Research on Modeling and Scheduling of Virtual Power Plant with Dual Demand Response

doi:10.16182/j.issn1004731x.joss.21-1234

Abstract

Abstract:

Virtual power plant technology provides an effective means to aggregate distributed power and user side resources to participate in power scheduling. Most of the existing research focus on the scheduling optimization of distributed energy instead of the demand response of user side. The user side resources are divided into contracted reliable response load and non-contracted random response load, and the load response is regulated through price adjustment mechanism to adapt to the change of distributed. A virtual power plant optimal scheduling model with dual demands response is constructed, in which the maximizing overall profit of the power grid is set to be the optimization objective, and an improved differential evolution algorithm with constraint penalty is used to optimize the model. Simulation results shows that, compared with the scheduling model without demand response, the proposed model can increase the revenue by 10%.

Key words: virtual power plant, dual demands response, differential evolution algorithm, constraints, scheduling

CLC Number:

TP391.9

Qiang Chen, Yi Wang, Kangshun Li. Research on Modeling and Scheduling of Virtual Power Plant with Dual Demand Response[J]. Journal of System Simulation, 2023, 35(4): 822-832.

Figures/Tables 9

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References 27

1	李媛. 虚拟电厂优化调度与运行仿真研究[D]. 北京: 华北电力大学, 2021.
	Li Yuan. Research on Optimal Dispatching and Operation Simulation of Virtual Power Plant[D]. Beijing: North China Electric Power University, 2021.
2	Koraki, Strunz. Wind and Solar Power Integration in Electricity Markets and Distribution Networks Through Service-Centric Virtual Power Plants[J]. IEEE Transactions on Power Systems(S0885-8950), 2018, 33(1): 473-485.
3	Wu H, Liu X, Ye B, et al. Optimal Dispatch and Bidding Strategy of a Virtual Power Plant Based on a Stackelberg Game[J]. IET Generation Transmission & Distribution(S1751-8687), 2016, 14(4): 4789-4796.
4	Shafiekhani M, Badri A, Shafie-Khah M, et al. Strategic Bidding of Virtual Power Plant in Energy Markets: A Bi-Level Multi-Objective Approach[J]. International Journal of Electrical Power & Energy Systems(S0142-0615), 2019, 113(12): 208-219.
5	唐福斌. 基于混合储能的虚拟电厂热电联合优化调度[D]. 北京: 华北电力大学, 2021.
	Tang Fubin. Optimal Scheduling of Combined Heat and Power Virtual Power Plant Based on Hybrid Energy Storage[D]. Beiging: North China Electric Power University, 2021.
6	Heydari R, Nikoukar J, Gandomkar. Optimal Operation of Virtual Power Plant with Considering the Demand Response and Electric Vehicles[J]. Journal of Electrical Engineering & Technology(S1975-0102), 2021, 16(5): 2407-2419.
7	刘丽军, 罗宁, 吴桐, 等. 基于混合整数二阶锥规划的考虑需求侧响应虚拟电厂优化调度[J]. 太阳能学报, 2021, 42(8): 96-104.
	Liu Lijun, Luo Ning, Wu Tong, et al. Optimal Scheduling of Virtual Power Plant Considering Demand Side Response Based on Mixed Integer Second-order Cone Programming[J]. Acta Energiae Solaris Sinica, 2021, 42(8): 96-104.
8	刘方, 徐耀杰, 杨秀, 等. 考虑电能交互共享的虚拟电厂集群多时间尺度协调运行策略[J]. 电网技术, 2022, 46(2): 642-656.
	Liu Fang, Xu Yaojie, Yang Xiu, et al. Multi-time Scale Coordinated Operation Strategy of Virtual Power Plant Cluster Considering Power Interactive Sharing[J]. Power System Technology, 2022, 46(2): 642-656.
9	Dong Lianxin, Fan Shuai, Wang Zhihua, et al. An Adaptive Decentralized Economic Dispatch Method for Virtual Power Plant[J]. Applied Energy(S0306-2619), 2021, 300: 1-15.
10	孙乐平, 韩帅, 吴宛潞, 等. 基于经济模型预测控制的多虚拟电厂两阶段协调优化调度[J]. 储能科学与技术, 2021, 10(5): 1845-1853.
	Sun Leping, Han Shuai, Wu Wanlu, et al. Coordinated Optimal Scheduling of Multiple Virtual Power Plants in Multiple Time Scales Based on Economic Model Predictive Control[J]. Energy Storage Science and Technology, 2021, 10(5): 1845-1853.
11	Lin Wheimin, Yang Chungyuen, Wu Zongyo, et al. Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk[J]. Applied Sciences(S2076-3417), 2021, 11(16): 1-16.
12	Cui Yong, Xiao Fei, Gu Jun, et al. Primary Frequency Regulation of Intelligent Building-type Virtual Power Plant[J]. Journal of Physics: Conference Series(S1742-6588), 2021, 1966(1): 1-6.
13	林毓军, 苗世洪, 杨炜晨, 等. 面向多重不确定性环境的虚拟电厂日前优化调度策略[J]. 电力自动化设备, 2021, 41(12): 143-150.
	Lin Yujun, Miao Shihong, Yang Weichen, et al. Day-ahead Optimal Schduling Strategy of Virtual Power Plant for Multiple Uncertainties[J]. Electric Power Automation Equipment, 2021, 41(12): 143-150.
14	Tan C, Wang J, Geng S, et al. Three-Level Market Optimization Model of Virtual Power Plant with Carbon Capture Equipment Considering Copula-CVaR Theory[J]. Energy(S0360-5442), 2021, 237: 1-18.
15	周步祥, 张越, 臧天磊, 等. 基于区块链的多虚拟电厂主从博弈优化运行[J]. 电力系统自动化, 2022, 46(1): 155-163.
	Zhou Buxiang, Zhang Yue, Zang Tianlei, et al. Optimal Operation of Multi-virtual Power Plants Based on Blockchain Master-slave Game[J]. Automation of Electric Power Systems, 2022, 46(1): 155-163.
16	袁桂丽, 贾新潮, 陈少梁, 等. 虚拟电厂源-荷协调多目标优化调[J]. 太阳能学报, 2021, 42(5): 105-112.
	Yuan Guili, Jia Xinchao, Chen Shaoliang, et al. Virtual Power Plant Source Load Coordinated Multi-objective Optimal Scheduling[J]. Acta Energiae Solaris Sinica, 2021, 42(5): 105-112.
17	Geng Shiping, Tan Caixia, Niu Dongxiao, et al. Optimal Allocation Model of Virtual Power Plant Capacity Considering Electric Vehicles[J]. Mathematical Problems in Engineering(S1024-123X), 2021: 1-19.
18	Lin Whei, Min Yang, Yuen Chung, et al. Optimal Control of a Virtual Power Plant by Maximizing Conditional Value-at-Risk[J]. Applied Sciences(S2076-3417), 2021, 11(16): 1-16.
19	金鑫, 梅雪峰, 冀伟超, 等. 基于电网结构和气象特征的分布式光伏升尺度虚拟等效电站预测技术研究及应用[J]. 电工技术, 2021, 543(9): 26-30.
	Jin Xin, Mei Xuefeng, Ji Weichao, et al. Research and Application of Distributed Photovoltaic Scaling Virtual Equivalent Power Station Prediction Technology Based on Power Grid Structure and Meteorological Characteristics[J]. Electric Engineering, 2021, 543(9): 26-30.
20	张惠忠, 周嘉新, 张雅雯. 含源—荷—储的虚拟电厂经济性优化运行研究[J]. 电气传动, 2021, 51(9): 55-60, 66.
	Zhang Huizhong, Zhou Jiaxin, Zhang Yawen. Research on Economic Optimization Operation of Virtual Power Plant with Source-Load-Storage[J]. Electric Drive, 2021, 51(9): 55-60, 66.
21	汪洋叶, 赵力航, 常伟光, 等. 基于模型预测控制的虚拟电厂储能系统能量协同优化调控方法[J]. 智慧电力, 2021, 49(7): 16-22.
	Wang Yangye, Zhao Lihang, Chang Weiguang, et al. Model Predictive Control Based Energy Collaborative Optimization Control Method for Energy Storage System of Virtual Power Plant[J]. Smart Power, 2021, 49(7): 16-22.
22	张涛, 李逸鸿, 郭玥彤, 等. 考虑虚拟电厂调度方式的售电公司多时间尺度滚动优化[J]. 电力系统保护与控制, 2021, 49(11): 79-87.
	Zhang Tao, Li Yihong, Guo Yuetong, et al. Multi-time Scale Rolling Optimization of Electricity Retailers Considering Virtual Power Plant Scheduling[J]. Power System Protection and Control, 2021, 49(11): 79-87.
23	Zahra Azimi, Rahmat-Allah Hooshmand, Soodabeh Soleymani. Optimal Integration of Demand Response Programs and Electric Vehicles in Coordinated Energy Management of Industrial Virtual Power Plants[J]. Journal of Energy Storage(S2352-152X), 2021, 41: 1-15.
24	鲍鹏. 考虑电解铝负荷响应的源网荷协同有功/频率控制研究[D]. 济南: 山东大学, 2021.
	Bao Peng. Cooperative Active Power/Frequency Control from Supply, Network, and Demand Sides Considering Participation of Aluminum Smelter Load[D]. Jiʹnan: Shandong University, 2021.
25	杨梓俊, 荆江平, 邓星, 等. 虚拟电厂参与江苏电网辅助服务市场的探讨[J]. 电力需求侧管理, 2021, 23(4): 90-95.
	Yang Zijun, Jing Jiangping, Deng Xing, et al. Discussion on Virtual Power Plant Participating in Ancillary Service Market of Jiangsu Power Grid[J]. Power Demand Side Management, 2021, 23(4): 90-95.
26	Storn R, Price K. Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J]. Journal of Global Optimization(S0925-5001), 1997, 11(4): 341-359.
27	Mallipeddi R, Suganthan P N. Problem Definitions and Evaluation Criteria for the CEC 2010 Competition on Constrained Real-parameter Optimization[R]. Singapore: Nanyang Technological University, 2010.

CEC2010 10维	GA mean ± std	PSO mean ± std	DE mean ± std	DE-PE mean ± std
F1	-7.10e-1 ± 3.86e-2	-3.34e-1 ± 8.09e-2	-3.89e-1 ± 5.25e-2	-7.24e-1 ± 2.69e-2
F2	3.51e+0 ± 1.12e+0	3.15e+0 ± 1.09e-1	1.84e+0 ± 0.00e+0	1.80e+0 ± 0.00e+0
F3	1.15e+13 ± 2.72e+13	1.11e+13 ± 2.93e+13	2.55e+12 ± 1.01e+12	6.80e+11 ± 8.16e+10
F4	5.02e+0 ± 5.63e+0	6.32e+0 ± 9.94e-1	4.95e+0 ± 1.00e+0	4.76e+0 ± 5.92e+0
F5	5.24e+2 ± 0.00e+0	4.86e+2 ± 1.08e+2	2.28e+2 ± 3.82e+2	1.50e+2 ± 9.17e+1
F6	3.65e+2 ± 9.62e+1	4.37e+2 ± 1.39e+2	4.38e+2 ± 8.50e+1	3.08e+2 ± 4.09e+2
F7	7.12e+2 ± 2.32e+3	6.46e+5 ± 1.48e+6	1.82e+9 ± 1.25e+9	1.36e+8 ± 2.22e+8
F8	5.38e+9 ± 0.00e+0	2.12e+10 ± 5.47e+9	2.69e+9 ± 3.82e+8	1.85e+9 ± 1.22e+8
F9	3.51e+13 ± 3.03e+13	7.32e+12 ± 4.81e+12	5.61e+12 ± 1.20e+12	5.56e+12 ± 1.88e+12
F10	1.16e+13 ± 7.21e+12	6.38e+12 ± 5.01e+12	1.82e+12 ± 2.59e+12	1.54e+12 ± 5.26e+11
F11	4.56e-2 ± 9.04e-3	9.44e-2 ± 8.37e-2	-1.08e+0 ± 1.12e+0	-1.72e+0 ± 2.72 e-1
F12	3.84e+1 ± 2.15e+0	2.05e+1 ± 1.64e+0	2.87e+1 ± 2.38e+0	2.22e+1 ± 1.27e+0
F13	-6.50e+1 ± 2.80e+0	-5.42e+1 ± 3.76e+0	-4.19e+1 ± 2.91e+0	-7.16e+1 ± 8.75e+0
F14	8.00e+13 ± 9.81e+12	8.18e+13 ± 7.00e+13	1.43e+12 ± 3.71e+11	9.84e+10 ± 4.55e+10
F15	4.75e+13 ± 7.90e+12	1.00e+14 ± 9.70e+13	2.17e+13 ± 2.36e+12	1.13e+12 ± 1.39e+11
F16	1.48e+0 ± 4.92e-2	1.05e+0 ± 3.63e-2	9.81e-2 ± 9.11e-2	2.63e-2 ± 3.71e-2
F17	5.37e+2 ± 2.03e+2	5.02e+2 ± 4.32e+2	1.50e+3 ± 3.39e+1	1.14e+3 ± 6.65e+0
F18	1.36e+4 ± 6.57e+3	2.10e+4 ± 1.13e+4	9.17e+3 ± 0.00e+0	6.24e+3 ± 1.62e+3

需求响应模型	收益
无需求响应	21 000
仅可靠需求响应	19 400
双重需求响应	23 100

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