Two-Stage Risk-Based Scheduling for Electricity-Biogas Rural Microgrids with Biomass Fermentation

Electricity-biogas rural microgrids (EBRMs) possess significant economic potential and the ability to coordinate multiple energy sources, making them a promising paradigm for energy management in rural areas. This paper proposes an energy scheduling method for an EBRM that utilizes renewable and biomass resources, with the goal of economically meeting rural electricity demand. The biogas fermentation process is modeled as a nonlinear biochemical dynamic system, where biogas production is governed by differential equations involving fermentation temperature and total solids (TS) concentration. A practical biogas fermentation model, which considers the dynamics of temperature and TS, is developed and subsequently linearized using McCormick relaxation. To more effectively capture renewable energy uncertainties beyond the tail focus of Conditional Value-at-Risk (CVaR), a Gini-weighted CVaR (GWCVaR) model is introduced to quantify the risk of load shedding, formulating a stochastic mixed-integer programming problem. To address the mismatch between long-term biogas scheduling and day-ahead microgrid dispatch, a two-stage optimization strategy is proposed. Finally, simulation results demonstrate its effectiveness. The proposed method achieves a 26.2% reduction in operating cost compared to a baseline without biogas integration, while the integrated GWCVaR reduces load-shedding risk by over 85% with only a 3.4% cost penalty.