A Dynamic Data-Driven Optimization Framework for Demand Side Management in Microgrids

The efficient utilization of distributed generation resources (DGs) and demand side management (DSM) in large-scale power systems play a crucial role in satisfying and controlling electricity demand through an economically viable and environmentally friendly way. However, uncertainties in power generation from DGs, variations in load demand, and conflicts in objectives (emission, cost, etc.) pose major challenges to determine the optimal operation planning of microgrids. In this chapter, we propose a dynamic data-driven multi-objective optimization model for a day-ahead operation planning for microgrids, integrating interruption load management (ILM) as a DSM program, while collectively considering the total cost and emissions as objective functions. The proposed model includes three modules that interact with each other: (1) a simulation module that captures the behavior of operating components such as solar panels, wind turbines, etc. and provides the data for the optimization model; (2) an optimization module that determines the optimal operational plan, which includes utilization of diesel generators, purchased electricity from utility and interrupted load, considering cost and emissions objective functions using ?-constraint method; and (3) a rule-based real-time decision making module that adapts the operation plan from the optimization model based on dynamic data from the microgrid and sends the revised plan back to the microgrid. The capabilities and performance of the proposed dynamic data-driven optimization framework are demonstrated through a case study of a typical electrical power system. The resultant operation plan is quite promising regarding total cost and CO2 emission.