Improved Light Robust Optimization Strategy for Virtual Power Plant Operations With Fluctuating Demand

Abstract

Aggregating loads and resources on both the supply and demand side of a virtual power plant (VPP) can enhance coordination between distributed generation systems and the power grid, ultimately improving the utilization rate and economic benefits of renewable energy. The energy storage system (ESS) has the added benefit of flexible demand-side resources, which can effectively suppress output uncertainty of distributed units and balance load fluctuations. This paper proposes an improved light robust (ILR) optimization method for the ESS’s demand-side resource, which optimizes the conservatism caused by robust optimization (RO) in solving the VPP optimal scheme, ultimately reducing running costs. By analyzing random factors in the VPP operation process, the ILR dynamically calls ESS backup power to participate in system operation when load fluctuation and output are inaccurate or uncertain, provided operational constraints are met. This approach balances both economy and optimization, ensuring the balance between supply and load fluctuates, improving the utilization rate of ESS backup power, and reducing the total operating cost of VPP operation. The case study show that the model can effectively utilize the reserve energy of the ESS to cope with the load fluctuations during normal operation of the system, improve the economy while ensuring the safe operation of the virtual power plant system, and leave some optimization space for decision-makers by utilizing the demand-side resource characteristics of the energy storage system.

Keywords

• Improve light robust
• energy storage system
• demand-side resources
• uncertainty demand
• virtual power plant