Option-Based Demand Response Management for Electric Vehicle Aggregator
Shuying Lai, Jing Qiu, Yuechuan Tao, Junhua Zhao
Abstract
Renewable energy resources, such as wind power, can serve as alternative energy sources of fossil fuel to reduce carbon emissions. However, the intermittent feature of wind power generation can lead to an imbalance between energy demand and supply. Thus, in this paper, an option-based demand response (DR) strategy is proposed to mitigate the demand-supply imbalance and save energy costs for both the retailer and the electric vehicle aggregator (EVA) who manages the electric vehicles (EVs). In the first part, option-based DR is conducted by the retailer to incentivize EVA to either charge or discharge electricity (DR decisions) from EVs that are at the parking lot. Additionally, a robust optimization method is formulated to model the wind power and EV behavior uncertainty impact on the DR decisions. To tackle the computational complexity of robust optimization, a dual approximation approach is applied. In the second part, a clustering-based Nucleolus method is formulated to allocate the cost saving of the EVA resulting from the DR decisions among EV users in the first part and ensure their satisfaction to stay in the grand coalition managed by the EVA. The time for finding the nucleoli (the optimal cost-saving allocation) can be shortened via the clustering technique and nested linear program. According to the simulation results, the proposed DR strategy can save energy costs for the retailer and the EVA at 26.2% and 6% compared with using the price-based DR strategy. For the cost-saving allocation, the formulated clustering-based Nucleolus allocation method can ensure the willingness of the EV users to participate in the DR strategy.