Demand Response Through Electric Vehicle Aggregator with Price-Adaptive Dispatch considering Energy-mix Distributed System
Abstract
This paper presents a three-level hierarchical optimization framework comprising of Level 1 (Electric Vehicle Aggregator, EVA), Level 2 (Distribution System Operator, DSO), and Level 3 (Electric Vehicle Load Aggregator, EVLA) for electric vehicle aggregation with price-adaptive dispatch in renewable-integrated distribution systems. The framework coordinates electric vehicle aggregators, distribution system operators, and load aggregators using dynamic location marginal pricing (DLMP) to manage high renewable penetration. Spectral clustering segments 1,000 electric vehicles into 20 clusters, implemented on the IEEE 33-bus network using quantum binary optimization over 24-hour operation. Results demonstrate 98.41% system efficiency, 99.4% renewable penetration with zero curtailment, and optimal power factors above 0.90 across EV clusters. Economic analysis shows $0.032/kWh EV charging costs and $29,118 renewable savings. Comparative analysis with Particle Swarm Optimization (PSO) validates QUBO’s superiority, demonstrating significantly lower system costs, higher efficiency, zero constraint violations, and superior power quality performance. Robustness validation confirms practical applicability under ±20% forecast errors and 70-100% EV participation rates, establishing a viable pathway for sustainable transportation electrification.
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