Optimal Planning for an Integrating Thermal–CHP–Boiler Units With a High Penetration Wind Farm Considering Economic and Environmental Factors

This article presents a novel approach for optimal stochastic economic-emission dispatch in a multisource power system, incorporating thermal, combined heat and power (CHP), boiler units, and wind farms. The main objective is to minimize both operating costs and environmental pollution while considering uncertainties in electrical and heat loads, as well as wind power generation. The study also explores the participation of flexible loads in demand–response programs (DRPs) for both electricity and thermal energy. A hybrid self-adjusting algorithm, hybrid multiobjective gray wolf optimizer–lightning search algorithm (hMOGWO–LSA), is proposed, excelling in comprehensively searching the solution space and avoiding local optima. The optimization results across four sections consistently demonstrate the superior accuracy of hMOGWO–LSA compared to other multiobjective meta-heuristic algorithms. Additionally, the findings show that uncertainties related to unit participation led to increased costs and emissions, but responsive loads in DRPs can mitigate this effect, achieving a 4.5% reduction in both cost and pollution. Sensitivity analysis reveals that uncertainties in power generation (electricity, heat, and wind) significantly impact costs and emissions, with a maximum increase of 14.13% in emissions and 15.41% in costs as uncertainties rise from 5% to 20%.