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Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited to the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.

Community Energy Markets (CEMs) enable trading opportunities between participants in a community to achieve savings and profits. However, the market design and the behaviour of participants are key factors that determine the success of such markets. To this end, this research presents a CEM model and conducts agent-based simulations to study the benefits of the CEM to consumers and prosumers. The proposed market structure is an hour-ahead periodic double auction. In particular, market rules are proposed that incentivise the provision of energy supply to the community and the investment in energy storage. Furthermore, a trading strategy is introduced that leverages energy flexibility created by the storage devices. Finally, as well as the hour-ahead market, we include a minute-by-minute balancing as part of the CEM’s energy exchange mechanism. The balancing approach is introduced to account for a community budget deficit caused by the time difference between supply and demand. The proposed market results in cost savings for consumers and profit for prosumers similar to existing approaches, while increasing the energy suppliers’ percentage of financial benefits from 50% to a range between 60−96% depending on the community configuration. Moreover, the market model accounts for uncertainties in supply and demand and suggests a methodology to overcome the community budget deficit.

Motivated by the high electric vehicle (EV) penetration percentages foreseen for the near future, this paper studies the participation of large fleets of EVs in electricity day-ahead markets. Specifically, we consider a scenario where a number of independent and self-interested EV aggregators participate in the day-ahead market to purchase energy to satisfy their clients’ driving needs. In this scenario, independent bidding can drive prices up unnecessarily, resulting in increased electricity costs for all participants. Inter-aggregator cooperation can mitigate this by producing coordinated bids. However, this is challenging due to the self-interested nature of the aggregators, who may try to manipulate the system in order to obtain personal benefit. In order to overcome this issue, we employ techniques from mechanism design to develop a coordination mechanism which incentivises self-interested EV aggregators to report their energy requirements truthfully to a third-party coordinator. This coordinator is then able to employ a day-ahead bidding algorithm to optimise the global bids on their behalf, extending the benefits of smart bidding to groups of competing EV aggregators. Importantly, the proposed coordination mechanism is straightforward to implement and does not require any additional infrastructure. To ensure scalability and computational tractability, a novel price-maker day-ahead bidding algorithm is proposed, which is formulated in terms of simple energy requirement constraints. The coordination mechanism substantially reduces bidding costs, as shown in a case study which uses real market and driver data from the Iberian Peninsula.