A Resource Allocation Strategy for Virtual Power Plants Based on URLLC to Minimize Control Costs

With the swift growth of renewable energy and the evolution of smart grid technologies, the concept of the Virtual Power Plant (VPP) has risen as a pioneering strategy for energy management, marking a pivotal area of study within power system research. Nonetheless, the effectiveness of VPPs hinges on the implementation of Ultra-Reliable and Low-Latency Communications (URLLC). In VPP operations, communication lags or data packet losses can result in the delayed or failed execution of control instructions, impacting both the stability of the electrical grid and the efficacy of energy management. Against this backdrop, this paper aims to bolster the control capabilities of VPPs by integrating control demands with URLLC constraints. We have crafted an optimization problem designed to reduce control expenditures in VPP operations through strategic distribution of potential data packet losses and wireless resources, all the while guaranteeing the stability of the power grid and the productivity of energy transactions. To tackle this issue, we have developed a Greedy Choice-Local Search Strategy algorithm (GC-LSS) that seeks to achieve the lowest possible control costs within the bounds of communication constraints. The simulation outcomes align with the expected parameter variations, and our proposed method demonstrates superior performance compared to random allocation methods.