Risk-based industrial load management with integrated distributed energy resources to enhance grid flexibility and market participation

Industrial loads, with their inherent flexibility and growing integration of energy storage systems (ESSs) and photovoltaic (PV) generation, present a significant opportunity to enhance grid stability through ancillary services (ASs) and unlock financial benefits. However, uncertainties in renewable generation and market prices can complicate decision-making, particularly for risk-averse industrial entities. To address these challenges, this study develops a risk-based, mixed-integer linear programming (MILP) optimization model for day-ahead scheduling. This model leverages flexible assets and incorporates the Conditional Value-at-Risk (CVaR) technique to evaluate how risk preferences impact participation in energy and AS markets. Moreover, the proposed model integrates a detailed characterization of industrial sub-loads, assesses load flexibility, and accounts for the combined effects of renewable energy sources (RESs) and ESSs. Four case studies are used to analyze the participation of industrial loads, PV, and ESS in AS markets, investigating the influence of risk preferences and AS participation strategies. The case studies demonstrate that engaging in AS markets yields financial gains regardless of risk preference. The results emphasize the critical role of flexible assets in enhancing system flexibility, promoting greater involvement in energy and AS markets, and improving grid support capabilities.