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Master of Science Thesis
KTH School of Industrial Engineering and Management
Energy Technology: TRITA-ITM-EX 2021:77
Division of Heat & Power Technology
SE-100 44 STOCKHOLM
Towards Circular Economy:
Technoeconomic assessment of second-
life EV batteries for energy storage
applications in public buildings
Maria Gris Trillo
Master of Science Thesis in Energy Technology
TRITA-ITM-EX 2021:77
Towards Circular Economy:
Technoeconomic assessment of second-life
EV batteries for energy storage applications
in public buildings
Maria Gris Trillo
Approved
2021-03-26
Examiner
Supervisor
Francisco Díaz González, UPC Barcelona
Abstract
With the accelerated tendency of renewable energy penetration in the electricity grid, energy storage becomes a crucial asset for matching generation and demand. The growth of energy storage systems requires adequate new policies and regulatory frameworks. The battery value chain also requests for new ways of end-of-life management since battery recycling is not a viable single option yet. This is where circular economy offers different solutions and alternatives for prolonging the battery life and reducing the negative impact. This study analyses the technoeconomic feasibility of giving electric-vehicle (EV) batteries a second life as stationary energy storage systems in buildings with integrated on-site renewable energy production, such as for instance PV panels. Four different scenarios have been considered, including the refurbishment of the battery or its direct reuse, taking into account the degradation of capacity and thus, the amortisation price; against the possible load shifting benefit and the reduction of contracted grid power for the building. Results show that, effectively, the reuse of batteries for stationary energy storage is economically justified but may not be worth only in self-consumption applications, that is, for prosumers with some little renewable generation installed on site. The simulations reveal less than 2% relative energy cost savings on annual basis and up to 25% savings related to reduction of grid-contracted peak power, for the chosen case study of a mid-size office building. Second-life battery applications are still dependent on the development of tools for estimating and monitoring the battery’s state of health and potential performance in the new setting, for the technology to succeed. The increasing interest and necessity for circular economy together with the high volume of EV batteries expected to be released on the second-hand market, not suitable for automotive purposes anymore but reasonably applicable for stationary energy storage, will place this topic in the spotlight in the near future.
SAMMANFATTNING
Den fortsätta trenden för utvidgning av förnybar energi i elnätet gör att energilagring blir en ännu
viktigare tillgång för balansen mellan elproduktion och efterfrågan. Nya policyer och regelverk
krävs för att understödja en bredare tillämpning av småskaliga energilagringssystem.
Batteriets värdekedja kräver också nya sätt att hantera uttömda material eftersom batteriåtervinning
ännu inte hunnit utvecklas som ett genomförbart alternativ. En cirkulär ekonomi borde erbjuda
olika lösningar inte endast för materialåtervinning utan också gentemot förlängning av livslängden
och fördröjning av återvinningsprocessen tills nya metoder och verktyg finns på plats för effektiv
hantering med minimal miljöpåverkan.
Denna studie analyserar den teknoekonomiska genomförbarheten att ge begagnade batterier från
elektriska fordon (EV) en andra tillämpning, typ en utvidgad livslängd, som stationära
energilagringssystem för mellanstora kontorsbyggnader med integrerad lokal elproduktion såsom
t.ex. solpaneler på taket. Fyra olika scenarier har beaktats, inklusive delvis renovering av batteriet
eller dess direkta återanvändning, med hänsyn tagen till kapacitetsnedbrytningen och därmed
amorteringspriset, som vägs mot fördelarna i form av en uppnåelig tidsförskjutning av elbehovet
och minskning av kontrakterad nätkraft för byggnaden.
Resultaten visar att återanvändning av elfordonsbatterier för stationär energilagring är ekonomiskt
motiverad men troligen inte alltid värt i applikationer med låg förbrukning och låg egenproduktion
av förnyelsebar elkraft. Simuleringarna avslöjar mindre än 2% relativa energikostnadsbesparingar
på årsbasis och upp till 25% besparingar relaterade till minskning av nätavtagen toppeffekt för den
valda fallstudien av en medelstor kontorsbyggnad.
Praktiska tillämpningar av begagnade batterier är fortfarande beroende av utvecklingen av verktyg
för uppskattning och övervakning av batteriets hälsotillstånd och potentiella prestanda i den nya
installationen, för att konceptet skulle kunna bevisa sitt värde. Det ökande intresset och
nödvändigheten för cirkulär ekonomi tillsammans med den stora volymen EV-batterier som
förväntas släppas på den begagnade marknaden, inte längre lämpliga för fordonsändamål men
rimligt användbara för stationära energilagringssystem, kommer att föra detta ämnesområde in i
rampljuset inom en snar framtid.
i
Contents
1 INTRODUCTION ......................................................................................................... 1
1.1 Background .......................................................................................................................... 1 1.2 Motivation ............................................................................................................................ 4 1.3 Objectives ............................................................................................................................ 6 1.4 Methods and analytical framework/Research approaches ................................................. 7
2 CIRCULAR ECONOMY ............................................................................................... 8
2.1 Concept definition ............................................................................................................... 8 2.2 Forecast of the environmental impact of material use (predictions) ................................... 9 2.3 Global resource outlook (impacts) .................................................................................... 10 2.4 Action ................................................................................................................................. 11
3 BATTERY VALUE CHAIN ........................................................................................ 13
3.1 Working principle .............................................................................................................. 13 3.2 Main Lithium-ion battery types ......................................................................................... 14 3.3 Battery value chain for EV and Industry ........................................................................... 19
3.3.1 Raw materials ........................................................................................................................................19 3.3.2 Active materials synthesis ...................................................................................................................21 3.3.3 Cell manufacturing...............................................................................................................................21 3.3.4 Module and system assembling .........................................................................................................22 3.3.5 Application and integration ................................................................................................................22 3.3.6 Recycling and second life....................................................................................................................22
4 STUDY CASE ............................................................................................................... 26
4.1 Modelling concepts ............................................................................................................ 28 4.1.1 Modelling of the battery degradation ...............................................................................................28 4.1.2 Prices ......................................................................................................................................................35
4.2 Testing procedure .............................................................................................................. 37 4.3 Mathematical formulation ................................................................................................. 38
4.3.1 Overview of sets, parameters and variables ....................................................................................38 4.3.2 General constraints ..............................................................................................................................39 4.3.3 Prosumer model ...................................................................................................................................40
5 RESULTS AND DISCUSSION ................................................................................... 42
5.1 Simulation results .............................................................................................................. 42 5.2 Discussion .......................................................................................................................... 44
5.2.1 Peak load reduction target ..................................................................................................................44 5.2.2 Savings estimation ...............................................................................................................................46
6 CONCLUSIONS .......................................................................................................... 50
REFERENCES .................................................................................................................. 51
APPENDIX ....................................................................................................................... 54
Directly reused battery (0.053 amortisation) ..................................................................................................54 Refurbished battery (0.0905 amortisation) .....................................................................................................62 Energy-related costs: ..........................................................................................................................................70
ii
List of Figures
Figure.1: Total primary energy supply (TPES) by source, Worldwide 1990-2017………………….1
Figure.2: Energy-related CO2 emissions and reductions in the sustainable Development Scenario by source………………………………………………………………………………………..2
Figure 3: Cumulative global energy storage deployments.…………………………….………….3
Figure 4: Total final consumption (TFC) by sector, Europe 1990-2017.IEA…………………….5
Figure 5: Electric vehicle stock in the EV3030 scenario, 2018-2030.…………………………….6
Figure 6: The Circular Economy System diagram……………………………………………….9
Figure 7: Growth of materials use and GDP, 2011-2060……………………………………….10
Figure 8: Global resources outlook 2015-206…………………………………………………..10
Figure 9: Achieving resource decoupling as a result of policy packages………………………...12
Figure 10: Li-ion battery structure diagram…………………………………………………….13
Figure 11: Average Li-cobalt battery…………………………………………………………....18
Figure 12: Pure Li-manganese battery …………………………………………………………18
Figure 13: Typical NMC battery………………………………………………………………..18
Figure 14: Standard LFP battery……………………………………………………………….18
Figure 15: Snapshot of NCA…………………………………………………………………...18
Figure 16: Chart of Li-titanate………………………………………………………………….18
Figure 17: EV and Industry Batteries’ value chain……………………………………………...19
Figure 18: Capital investment cell manufacturing vs. module and system assembly…………… 22
Figure 19: schematic of the methods and processes involved in the consumed LIBs recycling…23
Figure 20: Closed loop for LIBs life……………………………………………………………25
Figure 21: Virtual map of UPC campus Terrassa………………………………………………26
Figure 22: Aerial picture of the building………………………………………………………..27
Figure 23: Picture of the building………………………………………………………………27
Figure 24: Daily total consumption, HVAC consumption and PV generation………………….27
Figure 25: Ri-SOC plot with different cycles…………………………………………………...28
Figure 26: Maximum charge storage capacity for each cycle number as a function of T………..29
Figure 27: Capacity degradation curves for different discharge C-rates………………………....30
Figure 28: Comparison of calendar aging and cyclic aging for three temperatures investigated…30
Figure 29: Alterations of the voltage vs. capacity at different cycles……………………………31
Figure 30: Cycle life at different DoD………………………………………………………….32
Figure 31: Winter season typical sypply/demand scenario……………………………………...41
Figure 32: Spring season typical sypply/demand scenario ……………………………………...41
Figure 33: Summer season typical sypply/demand scenario …………………………………....42
Figure 34: Autumn season typical sypply/demand scenario …………………………………....42
iii
Figure 35: Peak reduction……………………………………………………………………....43
Figure 36: Peak reduction……………………………………………...…………….…………44
List of Tables
Table 1: Energy consumption by sector…………………………………………………………4
Table 2: Characteristics of Lithium Cobalt Oxide……………………………………………....15
Table 3: Characteristics of Lithium Manganese Oxide……………………………………….…15
Table 4: Characteristics of Lithium Nickel Manganese Cobalt Oxide…………………………..16
Table 5: Characteristics of Lithium Iron Phosphate……………………………………………16
Table 6: Characteristics of Lithium Nickel Cobalt Aluminium Oxide…………………………..17
Table 7: Characteristics of Lithium Titanate……………………………………………………17
Table 8: Main characteristics of critical raw materials involved in a battery……………………..20
Table 9: Pretreatment methods comparison……………………………………………………23
Table 10: Comparison for metal-extraction processes………………………………………….24
Table 11: Gaia building electricity consumption and generation………………………………..28
Table 12: Battery components’ prices…………………………………………………………..33
Table 13: Invoice periods of the Spanish tariff 3.0A.…………………………………………...34
Table 14: Retailer power pries for each invoice period…………………………………………35
Table 15: Retailer consumption pries for each invoice period………………………………….35
Table 16: Sets used in the simulation…………………………………………………………...36
Table 17: Parameters used in the simulation.…………………………………………………...36
Table 18: Variables in the simulation…………………………………………………………...37
Table 19: Peak reduction cases..…………………….…………………………….……………43
Table 20: Costs breakdown........…………………….…………………………….……………44
Table 21: Annual energy costs and savings.…………………….………………………………45
Table 22: Power term invoice conditions………………………………………………………46
Table 23: Power savings………………………………………………………………………..46
iv
Abbreviations and Nomenclature
BESS: battery energy storage system
BMS: battery management system
CAES: compressed air energy storage
DoD: Depth of Discharge
EGD: European Green Deal
EGDIP: European Green Deal Investment Plan
EIB: European Investment Bank
EVs: electric vehicles
ICE: internal combustion engine
LIBs: Lithium ion batteries
OEM: original equipment manufacturer
SEIP: Sustainable Europe Investment Plan
SoC: State of Charge
SoH: State of Health
UPC: Universitat Politécnica de Catalunya (BarcelonaTech)
WEO: World Energy Outlook
v
ACKNOWLEDGMENTS
I would like to acknowledge everyone who played a role in this Master Thesis. First of all, my
supervisor, Francisco Díaz González, who has provided valuable guidance and advice during this
research. Secondly, I would like to thank Pau Lloret for his help concerning the technical aspects.
Also I would like to express my gratitude to Miroslav Petrov, for making this project possible.
Additionally, many thanks to all the professors and PhD students in the electrochemistry
department at KTH for introducing me to this topic and for being sources of inspiration in the
classes and in the laboratory sessions they taught.
Finally, special thanks to my friends, family and Joel, who supported and encouraged me.
1
1 INTRODUCTION
1.1 Background
Energy is of crucial importance in our society and it has penetrated in almost all facets of the social
domain and is a pillar of the economy. In a world where the major primary energy supply is still
lead by coal and oil as it can be observed in Fig.1, a general awareness of their harmful effects has
been increasing the last few decades.
Fig.1: Total primary energy supply (TPES) by source, Worldwide 1990-2017. Source: IEA
One of the battles humanity is fighting nowadays is the climate crisis with the drawback of a
constant raise of energy demand. The objective of the Paris Agreement focuses on the need of
world CO2 emissions to be dropped drastically to reach a sustainable development scenario that
maintains the average global temperature increase below 2ºC above preindustrial levels and trying
to limit it to 1.5ºC (IEA, 2019). To achieve this goal both technology and policy need to take action
and work aligned, since less effective scenarios are drawn if one or another fail to meet their goals.
The International Energy Agency (IEA) published the World Energy Outlook (WEO) 2019, which
included The Sustainable Development Scenario. The WEO implies what should be done in order
to meet climate goals also aligned to what new policies are establishing. The following graph shows
different future scenarios depending on the amount of adopted measures:
2
Fig.2: Energy-related CO2 emissions and reductions in the sustainable Development Scenario by source.
Source: IEA
As it can be observed from the graph above, current trends of energy-related CO2 emissions will
lead to a scenario of 45 GT by 2040. However, integrating all the changes shown in the graph and
thus following the opposite tendency 0 emissions leads to an incredible drop of CO2 emissions
down to 10 GT in 2050. Although this is very promising it is not an easy task and even the current
target is not consistent with new stated policies. According to up-to-date policies, the future
emission scenario would flatter the rapid increasing curve tendency but it would not decrease the
current emissions as it can be noticed in the graph. These divergences between different scenarios
highlight the importance of the decisions made by governments in the few next months and years,
which will determine the new policies and the investment in technology development crucial for
accomplishing the lowest emission scenario.
Everything points to a huge investment in renewables replacing systems powered by fossil fuels, as
it can be observed in the trends of the past years. However, their stochastic and low predictable
nature dependant on weather and season together with their immediate consumption required
makes it difficult to maintain a high reliability and security in the supply of those renewable energy
sources.
Therefore, energy storage has grown drastically these past few years not only helping address the
intermittency of renewables but also responding rapidly to large fluctuations in demand, making
the grid more responsive and reducing the need to build backup power plants (Zablocki, 2019).
However, this growth of storage systems needs the adequate new policies and regulatory
frameworks in the electricity sector since there will be many different scenarios not contemplated
nor planned until now.
3
This future electricity market will need new rules to beneficiate all parts involved and prevent any
technological or legal right confusion (Bioenergy International, 2019).
In the upcoming years the energy storage market is going to experience a huge growth hitting the
741 GWh of global cumulative capacity in 2030 led by the U.S. and China, with 49 and 21 percent
respectively (McCarthy & Xu, 2020).
Fig.3: Cumulative global energy storage deployments. Source: Wood Mackenzie
All the research efforts that are being focused towards this technology will make reduce its price.
Still, a huge investment will be required to reach that capacity. However, since energy storage will
become a key grid asset, all market players will have to take part in this transition.
The success of an energy storage facility lies on the response capability in front of a demand
variation, the amount of energy lost in the storage system, the overall energy storage capacity and
the velocity in the recharging process rate (Zablocki, 2019).
Among some of the different ways to store electricity the most relevant ones that are currently
being investigated are hydrogen (fuel cells), supercapacitors, compressed air and batteries. In the
hydrogen storage, electricity is used to convert water and oxygen into hydrogen, which can be easily
stored and re-converted into the desired form (electricity, heat etc.). This technology presents many
advantages. Thanks to the large amounts of power and low cost for storing once transformed into
hydrogen, this technology is very suitable for industrial processes. Moreover, hydrogen storage is
a long-term storage system, which can last as long as needed. Secondly, supercapacitors are a very
high power-density storage system, being able to release high amounts of power in short periods
of time. They also have unlimited lifetime as their capacity is not affected by the amount of cycles
performed. However, supercapacitors are a short-term energy storage system only being able to
store energy up to some minutes. Thus, they are used for system disturbances providing short
electricity bursts when necessary. Furthermore, compressed-air energy storage (CAES) is a long-
term energy storage system that can store energy up to a week. And finally, batteries are an energy
storing system for comparatively short periods of time, from hours up to few days. They can be
employed in the frequency and voltage stabilisation of the power system, also helping in the
demand balance.
4
Energy storage is already an essential mechanism in the power and transportation sectors. While
there are several ways to store electricity nowadays, current tendency is pointing to Li-ion batteries
as the most viable solution in the short term. The key points to make a technology a success and
competitive are increasing their performance and reducing their cost. Li-ion batteries price has
dropped drastically from 1000 $/kWh to 200 $/kWh in the last 6 years and their energy density
has doubled. These aspects make them good enough for EVs industry and are already competitive
compared to internal combustion engines (ICE) because the lifecycle and cost can beat that of
fossil fuel vehicle. However, batteries haven’t hit in heavy vehicles and aviation yet. It is believed
this will occur by 2030-2040. Batteries will first be used on smaller planes, possibly many small
engines in one plane to provide more reliability and flexibility. In the aviation sector energy density
plays a crucial figure since there is the need to keep a low weight without loosing power range.
Battery energy density is rising by a significant 2 to 3 per cent each year. However, Tesla’s cars still
overcome these numbers with each iteration. “It’s not the same ballpark as Moore’s Law progress
because it’s chemistry, not electronics, but it’s still very good.” (Adams, 2017).
1.2 Motivation
For the last seven years I have been studying engineering and for the past three focusing on
renewable energy. My goal is to help society overcome the climate crisis providing the clean
alternatives to maintain our current life style as much as possible, in terms of energy use. Clearly,
there are some other aspects that need to be changed in order to successfully meet the goal of
preserving the environment, such as plastic use and waste management. Going back to energy use,
the current energy distribution in Europe is the following showed in Table 1 and the tendency
evolution of each sector is presented in Figure 4:
Table 1: Energy consumption by sector. Source: IEA, data and statistics.
Sector ktoe MWh %
Industry 333.947 3.883.803.610 23,79
Residential 346.149 4.025.834.259 24,66
Transport 391.169 4.549.295.470 27,87
Commercial and public services 179.653 2.089.364.390 12,80
Agriculture/Forestry 32.469 377.614.470 2,31
Fishing 2.030 23.608.900 0,14
Non-energy use1 118.264 1.375.410.320 8,43
1 Non-energy use: Non energy use includes energy products used as raw materials in the different sectors; that is not consumed as a fuel or transformed into another fuel.
5
Fig. 4: Total final energy consumption (TFC) by sector, Europe 1990-2017.
Source: IEA World Energy Balances 2019.
The industrial sector, for its economic resources and cover surface [m2] in the buildings, is one of
the main vectors to integrate renewables. In fact, it can generate more energy than its own building
consumption depending on the hour of the day and the economic activity carried out.
Thus, in order to maximize the local generation, renewable, and a neutral CO2 industrial sector,
batteries are fundamental tools.
The battery storage capacity of an EV is usually much larger than an industry storage system. The
requirements for a battery used in the transport sector are more demanding given their main
competitive characteristic. Once a little capacity is lost for the aging process, the car range is
reduced, compromising their good performance and eventually being replaced for a new EV.
However, the old batteries are still at a very high percentage of their initial capacity although not
being suitable for their original purpose. For instance, Tesla Model 3 has a battery degradation of
7% after 250.000 miles (Kane, 2017). Hence, a new employment needs to be found for these
batteries that are far from being at the end of their life.
As mentioned before, industry storage systems are not as size and capacity demanding as EV. Thus,
discarded EV batteries can be reused and given a second life in the industry sector, preventing the
over production of batteries risking the Earth resources of Lithium, taking also into account that
the scarcity of this material would eventually lead to a commercial fight and an important raise in
the prices.
6
Fig. 5: Electric vehicle stock in the EV3030 scenario, 2018-2030. Source: IEA Global EV Outlook 2019.
However, second life batteries will only be a considerable resource in 5-10 years time. Before being
a substantial source of second life batteries, EVs need to experiment a great increase in the vehicles
selling share. Current tendency points to a promising scenario, where there would be 250 million
vehicles by 2030 as revealed in Fig. 5. As this number increases, so do will the potential second life
batteries, with some years delay due to their improved first life performance.
Moreover, the batteries’ second life approach is particularly important due to the lack of recycling
capacity of the Li-ion batteries, which is totally insufficient in Europe. This is why the concept of
circular economy is interesting for the decarbonisation of the industrial sector and to reduce the
impact of the use of batteries in the natural environment.
1.3 Objectives
This project considers this current situation and proposes two major objectives. First, a definition
of the battery value chain from the cell manufacturer to the end user and secondly, a study of the
synergies between these two sectors: electro mobility and industry in order to quantify the impact
of giving a second life to these batteries, from an economic point of view. Such study will be done
by analysing two scenarios for the second life battery with the help of a simulation tool to check
the potential savings.
The concrete technical objectives are to significantly reduce the energy bill, both reducing the
electricity costs and power costs. In terms of power, since the battery is thought to be able to
reduce the demand peaks considerably, the target is to reduce 25% of the contracted power. Also,
the battery degradation is key to determine the viability of the project proposal.
The main research focus herein is therefore related to the feasibility and profitability of installing
second-life batteries from EVs to medium-scale energy storage applications in office buildings.
7
1.4 Methods, analytical framework and research
approaches
Research approaches and methods are procedures and strategies that define the way assumptions
are done and detailed data is collected and analysed to subsequently reach conclusions and results.
The choice of approach depends on the nature of the topic of study or the research problem.
This study will account with quantitative as well as qualitative methods. In order to give the most
accurate future scenario for batteries impact in their second life in the industrial sector, a
quantitative study will be executed with data provided by the energy resources and water
information system (SIRENA) from UPC campus and a simulation tool that accounts with a series
of equations describing the battery model, the different constraints, etc.
Afterwards, results will be analysed in order to reach conclusions about the feasibility, advantages
and drawbacks of the model proposed.
8
2 CIRCULAR ECONOMY
The key difference between the cradle-to-grave dynamic active throughout all modern production
history and the cradle-to-cradle system intended to accomplish, is the same as in between current
resources value chain and circular economy value cycles.
Two concepts arise from this difference: eco-efficiency and eco-effectiveness. The first one
assumes a linear flow of resources and materials with only one-way direction. This involves the
natural resources extraction, processing and transformation until the product desired, for finally
discarding it. In this value chain, the eco-efficient methods pursue only to diminish the quantity
and harmfulness of the material stream, but are unable to change the linear flow. Some of the
materials in these products are not recycled nor reused but undergo a downcycling process, which
downgrades material worth and thus, restricts its usage. This is only a transitional step since it does
not prevent the cradle-to-grave line.
Contrary to this methodology of resource reduction, eco-effectiveness aims to convert the products
and materials that have reached their end of life so they can be reused at the same value level for
any another purpose. This would establish an ecologically friendly as well as an economically
supportive system. It would be possible by creating a cradle-to-cradle cyclical strategic structure,
were materials maintain their worth and are used again as resources for a different aim (Webster,
Bleriot, & Johnson, 2012).
2.1 Concept definition
Current developed economies and societies are used to a fast use and throw away model that has
already compromised a wide range of natural resources on Earth. The material extraction not only
causes scarcity in natural resources, but also has an immense impact in the environment in terms
of land, water and air pollution. Moreover, it affects the ecosystems of millions of animals that
could be at risk of extinction with all its repercussions in the complex bionetworks.
As technology keeps evolving and our everyday life is more dependent on devices and material
assets, this devastating trend will not improve. Demand and waste are still correlated in the
resources equation governing our economy up to date. Following this model, the only way to
reduce the resources and the waste would be directly reducing the resource extraction. However,
this approach would not be considered an option from the consumer comfort point of view, as it
would cost the loss of facilities. Subsequently, this model does not meet our new need of urgently
turning eco-friendly maintaining our lifestyles. From a holistic perspective, modern lifestyle should
be reconsidered as an attempt to help reduce our footprint, but this is not a technical issue and
thus, is out of the scope of present work.
Hence, the new model needed is a circular economy, redefining growth decoupled from finite
resources consumption and redesigning the waste management systems. Together with renewable
energy sources transition, the circular model will bring benefits to the natural environment and
societies besides boosting economic activity.
This new system concept diagram flows as a ‘value circle’ instead of a ‘value chain’. The outline of
a circular economy could be sketch as follows:
9
Fig. 6: The Circular Economy System diagram. Source: Ellen MacArthur Foundation.
At the highest and more general level, the main objectives of a circular economy is to preserve and
regenerate natural capital by controlling finite stocks and balancing their use with renewable
resource flows. In the circular value flow, the goal is to optimise the resources yields by reusing
materials and components as much as possible and giving them different uses and shapes if needed
to get all their potential in the loop. Finally, raise system effectiveness by minimizing negative
externalities out of the value cycle.
2.2 Forecast of the environmental impact of material use
(predictions)
With current trend of growth, global population could reach 9.600 million by 2050 and therefore
more natural resources will be required to withstand living standards, up to the corresponding
resources of three planets Earth (United Nations, 2020).
Also, socioeconomic trends will determine the future material use. The three main drivers are
income convergence among countries, a structural change and technology developments (OECD,
2019).
All countries will face an improvement in living conditions and reach those of the wealthiest
countries. Emerging and developing countries will grow at higher rates than in the OECD region
(OECD, 2019). This may cause a boom in their construction demand and thus, a higher demand
of materials. It is believed that demand for services coming from any kind of customer (from
households, large companies or governments) will surpass that of agricultural or industrial supplies.
10
This structural change will lead to a less intense material use since agricultural and industrial sectors
have higher material intensity than the services ones.
Fig. 7: Growth of materials use and GDP, 2011-2060. Source: OECD.
As it can be seen in Fig. 7, GDP in developing countries will grow rapidly while Material Use will
not experiment such a rapid increase due to the mentioned less material intensive structural change.
2.3 Global resource outlook (impacts)
There are a wide range of environmental impacts related to material extraction, processing and use,
such as acidification, climate change, human toxicity, land use, photochemical oxidation, aquatic
and terrestrial ecotoxicity among others. More concretely, the resource provision involves
greenhouse gas (GHG) emissions from mining and treating raw materials, while the use (e.g. fossil
fuels) can cause air pollution produced by their combustion.
Products of the primary resources can also have serious environmental impacts at the end of their
useful life, if the waste management is not properly accomplished. The consequences of using iron,
aluminum, copper, zinc, lead, nickel and manganese are estimated to more than double by 2060
(OECD, 2019).
Resource2 extraction and processing cause half of the greenhouse emissions alongside 90% of
biodiversity loss and water stress (European Commission, 2020).
Past and current trends point to the following evolution from 2015 to 2060:
Fig. 8: Global resources outlook 2015-2060. Source: International resource panel, 2019.
2 Resource here encloses materials, fuels and food.
11
2.4 Action
Given the negative projections related to material use there is a need for urgent action.
Governments face a truly difficult challenge where a big transformation has to be shaped in several
new policies to address the dark resources forecast. All of the new political decisions and new
policies have to pursue the transition to a circular economy, where natural resources consumption
and environmental impacts disassociation from economic activity are key factors.
In a circular economy there exists a bidirectional correlation between mitigation of climate change
and resource efficiency. Shifting to a low-carbon emissions economy already involves taking action
in terms of resource-efficiency, and an enhancement of resource efficiency in policies will make a
repercussion on the climate crisis.
Following this purpose, the European Commission disclosed a roadmap concerning a sustainable
economy and with the goal of reaching a climate-neutral circular economy, where the economic
progress is not linked to resources use. This initiative known as The European Green Deal (EGD)
and presented on 11 December 2019 will be accompanied by a series of new policies, which are
fundamental to establish the bases for accomplishing this environmental challenge.
President of the European Commission Ursula von der Leyen claimed that “the European Green Deal
is our new growth strategy – for a growth that gives back more than it takes away”.
And as the First Vice-President of the European Commission Frans Timmermans added, “our plan
sets out how to cut emissions, restore the health of our natural environment, protect our wildlife, create new economic
opportunities, and improve the quality of life of our”.
Thus, the EGD main objectives are to cut to zero the GHG emissions by 2050, to make EU’s
economy sustainable by disjoining it from resource use and to make sure it is an inclusive transition
(European Comission, 2019). The actions postulated on its guideline comprised an enhancement
of the resource use efficiency through a clean, circular economy and a recovery of biodiversity.
The circular economy plan will highlight the reduction and reuse of materials before recycling them.
Also, the first sectors to be tackled are those with major resource intensity such as textiles,
construction, electronics and plastics. Another crucial strategy proposed by the Commission is the
support to business models based on renting services (e.g. cars, bycicles, scooters) that will enlarge
the use rate of those goods and lower their consumption from the levels they would have as if they
were an owned product (European Comission, 2019).
To set a long-term direction to meet the targets stated above and make this plan a reality, all sectors
of the economy will have to join the transition. It will require taking some actions from their side
involving investing in environmentally friendly technologies and transport, reinforcing industry
innovation, strengthening the decarbonisation of the energy sector, guaranteeing the buildings’
energy efficiency trend and collaborating internationally to expand sustainable standards. In order
to truly meet these commitments, the European Climate Law has been announced to make a legal
obligation and activate investment towards the European Green Deal (European Commission,
2019).
A fair transition fund will leverage private and public money including with the help of the
European Investment Bank (EIB), which will deliver a sustainable investment plan. The European
Green Deal Investment Plan (EGDIP), also known as Sustainable Europe Investment Plan (SEIP),
is the financing support of the EGD, which will employ at least 1 trillion euros in the following
12
decade. This plan includes the Just Transition Mechanism that accounts with minimum 100 billion
euros destined for funding the regions most impacted by the green transition throughout the period
2021-2027 (European Comission, 2019).
All in all, achieving the EU’s climate neutrality demands a new industrial policy based on circular
economy. The most important part of the EGD together with the financing is the new policy
framework, which will determine the actions required to accomplish by the different actors
involved in this huge transition.
Further, the OECD has raised a project for resource efficiency and circular economy as well. The
OECD’s RE-CIRCLE project goal is to predict the effects of unceasing natural resources use, and
forecast the outcome of applying new policies to recognize which ones would have the highest
impact to encourage circular economy transition (OECD, 2018).
Succeeding in the decoupling of resources and economy will bring significant improvement in
human well-being and environmental pressure, even restoring ecological impacts made in the past,
as well as boosting the economic growth. The most substantial change of trends could be glimpsed
in the increase of global GDP and area of forest and natural habitat, and decrease of global material
extraction, greenhouse gas emissions, area of agricultural land and global pastureland as shown in
Fig. 9.
Fig. 9: Achieving resource decoupling as a result of policy packages. Source: International resource panel, 2019.
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3 BATTERY VALUE CHAIN
3.1 Working principle
The cell, which is the core element of a battery, can take different shapes and sizes but its working
principle remains the same. During the charge and discharge process lithium atoms split into ions
and electrons, which migrate between the two electrodes and circulate across the external circuit
respectively. In the discharge, the oxidation takes place in the anode and the lithium ions travel
from the anode to the cathode, where the reduction occurs, while the electrons circulate through
the external circuit providing energy. Contrary, the charge process absorbs energy, the lithium ions
travel form the cathode to the anode as well as the electrons in the external circuit. Lithium is a
critical component in a battery but it is not the limiting one. The greater volume of lithium, the
higher the capacity, and the larger the potential difference among anode and cathode, the higher
the voltage.
The cell comprises three main layers. At the anode, there is a current collector commonly made of
copper and covered with a film of active material, usually natural graphite with some mixture of
chemical additives used to increase the conductivity. At the cathode, there is also a metallic current
collector in this case made of aluminium and again coated with active material3, conductive
additives and binder, which acts as an adhesive between the active material and conductive additive.
And the third layer consists of a membrane between anode and cathode known as separator.
These three layers are wetted with the electrolyte, a high ionic conductive mixture of solvents, salts
and additives that increases efficiency in lithium ions movement through the active material and
separator.
Fig. 10: Li-ion battery structure diagram. Source: U.S. Department of Energy. Office of Basic Energy Sciences
3 Active material: depends on the type of Lithium-ion battery. Usually consists of a mixture of several transition metals such as cobalt, nickel, manganese, iron, and/or aluminium.
14
Before batteries are ready for commercialisation, an electrical insulation layer known as solid
electrolyte interface (SEI) has to be built for the good functioning of the battery. The creation of
this layer is carried out under specific conditions of battery cycling. It provides sufficient ionic
conductivity (lowering a bit the capacity) for an adequate performance but prevents the electrons
to circulate through the electrodes, which is crucial for the battery working principle.
SEI growth is a consequence of irrecoverable decomposition of the electrolyte forming a solid layer
on the surface of the negative electrode active material.
Given that the electrolyte is almost unable to penetrate the SEI, once the first layer has formed, it
will not reach the active material and thus, will not promote further SEI growth. However, if there
was an enduring SEI growth there would be an unceasing loss of lithium, which would lead to a
slow capacity decline (Pinson & Bazant, 2012).
Most of current research is focusing on battery performance during discharge and much little
attention is put in increasing battery lifespan, which is limited by the irreversibilities that occur in
the electrochemical reactions.
3.2 Main Lithium-ion battery types
In order to understand the batteries value chain properly, a deep study of the activities and agents
involved in each of the stages is carried out. Considering the amount of diverse types of batteries
and all their end purposes and end-users, the focus will specifically be put in Lithium ion batteries
(LIBs). This kind of batteries is widely adopted in several sectors such as phone devices and EVs
among the most important ones.
However, the demand on the industry sector as an energy storage device is gaining weight nowadays
thanks to the already mentioned raise of renewables mostly.
There are some requirements that need to be meet for a battery to be viable and reach basic
functioning. As an electric storage device the following eight characteristics are of major relevance:
- High specific energy [Ah/kg] - Long life - Low toxicity
- High specific power - Safety - Fast charging
- Affordable price - Wide operating range
In addition, it is very important for a battery to have low self-discharge and instant start-up when
required. However, all batteries have some self-discharge, which is aggravated and intensified with
age and temperature (Battery University, 2017).
There are many different types of Lithium-ion batteries. This kind of batteries is named after the
active materials of what they are composed of. The most common ones are Lithium Cobalt Oxide
(LiCoO2) — LCO, Lithium Manganese Oxide (LiMn2O4) — LMO, Lithium Nickel Manganese
Cobalt Oxide (LiNiMnCoO2) — NMC, Lithium Iron Phosphate (LiFePO4) — LFP, Lithium
Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) — NCA and Lithium Titanate (Li2TiO3) — LTO.
Each of them has different characteristics such as voltages; specific energy; cycle life, which
depends on the cycling conditions; cost and applications. The following tables summarize the main
features of these types of batteries:
15
Lithium Cobalt Oxide (LiCoO2) – LCO
Table 2: Characteristics of lithium cobalt oxide. Source: Battery University.
This battery excels on high specific energy but has low specific power (load capability) and limited
life span.
Lithium-Manganese Oxide (LiMn2O4) – LMO
Table 3: Characteristics of Lithium Manganese Oxide. Source: Battery University.
LiMn2O4 cathode Graphite anode Since 1996
Voltages 3.70V, 3.80V nominal; typical operating range 3.0–4.2V/cell
Specific energy (capacity) 100–150Wh/kg
Cycle life Short
Applications Power tools, medical devices, electric powertrains
Lithium-manganese offers improvements in specific power and safety, but diminishes the capacity
decreasing the performance with respect to Lithium-cobalt.
Cathode (~60% Co) Graphite anode Since 1991
Voltages 3.60V nominal; usual operating range 3.0–4.2V/cell
Specific energy (capacity) 150–200Wh/kg.
Cycle life Limited
Applications Mobile phones, tablets, laptops, cameras
16
Lithium Nickel Manganese Cobalt Oxide (LiNiMnCoO2) – NMC
Table 4: Characteristics of lithium nickel manganese cobalt oxide (NMC). Source: Battery
University.
LiNiMnCoO2 cathode Graphite anode Since 2008
Voltages 3.60V, 3.70V nominal; typical operating range 3.0–4.2V/cell
Specific energy (capacity) 150–220Wh/kg
Cycle life Long
Applications E-bikes, medical devices, EVs, industrial
In this battery the addition of nickel and manganese play an important role. Nickel is recognised
for its great specific energy but reduced stability while manganese establishes a spinel structure,
which provides low internal resistance, but also provides low specific energy. However, the
combination of both metals boosts each other advantages.
Thus, this battery offers high capacity and high power, serving as both energy cell and power cell,
which is known as hybrid cell. Also, not using cobalt decreases the cost significantly and is making
this relatively new battery the dominant for cathode chemistry.
Lithium Iron Phosphate (LiFePO4) – LFP
Table 5: Characteristics of lithium iron phosphate. Source: Battery University.
LiFePO4 cathode Graphite anode Since 1996
Voltages 3.20, 3.30V nominal; typical operating range 2.5–3.65V/cell
Specific energy (capacity) 90–120Wh/kg
Cycle life Long
Applications E-bikes, medical devices, EVs, industrial
Lithium-phosphate battery is not stressed at sustained high voltage levels as it happens to other
lithium-ion systems, making it a safe battery with a very high thermal runaway (270ºC). Low specific
energy and high self-discharge but one of the fastest (high power) lithium-ion batteries.
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Lithium Nickel Cobalt Aluminium Oxide (LiNiCoAlO2) – NCA
Table 6: Characteristics of Lithium Nickel Cobalt Aluminium Oxide. Source: Battery University.
LiNiCoAlO2 cathode (~9% Co) Graphite anode Since 1999
Voltages 3.60V nominal; typical operating range 3.0–4.2V/cell
Specific energy (capacity) 200-260Wh/kg
Cycle life Short
Applications Medical devices, industrial, electric powertrain (Tesla)
In this battery the addition of aluminium provides more stability than in nickel oxide. For its high
capacity it is used as an energy cell.
Lithium Titanate (Li2TiO3) – LTO
Table 7: Characteristics of lithium titanate. Source: Battery University.
LMO or NMC cathode Li2TiO3 anode Commercially available since 2008
Voltages 2.40V nominal; typical operating range 1.8–2.85V/cell
Specific energy (capacity) 50–80Wh/kg
Cycle life Very long
Applications
UPS, electric powertrain (Mitsubishi i-MiEV, Honda Fit
EV),
solar-powered street lighting
In this case, the graphite anode is replaced by a lithium-titanate, which arranges into a spinel
structure. With such configuration, zero-tension can be reached, SEI is not formed when fast
charging at low temperature and consequently there is not lithium loss. For all these reasons the
lifespan is the highest of all Li-ion types, it can be ultra-fast charged and discharged at a current of
10 times the rated capacity. However, the extremely high cost of this technology makes it only
available for very specific and special applications, far from massive usage.
As it can be observed from the tables above, the trend in newer systems is to incorporate materials
such as nickel, manganese and aluminium to benefit from their singular and distinctive
characteristics to enhance batteries performance. The following radar or spider charts plot the
18
values over a graded scale of the most important variables for each battery (Battery University,
2017)
LCO LMO
Fig. 11: Average Li-cobalt battery. Fig. 12: Pure Li-manganese battery.
Source: Cadex Source: Boston Consulting Group
NMC LFP
Fig. 13: Typical NMC battery. Fig. 14: Standard LFP battery.
Source: Boston Consulting Group Source: Cadex.
NCA LTO
Fig. 15: Snapshot of NCA. Fig. 16: Chart of Li-titanate.
Source: Cadex. Source: Boston Consulting Group.
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3.3 Battery value chain for EV and Industry
Since the objective of the present work is to study the effect of second life batteries in the value
chain and explore the synergies between both first and end user in terms of bill and material
management savings afterwards, only one general value chain will be considered to analyse the
most important aspects relevant to the case study.
The value chain for lithium-ion batteries comprises several phases from the cell manufacturer to
the end user. Depending on who the end user is, this value chain will be split at the application and
integration point to reach each end user’s needs.
Regardless of the battery application the essential approach of the value chain is shared. The
following diagram shows the main stages.
Fig. 17: EV and Industry Batteries’ value chain.
The firsts and common segments in the value chain of batteries are raw material mining and
processing, cell manufacturing and system and module assembly. Although the energy storage
system end use determines the following stages of the manufacturing and integration process, the
final stage can also be common. When batteries reach the end of life for their first designed
purpose, they can be recycled or reused for another application instead.
Also, it is worth noting that certain companies deal with various segments of the value chain such
as some chemical industries covering the recycling and also the materials processing stages.
3.3.1 Raw materials
Natural resources are the departing point of the battery value chain journey. As previously
mentioned, there is a wide variety of elements used for Li-ion battery cells, including lithium (Li),
nickel (Ni), cobalt (Co), manganese (Mn), aluminium (Al), tin (Sn), titanium (Ti) and carbon (C)
mostly in natural graphite form. All these elements are obtained from raw material mining or earth
and water surface.
Some of these resources are of high importance to the EU economy and have a high supply-risk.
For both these features they are designated as “critical raw materials (CRMs)”. The European
Commission published the first list of 14 CRMs in 2011 (European Commission, 2011), which is
updated every 3 years to update production, technological progresses and market trends (European
Comission, 2020). The first reviewed list in 2014 contained 20 CRMs and the third one in 2017
comprised 27 CRMs. Further, in January 2018, the Commission issued a document featuring the
20
CRMs capacity for circular usage (Directorate-General for Internal Market, Industry,
Entrepreneurship; European Commission, 2018) and finally, in May 2019 the 'Recovery of critical and
other raw materials from mining waste and landfills - state of play on existing practices' was published presenting
the existing methods and processes for raw material recovery from mining waste and landfills
(Blengini, et al., 2019).
According to the last CRMs list published September the 5th 2020, which enclosed a larger number
of materials, among the 83 materials 63 where considered individually and the rest arranged in 3
groups: 10 in heavy rare earth (HREEs), 5 in light rare earth (LREEs) and 5 in platinum metals
(PGMs) (European Commission, 2020). To compare with previous lists, in 2011 41 elements were
assessed, 54 in 2014 and 61 in 2017. The final 2020 list identifies 30 Critical Raw Materials
(European Commission, 2020):
Among the materials used in Li-ion cells, cobalt, lithium, phosphate and natural graphite are
considered critical raw materials. In the following table the main global producer for each of them,
the stages assessed as critical, the economic importance and supply risk indexes and the recycling
rates are presented (European Commission, 2020):
Table 8: Main characteristics of critical raw materials involved in a battery. Source: European
Commission.
CRM Main global
producers
Stages
assessed as
critical
Substitution
indexes EI/SR4
EoL
recycling
input rate5
Cobalt
Congo, DR (59%)
China (7%)
Canada (5%)
Australia (4%)
mining/ extraction
0.92 / 0.92 22%
Lithium Chile (44%) processing/ refining
0.93/0.93 0%
Natural graphite
China (69%)
India (12%)
Brazil (8%)
mining/ extraction
0.95 / 0.97 3%
Phosphorus
China (74%)
Vietnam (9%)
Kazakhstan (9%)
United States (8%)
processing/ refining
0.99 / 0.99 0%
4 ‘Substitution index’ is a method to numerically determine the hardship in substituting the CRM. It is estimated for both Economic Importance (EI) and Supply Risk (SR) factors. The value ranges from 0 (substitutable) to 1 (irreplaceable). 5 ‘End-of-life recycling input rate’ calculates the ratio between the recycled scrap and the EU demand.
21
3.3.2 Active materials synthesis
Active materials are the core elements of a battery since are those participating in the
electrochemical reactions. These materials include anode, cathode (both electrodes) and the
electrolyte.
For the synthesis of these materials there are many different techniques. For example, for lithium
iron phosphate the process consists of continuous ball-milling6 at high temperature with shearing
capability, whereas for lithium nickel manganese cobalt oxide entails a batch wet synthesis on
organic or aqueous solvent.
Mechanochemichal (MC) methods have been widely used for preparing lithium-ion batteries
materials over the last years. MC methods shorten the synthesis process, display enhanced cycling
behaviour as well as diminish the energy used and material cost compared to previous procedures
including high temperature solid state reactions. However, current trends are pointing to
nanotechnology, as it will offer new options for the cathode synthesis materials (Uddin, Alaboina,
& Cho, 2017).
3.3.3 Cell manufacturing
The cell manufacturing process comprises four main steps: active material preparation, electrodes
manufacturing, cell assembly and cell formation.
First, for the active material arrangement, cathode material and graphite anode material are
separately placed into two tanks where they are mixed with binder, additives and solvents to
produce an ink.
Then, two metallic substrates, copper for the anode and aluminium for the cathode are covered
with the ink through a slot-die process. Once coated, these foils are put in an oven so that the
solvent evaporates and thus, a metallic bar coated with a solid substrate is the remaining product.
This foil then goes through a calendaring process where it reduces its thickness by roller
compression until a right porosity level is reached.
The next stage is to cut in smaller rolls the two large electrode rolls. If the cell assembly process
involves electrode piling, in order to obtain the electrode sheets it is required a roll-notching7 step.
In third place, the cell assembly involves the compilation of the separators and electrodes together.
The technique to do so is to coil together a separator (insulating sheet), the anode, another
separator and finally the cathode. Depending on the battery configuration these layers will be
enclosed in a cylindrical or prismatic casing, or will be assembled in single-sheet stacking or Z-
folding among other structures. Next, the battery is sealed and the metal contacts are adhered.
And finally, the cell formation or aging step consists of charging and discharging the new assembled
cell under very specific parameters depending on the chemistry composition, format and future
battery application. One of the objectives is to form the SEI, which is composed of lithium
carbonate and lithium oxide and grows in the anode active material surface. These initial charging
and discharging tests are also used to detect any faults errors and discard malfunctioning cells.
6 Ball-milling: grinding process into extremely fine dust. 7 Notching: metal cutting process.
22
3.3.4 Module and system assembling
Once the cells are ready, the modules can be built. The system assembly entails a few steps to
obtain the final module. The new cells are cabled and coupled together, and then inserted into a
casing made of plastic or metal. Additionally, to conclude with the final module, a control card is
connected, comprising a battery management system (BMS). Usually the modules account with 4
to 50 cells, reaching up to few kilowatt-hours.
Module and system assembly are way less capital intensive compared to cell manufacturing, around
5-7 times.
Fig. 18: Capital investment cell manufacturing vs. module and system assembly. Source: Saft
3.3.5 Application and integration
This stage is different for each battery end use. Depending on the battery first life purpose, the
battery pack will be integrated into the vehicle structure, comprising the battery-car interface
(connectors, plugs, mounts). This task is carried out by automotive OEMs (original equipment
manufacturer), such as Chrysler (Fiat), Ford, GM, Nissan and Tesla among the most important
ones (Lowe, Tokuoka, Trigg , & Gereffi , 2010).
In the other hand, stationary battery uses include off-grid applications, where storage is part of a
bigger energy solution; utility application, where energy storage can provide system reliability,
peaking capability, frequency response, regulation, power quality, forecast error mitigation and
renewable restriction mitigation among other values; and finally behind the meter, providing the
electricity consumer benefits determined by the tariff structure and in terms of power quality from
the grid.
3.3.6 Recycling and second life
Once the battery has reached the end of its first purpose life due to a capacity loss that no longer
satisfies the automotive industry needs, the objective is to repurpose the use of this battery instead
of discarding it right away in order to create a circular economy around them. The next steps are
conditioned by a series of economic and technical aspects that will be further discussed. However,
if the battery has already reached the end of its second life, meaning it has already been used for a
stationary purpose not as demanding as the first one, it will be recycled.
23
Fig. 19: Schematic of the methods and processes involved in the consumed LIBs recycling. Source: (Zheng, et al., A
Mini-Review on Metal Recycling from Spent Lithium Ion Batteries, 2018)
This process involves different stages, comprising pretreatments, metal-extraction and product
preparation. Initially, the battery is discharged for security reasons, the BMS, the battery cooling
system and packaging are disassembled, removed and handled separately. There are different
pretreatment methods: solvent dissolution; NaOH dissolution; ultrasonic assisted separation,
which allows stripping the cathode material thanks to cavitation effect; thermal treatment, to
decompose the binder and thus, reduce the bonding force between particles; and mechanical
methods including sieving, crushing and magnetic separation. Table 9 summarizes the advantages
and disadvantages of each process.
Table 9: Pretreatment methods comparison. Source: (Zhang, He, Wang, Ge, & Zhu, 2014).
Technology Advantages Disadvantages
Solvent dissolution
High separation efficiency Expensive solvent, environmental hazards
NaOH dissolution
High separation efficiency
Simple operation
Difficult aluminium recovery
Alkali wastewater emission
Ultrasonic-assisted
separation
Simple operation
Practically no exhaust emission
Noise pollution
High device investment
Thermal treatment
Simple operation
High output quantity
High energy consumption
High device investment
Toxic gas release
Mechanical methods
Simple and useful operation
Toxic gas release
Cannot separate all kind of components in spent LIBs completely
24
Some of these techniques have a more efficient separation process such as solvent dissolution and
NaOH dissolution whereas the others occur to be simpler operations.
Once opened, to inactivate the harmful substances, liquid nitrogen is used. Also, the cathode, anode
and separator are removed and put in an oven for 24h in order to dry them.
Next, each electrode is further separated for the metal extraction, which is done by pyrometallury,
hydrometallurgy, biometallurgy or a combination of these. This process consists on transforming
the solid metals into their liquid state to enable the later separation and retrieval of the metal
components. Again, the advantages and disadvantages are presented in a summary table:
Table 10: Comparison for metal-extraction processes. Source: (Georgi-Maschlera, Friedricha,
Weyheb, Heegnc, & Rutzc, 2012)
Technology Advantages Disadvantages
Pyrometallurgy Great capacity
Simple operation
High temperature and energy
consumption
Low metal recovery rate
Waste gas and dust
Hydrometallurgy
Low energy consumption
High metal recovery rate
High product purity
Long recovery process
High chemicals consumption
Waste water
Biometallurgy
Low energy consumption
Mild operating conditions
High metal recovery rate
Long reaction period
Bacteria are difficult to cultivate
This step of metal-extraction is critical to the whole process. The methods implemented are gaining
efficiency and capacity but still are very harmful for the environment because of the wastewater,
the management of chemicals involved and exhaust gas. Hence, further attention and research in
secondary pollution is needed to achieve a successful recycling process.
Subsequently, in the preparation step metal components present in the liquid mixture can be
recovered by a combination of solvent extraction, chemical precipitation and crystallization. For
the cathode material preparation, since the dissolved metal ions such as Ni, Mn and Co are difficult
to separate due to their nature similarity, a precursor material is used to ease the separation.
Then, the cathode material is regenerated through co-precipitation and sol-gel, both these are
synthesizing methods.
25
Further maturity in cathode recycling processes is key to produce elements that can already be used
in new batteries lowering or eradicating additional expensive reprocessing (Green Car Congress,
2019). In general terms, new batteries will be design to ease the recycling process and therefore,
reduce the whole battery life cost.
The final goal is to reach a close-loop, see Fig. 20, where spent batteries are recycled diminishing
processing steps and thus, reducing waste and energy consumption and positively affecting battery
production costs.
Figure 20: Closed loop for LIBs life. Source: Argonne National Laboratory
Currently Europe does not have the capacity to recycle all the batteries that are in use and thus,
even more emphasis needs to be placed in the repurposing and second-life of batteries to give time
to further develop the recycling industry.
The upcoming worldwide rise of EVs will certainly bring new strategies for the collection and waste
batteries management. Once the batteries are removed from the EVs their categorization and
testing are crucial for determining a suitable second-use.
26
4 STUDY CASE
From the battery collection until the second-use there are different strategies and approaches to
proceed. Depending on the state of the battery, the battery pack is tested without being
disassembled and, if it is apt and meets the market requirements for the second-use proposed,
directly reused; in the other hand, the battery pack can be dismantled at a module level involving
more technical procedures, materials and components to rebuild a new battery pack that will rise
the cost of the operation. However, this second repurposed battery would be more adaptable for
particular uses. The first strategy is known as “direct reuse” while the second one as “battery
repurposing/refurbishing” (Canals Casals & Amante Garcia, 2016).
The objective of this chapter is to introduce two different ways of proceeding with second life
batteries and the economic effects, pursuing the best scenario for the prosumer using the available
flexibility. To do so, an algorithm will be used to perform a consumption profile calculation using
a second life battery. This algorithm with the code for the calculation step will be taken from The
European project INVADE (Smart system of renewable energy storage based on INtegrated EVs and bAtteries
to empower mobile, Distributed and centralised Energy storage in the distribution grid) (INVADE, 2020).
INVADE is a 16 million euros budget project being one of the largest European research and
innovation in the field of SmartGrid & Storage. Both the present work and INVADE project
ultimately seek to increase renewable sources integration in the power system. However, while this
project only includes stationary storage, INVADE project incorporates both mobile (EVs) as well
as stationary storage and thus, those parts of the algorithm corresponding to EVs storage will have
to be omitted.
In the present work UPC public consumption and generation data will be used to perform the case
study. This data will be acquired from the UPC SIRENA (Sistema d’Informació de Recursos
Energètics i Aigua) tool (UPC, 2007), which was launched in 2007. SIRENA platform offers
publicly accessible data measured by the smart meters distributed throughout the installations of
the UPC for research and dissemination purposes (UPC, 2007). SIRENA’s main purpose is to lead
the new energy saving measures and get track of their implementation effect. UPC is a good model
to carry out such study since it has a plan for reducing energy consumption and implementing
renewable energy systems (solar PV) and thus, our second life batteries could benefit UPC both
technically and economically.
The targeted building is TR14 Gaia in UPC campus Terrassa. This building is intended to locate
university-company projects, technology-based companies, research centres and innovation units.
The solar power plant installed on the roof has 120 photovoltaic panels, with a power of 25kW.
Figure 21: Virtual map of UPC campus Terrassa. Source: UPC
27
Due to environmental conditions, aging in the PV panels and other technical aspects unknown,
the PV installation has been highly downgraded and performs far below the expectations. The
output peak power has been measured at steady levels lower than the 25 kW installed, reaching
down to about 12 kW.
Figure 22: Aerial picture of the building. Source: UPC Figure 23: Picture of the building. Source: UPC
A first description of the building and its main consumptions yield the following data:
About architecture, the total footprint of the building 7.247 m2, distributed in 3 floors and
a basement. It was designed and executed between 2006 and 2012 by the company CDB
Arquitectura (CDB Arquitectura, 2006). The total budget of the project was 10.6 M€. The
building is committed to isotropy in response to the demands of this type of buildings,
maximizing the continuity in the spaces and modularity to allow substitution and adaptation
according to requirements.
About its purpose, this building is embedded into the university campus of the UPC in
Terrassa, and it is usually used to host research centres, innovation hubs and start-ups.
About its electricity consumption and generation monitoring assets, according to SIRENA
platform, data is accessible for the smart meter at the point of connection to the grid; at the point
of connection of the PV installation with the rest of the building infrastructure; for heating,
ventilation and air conditioning (HVAC equipment).
All above-mentioned electrical loads are considered as inflexible for the purposes of the present
work. Only the data measured at the point of connection of the building with the electrical network
will be considered for optimization purposes. With the aim of completing the description of the
building, figure 24 depicts the daily total consumption, HVAC consumption and PV generation.
As noticeable below, the grid consumption of the building rises in summer due to HVAC
equipment. In terms of PV generation, the levels stay low compared to the consumption
throughout the year. Table 11 complements this overall description of the building electricity
consumption and generation.
28
Figure 24: Daily total consumption, HVAC consumption and PV generation. Source: SIRENA UPC.
Table 11: Gaia building electricity consumption and generation.
Grid consumption
(kWh) HVAC (kWh)
PV generation (kWh)
Total 275142 55473 20864
Average 753 151 57
Median 724 70 62
Max 1825 667 110
Min 176 0.3 0
4.1 Modelling concepts
4.1.1 Modelling of the battery degradation
Nowadays batteries have two major drawbacks: they are still expensive to manufacture and degrade
over time. As mentioned before (in chapter 3.1) lithium ions move between battery electrodes, but
as they go back and forth across the layers some of the ions get trapped diminishing it and being
an obstacle for the remaining cycleable lithium.
As a result battery cells slowly degrade after recurrent cycling and the cell capacity fades as its
resistance increases, reducing the battery safety and efficiency. As it can be observed in Figure 25,
the more cycles the battery carries, the higher the resistance leading to a loss of capacity in the
battery.
29
Figure 25: Ri-SOC plot with different cycles. Source: (Wang & Bao, 2017)
The battery cycle life refers to the amount of charging-discharging cycles a battery can undertake
before the battery capacity drops under a certain amount of the nominal value and needs to be
substituted. The main factors affecting to battery degradation are temperature,
charging/discharging current rate (C-rate), SoC and DoD.
4.1.1.1 Temperature
The first one affects in both extremes, low and high temperature. The low temperature mainly
affects the electrolyte increasing its viscosity, which reduces the ionic conductivity, increases the
impedance of the relocation of the chemical ions and thus, raises the resistance (Ma, et al., 2018).
On the contrary, the high temperature, although it improves Li-ion battery’s performance
temporarily by increasing its capacity, contributes to an advanced degradation rate mostly due to
the alteration of the electrode surface films (Leng, 2015).
In Figure 26 the temperature effect is detected in the different capacity drop evolution. As
mentioned above, high temperature can contribute to a transitory higher capacity, which would
make higher temperatures of 45 and 55 ºC the most suitable ones. However, it also increases
resistance and degradation as well as a low conductivity leading to a drop in capacity for high
temperatures. In this example 45 ºC would be the optimal temperature.
30
Figure 26: Maximum charge storage capacity for each cycle number as a function of temperature. Source: Battery
and Energy Technologies, MPower UK.
4.1.1.2 Current rate
Secondly, current rate (C-rate), which measures the speed at which a battery is charged/discharged
compared to its maximum capacity, also influences battery degradation. A 1C rate implies that the
charge/discharge current will charge/discharge the battery entirely in 1 hour (MIT Electric Vehicle
Team, 2008). Higher C-rates will shorten the time while lower C-rates will lengthen the
charging/discharging time.
At high C-rate, quick charge/discharge, chemical compounds in the battery will not have enough
time to react and move. Only part of the active material is transformed and thus, little energy is
obtained. Yet, at low C-rate, slow charge/discharge, more energy is released and the capacity is
higher (Honsberg & Bowden, 2019).
The C-rate "number" can be obtained from the current at which the battery is being
charged/discharged over the nominal battery capacity as follows:
𝐶 − 𝑟𝑎𝑡𝑒 =𝐼𝑐ℎ/𝑑𝑠𝑐ℎ
𝐶𝑛𝑜𝑚
31
Figure 27: Capacity degradation curves for different discharge C-rates. Source: (Perez, Montoya, & Quintero,
2018)
Figure 27 shows the faster degradation curve of a battery when discharged at higher C-rates. Lower
C-rates have lower capacity loss because at lower rates more active material has time to be
transformed and react.
4.1.1.3 State of Charge
The State of Charge (SoC) indicates the remaining energy in a battery expressed as a percentage of
the maximum capacity.
As shown in Figure 28, cycling at high states of charge of the battery shortens longevity in capacity,
while cycling at low states of charge prolongs capacity retention. This charging at lower states
combined with shallow cycling (as will be explained in the following point of DoD), are the clue
for diminishing capacity loss in battery cycling.
Figure 28: Comparison of calendar aging and cyclic aging for three temperatures investigated. Source: (Keil &
Jossen, 2015)
The voltage at which a battery is charged also contributes to the lifetime and capacity levels.
Lowering the peak charge voltage expands the cycle life.
32
However, lowering the voltage decreases the capacity stored. For the electric vehicle industry the
major concern is longevity and for this purpose the optimal charge voltage is 3.92V/cell (Battery
University, 2020).
Moreover, the State of Health of the battery is easily obtained by comparing how much electrical
charge is currently needed to move the battery from one point to another on the charge curve vs.
the amount it originally required when it was new. This calculation is carried out by the BMS.
Therefore, as the cell degrades less charge will be needed to move up the charging curve, since the
capacity will have diminished and it will reach the maximum earlier.
In Figure 29 this phenomenon is detected. The new battery with little cycles (blue line 5 cycles)
reaches both maximum voltage and capacity, while the old battery (purple line 800 cycles) reaches
sooner the maximum voltage only reaching at this point 0.7 times of the initial maximum capacity.
To exemplify an intermediate point, at 4.0 V at charge, the old battery already reaches 0.4 times the
initial maximum capacity whereas the new battery is at 0.8 times the maximum capacity.
Figure 29: Alterations of the voltage vs. capacity at different cycles. Source: (Huang & Tseng, 2017).
As it can be observed in Figure 28, same SoC level at lower capacities indicate a poorer SoH of the
battery.
4.1.1.4 Depth of discharge
The Depth of Discharge (DoD) is the percentage of the battery capacity discharged relative to the
maximum and thus, is complementary to SoC. Many studies have concluded that deeper DoD
shortens the lifetime of the battery whereas smaller DoD prolongs it. For example, a battery could
reach 15.000 cycles at a 10% DoD, but barely 3.000 at 80% DoD (Thoubboron, 2019).
33
Figure 30: Cycle life at different DoD. Source: (Wikner & Thiringer, 2018)
However, in the present work a specific DoD cannot be taken since the battery energy storage
system (BESS) will be driven by the cost of cycling the battery or energy prices in the energy market
tariffs and thus, will not be following a regular cycling (Wang, Zhou, Botterud, Zhang, & Ding,
2016).
After reviewing the main battery degrading causes, a numeric effect in terms of price is required.
Although Li-NMCs are one of the most long-lasting batteries nowadays, due to this degradation
and capacity fade, these batteries have a limited lifespan. Lifespan expected from manufacturers is
at least 2000 cycles but if used properly, can last up to 3000 cycles for the first life of the battery
(Lerma, 2019).
When quantifying a battery lifespan by a number cycles, 2000-3000 for example, it is referred to a
certain storage capacity, not zero. Battery capacity indicates the amount of charge battery cells can
deliver at the rated voltage and is directly correlated with the amount of electrode material
remaining. The first life ends when the capacity reaches 80% generally, and the second life up to
50% of the original new battery capacity.
For the current optimization, since a specific capacity value for the whole horizon of the problem
is needed, an average value between 80% and 50% is taken, for example 65%. Addressing the
typical size of second life battery from an EV type A, for instance Nissan Leaf or Volkswagen Golf,
it would correspond to 40-50kWh of capacity. Considering the average capacity for second life
mentioned above of 65%, the battery could offer 30kWh of storage capacity. Such capacity both
in terms of energy and power could fit with the requirements of a battery while associated to a PV
system rated at few tens of kW of installed power eg. PV system installed rated at 25kW as the one
in the building adopted for this work.
34
Concerning the study case, there is a cost associated to each charging and discharging cycle due to
degradation. This expense is the amortisation cost, which will be calculated according to the
lifespan of the second life battery. After the batteries are no longer suitable for transportation
purposes, they still have around 80% of their initial capacity as mentioned before.
According to the two study cases, refurbished and directly reused, different costs for the second
life battery will be considered. The directly reused battery will be cheaper since it has not been
improved and thus, its capacity will fade sooner, while the refurbished battery will be more
expensive because the BMS and other components will be changed in order to prolong the battery
life without reaching the cells recycling point.
Consistent with conversations with manufacturers and companies in second life batteries sector,
the battery prices considered for the study case are:
Table 12: Battery components’ prices. Source: Conversations with manufacturers.
Li-ion NMC refurbished Li-ion NMC not refurbished
Remaining
cycles 2000 cycles 1800 cycles
Cell 91 €/kWh 65 €/kWh
BMS 40 €/kWh 10 €/kWh
Packaging 50 €/kWh 20 €/kWh
Total 181 €/kWh 95 €/kWh
As specified in table 12 the cycles considered for the second life are 2000 for the refurbished ones,
since the technical improvements extend the battery life, and 1800 for the not refurbished battery.
However, the renewed one is more expensive than the directly reused for the investment required
in upgrading it. Therefore the amortisation costs for each case can be calculated as follows:
Li-ion NMC refurbished:
181€/𝑘𝑊ℎ
2000 𝑐𝑦𝑐𝑙𝑒𝑠= 0.0905 €/𝑘𝑊ℎ − 𝑐𝑦𝑐𝑙𝑒
Li-ion NMC not refurbished:
95€/𝑘𝑊ℎ
1800 𝑐𝑦𝑐𝑙𝑒𝑠= 0.053 €/𝑘𝑊ℎ − 𝑐𝑦𝑐𝑙𝑒
These prices are set per kWh since the algorithm works with any battery and thus, the capacity of
30kWh is also specified in the simulation process.
35
4.1.2 Prices
The electricity price will be determinant for the final decision to proceed with the flexibility request
from the storage system and thus, it is a crucial variable. The electricity price is determined by more
than one factor that affects the energy price. As for the electricity bill, there are 4 concepts that are
typically broken down:
- Power term: determines the fixed amount the consumer pays for being connected to the
grid at a certain load capacity.
- Equipment rental: It is charged to all those customers who do not own their electric meter.
Not applicable in this case.
- Consumption term: price for the energy consumed by a customer during a specific billing
period. Costs depend on the tariff contracted.
- Tax on electricity: Taxes the cost of manufacturing electricity and the term of consumption.
The overall objective is to follow a battery charging/discharging approach so that the total costs
are minimized. In order to achieve so, the model must take into account all of the listed points
when making the decisions and drawing a strategy:
- Lowering the purchase above the max contracted power.
- Shifting purchase from high price hours to low price hours.
- Taking into account the battery amortisation price when activating a flexibility request,
meaning a comparison between this cost and the grid electricity cost in that time period is
required.
Once the prices and the rate periods are set, the data is ready to be entered into the algorithm. The
main outputs of these simulations will be the optimal usage of the second life battery and the energy
costs and savings from using each battery (refurbished /directly reused) in terms of electricity bill
and battery amortisation.
4.1.2.1 Power term
In the present case, the tariff contracted is the Spanish rate 3.0A, which is the network access rate
determined by law for all low-voltage supply points with more than 15 kW of contracted power.
There are three invoice periods depending on the hour of the day. These invoice periods in the
region corresponding to the study case building (Iberian Peninsula) are listed in Table 13.
Moreover, the prices corresponding to each invoice period are determined by each retailer
contracted. Som Energia has been considered the electricity retailer for the present work. Their
power listed prices for the 3.0A rate and mentioned periods are presented in Table 14.
36
Table 13: Invoice periods of the Spanish tariff 3.0A. Source: Som Energia
Table 14: Retailer power pries for each invoice period. Source: Som Energia
For the simulation carried out in this paper, a single representative number needs to be taken. Then,
the value taken is the weighted average taking into account all invoicing periods and their
corresponding hours. This gives a value of 24.43 €/kW-year and will be used to calculate the power
expenses related to the year simulated.
4.1.2.2 Consumption term
As indicated in the power term section, the tariff contracted is the Spanish rate 3.0A. The listed
prices from Som Energia for the 3.0A rate and corresponding the three invoice periods are:
Table 15: Retailer consumption pries for each invoice period. Source: Som Energia.
Periods Jan - March
Oct - Dec April- Sept
P1 – tip period 18 - 22 h 11 - 15 h
P2 – flat period
8 - 18 h
22 - 24 h
8 - 11 h
15 - 24 h
P3 – valley period 0 - 8 h 0 - 8 h
P1 – tip period 40.718885 €/kW-year
P2 – flat period
24.437330 €/kW-year
P3 – valley period 16.291555 €/kW-year
P1 – tip period 0.13 €/kWh
P2 – flat period
0.093 €/kWh
P3 – valley period 0.066 €/kWh
37
4.1.2.3 Taxes
Taxes considered in the present work are:
- Electricity Tax: regulated by the government, applied to the sum of the terms energy and
power and equal to 5.11%.
- VAT: applied to the sum of the four concepts mentioned above (power term, equipment
rental, consumption term and tax on electricity) and equal to 21%.
4.2 Testing procedure
Fort he simulation process, 15 min have been set for the time resolution since it is the reference
number for all ancillary services markets (European Comission, Directorate-General for Energy,
2016). Given the high time resolution, the optimisation cannot run for a whole year at once due to
computational burden. Hence, one week per month is taken as a sample and the results will be
extrapolated to the other three remaining weeks to get an entire year result. Also, this
representative-week model can be done because the optimisation algorithm does not take into
account the battery installation cost and only considers the battery usage.
This study is a planning assessment and thus, the main goal is to find the technical and economic
viability for the second life battery installation in buildings. In this way, the present work relies on
historical data and evaluates a long operational period, one year, and applies the “rolling horizon”
technique, which means running the optimisation every hour to correct the photovoltaic resource
and demand forecast for the best battery operation adjustment.
In terms of contracted power, there have been three scenarios:
Initially, the power contracted was of 80 kW. In this situation, no battery was installed yet. This scenario has been used to check the load profile of the building and compute the current electricity bill. This is the base case without battery.
The evaluations of the base case without battery lead us to propose a second scenario with a lower contracted power, now including the battery for energy optimisation purposes. In particular, 5% reduction is proposed leading to a contracted power of 76 kW. This case was named as baseline optimisation scenario, where the system still had enough room for further power reduction.
This baseline scenario will be adjusted in terms of power until reaching the boundaries of optimisation feasibility. A presented later in chapter 5 and addressing the target of the project of reducing the contracted power of the building up to 25%, the minimum contracted power feasible for the optimization process is 60 kW. This means that the contracted power will be reduced until the mathematical optimisation reaches a point of no solution, according to the parameters characterizing the case study. This scenario will be identified as power reduction case.
The simulation procedure will be carried out four times, twice for a reconditioned battery and twice for a directly reused battery. The difference will stand in the amortization price and degradation (number of cycles the battery will be able to handle) in one hand, and in the power contracted in the other.
38
4.3 Mathematical formulation
4.3.1 Overview of sets, parameters and variables
4.3.1.1 Sets
Table 16: Sets used in the simulation. Source: INVADE project
4.3.1.2 Parameters
Table 17: Parameters used in the simulation. Source: INVADE project
T Set of periods/time slots in the planning horizon
Tc Subset of periods where curtailment is allowed
B Set of battery units
L Set of load units
Li Subset of inflexible load units
G Set of generation units
Gd Subset of curtailable disconnectable generation units
𝑷𝒕𝒓𝒆𝒕𝒂𝒊𝒍−𝒃𝒖𝒚
Price at energy part of retail contract for buying electricity in period
t [€/kWh]
𝑷𝒕𝒓𝒆𝒕𝒂𝒊𝒍−𝒔𝒆𝒍𝒍
Price at energy part of retail contract for selling electricity in period t
[€/kWh]
𝑷𝑽𝑨𝑻 Addition of VAT to the bought amount [fraction]
𝑿𝒊𝒎𝒑−𝒄𝒂𝒑 Maximum import capacity [average kW]
𝑿𝒆𝒙𝒑−𝒄𝒂𝒑 Maximum export capacity [average kW]
M Limitation of basis for peak fee [kW]
𝑶𝒃𝒎𝒊𝒏 Minimum state of charge for battery b [kWh]
𝑶𝒃𝒎𝒂𝒙 Maximum state of charge for battery b [kWh]
𝑶𝒃𝒊𝒏𝒊 Initial state of charge for battery b [kWh]
𝑨𝒃𝒄𝒉 Efficiency parameter for charging storage unit b [#]
𝑨𝒃𝒅𝒊𝒔 Efficiency parameter for discharging storage unit b [#]
𝑷𝒃,𝒕𝑩,𝒄𝒉 Price for charging battery unit b at period t [€/kWh]
𝑷𝒃,𝒕𝑩,𝒅𝒊𝒔 Price for discharging battery unit b at period t [€/kWh]
𝑾𝒍,𝒕𝒍𝒐𝒂𝒅 Baseline consumption at load unit l in period t [kWh]
𝑾𝒈,𝒕𝒑𝒓𝒐𝒅
Baseline production from generation unit g in period t [ kWh]
𝑵𝒉𝒐𝒖𝒓 Periods per hour [#]
39
4.3.1.3 Variables
Table 18: Variables in the simulation. Source: INVADE project
4.3.2 General constraints
4.3.2.1 Battery model
Each battery, referred as unit b, accounts with efficiency parameters both for charging and
discharging sequences (𝑨𝒃𝒄𝒉, 𝑨𝒃
𝒅𝒊𝒔). The battery current state of charge, 𝜎𝑏,𝑡𝑠𝑜𝑐 , depends on the SoC
in the previous period and on the charging/discharging (𝝈𝒃,𝒕𝒄𝒉 , 𝝈𝒃,𝒕
𝒅𝒊𝒔) in the current period.
𝜎𝑏,𝑡𝑠𝑜𝑐 = 𝜎𝑏,𝑡−1
𝑠𝑜𝑐 + 𝜎𝑏,𝑡𝑐ℎ ∗ 𝐴𝑏
𝑐ℎ −𝜎𝑏,𝑡
𝑑𝑖𝑠
𝐴𝑏𝑑𝑖𝑠 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 1)
The SoC must be between the limits set:
𝑂𝑏𝑚𝑖𝑛 ≤ 𝜎𝑏,𝑡
𝑠𝑜𝑐 ≤ 𝑂𝑏𝑚𝑎𝑥 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 2)
𝑿𝒕𝒃𝒖𝒚
Amount of electricity bought in period t [kWh]
𝑿𝒕𝒔𝒆𝒍𝒍 Amount of electricity sold in period t [kWh]
𝑿𝒕𝒔𝒆𝒍𝒍 Amount of electricity sold in period t [kWh]
𝑿𝒑𝒆𝒂𝒌 Basis for calculation of peak fee in cases where this is a part of the
grid contract [kW]
𝝍𝒈,𝒕 Amount of electricity produced from generating unit g in period t
[kWh]
𝜻𝒈𝒆𝒏 Total cost for utilizing generation flexibility [€] (curtailable
disconnectable).
𝝎𝒍,𝒕 Amount of electricity consumed from load unit l in period t [kWh]
𝝈𝒃,𝒕𝒄𝒉 Amount of electricity charged to battery unit b in period t [kWh]
𝝈𝒃,𝒕𝒅𝒊𝒔
Amount of electricity discharged from battery unit b in period t
[kWh]
𝝈𝒃,𝒕𝒔𝒐𝒄 Amount of energy in battery b in period t
𝜻𝒇𝒍𝒆𝒙𝒊𝒃𝒊𝒍𝒊𝒕𝒚 Total cost for utilizing internal flexibility [€]
𝜹𝒕𝒃𝒖𝒚
Binary variable = 1 if site is importing/buying electricity in period t,
else = 0
𝜹𝒕𝒔𝒆𝒍𝒍
Binary variable = 1 if site is exporting/selling electricity in period t,
else = 0
𝜹𝒈,𝒕𝒈𝒆𝒏 Binary variable = 0 if generating unit g is disconnected in period t,
else 1
40
Also, the initial SoC of the battery must be equal or lower to the SoC of the last time period. This
constrain ensures that the SoC at the end of the optimisation period is high enough so that the
battery can operate in subsequent periods.
𝑂𝑏𝑖𝑛𝑖𝑡 ≤ 𝜎𝑏,𝑇
𝑠𝑜𝑐 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 3)
Charging and discharging must not exceed their maximum levels:
𝜎𝑏,𝑡𝑐ℎ ≤
𝑄𝑏𝑐ℎ
𝑁ℎ𝑜𝑢𝑟 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 4)
𝜎𝑏,𝑡𝑑𝑖𝑠 ≤
𝑄𝑏𝑑𝑖𝑠
𝑁ℎ𝑜𝑢𝑟 , ∀ 𝑏 𝜖 𝐵, 𝑡 𝜖 𝑇 (Eq. 4)
4.3.2.2 Load model
For the present work the load studied includes the whole building and thus, it is considered as an
inflexible load unit. Other types of loads could be shiftable or curtailable. For the inflexible loads,
the planned load 𝜔𝑙,𝑡 needs to match the anticipated load 𝑊𝑙,𝑡𝑙𝑜𝑎𝑑 .
𝜔𝑙,𝑡 = 𝑊𝑙,𝑡𝑙𝑜𝑎𝑑 ∀ 𝑙 𝜖 𝐿𝑖 , 𝑡 𝜖 𝑇 (Eq. 5)
4.3.2.3 Generator model
In this study case the generation units are curtailable disconnectable. Solar photovoltaic panels
cannot regulate and reduce their power, but offer this slight flexibility by disconnecting them. For
curtailable disconnectable units, intended production must be either 0 or equivalent to expected
production.
𝜓𝑔,𝑡 = 𝛿𝑔,𝑡𝑔𝑒𝑛
𝑊𝑔,𝑡𝑝𝑟𝑜𝑑, ∀ 𝑔 𝜖 𝐺𝑑 , 𝑡 𝜖 𝑇 (Eq. 6)
4.3.3 Prosumer model
4.3.3.1 Prosumer services objective function
Since the main purpose of this study is helping reduce the energy costs for the prosumer by using
energy storage, the objective function seeks to minimize the electricity costs, electricity taxes and
costs for operating flexibility.
min 𝑧 = ∑[(𝑃𝑡𝑟𝑒𝑡𝑎𝑖𝑙−𝑏𝑢𝑦
+ 𝑃𝑡𝑡𝑎𝑥)𝒳𝑡
𝑏𝑢𝑦𝑃𝑉𝐴𝑇 − (𝑃𝑡
𝑟𝑒𝑡𝑎𝑖𝑙−𝑠𝑒𝑙𝑙)𝒳𝑡𝑠𝑒𝑙𝑙] + 𝜁𝑓𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦
𝑡∈𝑇
(Eq. 7)
41
4.3.3.2 Prosumer services constraints
There must exist a balance between generation, consumption and battery charge/discharge, and
import/export from/to the grid in every time period.
∑ 𝜓𝑔,𝑡
𝑔∈𝐺
+ ∑ 𝜎𝑏,𝑡𝑑𝑖𝑠
𝑏∈𝐵
+ 𝜒𝑡𝑏𝑢𝑦
= 𝜒𝑡𝑠𝑒𝑙𝑙 + ∑ 𝜔𝑙,𝑡
𝑙∈𝐿
+ ∑ 𝜎𝑏,𝑡𝑐ℎ
𝑏∈𝐵
, ∀ 𝑡 𝜖 𝑇
(Eq. 8)
Additionally, two binary variables that take value 1 if the location is importing or exporting, which
corresponds to buying and selling respectively, else value 0. With this constraint, the site cannot be
buying and selling energy at the same time.
𝛿𝑡𝑏𝑢𝑦
+ 𝛿𝑡𝑠𝑒𝑙𝑙 ≤ 1, ∀ 𝑡 𝜖 𝑇 (Eq. 9)
Finally, last restriction consists on limiting the energy bought/sold to capacity limits.
𝜒𝑡𝑏𝑢𝑦
≤ 𝛿𝑡𝑏𝑢𝑦
Χ𝑖𝑚𝑝−𝑐𝑎𝑝, ∀ 𝑡 𝜖 𝑇 (Eq. 10)
𝜒𝑡𝑠𝑒𝑙𝑙 ≤ 𝛿𝑡
𝑠𝑒𝑙𝑙Χ𝑒𝑥𝑝−𝑐𝑎𝑝, ∀ 𝑡 𝜖 𝑇 (Eq. 11)
42
5 RESULTS AND DISCUSSION
5.1 Simulation results
The results for both refurbished and directly reused battery, which come from the analysed data of
SIRENA UPC, are plotted in twelve different graphs, forty-eight in total, which are presented in
the Appendix, one for each month and only taking one week as an average model to be extrapolated
as mentioned before. The simulation cases are two for 76kW power case, and 2 for the power
reduction scenario of 60kW. For each power scenario has been simulated a refurbished battery and
a directly reused battery differentiating the amortisation. Thus, the 4 simulated cases are:
Directly reused at 76kW,
Directly reused at 60kW,
Refurbished at 76kW and
Refurbished at 60kW.
The graphs are plotting the evolution lines corresponding to the load demand (building) presented
as “inflexible load”; PV generation pattern; the battery charge/discharge behaviour; the base net
load representing the PV generation minus the load demand without any battery implication; the
net energy exchange, which is the base net load plus/minus the battery use; the battery state of
charge; the momentary battery power; and the total electricity purchase price at which energy is
being bought from the retailer at each of the three invoice periods. The load profile of the building
represents the typical occupation of a public office building during working hours. The building
load is drastically reduced during weekend days and at nights.
As stated in section 4.1, the baseline scenario is run at 76 kW as contracted power. The second
simulation adjusting the power term thanks to the battery integration corresponds to a contracted
power of 60 kW, aligning with the 20% reduction objective. This value has been taken from the
results of the first simulation, where it was clear that it could be adjusted since only punctual peaks
were found, which can be attributed to measurement errors or punctual phenomenon that would
be more cost-effective to pay the eventual penalty rather than raising the power level only for this.
The simulation is run for a time period of a week, as a representative slot time per each month, the
results are extrapolated to a month and finally computing the annual results. The first week of each
month has been taken regardless of the weekly day it started. As a consequence the results shown
throughout the project do not reflect a Monday-Sunday week. The model months are of 28 days
to match with exactly 4 weeks for simplification purposes, so the remaining days have been
neglected. The week sequence simulation therefore operates with 672 input/output data points,
corresponding to the 15-minute frequency testing explained in chapter 4.2.
To exemplify the results, a set of graphs presenting typical seasons with representative range of
supply/demand conditions for one of the cases is presented below. The chosen case is the “directly
reused battery (0.053 amortisation cost) with the baseline power case scenario of 76 kW”. In all
graphs presented below the left hand axis of the graphs entails the energy in kWh since it plots the
electricity exchanged with the grid by the building (per each 15-minute time slot) including the load
demand variation, battery activity and local PV generation. The right hand axis refers to the battery
SoC and grid electricity price levels.
43
Figure 31: Winter Season typical supply/demand scenario (February).
In case of a winter month, the climatology does not provide many PV generation (negative curve
since it is an input energy into the system) compared to the inflexible load of the building. With
this, the storage system participation is minor, since most of the PV generation is immediately
consumed. This can be observed comparing the base net load and the net energy exchange.
During the weekend, which are the two days where the electricity consumption is nearly zero in
the building, all of the PV output is aimed at charging the battery with certain small exceptions
when local power generation is exported to the grid. Therefore, the energy storage system state of
charge is at its maximum at the beginning of each new week.
Figure 32: Spring Season typical supply/demand scenario (April).
During spring season there is more fluctuation given the higher amount of sun hours and thus, of
PV generation. Especially at midday, an increase of PV generation can be detected. Also, there is a
charging process of the battery during weekends as in the previous case.
44
Figure 33: Summer Season typical supply/demand scenario (July).
In summer, PV generation increases but also the consumption rises due to air conditioning load.
The simulation result shows the frequent peaks of building load are diminished thanks to the
battery intervention. Again, during the weekend the battery is able to recharge fully.
Figure 34: Autumn Season typical supply/demand scenario (October).
In autumn, the building demand is still high but also deeply fluctuating. This might happen because
the air conditioning load is still turned on intermittently.
5.2 Discussion
5.2.1 Peak load reduction target
The simulation graphs results, which there are 48 of them, correspond to one representative week
of each month and show the 4 different cases mentioned: refurbished battery, directly reused,
76kW baseline scenario and 60kW reduced power scenario.
Thanks to the battery installation, a peak demand reduction can be observed (see Appendix) in
some extreme cases, where the ‘base net load’ is mitigated to the final ‘net energy exchange’. As it
can be expected, ‘power storage’ in undertakes a negative peak meaning it is delivering energy to
the system. In this example can be clearly observed the value and effectiveness of a storage system
in terms of energy and power reduction when a peak consumption episode occurs.
45
The following excerpt from a graph observes the peak reduction event:
Figure 35: Peak reduction. Directly reused battery (0.053 amortisation).Baseline case 76kW August.
The results of the optimization, yields that for load peak shaving purposes, the stress of the battery
is usually important both in terms of depth of discharge and peak power exchanged. For instance,
note the instantaneous peak power developed by the battery highlighted in the figure 35 above
(around 32 kW) gives a SoC variation of 26% calculated from the SoC before the peak reduction.
This important instantaneous power exchange by the battery affects its lifespan. Thus, increasing
the battery ratings is identified as an option for extending battery lifespan in other scenarios.
Further, evaluating other peaks and as observable in table 19, no difference is found regarding
seasonality. In most cases reported in this table the instantaneous peaks developed by the battery
are between 28-30 kW. Considering the ratings of the battery, 30 kWh, the battery would work
near the nominal current for peak reduction purposes. In energy terms the SoC does not overcome
such a drastic reduction as in power terms.
See the Appendix for the source graphs corresponding to table 19.
Table 19: Peak reduction cases
Instantaneous peak power
developed by the battery
SoC variation
(%)
August 76kW- 0.053 32 kW 26%
December 60 kW 0.053 28 kW 23%
August 76 kW 0.0905 32 kW 26%
September 76 kW 0.0905 12 kW 10%
September 60 kW 0.0905 32 kW 26%
46
Figure 36: Peak reduction. Directly reused battery (0.053 amortisation).Baseline case 76kW September
Another insight about peak load reduction is addressing the output. As noted in Figure 36, the PV
generation is not coincident with the maximum peak of demand usually at the beginning of the
day. Thus, the battery storage helps reducing that peak, which could not be addressed efficiently in
any other way.
Even when the rate of local renewable generation in the building is increased, the existing challenge
with sharp load peaks would remain unresolved. In such case and for power reduction purposes
the battery becomes the main solution.
In addition, the battery appears to be stressed in some cases, almost reaching the 100% SoC and
also 0%. These extremes in the SoC and huge DoD affect the battery aging and degradation
process. Then, trying to use a bigger battery would involve a higher investment cost but it would
be worth it in the long term if the capacity fades slower than the smaller battery.
To generalise on the feasibility of extending battery capacity, extensive further research is needed.
There is a trade-off between technical performance and initial investment to be formally explored.
5.2.2 Savings estimation
Alongside the graphs of the battery use, demand load, etc. the simulation also provides information
about the energy savings, which already take into account the amortisation cost of having the
storage system, referred as flexibility cost. All the calculations for energy savings and all the data
from the simulation are again presented in the ”Energy-related costs” section of the Appendix.
Similar to the treatment of energy results, the cost estimation can be extrapolated from one week
to the whole month and thus calculate the total amount for one year, summing up the 12 months.
As an example, baseline case 76kW directly reused (0.053 amortisation) is presented in table 20.
Furthermore, it should be noted that the total weekly costs are equal to the total weekly electricity
costs plus the total weekly flexibility costs (A=B+C).
47
Table 20: Costs breakdown assessment strategy
Electricity consumption costs Baseline case 76 kW
Weekly cost
(A=B+C)
Weekly electricity cost
(B)
Weekly flexibility cost
(C)
Weekly cost base case
(D)
Monthly cost base case (E=Dx4)
Monthly total cost (F=Ax4)
January 183.2101783 182.4011301 0.809048244 185.4762937 741.9051748 732.8407132
February 148.4487534 147.0175018 1.431251634 152.0041695 608.016678 593.7950136
March 86.23954438 81.93143246 4.308111917 92.59085007 370.3634003 344.9581775
April 88.25154156 87.03161529 1.219926268 94.03290726 376.131629 353.0061662
May 73.74772933 69.44135253 4.306376798 81.03187522 324.1275009 294.9909173
June 275.1925852 273.7881696 1.404415599 278.780733 1115.122932 1100.770341
July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988
August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159
September 420.7555532 418.311617 2.443936184 424.8965521 1699.586208 1683.022213
October 280.9540873 278.6547641 2.299323251 284.0274775 1136.10991 1123.816349
November 150.8715553 147.5662055 3.305349765 155.207416 620.829664 603.4862212
December 253.0762903 252.3850698 0.691220561 254.0222983 1016.089193 1012.305161
Total annual costs 11892.14632 11709.72842
Total annual savings 182.4179014
Percentage savings 1.53%
The summary of cost breakdown into months can be found in the Appendix. These savings are
presented in table 21 for the whole year, for both battery types and for each power limit:
Table 21: Annual energy costs and savings.
Directly reused
Costs Savings %Savings
76 kW power limit 11709.73 € 182.42 € 1.53%
60kW power limit 11718.60 € 173.54 € 1.46%
Refurbished
Cost Savings %Savings
76 kW power limit 11783.69 € 108.45€ 0.91%
60kW power limit 11798.36 € 93.78 € 0.79%
48
Percentage savings are based on the baseline cost, which is the cost associated to the energy needed
when only taking into account the demand and the PV generated (without any battery present in
the system).
Moreover, the savings for reducing the power term are calculated. Observing the simulation results
graphs, particularly the base net load, which draws the profile of the building inflexible demand
minus the instantaneous PV generation (cannot be stored and used when needed without a battery),
the power contracted before the battery installation can be considered 80kW.
Given the most demanding months are usually July and August for refrigeration reasons, these will
be tackled to check the power needs. In these months, the energy frequently surpasses 15 kWh
almost reaching the 20kWh each quarter of an hour according to simulation time resolution, which
corresponds to a power of 80 kW hourly. As Som Energia states in the power contract, the invoice
can be variable every month following the conditions presented in table 22:
Table 22: Power term invoice conditions. Souce: Som Energia, Nov. 2020.
For simplification purposes, neither the lower nor upper cases will be considered. The lower case,
less than 85%, if it is not taken into consideration in either case (80kW, 76kW, 60kW) it will not
be reflected in the final comparison. Same with the upper case, since there are no significant power
surpluses and only punctual episodes, which could even be considered meter reading errors, this
case is neglected too. And finally, the average power case, will contemplate the power contracted,
and not the power used, since the simulation does not provide this value and it is not possible to
calculate form the graphs due to its instantaneity. Therefore, the power term savings are calculated
as follows.
76kW power baseline case:
(80 𝑘𝑊 − 76𝑘𝑊) ∗ 24.43€
𝑘𝑊 − 𝑦𝑒𝑎𝑟= 97.72€/year
60kW power reduction case:
(80 𝑘𝑊 − 60𝑘𝑊) ∗ 24.43€
𝑘𝑊 − 𝑦𝑒𝑎𝑟= 488.6€/year
Power used Power invoiced
< 85% of the contracted power 85% of the contracted power will be charged
Between 85% and 105% of the contracted power The power used will be charged
>105% of the contracted power
The power used + a penalisation (double of the
difference between the registered value and the
value corresponding to 105% of the power
contracted) will be charged
49
Table 23: Power savings.
The percentage savings are calculated with respect to the base case of 80kW, not with respect to
the baseline case, which is already part of the study case.
As it can be observed from the results presented, the larger savings in absolute number are in the
energy bill, although they represent a very small percentage. On the contrary, the power savings
are a small amount compared to energy ones, but significantly higher in percentage, specially the
60kW case.
Costs [€/year] Savings
80kW without battery 1954.40 -
76kW baseline case 1856.68 5%
60kW reduction case 1465.80 25%
50
6 CONCLUSIONS
As to conclude with the present study and answering to the main research question, it is feasible
to install a second-life battery from EVs addressing the specifications of the evaluated case study
of a typical public building of medium size, such as a university office building. The integration of
the battery in the building electricity system permits a substantial reduction of the power contracted
from the grid and optimizes the load interaction with the grid. The energy bill has been reduced,
both the electricity costs and peak power costs are decreased. Particularly the peak load shaving
possibility has been highlighted, which corresponds to the 25% reduction target.
Besides the technical objectives of this study, the battery supply chain challenge had been discussed
and tackled. As expected, way more effort needs to be put into battery recycling, as it is still not a
feasible solution nowadays. Also, progress must be made in storage management and control tools
in order to get more information of the battery status in real time, as well as in the definition of
business models that consider the provision of more than one facility for the battery. A first and
important step in developing a circular economy around energy storage is thus, to give EV batteries
a second life for stationary services.
Results reveal very little relative savings in terms of electricity bill, in the order of 1% - 2% for the
particular case study. However, with the growing scale of energy storage applications, this relatively
inconspicuous benefit could expand to more sizeable levels in absolute numbers. On the other
hand, the reduction of contracted peak power for the building due to the battery peak mitigation
is considerably higher in percentage, up to 25%, but lower in absolute terms.
Furthermore, the battery amortisation costs are not significant in the computation yet meaningful
for the evaluation case. These results confirm how a directly reused battery has of course lower
amortisation costs because it has not been treated nor refurbished. Also, the poorer capacity of the
directly reused battery, compared to the refurbished one, does not affect given the energy demand
values in this project are not very high. Still, further research should be done in order to determine
if this would have a considerable effect in a larger scale.
As the main weak point is that the energy produced by the PV installed is almost immediately used
with very little battery intervention, an additional work on the topic could study the impact of
having larger renewable assets achieving higher renewable availability. With a large surplus of local
renewable energy production, other services could be provided apart from self-consumption. The
battery could provide congestion management services as well as voltage regulation, not only to
the prosumer, but also to third parties interested.
The outcomes of the project demonstrate that second-life battery application for stationary energy
storage is a feasible technology, but it is still necessary to develop tools for estimating the state of
health of batteries and monitoring their performance in the new environment, as the battery might
already be underperforming once rejected from the initial application. However, with the increasing
interest and necessity of circular economy, the high volume of EV batteries to be replaced as
unsuitable for automotive purposes anymore, the expanding deployment of renewable assets and
the insufficient end-of-life material recycling capacity, there will be a boost of research in this area,
which would produce a huge positive impulse for this technology.
51
REFERENCES
Adams, E. (2017, 05 31). WIRED. Retrieved April 16, 2020 from The Age of Electric Aviation Is Just 30
Years Away: https://www.wired.com/2017/05/electric-airplanes-2/
Battery University. (2020, September 17). How to Prolong Lithium-based Batteries. Retrieved September 21, 2020
from Learn about batteries:
https://batteryuniversity.com/learn/article/how_to_prolong_lithium_based_batteries
Battery University. (2017, April 9). Learn About Batteries. Retrieved June 8, 2020 from Battery University:
https://batteryuniversity.com/index.php/learn/article/the_octagon_battery_what_makes_a_battery_a_b
attery
Bioenergy International. (2019, November 13). Bioenergy International. Retrieved June 15, 2020 from IEA's
World Energy Outlook 2019 shows deep disparities in the global system:
https://bioenergyinternational.com/markets-finance/ieas-world-energy-outlook-2019-shows-deep-
disparities-in-the-global-system
Blengini, G., Mathieux, F. M., Nyberg, M., Viegas, H. (., Salminen, J., Garbarino, E., et al. (2019). Recovery of
critical and other raw materials from mining waste and landfills: State of play on existing practices. Luxembourg, 2019:
Publications Office of the European Union.
Canals Casals, L., & Amante Garcia, B. (2016). Assessing Electric Vehicles Battery Second Life Remanufacture and
Management. Barcelona: Journal of Green Engineering, Vol. 6, 77–98. doi: 10.13052/jge1904-4720.614 ⃝c
2016 River Publishers.
CDB Arquitectura. (2006, - -). CDB Arquitectura. Retrieved 03 23, 2021 from Edificio Gaia:
https://cdbarquitectura.com/project/edificio-gaia/
Directorate-General for Internal Market, Industry, Entrepreneurship; European Commission. (2018,
November 5). Publications Office of the EU. Retrieved May 25, 2020 from Report on critical raw materials and
the circular economy: https://op.europa.eu/en/publication-detail/-/publication/d1be1b43-e18f-11e8-
b690-01aa75ed71a1/language-en/format-PDF/source-80004733
European Comission. (2017, September 13). COMMUNICATION FROM THE COMMISSION TO THE
EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL
COMMITTEE AND THE COMMITTEE OF THE REGIONS on the 2017 list of Critical Raw Materials for
the EU. Retrieved May 25, 2020 from List of Critical Raw Materials for the EU 2017: https://eur-
lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52017DC0490&from=EN
European Comission. (2020, - -). Internal Market, Industry, Entrepreneurship and SMEs. Retrieved May 25, 2020
from Critical raw materials: https://ec.europa.eu/growth/sectors/raw-materials/specific-
interest/critical_en
European Comission. (2019, December 11). Press corner. Retrieved April 27, 2020 from The European Green
Deal Investment Plan and Just Transition Mechanism explained:
https://ec.europa.eu/commission/presscorner/detail/en/qanda_20_24
European Comission. (2019, December 11). Striving to be the first climate-neutral continent. Retrieved April 27,
2020 from A European Green Deal: https://ec.europa.eu/info/strategy/priorities-2019-2024/european-
green-deal_en
European Comission. (2019, December 11). Sustainable industry. Retrieved April 30, 2020 from The
European Green Deal: https://ec.europa.eu/commission/presscorner/detail/en/fs_19_6724
European Comission, Directorate-General for Energy. (2016). METIS Technical Note T4 Overview of European
Electricity Markets. Brussels: European Commission.
52
European Commission. (2020). "European Commission, Study on the EU’s list of Critical Raw Materials – Final
Report (2020)". Luxembourg: Publications Office of the European Union, 2020.
European Commission. (2019, December 11). Climate Action. Retrieved April 27, 2020 from European
Climate Law: https://ec.europa.eu/clima/policies/eu-climate-action/law_en
European Commission. (2020, March 11). Internal Market, Industry, Entrepreneurship and SMEs. Retrieved April
27, 2020 from New Circular Economy Action Plan shows the way to a climate-neutral, competitive
economy of empowered consumers: https://ec.europa.eu/growth/content/new-circular-economy-action-
plan-shows-way-climate-neutral-competitive-economy-empowered_en
European Commission. (2011). TACKLING THE CHALLENGES IN COMMODITY MARKETS
AND ON RAW MATERIALS. COMMUNICATION FROM THE COMMISSION TO THE
EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL
COMMITTEE AND THE COMMITTEE OF THE REGIONS (p. 22). Brussels: European Comission.
Georgi-Maschlera, T., Friedricha, B., Weyheb, R., Heegnc, H., & Rutzc, M. (2012). Development of a
recycling process for Li-ion batteries. Journal of Power Sources , 173–182.
Green Car Congress. (2019, February 16). Green Car Congress. Retrieved June 18, 2020 from DOE launches
its first Li-ion battery recycling R&D center: ReCell; driving toward closed-loop recycling:
https://www.greencarcongress.com/2019/02/20190216-recell.html
Honsberg, C., & Bowden, S. (2019, - -). Battery Capacity. Retrieved September 14, 2020 from Photovoltaics
Education Website: https://www.pveducation.org/pvcdrom/battery-characteristics/battery-capacity
Huang, S.-C., & Tseng, K.-H. (2017). Online SOC and SOH Estimation Model for Lithium-Ion Batteries.
Energies , 512.
IEA. (2019, November 1). International Energy Agency. Retrieved March 20, 2020 from World Energy Model:
https://www.iea.org/reports/world-energy-model/sustainable-development-scenario
INVADE. (2020, - -). INVADE. Retrieved - -, 2020 from INVADE: https://h2020invade.eu
John, J. S. (2019, April 10). Green Tech Media. Retrieved March 20, 2020 from Energy Storage:
https://www.greentechmedia.com/articles/read/global-energy-storage-to-hit-158-gigawatt-hours-by-
2024-with-u-s-and-china
Kane, M. (2017, August 30). INSIDEEVs. Retrieved April 21, 2020 from News:
https://insideevs.com/news/334714/tesla-model-s-surpasses-250000-miles-just-7-battery-degradation/
Keil, P., & Jossen, A. (2015, May). Aging of Lithium-Ion Batteries in Electric Vehicles: Impact of
Regenerative Braking. Conference: EVS28 - The 28th International Electric Vehicle Symposium and Exhibition , 11.
Leng, F. T. (2015). Effect of Temperature on the Aging rate of Li Ion Battery Operating above Room
Temperature. Scientific Reports , 12.
Lerma, A. (2019, March 14). Flux Power. Retrieved August 31, 2020 from Lithium-Ion vs Lead Acid Battery
Life: https://www.fluxpower.com/blog/lithium-ion-vs.-lead-acid-battery-life
Lowe, M., Tokuoka, S., Trigg , T., & Gereffi , G. (2010, October 5). Lithium-ion Batteries for Electric Vehicles:
THE U.S. VALUE CHAIN. Retrieved June 6, 2020 from Research Gate:
https://www.researchgate.net/publication/294580055_Lithium-
ion_Batteries_for_Electric_Vehicles_the_US_Value_Chain
Ma, S., Jiang, M., Tao, P., Songa, C., Wua , J., Wangb , J., et al. (2018). Temperature effect and thermal
impact in lithium-ion batteries. Progress in Natural Science: Materials International , Pages 653-666.
McCarthy, R., & Xu, L. (2020, September 30). Green Tech Media. Retrieved November 16, 2020 from
RESEARCH SPOTLIGHT: https://www.greentechmedia.com/articles/read/woodmac-global-storage-
to-reach-741-gigawatt-hours-by-2030
53
MIT Electric Vehicle Team. (2008, December -). MIT Electric Vehicle Team. Retrieved September 14, 2020
from A Guide to Understanding Battery Specifications:
http://web.mit.edu/evt/summary_battery_specifications.pdf
OECD. (2018, April). Resource productivity and waste. Retrieved May 13, 2020 from RE-CIRCLE: resource
efficiency and circular economy: https://www.oecd.org/environment/waste/recircle.htm
OECD, G. M. (2019, February 12). Economic Drivers and Environmental Consequences. Retrieved April 21, 2020
from OECD publishing: https://read.oecd-ilibrary.org/environment/global-material-resources-outlook-
to-2060_9789264307452-en#page20
Perez, A., Montoya, F., & Quintero, V. (2018). Characterizing the Degradation Process of Lithium-ion
Batteries Using a Similarity-Based-Modeling Approach. Fourth European Conference of the Prognostics and Health
Management Society (pp. -). Utrecht, The Netherlands: -.
Pinson, M., & Bazant, M. (2012). Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging
and Lifetime Prediction. Cambridge, MA 02139, Departments of Physics, Chemical Engineering, and
Mathematics. Massachusetts Institute of Technology, : Journal of The Electrochemical Society.
Thoubboron, K. (2019, June 3). Energysage. Retrieved September 19, 2020 from Smarter energy decisions,
Depth of discharge (DoD): What does it mean for your battery, and why is it important?:
https://news.energysage.com/depth-discharge-dod-mean-battery-important/
Uddin, M.-J., Alaboina, P., & Cho, S.-J. (2017, September -). Nanostructured cathode materials synthesis for lithium-
ion batteries. Retrieved May 30, 2020 from Science Direct:
https://www.sciencedirect.com/science/article/pii/S2468606917300746
United Nations. (2020, - -). Goal 12: Ensure sustainable consumption and production patterns. Retrieved May 27,
2020 from Sustainable Development Goals: https://www.un.org/sustainabledevelopment/sustainable-
consumption-production/
UPC. (2007, - -). SIRENA UPC. Retrieved March 12, 2021 from SIRENA UPC: sirena.app.dexma.com
Wang, D., & Bao, Y. S. (2017). Online Lithium-Ion Battery Internal Resistance. Measurement Application in State-of-
Charge. Estimation Using the Extended Kalman Filter. -: -.
Wang, Y., Zhou, Z., Botterud, A., Zhang, K., & Ding, Q. (2016, October). Stochastic coordinated operation
of wind and battery energy storage system considering battery degradation. Journal of Modern Power Systems
and Clean Energy 4 , 12.
Webster, K., Bleriot, J., & Johnson, E. C. (2012, October 09). Efficiency vs Effectiveness. Retrieved May 24,
2020 from Ellen MacArthur Foundation: https://www.ellenmacarthurfoundation.org/news/efficiency-vs-
effectiveness
Wikner, E., & Thiringer, T. (2018). Extending Battery Lifetime by Avoiding High SOC. Chalmers University of
Technology, Department of Electrical Engineering. Gothenburg: Plug-in Hybrid Electric Vehicle (PHEV)).
Zablocki, A. (2019, February 22). Fact Sheet: Energy Storage (2019). (A. L. Carol Werner, Producer) Retrieved
March 15, 2020 from EESI: https://www.eesi.org/papers/view/energy-storage-2019
Zhang, T., He, Y., Wang, F., Ge, L., & Zhu, X. (2014). Chemical and process mineralogical characterizations
of spent lithium-ion batteries; an approach by multi-analytical techniques. Waste Management , 34 (6), 1051-
1058.
Zheng, X., Zhu, Z., Lin, X., Zhang, Y., He, Y., Caoa, H., et al. (2018). A Mini-Review on Metal Recycling
from Spent Lithium Ion Batteries. Engineering , 4 (3), 361-370.
54
APPENDIX
Directly reused battery (0.053 amortisation)
Baseline power case: 76kW
January:
February:
March:
55
April:
May:
June:
56
July:
August:
September:
57
October:
November:
December:
58
Power reduction case 60 kW
January:
February:
March:
59
April:
May:
June:
60
July:
August:
September:
61
October:
November:
December:
62
Refurbished battery (0.0905 amortisation)
Baseline power case 76 kW
January:
February:
March:
63
April:
May:
June:
64
July:
August:
September:
65
October:
November:
December:
66
Power reduction case 60kW
January:
February:
March:
67
April:
May:
June:
68
July:
August:
September:
69
October:
November:
December:
70
Energy-related costs:
Please note, as stated in section 5.2.2., the costs have been extrapolated to a whole month from one week,
and then summed up to a whole year to have the final annual costs. Also notice that, the total weekly costs
are equal to the total weekly electricity costs plus the total weekly flexibility costs (A=B+C).
Directly reused:
Electricity consumption costs Baseline case 76 kW
Weekly cost
(A=B+C)
Weekly electricity cost
(B)
Weekly flexibility cost
(C)
Weekly cost base case
(D)
Monthly cost base case (E=Dx4)
Monthly total cost (F=Ax4)
January 183.2101783 182.4011301 0.809048244 185.4762937 741.9051748 732.8407132
February 148.4487534 147.0175018 1.431251634 152.0041695 608.016678 593.7950136
March 86.23954438 81.93143246 4.308111917 92.59085007 370.3634003 344.9581775
April 88.25154156 87.03161529 1.219926268 94.03290726 376.131629 353.0061662
May 73.74772933 69.44135253 4.306376798 81.03187522 324.1275009 294.9909173
June 275.1925852 273.7881696 1.404415599 278.780733 1115.122932 1100.770341
July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988
August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159
September 420.7555532 418.311617 2.443936184 424.8965521 1699.586208 1683.022213
October 280.9540873 278.6547641 2.299323251 284.0274775 1136.10991 1123.816349
November 150.8715553 147.5662055 3.305349765 155.207416 620.829664 603.4862212
December 253.0762903 252.3850698 0.691220561 254.0222983 1016.089193 1012.305161
Total annual costs 11892.14632 11709.72842
Total annual savings 182.4179014
Percentage savings 1.53%
Electricity consumption costs Power reduction caseat 60 kW
Weekly cost
(A=B+C)
Weekly electricity cost
(B)
Weekly flexibility cost
(C)
Weekly cost base case
(D)
Monthly cost base case (E=Dx4)
Monthly total cost (F=Ax4)
January 183.2101783 182.4011301 0.809048247 185.4762937 741.9051748 732.8407132
February 148.4487534 147.0175018 1.431251633 152.0041695 608.016678 593.7950136
March 86.23954438 81.93143246 4.30811192 92.59085007 370.3634003 344.9581775
April 88.25153745 87.03161339 1.219924064 94.03290726 376.131629 353.0061498
May 73.74772933 69.44135252 4.306376802 81.03187522 324.1275009 294.9909173
June 275.1925852 273.7881696 1.404415601 278.780733 1115.122932 1100.770341
July 522.8372469 519.4592611 3.377985826 525.551889 2102.207556 2091.348988
August 443.8470397 437.9791134 5.867926271 445.4141185 1781.656474 1775.388159
September 422.963096 418.4896692 4.473426826 424.8965521 1699.586208 1691.852384
October 280.9540909 278.6547679 2.299322977 284.0274775 1136.10991 1123.816364
November 150.8827574 147.5761503 3.306607085 155.207416 620.829664 603.5310296
December 253.0762904 252.3850698 0.691220565 254.0222983 1016.089193 1012.305162
Total anual costs 11892.14632 11718.6034
Total anual savings 173.5429232
Percentage savings 1.46%
71
Refurbished:
Electricity consumption costs Baseline case 76 kW
Weekly cost
(A=B+C)
Weekly electricity cost
(B)
Weekly flexibility cost
(C)
Weekly cost base case
(D)
Monthly cost base case (E=Dx4)
Monthly total cost (F=Ax4)
January 183.6664538 182.7451678 0.921286002 185.4762937 741.9051748 734.6658152
February 149.349966 147.4370613 1.912904683 152.0041695 608.016678 597.399864
March 88.3551685 84.97312146 3.382047041 92.59085007 370.3634003 353.420674
April 89.72504921 88.55318668 1.171862521 94.03290726 376.131629 358.9001968
May 75.88916474 72.18382999 3.705334752 81.03187522 324.1275009 303.556659
June 276.0341635 274.412098 1.622065498 278.780733 1115.122932 1104.136654
July 525.1131169 520.9249733 4.188143552 525.551889 2102.207556 2100.452468
August 447.3130588 440.001245 7.311813765 445.4141185 1781.656474 1789.252235
September 422.6889896 420.257613 2.431376584 424.8965521 1699.586208 1690.755958
October 282.0132467 280.3347591 1.678487599 284.0274775 1136.10991 1128.052987
November 152.4007679 149.9873542 2.413413745 155.207416 620.829664 609.6030716
December 253.3743654 252.9253214 0.449044032 254.0222983 1016.089193 1013.497462
Total annual costs 11892.14632 11783.69404
Total annual savings 108.4522764
Percentage savings 0.91%
Electricity consumption costs Power reduction caseat 60 kW
Weekly cost
(A=B+C)
Weekly electricity cost
(B)
Weekly flexibility cost
(C)
Weekly cost base case
(D)
Monthly cost base case (E=Dx4)
Monthly total cost (F=Ax4)
January 183.6664538 182.7451678 0.921286019 185.4762937 741.9051748 734.6658152
February 149.349966 147.4370613 1.912904686 152.0041695 608.016678 597.399864
March 88.3551685 84.97312146 3.382047041 92.59085007 370.3634003 353.420674
April 89.7250492 88.55318585 1.171863356 94.03290726 376.131629 358.9001968
May 75.88916474 72.18382998 3.705334759 81.03187522 324.1275009 303.556659
June 276.0341635 274.412098 1.622065501 278.780733 1115.122932 1104.136654
July 525.1131169 520.9249733 4.188143552 525.551889 2102.207556 2100.452468
August 447.3130588 440.001245 7.311813765 445.4141185 1781.656474 1789.252235
September 426.3434182 420.4074464 5.935971855 424.8965521 1699.586208 1705.373673
October 282.0132467 280.3347591 1.678487598 284.0274775 1136.10991 1128.052987
November 152.4133475 149.995777 2.417570552 155.207416 620.829664 609.65339
December 253.3743653 252.9253214 0.449043934 254.0222983 1016.089193 1013.497461
Total annual costs 11892.14632 11798.36208
Total annual savings 93.78424404
Percentage savings 0.79%
72
Power-related costs:
Power term costs and savings
Power price considered: 24.43euros/kW-year Costs Savings
80kW without battery 1954.40 €/year -
76kW baseline case 1856.68 €/year 5%
60kW reduction case 1465.80 €/year 25%
Total costs:
Directly reused Refurbished
76 kW power limit 13,566.41 € 13,640.37 €
60kW power limit 13,184.40 € 13,264.16 €