Integrating Advanced Machine Learning for Stable Real-Time Electricity Pricing in Smart Grids

Real-time electricity pricing (RTP) is pivotal for harnessing the full potential of smart energy grids. Despite consensus on its benefits, the implementation of RTP schemes remains a challenge. Traditional approaches, relying on day-ahead price forecasts, often lead to uniform consumer responses, causing demand and price fluctuations that threaten system stability and increase financial risks. Addressing this, we propose a refined game-theoretic framework that incorporates advanced machine learning techniques for improved demand response prediction. By enabling consumers to learn from historical data with greater accuracy, our approach minimizes regrets and stabilizes real-time prices, leading to more consistent load curves across the grid. This enhancement not only promises reduced volatility but is also underpinned by mean-field equilibria theories in multi-armed bandit games, offering a more reliable and efficient energy management solution.