Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis

This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.

► Evaluation and comparison in a realistic case-study of two new optimization approaches. ► Evaluation of the EV variables forecast's quality and value. ► Optimized bids allow a discount in the retailing tariff. ► The optimized coordination of EV charging decreases significantly the forecast errors.