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.

中文翻译：

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基于贝叶斯最优算法的家庭微电网数据驱动能源管理


微电网是未来智能电网的关键解决方案，它通过集成分布式可再生发电机和存储系统来有效地满足当地需求。然而，由于分布式可再生能源的间歇性和不确定性，微电网的可靠性和经济运行面临越来越多的新挑战。传统上，经济调度问题被认为是解决目标函数先验已知的离线或在线优化问题。然而，准确确定的函数表达式很难制定，错误的表达式可能会导致电费浪费并引发安全问题。因此，需要重新表述经济调度问题，并以数据驱动的方式解决它。本文针对单个并网家庭微电网提出了一种基于贝叶斯优化算法（BOA）的数据驱动能源管理解决方案。所提出的解决方案在没有闭合形式目标函数表达式的情况下制定优化问题，并使用基于 BOA 的数据驱动框架来解决它。所提出的解决方案是一种黑盒函数顺序全局优化策略，不需要对目标函数进行导数运算。此外，它还可以解决微电网运行和参数预测的不确定性。仿真结果证明了所提出解决方案的有效性。