Low-Carbon Dispatch Method for Active Distribution Network Based on Carbon Emission Flow Theory

Jiang Bian, Yang Wang, Zhaoshuai Dang (), Tianchun Xiang, Zhiyong Gan and Ting Yang
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Jiang Bian: Electric Power Science Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
Yang Wang: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Zhaoshuai Dang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Tianchun Xiang: State Grid Tianjin Electric Power Company, Tianjin 300010, China
Zhiyong Gan: Electric Power Science Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China
Ting Yang: School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energies, 2024, vol. 17, issue 22, 1-19

Abstract: In the context of integrating renewable energy sources such as wind and solar energy sources into distribution networks, this paper proposes a proactive low-carbon dispatch model for active distribution networks based on carbon flow calculation theory. This model aims to achieve accurate carbon measurement across all operational aspects of distribution networks, reduce their carbon emissions through controlling unit operations, and ensure stable and safe operation. First, we propose a method for measuring carbon emission intensity on the source and network sides of active distribution networks with network losses, allowing for the calculation of total carbon emissions throughout the operation of networks and their equipment. Next, based on the carbon flow distribution of distribution networks, we construct a low-carbon dispatch model and formulate its optimization problem within a Markov Decision Process framework. We improve the Soft Actor–Critic (SAC) algorithm by adopting a Gaussian-distribution-based reward function to train and deploy agents for optimal low-carbon dispatch. Finally, the effectiveness of the proposed model and the superiority of the improved algorithm are demonstrated using a modified IEEE 33-bus distribution network test case.

Keywords: Index Terms —artificial neural networks; decision support systems; green cleaning; power system control (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: 2024
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