Two-Stage Robust Optimization for Prosumers Considering Uncertainties from Sustainable Energy of Wind Power Generation and Load Demand Based on Nested C&CG Algorithm

Qiang Zhou, Jianmei Zhang, Pengfei Gao, Ruixiao Zhang, Lijuan Liu, Sheng Wang, Lin Cheng, Wei Wang and Shiyou Yang ()
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Qiang Zhou: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Jianmei Zhang: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Pengfei Gao: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Ruixiao Zhang: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Lijuan Liu: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Sheng Wang: Gansu Key Laboratory of Renewable Energy Integration Operation and Control, State Grid Gansu Electric Power Research Institute, Lanzhou 730070, China
Lin Cheng: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Wei Wang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Shiyou Yang: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Sustainability, 2023, vol. 15, issue 12, 1-23

Abstract: This paper develops a two-stage robust optimization (TSRO) model for prosumers considering multiple uncertainties from the sustainable energy of wind power generation and load demand and extends the existing nested column-and-constraint generation (C&CG) algorithm to solve the corresponding optimization problem. First, considering the impact of these uncertainties on market trading strategies of prosumers, a box uncertainty set is introduced to characterize the multiple uncertainties; a TSRO model for prosumers considering multiple uncertainties is then constructed. Second, the existing nested C&CG algorithm is extended to solve the corresponding optimization problem of which the second-stage optimization is a bi-level one and the inner level is a non-convex optimization problem containing 0–1 decision variables. Finally, a case study is solved. The optimized final overall operating cost of prosumers under the proposed model is CNY 3201.03; the extended algorithm requires only four iterations to converge to the final solution. If a convergence accuracy of 10 −6 is used, the final solution time of the extended algorithm is only 9.75 s. The case study result shows that prosumers dispatch the ESS to store surplus wind power generated during the nighttime period and release the stored electricity when the wind power generation is insufficient during the daytime period. It can contribute to promoting the local accommodation of renewable energy and improving the efficiency of renewable energy utilization. The market trading strategy and scheduling results of the energy storage system (ESS) are affected by multiple uncertainties. Moreover, the extended nested C&CG algorithm has a high convergence accuracy and a fast convergence speed.

Keywords: prosumer; nested column-and-constraint generation algorithm; two-stage robust optimization; multiple uncertainties (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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