Enhancing Flexibility and Cost-Effectiveness of Virtual Power Plants Through Multi-market Profit Optimization and Interruptible Loads Integration

Virtual Power Plants (VPPs) are a forward-looking avenue for seamlessly integrating renewable energy resources into the power grid. The primary aim of this research is to enhance the adaptability of VPP scheduling, curtail the expenses related to power generation, and maximize overall advantages, all grounded in prior investigations. This research takes into account the fluctuating nature of energy demand and introduces a comprehensive economic optimization scheduling model tailored for VPPs, which harmonizes the operations of energy sources, loads, and storage systems. The central goal of this model revolves around the minimization of forecasting inaccuracies while concurrently bolstering VPP revenue. The proposed scheduling model leverages multi-period scale optimization and a multi-market profitability framework. To tackle this model and fine-tune energy production within the VPP, we employ a particle swarm algorithm. The outcomes of our simulations vividly reveal that by synergizing an appropriate percentage of interruptible loads with a strategically-oriented operational approach, VPP flexibility and cost-effectiveness are significantly fortified.