Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment

Wei Liao, Yi Yang (), Qingwei Wang, Ruoyu Wang, Xieli Fu, Yinghua Xie and Junhua Zhao
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Wei Liao: Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China
Yi Yang: Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, China
Qingwei Wang: Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China
Ruoyu Wang: Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China
Xieli Fu: Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China
Yinghua Xie: Shenzhen Power Supply Co., Ltd., China Southern Power Grid, Shenzhen 518000, China
Junhua Zhao: Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518000, China

Energies, 2023, vol. 16, issue 12, 1-17

Abstract: The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy.

Keywords: electricity spot market; system planning; social welfare; hybrid experimental learning; data-driven (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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