Optimal scheduling of a microgrid in a volatile electricity market environment: Portfolio optimization approach

This paper proposes an optimal scheduling strategy for a microgrid participating in a volatile electricity market. The microgrid system includes photovoltaic generators, a wind turbine, a load, grid connection, and a battery storage system. An optimal microgrid operation is achieved by maximizing the utility function represented by the exponential rate of growth of the electricity market value through electricity transactions between the microgrid and main grid, on the premise of satisfying the power balance and generation limit of system components. The uncertainties occurring during the microgrid operation are represented by generator output, load demand, and electricity price fluctuation. The proposed strategy utilizes the Kelly Criterion, an optimal strategy that maximizes the growth rate of an asset's net worth over repeated investments, coupled with an artificial neural network forecast of electricity price to deal with the volatile energy market. The proposed algorithm provides significant improvements in microgrid scheduling by eliminating the reliance on renewable generation and load forecasts, which makes it computationally inexpensive and thus feasible for real-time implementation. In representative case scenarios, using real-world tracers, we show that the algorithm has no dependency on meteorological forecasts and performs optimally in a volatile electricity market.