A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized...

Area of Science:

• Renewable Energy Systems
• Optimization Algorithms
• Sustainable Energy Planning

Background:

• Off-grid electrification is crucial for sustainable development but faces challenges in capacity planning due to fluctuating demand and intermittent renewable energy sources (RESs).
• Existing optimization techniques for microgrid capacity planning are often computationally demanding, prone to premature convergence, and may not adequately balance exploration and exploitation.
• The integration of energy management schemes (EMS) with capacity planning is often overlooked in microgrid research.

Purpose of the Study:

• To propose and evaluate a rule-based energy management scheme (REMS) optimized by the grasshopper optimization algorithm (GOA) for long-term capacity planning of grid-independent microgrids.
• To minimize the cost of energy (COE) and the probability of power supply deficiency (DPSP) in microgrids.
• To assess the resiliency and operational limits of battery storage within the proposed system.

Main Methods:

• Development of a rule-based EMS (REMS) to prioritize RES usage and coordinate microgrid components (wind turbine, photovoltaic, battery bank, diesel generator).
• Optimization of the REMS using the grasshopper optimization algorithm (GOA) for long-term capacity planning.
• Comparison of the REMS-GOA performance against particle swarm optimization (PSO) and cuckoo search algorithm (CSA) through long-term simulations.

Main Results:

• The REMS-GOA demonstrated superior convergence to the optimal solution compared to PSO and CSA.
• The proposed system achieved significant reductions: 92.4% in fuel consumption, 92.3% in emissions, and 79.8% in COE compared to conventional diesel-only systems.
• REMS-GOA yielded the lowest COE ($0.3656/kWh) at 0% DPSP, outperforming REMS-CSA ($0.3662/kWh) and REMS-PSO ($0.3674/kWh).

Conclusions:

• The proposed REMS optimized with GOA is an efficient and effective technique for the capacity planning of grid-independent microgrids.
• The integrated approach enhances the adoption of cleaner energy systems by significantly reducing operational costs and environmental impact.
• Sensitivity analysis confirmed the robustness of the system against input uncertainties.