A Study on Capacity Credit and ESS Evaluation for WTG and Multi-ESS in Power System

10th IFAC Symposium on Control of Power and Energy Systems 10th IFAC Symposium on 4-6, Control Tokyo, Japan, September 2018of Power and Energy Systems Available www.sciencedirect.com 10th IFAC Symposium on 4-6, Control andonline EnergyatSystems Tokyo, Japan, September 2018of Power 10th IFAC Symposium on 4-6, Control Tokyo, Japan, September 2018of Power and Energy Systems Tokyo, Japan, September 4-6, 2018

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IFAC PapersOnLine 51-28 (2018) 516–521

A Study on Capacity Credit and ESS Evaluation for WTG and Multi-ESS in A Study on Capacity Credit and ESS Evaluation for WTG and Multi-ESS in System A Study on Capacity Credit andPower ESS Evaluation for WTG and Multi-ESS in System A Study on Capacity Credit andPower ESS Evaluation for WTG and Multi-ESS in Power System Ungjin Oh*, Jaeseok Choi**, Kyu-Ho Junmin Cha****, Kwang Y. Lee***** PowerKim***, System

Ungjin Oh*, Jaeseok Choi**, Kyu-Ho Kim***, Junmin Cha****, Kwang Y. Lee***** Ungjin Oh*, of Jaeseok Choi**, Kyu-HoGyeonsang Kim***, Junmin Cha****, Kwang Lee***** * Dept. Electrical Engineering, National University, Jinju,Y. Korea, Ungjin Oh*, of Jaeseok Choi**, Kyu-HoGyeonsang Kim***, Junmin Cha****, Kwang Y. Lee***** * Dept. Electrical Engineering, National University, Jinju, Korea, (Tel:82-55-772-1715; e-mail: [email protected]) * Dept. of Electrical Engineering, Gyeonsang National University, Jinju, Korea, (Tel:82-55-772-1715; e-mail: [email protected]) ** Dept.*of Electrical Engineering, RIGET,Gyeonsang ERI, Gyeongsang University, Jinju, Korea, Dept. of Electrical Engineering, NationalNational University, Jinju, Korea, (Tel:82-55-772-1715; e-mail: [email protected]) ** Dept. of Electrical Engineering, RIGET, ERI, Gyeongsang National University, Jinju, Korea, (e-mail: [email protected]) (Tel:82-55-772-1715; e-mail: [email protected]) ** Dept. of Electrical Engineering,(e-mail: RIGET,[email protected]) ERI, Gyeongsang National University, Jinju, Korea, *** of Dept. of Electrical Engineering, National National University, Anseong,Jinju, Korea, ** Dept. Electrical Engineering, RIGET,Hankyong ERI, Gyeongsang University, Korea, (e-mail: [email protected]) *** Dept. of Electrical Engineering, Hankyong National University, Anseong, Korea, (e-mail: [email protected]) (e-mail: [email protected]) *** Dept. of Electrical Engineering, Hankyong National University, Anseong, Korea, (e-mail: [email protected]) ****ofDept. of Electrical Engineering, Daejin University, Pocheon, Korea, *** Dept. Electrical Engineering, Hankyong National University, Anseong, Korea, (e-mail: [email protected]) **** Dept. of Electrical Engineering, Daejin University, Pocheon, Korea, (e-mail: [email protected]) (e-mail: [email protected]) **** Dept. of Electrical Engineering, Daejin University, Pocheon, Korea, [email protected]) ***** Dept. Electrical and(e-mail: Computer Engineering, Baylor University, TX, USA **** of Dept. of Electrical Engineering, Daejin University, Pocheon,Waco, Korea, (e-mail: [email protected]) ***** Dept. of Electrical and Computer Engineering, Baylor University, Waco, TX, USA (e-mail: [email protected]) (e-mail: [email protected]) ***** Dept. of Electrical and Computer Engineering, Baylor University, Waco, TX, USA (e-mail: [email protected]) ***** Dept. of Electrical and Computer Engineering, Baylor University, Waco, TX, USA (e-mail: [email protected]) (e-mail: [email protected]) Abstract: This paper proposes a new method for evaluating Effective Load Carrying Capability (ELCC) Abstract: ThisCredit paper(CC) proposes a new method for evaluating Carrying Capability (ELCC) and Capacity of a power system including WindEffective Turbine Load Generator (WTG) combined with Abstract: ThisCredit paper(CC) proposes a new method for evaluating Effective Load Carrying Capability (ELCC) and Capacity of a power system including Wind Turbine Generator (WTG) combined with Multi Energy Storage System (ESS). The WTG can only generate electrical power when the wind is Abstract: This paper proposes a new method for evaluating Effective Load Carrying Capability (ELCC) and Capacity Credit (CC) of a (ESS). power system including Wind Turbineelectrical Generator (WTG) combined with Multi Energy Storage System The WTG can only generate power when thereliability wind is available. Because of the fluctuation of wind speed, WTG generates intermittent power. In the and Capacity Credit (CC) of a power system including Wind Turbine Generator (WTG) combined with Multi Energy Storage System (ESS).ofThe can onlygenerates generate intermittent electrical power thereliability wind is available. Because ofsystem, the fluctuation windWTG speed, WTG power.when Incharacteristics the viewpoint of power the(ESS). intermittent power of WTG is similar to the probabilistic Multi Energy Storage System The WTG can onlygenerates generate electrical power when thereliability wind is available. Because of the fluctuation of wind speed, WTG intermittent power. In the viewpoint of on-off powerofpower system, thetointermittent power of WTG isofsimilar to the probabilistic characteristics based on the due the mechanical availability conventional generators. Therefore, high available. Because the fluctuation of wind speed, WTG generates intermittent power. In the reliability viewpoint of on-off power power system,due thetointermittent power of WTG isofsimilar to the probabilistic characteristics based on the the mechanical availability conventional generators. Therefore, high penetration of WTG will cause difficulties in power system operation. The high penetration of numerous viewpoint of power system, the intermittent power of WTG is similar to the probabilistic characteristics based on theofon-off power due to the mechanical availability of conventional generators. Therefore, high penetration WTG will can cause difficulties in power system operation. Thestability. high penetration ofdevelops numerous and large capacity WTG risk the system adequacy, quality, and This study based on the power due to thepower mechanical availability of conventional generators. Therefore, higha penetration ofon-off WTG will can cause difficulties in power system operation. Thestability. high penetration ofdevelops numerous and large capacity WTG risk the power system adequacy, quality, and This study a new method to assess how much the penetration of WTG can be extended when WTG is combined with penetration of WTG will cause difficulties in power system operation. The high penetration of numerous and large capacity WTG canmuch risk the power system adequacy, quality, and stability. Thisisstudy develops a new method to assess how the penetration of WTG can be extended when WTG combined with ESS.large Thiscapacity paper demonstrates various case studies of ELCC and CC of a power system WTG and WTG canmuch risk the system adequacy, quality, and stability. Thisiscontaining study develops new method to assess how the power penetration ofof WTG canand be CC extended whensystem WTG combined witha ESS. This paper demonstrates various case studies ELCC of a power containing WTG combined with ESSs athe model system similar tocan Jeju power system in Korea. new method to Multi assess how using much penetration beisland extended when WTG iscontaining combinedWTG with ESS. This with paper demonstrates various casesystem studiesof ofWTG ELCC and CC ofpower a power system combined Multi ESSs using a model similar to Jeju island system in Korea. ESS. This paper demonstrates various case studies of ELCC and CC of a power system containing WTG combined with Multi ESSsReliability, using a model system similar to Jeju island system insimulation Korea. © 2018, IFAC (International Federation of Automatic Control) Hosting bypower Elsevier Ltd. All rights reserved. Keywords: Power System Wind Turbine Generator (WTG), Monte Carlo (MCS), combined with Multi ESSsReliability, using a model system similar to Jeju island power system insimulation Korea. (MCS), Keywords: Power System Wind Turbine Generator (WTG), Monte Carlo Energy Storage System (ESS), Effective Load Carrying Capability (ELCC), Capacity Credit (CC) Keywords: Power System Reliability, Wind Generator (WTG), Monte CarloCredit simulation Energy Storage System (ESS), Effective LoadTurbine Carrying Capability (ELCC), Capacity (CC) (MCS), Keywords: Power System Reliability, Wind Generator (WTG), Monte CarloCredit simulation Energy Storage System (ESS), Effective LoadTurbine Carrying Capability (ELCC), Capacity (CC) (MCS), Energy Storage System (ESS), Effective Load Carrying Capability (ELCC), Capacity Credit (CC) In order to relieve a rapid output variation and dispel the 1. INTRODUCTION In order ofto supply relieve reliability a rapid output variation and Generators dispel the anxiety of Wind Turbine 1. INTRODUCTION In order ofto supply relieve reliability a rapid output variation and Generators dispel the anxiety of Wind Turbine 1. INTRODUCTION (WTGs), which have highest output variability among all In order ofto relieve a rapid output variation and Generators dispel the supply reliability of Wind Turbine 1. INTRODUCTION To conserve conventional energy resources and reduce anxiety (WTGs), which have highest output variability among all renewable generators, this study aims to evaluate how much anxiety of supply reliability of Wind Turbine Generators To conserve conventional energy resources and reduce which have this highest output variability among all emission, it isconventional not only energy expectedresources that penetration of (WTGs), renewable generators, study aims to evaluate how much To conserve and reduce more a WTG can increase supply capacity, when combined which have this highest output variability among all emission, itenergy isconventional not only energy expected that penetration of (WTGs), renewable generators, study aims to evaluate how much renewable generation will be accelerated but also To conserve resources and reduce more a WTG can increase supply capacity, when combined emission, itenergy is not only expected that penetration of with ESS. Thecan ELCC andstudy Capacity Credit (CC)how in power renewable generators, this aims to evaluate much renewable generation will be accelerated but also more a WTG increase supply capacity, when combined Battery Energy System (BESS) be increased emission, itenergy isStorage not only expected that also penetration of with Thecan ELCC andESS Capacity Credit (CC) from in power renewable generation will be will accelerated but also systems equipped with are evaluated, the more ESS. a WTG increase supply capacity, when combined Battery Energy Storage System (BESS) will also berenewable increased with ESS. The ELCC andESS Capacity Credit (CC) from in power to relieve a much output variability because the renewable energy generation will be accelerated but also systems equipped with are evaluated, the Battery Energy Storage System (BESS) will also be increased perspective of Loss of Load Expectation (LOLE), which is with ESS. The ELCC and Capacity Credit (CC) in power to relieve a much output variability because thebeTherefore, renewable systems equipped with ESS are evaluated, from istheaa energy resources have uncertainty in availabilities. Battery Energy Storage System (BESS) will also increased perspective of Loss of Load Expectation (LOLE), which to relieve a muchhave output variability because the Therefore, renewable representative indexof ofLoad probabilistic supply reliability, toa systems equipped with ESS are evaluated, from isthe energy resources uncertainty in on availabilities. of Loss Expectation (LOLE), which ato new agenda hashave been underpinned the discussion how to perspective relieve a much output variability because the Therefore, renewable representative index ofLoad probabilistic supply reliability, toa resources uncertainty in on availabilities. suggest a new method of estimating the capacity of ESS perspective of Loss of Expectation (LOLE), which is aenergy new agenda has been underpinned the discussion how to representative index of of probabilistic supply reliability, to evaluate the generating resources in ofon renewable energy with resources have uncertainty availabilities. Therefore, suggest a new method estimating the capacity of ESS aenergy new agenda has been underpinned the discussion how to installation, andindex the usefulness of ESS supply is onofmodel representative of of probabilistic reliability, to evaluate the generating resources ofonrenewable energy with suggest a new method estimating theanalyzed capacity ESS uncertainty in terms of reliability and how to recognize amuch new agenda has been underpinned the discussion how to installation, and the usefulness of ESS is analyzed on model evaluate the generating resources of renewable energy with systems of similar size to the Jeju island in Korea from suggest a new method of estimating the capacity of ESS much uncertainty in terms of reliability and how to recognize installation, and the usefulness ESSisland is analyzed on model the value of the supply capacity. evaluate the generating resources of renewable energy with diversified systems ofperspectives. similar size to theof Jeju in Korea from much uncertainty in terms of reliability and how to recognize installation, and the usefulness ESSisland is analyzed on model the value of the supply capacity. systems ofperspectives. similar size to theofJeju in Korea from much uncertainty in terms of reliability and how to recognize diversified the value of thethe supply capacity. systems ofperspectives. similar size to the Jeju island in Korea from To evaluate actual supply capacity of generating diversified the value of thethe supply capacity. To evaluate actual supply capacity generating 2. EFFECTIVE LOAD CARRYING CAPABILITY AND perspectives. resources which have uncertainty, Garver of first diversified To evaluate the actual supply capacity of(1966) generating 2. EFFECTIVE LOAD CARRYING CAPABILITY AND resources which have uncertainty, Garver (1966) first CAPACITY CREDIT developed the concept of Effective Carrying Capability To evaluate the have actual supply Load capacity of(1966) generating 2. EFFECTIVE LOAD CARRYING CAPABILITY AND resources which uncertainty, Garver first CAPACITY CREDIT developed the concept of Effective Load Carrying Capability 2. EFFECTIVE LOAD CARRYING CAPABILITY AND (ELCC) to show an actual load to take charge of the resources which have uncertainty, Garver (1966) first CAPACITY CREDIT developedtotheshow concept of Effective Load Carrying Capability (ELCC) an actual load to take charge of the 2.1 Effective Load Carrying Capability CAPACITY CREDIT generators which are penetrated and applied the reliability developedtotheshow concept of Effective Loadtake Carrying Capability (ELCC) an penetrated actual load charge of the 2.1 Effective Load Carrying Capability generators which andto applied the estimationtotechnique the probability of reliability generator (ELCC) show are anconsidering actual load to take charge of the 2.1 Effective Load Carrying Capability generators which are penetrated and applied the reliability estimation technique considering the probability of generator 2.1 Effective Load Carrying Forced Outage (FOR), and used in the practice for Garver (1966) proposed theCapability concept of ELCC for the first generators which Rate areconsidering penetrated andprobability applied reliability estimation technique the of generator Forced Outage Rateconsidering (FOR), and used in many practice for Garver (1966) proposed the concept of ELCC the first generation expansion planning. Since then, related time to evaluate the real supply capacity of thefor estimation technique the probability of generator Forced Outage Rate planning. (FOR), and used in many practice for Garver (1966) proposed the concept of ELCC forgenerating the first generation expansion Since then, related time to evaluate the real supply capacity of the generating research have been proceeding, theinELCC research resources havingproposed uncertainties, such asof theELCC FOR of thethepower Forced Outage Rate (FOR), however, and used practice for Garver (1966) the concept forgenerating first generation expansion planning. Since then, many related time to evaluate the real supply capacity of the research have been proceeding, however, the been ELCC research resources having uncertainties, such as theaspects. FOR of generating the power on power system considering the ESS has not found yet. system in the probabilistic reliability This was generation expansion planning. Since then, many related time to evaluate the real supply capacity of the research been proceeding, the been ELCC research resources having uncertainties,reliability such as theaspects. FOR of This the power on powerhave system considering thehowever, ESS has not found yet. system in the probabilistic was defined as "increasable load as seen from stochastic target research have been proceeding, however, the ELCC research resources having uncertainties, such as the FOR of the power on power system considering the ESS has not been found yet. system in the probabilistic reliability aspects. Thistarget was defined as "increasable load as seen from stochastic risk level of the power system before and This aftertarget the on power system considering the ESS has not been found yet. system in the probabilistic reliability aspects. was defined as "increasable load as seen from stochastic risk level of the power system before and aftertarget the defined as "increasable load as seen from stochastic risk level of the power system before and after the risk level of the 2405-8963 © IFAC (International Federation of Automatic Control) by Elsevier Ltd. power All rightssystem reserved.before and after the Copyright © 2018, 2018 IFAC 516Hosting Copyright 2018 responsibility IFAC 516Control. Peer review©under of International Federation of Automatic Copyright © 2018 IFAC 516 10.1016/j.ifacol.2018.11.755 Copyright © 2018 IFAC 516

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penetration of the power". The Fig. 1 describes the Concept of ELCC.

capacity (overall) is very important to determine the appropriate scale of renewable energy generating resources. The ELCC as stated above is increased continuously according to the increasing penetration of generators. Therefore, it is difficult to determine the adequate capacity (i.e., vale) of new facilities with ELCC only. Especially, in the case of generating resources of renewable energy with much volatility is more difficult. Therefore, substantial gain ratio (effective rate) for a certain investment can be expressed if the ELCC is expressed with the percentage after dividing it by the penetrating capacity, because it means the effective carrying capacity for the penetrating capacity. This is related deeply to facility utilization and involves the economics of new generating resources as well. This is defined as the CC and used as the index for valuation of renewable energy which has much volatility. The capacity credit is defined as

Fig. 1. Concept of Effective Load Carrying Capability. The confidence index which is generally used at this time is LOLE: NT

LOLE = å P (CGt < Lt ) ´ Dtt t =1

Capacity Credit (CC ) =

[hours/year] (1)

t =1

[hours/year] (2)

It is not easy to estimate the LOLE of a power system, which is combined with ESS and WTG as in (2). The researchers on this study already developed the algorithms and programs and published them in detail. The Fig. 2 shows again the model when multiple WTGs are installed with Multi-ESS on this study.

Equation (1) is the LOLE without WTG and (2) is the LOLE including the wand generator and ESS. Subscript "t" in (1) and (2) is step number of load when the load is modelied by the discrete load duration curve (i.e., number for the load time zone), and NT is the total step number of load steps. Therefore, ELCC(WTG+ESS) of the WTG in combination with ESS is formulated as (3) and proposed newly for simulation of CGt+CWTGt+CESSt is developed. NT

t =1

Gt

< Lt ) =

NT

å P[(CGt + CWTGt + CESSt ) < (Lt + ELCC(WTG + ESS ) )]

(4)

2.3 Multi-ESS Operation Model

where, P: The probability of energy not served CGt: Load capacity of ordinary generators in the t-th load time zone [MW] CESSt: Output of ESS in the t-th load time zone [MW] Lt: Load capacity in the t-th load time zone [MW] Δtt: Duration time in the t-th load time zone [hours]

å P(C

ELCC ´ 100 [%] CA

where, CA: Input capacity of renewable generator [MW]

NT

LOLE = å P (CGt + CWTGt + CESSt < Lt ) ´ Dtt

517

(3)

t =1

This is because (1) is the LOLE before the WTG penetration and (2) is the reduced LOLE after penetration of the WTG plus ESS, which mean the extra load [MM] can be provided for maintenance on titration level of confidence.

Fig. 2. Models of Conventional Generator (CG) and WTG combined with Multi ESS used in the study.

2.2 Capacity Credit

This study evaluates quantitatively the extent of expansion and effect of the supply capacity when ESS is installed in a wind farm by assessing the ELCC and CC.

Recently, considering the FOR of the generator, the evaluation of the ELCC which can evaluate the effective supply capability has shed new lights on the renewable generation sources of which the variability of output is big due to the uncertainty of the supply of the resource (wind speed or the rays of the sun). In particular, evaluation of the effective supply capacity due to the increase in installed

Here, an operation scenario was developed where X% is assumed to be the maximum permissible wind power in percentage of the system load at any point in time. This means the permissible rates will not change even though the load varies over time. 517

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2.4 Multi-ESS Operation Rule

3. WTG+ESS CAPACITY CREDIT

The basic purpose of ESS installation is to prevent power supply from being disrupted by considering only the reliable operation. Therefore, to prevent power supply from being disrupted, the choice is to determine the discharging mode of ESS first and then determine the charging mode of ESS.

In this study, a new formula is developed and formulated to evaluate CC when the ESS is installed in the renewable energy generating plants with uncertainty such as the WTG using ELCC, Capacity Credit (CC ) =

A. Discharging Mode Select the largest ESS discharging capacity first for discharging in the following cases.

ELCC ´ 100 [%] CGWTG + CGESS

(5)

where, CGWTG: Total Capacity of WTG [MW] CGESS: ESS Capacity [MW]

- If the total output of the existing generator is less than 1−X%.

4. PROBABILISTIC OUTPUT PREDICTION MODEL OF WTG

- If the sum of the output of wind power and that of the existing generator is less than load.

The Weibull probability distribution is used in this paper to posit the wind speed distribution per WTG complex. Also, the scale and shape variables are used to estimate the wind speed for the MCS. Then, based on the wind speed estimated, the i-th WTG's output in k-th state is calculated(WGi,k). Thus, the TWk[MW] is formulated as in (6).

B. Charging Mode In the charging mode, when the total of wind power generation exceeds X%, each ESS installed in the WTG is charged with the excess of the WTG output to each ESS. At this time, the discharge mode of ESS is excluded. Here, the following conditions are considered as a method of appropriately distributing the charged amount. - Except for the discharge mode of ESS, the rechargeable state of the remaining ESS is searched. And first, charge the maximum chargeable ESS. The charging and discharging conditions of the proposed model (Fig. 2) are summarized in the following Table 1 based on the charging / discharging operation mode described above. Here, SGw: Maximum permissible output per load of the WTG [MW], SGc: Compulsory output of the CG [MW], SGk: Total capacity of the CG and WTG [MW] Table 1. Charge and Discharge Conditions SGw + + − −

SGc + − + −

SGk + + (−) + −

Charging ○ ● × ×

Fig. 4. Total generation model of WTG proposed in this paper for MCS

Discharging × ○ × ○

TWk =

å

iÎW NGW

WGi , k

(6)

where, ΩNGW: WTG set, NGW: Number of WTG WGi,k : output of the i-th WTG at k-th state [MW]

However, the difference between the proposed multi-ESS model (Fig.2) and the conventional Single-ESS model (Fig. 3) is shown in the black mark (●) in Table 1. This means that if the ESS needs to be discharged, another ESS can charge the output of the extra WTG.

5. RELIABILITY EVALUATION AND ESS RELIABILITY CONTRIBUTION FUNCTION The reliability index was calculated by summing and averaging the results after obtaining the loss of load expectation (LOLE [hours/year]) and expected energy not served (EENS [MWh/year]) for each state. For the energy index of reliability (EIR [pu]) defined from the EENS, the higher the value is, the higher the reliability is. This is formulated in the following equations:

Fig. 3. Conventional Model (WTG combined with single-ESS)

LOLE =

518

1 NY

åt

k ÎWD

k

(7)

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EENS =

1 NY

Ungjin Oh et al. / IFAC PapersOnLine 51-28 (2018) 516–521

å (TG

k ÎWD

Dk

EENS EIR = 1 TDE

+ EU k )

519

7. CASE STUDY

(8)

7.1 Model Power System

(9)

This study evaluates the reliability in terms of the supply adequacy by applying the developed model to a sample power system integrated with three wind farms connected with ESS. The sample power system and three wind farms are shown in Fig. 6, which is similar to the Jeju power system.

where, NY: Years of Monte Carlo simulation ΩD: A set of discharge modes NSS: Total number of samples (states) EUk: Discharging control energy as a negative value [MWh] TGDk: Discharge energy indispensable for eliminating any lack of supply for load [MWh] 6. FLOW CHART The researchers evaluated the ELCC and CC for the question, "How much can they provide the actual load, maintaining the system reliability?" Using the programs developed by these authors, we can simulate the CC and ELCC of the combined power system with WTG and multi-ESS.

Fig. 6. Model Power system The generator data of the conventional generator and WTG are given in Table 2. Tables 3 shows the WTG characteristics and wind speed data of WF1, WF2 and WF3 wind farms. Table 4 shows the multi-ESS specifications for the system. Table 2. Generator data Name

Type

Capacity [MW]

Num.

α [Gcal/ MW2h]

β [Gcal/ MWh]

γ [Gcal/ hour]

Fuel cost (f) [$/Gcal]

FOR

1

HVDC

DC

150

2

0.004

1.512

45.207

43.300

0.028

2

NMJ3

T/P

100

2

0.004

1.512

45.207

43.300

0.012

3

JJU1

T/P

10

1

0.062

2.100

5.971

43.599

0.015

4

JJU2

T/P

75

2

0.003

1.832

30.231

43.599

0.012

5

HLM1

G/T

35

2

0.004

2.401

20.320

77.909

0.013

6

HLM1

S/T

35

1

0.004

2.401

20.320

77.909

0.013

7

JJU3

D/P

40

1

0.025

0.364

28.484

43.599

0.018

8

NMJ1

D/P

10

4

0.006

1.999

1.360

43.300

0.018

Table 3. Data of wind speed and WTG for wind farms WF1

WF2

WF3

WTG capacity

150 MW

90 MW

60 MW

Cut-in speed (Vci)

5 m/s

5 m/s

5 m/s

Rated speed (VR)

10 m/s

10 m/s

10 m/s

Cut-out speed (Vco)

25 m/s

25 m/s

25 m/s

Table 4. Multi-ESS specification ESS

Max. Capacity (ESM) [MWh]

Min. Capacity (ESm) [MWh]

Time length for charge/discharge [hours]

X% [pu]

Initial SOC of ESS [MWh]

WF1 ESS1

150

10

1

0.1

15

WF2 ESS2

90

10

1

0.1

15

WF3 ESS3

60

10

1

0.1

15

The load variation curve of the model power system is used in this case study, as shown in Fig. 7.

Fig. 5. Flow chart for reliability evaluation of WTG & ESS model proposed in this study. 519

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2) ESS Capacity Credit (CC) = 18.67% - ELCC = 840 - 784 = 56[MW] - C.C. =

56 ´ 100 = 18.67[%] 300

3) WTG+ESS Capacity Credit (CC) = 16.67%

- ELCC = 840 - 740 = 100[MW] - C.C. = Fig. 7. Load variation curve of the model power system.

100 ´ 100 = 16.67[%] 600

We can see that ELCC has been increased about 2.3 times from 44[MW] to 100[MW], and C.C. has been also increased around 2[%] from 14.67[%] to 16.67[%] by installing the ESS in the WTG. It is noted that effectiveness of ESS increases ELCC and CC in the WTG.

7.2 Basic analysis of ELCC and CC when WTG is combined with Multi-ESS (X%=0.1) First of all, three virtual system models, as shown in Fig. 8 are supposed to look at variety in terms of reliability of ELCC and CC when the WTGs and ESS are installed.

Table 5. Reliability evaluation result of model systems ELCC [MW]

Capacity Credit [%]

WTG

44

14.67

ESS

56

18.67

WTG+ESS

100

16.67

7.3 Comparison of WTG and Single-ESS combined power system System A

System B

System C

Fig. 8. Three model systems for case study. Fig. 9 shows the variation of LOLE of the model system in Fig. 8 when the peak load (Lp) is changed. The reliability of the system is set to LOLE=10[hours/year].

Fig. 10. Model System of a Single-ESS. To delve into the effects of multi-ESS on the reliability where an ESS is installed for each of the WTG sources, the multiESS is compared with the single-ESS where an ESS is installed for multiple WTGs as in Fig. 10. Table 6 compares the calculated results from these two model systems. In this paper, it confirmed that Multi-ESS is about 2 times as efficient as the Single-ESS in both ELCC and CC.

Fig. 9. Variation of LOLE of three model systems according to the changing of peak load. The ELCC and CC for WTG, and installed ESS in the WTG in Fig. 9 is calculated as follows, and tabulated in Table 5.

Table 6. ELCC and CC indexes Comparison Between Single-ESS and Multi-ESS

1) WTG Capacity Credit (CC) = 14.67%

Single-ESS

Multi-ESS

ELCC [MW]

57

100

Capacity Credit [%]

9.5

16.67

- ELCC = 784 - 740 = 44[MW] - C.C. =

44 ´ 100 = 14.67[%] 300 520

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8. CONCLUSION This study proposes a new scheme which evaluates how much value the ESS has when ESS has been combined with the wind turbine generator, whose uncertainty of the output is big due to the variability of the resource supply. Especially, the evaluation of the effective supply ability according to the increase of investment fee, the increase of facility capacity is very important in determining the appropriateness of the new regeneration sources. To this end, this research has formalized newly the formula which can calculate the effective load carrying capability (ELCC) of the power system with WTG combined with ESS. Further, if the ELCC is divided by input capacity and expressed in percentage, it can indicate a certain practical profit rate about investment. This is re-defined as capacity credit (CC) recently and is widely being utilized as the index for value assessment of the renewable energy with large variability, and thus this research also has proposed a new capacity credit considering the ESS. Lastly, this study has verified the effectiveness of proposed method by determining the appropriate capacity of ESS and WTG under various cases for model systems similar to Jeju island power system. ACKNOWLEDOGEMENT This work was supported by the Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Ministry of Trade, Industry and Energy. (No. 201740302014 40) and Korea Electric Power Co. (KEPCO), RIGET and ERI. REFERENCES Billinton, R., Huang, D. and Karki, R. (2010). Wind Power Planning and Operating Capacity Credit Assessment. PMAPS 2010, Singapore, pp.814-819. Billinton, R. and Li, W. (1994). Reliability Assessment of Electric Power Systems Using Monte Carlo Methods. Plenum Press. Choi, J.S., Lim, J.T. and Lee, K.Y. (2013). DSM Considered Probabilistic Reliability Evaluation and an Information System for Power Systems Including Wind Turbine Generators. IEEE Trans. on SG, 4(1), pp.425-432. Choi, J.S. (2013). Power System Reliability Evaluation Engineering, G&U Press. (in Korean, ISBN 978-896257-060-1). Cho, K.H., Park, J.J. and Choi, J.S. (2012). Probabilistic Reliability Based Grid Expansion Planning of Power System Including Wind Turbine Generators. Journal of Electrical Engineering & Technology, 7(5), pp.698-704. Carmen, L., Borges, T. and Cantarino, E. (2011). Microgrids Reliability Evaluation with Renewable Distributed Generation and Storage Systems. The 18th IFAC World Congress, Milano (Italy), pp.11695-11700. Do, D.P.N., Lee, Y.C. and Choi, J.S. (2016). Hourly average wind speed simulation and forecast based on ARMA model in Jeju island, Korea. Journal of Electrical Engineering & Technology, 11(6), pp.1548-1555.

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