A Comparative Framework For Competitive Power Markets Under Green Energy Integration

Abstract

The rapid growth in industry, urbanization, and population expansion results in an increase in world energy demand. This relies heavily on the main grid, which needs a large amount of natural resources that are depleting day by day. The use of natural resources for power generation leads to environmental pollution and poses significant long-term sustainability challenges. In response, renewable energy sources have emerged as viable alternatives due to their clean, sustainable, and environmentally friendly characteristics. The global transition toward green energy is therefore very much required for achieving sustainable development and meeting future energy needs. In this context, green energy management and forecasting have become critical components of modern power systems. This approach uses renewable sources to share the load with the main grid; ultimately, it reduces the burden on conventional generations. Furthermore, it provides a platform for every utility to buy and sell energy based on their deficiency and excess availability. This paper presents a comprehensive comparison of conventional and renewable energy trading markets using Machine Learning. This study mainly considers the impact of renewable energy on price variation and overall power market stability. Key parameters such as Market Clearing Price, Market Clearing Volume, purchase bids, and sell bids are utilized to evaluate the performance of the Day-Ahead Market, Green Day-Ahead Market, and Real-Time Market. Results indicate that the Green Day-Ahead Market offers improved stability and decarburization compared to the Day-Ahead Market and Real-Time Market. Also, the study suggests the use of AI-driven predictive methodology to improve market efficiency and enhance renewable energy involvement. These findings contribute to building resilient, low-carbon, and economically efficient electricity markets.