Multi-Objective Robust Scheduling Optimization Model of Wind, Photovoltaic Power, and BESS Based on the Pareto Principle

Guan Wang, Zhongfu Tan, Qingkun Tan, Shenbo Yang, Hongyu Lin, Xionghua Ji, Gejirifu De and Xueying Song
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Guan Wang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Zhongfu Tan: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Qingkun Tan: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Shenbo Yang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Hongyu Lin: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xionghua Ji: School of Economics and Management, Yan’an University, Yan’an 716000, China
Gejirifu De: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Xueying Song: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2019, vol. 11, issue 2, 1-14

Abstract: With the increasing proportion of distributed power supplies connected to the power grid, the application of a battery energy storage system (BESS) to a power system leads to new ideas of effectively solving the problem of distributed power grid connections. There is obvious uncertainty involved in distributed power output, and these uncertainties must be considered when optimizing the scheduling of virtual power plants. In this context, scene simulation technology was used to manage the uncertainty of wind power and photovoltaic output, forming a classic scenario. In this study, to reduce the influence of the uncertainty of wind and photovoltaic power output on the stable operation of the system, the time-of-use (TOU) prices and BESS were incorporated into the optimal scheduling problem that is inherent in wind and photovoltaic power. First, this study used the golden section method to simulate the wind and photovoltaic power output; second, the day-ahead wind and photovoltaic power output were used as the random variables; third, a wind and photovoltaic power BESS robust scheduling model that considers the TOU price was constructed. Finally, this paper presents the Institute of Electrical and Electronics Engineers (IEEE) 30 bus system in an example simulation, where the solution set is based on the Pareto principle, and the global optimal solution can be obtained by the robust optimization model. The results show that the cooperation between the TOU price and BESS can counteract wind and photovoltaic power uncertainties, improve system efficiency, and reduce the coal consumption of the system. The example analysis proves that the proposed model is practical and effective. By accounting for the influence of uncertainty of the optimal scheduling model, the actual operating cost can be reduced, and the robustness of the optimization strategy can be improved.

Keywords: TOU price; BESS; wind and photovoltaic power; the golden section method; robust scheduling optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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