Energy management strategies in hybrid power systems : review

Abstract

Given the strong push to stop climate change, cut greenhouse gases and ensure a safe energy supply, the world’s energy sector is now strongly supporting sustainability. Among alternative power systems, Hybrid Power Systems (HPS) take advantages of various kinds of energy. Regardless, these systems operate well and reliably because of the policies outlines in Energy Management Strategies (EMS). In this study, EMS used in hybrid power systems is broken down into strategies that depend on rules, optimization methods or artificial intelligence. It uses a side-by-side review of reports and documents to find out what benefits and disadvantages every EMS type has in different places. When the circumstances change fast, the simple rule-based approaches shown in research are less useful. With optimization, the company can handle expenses better and grow its renewable use by up to 15-30%, in comparison to regular methods. The use of AI in strategies including machine learning and deep reinforcement learning improves how risks are managed, faster decisions are made and the system remains adaptable. The use of these strategies has made the system work better and protected the environment greatly. Even so such approaches require the use of advanced computers and data infrastructure. Different types of EMS are reviewed to match them to either urban or rural areas and what is best for each place is discussed. It recommends that energy systems should be built by combining optimization and AI for them to be efficient and support growth. They support scientists, professionals and authorities in developing new energy management systems so more people in less developed areas are able to use energy.