Multi-Objective EV Aggregator Profit and Voltage Deviation Optimization in Day-ahead Market

Le résumé fourni par la source

The increasing adoption of electric vehicles (EVs) presents voltage stability challenges in low-voltage (LV) residential distribution networks. EV aggregators can mitigate these issues by coordinating EV charging and discharging while enhancing economic returns through participation in day-ahead market with ancillary services. This paper proposes a novel multi-objective optimization (MOO) approach to maximize the aggregator’s profit including revenues from energy arbitrage, reserve capacity, regulation services and battery degradation costs while simultaneously minimizing voltage deviations within the distribution network. An augmented epsilon-constraint (AUGMENCON) method is implemented to explore the optimal trade-offs between profitability and voltage stability. The implemented method outperforms the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in producing a more non-dominated Pareto front. The methodology is validated on an IEEE 33-bus LV residential network using the MATPOWER toolbox in MATLAB 2024b, demonstrating the feasibility and effectiveness of balancing economic incentives with voltage regulation constraints.

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