Work Package 3 iDistributedPV: Solar PV on the ... · PV installation and the storage system may as well be tuned in order to make a deeper analysis. After the simulations are carried

This project has received funding from the European Union’s Horizon 2020 research and innovation

programme under grant agreement No 764452

Deliverable D3.1: Effective large distribution solar PV penetration

validation framework

Work Package 3

iDistributedPV: Solar PV on the Distribution Grid: Smart

Integrated Solutions of Distributed Generation based on Solar

PV, Energy Storage Devices and Active Demand Management Horizon 2020-LCE-2017-RES-CSA

Project Number 764452

June 30, 2018

Consortium members: Fraunhofer-Institute for Solar Energy Systems ISE (Task lead)

Asociación de Empresas de Energías Renovables

Institute of Power Engineering

Enea Operator Sp. z o.o.

ExideTechnologies

Kostal Solar Electric Iberia, S.L.

Deloitte Advisory, S.L.

Institute of Communication and Computer Systems - National Technical University of Athens

Hellenic Electricity Distribution Network Operator S.Α

Lietuvos energetikos institutas

Renerga UAB

Novareckon S.R.L.

Deutsche Energie-Agentur GmbH (dena) – German Energy Agency

Page: 1 Date: 30/06/2018

Contents 1. Assessment methodology and simulation tools ................................................................... 4

1.1. Distributed solar photovoltaic sizing ............................................................................. 5

1.1.1. Prosumer Solution Simulation Tool ....................................................................... 6

1.1.2. Economic Analysis Tool ......................................................................................... 7

1.2. Analysis of the penetration of renewable generation into power networks ............... 9

1.2.1. N-1 contingency analysis ..................................................................................... 10

1.2.2. Voltage analysis ................................................................................................... 12

1.2.3. Frequency analysis .............................................................................................. 12

2. Case studies ......................................................................................................................... 13

2.1. Germany ...................................................................................................................... 13

2.1.1. Solutions .............................................................................................................. 13

2.1.2. Gathered data ..................................................................................................... 14

2.1.3. Grid analysis information .................................................................................... 16

2.1.4. Assessable KPIs .................................................................................................... 17

2.2. Greece ......................................................................................................................... 18

2.2.1. Solutions .............................................................................................................. 18

2.2.2. Gathered data ..................................................................................................... 19


2.2.4. Assessable KPIs .................................................................................................... 23

2.3. Lithuania ...................................................................................................................... 24

2.3.1. Solutions .............................................................................................................. 24

2.3.2. Gathered data ..................................................................................................... 25


2.3.4. Assessable KPIs .................................................................................................... 32

2.4. Poland .......................................................................................................................... 33

2.4.1. Solutions .............................................................................................................. 33

2.4.2. Gathered data ..................................................................................................... 34


2.4.4. Assessable KPIs .................................................................................................... 40

2.5. Spain ............................................................................................................................ 41

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2.5.1. Solutions .............................................................................................................. 41

2.5.2. Gathered data ..................................................................................................... 42


2.5.4. Assessable KPIs .................................................................................................... 52

3. References ........................................................................................................................... 53

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Index of tables

Table 1. Input data for the country comparison ........................................................................... 8

Table 2. Average yearly consumption for the German Case studies .......................................... 14

Table 3. Electricity tariffs data for the German Case studies ...................................................... 14

Table 4. Electricity market framework data for the German Case studies ................................. 14

Table 5. Panels and inverters data for the German Case studies ............................................... 15

Table 6. Storage systems data for the German Case studies ...................................................... 15

Table 7. Simulation drivers data for the German Case studies ................................................... 15

Table 8. Assessable KPIs for the German Case studies ............................................................... 17

Table 9. Electricity tariffs data for homeowners for the Greek Case studies ............................. 20

Table 10. Electricity tariffs data for companies for the Greek Case studies ............................... 20

Table 11. Electricity tariffs data for municipal buildings for the Greek Case studies ................. 20

Table 13. Electricity market framework data for the Greek Case studies .................................. 21

Table 14. Panels and inverters data for the Greek Case studies ................................................. 21

Table 15. Storage systems data for the Greek Case studies ....................................................... 22

Table 16. Simulation drivers data for the Greek Case studies .................................................... 22

Table 17. Assessable KPIs for the Greek Case studies ................................................................. 23

Table 18. Average yearly consumption for the Lithuanian Case studies .................................... 27

Table 19. Electricity tariffs data for the Lithuanian Case studies ................................................ 28

Table 20. Electricity market framework data for the Lithuanian Case studies ........................... 29

Table 21. Panels and inverters data for the Lithuanian Case studies ......................................... 30

Table 22. Storage systems data for the Lithuanian Case studies ................................................ 31

Table 23. Simulation drivers data for the Lithuanian Case studies ............................................. 31

Table 24. Assessable KPIs for the Lithuanian Case studies ......................................................... 32

Table 25. Average yearly consumption for the Polish Case studies ........................................... 35

Table 26. Electricity tariffs data in 2018 for the Polish Case studies .......................................... 37

Table 27. Electricity market framework data for the Polish Case studies .................................. 38

Table 28. Panels and inverters data for the Polish Case studies ................................................. 38

Table 29. Simulation drivers data for the Polish Case studies .................................................... 39

Table 30. Assessable KPIs for the Polish Case studies ................................................................. 40

Table 31. Average yearly consumption for the Spanish Case studies ......................................... 43

Table 32. Electricity tariffs data for the Spanish Case studies .................................................... 45

Table 33. Electricity market framework data for the Spanish Case studies ............................... 47

Table 34. Panels and inverters data for the Spanish Case studies .............................................. 47

Table 35. Storage systems data for the Spanish Case studies .................................................... 48

Table 36. Simulation drivers data for the Spanish Case studies ................................................. 49

Table 37. Assessable KPIs for the Spanish Case studies .............................................................. 52

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This document is a deliverable of the project “iDistributedPV: Solar PV on the Distribution Grid:

Smart Integrated Solutions of Distributed Generation based on Solar PV, Energy Storage Devices

and Active Demand Management”. In this document, the methodology employed to the

simulation of the distributed solar PV solutions in the different countries is explained in the first

chapter. Furthermore, in the second chapter, the case study environments are described

including the different characteristics of the countries where they will be developed as well as

the approaches that should be tested in the simulations.

1. Assessment methodology and simulation tools

The most promising distributed solar PV solutions identified in deliverable 2.1. will be evaluated

by analysing technical and economic criteria.

Regarding economic terms, the following aspects will be assessed:

The estimation of the cost of the solutions (investments, operation and maintenance costs,

etc.),

The identification of the economic flows among the different players,

The revenue model,

The analysis of the return of the investment,

The profitability of the projects,

The calculation of the LCOE of the solution,

The comparative evaluation with the current supply solutions.

In technical terms, which will be based on dynamic and static assessments, the following issues

will be analysed:

The reliability and security grid analysis,

The contingency assessment,

The voltage control,

The maximum solar PV capacity that could be integrated in safe conditions,

The impact of the solutions on the frequency control.

Additionally, the feasibility of the solutions will be evaluated according to the key performance

indicators defined in the deliverable 2.2. “Assessment methodology based on Evaluation

Procedures and Indicative Key Performance Indicators”.

Hence, an accurate methodology must be designed to carry out the case studies that will be

developed including the issues addressed below:

The different roles of the players in the case studies,

The operational procedure that should be tested,

The system security and feasibility criteria that should be evaluated,

The necessary information to develop the case studies,

Page: 5 Date: 30/06/2018

The information flows that must be implemented,

The economic flows that must be taken into consideration,

The tools that will support the assessment and the configuration criteria.

1.1. Distributed solar photovoltaic sizing

With the aim of evaluating the proper size of the solution, several simulations will be carried out

analysing different power capacities of the installed PV solution as well as different capacities of

the storage system, if applicable. Apart from the capacities, other technical aspects of both the

PV installation and the storage system may as well be tuned in order to make a deeper analysis.

After the simulations are carried out, these studied capacities will be compared to each other

taking into account a set of KPIs in order to assess which one is the most suitable in the different

solutions.

The five partners involved in the simulations will choose which ones of the proposed solutions

fits better with the regulatory criteria and procedures of their country, and will carry out several

simulations in order to size those installations according to their specific needs. Therefore, the

irradiation of the location, the consumption profile of the consumers, the electricity tariff, the

electricity market price, technical characteristics of the devices as well as their costs among

other parameters will play an important role in the results. The first step to carry out the

simulation will be gather all the data related to the consumers in order to customize the tool

with the real data of each solution. Once the data is recovered, the simulation will be ready to

start.

Figure 1. PV sizing simulation methodology

Data gathering

• Irradiance of the location

• Electricity consumption, share of self-consumption

• Contracted electricity tariff

• Electricity market price and taxes on self-consumption and / or electricity exported

• Technical characteristics of the installed devices: panels, inverters and energy storage

• Data related to maintenance costs, useful life, rate of carbon emissions avoided, etc

Simulation

• Set the main parameters related to the simulation process: number of simulations, decision-making horizon, study horizon, etc

Results analysis

• Analysis of the resulted KPIs and cash flows

• Study and comparison of the different simulated sized

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1.1.1. Prosumer Solution Simulation Tool

The Prosumer Solution Simulation tool developed by Deloitte performs a technical and

economic analysis about integrating photovoltaic (PV) systems into households, shops, offices,

industries and other type of buildings. Every case is addressed differently, since energy

consumption and the demand profile varies from case to case. Moreover, hourly discrimination

tariffs must comply with the technical aspects of these PV systems and usually depend on the

size of the system.

The tool needs the hourly irradiation profile and the area covered by the panels to calculate the

hourly energy production, along with the location of the simulated installation. The irradiation

profile can be obtained directly from weather stations or from estimations found in the

Photovoltaic Geographical Information System (PVGIS). The former data is more precise;

however, it is not always possible to have free access to data stored in weather stations or they

may be too far from the location studied to deploy the PV panels. If this is the case, PVGIS offers

data precise enough to perform reliable assessments and it is already integrated in the tool to

acquire the radiation data by introducing coordinates.

Solar irradiation data is not constant throughout the simulation. Data from previous years is

studied, and a mean and an uncertainty are obtained. Random profiles are generated and then

the probability of each generated profile to take place is assessed. Therefore, solar irradiation

values slightly differ from one year to another in the simulation.

Technical characteristics of the different devices comprising the PV system are included. For

instance, efficiency of the panels, inverters and batteries; life cycling of batteries; ageing of

panels leading to a reduction in the peak power they exhibit; capacity of the battery; and so on.

This aims to perform simulations as realistic as possible.

The tool evaluates the abovementioned parameters, energy production, demand profile,

characteristics of the system, hourly electricity price etc., and determines whether the best

option is self-consume the electricity generated, store it, sell it or buy energy from the grid to

store it. The last case may occur when the price per kWh is low but it is expected to increase in

the upcoming hours, and there is available capacity in the battery.

Together with the income and savings this system yields, a cost analysis is also conducted. The

complete economic analysis offers the internal rate of return (IRR) of the project, the net present

value (NPV) and the payback period among other KPIs.

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1.1.2. Economic Analysis Tool

In the iDistributedPV project, case studies in five European countries will be analysed in detail.

However, to make the results comparable to the conditions in other European countries, a rough

overview on the economic viability of several solutions in all European countries will be done.

Due to the fact that a large data set is needed for the economic conditions but the technical

calculations do not need to be detailed, a separate tool was developed by Fraunhofer ISE for

this purpose. The tool is set up in Excel and calculates the following values for each country (see

also the description of the KPIs in Deliverable 2.2):

Degree of self-sufficiency

Payback period

Net present value

Internal rate of return

LCOE

LCOS

Avoided CO2 emissions

Reduced network usage fee

Reduced cost of network losses

Table 1 shows the input data of the tool; filled with the German data as an example. Values in

red are to be filled for each country, while values in black are kept constant among the different

countries. Values in grey are calculated by the tool when the data is entered.

Electricity flow is separated into physical and accounting to be able to present the case of net

metering. In the case of feed-in tariff (FIT), physical and accounting electricity flows are the

same: If 30 % of the electricity flow is consumed directly and 70 % is provided to the grid, then

on the accounting side 30 % is considered as own consumption and 70 % will be rewarded with

FIT. In the case of net metering with 30 % physical own consumption, yet 100 % of the electricity

may be rewarded with the savings of the household electricity price, due to the energy balance

over the billing period (often a year). Thus, for accounting, own consumption will be 100 % with

no grid feed-in. The accounting values are therefore used to calculate the cash flow and

economic parameters. The physical flow is used to calculate the avoided network losses.

Due to the large variety of electricity pricing mechanisms, this simplified tool does not consider

variable electricity prices. The amount of savings calculated in the tool may therefore be lower

than the actual values. The purpose is to give a rough comparison between the situations in

various European countries; detailed analyses need to be done in order to obtain exact values

for the economic viability of the solutions.

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Table 1. Input data for the country comparison

PV system Capacity 5.8 kWp

Lifetime 20 years

Annual electricity generation 743 kWh/(kWp*a)

Degradation rate 0.25% per year

Battery system Battery Capacity / PV Power ratio 1.00 kWh/kWp Calendar life 10 years

System efficiency (round trip) 90%

Consumption Annual consumption 4,800 kWh/year

electricity flow - accounting

Direct self-consumption 30% Self-consumption from battery 60% Feed-in 10%

electricity flow - physical

Direct self-consumption 30% Self-consumption from battery 60% Feed-in 10%

Investment cost PV system cost 1,300 €/kWp

Battery system 860 €/kWh

Battery replacement 516 €/kWh

CAPEX PV system 7556 €

Battery system 4998 €

replacement Battery replacement cost 2999 €

OPEX Fixed operating costs (depends on CAPEX) 32.50 €/kWp

Insurance (% of CAPEX per year) 0%

Total annual costs 188.90 €/year

Residual Residual value for PV - €

Capital Structure

share Debt 80%

Equity 20%

interest rate (real) Debt 1.50%

Equity 3.00%

WACC real 1.80% Electricity price generation 0.0725 €/kWh

transmission and distribution 0.0657 €/kWh

total without VAT 0.1382 €/kWh

VAT 0.0487 €/kWh

total with VAT (household price el.) 0.3048 €/kWh

Support schemes feed-in tariff 0.12 €/kWh

FiT duration 20 years

Subsidy on PV system 0% of investment

Subsidy on battery system 0% of investment

Environmental CO2 emissions of electricity mix 615 gCO2eq./kWh

Grid losses In transmission and distribution grid 4 %

Page: 9 Date: 30/06/2018

1.2. Analysis of the penetration of renewable generation into power

networks

Electricity networks can be divided into two major subsections: transmission networks and

distribution networks.

The transmission network consists of high to very high voltage power lines designed to transfer

bulk power from major generators to areas of demand; in general, the higher the voltage, the

larger the capacity. Only the largest customers are connected to the transmission network.

Transmission network voltages are typically above 100 kV. The networks are designed to be

extremely robust, so they can continue to fulfil their function even in the event of several

simultaneous network failures. Failure of a single element, such as a transformer or transmission

line, is referred to as an “N-1” event, and transmission systems should be capable of

withstanding any such event. Loading of components and voltage levels should be kept under

the established permissible levels in case of contingences [1].

Transmission consists mainly of overhead lines. Although underground lines offer the advantage

of being less visually intrusive and raising less environmental objections, they incur higher initial

investment costs and have a lower transmission capacity.

Transmission systems are operated by transmission system operators (TSOs). They are actively

managed through power system control centres, also known as dispatch centres. Balancing

power entering and leaving the high voltage network, and reconfiguring the network to cope

with planned and forced outages, is a 24-hour activity.

On the other hand, distribution networks are usually below 100 kV and their purpose is to

distribute power from the transmission network to customers. However, at present, with the

presence of PV and other renewable power plants, more generation is being connected to

distribution networks. Generation connected to distribution networks is known as distributed

generation. Distribution networks contains more customers than transmission networks and

their reliability decreases as voltage levels decrease [1]. As with transmission networks,

distribution networks are operated by distribution system operators (DSOs).

Furthermore, low-voltage network is a part of the electric power distribution that carries electric

energy from distribution transformers to electricity meters of end customers. They are operated

at a low voltage level, which is typically equal to the main voltage of electric appliances.

The integration of solar PV power can result in improvements of the grids or can have negative

impacts on the steady state system operation parameters. The effects/impacts cannot be

generalized for all types of grids across Europe. For effective understanding of the effects before

integration, studies are needed to be carried out for a particular grid in countries in which there

is need to integrate more solar PV system to their power transmission or distribution grids. The

impact of photovoltaics integration has potential to cause [2, 3]:

Page: 10 Date: 30/06/2018

Changes to the feeder voltage level, voltage profile, line loading, power quality of the

system, system losses, power factor, fault currents, system stability (transient and voltage

stability), inertia of the system and mismatch in generation and load.

Changes in operations of voltage-control and regulation devices (frequent operations). This

can in turn, impact on maintenance costs, reliability and life span of the devices.

Change in direction of power flow. There is a very high chance of reverse power flow and

this can impact on protections relays (directional).

The analysis will cover the integration of solar power to an existing grid and study of the impacts

that the integration can cause on an existing grid at different levels of voltage: transmission,

distribution and low voltage. In the integration process, different penetration levels (10%, 30%,

60% and 90%, although these values may be changed) of the solar PV power will be implemented

and analysed, being compared with a baseline scenario. This scenario is set to the current grid

status, or its status in a determined period of time. It involves the modelling of power grids in

power system simulation software without including the solar PV solutions. The loading capacity

and voltage profile/stability of buses at the point of connection will be studied not only in steady

state but also in dynamic state. Solar PV power in combination with different operation

strategies (e.g. peak saving) will be modelled and added to the grid at different penetration

levels as mentioned before. In addition, the possible impacts will be investigated.

It must be pointed out that several software programmes are available to test the stability of

the grid, among others, PSSE, OpenDSS and PowerFactory can be found. The different case

studied will not be developed employing the same software as not all the partners have available

the same licenses. However, the analysis to be tested will comply with the same requirements

in all the case studied, no matter which software will be employed.

1.2.1. N-1 contingency analysis

Contingency means the identified and possible or already occurred faults that grid elements can

undergo, within or outside a transmission system operator's (TSO's) Responsibility Area,

including not only the transmission system elements, but also significant grid users and

distribution network elements if relevant for the transmission system operational security.

Therefore, internal contingency is a contingency within the TSO's responsibility area and

external contingency is a contingency outside the TSO's responsibility area, with an influence

factor higher than the contingency influence threshold.

Regarding chapter 5 of the Network Code on System Operation, which makes reference to the

contingency analysis and handling, each TSO has the following duties [4]:

Each TSO shall establish a contingency list, including the internal and external contingencies

of its observability area, by assessing whether any of those contingencies endangers the

operational security of the TSO’s control area. The contingency list shall include both

https://www.emissions-euets.com/internal-electricity-market-glossary/815-fault

https://www.emissions-euets.com/internal-electricity-market-glossary/429-significant-grid-user

Page: 11 Date: 30/06/2018

ordinary contingencies and exceptional contingencies identified by application of the

methodology developed pursuant to Article 75.

Each TSO shall perform contingency analysis in its observability area in order to identify the

contingencies which endanger or may endanger the operational security of its control area

and to identify the remedial actions that may be necessary to address the contingencies,

including mitigation of the impact of exceptional contingencies.

Each TSO shall ensure that potential violations of the operational security limits in its control

area which are identified by the contingency analysis do not endanger the operational

security of its transmission system or of interconnected transmission systems.

Each TSO shall perform contingency analysis based on the forecast of operational data and

on real time operational data from its observability area. The starting point for the

contingency analysis in the N-Simulation shall be the relevant topology of the transmission

system which shall include planned outages in the operational planning phases.

Each TSO shall assess the risks associated with the contingencies after simulating each

contingency from its contingency list and after assessing whether it can maintain its

transmission system within the operational security limits in the (N-1) situation.

When a TSO assesses that the risks associated with a contingency are so significant that it

might not be able to prepare and activate remedial actions in a timely manner to prevent

non-compliance with the (N-1) criterion or that there is a risk of propagation of a disturbance

to the interconnected transmission system, the TSO shall prepare and activate remedial

actions to achieve compliance with the (N-1) criterion as soon as possible.

In case of an (N-1) situation caused by a disturbance, each TSO shall activate a remedial

action in order to ensure that the transmission system is restored to a normal state as soon

as possible and that this (N-1) situation becomes the new N-Situation.

Hence, a contingency analysis is a must in order to assess the level of penetration of solar PV

within the grid and analyse its operational security, as well as identify the remedial actions that

may be necessary to address. The following figure shows the traditional N-1 analysis

methodology.

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Figure 2. N-1 analysis methodology

Where:

Base case: the power system in its normal steady-state, operation, with all elements in

service that are expected to be in service.

Primary contingency: a loss of one or more system elements that occurs first. A primary

contingency may be a planned or unplanned event.

System adjustments: a set of corrective actions executed automatically by a control system

or manually by a system operator to mitigate the effects of a contingency or strengthen the

system to withstand a possible future contingency. System adjustments may include the

opening or closing of a transmission element; the opening, closing, or redispatch of a

generator; the changing of a phase shifting transformer angle; the opening, closing, or

changing of a switched shunt set point; or the curtailment of load. System adjustments may

include actions that occur every time a certain contingency occurs or actions that occur only

when certain system conditions are met.

1.2.2. Voltage analysis

Voltage levels will be assessed both at transmission and distribution level to analyse the

influence of new solar PV generation on the grid.

1.2.3. Frequency analysis

Dynamic simulations to evaluate the impact of solar PV and battery storage penetration will be

performed at distribution level.

Page: 13 Date: 30/06/2018

2. Case studies

The methodology explained in the previous chapter will be implemented in all the case studies

were the simulations will be developed: Germany, Greece, Lithuania, Poland, and Spain.

Each country will choose the most appropriate solutions to be analysed within its borders.

Therefore, each country must gather the necessary data to develop the different simulations as

a first step to achieve the goal of the work. In the following subchapters, the solutions together

with the necessary data for the distributed solar PV sizing simulation and the analysis of

penetration of renewable generation into power networks simulation are stated. Once the data

is gathered, the inputs of the simulation will be set and the process will be ready to run.

2.1. Germany

2.1.1. Solutions

Homeowner as PV Battery Systems

Many residential battery systems (> 80 000) are installed in Germany already and further growth

of the number of systems is expected. Therefore the impact of a massive rollout on the

electricity system is to be analysed. Also various operational strategies should be analysed to

compare their system impact and to be able to develop recommendations for which operational

strategies to incentive. On the economic side, the impact of grid fees and their possible future

change will be analysed

Controllable load as Homeowner with PV Battery Systems and Electrical Vehicles

When buying an electrical vehicle many customers also think about building a PV system on their

roof to charge the EV battery with green and local electricity. A stationary storage system can

help to further increase the share of local and green electricity for transportation and reduce

gird stress due to high charging powers from the grid. General and seasonal effects are to be

analysed.

Multifamily house

After the introduction of a support scheme for tenant's electricity in Germany, this concept may

be followed by more and more investors. The effect of a massive rollout in Germany cities on

the grid will be examined.

Virtual power plant (VPP) as pool of PV Battery Systems providing Frequency Control Reserve

Pooling of PV home storage systems to virtual power plants for the provision of frequency

control is under discussion in Germany. From an economic and grid point of view this

development will be examined.

Page: 14 Date: 30/06/2018

2.1.2. Gathered data

Radiation profiles

Next to radiation profiles, PV generation measured on the AC side in the flied will be used as

input, depending in the simulation tool used (different for different KPIs).

Demand profiles

Demand profiles will mainly come from Synpro, a tool developed at Fraunhofer ISE to provide

synthetic load profiles. Partly measured profiles will be used.

Table 2. Average yearly consumption for the German Case studies

Parameters Homeowner Controllable

load

Multifamily

house VPP

Average yearly consumption [MWh] ≈ 6 10 20 6* X

Electricity tariffs

In Germany, for the solutions addressed, tariffs are equal for the whole year (no peak and

baseload prices).

Table 3. Electricity tariffs data for the German Case studies


load

Multifamily

house VPP

Tariff prices [€/kWh] 0.234 0.234 0.234 0.234

Electricity market framework

Table 4. Electricity market framework data for the German Case studies


load

Multifamily

house VPP

Feed in -

Revenues

Electricity market

price [€/MWh]

Feed in premium

[€/MWh]

Feed in tariff

[€/MWh] 122 122 122 / 118.7 122

Self-

consumption

tax

Fixed

term per

year

[€/kW]

Peak 2.716

Shoulder

Off-peak

Peak

Page: 15 Date: 30/06/2018

Usage

charge

[€/kWh]

Shoulder

Off-peak

Tax on energy

exported to

the grid

Toll [€/MWh]

Tax on benefits [%]

Panels and inverters

Table 5. Panels and inverters data for the German Case studies


load

Multifamily

house VPP

Total efficiency [%] 15 15 15 15

PV panel size [m2] 1.7 1.7 1.7 1.7

Nominal power per panel [W] 250 250 250 250

PV degradation rate per year [%] 0.5 0.5 0.5 0.5

Installed power capacity [kW] 2-10 2-10 5-40 2-10 * X

Price Fixed price [€]

Estimated price [€/kW] 1500 1500 1500 1500

Storage systems

Table 6. Storage systems data for the German Case studies


load

Multifamily

house VPP

Technology [-] Lithium ion Lithium ion Lithium ion Lithium ion

Cycle efficiency [%] 90 90 90 90

Charge / discharge rate [%] 50 50 50 100

Capacity [kWh] 0-12 0-12 0-25 0-125

Depth of discharge [%] 80 80 80 80

Life cycles [-] 8000 8000 8000 8000

Degradation rate per year [%] 0.5 0.5 0.5 0.5


Estimated price [€/kWh] 1060 1060 1060 1060

Simulation drivers

Table 7. Simulation drivers data for the German Case studies


load

Multifamily

house VPP

Operational parameters

Panels and

inverters 2 2 2 2

Page: 16 Date: 30/06/2018

Annual

maintenance

costs [%]

Storage system 0 0 0 0

Depreciation

[useful years]

Panels and

inverters 20 20 20 20


Financial and tax rates

Corporate tax rate [%] 0 0 0 0

WACC [%] 1.8 1.8 1.8 1.8

Inflation rate [%] 0 0 0 0

Simulation parameters

Study horizon [years] 20 20 20 20

Nº of simulations [-] 1000 1000 1000 1000

Decision-making horizon [hours] 23 23 23 23

Rate of avoided carbon dioxide

emissions [kg/kWh] 0.615 0. 615 0. 615 0. 615

2.1.3. Grid analysis information

In order to investigate possible future grid loads, in the iDistributedPV project a probabilistic

load-flow analysis is carried out for a synthetic MV distribution grid including underlying LV

distribution grids, see Figure 3. All in all the investigated grid contains 1074 nodes and 1012

lines. 1027 are located in the LV level 46 in medium voltage and one in high voltage level (HV).

Figure 3. 20 kV medium voltage distribution grid with 36 underlying low voltage grids

Page: 17 Date: 30/06/2018

The probabilistic load-flow calculations are carried out for different scenarios concerning RES

penetration and changes in operation of storage technologies. For each scenario several

combinations are calculated in which size and position of RES is changed as well as the places of

EV and heat pumps and batteries.

Since voltage variations of the HV grid are not taken into account for this simulations, 2 % voltage

deviation in each direction are reserved for it. Consequently in this investigations voltage band

violations are defined as a deviation of 8 % percent from nominal voltage of the respectively

voltage level. An asset is thermally overloaded when the actual current exceeds its rated current.

Due to computational restrictions it is not possible to store all line usage rates and voltage

magnitudes. Hence for characterizing general grid behaviour characteristic nodes and lines were

defined. Their usage rates and voltage magnitudes were stored over time. Additionally thermal

overloads or voltage band violations were stored. In order to evaluate the necessity of additional

effort in inverter technology, inverter operating points are stored.

2.1.4. Assessable KPIs

With probabilistic load flow simulation of the distribution grid it is possible to calculate for all

scenarios nodal voltages and usage of grid elements. Every scenario is defined by a certain

amount of installed renewables. For every penetration the KPI marked below will be evaluated.

Table 8. Assessable KPIs for the German Case studies

Key Information Indicators Homeowner Controllable

load

Multifamily

house

Virtual power

plant

Economic – on site


Net Present Value (NPV)

Internal Rate of Return (IRR)

Paybak time

Reduced exposure to volatility of electricity

prices

Levelized Cost of Electricity (LCOE)

Technical – on site


Reduction peak demand ratio

Technical – grid

Energy exchange with the grid

Hosting capacity of solar PV

Reduction in solar PV production cut-off due to

congestion

Voltage stability Vmax

Voltage stability Vmin

Reduction of over voltage in the grid

Reduction of under voltage in the grid

Increase frequency quality performance

Page: 18 Date: 30/06/2018

Time of a certain frequency variation

Average outage duration for each customer

served (SAID)

Average number of interruptions in the supply

of a customer (SAIFI)

Increased efficiency in preventive control and

emergency control

Increased demand side participation

Actual availability of network capacity

Regulative

Reduction in time to connect new user

Increase in coordinated operation between

TSOs and DSOs

Environmental

Quantified reduction of carbon emissions

2.2. Greece

2.2.1. Solutions

Three different solutions will be tested in Greece and the main reason for choosing them is that

they are typical type of consumers.

Homeowner

For the analysis of this solution a typical single-family detached house has been chosen. The

household is located at the Meltemi summer camp, which is an area of 200 houses in Rafina, a

province of Athens.

One of the reasons for choosing this solution was to evaluate the impact of large scale PV

penetration of PV in the frame of the new regulations about net-metering scheme. Furthermore,

the availability of the energy consumption data of this household makes it an ideal solution for

simulation purposes.

Company as investor

Typical type of consumer in Greece.

HEDNO (Hellenic Electricity Distribution Network Operator) is one of the project partners and

will provide the necessary data to ICCS in order to perform the simulations. HEDNO’s

department of information and telecommunications has been chosen for this solution because

of the availability of electric consumption data. The building is located at the area of Mesogeia,

Athens.

Page: 19 Date: 30/06/2018

Municipal building

The town hall of Rafina, a province of Athens as already mentioned, will be analysed for this

solution. It’s a typical municipal building in Greece, which mainly consumes electric power in the

daytime. Consumption pattern from this type of building is quite interesting also due to the

inactive hours of the weekend.

Another reason for choosing this solution is that municipal buildings will play a major role in the

localized energy policy that the new law about Energy Communities in Greece is trying to

establish.


Radiation profiles

The radiation data that will be used were obtained from the solar radiation database CMSAF-

PVGIS and they contain data in an hourly resolution from the year 2007 to the year 2016. The

irradiation of year 2016 is presented as an example IN Figure 4.

Figure 4 Yearly irradiance profile of Meltemi, Athens, Greece (2016)

Demand profiles

For the homeowner solution, data from three different households were obtained. The time

period that these houses were monitored was from 11 July 2017 until 1 June 2018. The houses

that were chosen for the simulations are occupied on a year-round basis.

For the other two solutions the available data is for a much longer period of time. The energy

consumption of the HEDNO’s building has been meausured from 29 July 2014 until 31 December

2017 and the Town Hall of Rafina from September 2015 until April 2018.

Page: 20 Date: 30/06/2018

Electricity tariffs

There are five different electricity suppliers in Greece, PPC S.A., Elpedison, Protergia, NRG,

Voltera. Each of these suppliers has different electricity tariffs based on the energy consumption

of the customer. Electricity tariffs also vary by type of customer by residential, commercial, and

municipal building connections. The three tables below shows the tariff prices for each of the

three solutions that have been chosen.

Table 9. Electricity tariffs data for homeowners for the Greek Case studies

Parameters Homeowner

Tariff prices

[€/kWh]

Upper price

[€cent/kWh]

PPC : Energy consumption in a 4

months period is 0 – 2000 kWh 9.460

PPC : Energy consumption in a 4

months period is > 2000 kWh

10,252

(for every kWh that

exceeds 2000kW)

Lower price

[€cent/kWh] Elpedison 8.950

Night tariff

[€cent/kWh] PPC, Protergia, Elpedison 6.610

Table 10. Electricity tariffs data for companies for the Greek Case studies

Parameters

Time zone Energy fee (€/kWh) Fixed rate (€/kW)

8:00-22:00 during the whole year 0.11346

0.053 22:00-8:00 during the whole year 0.06610

Table 11. Electricity tariffs data for municipal buildings for the Greek Case studies

Parameters

Time zone Power fee

(€/kW/month) Energy fee

(€/kWh) Fixed rate

The whole year 1.1 0.08259 0.53

Page: 21 Date: 30/06/2018


Table 12. Electricity market framework data for the Greek Case studies

Parameters Homeowner Company as

investor

Municipal

building

Feed in -

Revenues

Electricity market price

[€/MWh]

Feed in premium

[€/MWh]

Feed in tariff [€/MWh]

Self-

consumption

tax There aren’t taxes and levies for own-consumption or for the energy

exported to the grid Tax on energy

exported to the

grid


In the next categories, mean values for the panels, the inverters and the storage systems that

are in the market and are representative in each country will be used in order to achieve some

typical results during the simulation process.

Table 13. Panels and inverters data for the Greek Case studies

Parameters Homeowner Contractor

concept

Municipal

building

Total efficiency [%] 15 15 15

PV panel size [m2] 1.7 1.7 1.7

Nominal power per panel [W] 250 250 250

PV degradation rate per year [%] 0.5 0.5 0.5

Installed power capacity [kW] 2-6 10-60 10-40

Price

Fixed price [€]

Estimated price [€/kW] 1000 1000 1000

Storage systems

In the next categories, mean values for the panels, the inverters and the storage systems that

are in the market and are representative in each country will be used in order to achieve some

typical results during the simulation process.

Page: 22 Date: 30/06/2018

Table 14. Storage systems data for the Greek Case studies


concept

Municipal

building

Technology [-] Lithium ion Lithium ion Lithium ion

Cycle efficiency [%] 90 90 90

Charge / discharge rate [%] 60 60 60

Capacity [kWh] 0-3 10.5-30 5-25

Depth of discharge [%] 80 80 80

Life cycles [-] 8000 8000 8000

Degradation rate per year [%] 0.5 0.5 0.5


Estimated price [€/kWh] 600 600 600

Simulation drivers

Table 15. Simulation drivers data for the Greek Case studies


concept

Municipal

building


Annual

maintenance

costs [%]

Panels and inverters 2 1 1

Storage system 2 1 1

Depreciation

[useful years]

Panels and inverters 20 20 20

Storage system 10 10 10


Corporate tax rate [%] 0 25 25

WACC [%] 6.5 6.5 6.5

Inflation rate [%] 0 0 0


Study horizon [years] 20 20 20

Nº of simulations [-] 1000 1000 1000

Decision-making horizon [hours] 23 23 23


emissions [kg/kWh] 0.36 0.36 0.36


The simulation process will be carried out in low voltage and medium voltage level. The tool that

will be used in order to develop these simulations is the PowerFactory software. HEDNO will

Page: 23 Date: 30/06/2018

provide the necessary grid information, while ICCS will analyse these data and perform the

simulations on PowerFactory.

The low voltage grid that will be simulated is the summer camp Meltemi, an area of 200 houses

at the municipality of Rafina, a province of Athens. Some of the houses are not yearly occupied

and are used only as summer residences and this will give us the opportunity to test different

options of PV penetration.

For the medium voltage analysis, the distribution grid of Mesogeia in Athens will be simulated.

One medium voltage line which ends at the low voltage grid mentioned above, a second one

which includes the Town Hall of Rafina, one of the solutions that will be also analysed and

another one which includes the HEDNO’s building, which has also already been mentioned will

be simulated.


Table 16. Assessable KPIs for the Greek Case studies

Key Information Indicators Homeowner Company as

investor

Municipal

building





Paybak time


prices





Technical – grid




congestion








served (SAID)




emergency control

Page: 24 Date: 30/06/2018



Regulative



TSOs and DSOs

Environmental


2.3. Lithuania

2.3.1. Solutions

When deciding which solutions from those given in deliverable 2.1 should be tested in Lithuania,

several factors were considered in order to analyse the most promising ones to be implemented

in the country. Among the different reasons to decide, regulative criteria, data availability,

specific characteristics of the demand profile, etc. were some of the main reasons. Special

attention is paid to the new initiative described in new Energy strategy (approved by Parliament;

2018). It provides some new initiatives:

1) To have 34 000 energy prosumers in 2020;

2) 30 % of consumers will be active and participate in the energy market in 2030;

3) 50 % of consumers will be active and participate in the energy market in 2050.

Homeowner

A single-family house with solar PV and storage system, which is a popular business model in

Europe, will be analysed. PV electricity will be consumed when possible and excess electricity

will be either stored for later use or exported to the public grid. Two storage options will be

analysed: batteries and use of net metering scheme available today in Lithuania. Therefore,

savings will be generated by the avoidance of purchasing electricity from a supplier and possibly

by selling the excess electricity. Despite of the fact that limited information from family-

members houses located in Lithuania were available now it is expected that during

implementation new energy strategy homeowners will become main players in prosumers

market.

Company as investor

The case of the hotel “Grand SPA Lietuva“, which is located in Druskininkai city will be analysed.

The case of the hotel The hotel belongs (same as project participant UAB Renerga) to the same

UAB koncernas ACHEMOS GRUPĖ, which is one of the largest national capital business groups in

Lithuania. It seeks to become green energy hotel in near future. Data on electricity consumption

Page: 25 Date: 30/06/2018

at hotel is available and possibility to invest in PV and storage solution or use net metering

scheme is under consideration by hotel owners

Contractor concept

Lithuanian Energy Institute, Lithuania, will be analysed. This solution is quite widespread in

Europe especially among companies that are not interested in owning physical assets. This fact,

together with the availability of real data for the simulation, makes this solution highly

interesting.

The consumption profile shows also some features that can play an attractive role in electricity

market as it has almost no consumption during the weekends, the use of batteries and net

metering scheme will be analysed.

Controllable load

When buying an electrical vehicle many customers also think about building a PV system on their

roof to charge the EV battery with green and local electricity. A stationary storage system can

help to further increase the share of local and green electricity for transportation and reduce

gird stress due to high charging powers from the grid. General and seasonal effects are to be

analysed. Fast charging station for electrical vehicles near hotel “Druskininkai” will be analysed.

The station was introduced into market at September 22th, 2017. It is produced by DELTA

Electronics. The power of the station is up to 120 kW, which allows fast charging for up to 4

vehicles at the time.


Examples of the data gathered for the simulations are shown in the tables below. It must be

mentioned that installation of electricity consumption monitoring in an hourly resolution for the

non-industry users were actively started in the middle of 2017. Data for Homeowner and

Controllable load are still in gathering stage in order to have better statistics for precise

modelling calculations.

Radiation profiles

Different solutions which are in two cities of Lithuania will be studied during project

implementation. Objects for Homeowner and contractor concepts will be in Kaunas, Lithuania

and objects for Company as investor and Controllable load will be located in international all-

year-round therapeutic resort of mineral water, mud and climate - Druskininkai, Lithuania. The

radiation profiles were obtained from the solar radiation database CMSAF-PVGIS.

Page: 26 Date: 30/06/2018

Figure 5. Yearly irradiance of Kaunas and Druskininkai, Lithuania.

Demand profiles

The consumption profiles were obtained from the monitoring over a year in an hourly

resolution. The monitoring system for Contractor concept which will be implemented in

Lithuanian Energy Institute starts from 18/05/2017 than monitoring over a year in an hourly

resolution was installed. For the Company as investor concept data starts from 01/01/2007 till

12/30/2017.

Page: 27 Date: 30/06/2018

Table 17. Average yearly consumption for the Lithuanian Case studies


investor

Contractor

concept

Controllable

load

Average yearly consumption

[MWh] 6 551 892 4.2

Figure 6. Company as investor yearly consumption profile

Figure 7. Contractor concept yearly consumption profile

Page: 28 Date: 30/06/2018

Figure 8. Controllable load concept consumption profile

Electricity tariffs

The price of the electricity that each solution demands will depend on the electricity tariff that

each consumer has contracted with electricity retailer. In cases of Homeowner, the consumers

pay fixed tariff price. In cases of Company as investor and Controllable load the consumers pay

fixed tariff price + additional tax on the tariff which is 0.7502 €/kW/month (One-time zone

energy component). In case of Contractor concept, consumer has tariff which is split in three

main subcategories: peak, shoulder and off peak (which is additionally split in two parts: off

peak, which is between 23:00-7:00 hours and off peak, which is calculated for Sunday, Saturday

and National Weekends between 0-24:00; in order to simplify calculation we selected higher

tariff price for off peak calculations – 0.0664 €/kWh). There is additional tax on the tariff for

Contractor concept consumer which is 0.9559 €/kW/month.

Table 18. Electricity tariffs data for the Lithuanian Case studies


Company

as

investor

Contractor

concept

Controllable

load

Tariff

prices

[€/kWh]

Summer

Peak 0.113 0.1025 0.09004 0.1025

Shoulder 0.113 0.1025 0.0974 0.1025

Off-peak 0.113 0.1025 0.0664 0.1025

Winter

Peak 0.113 0.1025 0.09004 0.1025

Shoulder 0.113 0.1025 0.0974 0.1025

Off-peak 0.113 0.1025 0.0664 0.1025

Page: 29 Date: 30/06/2018

Tariff

periods [h]

Summer

Peak 0-24 0-24

8-11 (Oct-Mar)

18-20 (Apr; Sep)

9-12 (May-Aug)

0-24

Shoulder 0-24 0-24 others 0-24

Off-peak 0-24 0-24 23-7 0-24

Winter

Peak 0-24 0-24

8-11 (Oct-Mar)

18-20 (Apr; Sep)

9-12 (May-Aug)

0-24

Shoulder 0-24 0-24 others 0-24

Off-peak 0-24 0-24 23-7 0-24


According new Energy strategy (approved by Parliament; 2018), net metering scheme will be

used for energy storage which will be produced by renewables and is expected to bring around

200 MW of PV power online by 2020. Prior to the approval of new Energy strategy, only public

institutions were entitled to install PV systems between 10 and 100 kW and now it is additionally

expanding for the businesses and farmers. Residential PV installations could be up to 10 kW for

homeowners. Several algorithms for energy storage were proposed by national distribution

company ESO (AB Energijos Skirstymo Operatorius). The main of them are related to 0.4 kV grid:

fixed payment for energy storage – 0.03876 €/kWh or prosumer could leave 38 % of produced

electricity to ESO and use 68 % for own use free of charge.

As additional support scheme, The Environmental Project Management Agency under the

Ministry of Environment of the Republic of Lithuania announced call for proposals for residential

PV installations which could compensate up to 336 €/kW installed.

Table 19. Electricity market framework data for the Lithuanian Case studies

Parameters Homeowner Company

as investor

Contractor

concept

Controllable

load

Feed in -

Revenues

Electricity market

price [€/MWh]

Feed in premium

[€/MWh]

Feed in tariff

[€/MWh]

Self-

consumption

tax

Electricity storage

charge [€/kWh] 0.03876 0.03876 0.03876 0.03876

Toll [€/MWh]

Page: 30 Date: 30/06/2018

Tax on energy

exported to

the grid

Tax on benefits [%]


Polycrystalline PV modules will be employed in the different solutions. This technology has a

major cost than others but also its efficiency is higher, standing between 17 and 18%. However,

the presence of inverters will decrease the total efficiency of the installation to 15%.

The sizing assessment of the solution is one of the main aims of this study. Therefore, several

installed capacities will be simulated in order to see which one should be implemented or

suggested.

Table 20. Panels and inverters data for the Lithuanian Case studies


as investor

Contractor

concept

Controllable

load


PV panel size [m2] 1.7 1.7 1.7 1.7



Installed power capacity [kW] 5-10 100 100 30-100



Storage systems

Lithium-ion batteries will be employed in the different solutions. They exhibit high energy and

power density and show a high efficiency.

The number of cycles is stablished as 8000 and, therefore, by the end of the horizon study they

will possible not have be realized. However, based on real cases and experiences, it will be

considered to replace the battery after ten years of use.

As in the previous case, several capacities will be analysed to carry out the sizing assessment of

the solutions.

Additionally, to Lithium batteries, hydrogen energy technologies for electricity storage will be

evaluated for the possible cases based on available information on worldwide market.

Page: 31 Date: 30/06/2018

Table 21. Storage systems data for the Lithuanian Case studies


as investor

Contractor

concept

Controllable

load




Capacity [kWh] 0-3 10.5-30 5-25 50-150


Life cycles [-] 8000 8000 8000 8000




Simulation drivers

To carry out the simulations, some extra data related to both the economic and technical aspects

is needed. The data is shown in the table below and follows the template of the Prosumer

Solution Simulation tool, see Annex I for detailed information of the different parameters.

To achieve a higher accuracy, the number of simulations will be set as 1000, which is supposed

to be a large enough number to avoid outliers. Moreover, the decision making will be stablished

as 23 to let the algorithm analyse a completely day ahead when taking a decision. It must also

be pointed out that the inflation will not be considered in the study as it is quite unpredictable

in the electricity markets and therefore the simulation will be carry out in real values.

Table 22. Simulation drivers data for the Lithuanian Case studies


Company

as

investor

Contractor

concept

Controllable

load


Annual

maintenance

costs [%]

Panels and

inverters 2 1 1 1


Depreciation

[useful years]

Panels and

inverters 20 20 20 20




WACC [%] 6.5 6.5 6.5 6.5


Page: 32 Date: 30/06/2018






emissions [kg/kWh] 0.36. 0.36 0.36 0.36


Simulations at transmission and distribution levels will be carried out based on national

regulation for the connections of new prosumers to the existing grid. Small power producers (<

10 kW) are obliged to connect to the 0.4 kV distribution grid therefore simulations for

Homeowner and Controllable load scenarios will be performed for the whole 0.4 kV distribution

grid. Simulations will analyse case then newly connected prosumer as well as other energy plants

(both installed and with valid permissions to connect to the grid) are generating power at

projected capacities and at the same time power demand from the corresponding transformer

station is equal to 0 kW. Simulations will analyse the influence for the stability of voltage levels,

loading of components and other standard characteristics specified by the grid operator.

Company as investor and Contractor concept scenarios include installation of 100 kW generation

power which will be connected to 0.4/10 kV transformer stations accordingly similar simulations

will be performed for both 0.4 kV and 10 kV distribution grid voltage levels.

All calculation will be performed strictly following the methodologies and recommendations

issued by the national grid operator and using PSS/E and/or EA-PSM software.


Due to the analysed solutions and the different simulations (static, quasi-static and Dynamic)

that will be carried out, it is expected to evaluate most of the key performance indicators

presented in the table in below.

Table 23. Assessable KPIs for the Lithuanian Case studies


investor

Contractor

concept

Controllable

load





Paybak time


prices




Page: 33 Date: 30/06/2018


Technical – grid




congestion








served (SAID)




emergency control



Regulative



TSOs and DSOs

Environmental


2.4. Poland

2.4.1. Solutions

Main factors taken into account which solutions are most promising in Poland:

- regulative criteria

- data availability

- specific characteristic of the demand profile

Homeowner

A double-family terrace house with solar PV. PV electricity will be consumed according to current

demand and its surplus will be exported to the public grid. Due to specific regulations it’s

possible to get back 80% of exported energy (in installations smaller than 10kWp). Hence,

savings will be generated by the avoidance of purchasing electricity from a supplier.

This solution will not only be analysed in Poland but also in the other countries that will carry

out simulations. However, due to the existence of specific regulations which treat DSO’s network

as virtual storage system it will be very interesting to analyse this solution here.

Page: 34 Date: 30/06/2018

Company as investor

Company based in Poznań with 8kWp PV installation will be an example of Company as investor

solution. Accessible demand profile is representative for average medium business in Poland.

Conducted simulations would reflect generation/demand dependencies.

Municipal building

The Culture Center in Śmigiel will be an example of municipal building. The most exceptional

characteristic of this building is its non – regular demand profile depended on both typical needs,

organized cultural events and different needs through a year (ex. school holidays).


The data needed for the simulations are shown in the tables below. Software used to analyse

the solutions will be the Prosumer Solution Simulation tool and DNV Synergi.

Radiation profiles

Radiation profiles will be used for each chosen locations. Example chart for Poznań for one year

period (2007) is shown below:

Figure 9. Yearly irradiance profile of Poznań, Poland

The radiation profiles were obtained from the solar radiation database CMSAF-PVGIS and they

contain data in an hourly resolution from the year 2007 to the year 2016

Demand profiles

The average yearly consumption is shown in figures below. As it can be observed in the following

figures, they show a very different pattern of consumption.

Page: 35 Date: 30/06/2018

Table 24. Average yearly consumption for the Polish Case studies


investor

Municipal

building

Average yearly consumption [MWh] 4 93 8

Figure 10. Company as Investor consumption profile

Figure 11. Homeowner consumption profile

0

10

20

30

40

01

.20

17

02

.20

17

03

.20

17

04

.20

17

05

.20

17

06

.20

17

07

.20

17

08

.20

17

09

.20

17

10

.20

17

11

.20

17

12

.20

17

01

.20

18

Ener

gy (

kWh

)

Company as Investor

0

2

4

6

8

02

.17

03

.17

04

.17

05

.17

06

.17

07

.17

08

.17

09

.17

10

.17

11

.17

12

.17

01

.18

02

.18

Ener

gy (

kWh

)

Homeowner

Page: 36 Date: 30/06/2018

Figure 12. Municipal building consumption profile

Electricity tariffs

Energy companies independently prepare tariffs according to the scope of activity (held

licenses). In the Ordinance of the Minister of Energy of December 29, 2017, regarding detailed

rules for formation and calculation of tariffs and settlements in trading in electricity (Journal of

Laws of 2017, item 2500), the following rules were defined: shaping tariffs for electricity, prices

and fees calculation, settlements with customers and between energy companies.

Tariffs for electricity are subject to approval by the President of the Energy Regulatory Office.

After conducting administrative proceedings, the President of the Energy Regulatory Office

approves or refuses to approve the tariff. However, the President of the Energy Regulatory

Office may exempt an energy company (seller) from obligation to submit tariffs for approval, if

finds that it works under competitive conditions. Currently, most sellers are required to submit

tariffs to approval.

Regardless of the above duty, sellers can present market offers (prices for consumers defined in

price lists not approved by the President of the Energy Regulatory Office, hence can offer better

and more personalised deals to wide customer groups. Whereas distributors - enterprises

operating in conditions of a natural monopoly, have the obligation to submit tariffs to full

validation, i.e. for all tariff groups.

0

1

2

3

4

5

01

.17

02

.17

03

.17

04

.17

05

.17

06

.17

07

.17

08

.17

09

.17

10

.17

11

.17

12

.17

01

.18

Ener

gy (

kWh

)

Municipal building

Page: 37 Date: 30/06/2018

An electricity consumer bears the following fees:

Purchase of electricity,

Energy distribution services, and other fees provided for in the tariff energy company.

Types of tariffs accepted for calculations of Polish case study:

G11 - tariff dedicated to households, (average household metered daily),

C11 - tariff dedicated to small and medium-sized companies and agricultural holdings, is

supplied from low-voltage power grids with a contracted capacity below 40 kW,

C21 - tariff dedicated to medium-sized companies with a connection capacity of over 40 kW.

In this case, price for energy is negotiable.

All the above tariffs are single-zone variants - a rate for price of electricity is immutable, it does

not depend on the given hour during a day or a day of a week.

Table 25. Electricity tariffs data in 2018 for the Polish Case studies

Tariffs G11 C11 C21

Energy prices (net) PLN/kWh 0.2432 0.3999 0.3095

€/kWh 0.0571 0.0939 0.0727

Energy prices ( gross) PLN/kWh 0.2991 0.4919 0.3807

€/kWh 0.0703 0.1155 0.0894

Energy prices with distribution fee (net) PLN/kWh 0.4186 0.3999

negotiable €/kWh 0.0983 0.0939

Energy prices with distribution fee (gross) PLN/kWh 0.5149 0.6668

negotiable €/kWh 0.1209 0.1566

Network fee PLN/kWh 3.74 3.74 3.74

€/kWh 0.8784 0.8761 0.8761

Subscription fee PLN/kWh 4.72 34.44 75

€/kWh 1.1086 8.0890 17.6156 Sources: Cennik ogólny ENEA.

Yearly average rates January-December 2017 – 1EUR – 4,2576PLN


Regarding the Polish regulation net-metering is the system for micro-installation in Poland. The

surplus of produced energy may be feed to the electricity grid, and may be balanced with the

energy consumed from the electricity network in relation:

1 to 0.7 for PV systems with the power between 10 kW and 40 kW and

1 to 0.8 for PV systems with less than 10 kW.

If the installation does not exceed 40 kW, but produces energy for the needs of the business an

owner may sell surplus produced, on the prices of competitive market, which is announced by

the President of the Regulatory Office Energy.

Page: 38 Date: 30/06/2018

In 2016, the average annual price of sales of electricity on the competitive market was 38.90

EUR/ MWh (169.70 PLN/MWh), in 2017 - 38.45 EUR/ MWh, (163.70 PLN/MWh), and for I quarter

of 2018 41.98 EUR/ MWh (174.95 PLN/MWh).

Table 26. Electricity market framework data for the Polish Case studies


investor

Municipal

building

4Feed in -

Revenues

Electricity market

price [€/MWh]

Feed in premium

[€/MWh]

Feed in tariff

[€/MWh]

Self-

consumption

tax

Fixed

term per

year

[€/kW]

Peak

Shoulder

Off-peak

Usage

charge

[€/kWh]

Peak

Shoulder

Off-peak

Tax on energy

exported to

the grid

Toll [€/MWh]

Tax on benefits [%]


Table 27. Panels and inverters data for the Polish Case studies


investor

Municipal

building

Total efficiency [%] 15 15 15

PV panel size [m2] 1.7 1.7 1.7

Nominal power per panel [W] 130 250 250

PV degradation rate per year [%] 0.5 0.5 0.5

Installed power capacity [kW] 4.16 8 3

Price Fixed price [€] 4510€1 6013€2 2255€3

Estimated price [€/kW]

1- (32pieces*600zł)/4.2576zł = 4510€

2- (32pieces*800zł)/ 4.2576zł = 6013€

3 - (12 pieces*800zł)/ 4.2576zł = 2255€

Page: 39 Date: 30/06/2018

Storage systems

Regarding the Polish regulation net-metering is the system for micro-installation in Poland. The

surplus of produced energy may be feed to the electricity grid, and may be balanced with the

energy consumed from the electricity network in relation:

1 to 0.7 for PV systems with the power between 10 kW and 40 kW and

1 to 0.8 for PV systems with less than 10 kW.

If the installation does not exceed 40 kW, but produces energy for the needs of the business an

owner may sell surplus produced, on the prices of competitive market, which is announced by

the President of the Regulatory Office Energy.

In view of the above we may treat distributed network as a virtual storage system with infinite

capacity.

Simulation drivers

Table 28. Simulation drivers data for the Polish Case studies


investor

Municipal

building


Annual

maintenance

costs [%]

Panels and

inverters 1% 2% 2%

Storage system None – grid

storage

None – grid

storage

None – grid

storage

Depreciation

[useful years]

Panels and

inverters 5% 5% 5%

Storage system None – grid

storage

None – grid

storage

None – grid

storage


Corporate tax rate [%] 19 19 19

WACC [%] 6 6 6

Inflation rate [%] 2 2 2


Study horizon [years] 20 20 20

Nº of simulations [-] 1 1 1

Decision-making horizon [hours] 23 23 23


emissions [kg/kWh] 0.36 0.36 0.36

Page: 40 Date: 30/06/2018


Grid analysis will be performed considering the following factors:

- Low voltage network limitations for PV highly saturated grid segments

- Impact of PV generation on medium voltage network

- Any possible grid limitations (overvoltage, over frequency) that can occur at low voltage and

medium voltage level.


Table 29. Assessable KPIs for the Polish Case studies


investor

Municipal

building





Paybak time


prices





Technical – grid




congestion








served (SAID)




emergency control / /



Regulative



TSOs and DSOs

Environmental


Page: 41 Date: 30/06/2018

2.5. Spain

2.5.1. Solutions

When deciding which solutions from those given in deliverable 2.1 should be tested in Spain,

several factors were taken into account in order to analyse the most promising ones to be

implemented in the country. Among the different reasons to decide, some examples are

regulative criteria, data availability, specific characteristics of the demand profile, etc.

Homeowner

A single-family house with solar PV and storage system, which is a popular business model in

Europe, will be analysed. PV electricity will be consumed when possible and excess electricity

will be either stored for later use or exported to the public grid. Therefore, savings will be

generated by the avoidance of purchasing electricity from a supplier and possibly by selling the

excess electricity.

This solution will not only be analysed in Spain but also in the other countries that will carry out

simulations. However, analysing this solution in Spain can be very interesting, given the high

degree of specificity of the Spanish self-consumption regulation. The simulation will also assess

what impact this regulation have over the profitability of those projects.

Furthermore, real consumption data is available from a four family-members house located in

Madrid, Spain. The demand profile shows a 50% lower consumption during summer, when the

PV production is higher.

Contractor concept

Deloitte’s headquarters in Madrid, Spain, will be analysed. This solution is quite widespread in

Europe especially among companies that are not interested in owning physical assets. This fact,

together with the availability of real data for the simulation, makes this solution highly

interesting. The regulation against PV self-consumption also affects this type of consumers.

In addition, the consumption profile shows some features that can play an attractive role in

electricity market as it has almost no consumption during the weekends, which means that

almost all the energy produced by the solution could be exported to grid during that time.

Municipal building

A public school in Getafe, Madrid, Spain, will be employed to assess the municipal building

solution. The most prominent characteristic of this building is its consumption profile. Schools

do not always have electricity demand during the summer period, as well as during the

weekends. Therefore, as the revenues are higher from avoiding to buy energy from the grid than

Page: 42 Date: 30/06/2018

from selling energy to the grid, it will be interesting to test the profitability and scalability of this

solution.

Furthermore, municipal buildings are also quite interesting due to the lighthouse effect that they

may have. This case is not exempt from this adverse regulation and therefore it would be a good

way to compare its impact.

Community storage

This is one of the newest and promising solutions that can have a huge penetration in the future,

as the concept of smarts grid grows and matures. For this reason, evaluating this case study may

be highly enriching. Nowadays, there are no real cases of this solution implemented in the

country, although a reduced number of case studies at a small scale have been developed.

Additionally, the number of self-consumption communities without storage is also very limited

to date, given that self-consumption in communities was prohibited by law until 2017.

The community storage will be understood as a group of houses that share a storage device.

Therefore, regarding the demand profile of the different houses, the same demand

characteristics of the homeowners will be assumed for the different houses of the community.


The data gathered for the simulations are shown in the tables below. As the software employed

to size the solutions will be the Prosumer Solution Simulation tool, these tables follows the same

pattern than the input windows of the tool.

Radiation profiles

The different solutions will be tested in Madrid, Spain. The region where they are located

presents an average annual irradiance of 1.75 MWh/m2, reaching its maximums values during

the summer season. The radiation profiles were obtained from the solar radiation database

CMSAF-PVGIS and they contain data in an hourly resolution from the year 2007 to the year 2016.

As an example, data from year 2007 in Madrid is shown in the Figure below.

Page: 43 Date: 30/06/2018

Figure 13. Yearly irradiance profile of Madrid, Spain

Demand profiles

The consumption profiles were obtained by monitoring the hourly demand of electricity over a

year in real installations. As it can be observed in the following figures, the different case studies

show a very different pattern of consumption. While the office and schools have a huge

consumption during the week, they show few consumption during the weekend. However, the

higher demand peak in a household appears around 13 and 20 hours. It can be also observed in

the following table that the average yearly consumption is highly different when evaluating the

different types of solutions.

Table 30. Average yearly consumption for the Spanish Case studies


concept

Municipal

building

Community

storage

Average yearly consumption [MWh] 6 198 158 130

Page: 44 Date: 30/06/2018

Figure 14. Household consumption yearly profile

Figure 15. Contractor concept consumption yearly profile

Page: 45 Date: 30/06/2018

Figure 16. Municipal building consumption yearly profile

Electricity tariffs

The price of the electricity that each solution demands will depend on the electricity tariff that

the solution has contracted with the electricity retailer. For example, the homeowner is

associated to a 2.0 DHS tariff, while the office and the school is associated to a 3.0 A. These

tariffs, with hourly discrimination, correspond to a contracted power capacity lower than 10 kW

and higher than 15 kW, respectively. The different prices and hours for each period are shown

in the table below.

Table 31. Electricity tariffs data for the Spanish Case studies


concept

Municipal

building

Community

storage

Tariff prices

[€/kWh]

Summer

Peak 0.17 0.11 0.11 0.17

Shoulder 0.11 0.10 0.10 0.11

Off-peak 0.08 0.07 0.07 0.08

Winter

Peak 0.17 0.11 0.11 0.17

Shoulder 0.11 0.10 0.10 0.11

Off-peak 0.08 0.07 0.07 0.08

Tariff periods

[h]

Summer

Peak 13-23 11-15 11-15 13-23

Shoulder Others Others Others Others

Off-peak 1-7 0-8 0-8 1-7

Winter

Peak 13-23 18-22 18-22 13-23

Shoulder Others Others Others Others

Off-peak 1-7 0-8 0-8 1-7

Page: 46 Date: 30/06/2018


According to the Spanish regulation, this type of solutions must pay a tax on self-consumption,

except the cases when the total installed power is lower than 10 kW, which may only be

applicable to the homeowner case study.

The self-consumption tax is composed of two terms: a fixed term and a variable term. The first

one is related to the power of the installation, while the second one affects to the self-consumed

energy. According to the tariff contracted by the user, the law sets the different costs for these

two terms.

It must be noted that in the vast majority of cases the fixed term is not billed, given that it is only

applicable to the difference between the contracted power and the billed power, and it is

common that the billed power corresponds to the contracted power (in tariffs with a contracted

power lower than 15 kW it is inevitable). Therefore, the most important tax is the one related

to the self-consumed energy. The equation which allows to calculate the cost of the variable

term is the following:

𝑇𝑠−𝑐 = 𝐸𝑠−𝑐 ∙ 𝑉𝑇𝑠−𝑐

Where:

Ts-c: Total tax billed (€)

Es-c: Total self-consumed energy (kWh)

VTs-c: Self- consumption tax applicable to each tariff (€/kWh)

In tariffs with more than one hourly discrimination period, the self-consumption taxes are billed

for each term.

When the solutions exports energy to the grid, its revenues will be determined by the Electricity

Market Price (EMP). This data is obtained from the Spanish division of the Iberian Market

Operator (OMIE) website and it corresponds to a five-year period, as required by the simulation

tool.

Furthermore, according to the Royal Decree 1544/2011 of the Spanish regulation, a toll of 0.5

€/MWh must be paid for all the energy exported. Moreover, there is a tax of 7%, applicable on

the benefits obtained for selling this energy, which must also be considered.

The table below summarizes the different sources of income, taxes and tolls that play a role on

the profitability of the solutions.

Page: 47 Date: 30/06/2018

Table 32. Electricity market framework data for the Spanish Case studies


concept

Municipal

building

Community

storage

Feed in -

Revenues

Electricity market price

[€/MWh]

Feed in premium

[€/MWh]

Feed in tariff [€/MWh]

Self-

consumption

tax

Fixed term

per year

[€/kW]

Peak 7.10 32.00 32.00 7.10

Shoulder 7.10 5.20 5.20 7.10

Off-peak 7.10 4.13 4.13 7.10

Usage

charge

[€/kWh]

Peak 0.05 0.02 0.02 0.05

Shoulder 0.01 0.01 0.01 0.01

Off-peak 0.06 0.01 0.01 0.01

Tax on energy

exported to

the grid

Toll [€/MWh] 0.5 0.5 0.5 0.5

Tax on benefits [%] 7 7 7 7


Monocrystalline PV modules will be employed in the different solutions. This technology has a

major cost than others but its efficiency is also higher, standing between 17 and 18%. However,

the presence of inverters, as well as all the possible losses that might affect the installation

(temperature, shading, soiling, etc.) will decrease the total efficiency of the installation to 15%.

The sizing assessment of the solution is one of the main aims of this study. Therefore, several

installed capacities will be simulated in order to see which one should be implemented or

suggested. In the table below, the different characteristics of the installation can be observed,

which includes panels and inverters, together with the range of installed power capacity that

will be studied.

Table 33. Panels and inverters data for the Spanish Case studies


concept

Municipal

building

Community

storage


PV panel size [m2] 1.7 1.7 1.7 1.7



Installed power capacity [kW] 2-6 10-60 10-40 50-100



Page: 48 Date: 30/06/2018

Storage systems

Lithium-ion batteries will be employed in the different solutions. They provide high energy and

power density and also show a high efficiency.

The number of cycles of operation of the batteries is established at 8,000, which by the end of

the simulated lifetime of the installation might not have been reached. However, based on real

cases and experiences, even though the total life cycles of the battery have not been exceeded,

the tool simulates a battery replacement rate of ten years.

As in the case of the PV installation, several capacities will be analysed in order to carry out the

sizing assessment of the solutions. In the table below, the different characteristics of the storage

systems together with the range capacities that will be studied are presented.

Table 34. Storage systems data for the Spanish Case studies


concept

Municipal

building

Community

storage




Capacity [kWh] 0-3 10.5-30 5-25 50-150


Life cycles [-] 8000 8000 8000 8000




Simulation drivers

In order to carry out the simulations, some extra data related to both the economic and technical

aspects is needed.

In order to achieve a higher accuracy, the number of simulations will be set at 1,000, which is

supposed to be large enough to avoid outliers. Moreover, the decision making will be stablished

at 23, in order to let the algorithm analyse a complete day ahead when taking a decision. It must

also be pointed out that the inflation rate will not be considered in the study, as it is quite

unpredictable in the electricity markets, and therefore the simulation will be carried out

considering current values.

Page: 49 Date: 30/06/2018

Table 35. Simulation drivers data for the Spanish Case studies


concept

Municipal

building

Community

storage


Annual

maintenance

costs [%]

Panels and inverters 2 2 2 2


Depreciation

[useful years]

Panels and inverters 20 20 20 20




WACC [%] 6.5 6.5 6.5 6.5







emissions [kg/kWh] 0.36 0.36 0.36 0.36


Simulations at transmission and distribution level will be carried out. The peninsular Spanish

transmission system will be employed in the simulations. Interconnections with France, Portugal

and Morocco must be also considered due to the impact that they have in the electricity

interconnecting grid performance. A distribution grid in the region of Madrid was built and

connected to the transmission system. This distribution grid is shown in the figure below and it

consists of five voltage levels: 400, 220, 132, 45 and 15 kV.

In order to develop the simulations, PowerFactory will be used, which is a leading power system

analysis software application for use in analysing generation, transmission, distribution and

industrial systems. An example of the Madrid grid model is depicted in the figure below.

Several levels of demand will be considered and different levels of solar PV penetration will be

assessed. This PV generation will be deployed in 43 nodes at 15 kV and 1 node at 45 kV.

The parameters evaluated will be grid losses, voltage levels and loading of components. N-1

contingency analysis will be performed to assess the behaviour of the system before and after

the PV solution is introduced. Security criteria must be met.

Page: 50

Date: 30/06/2018

Figure 17. Distribution grid of Madrid, Spain (part of the transmission grid is included)

Page: 51

Date: 30/06/2018

Figure 18. Example of PowerFactory grid modelling (one part of the distribution grid of Madrid

Page: 52

Date: 30/06/2018

At low voltage level, yearly simulations with an hourly time resolution will be performed. The PV

solutions proposed are mainly connected at low voltage level (< 1 kV). Therefore, assessing the

local impact these systems exerts on the low voltage grid is a key output of this project. Through

these quasi-static simulations, it can be appreciated when and where there could be

overloading, overvoltage or under-voltage in the grid.

The integration of batteries opens up the possibility to assess the improvement in the transient

behaviour of the system in case of faults and disturbances.


Given the analysed solutions and the different simulations (static, quasi-static and dynamic) that

will be carried out, it is expected to evaluate the key performance indicators presented in the

table below.

Table 36. Assessable KPIs for the Spanish Case studies

Key Information Indicators Homeowner Contractor

concept

Municipal

building

Community

storage





Paybak time


prices





Technical – grid




congestion








served (SAIDI)



Page: 53

Date: 30/06/2018


emergency control



Regulative



TSOs and DSOs

Environmental


3. References

1. No Title. Available at: https://www.wind-energy-the-facts.org/european-transmission-

and-distribution-networks.html (Accessed: 30 June 2018)

2. M. Bollen och H. Fainan, "Integration of Distributed Generation in the Power System",

Hoboken, New Jersey: John Wiley and Sons, Inc, 2011.

3. Enernex, ”Integration Issues and Simulation Challenges of High Penetration PV,” Enernex,

Knoxville, 2011.

4. No Title. Available at: https://www.entsoe.eu/publications/system-operations-reports/

(Accessed: 10 July 2018)

Documents

Work Package 3 iDistributedPV: Solar PV on the ... · PV installation and the storage system may as well be tuned in order to make a deeper analysis. After the simulations are carried