Reserve-Optimized Transmission-Distribution Coordination in Renewable Energy Systems

Li Chen and Dan Zhou ()
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Li Chen: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Dan Zhou: College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Energies, 2025, vol. 18, issue 18, 1-19

Abstract: To effectively address challenges posed by high-penetration renewable energy to power system operation and reserves, this paper proposes a novel research framework. The framework considers transmission–distribution coordinated dispatch and optimizes reserve capacity. First, the framework addresses the volatility and uncertainty of wind and solar power output. It constructs a three-dimensional objective function incorporating generation cost, spinning reserve cost, and linear wind/solar curtailment penalties as core components. The study uses the IEEE 30-bus system as the transmission network and the IEEE 33-bus system as the distribution network to build a transmission–distribution coordinated optimization model. Power dynamic mutual support across voltage levels is achieved through tie transformers. Second, the framework designs three typical scenarios for comparative analysis. These include separate dispatch of transmission and distribution networks, coordinated dispatch of transmission and distribution networks, and a fixed reserve ratio mode. The approach breaks through the limitations of traditional fixed reserve allocation. It optimizes the coordinated mechanism between reserve capacity spatiotemporal allocation and renewable energy accommodation. Case study results demonstrate that the proposed coordinated optimization scheme reduces total system operating costs and wind/solar curtailment rates. This is achieved by exploiting the potential of regulation resources on both the transmission and distribution sides. The results verify the significant advantages of transmission–distribution coordination in improving reserve resource allocation efficiency and promoting renewable energy accommodation. The approach helps enhance power grid operational economics and reliability.

Keywords: cooperative transmission and distribution scheduling; renewable energy consumption; reserve capacity optimization; reserve cost; improved particle swarm 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: 2025
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