Optimal techno-economic sizing of a multi-generation microgrid system with reduced dependency on grid for critical health-care, educational and industrial facilities.

Energy security and the quality of power supply are the key issues being faced with rising demand for electricity. The scarcity of fossil fuels and rising environmental concerns have put the focus on renewable sources of energy. However, obtaining firm power from them is a challenge. Even the complementary nature of energy delivery by different sources like solar and wind power systems may not entirely serve the purpose, as in multi-generation energy systems. Energy storage is set to play a key role in hybrid energy systems by smoothening the variations in intermittent generation and arresting the net load variations. However arriving at the optimal capacity and designing the optimal operating strategy for a battery storage system is the real challenge. A generalized model for techno-economic optimization has been developed and presented. This paper attempts to find the optimal sizes for a battery bank, solar PV system, Biomass and Diesel generator, integrated with a distribution system, in a microgrid configuration. Economic benefits of operating the battery system alongside the different distributed generators, in different regimes compared to existing tariff (of utility pricing like flat tariff, time of use, DG pricing etc.) has been shown. The developed model is solved using mixed integer linear programming (MILP) for four different microgrids healthcare, educational and industrial facilities. The proposed optimal model has been rigorously tested for all cases to evaluate robustness of the self-sustained asset configuration. The techno-economic analysis result (LCOE and NPV) shows that the obtained optimal asset configuration with renewable energy (RE) combination gives cost effective optimal solution with minimal carbon footprint. • A novel generic optimal asset configuration MILP model for multi-generation hybrid microgrids. • Generation Expansion Planning (GEP) based on the load forecast. • Budget constraint to enhance the practicality and optimal asset configuration. • A detailed economic analysis for ascertaining viability of the proposed solution. • Rigorously tested on four different types of actual microgrids. [ABSTRACT FROM AUTHOR]