Electric Vehicle Energy Management Considering Stakeholders' Interest in Smart Grids

Abstract

With the electrification in transportation systems, Electric Vehicles (EVs) have developed rapidly in recent years. At the same time, with large-scale EV integration to power grids, the charging behaviours of EVs bring both challenges and opportunities to power grids operation. This thesis focuses on the EV energy management in smart grids, and the EV energy management problem is studied considering three stakeholders' interests, i.e. EV owner, aggregator and grid, respectively. First, the economic relationship between EV owners and the aggregator is studied (EV owners' and aggregator's interest). Two multi-objective optimisation methods are applied to investigate the economic relationship between these two stakeholders and the aggregator{owner economic inconsistency issue is presented. To mediate this issue, a rebate factor is proposed in the model. The results show that a signi cant reduction in the EV owners' charging fee from self-scheduling can be achieved while the aggregator pro t is maximised. Second, the EV aggregator bidding strategy in the electricity market is studied (aggregator's interest). By jointly considering the reserve capacity in the day-ahead market and the uncertainty of reserve deployment requirements in the real-time market, a scenario-based stochastic programming method is used to maximise the expected aggregator pro t. The risk of the deployed reserve shortage is addressed by introducing a penalty factor in the model. In addition, an owner{aggregator contract is designed to mitigate the economic inconsistency issue between EV owners and the aggregator. The results show that the expected aggregator pro t is guaranteed by maximising reserve deployment payments and mitigating the penalties and thus the uncertainty of the reserve market is well managed. Third, the EV integration in a transmission system is studied (grid's interest) to achieve the coordination between generators and EVs. To tackle the challenge of large-scale EV integration problem, a bi-level scheduling strategy is proposed. The bi-level strategy clearly de nes the responsibility of transmission system operator and the aggregator. An EV information grouping method is designed, which could e ciently tackle the optimisation complexity problem. In addition, a detailed EV battery charging model is built. The results show that the total cost of the systems is minimised and EVs could shave the peak and ll the valley loads. This thesis discusses the EV energy management problem considering three stakeholders' interests, respectively. The proposed strategies in this thesis clearly evaluate and de ne the economic relationships and responsibility among EV owners, aggregator and the grid in managing EV charging and discharging behaviours. Based on three case studies conducted in this thesis, EV energy management could bene t the stakeholders as follows: (1) the EV owner charging fee is minimised while their driving requirements are satis ed; (2) the aggregator pro t is maximised by participation in the electricity market; (3) the cost of the system is minimised by achieving the coordination between EVs and generators.