Decentralized Scheduling Strategy for Multi-Energy Stations Aggregating Large-scale Flexible Resources

The growing adoption of distributed energy resources (DERs) has sparked greater research attention towards multi-energy cluster control stations. Although previous research highlights the substantial grid flexibility and economic advantages associated with multi-energy cluster control stations, there remains a notable gap in adequately addressing the integration of security constraints within multi-energy cluster control stations. This paper introduces a market-based control mechanism in market, aiming to effectively manage multi-energy cluster control stations in an economically viable manner while safeguarding the security of system operations. Given the dynamic behaviors of batteries and responsive demands, this study employs a model predictive control (MPC) technique to manage constraints across various time intervals and integrate predictions for both generation and consumption activities. To mitigate computational challenges and address concerns regarding individual privacy, we have devised an effective distributed algorithm tailored for solving the optimal power flow (OPF) problem. In simpler terms, we're adding extra factors to help us understand how different consumers who also produce power affect each other through the power grid. This helps us figure out prices for electricity in specific locations, considering factors like how much energy is being used, how congested the grid is, and any power loss along the way. We've tested this approach using a standard power system model, and our results show that it works effectively and efficiently.