Two-Layer Optimal Dispatch of Distribution Grids Considering Resilient Resources and New Energy Consumption During Cold Wave Weather

Lu Shen, Xing Luo, Wenlu Ji, Jinxi Yuan and Chong Wang ()
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Lu Shen: State Grid Nanjing Power Supply Company, Nanjing 210000, China
Xing Luo: State Grid Nanjing Power Supply Company, Nanjing 210000, China
Wenlu Ji: State Grid Nanjing Power Supply Company, Nanjing 210000, China
Jinxi Yuan: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China
Chong Wang: School of Electrical and Power Engineering, Hohai University, Nanjing 210098, China

Energies, 2025, vol. 18, issue 11, 1-26

Abstract: Within the context of global warming, the frequent occurrence of extreme weather may lead to problems, such as a sharp decrease in new energy output, insufficient system backups, and an increase in the amount of energy consumed by users, resulting in large-scale power shortages within the grid for a short period of time. With the increase in the numbers of electric vehicles (EVs) and microgrids (MGs), which are resilient resources, the ability of a system to participate in demand response (DR) is further improved, which may make up for short-term power shortages. In this paper, we first propose a charging and discharging model for EVs during the onset of a cold wave, and then perform load forecasting for EVs during cold wave weather based on user behavioral characteristics. Secondly, in order to accurately portray the flexible regulation capability of microgrids with massively flexible resource access, this paper adopts the convex packet fitting expression based on MGFOR to characterize the flexible regulation capability of MGs. Then, the Conditional Value at Risk (CVaR) is used to quantify the uncertainty of wind and solar power generation, and a two-layer model with the objective of minimizing the operation cost in the upper layer and maximizing the rate of new energy consumption in the lower layer is proposed and solved using Karush–Kuhn–Tucker (KKT) conditions. Finally, the proposed method is verified through examples to ensure the economic operation of the system and improve the new energy consumption rate of the system.

Keywords: resilient resource; two-layer model; load forecasting for EVs; MGFOR; CVaR (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: 2025
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