Optimizing Energy Trade in Virtual Power Plants with Lstm, Representative Houses and Dynamic programming

The radical change of the global energy system causes the development of Virtual Power Plants and microgrids in energy trading. Since the consumer behavior is volatile and not easily predictable, load forecasting is becoming a serious problem, also due to the influence of holidays and seasonal and special events that may affect energy demand in general. Virtual Power Plants and microgrids require precise load forecasting for real-time trading to ensure the balance between supply and demand. Thus, this paper proposes innovative forecasting techniques using Long Short-Term Memory and machine learning to address these challenges. New forecasting techniques would help to improve resource management and enhance the stability of the energy system while improving the integration of renewable energy and the efficiency of distributed energy resources. The use of a multivariate LSTM load forecasting model in this small work aims at making the energy supply and demand process more optimal between producers and consumers. For more accurate assessment of constantly changing customer attitudes to control the energy distribution process, virtual energy groups have been prepared aimed at clustering residential homes based on energy usage and weather forecasting pattern. Since the most similar homes are clustered according to their energy consumption, the most similar resident homes within such groups were selected, which was the basis for multivariate LSTM load forecasting. In the following study, such virtual consumer groups and the study of homes as a separate group will be analyzed based on the different ways of energy distribution on the needs of such virtual energy groups. This study provides the importance of virtual organizations and homes for load forecasting, increasing the efficiency of energy supply, network survivability, costs, and environmental protection. Efficient energy trading, guided by advanced forecasting techniques like LSTM, representative houses, and dynamic programming, is essential for optimizing Virtual Power Plants, ensuring precise resource allocation, and enhancing grid resilience, cost-effectiveness, and sustainability in the modern energy ecosystem. The study not only delves into the current challenges and methodologies, but also provides a trajectory for future work, envisaging continued advancements in optimizing energy trading systems.