Optimal Operation of Multi-Energy Microgrids Considering Network Constraints and Multiple Uncertainties

Multi-Energy Microgrids (MEMGs) offer great opportunities for enhancing energy systems efficiency and penetration of renewable energy sources (RESs). By coupling traditionally independent electrical and thermal networks with binding components such as Combined Heat and Power (CHP) units, system operators can benefit from higher energy utilization efficiency. However, MEMGs' operation is severely compromised by uncertainties existing in RES generation and electric loads, which can never be perfectly predicted. This affects considerably the MEMG day-ahead dispatch and poses major challenges on how to maintain reliable operating conditions. This work proposes a coordinated dispatch strategy to minimize the operating costs of a network-constrained MEMG consisting of combined electrical and thermal distribution networks when multiple uncertain parameters are considered. To capture uncertainty variability, the operation method was modeled as a stochastic programming problem. Moreover, the Monte Carlo sampling technique was used to generate a large number of random scenarios. The proposed stochastic operation method was tested on a 14-bus system to find the optimal day-ahead scheduling operation. A feasibility check of the stochastic and deterministic methods was then carried out based on a new set of uncertainties realizations. Results indicated the proposed operation model to minimize the operating costs and guarantee strong robustness against uncertainties' realizations.