Photovoltaic active power control based on BESS smoothing

2019 IFAC Workshop on 2019 IFAC Workshop on 2019 IFAC Workshop on 2019 IFAC Workshop on online at www.sciencedirect.com 2019 IFAC Workshop Control of Grid and Energy Control of Smart Smart Gridon and Renewable RenewableAvailable Energy Systems Systems Control of Smart Grid and Renewable Energy Systems 2019 IFAC Workshop on Control of Smart Grid and Renewable Control of Smart Grid and Renewable Energy Energy Systems Systems 2019 IFAC Workshop on Jeju, Korea, June 10-12, 2019 Jeju, Korea, June 10-12, 2019 Jeju, Korea, Korea, JuneGrid 10-12, 2019 Control of Smart and Renewable Energy Systems Jeju, June 10-12, 2019 Jeju, Korea, JuneGrid 10-12, 2019 Control of Smart and Renewable Energy Systems Jeju, Korea, June 10-12, 2019 Jeju, Korea, June 10-12, 2019

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IFAC PapersOnLine 52-4 (2019) 443–448

Photovoltaic Photovoltaic active active power power control control based based on on BESS BESS smoothing smoothing Photovoltaic active power control based on BESS smoothing Photovoltaic active power control based on BESS smoothing Photovoltaic active power control based on BESS smoothing Linjun Linjun Shi*, Shi*, Ladier Ladier Fa*, Fa*, Haoqing Haoqing Zhu*, Zhu*, Jiangfeng Jiangfeng Shi*, Shi*, Feng Feng Wu*, Wu*, Weiguo Weiguo He**, He**, Chun Chun Wang Wang **, **, Kwang Kwang Y. Y.

Lee***, Lee***, Linjun Shi*, Ladier Fa*, Haoqing Zhu*, Jiangfeng Shi*, Feng Wu*, Weiguo He**, Chun Wang **, Kwang Y. Lee***, Linjun Shi*, Ladier Fa*, Haoqing Zhu*, Jiangfeng Shi*, Feng Wu*, Weiguo He**, Chun Wang **, Kwang Y. Lee***, Linjun Shi*, Ladier Fa*, Haoqing Zhu*, Jiangfeng Shi*, Feng Wu*, Weiguo He**, Chun Wang **, Kwang Y. Lee***, Keman Lin* Keman Lin* Linjun Shi*, Ladier Fa*, Haoqing Zhu*, Jiangfeng Shi*, Feng Wu*, Weiguo He**, Chun Wang **, Kwang Y. Lee***, Keman Lin* Keman Lin* Linjun Shi*, Ladier Fa*, Haoqing Zhu*, Jiangfeng Shi*, Feng Wu*, Weiguo He**, Chun Wang **, Kwang Y. Lee***, Keman Lin* Keman Lin* Keman Lin* * College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 * College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 * College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 * College of Energy and Electrical Engineering, Hohai University, Nanjing, 211100 * College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 CHINA (Tel: 13951992305; 13951992305; e-mail: [email protected] ). Jiangsu CHINA (Tel: 13951992305; e-mail: [email protected] ). * College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 CHINA (Tel: e-mail: [email protected] ). CHINA (Tel: 13951992305; e-mail: [email protected] ). ***College of Energy and Electrical Engineering, Hohai University, Nanjing, Jiangsu 211100 CHINA (Tel: 13951992305; e-mail: [email protected] ). State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems ** State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems CHINA (Tel: 13951992305; e-mail: [email protected] ). ** State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems ** State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems CHINA (Tel: 13951992305; e-mail: [email protected] ). ** State China Key Laboratory of Operation and Control of Renewable Energy & Storage Systems Electric Power Research Institute, Nanjing, Jiangsu 210024 CHINA China Electric Power Research Institute, Nanjing, Jiangsu 210024 CHINA ** Key of and Control of Energy & Storage Electric Power Research Institute, Nanjing, Jiangsu 210024 China Electric Power Research Institute, Nanjing, Jiangsu 210024 CHINA ** State State China Key Laboratory Laboratory of Operation Operation and Control of Renewable Renewable Energy & CHINA Storage Systems Systems China Electric Power Research Institute, Nanjing, Jiangsu 210024 CHINA *** Department of Electrical Engineering, Baylor University *** Department of Electrical Engineering, Baylor University China Power Research Institute, Nanjing, Jiangsu 210024 CHINA *** Department of Electrical Engineering, Baylor University *** Department of Electrical Engineering, Baylor University China Electric Electric Power Research Institute, Nanjing, Jiangsu 210024 CHINA *** Department of Electrical Engineering, Baylor University Waco, TX 76798 USA (e-mail: [email protected]) Waco, TX USA (e-mail: [email protected]) *** Department Electrical Engineering, Baylor Waco, TX 76798 (e-mail: [email protected]) Waco, TX 76798 76798of USA (e-mail: [email protected]) *** Department ofUSA Electrical Engineering, Baylor University University Waco, TX 76798 USA (e-mail: [email protected]) Waco, TX 76798 USA (e-mail: [email protected]) Waco, TX 76798 USA (e-mail: [email protected]) Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get Abstract: The power fluctuation of photovoltaic (PV) is harmful to power systems, so the battery energy storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to get configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In storage system (BESS) was applied to smooth power fluctuation in PV. At present, the main ways to configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In storage system (BESS) was appliedempirical tofilter smooth power fluctuation in(EMD) PV. At present, the main ways to get get configuration of BESS are low-pass and spectrum compensation, which have some drawbacks. In this paper, a method that combines mode decomposition with wavelet analysis (WA) is this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is configuration of BESS are low-pass filter and spectrum compensation, which have some drawbacks. In this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV this paper, a method that combines empirical mode decomposition (EMD) with wavelet analysis (WA) is proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and proposed to get grid-connected active power expectation of PV properly. Based on simulation of PV output, the minimum sizing of BESS is determined by different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only output, minimum sizing of BESS is by different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and compensation, this method not efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only only output, the the minimum sizing ofaccurately, BESS is determined determined by spectrum different batteries’ state-of-charging (SOC) and efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only but also improves the effect to smoothing power fluctuation of acquires the capacity of BESS accurately, but also improves the effect to smoothing power fluctuation of acquires the capacity of BESS efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only accurately, but also improves the effect to smoothing power fluctuation of acquires the capacity of BESS accurately, but also improves the effect to smoothing power fluctuation of acquires the capacity of BESS efficiency. Comparing traditional low-pass filter and spectrum compensation, this method not only accurately, but also improves the effect to smoothing power fluctuation of acquires the capacity of BESS PV effectively. Finally, a case is proposed to verify correctness of the theory. PV effectively. Finally, a case is proposed to verify correctness of the theory. accurately, but also improves the effect to smoothing power fluctuation acquires the capacity of BESS PV effectively. Finally, a case is proposed to verify correctness of the theory. PV effectively. Finally, case is isaccurately, proposed but to verify verify correctness ofeffect the theory. theory. also improves theof to smoothing power fluctuation of of acquires the capacity of aaBESS PV effectively. Finally, case proposed to correctness the PV effectively. Finally, case is proposed to correctness of the Keywords: Photovoltaic storage capacity; mode decomposition (EMD); Keywords: Photovoltaic output; Sizing energy storage capacity; Empirical mode decomposition (EMD); © 2019, IFAC (International Federation ofenergy Automatic Control) Hosting Elsevier Ltd. All rights reserved. PV effectively. Finally, aaoutput; case is Sizing proposed to verify verify correctness ofEmpirical thebytheory. theory. Keywords: Photovoltaic output; Sizing energy storage capacity; Empirical mode decomposition (EMD); Keywords: Photovoltaic output; Sizing energy storage capacity; Empirical mode decomposition (EMD); Keywords: Photovoltaic output; Sizing energy storage capacity; Empirical mode decomposition (EMD); Wavelet analysis. Wavelet analysis. Keywords: Photovoltaic Wavelet analysis. Wavelet analysis. Keywords: Photovoltaic output; output; Sizing Sizing energy energy storage storage capacity; capacity; Empirical Empirical mode mode decomposition decomposition (EMD); (EMD); Wavelet analysis. Wavelet analysis. analysis. Wavelet grid-connected grid-connected reference reference can can be be obtained obtained by by spectral spectral analysis analysis grid-connected reference can be obtained by spectral analysis 1. INTRODUCTION grid-connected reference can be obtained by spectral analysis 1. INTRODUCTION grid-connected reference can be obtained by spectral analysis 1. INTRODUCTION and compensated for the right frequency band. 1. INTRODUCTION and compensated for the right frequency band. grid-connected reference can be obtained by spectral analysis 1. INTRODUCTION and for frequency and compensated for the right frequency band. grid-connected reference can be obtainedband. by spectral analysis 1. and compensated compensated for the the right right frequency band. With the increasing penetration of photovoltaic (PV) in 1. INTRODUCTION INTRODUCTION With the increasing penetration of photovoltaic (PV) in and compensated for the right frequency band. With the increasing penetration of photovoltaic (PV) in The method named empirical mode decomposition (EMD) With the increasing penetration of photovoltaic (PV) in and compensated for the right frequency band. The method named empirical mode decomposition (EMD) With the increasing penetration of photovoltaic (PV) in The method named empirical mode decomposition (EMD) power systems, the of stability operation The method named empirical mode decomposition (EMD) power the systems, the problems problems of safety, safety, stability and and operation With increasing penetration of photovoltaic (PV) in The method named empirical mode decomposition (EMD) power systems, the problems of safety, stability and operation (Huang et al., 1998) has a good performance in processing power systems, the problems of safety, stability and operation With the increasing penetration of photovoltaic (PV) in (Huang et al., 1998) has a good performance in processing The method named empirical mode decomposition (EMD) power systems, the problems of safety, stability and operation (Huang et al., 1998) has a good performance in processing become increasingly acute. More and more attention has been (Huang et al., 1998) has a good performance in processing The method named empirical mode decomposition (EMD) become increasingly acute. More and more attention has been power systems, the problems of safety, stability and operation (Huang et al., 1998) has a good performance in processing become increasingly acute. More More and more attention has been been non-stationary signal and has widely in become increasingly acute. and more attention has non-stationary signal has and ait it good has been been widely used used in many many power systems, the problems of safety, stability and operation (Huang et 1998) performance in processing become increasingly More and more attention been signal and has been widely used in many paid influence of penetration PV on (Yun et non-stationary signal and it has been widely used in many (Huang et al., al.,(Hu 1998) has ait performance in processing paid to to the the influence acute. of high high penetration PV on grid gridhas (Yun et non-stationary become increasingly acute. More and more attention has been non-stationary signal and2012). it good has EMD been widely used in 1998) many paid to the influence of high penetration PV on grid (Yun et applications et al., (Huang et al., paid to the influence of high penetration PV on grid (Yun et applications (Hu et al., 2012). EMD (Huang et al., 1998) become increasingly acute. More and more attention has been non-stationary signal and it has been widely used in many paid to the influence of high penetration PV on grid (Yun et applications (Hu et al., 2012). EMD (Huang et al., 1998) al., 2007.). The output of PV is fluctuant because of the applications (Hu et al., 2012). EMD (Huang et al., 1998) non-stationary signal and it has been widely used in many al., 2007.). The output of PV is fluctuant because of the paid to the influence of high penetration PV on grid (Yun et applications (Hu et al., 2012). EMD (Huang et al., 1998) al., 2007.). The output of PV is fluctuant because of the does to set principal functions in and its al., 2007.). The output of PV is fluctuant because of the paid to theofinfluence of high penetration PV on grid (Yun et applications does not not need need(Hu to et set al., principal functions in advance, advance, and its 2012). EMD (Huang et 1998) al., 2007.). of PV is fluctuant because of the does not need to set principal functions in advance, and its volatility solar and changes, which has does to set principal functions in and its applications et 2012). EMD analysis (Huang et al., al.,(Huang 1998) volatility of The solaroutput and weather weather changes, which has negative negative al., 2007.). The output of PV is fluctuant because of the does not not need need(Hu to set al., principal functions in advance, advance, and its volatility of solar and weather changes, which has negative performance is superior to the wavelet (WA) volatility of solar and weather changes, which has negative performance is superior to the wavelet analysis (WA) (Huang al., 2007.). The output of PV is fluctuant because of the does not need to set principal functions in advance, and its volatility of solar and weather changes, which has negative performance is superior to the wavelet analysis (WA) (Huang effects on safety and stability of power grid and power performance superior to the wavelet analysis (WA) (Huang does not needis to set principal functions in advance, and its effects on safety and stability of power grid and power volatility of solar and weather changes, which has negative performance is superior to the wavelet analysis (WA) (Huang effects on safety and stability of power grid and power et al., 2003). effects on safety and et stability of power power gridhas anddiminish power performance et al., al., 2003). 2003). is superior to the wavelet analysis (WA) (Huang volatility ofsafety solar and weather changes, which negative effects and of grid power et quality (Mairajuddin al., In to et performance quality on (Mairajuddin etstability al., 2009). 2009). In order order toand diminish effects on safety and stability of power grid and power et al., al., 2003). 2003). is superior to the wavelet analysis (WA) (Huang quality (Mairajuddin et al., 2009). In order to diminish quality (Mairajuddin et al., 2009). In order to diminish effects on safety and stability of power grid and power et al., 2003). quality (Mairajuddin et al., 2009). In order to diminish impact of PV, there are some restrictions to the PV output, In this paper, et al., 2003). In this paper, aaa new new power power smoothing smoothing method method is is proposed proposed impact of PV, there are some restrictions to the PV output, quality (Mairajuddin et al., 2009). In order to diminish impact of PV, there are restrictions to the output, In paper, new power smoothing method is impact of PV, there are some restrictions to the PV output, In this paper, new power smoothing method is proposed quality (Mairajuddin et some al., power 2009). In order toPV diminish In this this paper, aaEMD new with power smoothing method is proposed proposed impact of PV, there are some restrictions to the PV output, for example, the maximum fluctuation ratio in one that combines WA to obtain the grid-connected that combines EMD with WA to obtain the grid-connected for example, the maximum power fluctuation ratio in one impact of PV, there are some restrictions to the PV output, In this paper, a new power smoothing method is proposed for example, the maximum power fluctuation ratio in one that combines EMD with WA to obtain the grid-connected for example, the maximum power fluctuation ratio in one that combines EMD with WA to obtain the grid-connected impact of PV, there are some restrictions to the PV output, In this paper, a new power smoothing method is proposed that combines EMD with WA to obtain the grid-connected for example, the maximum power fluctuation ratio in one minute must be less than 10% of the installed PV capacity in power reference of PV, which makes full use power reference of with PV, WA which makesthe fullgrid-connected use of of the the minute must be betheless less than 10% 10%power of the the fluctuation installed PV PVratio capacity in that for example, maximum in combines EMD to minute must be than 10% of the installed PV capacity in power reference of PV, which makes full use of the minute must than of installed capacity in power reference of PV, which makes full use for example, theless maximum power fluctuation ratio in one one that combines EMD with to obtain obtain grid-connected power reference of effective PV, WA which makesthe full use of of the the minute must be less than 10% of the installed PV capacity in China (GB/T China, 19964-2012). characteristics and information of photovoltaic characteristics and effective information of photovoltaic China (GB/T China, 19964-2012). minute must be less than 10% of the installed PV capacity in power reference of PV, which makes full use of the China (GB/T China, 19964-2012). characteristics and effective information of photovoltaic China (GB/T China, 19964-2012). information of photovoltaic minute must be less than 10% of the installed PV capacity in characteristics power referenceand of effective PV, which makes full use such of the characteristics and effective information of photovoltaic China (GB/T China, 19964-2012). power generation. Compared with the other methods, power generation. generation. Compared with the other other methods, methods, such as as China (GB/T China, 19964-2012). characteristics and effective information of photovoltaic power Compared with the such Energy storage devices are applied to smooth the output power generation. Compared with the other methods, such as China (GB/T China, 19964-2012). characteristics and effective information ofcompensation, photovoltaic Energy storage devices are applied to smooth the output power generation. Compared with the other methods, such as as Energy storage devices are applied to smooth the output low-pass filter and conventional spectrum Energy storage devices are applied to smooth the output low-pass filter and conventional spectrum compensation, power generation. Compared with the other methods, such as Energy storage devices are applied to smooth the output low-pass filter and conventional spectrum compensation, fluctuation of properly et 2004; et filter and conventional spectrum compensation, power generation. Compared with the other methods, such as fluctuation of PV PVdevices properlyare(Barton (Barton et al., al., 2004; Wang Wang et al., al., low-pass Energy storage applied to smooth the output low-pass filter and conventional spectrum compensation, fluctuation of PV properly (Barton et al., 2004; Wang et al., superiority of the new method is verified in a study case. fluctuation of PV properly (Barton et al., 2004; Wang et al., Energy storage devices are(Barton applied to smooth the output superiorityfilter of the theand new conventional method is is verified verified in aa study study case. spectrum compensation, fluctuation of PV properly al., 2004;the Wang et al., low-pass superiority of new method in case. 2012), due its high cost. key to minimum superiority of new method in case. low-pass spectrum compensation, 2012), due to to its high cost. The The key et to calculate calculate the minimum fluctuation of PV properly (Barton et al., Wang et superiorityfilter of the theand new conventional method is is verified verified in aa study study case. 2012), due to its high cost. The key to calculate the minimum 2012), due to cost. The key to calculate the minimum fluctuation of its PVhigh properly (Barton et al., 2004; 2004; Wang et al., al., superiority of the new method is verified in a study case. 2012), due to its high cost. The key to calculate the minimum energy to be stored is to determine the reference value of 2. PV REFERENCE POWER CACULATION BASED superiority of the new POWER method isCACULATION verified in a study case. ON 2. PV REFERENCE POWER CACULATION BASED ON energy to be stored is to determine the reference value of 2012), due to its high cost. The key to calculate the minimum energy to be stored is to determine the reference value of 2. PV REFERENCE BASED energy to be stored is to determine reference value of 2. CACULATION BASED ON 2012), due to for its highgrid-connected. cost. The key to the calculate the minimum 2. PV PV REFERENCE REFERENCE POWER POWER CACULATION BASED ON ON energy to be stored is to determine the reference value of active power PV EMD AND WA EMD AND WA active power for PV grid-connected. energy to be stored is to determine the reference value of 2. PV REFERENCE POWER CACULATION BASED ON active power for PV grid-connected. EMD AND WA active power PV grid-connected. EMD AND WA energy to be for stored is to determine the reference value of 2. PV REFERENCE POWER CACULATION BASED ON EMD AND WA active power for PV grid-connected. active power for grid-connected. EMD It common method to active for PV PV grid-connected. EMD AND AND WA WA It is is aaaapower common method to obtain obtain the the scheduling scheduling power power of of 2.1 It is common method to obtain the scheduling power of 2.1 Empirical Empirical mode mode decomposition decomposition It is common method to obtain the scheduling power of 2.1 Empirical mode decomposition It is a common method to obtain the scheduling power of 2.1 Empirical mode decomposition intermittent renewable energy including PV by low pass 2.1 Empirical mode decomposition intermittent renewable energy including PV by low pass It is a common method to obtain the scheduling power of intermittent renewable energy including PV by by low pass mode decomposition intermittent renewable including PV low pass It is a common methodenergy to obtain the scheduling power of 2.1 Empirical intermittent renewable by low filtering al., Jiang al., However, it has Empirical mode decomposition filtering (Li (Li et et al., 2012; 2012;energy Jiang et etincluding al., 2013). 2013).PV However, itpass has 2.1 The EMD must meet the intermittent renewable energy including PV by low pass filtering (Li et al., 2012; Jiang et al., 2013). However, it has The EMD must meet the three three principles principles (Huang (Huang et et al., al., 1998): 1998): The EMD must meet the principles (Huang et 1998): filtering (Li et al., 2012; Jiang et al., 2013). However, it has intermittent renewable energy including PV by low pass filtering (Li et al., 2012; Jiang et al., 2013). However, it has The EMD must meet the three principles (Huang et al., 1998): some drawbacks such as time delay, low traceability and poor The EMD must meet the three three principles (Huang et al., al., 1998): some drawbacks such as time delay, low traceability and poor 1) At least one maximum value and one minimum value of filtering (Li et al., 2012; Jiang et al., 2013). However, it has some drawbacks such as time delay, low traceability and poor 1) At least one maximum value and one minimum value of The EMD must meet the three principles (Huang et al., 1998): 1) At least one maximum value and one minimum value of some drawbacks such as time delay, low traceability and poor filtering (Li et al., 2012; Jiang et al., 2013). However, it has some drawbacks such as time delay, low traceability and poor 1) At least one maximum value and one minimum value of The EMD must meet the three principles (Huang et al., 1998): characterization in some cases. Although Nguyen et al. (2014) 1) At least one maximum value and one minimum value of (2014) characterization in some cases. Although Nguyen et al. input signals are needed. 2) Time characteristic scale is some drawbacks such as time delay, low traceability and poor characterization in some cases. Although Nguyen et al. (2014) input signals are needed. 2) Time characteristic scale is 1) At least one maximum value and one minimum value of input signals are needed. 2) Time characteristic scale is characterization in some cases. Although Nguyen et al. al. (2014) some drawbacks such as time delay, low traceability and poor characterization in some cases. Although Nguyen et (2014) input signals are needed. 2) Time characteristic scale is 1) At least one maximum value and one minimum value of proposed a method based on zero-phase low-pass filter to input signals are needed. 2) Time characteristic scale is proposed a method based on zero-phase low-pass filter to determined by the time interval of continuous extreme value. characterization in some cases. Although Nguyen et al. (2014) proposed a method based on zero-phase low-pass filter to determined by the time interval of continuous extreme value. input signals are needed. 2) Time characteristic scale is determined by the time interval of continuous extreme value. proposed aa method based on zero-phase low-pass filter to characterization in some cases. Although Nguyen etal. al. (2014) proposed method based on zero-phase low-pass filter to determined by the time interval of continuous extreme value. input signals are needed. 2) Time characteristic scale is eliminate the problem of time delay, and Jiang et (2013) determined by the time interval of continuous extreme value. eliminate the problem of time delay, and Jiang et al. (2013) 3) Extreme value can be obtained by the differential, if there proposed aa method on zero-phase low-pass filter to eliminate problem of delay, and Jiang et (2013) 3) Extreme Extreme by value can beinterval obtained by the differential, differential, ifvalue. there the time of continuous extreme 3) value can be obtained by the if there eliminate the problembased of time time delay, and control Jiang ettoal. al.improve (2013) proposed the based on zero-phase low-pass filter to determined eliminate the problem of time delay, and Jiang et al. (2013) 3) Extreme value be obtained by the there determined by thecan time ofextreme continuous extremeif presented aa method two-time-scale coordination 3) only Extreme value can beinterval obtained by the differential, differential, ifvalue. there presented a two-time-scale coordination control to improve is one inflection point but no value. eliminate the problem of time delay, and Jiang et al. (2013) presented two-time-scale coordination control to improve is only one inflection point but no extreme value. 3) Extreme value can be obtained by the differential, if there is only one inflection point but no extreme value. presented aa two-time-scale coordination eliminate the problem ofenergy time delay, and control Jiang etto al.improve (2013) presented two-time-scale coordination control to improve is only one inflection but no extreme value. 3) Extreme value canpoint be obtained by the differential, if there the traceability of wind by using the predicted wind is only one inflection point but no extreme value. the traceability of wind energy by using the predicted wind presented a two-time-scale coordination control to improve the traceability of wind energy by using the predicted wind is only one point but no extreme value. the traceability of wind energy by using the predicted wind presented a two-time-scale coordination control to improve In addition, the basic unit that makes up the traceability of wind energy by using the predicted wind is one inflection inflection extreme In only addition, the most most point basic but unitno that makesvalue. up of of the the original original power in short time, these methods were complicated. In the In addition, the most basic unit that makes up the original power in short time, these methods were complicated. In the the traceability of wind energy by using the predicted wind In addition, the most basic unit that makes up of the original power in short time, these methods were complicated. In the In addition, the most basic unit that makes up of ofor theempirical original power in short time, these methods were complicated. In the the traceability of wind energy by using the predicted wind signal is called intrinsic mode function (IMF), power in short time, these methods were complicated. In the signal is called intrinsic mode function (IMF), or empirical In addition, the most basic unit that makes up of the original meantime, the method of spectrum compensation appears to signal is called intrinsic mode function (IMF), or empirical meantime, the method of spectrum compensation appears to power in short time, these methods were complicated. In the signal is called intrinsic mode function (IMF), or empirical meantime, the method of spectrum compensation appears to In addition, the most basic unit that makes up of the original signal is called intrinsic mode function (IMF), or empirical meantime, the method of spectrum compensation appears to power in short time, these methods were complicated. In the mode component. The IMF has the following two constraints: meantime, the method of spectrum compensation appears to mode component. component. The IMF IMF has function the following following twoorconstraints: constraints: signal is called intrinsic mode (IMF), empirical be promising in the literature (Wang et al., 2012), where the mode The has the two be promising in the literature (Wang et al., 2012), where the meantime, the method of compensation appears to component. The IMF has the following two be promising in the literature (Wang et al., 2012), where the signal isnumber called of intrinsic mode (IMF), orconstraints: empirical mode component. The IMF has function the following two constraints: be promising in the literature (Wang et al., 2012), where the meantime, theof method of spectrum spectrum compensation appears to mode 1) The extreme and the number of zero crossing be promising in the literature (Wang et al., 2012), where the 1) The number of extreme and the number of zero crossing mode component. The IMF has the following two constraints: intermittency renewable power’s (including wind and solar) 1) The number of extreme and the number of zero (including windwhere and solar) solar) intermittency of renewable power’s be promising in the literature (Wang et al., 2012), the 1) The number of extreme and the number of zero crossing intermittency of renewable power’s (including wind and mode component. The IMF has the following two constraints: 1) The number of extreme and the number of zero crossing crossing intermittency power’s wind and solar) be promising of in renewable the literature (Wang(including et al., 2012), where the 1) intermittency of renewable power’s (including wind and solar) The number of extreme and the number of zero crossing intermittency of renewable power’s (including wind and 1) The by number extreme andreserved. the number of zero crossing intermittency of IFAC renewable power’sFederation (including and solar) solar) 2405-8963 © 2019, (International of wind Automatic Control) Hosting ElsevierofLtd. All rights

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2019 IFAC CSGRES 444 Jeju, Korea, June 10-12, 2019

Linjun Shi et al. / IFAC PapersOnLine 52-4 (2019) 443–448

must be equal, or differ at most by 1; 2) The local extreme value’s envelope curve (including local maximum envelope curve and local minimum envelope curve) of any point on the data obtained by interpolation method, and mean value of the two envelope curves should be zero. The process to obtain the IMFs from the given signal is called sifting, and its detail steps are in (Chen, 2010). In the sifting process of EMD, its decomposition is orderly since high frequency IMFs are sifted before low frequency IMFs for its successive extraction. Therefore, we can analyze the signal’s physical characteristics by selecting several IMFs discretionarily. Because of the cubic spline interpolation’s characteristics, there are endpoint effects in EMD (Chen, 2010). In order to eliminate the endpoint effects, mirror boundary extension (Huang et al., 2003) is used in this paper. This method regards endpoint as extension center to eliminate the singular endpoint.

SNR

(

t

In this formula, frequency domain; unanalyzed signal.

2

X( )

(

e

j

)d

(3)

10 log[

n

[ x ( n)

xˆˆ((n)] )2

]

(4)

2.3 Combination of EMD and WA

In this formula, WTx is a wavelet transform; α is a positive scale factor used to stretch the basic wavelet function (t) ; is a displacement parameters; X(t) is the unanalyzed signal. The frequency domain representation of (1) is expressed as follows: WTx ( , )

2

xˆ (n)] )

In principle, when the value of RMSE is smaller and the value of SNR is greater, the number of decomposition layers is more reasonable.

(1)

))dt dt

[ x ( n)

Among them, x(n) is the original signal, xˆ(n) is the denoised signal, and n =1,2,3,…, N is number of layers.

The wavelet transform is a transformation. First, shift a wavelet function, and then the inner product is made with the unanalyzed signal at different scales, as shown in the follows (Cai et al., 2011; Yuan, 2008): X (t )

1 N

x 2 ( n)

Wavelet analysis (WA) is developed on the basis of Fourier transform (Cai et al., 2011). Due to the merits of WA, such as excellent capability of frequency analysis, time-domain localization analysis as well as de-noising, a large number of scholars or experts pay more attention to this field, and many most of them have made significant achievements by using WA.

1

In this paper, wavelet symlets 7(sym7) is selected as a basic wavelet and mandatory de-noising threshold value is selected as a threshold function. According to the characteristics of data, the wavelet decomposition layer is selected by empirical method. If the number of layers is small, the signal will not meet the requirements by de-noising. On the contrary, if the number of layers is large, the computation will increase significantly. In this paper, both the root mean square error (RMSE) and signal-to-noise ratio (SNR) are used to decide the number of layers: RMSE

2.2 Wavelet analysis and de-noising

WTx ( , )

determine the decomposition layers, and the last one is how to get the threshold function.

(2)

( ) is the representation of

(t) in 2 f , and f is is the frequency of the

Since EMD does not require any basic functions in advance, it can be applied conveniently. In addition, EMD is effective in matching nonlinear and linear signals by Fourier transform with higher harmonic frequency component (Hu et al.,2012). However, the IMF contains noise, and the simple combination of several IMFs may also lose useful signals, so it is necessary for IMF to be de-noised. On the other hand, Noise can be separated from the useful signals by WA, since WA has its unique advantages in the noise removal. Therefore the method of combining EMD and WA is proposed in the paper. First, the original PV output signals are decomposed into IMFs by EMD. Second, each IMFs are de-noised by WA. Third, the ideal waveform can be obtained by regrouping IMFs which satisfied with the constraint conditions in (GB/T China, 19964-2012). The value of the waveform is the reference value for the grid-connected active power of PV. 3. CASE STUDY The PV station with a battery energy storage system (BESS) is shown in Fig. 1.

The WA is used to de-noising or to analyze characteristics of the original signal. Since the wavelet coefficients of original signal and noise are different, de-noising by WA can be easily implemented. The noise vector is in the form of Gauss transformation as well as its orthogonal transformation (Cai et al., 2011). The results show that when the wavelet transform scale is larger, the Gaussian white noise is smaller and the de-noising effect is better (Yuan, 2008).

From Fig. 2, the maximum active power (Pg) fluctuation ratio (fmax) in one minute is 54.4%, which far exceeds the maximum power fluctuation requirement of less than 10%. Therefore, a BESS is installed in the 10kV AC bus as shown in Fig. 1.

In this paper, the de-noising by wavelet threshold is adopted. However, there are some issues in the process: The first one is how to select wavelet function, the second one is how to 479

2019 IFAC CSGRES Jeju, Korea, June 10-12, 2019

10kv AC bus

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Power network

Step-up transformer

BESS AC converter

445

Step-up transformer AC DC

DC PV converter

BESS

Load

PV

Fig.1 The PV power station system diagram. Fig.3 Decomposition of the original PV output signals with EMD. As can see from Fig. 3, the signals are decomposed by EMD from high to low frequency. The first IMF has the highest frequency and the densest signal, while the seventh IMF has the lowest frequency. (2) The choice of decomposition layers Sym7 is selected for the original power signal from the gridconnected PV power plant in this case. The value of RMSE and SNR can be obtained from the wavelet decompositions in three layers, four layers and five layers respectively by threshold de-noising. The results are given in Table 1. Fig.2 The measured original PV power (Pg). In Case I, the reference value of active power for PV gridconnected is obtained by combining EMD and WA, while the reference value is acquired by traditional low-pass filter. Based on the reference active power, the optimal sizing of BESS was calculated to smooth the output power fluctuations. Finally, comparison and analysis of the results in the two cases are given in the paper. 3.1 Case I The first case is the optimal sizing of the BESS based on the combination of EMD and WA.

As can see from Table 1, the value of RMSE is the smallest and SNR is the largest when the number of decomposition layers is three. The decomposition effect is the best under this situation, which is used as the basis for the subsequent analyses. Table 1. The value of RMSE and SNR in different decomposition layers Number of Decomposition Layers

RMSE

SNR

Three layers

20.5149

46.7367

Four layers

29.4831

39.4834

Five layers

44.8509

31.0929

(3) WA applies to decomposed IMFs

(1) Decomposition by EMD

The WA is performed on the seven IMFs in Fig. 3 individually based on the three decomposition layer.

The original PV output signals are decomposed by EMD, and seven IMFs at different levels can be obtained as shown in Fig. 3.

The third IMF applied with WA is shown in Fig. 4. It can be seen that the signals can keep their own basic characteristics by WA. Other IMFs are de-noised similarly.

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Fig.6 The ideal and actual compensation of BESS.

Fig.4 De-noising of the third IMF component by WA

From the actual compensation of BESS (Pb), the actual reference value of active power for PV grid-connected P (P1) The charging and discharging efficiency of BESS is equal to can be obtained (P1= Pg - Pb). The maximum fluctuation ratio 0.928, and the maximum and minimum state-of-charge (SOC) of P1 is 5.69% in one minute, which exceeds the theoretical of BESS is 1 and 0.3 respectively. The maximum active fluctuation ratio, but still satisfies the regulation (less than power fluctuation ratio in one minute should be less than 10% 10%). From Fig. 6, the maximum output of BESS is according to (GB/T China, 19964-2012), and BESS is 122.1642 kW, which can be the rated power of BESS, PBESS. adopted to reduce the fluctuation ratio. Based on trial and The comparison of fluctuation ratios for P1 and Pg is shown error method (Li, 2014), the last four IMFs de-noised by WA in Fig. 7, where all fluctuation ratios of P1 are less than 10%. are combined to be P0 as shown in Fig. 5, which is used as an ideal reference value of active power for PV gridconnected. The maximum active power fluctuation ratio of P0 is 5.08%, which is less than 10%. While P01 is combined by last four IMFs undecomposed in Fig. 5. The maximum active power fluctuation ratio of P01 is 10.02%. Therefore, P0 is better than P01. (4) Sizing of BESS

Fig.7 The comparison of power fluctuation ratios. The energy fluctuation of BESS can be obtained by integrating the power fluctuation in Fig. 6: EBESS , acu [n]

n 0

( Pb [n] T ), n

0,1,

,M

(5)

In these formulae, EBESS , acu is accumulating energy of BESS

Fig.5 The original vs. de-noised signal and undecomposed signal.

p at every sampling point n, M is the total number of sampling points, and T is sample interval.

Since P0 satisfies the fluctuation ratio in (GB/T China, 19964-2012), the difference between the actual PV output Pg and ideal reference value of active power for PV gridconnected P0 is compensated by the BESS. From Fig. 5, the ideal compensation of BESS Pb0 (Pb0=Pg-P0) is obtained. Considering the efficiency of the BESS, the actual compensation waveform of BESS Pb is in Fig. 6.

Based on (5) and Fig. 6, the energy fluctuation EBESS , acu and SOC are plotted in Fig. 8. The capacity of the BESS, EBESS, is 25.612 kWh by this simulation (Wang et al., 2012).

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From Fig. 9 and Table 2, only fc =1/660 Hz satisfies the regulation (less than 10%). The case in which fc=1/660 Hz ought to compare with the combination of EMD with WA. 3.3 Comparison and analysis Two different methods are given in Cases I and II to calculate the reference value of active power for PV grid-connected. Fig. 10 shows the comparison of these two methods. From Fig. 10, the original signals cannot be reflected very well by low-pass filter because of the time-delay. However, the method combined EMD with WA shows that the original signals’ characteristics are reflected very closely. Table 3 shows that, on the premise of meeting the requirement (less than 10%), BESS requires smaller capacity and power configuration by combining EMD with WA, and smoothing effect is better in this method.

Fig.8 Energy fluctuation and SOC of BESS. As can be seen from Fig. 8, the charged state of BESS returns to the initial value at the end of a cycle, and there is good complementary relationship between the fluctuation of stored energy and charging state of battery. 3.2 Case II In this case, PV output is smoothed by traditional low-pass filter. It has different effect for different time constant T of the filter. When the cut-off frequency fc is 1/60 Hz to 1/660 Hz, different smoothed PV output are obtained in Fig. 9. The results are shown in Table 2. Table 2. Power and capacity of BESS for different cut-off frequency and time constant fc (Hz)

1/60

1/180

1/360

1/660

T (s)

573

1719.75

3437.7

6305.7

fmax (%)

24.42

14.63

10.08

5.87

PBESS (kW)

69.3164

100.7171

122.5237

135.8726

EBESS (kWh)

9.1593

26.1127

47.7697

77.4984

Fig.10 PV output in different method Table 3. The results of capacity, power, and fluctuation by two method in two cases Capacity of BESS/kWh

Power of BESS/kW

The maximum fluctuation ratio/%

Combining EMD with WA

25.612

122.1642

5.69

Low-pass filter

77.4984

135.8926

5.87

4. CONCLUSIONS In this paper, a new method based on the combination of empirical mode decomposition (EMD) and wavelet analysis (WA) is proposed to acquire reference value of active power for PV grid-connected. The original PV output signals are decomposed into several intrinsic mode functions (IMFs) by EMD, and then reconstructed after WA. Finally, the reference value of active power for PV grid-connected can be obtained. Comparing with the conventional low-pass filter, this method can keep original signals’ characteristics with smaller capacity of battery energy storage system (BESS). The

Fig.9 Comparison of waveform for different time constant T.

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effectiveness and superiority is demonstrated by comparing the two cases.

Yuan, L. (2008). Application of Wavelet Transformation in Signal Denoising. Ph.D. dissertation, China University of Geosciences (Beijing). Li, W. (2014). Fault Diagnosis of Diesel Engine Based on Empirical Mode Decomposition and Wavelet Threshold De-noising. Ph.D. dissertation, North University of China. Technical requirements for connecting Photovoltaic power station to power system, GB/T China, 19964-2012

ACKNOWLEDGEMENTS This work was supported in part by the Research and application on key technologies of multi-layer intelligent operation control for large-scale renewable energy plant (NY71-16-040). REFERENCES Yun, T. T., & Kirschen, D. S. (2007). Impact on the power system of a large penetration of photovoltaic generation. 2007 IEEE Power Engineering Society General Meeting (PESGM), 1-8. Mairajuddin, M., Shameem, A. L., & Shiekh, J. I. (2009). Super-capacitor based energy storage system for improved load frequency control. Electric Power Systems Research, vol. 79, pp. 226-233. Barton, J. P. & Infield, D. G. (2004). Energy storage and its use with intermittent renewable energy, IEEE Transactions on Energy Conversion,19(2), 441-448. Wang, C., Yu, B., Xiao, J., & Guo, L. (2012). izing of energy storage systems for output smoothing of renewable energy systems. Proceedings of the CSEE, 32(16),1-8. Li, B. & Guo, J. (2012). A control strategy for battery energy storage system to level wind power output. Power System Technology, 36(8), 39-43. Jiang, Q., & Wang, H. (2013). Two-time-scale coordination control for a battery energy storage system to mitigate wind power fluctuations. IEEE Transactions on Energy Conversion, 28(1), 52-61. Nguyen, C. L., & Lee, H. H. (2014). Optimization of wind power dispatch to minimize energy storage system capacity, Journal of Electrical Engineering & Technology, 9(3), 1080–1088. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shi, H. H., Zheng, Q., Yen N., Tung, C. C., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationarity time series analysis. Proceedings: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995. Hu, A., Ma, W., & Tang, G. (2012). Rolling bearing fault feature extraction method based on ensemble empirical mode decomposition and Kurtosis criterion. Proceeding of the CSEE, 32(11), 106-111. Huang, N. E., Wu, M. C., Long, S. R., Shen, S. S. P., Qu, W., Gloersen, P., & Fan, K. L. (2003). A confidence limit for the empirical mode decomposition and the Hilbert spectral analysis. Proceedings: Mathematical, Physical and Engineering Sciences, 459(2037), 2317-2345. Chen, Z. (2010). Self-adaptive Remote Sensing Image Fusion Based on Empirical Mode Decomposition and SavitzkyGolay Method. Ph.D. dissertation, East China Normal University. Cai, Y., Li, A., Zhang, W., & Xu, P. (2011), HHT end effect processing method based on maximum Lyapunov index boundary extension model, Chinese Journal of Scientific Instrument, 32(6), 1330-1336.

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