This work proposes a methodology for the optimal training of rule-based management strategies, to be directly implemented in the industrial controller of hybrid off-grid microgrids. The parameters defining the control rules are optimally tuned resorting to different evolutionary algorithms, based on the expected operating conditions. The performance of the resulting management heuristics is compared with conventional approaches to optimal scheduling, including Mixed Integer Linear Programming (MILP) optimization, direct evolutionary scheduling optimization, and traditional non-trained heuristics. Results show how the trained heuristics achieve a performance very close to the global optimum found by the MILP solution, outperforming the other methods, and providing a single-layer commitment and dispatch algorithm which is easily deployable in the microgrid controller.

中文翻译：

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基于可调整规则的创新策略用于混合微电网的预测管理

这项工作提出了一种用于基于规则的管理策略的最佳培训的方法，可以直接在混合离网微电网的工业控制器中实施。定义控制规则的参数可根据预期的运行条件，根据不同的进化算法进行优化调整。将所得管理启发式方法的性能与常规调度方法进行了比较，这些方法包括混合整数线性规划（MILP）优化，直接进化调度优化和传统的非训练式启发式方法。结果表明，经过训练的启发式方法如何达到非常接近于MILP解决方案发现的全局最优性能，并且胜过其他方法，