Long-Term Dispatch Method for Electro-Hydrogen Coupled Microgrids Considering a Tiered Carbon Trading Mechanism

Abstract

Electro-hydrogen coupled microgrids (EHCMs), in which water electrolyzers, hydrogen fuel cells, and power-electronic converters act as cascaded electromagnetic–electrochemical energy conversion interfaces, significantly enhance the renewable energy hosting capacity and flexible regulation capability of distributed energy systems through cross-temporal energy conversion and dynamic power regulation. However, few studies have addressed the medium-to-long-term operational optimization of EHCMs under carbon trading mechanisms. This paper proposes a medium-to-long-term low-carbon economic dispatch framework for EHCMs incorporating a tiered carbon trading mechanism. First, a tiered carbon trading model based on hierarchical pricing is established, in which differentiated carbon price brackets strengthen the low-carbon dispatch incentives of EHCMs and maximize their emission reduction potential. Second, a typical-day scenario generation method based on spectral joint clustering is introduced; exploiting eigen-decomposition of the normalized graph Laplacian—a spectral analysis technique sharing its mathematical foundation with modal decomposition methods widely used in computational electromagnetics—the method preserves the key statistical and temporal characteristics of multi-energy time series while significantly reducing the dimensionality of the long-term optimization problem. Subsequently, a medium-to-long-term operational optimization model of EHCMs is constructed, comprehensively considering electricity, heat, natural-gas, and hydrogen balance constraints. With the objective of minimizing the annual operating cost, the model reveals cross-seasonal hydrogen storage operation strategies for electro-hydrogen coupled micro-energy networks. Case studies of a large-scale microgrid in Shanxi, China, verify the effectiveness of the proposed method in enhancing system economics and reducing carbon emissions.