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

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 sustain- able 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.