Two-Stage Optimal Scheduling Based on the Meteorological Prediction of a Wind–Solar-Energy Storage System with Demand Response

Lu Wei (), Yiyin Li, Boyu Xie, Ke Xu and Gaojun Meng
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Lu Wei: Henan Meteorological Service Center, Zhengzhou 450003, China
Yiyin Li: Henan Meteorological Service Center, Zhengzhou 450003, China
Boyu Xie: Henan Meteorological Service Center, Zhengzhou 450003, China
Ke Xu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Gaojun Meng: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Energies, 2024, vol. 17, issue 6, 1-13

Abstract: With large-scale wind and solar power connected to the power grid, the randomness and volatility of its output have an increasingly serious adverse impact on power grid dispatching. Aiming at the system peak shaving problem caused by regional large-scale wind power photovoltaic grid connection, a new two-stage optimal scheduling model of wind solar energy storage system considering demand response is proposed. There is a need to comprehensively consider the power generation cost of various types of power sources, day-ahead load forecasting information, and other factors and plan the day-ahead output plan of the energy storage system with the minimum system operation cost as the optimization objective of day-ahead dispatching. The demand response strategy is introduced into the time-ahead optimal scheduling, and the optimization of the output value of the energy storage system in each period is studied with the goal of minimizing the system adjustment cost. The particle swarm optimization algorithm is used to solve the model, and the IEEE33 node system is used for an example simulation. The results show that using the demand response and the collaborative effect of the energy storage system can suppress the uncertainty of wind power and photovoltaic power, improve the utilization rate of the system, reduce the power generation cost of the system, and achieve significant comprehensive benefits.

Keywords: demand response; energy storage system; two-stage scheduling; particle swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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