Hybrid heuristic-MILP framework for techno-economic scheduling of microgrids in day-ahead markets with micro gas turbines and renewables

• Hybrid MILP–heuristic model optimizes day-ahead microgrid scheduling. • Achieves 21.4% annual energy cost reduction with LCoE = 0.126 €/kWh. • MGTs with NG–H 2 blending enable fuel-flexible, low-carbon power transition. • Monte Carlo Simulation quantifies uncertainty in NPV, IRR, ROI, and PBP. • Scalable to multi-community and regional grids with market integration. Rising global energy demand and decarbonization targets highlight the need for resilient, sustainable energy systems. Microgrids enhance flexibility by integrating renewable energy sources, dispatchable units, and storage, but their techno-economic scheduling in day-ahead markets remains challenging. This paper presents a scheduling framework that integrates micro gas turbines (MGTs), RES, battery storage, and boilers, supporting a gradual transition from natural gas (NG) to hydrogen (H 2 ). The framework combines rule-based heuristic scheduling with Mixed-Integer Linear Programming (MILP) to improve cost efficiency, energy resilience, and sustainability. A case study from Stavanger, Norway, demonstrates the model’s effectiveness. The optimized hybrid system achieves a unit electricity cost of 0.126 €/kWh, reducing annual costs by 21.4% compared to full grid reliance and 13.4% compared to a microgrid-only system. Validation against published case studies confirms the reliability of the proposed tool. Monte Carlo Simulation is applied to assess uncertainty and risk, with results showing an NPV of €1.76 million, IRR of 20.02%, and a payback period of 5.6 years. The study addresses the energy trilemma by reducing LCoE, improving system resilience through market-driven scheduling, and enabling a phased NG–H 2 fuel transition. The proposed framework provides a scalable pathway toward cost-efficient, hydrogen-integrated microgrids in day-ahead electricity markets.