Stochastic planning of islanded microgrids with uncertain multi-energy demands and renewable generations

Islanded microgrids (IMGs) are embedded power networks with distributed energy resources (DERs) providing a reliable and flexible energy option for off-grid customers. This work addresses the planning model of renewable-based IMGs feeding multi-energy demands considering investment and emission related objectives. The proposed solution is to determine the optimal mix and sizing of various energy sources in IMG, including renewables; for multiple energy demands. This study also presents a hybrid-scenario and Monte Carlo approach to gauge the uncertainty involved in multi-energy demands, i.e. electrical, heating, and cooling loads; together with correlation among wind and solar generations. The spatial interdependence among renewable generations is implemented using copula; that generates a synthetic set of stochastic correlated data. The combined load scenarios for multi-energy demands and renewable samples are implemented with the proposed hybrid approach in the formulated stochastic planning model. In this work, the formulated problem is proposed to solve using meta-heuristic multi-objective ant lion optimiser algorithm, that is validated on the test system. The superiority of the proposed approach is highlighted in comparison with other multi-objective optimisers. The multi-energy dispatch between associated sources and loads were simulated to show how the obtained capacity can suffice the seasonal multi-energy demands of a typical day considered.