Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators

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 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

Incentive based Residential Demand Aggregation

From the beginning of the twenty-first century, the electrical power industry has moved from traditional power systems toward smart grids. However, with the increasing amount of renewable energy resources integrated into the grid, there is a significant challenge in power system operation due to the intermittency and variability of the renewables. Therefore, the utilization of flexible and controllable demand-side resources to maintain power system efficiency and stability has become a fundamental goal of smart grid initiatives. Meanwhile, due to the development of communication and sensing technologies, intelligent demand-side management with automatic controls enables residential loads to participate in demand response programs. Therefore, the aggregate control of residential appliances is anticipated to be feasible technique in the near future, which will bring considerable benefits to both residential consumers and load-serving entities. Hence, this dissertation proposes a comprehensive optimal framework for incentive based residential demand aggregation. The contents of this dissertation include: 1) a hardware design of smart home energy management system, 2) a new model to assess the responsive residential demand to financial incentives, and 3) an online algorithm for scheduling residential appliances. The proposed framework is expected to generate optimal control strategies over residential appliances enrolled in incentive based DR programs in real time. To residential consumers, this framework will 1) provide easy-to-use smart energy management solution, 2) distribute financial rewards by their quantified contribution in DR events, and 3) maintain residents’ comfort-level expectations based on their energy usage preferences. To LSEs, this framework can 1) aggregate residential demand to enhance system reliability, stability and efficiency, and 2) minimize the total reward costs for executing incentive based DR programs. Since this framework benefits both load serving entities and residents, it can stimulate the potential capability of residential appliances enrolled in incentive based DR programs. Eventually, with the growing number of DR participants, this framework has the potential to be one of the most vital parts in providing effective demand-side ancillary services for the entire power system

Distributed multi-agent algorithm for residential energy management in smart grids

Distributed renewable power generators, such as solar cells and wind turbines are difficult to predict, making the demand-supply problem more complex than in the traditional energy production scenario. They also introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. In this article we describe a distributed algorithm for residential energy management in smart power grids. This algorithm consists of a market-oriented multi-agent system using virtual energy prices, levels of renewable energy in the real-time production mix, and historical price information, to achieve a shifting of loads to periods with a high production of renewable energy. Evaluations in our smart grid simulator for three scenarios show that the designed algorithm is capable of improving the self consumption of renewable energy in a residential area and reducing the average and peak loads for externally supplied power

Participation of Electric Vehicle Aggregators in Wholesale Electricity Markets: Recent Works and Future Directions

Electric Vehicles are key to reducing carbon emissions while bringing a revolution to the transportation sector. With the massive increase of EVs in road networks and the growing demand for charging services, the electric power grid faces enormous system reliability and operation stability challenges. Demand and supply disparities create inconsistency in the smooth delivery of electrical power. As a potential solution, EVs and their charging infrastructure can be aggregated to prevent the unwanted effects on power systems and also facilitate ancillary services to the power grid. When not need for transportation purposes, EVs can leverage their batteries for power grid services by participating in the electricity market via mechanisms coordinated by system operators. Hence, the market participation of EV infrastructure can help alleviate the power grid stress during peak periods. However, further research is needed to demonstrate the multiple benefits to both EV owners and power grid operators. This paper briefly overviews the existing literature on market participation of EV aggregators, discuss associated challenges and needs, and propose research directions for future research

Electric Vehicle Fleet Integration in the Danish EDISON Project:A Virtual Power Plant on the Island of Bornholm

The Danish EDISON project has been launched to investigate how a large fleet of electric vehicles (EVs) can be integrated in a way that supports the electric grid while benefitting both the individual car owners and society as a whole through reductions in CO 2 emissions. The consortium partners include energy companies, technology suppliers and research laboratories and institutes. The aim is to perform a thorough investigation of the challenges and opportunities of EVs and then to deliver a technical platform that can be demonstrated on the Danish island of Bornholm. To reach this goal, a vast amount of research is done in various areas of EV technology by the partners. This paper will focus on the ICT-based distributed software integration, which plays a major role for the success of EDISON. Key solution technologies and standards that will accommodate communication and optimize the coordination of EVs will be described as well as the simulation work that will help to reach the goals of the project

Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107