POLITECNICO DI MILANO

Scuola di Ingegneria Industriale e dell’informazione

Corso di Laurea Magistrale in Ingegneria Energetica

Modelling and Analysis of Battery Energy Storage

Systems for Primary Control Reserve Provision

Relatore: Prof. Marco MERLO

Correlatore: Ing. Claudio BRIVIO

Tesi di Laurea Magistrale di:

Pietro IURILLI Matr. 852664

Anno Accademico 2016 – 2017

“Your car goes where your eyes go”

Garth Stein

- I -

Ringraziamenti

Non sapevo a cosa andassi incontro iscrivendomi al Politecnico. Ho sudato

tanto, soprattutto nelle sessioni d’esame, per mantenere il ritmo scandito dai vari

appelli. Per fortuna, la compagnia è sempre stata ottima. Compagni da ogni parte

di Italia e anche dall’estero, tutti con la stessa pazza idea di voler fare gli

ingegneri. E così, tra una battuta durante le lezioni e discorsi infiniti nelle pause,

le tante ore di lezione si sono fatte più leggere. Ora, questo ciclo si chiude e sono

contento delle scelte che ho fatto.

Innanzitutto desidero ringraziare il Prof. Marco Merlo, che mi ha fatto

appassionare ad un mondo che prima conoscevo poco. I suoi consigli sono

sempre stati utili ad accendere la mia curiosità e a sfruttarla per sviluppare la

tesi.

Ringrazio Claudio, capace di capire a cosa stessi pensando e di

valorizzarlo nel modo giusto. Quante volte ho varcato la porta del tuo ufficio, per

chiederti consigli e risolvere problemi.

Ringrazio Matteo, che in questi mesi è diventato un grande amico,

dall’attività svolta insieme in laboratorio, alle gite in montagna.

Ringrazio i miei genitori, che mi hanno supportato fin dall’inizio (il che

significa dal primo anno di asilo). Ho sempre potuto seguire le mie passioni,

sapendo di avere un riferimento ed un appoggio, fino ad arrivare al traguardo

odierno. Ringrazio Anna, Giovanni e Chiara, perché, a modo nostro, ci vogliamo

bene e formiamo una bella squadra.

Ringrazio Elena, che con la sua semplicità e il suo affetto è sempre stata

capace di sostenermi e di condividere le scelte; di incoraggiarmi e di distrarmi

nei momenti difficili. Mi hai sopportato nei viaggi in treno e nella preparazione

degli esami e, ancora adesso, mi sopporti fuori dall’università.

Ringrazio tutti coloro che al momento opportuno sono stati al mio fianco:

gli amici ed in particolare il gruppo dell’oratorio; i compagni di università a

Milano ed Amburgo ed i colleghi di SOS Malnate tra un servizio e l’altro.

- III -

Extended abstract

1. Introduction

In the last years, in Europe, the use of

renewable energy sources has strongly

increased. In the field of electricity

production, the share of renewable sources

(RES-E) grows to 28.8% of EU-28’s gross

electricity production [1]. The integration

of RES-E plants into transmission and

distribution grids affects the network

operations. The discontinuous generation

and the priority in power dispatching of

these plants bring to a reduction of

resources dedicated to guarantee security

and reliability of networks.

Recent modifications to the European

network code allow RES-E plants and

Battery Energy Storage Systems (BESSs)

to provide the ancillary services. These are

a variety of services that guarantee the

reliability and security of electrical

networks [2].

The ability of BESSs to provide high

power capability in relation to energy

capacity and their fast response make these

systems suitable to provide different grid

services. In particular, these systems are

good candidates for the provision of

Primary Control Reserve (PCR). This

service is utilized to manage the energy

balance of the grid through the constant

control of the grid frequency. It must be

constantly guaranteed with a predefined

value of available reserve. An eventual

interruption in provision is a serious

problem and it causes penalty issues. On

this point, the service continuity of a BESS

is affected by the finite capacity of the

battery. Capacity depends on the size and

on the technology exploited. Furthermore,

the battery has an internal efficiency and it

is subjected to self-discharge.

The purpose of this work is to propose a

methodology capable to properly model

BESS behaviour in providing PCR at

distribution level. Different aspects are

analysed by setting different goals:

1. Selection of the proper battery model to

simulate the BESS with a technical

analysis of the service provision.

2. BESS design: economic comparison of

different BESS sizes.

3. Analysis of different SoC control

strategies able to improve the service

continuity of the BESS.

4. Comparison of the different SoC control

strategies both on a technical (i.e.

service provision) and an economic (i.e.

investment) perspectives.

This extended abstract is organized in five

sections: Section 2 presents a review on

battery technologies, battery models and an

overview of PCR service in Europe;

Section 3 describes the approach used to

develop the proposed methodology and all

its characteristics; Section 4 introduces the

Matlab™ Simulink™ model based on the

proposed methodology and presents the

case study; finally, Section 5 presents the

simulations and the discussion of the

results obtained.

Extended abstract

- IV -

2. Literature review

The literature review is developed on three

levels: characterization of different battery

technologies, description of different

battery models and overview of the PCR

service in Europe.

Battery technologies

The rechargeable batteries are one of the

most widely used ESS technologies in

industry and in daily life [3]. Different

technologies with different characteristics

are available depending on the material

utilized in the cell and on the chemical

reactions [4]:

- Lead-acid batteries: high specific power

but low specific energy: fast response

and low self-discharge rate. lifetime is

limited and it is reduced by deep cycles.

- Nickel based batteries: limited energy

density, available in a wide range of

sizes. but subjected to memory effect

that can decrease the available capacity.

- Sodium based batteries: thermal

management needed in order to

maintain the operational temperature;

high energy density, good specific

power and a high efficiency; the cycle

lifetime is high.

- Li-ion batteries: very high

reactivity with small response time.

Different chemistries are available, with

different characteristics, depending on

the materials utilized in the cell.

Among the various technology, the Li-ion

technology has been chosen as reference in

this work. Nowadays, the 75% of existing

BESSs for ancillary services provision is

based on the Li-ion technology [5].

Battery models

Different kind of battery models have been

developed to emulate the behaviour of the

internal components with different degrees

of complexity. Regardless the complexity

of the model, two main factors are

investigated: modelling of operating

condition (i.e. SoC estimation) and

modelling of aging (i.e. SoH estimation). It

is possible to define four general different

categories of battery models [6]:

- Electrochemical models: they have a

very high level of accuracy but they are

difficult to develop. They are used in

structural design of batteries; several

parameters must be evaluated through

different experiments [7].

- Analytical (empirical) models: based on

analytical equations that describe the

system; the electrochemical processes

are not considered but empirically

fitted. The empirical-analytical models

are the most often employed in

dimensioning tools due to their

simplicity.

- Electrical models: based on the

electrical properties of a battery; they

utilize an equivalent circuit to represent

battery dynamics. These models are

divided in two families, active and

passive models, depending on the OCV

representation (through active or

passive circuit elements) [8]. Then, they

can be represented in time domain or

frequency domain. These models can

be utilized for battery monitoring and

design [6];

- Stochastic models: they represent the

charging and discharging phenomena as

- V -

stochastic Markovian processes. The

physical aspects of battery operations

are not taken into account but end-of-

discharge and end-of-life can be

accurately predicted.

Between the different categories, the

electrical and the empirical models have

been selected for the analyses.

Overview on PCR service

The aim of primary control reserve is to

maintain a balance between electrical

generation and consumption within the

synchronous area [9]. A synchronous area

is an area covered by interconnected

systems whose control areas are

synchronously interconnected with control

areas of members of the association [10].

When the frequency varies by its nominal

value (50 Hz in Europe), the primary

control reserve is activated and the droop

control technique is utilized to provide the

service. The activation of the service is

automatic and is governed by the droop

control law. This law is characterized by

three main parameters: (i) the dead band

(DB), defined as a small band around the

nominal frequency where no power needs

to be provided; (ii) the regulation band,

defined as the maximum upward or

downward power (Pmax) that the

generator must make available to provide

PCR; (iii) the droop (σ), defined as the

slope of the curve, it describes the slower

or faster action capacity of the power

generator to a change of frequency. It is

defined as follows:

𝜎 = −∆�̇�

∆�̇� (I)

where ∆�̇� is the frequency variation in p.u.

of the nominal frequency value and ∆�̇� is

the power setpoint measured in stable

working condition in p.u. of the nominal

power of the generator (Pn).

By utilizing these parameters, a

representation of the droop control law is

given in Figure I. In Europe a general

electric network code has been developed

by ENTSO-E and it has been utilized by the

different countries to develop national

regulations. In Italy, the primary control

reserve service is described in the

“Allegato A-15” of the grid code [11]. All

the power units with a rated power higher

than 10 MW must guarantee the service

that is not remunerated. The dead band is

set equal to ± 20 mHz; the minimum value

of regulation band is set to 1.5% and the

droop range is between 2 and 5% at

distribution level [12].

Figure I – general droop control curve for Primary

Control Reserve provision

Around Europe, some countries have

already developed a dedicated regulation

for BESSs and a capacity market for PCR.

In Germany a specific droop control law

has been defined for BESSs, with

ΔPmax

-ΔPmax

ΔP

ΔfΔfmax

-Δfmax

DB

-DB

σ

σ

Extended abstract

- VI -

correspondent limits on SoC and different

possibilities to exchange energy in order to

guarantee service continuity [13]. In UK, a

new service called Enhanced Frequency

Response has been developed for BESSs,

defining two types of provision with

specific droop characteristics.

3. Proposed methodology

The proposed methodology wants to

properly model BESS behaviour in

providing PCR at distribution level. It is

based on three main assumptions: (i) the

frequency input signal is not affected by

the battery power output; this signal is

taken by measurements; (ii) the influence

of the temperature on the battery model is

neglected; (iii) the BESS is modelled in

individual configuration providing only

one service.

The schematic representation of the

methodology is given in Figure II. Given

the input signals, the model developed

herein, after named “BESS4PCR”,

includes all the sub-models necessary to

simulate the operation of a BESS.

Figure II – Schematic representation of the proposed

methodology

The performances are measured with the

SoC estimation and the service provision

reliability (technical point of view) and

with the NPV (economic point of view). A

detailed description of the components of

BESS4PCR model is given in the

followings.

Controller model

It includes specific decision-making rules

to elaborate the input signals. The simplest

control strategy is the application of droop

control technique with a fixed droop. Then,

other strategies have been implemented:

1. Dead band strategy: Exploitation of the

dead band range to bring the battery

SoC to the reference value with a power

setpoint-

2. SoC restoration with PCR interruption:

the restoration of the SoC is done by

interrupting the service provision and

imposing a power setpoint set to correct

the SoC.

3. SoC restoration with PCR provision: in

this case the restoration is parallel to

PCR provision (i.e. at the same time).

4. Variable droop strategy: utilization of

variable droop depending on the actual

SoC of the BESS; no power setpoint is

imposed but the droop characteristics is

modified.

Regulation model & Inverter model

The regulation model receives the

frequency signal and the parameters by the

controller in order to build the droop

control curve and to calculate the power

setpoint for the battery (𝛥�̇�). The inverter

model simulates the presence of an inverter

INPUTS

BESS4PCRmodel

OUTPUTS

REGULATION

MODEL

INVERTER

MODEL

CONTROLLER

MODEL

BATTERY

MODEL

PCR POWER

SET POINTS

SO

C E

ST

IMA

TIO

N

ECONOMIC

MODEL

- VII -

with a simple model based on equivalent

efficiency and response time.

Battery models

Two different models have been applied in

the methodology: the empirical and the

electrical battery model. The parameters

utilized in this work have been collected at

the Energy Storage Research Center

(ESReC) located in Nidau (CH). The

measurements were carried out within the

the framework of the collaboration

between Politecnico di Milano (DoE) and

CSEM-PV Center (Swiss Center for

Electronics and Microtecnology). The

details are given in [14]. The experimental

tests refer to LNCO cell (Li-ion) of Boston

Power (model SWING5300) [15]. This

work utilizes the results about efficiency

tests, OCV tests, EIS tests and aging tests.

Empirical battery model

The battery model receives the value of 𝛥�̇�

calculated by the regulation block and it

computes the real power 𝛥𝑃𝐵̇ (generator

convention) as follow:

∆𝑃𝐵̇ = { ∆�̇�

𝐶𝐻, ∆�̇� < 0

∆�̇� / 𝐷𝐼𝑆𝐶𝐻

, ∆�̇� ≥ 0 (II)

where CH and DISCH are respectively the

BESS charge and discharge efficiencies.

These values can be fixed to a constant or

variable as function of 𝛥𝑃𝐵̇ value. The

expression to evaluate the SoC variations

depends by the nominal power (Pn) and

nominal energy (En) of the battery and is

defined as:

∆𝑆𝑂𝐶 = ∫ ∆𝑃𝐵̇ 𝑃𝑛

𝑡+1

𝑡 𝑑𝑡

𝐸𝑛 (III)

To evaluate the service provision, it is

possible to evaluate the regulating energy

(EPCR) the BESS must provide as:

𝐸𝑃𝐶𝑅 = ∫ 𝛥�̇� 𝑃𝑛 𝑑𝑡 𝑡=𝑒𝑛𝑑

𝑡=𝑠𝑡𝑎𝑟𝑡

(IV)

The energy not provided EP is part of EPCR

and it is estimated as follow:

𝐸𝑝 = ∫ ∆�̇� 𝑃𝑛

𝑡=𝑒𝑛𝑑

𝑡=𝑠𝑡𝑎𝑟𝑡

𝑑𝑡|(𝑆𝑜𝐶𝑆𝑜𝐶𝑚𝑎𝑥)

(V)

Given these values, it is possible to

evaluate an indicator of the provision of

service by the BESS. This indicator is

called Loss of Regulation (LoR) and it is

defined as:

𝐿𝑜𝑅 =𝐸𝑃

𝐸𝑃𝐶𝑅 (VI)

Electrical battery model

Among the different types of electrical

battery models, a passive electrical model

has been chosen for this work, given in

[14]. This model has been developed

exploiting the results of OCV and EIS tests.

It is a passive electrical impedance-based

model in the time domain and it has been

modelled for Lithium-ion cells. By

utilizing combinations of impedances and

capacitances it evaluates different effects:

the electromagnetic effect; the double layer

and charge transfer effects; the diffusion of

charge carriers in the electrolyte and the

diffusion of charge carriers in the

crystalline structure of the active metal.

The representation of the model is given in

Figure III. In order to utilize the cell model

to simulate the BESS behaviour it is

Extended abstract

- VIII -

Figure III – Representation of the electrical circuit of the adopted electrical model [14]

necessary to scale down the inputs to the

cell. The real power Pcell required from or

injected to cell (generators convention) can

be computed as follows:

𝑃𝑐𝑒𝑙𝑙 = 𝐶𝑛,𝑐𝑒𝑙𝑙 ∙ 𝑉𝑛,𝑐𝑒𝑙𝑙 ∙ ∆�̇�

𝐸𝑃𝑅 (VII)

where Cn,cell and Vn,cell are respectively the

nominal capacity [Ah] and voltage [V] of

the cell. In this case, the energy not

provided by the battery model is linked to

the voltage limits of the cell. Specifically:

𝐸𝑃 = ∫ 𝛥�̇� 𝑃𝑛 𝑑𝑡 𝑡=𝑒𝑛𝑑

𝑡=𝑠𝑡𝑎𝑟𝑡

|(𝑉 < 𝑉𝑚𝑖𝑛)

𝑉 (𝑉> 𝑉𝑚𝑎𝑥)

(VIII)

The SoC estimation is related to the cell’s

OCV: a look-up table is utilized for the

conversion.

For both the empirical and electrical

models, the BESS lifetime is simply

defined as:

𝐿𝑇𝐵𝐸𝑆𝑆 = 𝑐𝑦𝑚𝑎𝑥

𝑐𝑦𝑡_𝑃𝐶𝑅 (IX)

developed by utilizing a maximum number

of cycles (cymax) and the number of cycles

done by the battery during the simulation

(cyt_PCR). The maximum number of cycles

can be fixed using database values or

variable as function of the C-rate.

Economic evaluation

The net present value (NPV) indicator has

been utilized to evaluate the investment as

follows:

𝑁𝑃𝑉 = 𝐼𝑛𝑣 + ∑𝐶𝐹(𝑦)

(1 + 𝑟)𝑦+ 𝑅𝑉(𝑇) [€]

𝑇

𝑦=1

(X)

where: Inv is the initial investment; T is the

investment period; RV is the residual value

of the BESS; r is the discount rate; CF(y)

is the net cash flow during the year y. CF

includes the revenues for PCR provision,

the penalties for service interruptions, the

revenues for energy transactions and the

eventual cost of replacement of the battery.

4. Case study

The methodology proposed in Section 3

has been implemented in a MATLAB™

Simulink™ tool. The model utilizes the

same block organization as Figure II. Three

different battery models have been

developed:

- Empirical FIX: this is the typical model

that can be found in literature [16]. It is

based on empirical model with constant

efficiency set to 95% and constant cymax

set to 5000.

- Empirical VAR: an efficiency curve is

coupled to the empirical model. The

value of efficiency varies as function of

Electrical and

magnetic effect

Double layer and

charge transfer –

electrode 1

Diffusion inside

the electrolyte

Diffusion inside

the electrodes

Double layer and

charge transfer –

electrode 2

RC,T 1 RC,T 2 R1,T R1,R

CD,L 1 CD,L 2 3/2 CD,T 1/2CD,R RΩ

ZCELLCD,R

{x5}{x5}

- IX -

the actual C-rate of the battery

(efficiency tests results [14]). This

variable is an indicator of the current

flowing in the battery; with the

empirical model the E-rate based on

energy is utilized. As regards lifetime,

the value of cymax depends on a battery

decay factor. The decay factor curve

derives by aging tests of [14].

- Electrical: based on the electrical model

described in Section 3 and selected by

[14]. The SoC estimation is based on

OCV tests results while the battery

lifetime on the aging tests results (to

evaluate cymax).

The frequency signal utilized has been

acquired within the framework of the IoT-

Storage Lab, with a 1 Hz sampling time for

1 month in Politecnico di Milano.

The constant technical and economic

parameters assumed in this work are listed

in Table I. The regulating power is the

power offered for PCR; all the simulations

have been performed with a constant

regulating power of 1MW. The ratio

between nominal energy and nominal

power of the battery (EPR) is fixed to 1h.

Then, by varying the regulation band

(introduced in Section 2), six different Pn-

En pairs have been analysed. For instance,

whit RB=10% the BESS is

10MW/10MWh; when RB=100% it is

1MW/1MWh; when RB=200% it is

0.5MW/0.5MWh. As regards the economic

parameters, the revenue of PCR is based on

the Central Europe Mechanism and fixed to

3500 €/MW [17]. The energy traded for

SoC restoration is valorized with Italian

PUN [18].

The penalty valorization is fixed to

150€/MWh [19].

5. Simulations, results, and discussion

Two sets of simulations have been

performed: the first related to the

comparison of battery models and BESS

design and the second to the comparison of

SoC control strategies.

Comparison of battery models

By coupling the three battery models with

the six different Pn-En pairs a total number

of 18 configurations has been simulated.

An overview of the configurations is given

in Table II.

The configurations 4, 10 and 16,

corresponding to 100% regulation band,

are chosen as reference. The main results

are shown in Figure IV. The representation

of the SoC profiles reflects the differences

in service provision. The empirical models

tend to maintain a higher level of charge

with respect to the electrical model. The

efficiency plays an important role in this

case: the empirical model with fixed

efficiency has shorter charging time; the

empirical model with variable efficiency

has a similar behaviour and the electrical

model has the lower charging time. The

LoR computation is activated when the

battery capacity is saturated. The two

empirical models with fixed and variable

efficiency obtained respectively 15.7% and

16.5% of LoR while the electrical model

obtained 21% of LoR. This higher value is

due to the LoR calculation based on

voltage limits saturation. In this way the

electrical model is not able to exploit the

whole SoC range to provide PCR. A focus

Extended abstract

- X -

Table II - Overview of the first set of simulations where the 3 battery model configurations have been simulated

with different battery sizes

Battery

model

Regulation band and battery size

RB=10%

10 MW/

10 MWh

RB=25%

4 MW/

4 MWh

RB=50%

2 MW/

2 MWh

RB=100%

1 MW/

1 MWh

RB=150%

0.67 MW/

0.67 MWh

RB=200%

0.5 MW/

0.5 MWh

Empirical

FIX

Configuration

1

Configuration

2

Configuration

3

Configuration

4

Configuration

5

Configuration

6

Empirical

VAR

Configuration

7

Configuration

8

Configuration

9

Configuration

10

Configuration

11

Configuration

12

Electrical Configuration 13

Configuration

14

Configuration

15

Configuration

16

Configuration

17

Configuration

18

on the behaviour of the electrical model is

given in Figure V. The voltage signal

saturates even if the SoC still remains

within the its limits. The higher the current

flowing in the battery, the faster the voltage

limits are reached. About the execution of

simulations, configuration 16 (electrical

model) required a simulation time 50 times

higher than configurations 4 and 10

(empirical models). The model complexity

is the main cause of this result. Looking at

all the configurations simulated, it is

possible to compare the service provision

by analysing the LoR. Figure VI-a shows

the estimated curve of LoR of the three

models as function of size (i.e. RB). In the

case of empirical FIX and the empirical

VAR models the LoR curve has a

logarithmic behaviour. In the case of

electrical model, the LoR increases linearly

with the regulation band. Configuration 18

has a double LoR with respect to

configurations 12 and 6. High current

causes higher fluctuations of the voltage

Table I - Technical and economic parameters adopted in the simulations

Description Parameter name Value

Regulating Power PReg 1 MW

Energy-Power ratio EPR 1 h

Regulation band RB [10 - 25 - 50 -100 - 150 - 200] %

Initial State of Charge SoC_start 50 %

Maximum SOC SoC_max 100 %

Minimum SOC SoC_min 0 %

Dead-band DB ±0.02 Hz

Maximum lifetime LTBESS,max 10 ys

Simulation span ΔT 30 d

Time-step Δt 1 s

Internal rate of return r 6%

Revenue of PCR RoPCR 3500 €/MW

Valorisation of LoR PLoR 150 €/MWh

Electricity price PUN PUN 50 €/MWh

Initial battery price Pbattery 400 €/kWh

Investment term T 10 ys

- XI -

Figure IV - 30 days simulations at 100% regulation band. Df and DP input signals and computed SoC profile of

the three different models

Figure V- Zoom on days from 10 to 20 of the simulation. C-rate, SoC, voltage and DP loss of the electrical model

with faster saturation of the signal and

consequent service interruption.

On an economic point of view the NPV is

utilized to compare the configurations

simulated. The estimated NPV curves are

shown in Figure VI-b. At low RB values,

the influence of the battery cost (big size)

gives negative NPVs. For higher RB values

the Empirical FIX model tends to maintain

higher NPVs than Empirical VAR and

Electrical battery models. These two

models, in all the configurations (from 7 to

18) have the same behaviour in terms of

NPV trend. Assuming higher penalties

valorization, the electrical model tends to

the lowest NPVs with respect to the other

models. This fact is mainly related to the

different LoR estimation already showed in

Figure VI-a. In general, a trade-off between

battery size and service provision is

showed. The optimal value of RB for the

electrical model is found equal to 110%

with a NPV of 0.57 M€/MWPCR; it

corresponds to a size of 0.9MW/0.9MWh.

Extended abstract

- XII -

Figure VI- Comparison of battery models: (a) estimated LoR curves and (b) estimated NPV curves as function of

the Regulation band

SoC control strategies implementation

The optimal configuration found for the

electrical model, with a battery size equal

to 0.9MW/0.9MWh has been utilized to

compare the SoC control strategies

introduced in Section 3. The reference

case, with fixed droop strategy, has been

called strategy 0.

The main specifics are given in the

followings:

A. Dead band strategy (strategy A): after a

sensitive analysis on different power

setpoints, the value of 12% of Preg has

been chosen to perform the simulation.

B. SoC restoration with PCR interruption

(strategy B): the power setpoint equal to

200% of Preg has been chosen to have

the lowest PCR interruption time;

C. SoC restoration without PCR interrup-

tion (strategy C): the power setpoint

equal to 50% of Preg has been chosen to

guarantee the restoration and service

provision at the same time;

D. Variable droop strategy (strategy D):

the droop is ranged between 0.027% and

0.068%. No power setpoint is imposed.

The main results of simulations are

grouped in Table III. In general, the

utilization of SoC control strategies

improves the service provision but, at the

same time, decreases the BESS lifetime

due to a higher utilization of the battery, in

terms of average C-rate and cycles.

However, the general decrease of LoR is

not proportional to the increase of NPV.

The battery replacements have a high

impact on the investment: for instance,

strategy D, with the highest NPV, maintain

the highest battery lifetime. On the

contrary, strategy B has the lowest NPV.

The high number of cycles per month and

the high average C-rate results in a low

BESS lifetime equal to 3.9 years. Strategy

C has a BESS lifetime similar to strategy B

but the very low value of LoR has a

positive impact on the final NPV.

In terms of cycles per month, strategy A is

the heaviest one. However, the utilization

of a low power setpoint results in a very

low average C-rate compared to the other

strategies. The BESS lifetime results

higher than strategies B and C.

-3

-2,5

-2

-1,5

-1

-0,5

0

0,5

1

0% 50% 100% 150% 200%

NP

V [

M€/M

WP

CR]

Regulation band

Electrical

Empirical (VAR)

Empirical (FIX)

R² = 0,976

R² = 0,9735

R² = 0,9742

0%

5%

10%

15%

20%

25%

30%

35%

40%

0% 50% 100% 150% 200%

Lo

R

Regulation band

Electrical

Empirical VAR

Empirical FIX

Lineare (Electrical)

Log. (Empirical VAR)

Log. (Empirical FIX)

(a) (b)

- XIII -

The utilization of a variable droop curve of

strategy D, without imposition of power

setpoint, results in a number of cycles per

month similar to strategy 0.

As regards the battery efficiency, strategies

0, C and D have a similar value, around

89.3%. Strategy A has the highest

efficiency, equal to 92.4% thanks to the

low average C-rate. Analogously, strategy

B has the lowest efficiency (85.3%) due to

the high average C-rate.

All the results highlight a trade-off between

the service provision reliability and the

economic value of the whole investment.

The grid operator will prefer higher

reliability while the provider will prefer

higher revenues from the investment. An

eventual increase of the penalties related to

interruption of service will move to the

utilization of the strategies that minimize

the LoR.

6. Conclusion

The proposed methodology gives all the

tools useful to evaluate the provision of

PCR by BESSs, including the control

model, the regulation model with reference

to the actual regulation and the battery

model.

The comparison of different empirical and

electrical battery models showed how the

complexity and the ability of the model to

reproduce the battery behaviour affect the

provision of PCR service. The utilization

of electrical variables gives a higher level

of information about the battery status and

a more realistic behaviour with the

saturation of capacity based on voltage

limits. However, as predictable, the higher

model complexity implies higher

simulation time than empirical models.

The BESS design showed how the

valorization of penalties for interruption of

service influence the final investment.

Different scenarios showed different

optimal BESS sizes.

The utilization of SoC control strategies

has two main aspects: on a technical point

of view it improves the service provision;

on an economic point of view it causes

higher costs related to more frequent

battery replacements. As for battery

design, the optimal choice is influenced by

penalties valorization. The action of grid

operator must be considered.

A specific remark can be done on the

battery model. Within the thesis work, a

laboratory activity on a commercial BESS

based on Sodium-Nickel technology

Table III - 30 days simulations at 110% regulation band. Technical and economic results of SoC

control strategy utilization compared to the reference case

SoC control strategy Strategy 0 Strategy A Strategy B Strategy C Strategy D

EPCR provided [MWh/month] 74.9 87.5 91.6 94.6 83.3

C-rate (average) 0.36 0.22 0.52 0.43 0.39

Cycles [#/month] 41.2 76.8 60.2 67.8 45.8

BESS lifetime [y] 7.5 5.1 3.9 4.1 6.5

Efficiency [%] 89.3% 92.4% 85.3% 89.4% 89.3%

LoR [%] 22.4% 9.1% 6.1% 2.2% 11.9%

NPV [M€/MWPCR] 0.57 0.62 0.50 0.59 0.67

Extended abstract

- XIV -

(ZEBRA) produced by FZSONICK [20]

has showed a completely different

behaviour by the Li-ion batteries. An

important consideration is that the battery

model must be modelled carefully

depending on the technology in order to

obtain valuable results. The assumptions

done for the Li-ion technology are not

completely applicable in this case: the

temperature of the battery is a governing

factor for ZEBRA batteries. A possible

improvement of the proposed methodology

is the introduction of the dependence on the

temperature.

- XV -

Contents

Ringraziamenti .................................................................................................................. I

Extended abstract ........................................................................................................... III

Contents ......................................................................................................................... XV

Abstract ........................................................................................................................ XIX

Sommario .................................................................................................................... XXI

1. INTRODUCTION ....................................................................................................... 1

1.1. PROBLEM FORMULATION ................................................................................ 7

2. LITERATURE REVIEW: BATTERY ENERGY STORAGE ........................................... 11

2.1. ELECTROCHEMICAL STORAGE: STRUCTURE AND TECHNOLOGIES .................. 11

2.2. LI-ION BATTERY TECHNOLOGY ..................................................................... 16

Cathode materials ................................................................................................ 16

Anode materials .................................................................................................. 17

Electrolytes.......................................................................................................... 18

Separators ............................................................................................................ 18

Li-ion chemistries ............................................................................................... 18

2.3. BATTERY MODELLING ................................................................................... 20

Empirical models ................................................................................................ 23

Electrical models ................................................................................................. 24

3. OVERVIEW OF PRIMARY CONTROL RESERVE ..................................................... 33

3.1. DROOP CONTROL TECHNIQUE ........................................................................ 34

3.2. REVIEW ON REGULATION SCHEMES IN PLACE IN EUROPE .............................. 36

German regulation for PCR provision by BESSs ............................................... 37

Enhanced Frequency Response: United Kingdom regulation ............................ 40

- XVI -

Central Europe Mechanism ................................................................................. 41

3.3. BESSS STATE OF THE ART IN EUROPE ........................................................... 43

Germany ............................................................................................................. 43

Denmark ............................................................................................................. 44

Switzerland .......................................................................................................... 45

Italy ............................................................................................................. 46

4. GOALS & METHODOLOGY OF THE RESEARCH ..................................................... 49

Differences between traditional power plants and BESSs .................................. 52

4.1. CONTROLLER MODEL .................................................................................... 53

The dead band strategy ........................................................................................ 54

The SoC restoration strategy ............................................................................... 55

Variable droop strategy ....................................................................................... 56

4.2. THE REGULATION MODEL .............................................................................. 58

4.3. THE INVERTER MODEL ................................................................................... 60

4.4. THE BATTERY MODEL .................................................................................... 61

Empirical battery model ...................................................................................... 62

Electrical battery model ....................................................................................... 63

Lifetime model .................................................................................................... 68

4.5. ECONOMIC EVALUATION ............................................................................... 69

5. THE CASE STUDY ................................................................................................... 71

5.1. APPLICATION OF PROPOSED METHODOLOGY ................................................. 71

Battery models ..................................................................................................... 73

5.2. SET UP OF SIMULATIONS ................................................................................ 77

Frequency trend ................................................................................................... 77

Technical parameters ........................................................................................... 79

Economic parameters .......................................................................................... 81

Configuration of the simulations ......................................................................... 83

- XVII -

6. SIMULATIONS, RESULTS AND DISCUSSION ............................................................ 85

6.1. COMPARISON OF EMPIRICAL AND ELECTRICAL BATTERY MODELS................. 86

LoR estimation: technical comparison ................................................................ 86

Optimal regulation band evaluation: economic comparison ............................... 92

6.2. SOC CONTROL STRATEGIES IMPLEMENTATION .............................................. 97

Reference case ..................................................................................................... 97

The dead band strategy ....................................................................................... 99

The SoC restoration with PCR service interruption .......................................... 103

The SoC restoration without PCR service interruption .................................... 106

Variable droop strategy ..................................................................................... 108

6.3. COMPARISON OF THE DIFFERENT SOC CONTROL STRATEGIES ..................... 111

Technical comparison ....................................................................................... 111

Economic comparison ....................................................................................... 112

7. CONCLUSION ....................................................................................................... 117

APPENDIX A: LABORATORY ACTIVITY ........................................................................ 121

System configuration and measurements setup ................................................ 122

Discharging tests results.................................................................................... 124

Charging tests results ........................................................................................ 127

Remarks .......................................................................................................... 130

APPENDIX B: DATA SHEETS .......................................................................................... 131

List of figures ............................................................................................................... 133

List of acronyms and symbols...................................................................................... 137

References .................................................................................................................... 139

- XIX -

Abstract

This thesis evaluates the utilization of a Battery Energy Storage System (BESS) for

grid-tied application: the Primary Control Reserve service provision. The work is based

on the development of a methodology devoted to properly simulate BESS dynamic

response. Firstly, an overview and description of the different battery technologies is

provided: the Lithium-ion technology is chosen as reference for this work. Then, the

proposed methodology is presented, which consists of five sections: (i) the controller

model, that elaborates the input signals and includes decision-making rules to control the

system; (ii) the regulation model that simulates the droop control proposed in this thesis,

to generate a power setpoint; (iii) the inverter model that simulate the presence of an

inverter; (iv) the battery model that simulates the battery behaviour and is the core of the

methodology; (v) the economic model that evaluates the investment on a defined period.

The methodology has been implemented in a Matlab™ Simulink™ model and applied to

a case study based on the Italian regulation.

The main analysis criteria are: (i) the comparison of service provision between two

different battery modelling approaches: empirical and electrical; (ii) the optimal

dimensioning of the system that guarantees a positive investment; (iii) the service

continuity, in order to limit the negative effect of finite capacity of the BESS by utilizing

SoC control strategies.

The main outcomes show that: (i) different battery models have a different level of

service provision; these differences are given by the nature of the models; (ii) the optimal

dimensioning of the BESS is strongly influenced by service provision only when the

penalty is high; (iii) SoC control strategies improve the provision of the service but they

can have a negative effect on the BESS lifetime and on the final value of the investment

due to heavier battery utilization.

Keywords: energy storage system, battery, primary frequency control, Li-ion technology,

distributed generation.

- XXI -

Sommario

Questo lavoro di tesi valuta l’utilizzo di un sistema di accumulo a batteria (BESS)

installato sulla rete elettrica ed utilizzato per fornire riserva di controllo primaria.

L’elaborato è basato sullo sviluppo di una metodologia che definisca un modello capace

di simulare opportunamente la risposta dinamica di un BESS. In primo luogo, una

panoramica sulle diverse tecnologie di batteria viene proposta: la tecnologia agli ioni di

litio (Li-ion) è scelta come riferimento per il lavoro. Quindi, viene presentata la

metodologia proposta che è composta da cinque sezioni: (i) il controllore, che elabora il

segnale in ingresso e lo processa con regole decisionali definite; (ii) il regolatore, che

simula l’utilizzo della curva di statismo per generare un valore di potenza; (iii) il modello

di inverter, che ne simula il comportamento; (iv) il modello di batteria che è il cuore del

modello e simula il comportamento di una batteria; (v) il modello economico, utilizzato

per valutare l’investimento su un periodo di tempo predefinito. La metodologia è stata

implementata in un modello Matlab™ Simulink™ e applicata ad un caso di studio basato

sulla regolamentazione italiana.

I principali criteri di analisi sono: (i) il confronto della fornitura di servizio tra

diversi approcci di modellazione di batteria: empirico ed elettrico; (ii) il dimensionamento

ottimale del sistema che garantisce un investimento proficuo; (iii) la continuità di

servizio, in modo da limitare l’effetto negativo della capacità limitata di un BESS

utilizzando delle strategie di controllo dello stato di carica.

I risultati dimostrano che: (i) i diversi tipi di modelli di batteria hanno un diverso

grado di fornitura del servizio; queste differenze sono dettate dalla natura stessa dei

modelli; (ii) il dimensionamento ottimale del BESS è fortemente influenzato dal livello

di fornitura del servizio solo quando la penalità è alta; (iii) l’utilizzazione di strategie di

controllo dello stato di carica migliorano la fornitura del servizio ma possono avere un

effetto negativo sulla vita utile del sistema e sul valore finale dell’investimento a causa di

una utilizzazione più pesante della batteria.

Parole chiave: sistema di accumulo, batteria, controllo primario di frequenza, tecnologia

Li-ion, generazione distribuita.

- 1 -

1. INTRODUCTION

In a world where the consumption of energy is strictly related to the level of

development of a country, the reliability of energy supplying is essential to properly feed

the needs. An efficient transmission network and a capillary distribution network are

necessary for electricity provision. These systems can work optimally when the whole

grid is balanced, that is when the energy demand equals the energy production second

after second.

Moreover, in the last years the attention to the consequences of human activities on

the ecological system of Earth has strongly increased. Thanks to international

cooperation, setting of goals to reduce emission and effective international and national

regulations, the production of energy has been modified. The utilization of renewable

energy sources is the core of the change in energy production and consumption.

In Europe, the use of renewable energy sources has strongly increased. The positive

development has been moved by the targets imposed by the Directive 2009/28/EC. Even

if the target fixed for the whole European Union (EU) is the “2020 strategy” with the 20%

of share of Renewable Energy Sources (RES), every single country has to make specific

efforts [21]. As regards the production of energy, Figure 1.1 shows the share of renewable

energy for every single country in percentage of the gross final energy consumption and

compare it to the imposed target. Some countries have already a share of RES higher than

20% and the 2020 goal is set to higher value, as about 50% for Sweden. In other cases,

the 2020 goal is lower than 20%, as Italy. Looking at the whole European Union the goal

is equal to 20%. The imposition of these targets favoured the diffusion of renewable

energy sources plants. As described in [1], from 2005 to 2015 the production of energy

from renewable sources within EU-28 increases of 71%, reaching 26.7% of total primary

energy production from all sources. Focussing on the electricity production, the share

grows to 28.8% of EU-28’s gross electricity production. This expansion of renewable

energy sources for electricity production (RES-E), is mainly related to wind energy, solar

power and solid biofuels. At the same time, the RES-E expansion had a big influence on

the transmission and distribution network systems of Europe. These systems were built

to satisfy the electricity demand, transferring energy from big power plants on the

transmission grid to the distribution grid, only one-way direction. A simplify scheme of

this grid is given in Figure 1.2. From the power plant the energy flow goes to the

substation with the transmission network; then the energy flows in the distribution

network and reaches the final consumers. Nowadays many small and domestic plants are

installed on the distribution grid, mainly RES plants. In Italy the authority for electricity

and gas (AEEGSI) is in charge to provide, yearly, a figure on the distributed generation

Chapter 1

- 2 -

[23]. In 2015, 22,2% of total gross national production was given by plants at the

distribution level; 79,9% of these plants utilize a renewable source. Analysing the RES-

E generation for every single Italian region, it is possible to represents the energy

production as in Figure 1.3. The trend shows the higher RES-E generation at distribution

level is in the big northern regions, where the population density and the number of small

and medium industries are high and in the southern regions, where the renewable sources

Figure 1.1 - Share of energy from renewable sources in the EU Member states [22]

Figure 1.2 – Schematic representation of one-way electrical networks

Distribution levelTransmission level

Energy flowEnergy flow

Introduction

- 3 -

have a higher availability than the northern regions. The presence of all these plants at

distribution level imply a bi-directional use of the grid that can cause a decreasing of

security and reliability levels. Many projects at national and international level are

evaluating solutions and the possible evolution of the actual system to more efficient

networks. A very attractive solution is the evolution of the actual networks to smart grids

[24]. The concept of smart grids integrates the production and the consumption of energy

with a strategical dispatching thanks to smart meters, communication systems, electrical

vehicle integration and energy storage [25]. A simple scheme is given in Figure 1.4. The

Figure 1.4 - Schematic representation of a bi-directional electrical network (smart grid)

Distribution levelTransmission level

Comunication system

Energy flow

Energy flow

Figure 1.3 - Energy production by RES-E generators at distribution level in Italy [23]

0

1000

2000

3000

4000

5000

6000

7000

8000E

nerg

y p

rod

ucti

on

[G

Wh

]

Chapter 1

- 4 -

evolution of smart grid is still not fast and technical solutions must be found to overcome

unbalancing of the grid. Therefore, before developing new solutions for the grid, it results

essential to understand how the security and the balance of the network is done nowadays.

A particular market is responsible of the security and balance of the electrical

network. It is called Ancillary Services Market. Many different services are provided

utilizing this specific market. In [2] a detailed description of these services is given.

Specifically:

- Load frequency control (active power reserve): this service guarantees the secure

operation of the electricity grid with a continuous use of control power in order

to balance capacity variations in the control area.

- Voltage support: the reactive power can affect the voltage of in a grid node.

Reference voltages for the feed-in nodes are given by the transmission system

operator (TSO) and the exchange of reactive power is allowed to maintain these

reference values.

- Compensation of active power losses: any transport of active or reactive power

is affected by losses at any different level of the electrical network. These losses

must be compensated. All the system operators, at transmission and distribution

levels, are responsible for the procurement of active power losses.

- Black start and islanded mode capability: the black start-enabled power stations

guarantee the restoration of the grid after major incidents. These power stations

must be able to go from idle to operation without any injection of grid-connected

electricity. As regards the island mode, a power station is able to ensure this

service if it can achieve and maintain a certain operating level without requiring

activation of the outgoing lines to the grid.

- System coordination: it covers all the services required at the transmission

system level in order to coordinate and ensure the reliable, secure and stable

operation of a national transmission system and its integration in the

international system.

- Operational measurement: it includes the installation, operation and

maintenance of the measuring and metering devices and data communication

equipment in the grid. It includes also the provision of information to ensure the

operation of the grid. The operational measurement is an important interface

between different grids.

Among the different services, one of the most particular is the frequency control.

This service guarantees three different types of active power reserve: primary control,

secondary control and tertiary control. The primary control reserve (PCR) restores the

balance between consumption and generation within seconds of the deviation occurring.

The activation is automatic and covers all the power units in the control area able to

Introduction

- 5 -

provide the service. The secondary control reserve (SCR) is utilized to maintain the

desired energy exchange of a specific area with the other part of the grid and to maintain

the frequency value at its nominal value. Also in this case the activation is automatic and

it is few seconds after the cause of control deviation. If this cause is not eliminated during

the period of activation of the secondary reserve, the tertiary reserve will be activated.

The tertiary control reserve (TCR) is activated as support to the SCR, in order to restore

a sufficient secondary control volume. Its activation can be automatic or manual. A

summary of the main characteristics of these three services in Europe is given in Table

1.1. Moreover, a simple scheme representing the activation of the power reserves on a

temporal scale is given in Figure 1.5.

Figure 1.5 - Load frequency control reserves activation

Power

Time [min]0 5 10 15 20 30 40

Primary control reserve

Secondary control reserve

Tertiary control reserve

Table 1.1 - Load frequency control in Europe [26]

Service Description Settling time

Primary control Automatic control, it is activated in response to a

frequency deviation from the nominal value. All

the control areas must provide the service

simultaneously.

30 seconds

Secondary control Automatic control, it is activated to keep or restore

the frequency to its nominal value to ensure the

full PCRs will be made available again. It is

activated only in the control area where the

imbalance take place.

5 minutes

Tertiary control Automatic or manual control, it is activated to

restore an adequate value of SCR

15 minutes

Chapter 1

- 6 -

The definition of the characteristics of the ancillary services and more in general of

the electrical grid utilization in Europe is given by the association of Transmission System

Operators for Electricity (ENTSO-E). This organization involves 35 countries and 42

transmission system operators. Its role is to support the TSOs in their regional strategies

providing useful IT-tools and models. In this way, ENTSO-E promotes the application of

EU energy policy and it is responsible of a common grid code, evaluating possible

improvements. In the last years, with the coordination of the European Agency for

Cooperation of Energy Regulators (ACER), the EU Commission presented useful

modifications to the network code. In the Winter Package an update of current EU rules

is present to allow renewable producers (RES-E based power plants) and battery energy

storage systems (BESSs) to fully participate in all market segments, with a progressive

shift from centralized conventional generation to decentralized [27]. In Italy, a first

important step in this direction has been made by the AEEGSI through the consultation

document (DCO) 298/16/R/eel. It proposed a reform of the regulatory framework for the

dispatching service currently in force, with the purpose to enable the active participation

of final users to the management of the power system. Almost one year later, the

resolution 300/2017/R/eel made the purpose real: RES-E plants have the possibility to

participate to the management of power as pilot projects. Moreover, a list of technical

rules and constraints for the utilization of storage systems in the network has been given.

In this way, RES-E plants are allowed to sell services designed to balance generation and

consumption on the Ancillary Services Market. Nevertheless, the unpredictable nature of

the primary resources for renewable energy generators still limits the operation. A

solution to exploit the potential of RES-E is the utilization of BESSs.

The ability of BESSs to provide high power capability in relation to energy

capacity, make these systems suitable to provide services on the distribution and

transmission systems not only coupled with RES-E units but also as individual units. A

typical BESS consists of a battery bank where multiple batteries are connected in series

parallel configurations to provide the desired storage capacity. A power converter and a

transformer are installed in order to interface the battery system with the external grid.

Usually, all the components of a BESS are installed in a container. In this way the

transportation and the final installation results easier. Several countries utilize BESS in

the electricity market as pilot projects or already as effective users, as the other power

units. For instance, in Italy the transmission system operator (TERNA) has in place pilot

projects to evaluate the benefits of BESSs [28]. The projects are related on security and

congestion of both the transmission and the distribution networks [29].

A peculiarity of BESSs is the ability to provide different services. In the field of

grid security and reliability, the BESSs are able to provide primary control reserve,

secondary control reserve, peak shaving, load shifting, voltage control and energy trading

Introduction

- 7 -

[30]. The BESSs utilization can be only for a service or for more services at the same

time (multi-service BESSs). Because of the batteries have a finite value of capacity, it is

better to exploit the other peculiar characteristics in the service provision. For instance,

the fast ramp rates of BESSs is an advantage to participate in services that required a fast

response. So that, the BESSs are good candidates for the provision of load frequency

control.

However, an important aspect must be considered: the service continuity. A

traditional plant has no limits of power output, i.e. it can produce energy continuously

until it has available fuel. A battery has a defined capacity. This capacity depends on the

battery technology and on the size. The utilization of a bigger battery size than the needed

one is not a practical solution for a technical and an economic point of view. Due to the

internal efficiency, even if the power provision of a battery is null, it will decrease the

level of state of charge (SoC) in time up to total discharge. On the economic point of

view, a bigger battery implies higher costs.

The interruption of service is a serious problem for BESSs: the battery could reach

level of SoC critical for its State of Healt (SoH). Consequently a BESS deactivation could

be necessary, driving to penalty costs. must be paid to the grid system operator. Therefore,

it is essential to optimize the design of the BESS and the choice of the control strategy. A

solution to guarantee the service continuity of these systems can be to give the possibility

to purchase and sell energy in the energy market in order to maintain a level of SoC that

is acceptable for service provision. Several works study the feasibility and the utilization

of BESSs in PCR provision [16], [31], [32].

1.1. PROBLEM FORMULATION

As mentioned, the main problem related to utilization of BESS for PCR service

provision is to find the optimal size of the battery to guarantee the service continuity. This

problem must be investigated during the evaluation of the feasibility of the project, on a

technical and an economic point of view.

The aim of this work is to propose a methodology capable to properly model BESS

behaviour in providing primary control reserve at distribution level. The analysis is

composed by two different aspects.

The first aspect is related to the definition of a proper battery model and to BESS

design. The choice of the battery model is on a technical basis, evaluating the behaviour

of the different models and their service provision. The optimal dimensioning of the

Chapter 1

- 8 -

BESS is investigated by an economic analysis: the feasibility of the investment is

evaluated for different battery sizes and for the different battery models.

The second aspect is related to the service continuity of the BESS. This factor is

governed by the control strategy that govern the system. A static battery control strategy

will bring the battery to the full charge or discharge states; on the contrary, a smarter

control strategy will maintain the battery state of charge in a suitable range for the

provision of PCR service.

All the analyses are based on a literature review of two main topics: the battery and

the primary control reserve. The structure of the thesis is outlined in the following.

In Chapter 2 an introduction on the different types of storage and on the

electrochemical storage system is given. Then, after the presentation of the different

electrochemical technologies, the Chapter is divided in two sections. In the first one, the

Li-ion battery technologies are presented; in the second one the battery models are

detailed. After a list of all the different battery models, the focus is on the empirical model

and the electrical model. These models will be implemented in the proposed

methodology.

In Chapter 3 the description of the PCR service is given. The droop control

technique is presented at a general level. A focus on the European context is done: the

Italian regulation is taken as example. Then, by the limitation of the Italian regulation,

the discussion moves on the German and English examples, where BESS’ regulations are

already in place. Moreover, a description of the Central Europe market for PCR is given.

To conclude, the Chapter includes some examples of existing BESSs in Europe. Both

systems for researching purposes and for market purposes are presented.

Afterwards the work is concentrated on the development of the dedicated

methodology to simulate the BESS providing PCR service and to the case study adopted.

In Chapter 4 the proposed methodology is shown. As the scheme that represents the

methodology, the Chapter is organized in different sections that describes every single

part. The different SoC control strategies that can be applied are listed with all the

governing equations. Then, the model that evaluate the droop control law and the battery

models applied are described, including the equations and the main variables included in

the calculations. In the conclusion, the Chapter presents the equations and expressions

utilized for the economic evaluation.

In Chapter 5 the proposed methodology is applied to the case study. The Matlab™

Simulink™ model utilized to create the model based on the methodology is described.

The setup of the simulations is included in the discussion, with all the technical and

Introduction

- 9 -

economic parameters necessary for the simulations. Moreover, a description of the

frequency signal utilized in the simulations is given.

In Chapter 6 the simulations performed with the model presented in Chapter 5 are

presented. In the first section the comparison of the empirical and electrical battery

models is given, with a technical and economic analyses. In the second section all the

different SoC control strategies presented in Chapter 4 have been applied to the electrical

battery model. The simulations results are analysed and discussed. To conclude, in the

third Section a comparison of the SoC control strategies and the reference case is given

on both a technical and economic point of view.

A laboratory activity is presented in Appendix A. The author has participated in the

analysis of the behaviour of a BESS commercial product for domestic application. The

system’s battery is based on Sodium-Nickel-Chloride technology (ZEBRA battery). The

tests have been a preliminary analysis in order to evaluate the possibility of PCR provision

at distribution level by the storage system.

- 11 -

2. LITERATURE REVIEW:

BATTERY ENERGY STORAGE

The battery energy storage is only one component of a large family of storage

systems called electrical storage systems (ESS). These systems can be differentiated in

different ways, such as the technology, the responsiveness or the storage duration. One

of the most utilized method of classification is based on the form of energy stored in the

system [3]. Specifically, it is possible to define the following categories:

- mechanical (pumped hydroelectric storage, compressed air energy storage and

flywheels);

- electrochemical (conventional rechargeable batteries and flow batteries);

- electrical (capacitors, supercapacitors and superconducting magnetic energy

storage);

- thermochemical (solar fuels);

- chemical (hydrogen storage with fuel cells);

- thermal energy storage (sensible heat storage and latent heat storage).

This Chapter considers two different aspects: the battery description on a technical point

of view and the battery modelling. Sections 2.1 describes the electrochemical cell

structure and the different available technologies; Section 2.2 makes a focus on the Li-

ion chemistries. Then, Section 2.3 is dedicated to battery modelling, with a specific focus

on the empirical and electrical battery models.

2.1. ELECTROCHEMICAL STORAGE: STRUCTURE AND TECHNOLOGIES

The electrochemical storage is also known as battery energy storage. The rechargeable

batteries are one of the most widely used ESS technologies in industry and in daily life.

They are used in naval/submarine applications, automotive industry, telecommunication

system, electrical systems and in uninterruptible power supply (UPS) [3]. In any

electrochemical process, the electrons flow from one chemical substance to another: this

reaction is called oxidation-reduction (REDOX) reaction. The electrons are transferred

by a chemical species to another. The oxidant is the species that gains electrons and is

reduced in the process, while the reductant is the species that loses electrons and is

oxidized in the process. The associated potential energy is computed as the difference

between the valence electrons in atoms of different elements. The overall reaction (2.1)

Chapter 2

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is electrically neutral and it is composed by two half reactions: the reduction (2.2) and the

oxidation (2.3). The reactions are composed as follows:

𝑎𝐴 + 𝑏𝐵 → 𝑐𝐶 + 𝑑𝐷 (2.1)

𝑎𝐴 + 𝑧𝑒 − → 𝑐𝐶 (2.2)

𝑏𝐵 → 𝑧𝑒 − + 𝑑𝐷 (2.3)

The battery cell is composed by two compartments where the different reaction take

place. A simplify scheme of an electrochemical battery cell is shown in Figure 2.1. The

main components are:

- the electrodes: one for each compartment, they are composed of solid metal.

They are named anode, where the oxidation takes place, and cathode, where the

reduction takes place;

- the electrolyte: it is a medium where the two electrodes are immersed and the

ions are free to move;

- the separator: it is a membrane or a salt bridge that allow ions the transfer of ions

between the two compartments in order to maintain electrical neutrality;

- the electrical connection: it is the physical connection made by a metallic

conductor between the anode and the cathode. The electrons flow always from

the anode to the cathode.

During the discharging process of the cell a spontaneous REDOX reaction takes place

and the energy released is converted into electrical energy. This specific cell is also known

Figure 2.1 - Simplify scheme of an electrochemical battery cell

Charge

Discharge

e-

e-

Cathode Anode

Electrolyte Separator

x+

x+Discharge

Charge

Literature review: battery energy storage

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as “Galvanic cell”. Instead, during the charging process an external electricity generator

is utilized in order to generate a potential difference between the electrodes, driving a

nonspontaneous REDOX reaction. This specific cell is called “Electrolytic cell”. The

combination of the two processes makes possible the utilization of the electrochemical

cells as electrical energy storage. The theoretical voltage and capacity of a cell is

depending by the type of anodic and cathodic materials. Knowing the oxidation and

reduction reactions, it is possible to evaluate the standard potential (E0) of a material by

utilizing the Faraday’s law as follows:

𝐸0 =∆𝐺0

𝑧 ∙ 𝐹 (2.4)

where ∆𝐺0 is the free Gibbs energy related to the reaction; z is the number of electrons

involved in stoichiometric reaction and F is the Faraday’s constant equal to 96500 C. By

the standard potential of the two materials it is possible to compute the potential of the

cell (i.e. the voltage) as follows:

𝐸𝑐𝑒𝑙𝑙0 = 𝐸𝑅𝑒𝑑,𝑐𝑎𝑡ℎ𝑜𝑑𝑒

0 − 𝐸𝑂𝑥,𝑎𝑛𝑜𝑑𝑒0 (2.5)

Due to the low value of standard potential, the cells are usually connected in series; then

other connections in parallel allow to have an appreciable value of capacity. Various cells

connected together form a battery; different batteries are connected into a battery pack

and different packs together constitute the battery system.

Different technologies are available, depending on the material utilized in the cell

and on the chemical reactions. Specifically [4]:

- Lead-acid batteries: these are the most widely used rechargeable batteries (for

instance it is utilized in the most of the vehicles all over the world) [3]. The

cathode is made of PbO2 and the anode is made of Pb; the electrolyte is sulfuric

acid. It is a mature and well-known technology and its cost is relatively low (100-

200 kWh). The efficiency is relatively high (60-80%) and thanks to a high

specific power, they are capable to manage high discharge currents. However,

the specific energy is low and the charging process is slow (up to 14-16 h to

saturate the charge). Lead-acid batteries have a fast time response and low self-

discharge rate (

Chapter 2

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chemistries are present: Nickel-Cadmium (NiCd) and Nickel-metal-Hydride

(NiMH). NiCd batteries are composed by two electrodes of nickel hydroxide and

metallic cadmium immersed in an aqueous alkali solution. The energy density is

limited but it is available in a wide range of sizes; with a proper maintenance

these batteries reach a high number of cycles. However, it is necessary a complex

algorithm for the charging process because they tend to overcharge. In case of

fast charge or discharge processes there is a high heat production. At ambient

temperatures the behaviour of NiCd batteries is good and they can be stored in

discharged state without losses of performance. Also in this case, deep cycles

reduce the battery lifetime. At last, these batteries are subjected to memory

effect: the available capacity can be decreased if the batteries are charged after

being only partially discharged. This effect can be corrected through a complete

discharging and charging cycle. NiMH have many advantages with respect to

NiCd: higher capacity (i.e. specific energy and energy density), lower influence

of the memory effect and absence of Cd that makes this solution more

environmentally friendly. However, the lifetime is reduced with respect to the

NiCd batteries.

- Sodium based batteries: the different Sodium chemistries are Sodium-Sulphur

(NaS) and Sodium-nickel-chloride (NaNiCl2). In the first case, NaS batteries

utilize inexpensive and abundant materials. The anode is made by molten sodium

and the cathode is made by molten sulphur; the electrolyte is solid and made by

beta alumina. To guarantee the liquid state of the electrodes, the reactions

normally require a temperature of 300-350°C. So that, an efficient thermal

management is needed. Furthermore, because of corrosive products and possible

dangerous reactions of molten (e.g. explosion with water) a hermetic and solid

case is necessary. This type of batteries has high energy density, good specific

power and a high efficiency. The battery self-discharge is practically null and

the cycle lifetime is high. However, the costs related to the operations,

specifically to ensure the operating temperature, results high. In Italy the

transmission system operator has a pilot project that utilize NaS technology. In

the next Chapter this project will be presented. As regards the second chemistry,

NaNiCl2 batteries, also known as ZEBRA batteries, have a behaviour similar to

NaS batteries. The anode is made by molten sodium and the cathode is made by

nickel chloride; the electrolyte is solid as in the previous case, made by beta

alumina. The temperature of operations is lower than the previous case and it is

about 245°C.The energy density and the specific power is slightly lower than

NaS technology. The battery self-discharge can arrive up to 10% per day.

However, a peculiar behaviour is these batteries are able to maintain their level

of charge when they are not working (i.e. not at the operational temperature).

Literature review: battery energy storage

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For instance, if the battery has 50% SoC and it is turned off, the salts will solidify

and maintain the level of charge. The battery lifetime is high but the price is

higher than other technologies. As NaS batteries, the operational costs must

include the thermal power needed to ensure the operating temperature.

- Li-ion batteries: the peculiarity of Li-ion batteries is the very high reactivity with

a small response time. This technology is the today’s most investigated one in

terms of material developing, cell and system assembling. The lithium-ions

technology includes various types of chemistries. They are Lithium Cobalt

Oxide (LCO), Lithium Manganese Oxide (LMO), Lithium Nickel Manganese

Cobalt Oxide (NMC or LMCO), Lithium Iron Phosphate (LFP) and Lithium

Titanate (LTO). A complete description is given in the next Section.

The anodic and cathodic reactions of these six different battery technologies and

the cell voltages are listed in Table 2.1.

Table 2.1 – Chemical reactions and cell voltages of the main battery technologies

Battery type Anode and Cathode reactions Cell voltage [V]

Lead-acid Pb + SO42− ↔ PbSO4 + 2 e

−

PbO2 + SO42− + 4 H+ + 2 e− ↔ PbSO4 + 2 H2O

2.0

Nickel-cadmium

(NiCd) Cd + 2 OH− ↔ Cd(OH)2 + 2 e

−

2 NiOOH + 2 H2O + 2 e− ↔ Ni(OH)2 + 2 OH

− 1.2

Nickel-metal-hydride

(NiMH) Ni(OH)2 + OH

− ↔ NiOOH + H2O + e−

H2O + e− ↔ 0.5 H2 + OH

− 1.2

Sodium-sulfur (NaS) 2 Na ↔ 2 Na+ + 2 e−

x S + 2 e− ↔ Ni + x S2− 2.0

Sodium-nickel-

chloride (NaNiCl2) 2 Na ↔ 2 Na+ + 2 e−

NiCl2 + 2 e− ↔ Ni + 2 Cl−

2.6

Litium-ion (Li-ion)

[graphite anode] LiXXO2 ↔ Li1−nXXO2 + n Li

+ + n e−

C + n Li+ + n e− ↔ LinC 3.6

Chapter 2

- 16 -

2.2. LI-ION BATTERY TECHNOLOGY

As already mentioned, the Li-ion technology includes different types of cells. As

regards to the electrodes, the cathode is an aluminium foil coated with the specific active

cathode material, while the anode is a copper foil coated with a specific active anode

material. These two coated foils are kept separated through the use of a proper material;

most of the time the separator is made of plastic material. These three elements form the

so called jellyroll and they are inserted into a container. The container can be a metal can,

a plastic enclosure or a metal foil-type pouch. Finally, the electrolytic liquid is injected

into the assembly which is then hermetically sealed. The different types of Li-Ion cells

are given by different combination of anode, cathode materials and of the electrolyte. A

list of the materials utilized in Li-ion battery is given above.

Cathode materials

The cathode materials are the major source of Li-ions [33]: they are transitional

metal oxides with lithium. The ions must be able to diffuse freely through their crystal

structure in order to classify them as cathode materials. The crystal structure can have

different morphological structures with increasing complexity. The materials currently in

use can be divided in:

- Layered rock salt structure materials (two-dimensional crystal morphology): the

most common example is lithium cobalt oxide (LiCoO2), but also lithiated nickel

as lithium nickel cobalt aluminium oxide (LiNiCoAlO2) or lithium nickel

manganese cobalt oxide (LiNiMnCoO2). They are characterized by high

structural stability; however, they are costly (due to their limited resources) and

hard to synthetize.

- Spinel structure materials (three-dimensional crystal morphology): the main

member of this category is lithium manganese oxide (LiMn2O4), which enables

Li-ions to diffuse in all three dimensions. This material is one of the oldest

compounds researched and still widely used. The advantages of the Spinel

structure are a lower cost and a lower environmental impact than layered rock

salt materials, but on the other side the energy density is lower.

- Olivine structure materials (one-dimensional crystal morphology): the main

compound of this category is lithium iron phosphate (LiFePO4). They are non-

toxic and they show lower capacity reduction during lifetime.

The cathode materials occupy 35% of the total cost of cells [34]. The reduction of

cathode material cost is a pursued strategy by cell manufacturers to increase

Literature review: battery energy storage

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competitiveness. As consequence, the layered rock salt structure materials are less

utilized than the other two types of material.

Anode materials

Most of the anodes currently utilized are carbon-based, however there are other

types of anodes that are commercialized. Specifically:

- Carbon-based anode: thanks to carbon anode, the Li-ion batteries became

commercially viable more than 20 years ago. The electrochemical activity in

carbon comes from the intercalation of Li between the graphene planes, which

offer good two-dimensional mechanical stability, electrical conductivity and Li-

ion transport. Carbon has many advantages: low potential vs Li, high Li

diffusivity, high electrical conductivity and low volume change during

charge/discharge processes. Moreover, it has a low cost and it has abundant

availability. Commercial carbon anodes can be divided into two types: graphitic

carbons, which have large graphite grains, and hard carbons, which have small

graphitic grains [35].

- Lithium titanium oxide (Li4Ti5O12): lithium titanate nanocrystals on the anode

surface instead of carbon has been recently introduced into the market.

Advantages are the high thermal stability (i.e. safety) and unequalled cycling

capabilities. The main drawbacks are the high cost of titanium and the low

voltage when coupled with any cathode materials [35].

- Alloying-metals: they are elements which electrochemically alloy and form

compound phases with Li. They can have extremely high volumetric and

gravimetric capacity, but they suffer from huge volume change which can cause

serious damage (fracturing) and hazards [36]. Among the top studied Li-alloy

anodes it is possible to mention: Li-Al (lithium aluminium) that suffer severe

fracturing; Silicon which has low potential vs Li, abundance, low cost, chemical

stability, and non-toxicity; Selenium which has similar properties with Silicon

except a lower gravimetric capacity; Germanium which does not suffer from

fracturing even at larger particles sizes but it is too expensive for most practical

application; Zinc, Cadmium and Lead which have good volumetric capacity but

suffer from low gravimetric capacity.

- Amorphous anode: This type of anode uses oxides in which Li2O are formed on

the initial charging of the battery. The Li2O acts as a ‘glue’ to keep particles of

the alloying material (such as Silicon or Selenium) together. Drawbacks of Li2O

is the low electrical conductivity and the large voltage hysteresis.

Chapter 2

- 18 -

Electrolytes

The electrolyte is a mi