Community Microgrid Operations Management

Community microgrids offer viable alternatives to centralized generation and distribution of electricity, while providing significant benefits to electricity customers and electric utilities. The design of an efficient master controller to be able to optimally schedule available distributed energy resources (DERs) and loads while maintaining the privacy of customers, however, is of utmost importance for the economics of operations in these systems. An efficient model for community microgrid optimal scheduling considering customer privacy issues is proposed in this chapter. The objective of the proposed economic operations model is to minimize the microgrid's total operations cost, which comprises the generation cost of local resources and the cost of energy purchases from the main grid for supplying local loads. Microgrid DERs and loads are separated into two categories: o communal and individual. Mathematically, it was formulated as a bi‐level model. The upper level is solved by the microgrid master controller to schedule communal DERs and loads and further calculate price signals for local controllers. The lower level is solved by local controllers to schedule individual DERs and loads, and accordingly determine the net load of each building. The Lagrangian relaxation method was employed to decouple the master controller and local controllers scheduling problems. Price signals, which are formed in the master controller and sent to local controllers, and load data, which are calculated in local controllers and sent to the master controller, coupled the two problems. Hourly prices determined in the master controller and utilized in local controllers are the only available signals used by the master controller to control individual DERs and loads. The proposed model ensures the privacy of individual DERs and loads within the microgrid, prevents direct control by the master controller, finds the microgrid optimal schedule, and efficiently handles islanding incidents. The proposed model was analyzed through numerical simulations, where it was shown that the optimal microgrid schedule could be obtained using iterations among the master controller and local controllers while maintaining individual customers’ privacy. Numerical simulations demonstrate the effectiveness of the proposed community microgrid optimal operations management model.