A novel solution strategy for scheduling optimization of virtual power plant considering multiple participants and Peak Energy Market

The increasing penetration of renewable energy sources presents significant operational challenges for power systems, prompting the need for an efficient and flexible Virtual Power Plant (VPP) framework capable of ensuring reliable, efficient, and market-oriented energy management. This study proposes a novel framework for VPPs to address the challenges of renewable energy integration, particularly the uncertainty of wind and photovoltaic power outputs. The framework integrates wind turbines (WT), photovoltaic systems (PV), energy storage systems (ESS), controllable distributed generators (CDG), and flexible loads (FL) into a VPP model. An adaptive Alternating Direction Method of Multipliers (ADMM) algorithm, enhanced by an adaptive step size, is applied to optimize the internal scheduling of VPP participants and bidding strategies for both the electricity and peak regulation markets. The results indicate that the proposed algorithm improves wind energy utilization by 8 % and increases overall profit by 12 %. Additionally, the algorithm's convergence speed is enhanced by 50 % compared to traditional methods. A comparative analysis of different algorithms demonstrates that the proposed ADMM approach outperforms other approaches in terms of scheduling performance, carbon emission cost reduction, and market profitability.