A risk-averse multi-timescale stochastic integration framework for a community multi-energy system under correlated uncertainties

Diverse correlated uncertainties may bring operational risks and lead to real-time energy shortages of the community multi-energy system (CMES), necessitating efficient coordination of heterogeneous resources through integrated optimization models. To address this problem, a risk-averse multi-timescale stochastic system integration framework for CMES under various correlated uncertainties is proposed in this paper. Operation optimization for the CMES is dynamically coordinated across different timescales, including day-ahead phase optimization (OPT2-DA), intraday phase optimization (OPT2-ID) and real-time local optimization (OPT1). In OPT2-DA, a conditional value at risk (CVaR)-based three-stage stochastic programming (SP) model is established to handle multi-dimensional correlated uncertainties and mitigate potential economic risks. The day-ahead strategy, including unit start-up status, day-ahead power purchase and energy storage system (ESS) dispatch, is optimized. In the subsequent OPT2-ID, as part of uncertainties gradually unfold, a two-stage SP model with rolling optimization is established, addressing prediction errors within the operation horizon. The intraday strategy, including unit output and intraday power purchase, is continually optimized for updating. Building on day-ahead and intraday strategies, OPT1 uses real-time rolling optimization to dynamically adjust operation strategies, ensuring real-time balance of energy supply and demand to prevent shortages. To model diverse correlated uncertainties from renewable energy sources (RESs), multi-energy loads and energy prices across different timescales, a new scenario generation and reduction method that combines Cholesky decomposition-based Latin hypercubic sampling (CD-LHS) and Gaussian mixture model (GMM)-based clustering is developed, forming a three-layer scenario tree. Finally, case studies verify the effectiveness of the proposed method, presenting a lower CVaR than the traditional risk-neutral method and achieving 100% energy supply adequacy.