EVA-centric QUBO optimization for active and reactive power coordination in DLMP-driven distribution systems

This research presents a bilevel optimization framework enhanced with quadratic unconstrained binary optimization (QUBO) for the reactive demand response (DR) driven by the locational marginal price (DLMP) of electric vehicle aggregators (EVA) in renewable integrated distribution systems. The methodology fundamentally reverses the traditional power system hierarchy by positioning the EVA as the Stackelberg leader and the distribution system operator (DSO) as the follower, acknowledging the growing influence of electric vehicles as dominant flexible resources. The proposed framework introduces the first comprehensive approach that simultaneously optimizes both active power charging schedules and reactive power provision through QUBO-enhanced spectral clustering with multimetric validation. It also introduces a novel post-optimization disaggregation methodology that distributes cluster-level decisions to individual vehicles while minimizing charging interruptions. The comprehensive evaluation of the IEEE 33 bus distribution system with 1000 electric vehicles demonstrates quantifiable economic benefits that include a 24. 70% reduction in DSO costs, a 75. 93% reduction in EVA payments, and 39. 55% total system cost savings compared to conventional approaches. The technical improvements include a 4. 64% reduction in power loss, 9. 86% improvement in voltage deviation, 6. 49% improvement in the load factor, and 86. 11% effectiveness of the voltage support. QUBO optimization achieves a 100% success rate with 72, 000 binary variables while introducing reactive power revenue streams generating 47. 20 per day, while maintaining 98. 7% customer satisfaction. The quantum-inspired optimization approach positions the framework for future enhancement as quantum computing technologies mature, providing immediate benefits through algorithms capable of handling the computational complexity of large-scale EV integration scenarios. The methodology has been compared against Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), and the QUBO problem has been simulated using a Linprog, a solver in Yalmip toolbox in MATLAB, and compared against a commercial solver, Gurobi, for computation analysis. The results establish performance benchmarks for advanced EV integration strategies, providing validation data for regulatory frameworks and investment decisions in distribution systems with high penetration of electric vehicles and renewable energy.