Energy system has been facing problems such as soaring energy cost and environmental concerns. Among different types of customers, large‐sized industrial building microgrids (μGs) with heavy load, that can contribute significantly to demand response and greenhouse gas (GHG) emissions reduction. Therefore, an optimal risk‐averse energy management strategy is required for this class of customers. The objective of this article is to devise an energy management system (EMS) for large‐scale industrial μG to reduce energy consumption cost and GHG emissions. Framework has been solved in MATLAB using conventional flower pollination algorithm (FPA). However, metaheuristic techniques such as FPA take large execution time and trap in local optimum. On the other hand, deterministic techniques cannot handle large problems, however, reach an optimum solution in a shorter time span. To address these issues of both classes of algorithms, article devises and validates hybrid modified FPA‐mixed‐integer linear programming solution algorithm. Simulations show that the proposed technique produces improved results with low execution time, providing a justification for the practical implementation of the concept in the smart energy distribution system.

中文翻译：

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面向大型工业建筑微电网的混合风险规避能源管理优化器

能源系统一直面临能源成本飞涨和环境问题等问题。在不同类型的客户中，重负荷的大型工业建筑微电网（μG）可以极大地促进需求响应和减少温室气体（GHG）排放。因此，此类客户需要最佳的规避风险的能源管理策略。本文的目的是设计一种用于大型工业μG的能源管理系统（EMS），以降低能源消耗成本和GHG排放量。使用常规的花授粉算法（FPA）在MATLAB中解决了该框架。但是，诸如FPA的元启发式技术会花费大量执行时间，并且陷入局部最优状态。另一方面，确定性技术无法处理大问题，但是，在较短的时间范围内达到最佳解决方案。为了解决这两类算法的这些问题，本文设计并验证了混合修改的FPA-混合整数线性规划解决方案算法。仿真表明，所提出的技术以较低的执行时间产生了改进的结果，为在智能能源分配系统中实际实施该概念提供了依据。