Intelligent power trading and scheduling strategies for multi-energy virtual power plant leveraging quantum physics-inspired optimization

The global transition towards sustainable energy systems necessitates advanced solutions for managing the integration of intermittent renewable resources into the power grid. This paper proposes a novel intelligent power trading and scheduling strategy for a multi-energy virtual power plant (VPP) that synergistically aggregates wind power, photovoltaic (PV) generation, a battery energy storage system (BESS), and fossil-fuel-based backup generation. To resolve the complex and inherently nonlinear optimization challenges posed by VPP operations, a metaheuristic optimization algorithm, drawing inspiration from the principles of quantum mechanics, namely the Quantum Particle Swarm Optimization (QPSO) algorithm, is developed and validated within this framework. The optimization model is formulated with a objective function aimed at minimizing total operating costs, which include generation, storage, and market-trading expenses, while adhering to a full set of physical constraints. The performance of the proposed QPSO-based framework is validated through extensive numerical simulations conducted under multiple renewable generation scenarios, including normal, solar-dominant, and wind-dominant conditions. The computational results confirm that the QPSO algorithm exhibits an advantage over the conventional Particle Swarm Optimization (PSO) method, delivering enhanced convergence rates and superior solution quality in case studies. Specifically, the proposed strategy yields significant cost reductions across different cases by optimizing the charge-discharge cycles of the BESS and substantially minimizing the reliance on costly and carbon-intensive fossil-fuel backup, demonstrating its value for sustainable VPP operation. • A quantum-inspired optimization algorithm for multi-energy VPP scheduling is developed. • A cost-minimization model integrating wind, solar, storage, and backup generation is established. • Operational cost reduction through optimized battery storage dispatch was achieved. • A scalable framework for intelligent VPP operation in renewable-rich grids was provided.