Quantum Particle Swarm Optimization-Based Robust Relay Power Allocation Strategy for Cyber-Physical Power System

Wireless communication has been widely applied in cyber-physical power systems due to its flexible deployment and cost-effectiveness. To address the problem that the critical business instructions cannot be transmitted reliably with priority under the conditions of limited communication resources and uncertain channel gains, a quantum particle swarm optimization (QPSO)-based robust relay power optimization allocation strategy is proposed. Specifically, a comprehensive importance evaluation method is first designed, which takes the expected transmission rates of business instructions as the QoS requirements and the corresponding power regulation amounts as the impacts of business instructions on the physical grid, to achieve fine-grained prioritization of instructions. Subsequently, based on the importance division, a robust relay power optimization model is established. In this model, the worst-case method is utilized to model the uncertainty of channel gain, and a resource-saving power allocation model is also adopted. Finally, an improved QPSO algorithm is developed, by introducing an adaptive contraction-expansion coefficient and a correction coefficient, to solve the proposed optimization problem with non-convex and nonlinear characteristics. Simulation results demonstrate that the proposed strategy can effectively enable the prioritized and reliable transmission of critical business instructions, especially, when the communication resources are insufficient, the proposed strategy can reduce the power loss cost by 7.53<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>. In addition, the grid cost is reduced by 24.05<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, 34.66<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, and 55.79<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula>, respectively, in different scenarios.