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Keywords

stepped carbon trading, demand-side response, distributed energy storage, collaborative planning, reinforcement learning algorithm

Abstract

With the aggravation of global climate change and the energy crisis, distributed energy storage systems play an important role in reducing users’ energy consumption costs and carbon emissions. However, few studies fully consider the synergy of various demand-side responses and flexible resources when planning distributed energy storage systems. To solve this problem, first, a stepped carbon trading calculation method considering carbon quotas and a characterization method for demand-side responses were proposed, and the elasticity matrix was used to characterize the transfer-type and reduction-type responses. Then, a collaborative planning method for distributed energy storage in industrial parks considering stepped carbon trading and demand-side responses was proposed, and the rapid solution of the optimization model was achieved through the twin delayed deep deterministic policy gradient algorithm. Finally, taking a certain industrial park as a case, it is verified that the proposed collaborative planning model can effectively integrate various demand-side responses and multiple flexible resources. The reduced system operation, maintenance, and carbon emission costs of the planning scheme can offset the investment cost, significantly improving the economy of the park operation, and proving the necessity and economic feasibility of flexible resource planning.

DOI

10.19781/j.issn.1673-9140.2026.03.017

First Page

173

Last Page

182

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