In the rapidly evolving energy sector, the integration of renewable energy sources into power grids presents both opportunities and challenges. A recent study published in the journal “IEEE Access” offers a novel approach to optimizing the operation of virtual power plants (VPPs), addressing the uncertainties inherent in renewable energy sources and flexible loads. The research, led by Shixun Qi of the Inner Mongolia Power Trading Center Company Ltd., introduces a distributed two-stage robust optimization method that could significantly enhance the economic and low-cost operation of VPPs.
Virtual power plants aggregate various distributed energy resources, including renewable energy sources like wind and solar power, energy storage devices, and controllable loads. As the proportion of renewable energy in VPPs continues to rise, the uncertainties in energy output and load demand become more pronounced, necessitating advanced optimization techniques. “The increasing integration of renewable energy and flexible loads into VPPs has made it crucial to develop robust optimization methods that can handle these uncertainties effectively,” Qi explains.
The study employs Monte Carlo scenario sampling and scenario reduction methods to depict typical scenarios of uncertainty, determining the output interval ranges for wind power, photovoltaic output, and load. Based on these intervals, the researchers established a distributed two-stage robust optimization model that considers the uncertainties of both renewable energy sources and loads across multiple VPPs. This model adopts a min-max-min structure to obtain the optimal dispatching scheme for VPP operating costs under the most extreme scenarios.
One of the key innovations in this research is the use of the Benders decomposition algorithm, which splits the model into a master problem and a subproblem. By combining duality theory, Karush-Kuhn-Tucker (KKT) conditions, and the Big-M method, the inner layer max-min optimization problem is transformed into a mixed integer linear programming problem. This approach ensures that the model can be solved efficiently and effectively, even under complex and uncertain conditions.
The numerical simulations conducted in the study validate the correctness and effectiveness of the proposed model. The results demonstrate that the model can significantly improve the economic and low-cost operation of VPPs, even when considering capacity configuration. “Our findings suggest that this robust optimization method can play a positive role in the economic operation of VPPs, helping to mitigate the impacts of uncertainty and enhance overall system performance,” Qi notes.
The implications of this research are far-reaching for the energy sector. As the integration of renewable energy sources continues to grow, the need for advanced optimization techniques that can handle uncertainty and ensure reliable and cost-effective operation becomes increasingly important. The distributed two-stage robust optimization method proposed in this study offers a promising solution to these challenges, paving the way for more efficient and resilient VPPs.
Published in the peer-reviewed journal “IEEE Access,” this research represents a significant step forward in the field of energy optimization. By addressing the uncertainties in renewable energy sources and flexible loads, the proposed method can help energy providers and grid operators make more informed decisions, ultimately leading to a more sustainable and efficient energy future. As the energy sector continues to evolve, the insights gained from this study will be invaluable in shaping future developments and ensuring the reliable operation of VPPs.