Robust optimization-based energy management system for integrating renewable energy resources and smart grid technology

Abstract

This study focuses on enhancing Energy Management (EM) by integrating Renewable Energy Resources (RERs) like Wind Energy (WE) and Solar Energy (SE) with grid technology. To analyze the proposed integration technology, an academic campus with interconnected group, cost-responsive demands was considered. These demands can be met using both the grid and RERs; however, EM challenges arise when integrating RERs with the grid. The Evolutionary Energy Preserve Rate Clustering Algorithm (EEPRCA) was utilized to analyze the EM, and the Robust Optimization (RO) strategy was used to predict the unpredictability associated with the power output of RERs, as well as the cost of power produced and energy received from or sold to the main grid. Additionally, Smart Grid (SG) technology was employed to enable bidirectional connectivity between the Energy Management System (EMS) and the main grid, as well as between the EMS and RERs. The EMS plans energy consumption at the start of the working day without SG. However, with SG, the EMS uses bidirectional communication to reschedule utilization based on energy prices and WE and SE generation. Using SG technology leads to a 32% improvement in total utility grid efficiency compared to an EMS without SG. Also, an EMS with SG maximizes the use of available wind and solar RERs, whereas without SG only 55% of these resources are utilized. From these outcomes, it is suggested that the proposed EMS integration strategy can efficiently manage energy, increase the consumption of RERs, and reduce the energy utilization cost for the academic campus.