Coordinated dispatch of electric, thermal, and hydrogen vectors in renewable-enriched microgrids using constrained harris hawks optimization under uncertainty

Microgrids (MGs) integrating renewable energy sources (RESs), plug-in hybrid electric vehicles (PHEVs), battery storage, and proton exchange membrane fuel cell-based combined heat and power (PEMFC-CHP) systems face increasing complexity due to uncertainty in both energy supply and demand, as well as dynamic electricity market prices. This paper proposes a comprehensive energy management strategy for renewable-enriched microgrids that simultaneously coordinate the dispatch of electric, thermal, and hydrogen energy vectors. The proposed system integrates photovoltaic (PV) and wind resources, a proton exchange membrane fuel cell combined heat and power unit (PEMFC-CHP), battery energy storage systems (BESS), plug-in hybrid electric vehicles (PHEVs), and a hydrogen production and storage subsystem. To address the inherent uncertainties in load demand, renewable generation, and market prices, a Monte Carlo Simulation (MCS)-based scenario framework is adopted. A constrained variant of the Harris Hawks Optimization (HHO) algorithm is introduced to solve the multi-objective optimization problem, minimizing total operational cost, carbon emissions, and load or storage violations. The optimization process enforces technical and economic constraints including power balance, storage capacity, thermal demand satisfaction, and hydrogen trading limits. The proposed framework is developed and simulated using MATLAB® software and validated on a modified 16-bus microgrid under multiple operational scenarios, ranging from uncontrolled PHEV charging to full vector coordination with PEMFC and CHP integration. Simulation results demonstrate that the proposed HHO-based energy management framework significantly outperforms benchmark algorithms in minimizing operational cost, emissions, and unmet energy demand. Case 6, which integrates smart PHEV charging with PEMFC-CHP coordination, achieves the most optimal performance—delivering the lowest cost (320 €), reduced emissions (520 kg CO2), and zero unmet load across all scenarios.