Data-Driven Energy Management in a Home Microgrid Based on Bayesian Optimal Algorithm

Abstract

Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the intermittent and uncertainty of distributed renewable energy, the reliability and economic operations of microgrid are facing increasing new challenges. Traditionally, economic dispatch issue is considered as solving an offline or online optimization problem whose objective function is prior known. However, accurate and determined function expression is difficult to formulate, and wrong expression may result in waste of electricity cost and causing security issues. Thus, it is desirable to reformulate the economic dispatch problem, and solve it in a data-driven way. This paper proposes a data-driven energy management solution based on Bayesian optimization algorithm (BOA) for a single grid-connected home microgrid. The proposed solution formulates the optimization problem without a closed-form objective function expression, and solves it using BOA-based data-driven framework. The proposed solution is a kind of black-box function sequential global optimization strategy, and does not require derivative operation on the objective function. Besides, it can also solve the microgrid operation and parameter prediction uncertainty. Simulation results demonstrate the effectiveness of the proposed solution.