Optimal control of a residential microgrid

We propose a generic mixed integer linear programming model to minimize the operating cost of a residential microgrid. We model supply and demand of both electrical and thermal energy as decision variables. The modeled microgrid is operated in grid-connected mode. It covers solar energy, distributed generators, energy storages, and loads, among them controllable load jobs released by home appliances and electric vehicles. We propose a model predictive control scheme to iteratively produce a control sequence for the studied microgrid. Our case study reveals the performance of minimum cost control by comparison with benchmark control policies. We consider three price scenarios in our analyses which include two market-based scenarios. Numerical results from our study indicate savings in annual operating cost between 3.1 and 7.6 percent.

► Solution of the unit commitment and economic dispatch problem in a residential microgrid including programmable load jobs, energy storages, and distributed generators.
► Simultaneous management of supply and demand.
► Interdependent electrical and thermal energy balance due to combined heat and power generation.
► Modeling based on commercially available technology and historical data; three different electric rate scenarios; a model predictive control scheme solves the problem over one year.
► Scheduling units according to price signals subject to technical constraints and user preferences decreases total annual operating cost by up to 7.6 percent, annual electricity cost of individual loads by even 30.4 percent.