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Electronics 2020, 9, 701; doi:10.3390/electronics9040701 www.mdpi.com/journal/electronics

Article

An Analysis of Battery Degradation in the Integrated

Energy Storage System with Solar

Photovoltaic Generation

Munsu Lee

1

, Jinhyeong Park

2

, Sun-Ik Na

3

, Hyung Sik Choi

4

, Byeong-Sik Bu

5

and Jonghoon Kim

2,

*

1

Department of Energy Science, Sungkyunkwan University, Suwon 16419, Korea; munsu@skku.edu

2

Department of Electrical Engineering, Chungnam National University, Daejeon 34134, Korea;

pig25t@o.cnu.ac.kr

3

Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul 08826, Korea;

nasunik@snu.ac.kr

4

Division of Policy Research, Green Technology Center, Seoul 08826, Korea; hyungsik.choi@gtck.re.kr

5

Planning and Supporting Division, Power Policy Group, Posco Energy, Seoul 06194, Korea;

bubs@poscoenergy.com

* Correspondence: whdgns0422@cnu.ac.kr

Received: 2 April 2020; Accepted: 23 April 2020; Published: 24 April 2020

Abstract: Renewable energy generation and energy storage systems are considered key technologies

for reducing greenhouse gas emissions. Energy system planning and operation requires more accurate

forecasts of intermittent renewable energy resources that consider the impact of battery degradation

on the system caused by the accumulation of charging and discharging cycles. In this study, a

statistical model is presented for forecasting a day-ahead photovoltaic (PV) generation considering

solar radiation and weather parameters. In addition, the technical performance of energy storage

systems (ESS) should be evaluated by considering battery degradation that occurs during the charge

and discharge cycles of the battery. In this study, a battery degradation model based on the data-

driven method is used. Based on a suitable forecasting model, ESS scheduling is performed to charge

the maximum amount of PV generation and discharge for the self-consumption of the customer load

when PV generation ends. Since the battery is highly dependent on operating conditions such as depth

of discharge, state of charge and temperature, two different ESS charge and discharge modes are

proposed. From the simulation with the battery degradation model using parameters derived from

experiments, we show that the battery is degraded along with charging cycles during testing periods.

Variations in state of health are observed owing to the different characteristics of the battery according

to the ESS operation modes, which are divided into the low and high SOC. Through experimental

validation, it is proved that the state of charge (SOC), 0.45 is the optimal threshold that can determine

the low and high SOC. Finally, the simulation results lead to the conclusion that the battery

degradation in different operation modes should be taken into account to extend the end of life

efficiently.

Keywords: energy storage system; PV forecasting; state of charge; state of health; battery degradation

1. Introduction

While an increase in fossil energy consumption contributes to global warming, countries are

seeking pathways to reduce greenhouse gas emissions by substituting fossil energy with renewable

energy after the Paris agreement in 2015 [1]. Because of increasing concerns on grid resiliency caused

by volatile renewable electricity generation from solar and wind, secure grid management has risen as

Electronics 2020, 9, 701 2 of 14

a crucial factor in energy transition for climate change mitigation. In particular, the energy storage

system (ESS) is expected to play a pivotal role in the distributed energy system which comprises

variable renewable energy resources [2,3].

ESS is one of the methods to transfer energy from the electrical power system into various

applications [4]. The ESS has been expected to bring benefits to market and system while stabilizing the

electric market price, freeing the volatility by renewable energy, avoiding transmission congestion

charges, and allowing a market-driven electricity dispatch through customer’s market participation [5].

However, there are a few countries that have set specific regulations or operating rules of the ESS in

the electricity markets.

Some reports discussed possible services of the ESS as power generation, transmission and

distribution (T&D) assets [6–8]. The main applications of the ESS can be grouped as balancing variable

renewable generation and increasing the reliability and asset utilization of the grid. From a large-scale

bulk storage to a substation storage and a distributed energy storage, detailed applications and their

relevant operational goals in the system were analyzed [5,6]. Those applications were assessed in terms

of size, duration, cycles and lifetime considering requirements for their necessity in industry and

market.

When the ESS has cost-effectiveness to replace some part of the conventional T&D infrastructure

in order to meet growing peak demand and reserve margin, it will get more importance in a position

to respond during periods of high prices and operate within seconds to minutes of a signal in electricity

market [7,9,10]. Therefore, it has been also discussed how to solve not only the barriers in market rules

and regulation, but also technological penetration and economic viability [9]. Some studies argued with

the number of challenges to restrict its deployment to different applications for electric supply and

ancillary services for electricity wholesale market, renewable integration to the grid system for utility

system and many others for end-users [11,12].

For the purpose of energy system planning and operation, it is required to get more accurate

forecasts of intermittent renewable energy resources while distributed energy resources (DER) are

increasing in the grid system [13]. Energy planning based on short-term to long-term forecasting is

utilized to maintain the reliability of the power system and the efficient system operation. According

to the forecast horizon which means the amount of time between the actual time and the effective time

of prediction, forecasting models can be divided into the following: very-short-term (1 minute–a few

minutes), short-term (1 hour–1 week), mid-term (1 month–1 year),and long-term (1–10 years) [14].

The integrated system of the ESS with renewable energy can play an important role to mitigate the

effects of the intermittent and uncertain energy sources when it is connected to the grid and decreases

the loss of capacity and power fluctuation [11]. Hence, the integrated system can decrease the

dependency of the electricity from the grid system and shift its use from the expensive peak period to

the non-peak period to lower the electricity bill of the customer. This can be achieved by storing surplus

power from the DER and using it to supply the electricity when the customer needs it.

From the perspective of the system operation, it is important to efficiently manage relevant

elements from energy generation to consumption in the energy system. According to its system design,

the system operator considers customer’s load, generation from renewable energy resources, and

participation in the electricity market such as demand response and ancillary services and other types

of incentives [6,7]. To find the economically optimal strategy, control of charging and discharging the

ESS is managed to earn maximum profit from savings of electricity tariff and incentives from the market

[15–17].

However, it is not easy to facilitate the roles of the ESS without understanding the characteristics

of the battery and the battery management system (BMS) which controls charging and discharging

power of the ESS. To set an appropriate management strategy, the technical performance of the ESS

must be evaluated considering forecasting errors that occurred by changes of the customer’s load and

intermittent characteristics of renewable energy and battery degradation. Among system-perspective

studies, there are only a few to consider the impact of the battery degradation to the system which is

Electronics 2020, 9, 701 3 of 14

caused by the accumulation of charging and discharging cycles [18]. Otherwise, it is not feasible to

calculate an accurate performance based on an initial value of the state of health (SOH) [19].

This study focuses on battery degradation in the self-consumption ESS, exploring a correlation

between the state of charge (SOC) and the SOH of the Li–phosphate (LFP) battery. Self-consumption

ESS in this study is supposed to charge the maximum Photovoltaic (PV) generation and discharge the

same amount of power to the load. Since the SOC of the battery is highly dependent on PV generation

and the self-consumption of the load, a day-ahead schedule of the ESS can be determined by the PV

generation forecast. To enhance the performance of PV generation forecasting, a statistical model is

presented as a case study considering solar radiation and weather parameters. As a result, based on the

better forecasting model, the ESS scheduling is performed to charge the maximum amount of PV

generation and discharge for the self-consumption of the customer’s load when the PV generation ends.

Once the charge and discharge cycles are determined, battery degradation can be estimated by the cycle

aging model using experimental parameters of the LFP. Finally, simulation results show that the battery

is degraded along with charging cycles during test periods and its change of the SOC is the main factor

that influences capacity loss.

2. Literature Review

A lot of research has been done to address how the battery loses its capacity or power output

during operation. Some studies argued that effective knowledge of battery states especially, the SOH is

crucial for battery health management [20,21] and the expected remaining useful life (RUL) can present

potential power of the battery for practical applications [22]. In addition, numerous amounts of

researches economically evaluated the battery. Because of advancements in material technology and

manufacturing process, the cost of the battery is decreasing following the path of PV modules, while

he battery pack price has fallen to $ 156/kWh in 2019 and is expected to decrease to $ 100/kWh in 2023

[23,24]. However, there is one study to consider degradation cost instead of replacement and

operational costs throughout battery lifetime [25] and another study to mention its cost of battery could

be equivalent to the cost of electricity generation [26]. Even if the ESS operation optimization also was

studied increasingly, there are few paying attention to the impact of battery degradation to the system.

Because of the complexity of the battery characteristics, there are a number of methods such as the

linear model, the nonlinear model, the Tremblay model and the Volterra model to estimate parameters

and internal states of batteries [27–29]. Those are mathematical models to fulfill the different

requirements such as battery life, efficiency, etc..

Basically, the solid electrolyte interface (SEI) layer is formed on the electrode surfaces from

decomposition products of electrolytes with the battery during the charge and discharge operations.

As active lithium-ions decrease in the battery, its available charge and discharge capacity fade,

following the loss of active lithium-ions. While the battery aging is an inevitable problem with charge

and discharge operation, there are a large number of studies to capture the major factors and simulate

the phenomena using various methods which can be classified into physical-chemical models and

empirical models [30]. The former ones are effective in understanding degradation under diﬀerent

conditions and the impact on diﬀerent aspects of the battery performance. However, there are

limitations that a detailed modeling is required for simulating the degradation process which results in

a long computation time. To overcome this problem, a semi-empirical model is developed by

considering simpliﬁed physical relationship and using the parameters with experimental data obtained

from aging experiments. The model with benefits of accurate performance using relatively simple

equations is constructed to investigate the impact of battery degradation at the different SOC ranges.

Because of the importance of prediction by using different types of forecasting models in energy

planning, many forecasting techniques were developed [31]. Depending on data availability and

objectives of the analysis and time horizon, a different approach needs to be considered among

quantitative forecasting techniques such as physical methods, statistical methods and advanced

methods which are based on the computational intelligence techniques [31]. In [32], the artificial neural

Electronics 2020, 9, 701 4 of 14

networks and the support vector machine techniques were proposed for energy generation forecasting

and condition-based maintenance strategies in PV plants. For planning and controlling electricity load,

short-term forecasting ranging from hours to days is effective and for a national or regional scope of

operations and the impact of policy in longer-term, long-term forecasting is utilized [33]. Because a day-

ahead prediction is essential for system operation, the short-term forecasting techniques have been

predominantly used in the power sector [33].

In the case of renewable energy, forecasting models can be divided into three types; measured data

of renewable energy generation systems, historical measured data of explanatory variables including

weather parameters such as temperature, humidity, pressure and other climate factors [34]. In addition,

there are hybrid forecasting methods with integration of machine learning algorithms and numerical

weather prediction. Since renewable energy is highly dependent on weather parameters and climate

conditions, it is a challenge to reduce forecasting error, capture rapid changes in the power output and

calculate net loads from DER [35]. Therefore, a reliable forecasting model helps decreasing mismatches

between the power contracted and actual delivery, which lead to less capital and operation cost [35,36].

Because of the high capability of regression, artificial intelligence techniques are recently used in

renewable energy and power system forecasting field [34]. Machine learning methods have been

proposed in the control strategy of power systems, which suggested an intelligent framework [37]. To

minimize forecasting errors occurred by a nonlinear relationship between input and output, support

vector machine, artificial neural network, extreme learning machine and adaptive fuzzy neural network

were frequently selected for forecasting solar radiation [38] and wind speed [39,40].

As mentioned above, there is little to be studied considering the impact of battery degradation to

PV-ESS operation in the case of charging and discharging the battery at the different SOC ranges. This

research aims to provide the method that incorporates partial cycles at the different SOC ranges for the

technical assessment.

3. Methodologies of PV Generation Forecasting

3.1. Input Data Collection and Preprocessing

Forecasting models for predicting PV generation are essential to schedule the ESS operation to

maximize charging PV generation. For developing those models, exogenous inputs such as weather,

solar radiation, and integrated system data are required to estimate potential power from PV generation

and determine the ESS schedule during the next 24 hours.

In general, atmosphere temperature, relative humidity and cloud cover are highly relevant

elements to PV generation. In this study, weather forecasts from the Korea Meteorological

Administration (KMA) which manages the official records of Korea’s meteorological data are used for

PV generation forecasting. To increase the accuracy of the model results, weather dataset from the close

weather station should be included in the model. Among various types of weather forecast data,

relevant factors should be selected as input variables considering correlation with PV generation.

In South Korea, the KMA provides 12 forecast elements 8 times per day by Dong-Nae Forecast

[41]. However, they do not provide the solar irradiance forecast which is the most essential for PV

generation forecasting. Thus, the ASHRAE Clear-Sky model is used exogenously to estimate the solar

radiation where the PV generator is located [42].

In particular, the presence of clouds affects the value of radiation on the earth’s surface which leads

to uncertainty of forecasting. Following the cloud amount, the sky condition can be represented by 4

groups following 10 scales of cloud amount; ‘clear’ (0 ≤ cloud amount ≤ 2), ‘partly cloudy’ (3 ≤ cloud

amount ≤ 5), ‘mostly cloudy’ (6 ≤ cloud amount ≤ 8) and ‘cloudy’ (9 ≤ cloud amount ≤ 10).

3.2. Solar Irradiance Estimation

Global Hourly Irradiance (GHI) can be calculated using different models such as the empirical

model, statistical model and advanced computational model based on artificial neural networks (ANN)

Electronics 2020, 9, 701 5 of 14

[43]. Because clouds can block the sun’s rays and affect PV generation with uncertainty, estimating the

radiation is complex [44]. However, the KMA is providing only 3-hour interval weather forecasts and

solar radiation forecast which is one of the most essential for PV generation analysis, is not included.

Therefore, the ASHRAE Clear-Sky model is used to estimate the radiation. The model is a type of

parametric model offering a relatively simple method and widely used in building community.

Since the estimated radiation is necessary for PV forecasting, the ASHRAE Clear-Sky model is

chosen to calculate the value for the PV forecasting model input [38]. In order to design the model,

environmental variables and solar geometry data should be used in the model. First of all, GHI (I) can

be calculated as shown in Equation (1) [42,45]:

=

+

+

(1)

where,

,

and

are hourly direct beam radiation, hourly diffuse radiation and hourly

reflected radiation, respectively.

Second, to get the direct beam normal to the sun’s rays (

), the individual value of radiation can

be calculated using the solar altitude angle (β) and two empirical coefficients (“apparent”

extraterrestrial flux (A) and a dimensionless factor called the optical depth (k)) which are fitted to a

trigonometric function of sine function of the day number of the year (n) as shown in (2). In addition,

variables such as the hour angle (H), the latitude of the location (L) and the solar declination (δ) are

used to calculate altitude angle (β) [42,45]:

=

/

(2)

where,

= 1160 +75 × sin360

365(−275)

= 0.174 +0.0035 ×360

365(−100)

sin = cos cos cos + sin sin

And, the direct beam radiation on a horizontal surface is represented using the direct beam normal

to rays (

) and its altitude angle in Equation (3) [42,45]:

=

sin

(3)

And, the diffuse radiation (

) is also expressed using

and the diffuse sky factor (C) in

Equation (4) [42,45]:

=

(4)

where,

= 0.095 +0.04 ×sin360

365( − 100)

3.3. PV Forecasting Based on the Statistical Model

Building an accurate forecasting model needs to explore relative variables and capture highly

correlated variables influencing the forecasted value. In a case of PV generation, those variables include

weather data such as temperature, humidity, cloud amount and solar irradiance and time. In that sense,

correlation analysis ensures understanding dependencies among variables and improving the

prediction of the model.

In order to evaluate the statistical correlation between PV generation and all other variables, 15-

minute interval PV generation data are used during training periods. It shows that the solar irradiance

Electronics 2020, 9, 701 6 of 14

variable is the most correlated and humidity and temperature variables present high correlation value

as shown in Table 1.

Table 1. Correlation of variables with photovoltaic (PV) generation.

Variable Correlation (%)

solar irradiance 85.1

humidity 47.8

temperature

47.6

sky condition 18.0

day 11.8

hour 4.2

PV generation forecasting model is developed with the autoregressive model using variables

selected by correlation analysis above such as weather forecast data (temperature and humidity) and

solar radiation derived from the ASHRAE Clear-Sky model. The forecasting model can be expressed as

follows [45]:

=

+

+

+

+

(5)

where,

indicates PV generation at forecasting time (t) and

indicates historical PV generation if

available or previous forecasts at a time (t-i). I

t

indicates radiation, T

t

indicates temperature, H

t

indicates

humidity at forecasting time (t) and ε

t

is a white noise with mean zero.

3.4. Performance Measure

While most studies provide characteristics of different models and their applicability, accuracy

and scope of the research, those models are investigated by performance index such as mean absolute

error (MAE), mean absolute percentage forecast error (MAPE) and root mean square error (RMSE) [41].

In this study, MAPE is used to determine the most accurate model as follows:

1

1

100

N

k k

kk

A F

MAPE N A

(6)

where, N is the number of forecasting value, A

k

is actual value and F

k

is forecasting value.

4. Simulation for Self-Consumption ESS with PV Generation

4.1. Simulation Design

For this study, we chose an integrated PV and ESS in Cheonnam province in South Korea as shown

in Table 2.

Table 2. System information on PV and energy storage system (ESS).

PV generation 360 kW

Operation mode Island mode

Battery type Li–phosphate

Round-trip efficiency 90%

ESS Rated Capacity 1 MWh

ESS Upper Limit 100%

The selected site in this study operates the commercial building with the PV-ESS integrated system

which is running in island mode. In this mode, the customer gets the electricity load from the DERs.

The need for ESS is to charge the amount of PV generation and discharge the same to the customer for

Electronics 2020, 9, 701 7 of 14

their self-consumption. To find out a day-ahead control strategy for searching the system’s charge and

discharge schedule, forecasting models are necessary to predict PV generation and the customer’s load

consumption. In general, ESS can minimize the electricity use purchased from the grid and lower the

electricity bill when charging electricity during off-peak time. In this study, PV generation installed for

self-consumption helps increasing independence from the utility by using ESS which operates

independently from the main grid.

The reference system used in this study consists of a solar PV generator, electrical ESS and power

conversion system (PCS). The battery type is LFP which has a higher charge and discharge cycle with

an excellent electrochemical performance than other types such as Li-ion and lead–acid batteries [46].

Due to its wide range of temperatures possibly from −30 °C to 60 °C, it has proven as a safe battery with

high chemical resistance to thermal runaway [47]. As shown in Table 2, it is assumed that the battery

operates between 0% and 100% of the full capacity and its initial value of the SOH is 100%.

It is assumed that ESS operates in island mode which is charging from PV generation during

daytime and discharging to the customer for their self-consumption. Since the battery is operating

independently from the main grid, it is not necessary to predict the customer’s load consumption. To

set a day-ahead ESS control, it is necessary to take into account battery degradation occurred by

charging and discharging cycles during test periods.

4.2. Battery Degradation based on Cycle Aging Model

Because of the battery’s characteristics of electrochemical dynamics and multi-physics coupling, it

is difficult to monitor internal states accurately and robustly [22]. Although there is a conflict between

energy consumption and battery life extension, there have been studies investigating methods to

minimize both energy consumption and battery aging in terms of energy management [44]. Therefore,

it is necessary to set a control strategy to optimize power management and battery aging without

performance loss.

To estimate the impact of battery degradation occurred by charging from PV generation, cycle

aging model is adopted as follows [30,48]:

( ) ( )

z

a rate

cycle

gas K

E C

Q SOC Ah

R T

(7)

where α and β are fitting coefficients, E

a

is the activation energy, η is the compensation factor of C

rate

,

R

gas

is the gas constant and T

K

is the ambient temperature in [K]. Ah is the ampere-hour throughput and

z is the power-law factor. The parameters such as fitting coefficients, activation energy, compensation

factor, gas constant and power law factor used in the model are derived from [30,48]. Fitting coefficients

α and β are defined by the SOC dependency and each fitting coefficient is varied by the SOC, 0.45, which is a

critical point to define the low and high SOC.

And then, to investigate charging cycles of ESS during test periods, a discrete-time model of cycle

aging can be defined as follows:

1

, 1 ,

( ) ( )

z

a rate

cycle n cycle n n

gas K

E C

Q Q A h z SO C A h

R T

(8)

where Q

cycle,n

and Q

cycle, n +1

are the capacity loss at the time instants t

n

and t

n+1

, respectively; ΔAh is the

ampere-hour throughput from t

n

to t

n+1

; Ah

n

and Ah

n+1

are the accumulated ampere-hour throughput

until the time instants t

n

and t

n+1

, respectively.

In this study, ampere-hour throughput is defined as the value that energy (E) is divided by

nominal voltage (V

nominal

) as follows:

1

nominal

1| ( ) |

3600

n

n

t

bat

t

E

Ah C t dt V

(9)

1n n

Ah Ah Ah

(10)

where C

bat

is the battery current for charge, E and V

nominal

are energy and a nominal voltage of the battery,

respectively.

Electronics 2020, 9, 701 8 of 14

5. Results and Discussions

5.1. Results of PV Generation Forecasting

To calculate the statistical model parameters, two weeks of PV generation data and relevant

datasets are used for training, and the selected model is tested with another ten days of data. When the

performance of the selected models by varying the time lag (p) is tested, the multivariate model, MV

hour

using 10 prior historical data with meteorological variables such as temperature, humidity, and

irradiance, is determined as the best model, which has the least MAPE during the overall forecasting

periods [45].

Figure 1 shows the PV forecasting using the selected short-term forecasting model. In both sunny

and cloudy day, it shows excellent performance. As a result, MAPE result on a sunny day (day 4) is

0.227 and on a cloudy day (day 3) is 0.386.

Figure 1. MV

hour

model forecast; (a) PV forecasting on a sunny day; (b) PV forecasting on a cloudy day.

The statistical test between forecasted and actual produced power is summarized in Table 3 which

presents the comparison of MAPE values between sunny days and cloudy days. For validating the

performance of PV generation forecasting, the least value of MAPE is observed by the model

differently. Table 3 shows that the performance of the model is better on sunny days (days 2, 4, 5, 8, 9

and 10).

Table 3. Comparison of mean absolute percentage forecast error (MAPE) between sunny days and

cloudy days.

Weather type

MV

hour

Sunny

0.167

Cloudy 0.327

5.2. Simulation Results of ESS Charge and Discharge Operation Modes

According to self-consumption operation, the ESS is scheduled to charge and discharge energy to

maximize charging PV generation based on PV forecasting. During testing periods of ten days, a day-

ahead PV forecasting is performed by MV

hour

model. While PV is generating electricity in the daytime

and discharging the same amount to the load, the ESS starts charging.

In this study, we proposed two different ESS operation modes to investigate the difference in

battery degradation depending on a different DOD (depth of discharge). In mode 1, the ESS is supposed

to charge maximum PV generation and start discharging energy to the load for self-consumption when

PV generation ends. In mode 2, the ESS is supposed to charge PV generation up to a certain point of the

SOC, 0.45 which is the optimal value for investigating the operational stress factor of DOD.

Electronics 2020, 9, 701 9 of 14

Based on mode 1, Figure 2 shows the ESS is scheduled to charge the maximum amount of PV

generation and discharge the same amount to the load. The daily peak of the SOC which is dependent

on PV generation and the peak in cloudy days (days 1, 3 and 7) is relatively lower than sunny days.

Figure 2. Self-consumption ESS schedule in mode 1.

Based on mode 2, the ESS is scheduled to charge the designated amount of PV generation and

discharge the same amount to the load as shown in Figure 3. When the ESS reaches the SOC, 0%, the

ESS operates to charge again until PV generation ends. The daily peak of the SOC is the same during

testing periods and the ESS operates two cycles per day except day 3 when is not charged to the SOC,

0.45.

Figure 3. Self-consumption ESS schedule in mode 2.

However, it is difficult to calculate the actual value of energy to be delivered to the load because

the battery loses its capacity along with the charge and discharge cycles. Thus, using the battery cycle

aging model is used to estimate capacity loss considering partial cycles at the different SOC ranges in a

daily basis.

-400

-300

-200

-100

0

100

200

-200%

-150%

-100%

-50%

0%

50%

100%

0000000000

Charging(+) and discharging(-) energy

[kW h]

SOC [%]

ESS charge (positive)

ESS discharge (negative)

SOC

Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10

-400

-300

-200

-100

0

100

200

-200%

-150%

-100%

-50%

0%

50%

100%

0000000000

Charging(+) and discharging(-) energy

[kW h]

SOC [%]

ESS charge (positive)

ESS discharge (negative)

SOC

Day1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day10

Electronics 2020, 9, 701 10 of 14

5.3. Results and Discussions

Starting with the ESS scheduling operation based on PV generation forecasting, capacity loss

estimation using its hourly SOC data from the charging cycle is performed to evaluate the battery’s

SOH. During whole testing periods, the results of battery degradation based on the ESS operation

modes are compared to the result from the actual value of PV generation as shown in Figure 4. The

battery degradation from the predicted ESS cycle in mode 1 is 0.37% and the SOH is 99.6% and the

battery degradation in mode 2 is 0.29% and the SOH is 99.7%. In mode 2, the result of the SOH is 0.1%

higher than mode 1 which indicates that the ESS operation with low DOD results in the less battery

degradation than the result with high DOD. As can be seen, the difference of the SOH between battery

degradation based on the actual value of PV generation and the predicted value is approximately 0.02%

which is owing to the high accuracy of the PV forecasting model.

Figure 4. Comparison of the day-ahead battery degradation estimations.

In general, the aging of the LFP battery indicates that the main factor of the battery degradation is

the high SOC occurred by the charging process, which is due to the electrochemical charge

overpotential when the SEI layer becomes large in high SOC region [49]. In the battery degradation

model, the SOC coefficients (α and β) are reflected as shown in (7), which is changed at the SOC, 0.45.

These coefficients are defined as the SOC dependence, which is determined from the curve fitting of

experimental data [30]. The reason of the change can be identified through the SOC-open circuit voltage

(OCV) relationship of the LFP battery as shown in Figure 5. The OCV is sharply changed in the low

and high SOC region (0.9 and 0.05) and the other region is relatively flat because the SEI formation is

varied little [49].

99.6

99.7

99.8

99.9

100.0

0 24 48 72 96 120 144 168 192 216 240

SOH [%]

Time [h]

SOH mode1 actual

SOH mode1 predict

SOH mode2 actual

SOH mode2 predict

Electronics 2020, 9, 701 11 of 14

Figure 5. Relationship between the state of charge (SOC)-open circuit voltage (OCV) curve.

However, from only the SOC-OCV curve, the optimal threshold is hard to be determined for

reflecting the SOC dependency. When the SOC-OCV relation is transformed to slope value as shown

in Figure 6, the minimum value is at the SOC, 0.45. From this result, the coefficients (α and β) can be

separately applied based on the SOC region and the optimal operation region of the battery is regulated

under the SOC, 0.45 for the prolonged battery life.

Figure 6. Slope of the linearized SOC-OCV curve.

6. Conclusions

This study presents PV generation forecasting models to find a control strategy for the integrated

system of PV and ESS. Compared to the model research using the statistical model with a single

variable, the PV forecasting technique with multivariate to increase the strength in a cloudy day can

bring about the best prediction of PV generation. Those models are developed and trained with two

weeks of 15-minutes interval generation data, weather data and solar radiation and tested with the next

ten days of actual PV generation.

Derived from the comparison of the forecasting results, renewable energy forecasting can be

separated by several steps. As mentioned earlier, the value of solar radiation should be input

exogenously because the KMA weather forecast does not provide solar radiation. Since the Clear-Sky

model is based on radiation estimation in a clear weather condition, other types of sky conditions

should be modeled by different variables. Hence, depending on the sky condition, the MV

hour

model

operates with weather parameters. The result shows the proposed model has high performance both

in sunny and cloudy days. However, it is necessary to consider predicting PV generation with an

2.5

2.7

2.9

3.1

3.3

3.5

0.0 0.2 0.4 0.6 0.8 1.0

Voltage [V]

SOC

0

2

4

6

8

10

0.0 0.2 0.4 0.6 0.8 1.0

Slope,

SOC

0.00

0.05

0.10

0.35 0.40 0.45 0.50 0.55 0.60

∆

∆

Electronics 2020, 9, 701 12 of 14

intelligent computational model that can perform better in the weather with high temporal variability

PV generation to increase its confidence in forecasting.

Although EOL (End of life) is one of the most important factors for determining the battery

replacement period, estimating EOL is difficult without measured data of battery capacity. In a realistic

environmental condition, only voltage, current and energy can be acquired, and the capacity can be

obtained limitedly by the specific experiment. In this study, a battery degradation model based on the

data-driven method is utilized. From the experiment with the battery degradation model using

parameters derived from experiments, it shows that the battery is degraded along with charging cycles

during testing periods and variations in state of health are observed owing to the different

characteristics of the battery according to the ESS operation modes. To separate the ESS operation

modes, the optimal value of the SOC is set to investigate the impact of different DOD to battery

degradation. From the simulation result, the result of the SOH in mode 2 which operates up to 0.45 of

the SOC is 0.1% higher than mode 1 using full operation range. This result indicates that the ESS

operation with low DOD results in less battery degradation than the result with high DOD. Finally, the

simulation result leads to the conclusion that the battery degradation in different operation modes

should be taken into account to extend the end of life efficiently.

However, avoiding high DOD to extend the EOL of the battery can bring a negative impact on the

customer’s benefit. Therefore, future works will develop the capacity loss model with a wide range of

battery aging data from experiments and suggest the optimal operating strategy under the various ESS

operation mode. This preliminary research will provide effective guidelines for future research, to

encourage more ESS integrating with renewable energy resources considering battery degradation.

Author Contributions: M.L. carried out the main research tasks, proposed forecasting models and wrote the full

manuscript and J.P. provided technical support to estimate the capacity loss with the battery degradation model.

S-I.N., H.S.C., B.-S.B. and J.K. validated the proposed strategy, the results and the whole manuscript All authors

have read and agreed to the published version of the manuscript.

Funding: This research received no external funding.

Acknowledgments: This work was supported by the projects of the Korea Electric Power Corporation (R19XO01-

45) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea

government (MOTIE) (20182410105280).

Conflicts of Interest: The authors declare no conflict of interest.

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