An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

Vasirani, Matteo; Kota, Ramachandra; Cavalcante, Renato L. G.; Ossowskí, Sascha; Jennings, Nicholas R. · 2013 · OpenAlex-citations

DOI: 10.1109/tsg.2013.2259270

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Summary

This paper addresses the economic and operational challenges of integrating intermittent wind power into electricity markets. Wind generation is unpredictable and often produces energy during low-demand periods, resulting in low prices and limited competitiveness compared to conventional sources. To mitigate these issues, the authors propose a Virtual Power Plant (VPP) model that aggregates wind generators with electric vehicles (EVs) acting as storage. This approach allows wind farms to store excess energy during low-price periods and release it during high-price periods, thereby smoothing supply and increasing profitability without requiring dedicated capital investment in storage infrastructure. The study employs an agent-based approach to model the VPP, treating wind farms and EVs as autonomous entities with private preferences. The core methodology involves a linear programming optimization model that schedules energy supply to the grid and storage usage in EV batteries. A key innovation is a novel payment scheme where EVs are compensated for storage usage not with cash, but with charging entitlements. This leverages the price differential between wholesale electricity costs (paid by the VPP) and retail prices (saved by EV owners), making participation profitable for EV owners while minimizing costs for the wind farm. The model accounts for battery conversion losses, depth-of-discharge constraints, and battery lifetime impacts. The operational design consists of two phases. First, a day-ahead optimization determines the optimal bid for the electricity market based on wind forecasts and available EV storage capacity. Second, a receding horizon optimization is applied during the delivery day to continuously adjust the schedule in response to real-time wind generation data and updated storage availability. This dynamic adjustment minimizes imbalance penalties incurred when actual generation deviates from contracted supply. The model incorporates constraints for energy balance, storage limits, and payment proportions, ensuring that the VPP remains feasible and profitable under uncertainty. The authors validate the approach through a realistic case study using real wind generation data, historical electricity market prices, and specific EV characteristics. The results demonstrate that the proposed VPP formation is economically feasible, yielding positive profits for both wind farms and EV owners. The study concludes that coupling wind generation with EV storage via an agent-based framework effectively addresses the intermittency of renewable energy and enables participation in short-term electricity markets. This work contributes to the Smart Grid domain by providing a viable mechanism for integrating distributed energy resources and storage owned by different stakeholders, offering a scalable alternative to traditional feed-in tariffs and dedicated storage investments.

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