MG-OPT: intelligent multi-objective Pareto-based optimization framework and transactive energy for Hybrid Renewable Energy Systems with hydrogen integration

This paper presents Microgrid Optimization (MG-OPT), an intelligent multi-objective framework for Zero-Net Emissions Hybrid Renewable Energy Systems utilizing Mixed-Integer Linear Programming and Pareto-based optimization. MG-OPT uniquely integrates photovoltaic, wind, and fuel cell sources with hydrogen storage, optimizing across four competing objectives: minimizing costs and emissions while maximizing renewable utilization and profit through transactive energy mechanisms. Simulation results across three scenarios demonstrate remarkable performance: the baseline configuration (30 kW) achieves 95 % emissions reduction with $31 daily profit; the EV-integrated system (40 kW) maintains 75 % emissions reduction with $28 daily profit; and the advanced configuration (50 kW) accomplishes complete decarbonization while generating $45 daily profit—all compared to conventional grid-only systems. Economic analysis reveals substantial long-term viability with net positive revenue ranging from $30,331 to $45,446 over six years. While hydrogen integration requires a higher initial investment, it enables complete emission elimination in advanced configurations while enhancing system flexibility and economic returns. This research provides decision-makers with optimal trade-off solutions bridging theoretical optimization and practical implementation requirements for sustainable energy systems.