Smart Grids Optimization for Energy Trading with AI Solutions

Abstract

Smart grids are changing the energy industry by making it easier to use green energy and handle energy more efficiently. Energy trading is an important part of smart grids because it lets buyers and sellers trade energy based on current supply and demand. But because smart grids are so complicated and changeable, it's hard to find the best way to trade energy in them. This paper gives an in-depth look at how to make trading energy in smart grids more efficient using AI tools. We look at the research that has already been done on smart grid planning and stress how important AI is for solving the problems that come up with trading energy. Next, we suggest a new approach that uses AI tools like machine learning, deep learning, and optimization methods to make trading energy in smart grids more efficient. The suggested system has several important parts, such as gathering and editing data, predicting demand, planning output, and bidding on the market. For demand predictions, machine learning models are used to guess how much energy will be used in the future. For generation schedule, optimization methods are used to find the best mix of generators based on the predicted demand. For market bids, deep learning models are used to find the best trade plan and make the most money. We test the suggested framework's performance with real-world data and show that it can help smart grids trade energy more efficiently. The results we got show that the suggested framework can make sharing energy a lot more profitable and efficient. This can help build energy systems that are both long-lasting and reliable.