Challenges associated with Hybrid Energy Systems: An artificial intelligence solution

Abstract

Hybrid Energy Systems (HES) combine multiple energy sources to maximize energy efficiency. Due to the unpredictability and dependence on the weather, integrating renewable energy sources (RES) is a viable option for distributed distribution (DG). To minimize environmental impact and meet the increasing energy demand–supply gap, scientists need to find alternative energy sources. Several studies have confirmed that HES is economically viable in remote areas, particularly in off-grid applications. Despite several improvements over the past few years, existing HES control systems are complex, costly, less reliable, and not sufficiently efficient. The purpose of this paper is to present the most common challenges faced by stand-alone hybrid energy systems and how the artificial intelligence (AI) technique has improved them. AI techniques are widely used in HES, and this study addressed how AI can solve classification, forecasting, networking, optimization, and control problems. This study provides an overview of the recent history of HES critical challenges in energy management, sizing, demand side management, and storage management; additionally, we have addressed several conceptual/theoretical problems, antecedents, and consequences that may be of interest or require further research. Companies must ensure their systems perform effectively and pay for their investments. Regardless of the system, failures and defects should be diagnosed and repaired as soon as possible. This can be achieved by increasing the system’s efficiency and preventing early-stage damage. Researchers and project managers who work on hybrid systems will find this paper to be an invaluable resource.

Keywords

• Artificial intelligence
• Hybrid energy
• Optimization
• Demand response
• Sizing