Risk-managed economic dispatch in hybrid hydrothermal-wind-solar systems: a novel multi-objective optimization approach

This study presents a novel multi-objective optimization framework for risk-managed economic dispatch in hybrid hydrothermal-wind-solar systems (HTWPS). To address the uncertainties in renewable energy generation, a quantitative regression approach combined with a multivariate Gaussian distribution is used for scenario generation. The model integrates spinning reserve (SR) constraints and a synchronous peak shaving strategy to enhance system stability and cost efficiency. A Multi-Objective Artificial Rabbits Optimization (MOARO) algorithm, incorporating Pareto criteria and fuzzy theory, is applied to optimize dispatch decisions while balancing cost and risk. Simulation results demonstrate that increasing the comprehensive utilization flow (DCUF) reduces operational risks and costs in the dry season by up to 25.4%, while in the wet season, risk remains stable due to SR constraints. Implementing thermal spinning reserves (HTSR) reduces operational risks by up to 79.1% but increases costs by 22.4%, highlighting a key trade-off. The synchronous peak shaving strategy lowers power abandonment risks by up to 75%. These findings emphasize the importance of integrating risk management in hybrid energy systems to improve operational reliability and economic performance.