Multi-scenario microgrid optimization arises regularly in real life. It refers to finding optimal scheduling strategies of a microgrid under multiple scenarios where each scenario corresponds to a specific working condition. For example, in an industrial park, there are often many users with different load demands. We need to efficiently find the optimal scheduling strategies for all users. The easiest way is to conduct the operation search for each user separately, which however, is obviously inefficient. Inspired by the underlying parallelism of evolutionary multi-objective optimization (EMO), this study proposes to optimize all scenarios simultaneously, i.e., finding the optimal scheduling strategies for all users in a single algorithm run. Specifically, the multi-scenario microgrid optimization problem is transformed into a bi-objective problem in which one objective is to minimize the number of scenarios and the other is to minimize the overall cost of the microgrid. The bi-objective problem is then solved by a typical EMO algorithm. The obtained Pareto optimal solutions correspond to the optimal scheduling strategies for different scenarios. Experimental results show that the proposed method is both effective and efficient, and can find more appropriate scheduling strategies than dealing with each scenario individually.

多场景微电网优化在现实生活中经常出现。它是指寻找微电网在多种场景下的最优调度策略，每种场景对应一种特定的工况。例如，在工业园区中，往往有许多用户，其负载需求各不相同。我们需要有效地为所有用户找到最佳的调度策略。最简单的方法是对每个用户单独进行操作搜索，但这显然效率低下。受进化多目标优化（EMO）底层并行性的启发，本研究提出同时优化所有场景，即在单个算法运行中为所有用户找到最佳调度策略。具体来说，多场景微电网优化问题转化为双目标问题，其中一个目标是最小化场景数量，另一个目标是最小化微电网的总体成本。然后通过典型的 EMO 算法解决双目标问题。得到的Pareto最优解对应于不同场景的最优调度策略。实验结果表明，该方法既有效又高效，并且能够找到比单独处理每个场景更合适的调度策略。