Coordinated Optimization of Demand Response and Reconfiguration for Distribution Networks with Two-Stage Strategy

To enhance distribution network flexibility and economy under conditions involving a high penetration of distributed energy resources, this paper proposes a two-stage optimization method considering demand response (DR). The first stage establishes a marginal cost-based DR model using a “base compensation + increasing marginal cost” mechanism to curb irrational user behaviors, reducing peak-hour power purchase costs. The second stage develops a dynamic reconfiguration model minimizing network losses, voltage deviation, and switch operation costs. Solved by an Improved Grey Wolf Optimizer (IGWO), it incorporates a segmented voltage compensation mechanism quantifying user satisfaction through differentiated coefficients. The two stages operate in a coordinated framework where “temporal load optimization” informs “spatial topology reconfiguration”. Case results demonstrate that this coordinated approach significantly reduces power purchase costs, improves voltage quality, and minimizes network losses, providing an effective solution for efficient distribution network operation.

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Publication Details

Journal
Processes
Published
2026-01-09
DOI
https://doi.org/10.3390/pr14020241
Primary Topic
Smart Grid Energy Management
Type
article
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article

Coordinated Optimization of Demand Response and Reconfiguration for Distribution Networks with Two-Stage Strategy

Lu Yin, Shuping Gao
Processes
Smart Grid Energy Management
article

Coordinated Optimization of Demand Response and Reconfiguration for Distribution Networks with Two-Stage Strategy

Lu Yin, Shuping Gao
article en

Abstract

To enhance distribution network flexibility and economy under conditions involving a high penetration of distributed energy resources, this paper proposes a two-stage optimization method considering demand response (DR). The first stage establishes a marginal cost-based DR model using a “base compensation + increasing marginal cost” mechanism to curb irrational user behaviors, reducing peak-hour power purchase costs. The second stage develops a dynamic reconfiguration model minimizing network losses, voltage deviation, and switch operation costs. Solved by an Improved Grey Wolf Optimizer (IGWO), it incorporates a segmented voltage compensation mechanism quantifying user satisfaction through differentiated coefficients. The two stages operate in a coordinated framework where “temporal load optimization” informs “spatial topology reconfiguration”. Case results demonstrate that this coordinated approach significantly reduces power purchase costs, improves voltage quality, and minimizes network losses, providing an effective solution for efficient distribution network operation.

ProcessesVol. 14(2)
Xi'an University of Science and Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 92%
Smart Grid Energy Management
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