Low-Carbon and Economic-Oriented Dispatch Method for Multi-Microgrid Considering Green Certificate: Carbon Trading Mechanism Driven by AI Reinforcement Learning-Enhanced Genetic Algorithm
Year: 2025
Type: article
Abstract: Aiming at the problem that the existing research mostly focuses on a single microgrid or an independent optimization goal and lacks the cooperative scheduling of multi-microgrids and the deep integration with the green certificate (GC) and carbon trading (CT) mechanisms, this paper proposes a low-carbon and economic-oriented dispatch method for multi-microgrids considering a GC-CT mechanism driven by an artificial intelligence (AI) reinforcement learning-enhanced genetic algorithm (GA). First of all, under the constructed architecture model of the GC-CT mechanism and multi-microgrid, this method constructs an optimal objective model that incorporates economic revenue and GC-CT costs. Secondly, regarding the two key parameters, crossover rate and mutation rate, which seriously influence the performance of the GA, this paper utilizes an AI reinforcement learning algorithm to adaptively adjust them and solves the constructed model based on the AI reinforcement learning-enhanced GA. Finally, based on a regional multi-microgrid system, the simulation results show that the proposed method can significantly improve the operating efficiency of the microgrid system after integrating the GC-CT mechanism into the microgrid system, which provides a theoretical framework and technical path for low-carbon and economic-oriented dispatch of multi-microgrids and helps the power system to evolve into a zero-carbon smart energy system.
Source: Processes
DOI: 10.3390/pr13082531
Authors: Yiqiao Cheng, Hongbo Zou, Fei Wang
Institutions: UNSW Sydney, China Three Gorges University, Wuhan University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4413213036