Techno-economic sizing and multi-objective energy management of AC multi-bus microgrids for enhanced reliability and cost efficiency: Application to small villages in Morocco

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Rural electrification in remote areas remains a critical challenge due to limited infrastructure and the need for reliable, cost-effective energy solutions. Microgrids offer promising solutions to address these challenges. In particular, multi-microgrid (MMG) configurations outperform standalone microgrids for scalable and sustainable rural energy access. However, their design and operation require advanced methods, including artificial intelligence (AI), to manage their complexity. This study seeks to develop a techno-economic model for the optimal sizing and energy management of an AC multi-bus microgrid. The model, implemented using MATLAB software, uses a two-stage framework that first determines the optimal sizing of distributed energy resources (DERs) using the intelligent genetic algorithm (GA), then coordinates real-time energy management across interconnected microgrids based on a multi-objective optimization. By incorporating detailed representations of active and reactive power flows, the approach ensures voltage stability and system resilience under both grid-connected and isolated operating conditions. An incentive-based demand response (IDR) program is also integrated to shift loads from on-peak to off-peak periods, reducing operational costs. A case study involving a modified IEEE 5-bus system demonstrates 24.6% reduction in operational costs through peer-to-peer (P2P) energy exchange and grid reliance minimization. Results highlight voltage stability (±5% deviation), effective battery utilization, and resilience in islanding mode, with 10% cost reduction from demand response.