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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.

Current approaches for assessing the effects of invasive alien species (IAS) are biased toward the negative effects of these species, resulting in an incomplete picture of their real effects. This can result in an inefficient IAS management. We address this issue by describing the INvasive Species Effects Assessment Tool (INSEAT) that enables expert elicitation for rapidly assessing the ecological consequences of IAS using the ecosystem services (ES) framework. INSEAT scores the ecosystem service “gains and losses” using a scale that accounted for the magnitude and the reversibility of its effects. We tested INSEAT on 18 IAS in Great Britain. Here, we highlighted four case studies: Harmonia axyridis (Harlequin ladybird), Astacus leptodactylus (Turkish crayfish), Pacifastacus leniusculus (Signal crayfish) and Impatiens glandulifera (Himalayan balsam). The results demonstrated that a collation of different experts’ opinions using INSEAT could yield valuable information on the invasive aliens’ ecological and social effects. The users can identify certain IAS as ES providers and the trade‐offs between the ES provision and loss associated with them. This practical tool can be useful for evidence‐based policy and management decisions that consider the potential role of invasive species in delivering human well‐being.

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.

We study the equilibrium distribution of relative strategy scores of agents in the asymmetric phase ( $$\alpha \equiv P/N\gtrsim 1$$ ) of the basic Minority Game using sign-payoff, with N agents holding two strategies over P histories. We formulate a statistical model that makes use of the gauge freedom with respect to the ordering of an agent’s strategies to quantify the correlation between the attendance and the distribution of strategies. The relative score $$x\in \mathbb {Z}$$ of the two strategies of an agent is described in terms of a one dimensional random walk with asymmetric jump probabilities, leading either to a static and asymmetric exponential distribution centered at $$x=0$$ for fickle agents or to diffusion with a positive or negative drift for frozen agents. In terms of scaled coordinates $$x/\sqrt{N}$$ and t / N the distributions are uniquely given by $$\alpha $$ and in quantitative agreement with direct simulations of the game. As the model avoids the reformulation in terms of a constrained minimization problem it can be used for arbitrary payoff functions with little calculational effort and provides a transparent and simple formulation of the dynamics of the basic Minority Game in the asymmetric phase.