This research presents a stochastic multi-objective optimization framework for the dispatch and decision-making processes in integrated electric and thermal energy systems (IETES). The approach considers uncertainties in renewable energy generation, load demands, and market prices to ensure reliable and efficient energy management. A multi-objective evolutionary algorithm is employed to simultaneously minimize operational cost, emission levels, and unmet energy demand. The model integrates both electric and thermal subsystems, including combined heat and power (CHP) units, boilers, and energy storage devices. Pareto-optimal solutions are evaluated using a decision-making tool to identify the best trade-offs among conflicting objectives. The results demonstrate the proposed framework’s robustness under stochastic conditions and its potential to support sustainable and economically viable energy planning in modern smart grids.

: Integrated energy systems, stochastic optimization, multi-objective dispatch

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