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An Analysis of Energy Storage Systems for Primary Frequency Control of Power Systems in South Korea
Available online at www.sciencedirect.com
ScienceDirect Energy Procedia 107 (2017) 116 – 121
3rd International Conference on Energy and Environment Research, ICEER 2016, 7-11 September 2016, Barcelona, Spain
An Analysis of Energy Storage Systems for Primary Frequency Control of Power Systems in South Korea Hyeon-Jin Moona, Ah-Yun Yuna, Eung-Sang Kimb and Seung-Il Moona* b
a School of Electrical and Computer Engineering, Seoul National Univ., 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea Smart Distribution Research Center, Korea Electrotechnology Research Institute, 12, Bulmosan-ro 10beon-gil, Seongsan-gu, Changwon-si, Gyeongsangnam-do, 51543, South Korea
Abstract Energy storage systems (ESSs) have a quick response and outstanding functions when used for frequency regulation. This paper examines the effect of an ESS used in conjunction with the primary frequency control (PFC) of a power system in South Korea. A simple low order system frequency response (SFR) model is used to undertake a simulation with MATLAB Simulink. The simulation includes gas, hydro and steam turbines and the ESS with the PFC. The results show that the ESS has a remarkable effect in the PFC and that it is more efficient in a weak grid. When more ESSs are added, the substitution effect is weaker according to the simulation results. © 2016 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment (http://creativecommons.org/licenses/by-nc-nd/4.0/). Research. Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment Research. Keywords: Energy Storage System (ESS); Frequency regulation; Primary Frequency Control (PFC)
1. Introduction The controlling frequency is an essential aspect when operating a power system. However, as the proportion of Renewable Energy Sources (RES) in power systems continues to increase, the stiffness of power systems is decreasing due to their very low or even lack of inertia and their intermittency, which weakens the stability of the
* Corresponding author. Tel.: +82-10-3798-1821; fax: +82-2-878-1452. E-mail address: [email protected]
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment Research. doi:10.1016/j.egypro.2016.12.143
117
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
power system [1]. A conventional generator can provide good frequency control but at a slow speed due to its physical and/or thermal limitations [2, 3]. To counteract the side effect of RESs, many studies to develop frequency operation schemes for power systems are being conducted. In a study, frequency control was provided by Doubly Fed Induction Generators (DFIG) and converter based wind turbines [1]. Latter, the PFC control scheme, which uses the power of electric vehicles, was introduced [4]. Other works, involved the use of ESSs for frequency control in power systems [5-7]. In particular, ESSs take center stage with regard to frequency control because they have a very rapid response and bidirectional transmission to/from a power system. This application of ESSs has been demonstrated in several countries [7, 8]. In South Korea, the Korea Electric Power Corporation (KEPCO) has installed a 200 MW ESS for frequency control. Moreover, KEPCO plans to increase ESS capacity to 500 MW for frequency regulation by 2017. In this paper, the effects of ESSs when used for PFC are analyzed in MATLAB Simulink using the SFR model, governor-turbine models, and an ESS model reflecting the power system in South Korea. The frequency response determined by a dynamic simulation and the results are analyzed. This paper focuses on the minimum frequency of the system because this factor is most critical in relation to PFC. 2. Simple power system modeling of a power system in South Korea 2.1. Frequency control of a power system The frequency of a power system is related to the balance of active power according to this equation.
M
d (Δf / f 0 ) + K Δf = −ΔP dt
(1)
Where M (= 2H) is the inertia constant of the system (s), f 0 is the system nominal frequency (Hz), K is the power/frequency characteristic of the system (PU/Hz), and ΔP is the amount of generator/load change (PU) [2]. When the active power of a load or a generator is suddenly changed, an active power imbalance occurs and the frequency deviates from the nominal frequency. Therefore, for stable operation of a power system, it is necessary to hold the system frequency at the nominal frequency. Generally, there are three control layers to sustain the system frequency [9]. Primary frequency control (PFC) is a rapid local control method that regulates the active power of generators and controllable loads. It is used to mitigate deviations of the system frequency. Secondary frequency control (SFC) is a centralized generation control method that regulates the active power of generators. It is used to restore the system frequency to its nominal value. Tertiary frequency control (TFC) refers to the manual control of the actions of dispatching and generation unit commitment. This control method is used when secondary control is incapable of restoring the system frequency. In South Korea, the Korea Power Exchange (KPX) is in charge of managing the frequency control of power systems using their energy management system (EMS) based on the operating rules of South Korea. In their operating rules, the nominal frequency is set to 60 ± 0.2 Hz and the amount of frequency regulation reserve exceeds 1,500 MW [10]. This regulation reserve is similar to the first and secondary control methods. 2.2. Simplified power system model An actual power system consists of many elements, such as the generator, load, transformer and others. Therefore, the system is constantly changing and is very difficult to model precisely. For this reason, a simple low order system frequency response (SFR) model is used to simulate power system frequency responses [11]. This simple model averages the frequency response of an actual power system to a first-order transfer function using the system inertia M and the load damping constant D. This model can show the short-term dynamic response of the frequency at a specific operating point. It is used to analyze the frequency responses of power systems with the governor-turbine model of generators to provide proper frequency control [11].
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Fig. 1. Simplified South Korean Power System Model with Governor-turbines, ESS Model.
The governor-turbine model contains negative feedback loop of frequency change to provide appropriate primary frequency control. Similarly, to investigate the frequency responses of power systems, the SFR model is devised by MATLAB Simulink as shown in Fig. 1. The modeling of governor-turbines such as gas, hydro, and steam turbines is referenced from the PSS/E model library (gas turbine: GAST, hydro turbine: HYGOV, steam turbine: TGOV1), and the values of the parameters are determined from KPX data obtained in actual tests [12]. The battery response model includes simple first-order time delay function (0.03 second) because the dynamic characteristics of ESSs are simple and very rapid [13]. 2.3. Parameters for the simulation The simulation parameters are shown in Table 1. The simulation had two parts. The first is the peak time case in which there are many generators and loads in the power system. The second part is the off-peak time case which uses a smaller power system than the peak time case. The base power in the simulation is assumed to be 80/55GW, and system inertia M is 20/15 seconds. These settings reflect the latest trends of electrical power consumption in South Korea and the findings of related research [14]. The load damping constant is 2% [15, 16]. The droop constant of the generator is assumed to be 5%. In particular, a conventional generator for PFC provides frequency reserve using a deloaded operation of approximately 5% of the rated power. Table 1. Simulation parameters Parameters
Peak
Off-peak
Base Power (GW)
80
55
System Inertia M (s)
20
15
Load Damping Constant (%)
2
Droop Constant of Generators (%)
5
Droop Constant of ESS (%)
0.25
Detection time of frequency (s)
0.1
Frequency Dead Band (Hz)
0.01
Nominal Frequency (Hz) Simulation Time (s) Generator trip Amount (MW) Trip Time (s)
60 60 1,000 10
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
On the other hand, an ESS can be used with its full power rating. For this reason, the droop constant of the ESS is set by calculating the ratio of the rated power and the amount of the frequency reserve. The obtained parameter value is 0.25% (5% by 5%). The frequency dead band and detection time were sourced from earlier work [5, 9]. The simulation time is 60 seconds. At 10 seconds, we assume that the 1,000 MW generator is tripped. 3. The effect evaluation of ESS on PFC
In this chapter, we undertake a simulation to analyze the effect of an ESS for PFC using our simplified power system model. This model uses specific snap shot data of the system. The peak time and off-peak time data of power system in South Korea are used to perform the simulation. The simulation is performed while changing the ratio of the PFC capacity by replacing a steam turbine to ESS. That is because the steam turbine is most general generator for PFC of power system in South Korea. The PFC capacity is based on actual operation data from KPX and is slightly revised for simplification. The amount of PFC capacity at the peak time and off-peak time are shown in Table 2 and Table 3, respectively. The frequency of the simulation is observed to evaluate the effect of the ESS on PFC because the main purpose of PFC is to relieve excessive drops in the system frequency. 3.1. Case study at the peak time The simulation result is shown in Fig. 2 (a). As shown in Table 2, case 1 uses normal frequency reserve without an ESS for PFC and cases 2 and 3 include 200 and 700 MW of ESS, respectively, instead of a steam turbine for PFC. That is because steam turbine has the lowest cost of generation of electric power in PFC. So, steam turbine is better to generate constant power for supplying load demand rather than participating PFC with a deloaded operation. The PFC operation resulting from a trip event at 10 seconds reduces the drop in the system frequency with a small amount of undershoot. Table 4 shows the results of the simulation. In case 1, the minimum frequency of the system is 59.9823. For case 2, it is 59.9827. The drop in frequency in case 2 is smaller than in case 1 due to the use of the ESS in the system in place of the steam turbine, for which the response is relatively slow in comparison to an ESS. Furthermore, in case 3, the observed minimum frequency is 59.9835, and there is a little undershoot due to the use of the 700 MW ESS. 3.2. Case study at the off-peak time In the same manner, the simulation result is shown in Fig. 2 (b) and Table 4. As shown in Table 3, case 4 assumes a normal frequency reserve capacity at the off-peak time, and cases 5 and 6 include 200 and 700 MW ESSs, respectively, instead of steam turbine for PFC. At the off-peak time in South Korea, steam turbine is mainly in charge of PFC. For simplicity of simulation, other generators participating to PFC are ignored. Minimum frequencies in cases 4 to 6 are 59.8623, 59.8731 and 59.8936, respectively. Undershoot of frequency due to generator trip is diminished by adding the ESS for PFC due to the very rapid response of the ESS. Table 2. The amount of PFC capacity at the peak time. Case Number
Amount of PFC Capacity (MW)
Case 1
Gas: 500, Hydro: 300, Steam: 700
Case 2
Gas: 500, Hydro: 300, Steam: 500, ESS: 200
Case 3
Gas: 500, Hydro: 300, ESS: 700
Table 3. The amount of PFC capacity at the off-peak time. Case Number
Amount of PFC Capacity (MW)
Case 4
Steam: 1,500
Case 5
Steam: 1,200, ESS: 200
Case 6
Steam: 800, ESS: 700
119
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Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
Fig. 2. Frequency responses of 1 GW generator trip in the power systems: (a) at the peak time; (b) at the off-peak time.
3.3. Comparison of the effect using ESS to PFC in different system state. At the peak time, there are many generators and loads. In other words, the systems have large rated power and system inertia levels. Accordingly, the speed of a frequency change is relatively slow and frequency drops are low, while at the off-peak time, systems have less inertia than in the former cases. Hence, the frequency drop in the simulation is greater and the frequency change speed is faster than in the peak time cases. In addition, the time of the minimum frequency in Fig. 2 (b) is earlier than in Fig. 2 (a) and the degree of undershoot at the off-peak time cases is higher than in the peak time cases. For this reason, the effect of an ESS for PFC is remarkable in the off-peak time cases. But when more ESSs are added, instead of the steam turbine, the substitution is less effective according to the simulation results. The improved effect α (%) mitigating the minimum frequency by using an ESS compared with conventional steam turbine for PFC can be calculated using this equation below.
α=
f Min ,base − f Min , ESS f 0 − f Min ,base
× 100
(2)
Where f Min,base is a minimum frequency of a case without ESS for PFC (Hz) and f Min, ESS is a minimum frequency of a case with ESS for PFC (Hz). According to the equation above, we can determine overall improved effect due to replacing a steam turbine with an ESS. The simulation assumes a specific operation state of power system in South Korea and uses simple power system and governor-turbine model. In addition, the performance of PFC depends on many factors such as controller setting, secondary frequency control, type of generators for PFC, and so on. Therefore, actual improved effect would be different from simulation results. Table 4. Results of the simulation with six cases. Case Number
Minimum
Frequency
Overall
Improved effect
Frequency (Hz)
on steady state (Hz)
improved effect α (%)
per 100 MW (%)
-
-
2.2598
1.1299
Case 1
59.9823
Case 2
59.9827
59.9840
Case 3
59.9835
6.7796
0.9685
Case 4
59.8623
-
-
Case 5
59.8731
7.8431
3.9215
Case 6
59.8936
22.7305
3.2472
59.9070
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
4. Conclusion
The effect of an ESS for PFC in South Korean power system has been presented in this paper. A simplification of the power system model is utilized in this study to examine the effect of such a system. The governor-turbine and the ESS model are made based on PSS/E model library. The simulation parameters were sourced from KPX and from earlier work. The simulation indicates that ESSs offer a strong advantage for PFC due to their quick and stable dynamic responses compared to conventional generators. Furthermore, ESSs can be more effective with a weak grid, which have low inertia levels and slow frequency responses. Therefore, the power system stability could be improved when an ESS is take part in PFC instead of conventional generators. Regarding this result, ESSs will play an important role with regard to PFC in future power systems. Acknowledgements
This work was supported by the Global Excellent Technology Innovation (20132010101890) of the Korea Institute of Energy Technology Evaluation and Planning(KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. This research was supported by Korea Electrotechnology Research Institute(KERI) primary research program through the National Research Council of Science & Technology(NST) funded by the Ministry of Science, ICT and Future Planning(MSIP). (No. 16-12-N0101-04). References [1] Ekanayake JB, Jenkins N, Strbac G. Frequency response from wind turbines. Wind Engineering 2008;32.6;573-586. [2] Kundur P. Power System Stability and Control. New York: McGraw-Hill; 1994. [3] Mu Y, et al. Primary frequency response from electric vehicles in the Great Britain power system. IEEE Transactions on Smart Grid 2013; 4.2;1142-1150. [4] Liu H, et al. Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands. IEEE Transactions on Power Systems 2013;28.3;480-3489. [5] Leitermann O. Energy storage for frequency regulation on the electric grid. Diss. Massachusetts Institute of Technology, 2012. [6] Aditya SK, Das D. Battery energy storage for load frequency control of an interconnected power system. Electric Power Systems Research 2001;58.3;179-185. [7] Divya KC, Østergaard J. Battery energy storage technology for power systems—An overview. Electric Power Systems Research 2009;79.4; 511-520. [8] Hsieh E, Johnson R. Frequency response from autonomous battery energy storage. Cigré Grid of the Future Symposium. 2012. [9] Rebours, YG, et al. A survey of frequency and voltage control ancillary services—Part I: Technical features. IEEE Transactions on power systems 2007; 22.1;350-357. [10] Kim JY, Cho KW, Moon SI. Study on the frequency regulation reserve criteria considering ESS. KIEE Summer Annual Conference 2014;465-466. [11] Anderson PM, Mirheydar M. A low-order system frequency response model. IEEE Transactions on Power Systems 1990:5.3;720-729. [12] Siemens Industry, Inc. PSS®E Model Library. New York: 2013. [13] Tan X, Li Q, Wang H. Advances and trends of energy storage technology in microgrid. International Journal of Electrical Power & Energy Systems 2013;44.1:179-191. [14] Inoue T, et al. Estimation of power system inertia constant and capacity of spinning-reserve support generators using measured frequency transients. IEEE Transactions on Power Systems 1997;12.1:136-143. [15] Jeong BS, Chun YH, Kim ID, Yang JJ. Assessment of the Generators Constant from Frequency Response Properties of Korean Power System. The transactions of The Korean Institute of Electrical Engineers, 2009;58:688-693. [16] Park JH, Yeo SM, Kim CH. Estimation of Inertia Constant for Korean Power System by Frequency Analysis. KIEE Summer Annual Conference 2009;207-208.
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ScienceDirect Energy Procedia 107 (2017) 116 – 121
3rd International Conference on Energy and Environment Research, ICEER 2016, 7-11 September 2016, Barcelona, Spain
An Analysis of Energy Storage Systems for Primary Frequency Control of Power Systems in South Korea Hyeon-Jin Moona, Ah-Yun Yuna, Eung-Sang Kimb and Seung-Il Moona* b
a School of Electrical and Computer Engineering, Seoul National Univ., 1, Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea Smart Distribution Research Center, Korea Electrotechnology Research Institute, 12, Bulmosan-ro 10beon-gil, Seongsan-gu, Changwon-si, Gyeongsangnam-do, 51543, South Korea
Abstract Energy storage systems (ESSs) have a quick response and outstanding functions when used for frequency regulation. This paper examines the effect of an ESS used in conjunction with the primary frequency control (PFC) of a power system in South Korea. A simple low order system frequency response (SFR) model is used to undertake a simulation with MATLAB Simulink. The simulation includes gas, hydro and steam turbines and the ESS with the PFC. The results show that the ESS has a remarkable effect in the PFC and that it is more efficient in a weak grid. When more ESSs are added, the substitution effect is weaker according to the simulation results. © 2016 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment (http://creativecommons.org/licenses/by-nc-nd/4.0/). Research. Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment Research. Keywords: Energy Storage System (ESS); Frequency regulation; Primary Frequency Control (PFC)
1. Introduction The controlling frequency is an essential aspect when operating a power system. However, as the proportion of Renewable Energy Sources (RES) in power systems continues to increase, the stiffness of power systems is decreasing due to their very low or even lack of inertia and their intermittency, which weakens the stability of the
* Corresponding author. Tel.: +82-10-3798-1821; fax: +82-2-878-1452. E-mail address: [email protected]
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 3rd International Conference on Energy and Environment Research. doi:10.1016/j.egypro.2016.12.143
117
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
power system [1]. A conventional generator can provide good frequency control but at a slow speed due to its physical and/or thermal limitations [2, 3]. To counteract the side effect of RESs, many studies to develop frequency operation schemes for power systems are being conducted. In a study, frequency control was provided by Doubly Fed Induction Generators (DFIG) and converter based wind turbines [1]. Latter, the PFC control scheme, which uses the power of electric vehicles, was introduced [4]. Other works, involved the use of ESSs for frequency control in power systems [5-7]. In particular, ESSs take center stage with regard to frequency control because they have a very rapid response and bidirectional transmission to/from a power system. This application of ESSs has been demonstrated in several countries [7, 8]. In South Korea, the Korea Electric Power Corporation (KEPCO) has installed a 200 MW ESS for frequency control. Moreover, KEPCO plans to increase ESS capacity to 500 MW for frequency regulation by 2017. In this paper, the effects of ESSs when used for PFC are analyzed in MATLAB Simulink using the SFR model, governor-turbine models, and an ESS model reflecting the power system in South Korea. The frequency response determined by a dynamic simulation and the results are analyzed. This paper focuses on the minimum frequency of the system because this factor is most critical in relation to PFC. 2. Simple power system modeling of a power system in South Korea 2.1. Frequency control of a power system The frequency of a power system is related to the balance of active power according to this equation.
M
d (Δf / f 0 ) + K Δf = −ΔP dt
(1)
Where M (= 2H) is the inertia constant of the system (s), f 0 is the system nominal frequency (Hz), K is the power/frequency characteristic of the system (PU/Hz), and ΔP is the amount of generator/load change (PU) [2]. When the active power of a load or a generator is suddenly changed, an active power imbalance occurs and the frequency deviates from the nominal frequency. Therefore, for stable operation of a power system, it is necessary to hold the system frequency at the nominal frequency. Generally, there are three control layers to sustain the system frequency [9]. Primary frequency control (PFC) is a rapid local control method that regulates the active power of generators and controllable loads. It is used to mitigate deviations of the system frequency. Secondary frequency control (SFC) is a centralized generation control method that regulates the active power of generators. It is used to restore the system frequency to its nominal value. Tertiary frequency control (TFC) refers to the manual control of the actions of dispatching and generation unit commitment. This control method is used when secondary control is incapable of restoring the system frequency. In South Korea, the Korea Power Exchange (KPX) is in charge of managing the frequency control of power systems using their energy management system (EMS) based on the operating rules of South Korea. In their operating rules, the nominal frequency is set to 60 ± 0.2 Hz and the amount of frequency regulation reserve exceeds 1,500 MW [10]. This regulation reserve is similar to the first and secondary control methods. 2.2. Simplified power system model An actual power system consists of many elements, such as the generator, load, transformer and others. Therefore, the system is constantly changing and is very difficult to model precisely. For this reason, a simple low order system frequency response (SFR) model is used to simulate power system frequency responses [11]. This simple model averages the frequency response of an actual power system to a first-order transfer function using the system inertia M and the load damping constant D. This model can show the short-term dynamic response of the frequency at a specific operating point. It is used to analyze the frequency responses of power systems with the governor-turbine model of generators to provide proper frequency control [11].
118
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
Fig. 1. Simplified South Korean Power System Model with Governor-turbines, ESS Model.
The governor-turbine model contains negative feedback loop of frequency change to provide appropriate primary frequency control. Similarly, to investigate the frequency responses of power systems, the SFR model is devised by MATLAB Simulink as shown in Fig. 1. The modeling of governor-turbines such as gas, hydro, and steam turbines is referenced from the PSS/E model library (gas turbine: GAST, hydro turbine: HYGOV, steam turbine: TGOV1), and the values of the parameters are determined from KPX data obtained in actual tests [12]. The battery response model includes simple first-order time delay function (0.03 second) because the dynamic characteristics of ESSs are simple and very rapid [13]. 2.3. Parameters for the simulation The simulation parameters are shown in Table 1. The simulation had two parts. The first is the peak time case in which there are many generators and loads in the power system. The second part is the off-peak time case which uses a smaller power system than the peak time case. The base power in the simulation is assumed to be 80/55GW, and system inertia M is 20/15 seconds. These settings reflect the latest trends of electrical power consumption in South Korea and the findings of related research [14]. The load damping constant is 2% [15, 16]. The droop constant of the generator is assumed to be 5%. In particular, a conventional generator for PFC provides frequency reserve using a deloaded operation of approximately 5% of the rated power. Table 1. Simulation parameters Parameters
Peak
Off-peak
Base Power (GW)
80
55
System Inertia M (s)
20
15
Load Damping Constant (%)
2
Droop Constant of Generators (%)
5
Droop Constant of ESS (%)
0.25
Detection time of frequency (s)
0.1
Frequency Dead Band (Hz)
0.01
Nominal Frequency (Hz) Simulation Time (s) Generator trip Amount (MW) Trip Time (s)
60 60 1,000 10
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
On the other hand, an ESS can be used with its full power rating. For this reason, the droop constant of the ESS is set by calculating the ratio of the rated power and the amount of the frequency reserve. The obtained parameter value is 0.25% (5% by 5%). The frequency dead band and detection time were sourced from earlier work [5, 9]. The simulation time is 60 seconds. At 10 seconds, we assume that the 1,000 MW generator is tripped. 3. The effect evaluation of ESS on PFC
In this chapter, we undertake a simulation to analyze the effect of an ESS for PFC using our simplified power system model. This model uses specific snap shot data of the system. The peak time and off-peak time data of power system in South Korea are used to perform the simulation. The simulation is performed while changing the ratio of the PFC capacity by replacing a steam turbine to ESS. That is because the steam turbine is most general generator for PFC of power system in South Korea. The PFC capacity is based on actual operation data from KPX and is slightly revised for simplification. The amount of PFC capacity at the peak time and off-peak time are shown in Table 2 and Table 3, respectively. The frequency of the simulation is observed to evaluate the effect of the ESS on PFC because the main purpose of PFC is to relieve excessive drops in the system frequency. 3.1. Case study at the peak time The simulation result is shown in Fig. 2 (a). As shown in Table 2, case 1 uses normal frequency reserve without an ESS for PFC and cases 2 and 3 include 200 and 700 MW of ESS, respectively, instead of a steam turbine for PFC. That is because steam turbine has the lowest cost of generation of electric power in PFC. So, steam turbine is better to generate constant power for supplying load demand rather than participating PFC with a deloaded operation. The PFC operation resulting from a trip event at 10 seconds reduces the drop in the system frequency with a small amount of undershoot. Table 4 shows the results of the simulation. In case 1, the minimum frequency of the system is 59.9823. For case 2, it is 59.9827. The drop in frequency in case 2 is smaller than in case 1 due to the use of the ESS in the system in place of the steam turbine, for which the response is relatively slow in comparison to an ESS. Furthermore, in case 3, the observed minimum frequency is 59.9835, and there is a little undershoot due to the use of the 700 MW ESS. 3.2. Case study at the off-peak time In the same manner, the simulation result is shown in Fig. 2 (b) and Table 4. As shown in Table 3, case 4 assumes a normal frequency reserve capacity at the off-peak time, and cases 5 and 6 include 200 and 700 MW ESSs, respectively, instead of steam turbine for PFC. At the off-peak time in South Korea, steam turbine is mainly in charge of PFC. For simplicity of simulation, other generators participating to PFC are ignored. Minimum frequencies in cases 4 to 6 are 59.8623, 59.8731 and 59.8936, respectively. Undershoot of frequency due to generator trip is diminished by adding the ESS for PFC due to the very rapid response of the ESS. Table 2. The amount of PFC capacity at the peak time. Case Number
Amount of PFC Capacity (MW)
Case 1
Gas: 500, Hydro: 300, Steam: 700
Case 2
Gas: 500, Hydro: 300, Steam: 500, ESS: 200
Case 3
Gas: 500, Hydro: 300, ESS: 700
Table 3. The amount of PFC capacity at the off-peak time. Case Number
Amount of PFC Capacity (MW)
Case 4
Steam: 1,500
Case 5
Steam: 1,200, ESS: 200
Case 6
Steam: 800, ESS: 700
119
120
Hyeon-Jin Moon et al. / Energy Procedia 107 (2017) 116 – 121
Fig. 2. Frequency responses of 1 GW generator trip in the power systems: (a) at the peak time; (b) at the off-peak time.
3.3. Comparison of the effect using ESS to PFC in different system state. At the peak time, there are many generators and loads. In other words, the systems have large rated power and system inertia levels. Accordingly, the speed of a frequency change is relatively slow and frequency drops are low, while at the off-peak time, systems have less inertia than in the former cases. Hence, the frequency drop in the simulation is greater and the frequency change speed is faster than in the peak time cases. In addition, the time of the minimum frequency in Fig. 2 (b) is earlier than in Fig. 2 (a) and the degree of undershoot at the off-peak time cases is higher than in the peak time cases. For this reason, the effect of an ESS for PFC is remarkable in the off-peak time cases. But when more ESSs are added, instead of the steam turbine, the substitution is less effective according to the simulation results. The improved effect α (%) mitigating the minimum frequency by using an ESS compared with conventional steam turbine for PFC can be calculated using this equation below.
α=
f Min ,base − f Min , ESS f 0 − f Min ,base
× 100
(2)
Where f Min,base is a minimum frequency of a case without ESS for PFC (Hz) and f Min, ESS is a minimum frequency of a case with ESS for PFC (Hz). According to the equation above, we can determine overall improved effect due to replacing a steam turbine with an ESS. The simulation assumes a specific operation state of power system in South Korea and uses simple power system and governor-turbine model. In addition, the performance of PFC depends on many factors such as controller setting, secondary frequency control, type of generators for PFC, and so on. Therefore, actual improved effect would be different from simulation results. Table 4. Results of the simulation with six cases. Case Number
Minimum
Frequency
Overall
Improved effect
Frequency (Hz)
on steady state (Hz)
improved effect α (%)
per 100 MW (%)
-
-
2.2598
1.1299
Case 1
59.9823
Case 2
59.9827
59.9840
Case 3
59.9835
6.7796
0.9685
Case 4
59.8623
-
-
Case 5
59.8731
7.8431
3.9215
Case 6
59.8936
22.7305
3.2472
59.9070
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4. Conclusion
The effect of an ESS for PFC in South Korean power system has been presented in this paper. A simplification of the power system model is utilized in this study to examine the effect of such a system. The governor-turbine and the ESS model are made based on PSS/E model library. The simulation parameters were sourced from KPX and from earlier work. The simulation indicates that ESSs offer a strong advantage for PFC due to their quick and stable dynamic responses compared to conventional generators. Furthermore, ESSs can be more effective with a weak grid, which have low inertia levels and slow frequency responses. Therefore, the power system stability could be improved when an ESS is take part in PFC instead of conventional generators. Regarding this result, ESSs will play an important role with regard to PFC in future power systems. Acknowledgements
This work was supported by the Global Excellent Technology Innovation (20132010101890) of the Korea Institute of Energy Technology Evaluation and Planning(KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. This research was supported by Korea Electrotechnology Research Institute(KERI) primary research program through the National Research Council of Science & Technology(NST) funded by the Ministry of Science, ICT and Future Planning(MSIP). (No. 16-12-N0101-04). References [1] Ekanayake JB, Jenkins N, Strbac G. Frequency response from wind turbines. Wind Engineering 2008;32.6;573-586. [2] Kundur P. Power System Stability and Control. New York: McGraw-Hill; 1994. [3] Mu Y, et al. Primary frequency response from electric vehicles in the Great Britain power system. IEEE Transactions on Smart Grid 2013; 4.2;1142-1150. [4] Liu H, et al. Decentralized vehicle-to-grid control for primary frequency regulation considering charging demands. IEEE Transactions on Power Systems 2013;28.3;480-3489. [5] Leitermann O. Energy storage for frequency regulation on the electric grid. Diss. Massachusetts Institute of Technology, 2012. [6] Aditya SK, Das D. Battery energy storage for load frequency control of an interconnected power system. Electric Power Systems Research 2001;58.3;179-185. [7] Divya KC, Østergaard J. Battery energy storage technology for power systems—An overview. Electric Power Systems Research 2009;79.4; 511-520. [8] Hsieh E, Johnson R. Frequency response from autonomous battery energy storage. Cigré Grid of the Future Symposium. 2012. [9] Rebours, YG, et al. A survey of frequency and voltage control ancillary services—Part I: Technical features. IEEE Transactions on power systems 2007; 22.1;350-357. [10] Kim JY, Cho KW, Moon SI. Study on the frequency regulation reserve criteria considering ESS. KIEE Summer Annual Conference 2014;465-466. [11] Anderson PM, Mirheydar M. A low-order system frequency response model. IEEE Transactions on Power Systems 1990:5.3;720-729. [12] Siemens Industry, Inc. PSS®E Model Library. New York: 2013. [13] Tan X, Li Q, Wang H. Advances and trends of energy storage technology in microgrid. International Journal of Electrical Power & Energy Systems 2013;44.1:179-191. [14] Inoue T, et al. Estimation of power system inertia constant and capacity of spinning-reserve support generators using measured frequency transients. IEEE Transactions on Power Systems 1997;12.1:136-143. [15] Jeong BS, Chun YH, Kim ID, Yang JJ. Assessment of the Generators Constant from Frequency Response Properties of Korean Power System. The transactions of The Korean Institute of Electrical Engineers, 2009;58:688-693. [16] Park JH, Yeo SM, Kim CH. Estimation of Inertia Constant for Korean Power System by Frequency Analysis. KIEE Summer Annual Conference 2009;207-208.
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