An integrated approach for cost-and emission optimal planning of coastal microgrid with demand-side management

In the realm of sustainable power systems, the pivotal role of demand-side management (DSM) in Microgrids (MGs) design and planning is increasingly recognized. This study accentuates the significance of DSM in improving operational efficiency, economic viability, and environmental sustainability of MGs, focusing on a remote community in the Gulf Al-Aqaba, Sinai Peninsula, Egypt. An innovative four-level approach is proposed for the techno-enviro-economic modelling, simulation, and optimization-based DSM of MGs, addressing the challenges posed by renewable energy integration and fluctuating electricity demands. The research explores three demand scenarios: a base load without DSM (SC-1), load shifting-based DSM (SC-2), and a combined load shifting/peak shaving DSM (SC-3), to demonstrate the adaptability and robustness of MGs under different conditions. The findings reveal significant improvements in MG performance, with peak load reduction from 73.66 kW in SC-1 to 50.82 kW in SC-2 and 44.82 kW in SC-3, highlighting the effectiveness of the applied DSM strategies. These strategies also yield economic benefits, evident in an 8% and 23% decrease in the total life cycle cost for hybrid and 100% renewable MG configurations under SC-2 and SC-3, respectively, compared to SC-1. Particularly noteworthy is the reduction in required capacities of key MG components in the hybrid MG scenario under SC-3, with reductions of 10% for solar photovoltaic, 33% for microturbine generator, and 40% for battery energy storage, implying cost savings and a more efficient MG setup. Additionally, the environmental impact is profound, with an 80.3% decrease in emissions for the hybrid MG setup under SC-3 compared to a standalone 50 kW MTG system under SC-1. This outcome highlights the potential of DSM in contributing to environmental sustainability, particularly in MG configurations involving renewable energy sources. In conclusion, the study provides comprehensive insights into the synergy between DSM and MG optimization, underscoring its essential role in addressing climate change and transitioning to sustainable energy systems.