Optimal control of microgrids - algorithms and field implementation

A microgrid is a collection of distributed generation assets, storage devices and electrical and/or thermal loads connected to each other. In this paper, a generic model-predictive control algorithm for microgrids is presented. The algorithm has been implemented at Bella Coola, a remote community in British Columbia, Canada. The approach comprises two parts: unit commitment to decide the optimal set of distributed generators that must be switched on to meet predicted load requirements, and convex optimal control to minimize operational costs once the commitment is known. The unit commitment problem is recast as a 0-1 Knapsack problem and is solved via dynamic programming, while the optimal dispatch problem is posed as a sparse linear programming problem and solved via off-the-shelf software. Worst-case complexity and scalability considerations, and not optimality, often drive algorithm choice in industrial control settings; therefore, the solution proposed in this paper is efficient and can be rigorously bounded in terms of memory and run-time. Simulation results using real field data, practical considerations, and details of the implementation at Bella Coola are provided.