Renewables integration into power systems through intelligent techniques: Implementation procedures, key features, and performance evaluation

Abstract

Integrating renewable energy sources (RESs) such as solar photovoltaic (PV), wind, biogas, and hydropower into the power system is a sustainable solution that can feasibly maintain the power supply and demand response. The uncertainty in solar irradiance and wind speed impedes the supply and demand response. The uncertainty problem can be solved by integrating an appropriate control technique that reasonably forecasts necessary information and maintains system operation. A critical analysis of different intelligent techniques with numerical data review, prediction accuracy, pros and cons, and techno-economic feasibility is necessary for the reader’s perception. This paper analyzes the 89 research works of different intelligent techniques integrated into RESs and energy storage systems (ESSs). The intelligent techniques are classified according to the considered resources, such as PV, wind, biogas, and hydropower to demonstrate a meaningful insight into the particular research field. The analysis provides adequate information on each considered technique presenting the implementation procedures, key features, and accuracy. The accuracy of each method is determined by integrating different feasibility metrics such as root mean square error (RMSE), root mean absolute error (RMAE), and root mean percentage error (RMPE). The intelligent integration into ESS emphasizes the possibility of enhancing the storage backup for RESs connected power distribution systems. The review analysis signifies the current view and potentiality of incorporating intelligent methods into power systems and demonstrates a significant insight into the research field.

Keywords

• Renewable energy integration
• Photovoltaic
• Wind
• Energy storage system
• Artificial intelligent
• Machine learning