Hybrid risk‐averse energy management optimizer for large‐scale industrial building microgrids

Abstract

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.