Decentralized Greedy-Based Algorithm for Smart Energy Management in Plug-in Electric Vehicle Energy Distribution Systems

Variations in electricity tariffs arising due to stochastic demand loads on the power grids have stimulated research in finding optimal charging/discharging scheduling solutions for electric vehicles (EVs). Most of the current EV scheduling solutions are either centralized, which suffer from low reliability and high complexity, while existing decentralized solutions do not facilitate the efficient scheduling of on-move EVs in large-scale networks considering a smart energy distribution system. Motivated by smart cities applications, we consider in this paper the optimal scheduling of EVs in a geographically large-scale smart energy distribution system where EVs have the flexibility of charging/discharging at spatially-deployed smart charging stations (CSs) operated by individual aggregators. In such a scenario, we define the social welfare maximization problem as the total profit of both supply and demand sides in the form of a mixed integer non-linear programming (MINLP) model. Due to the intractability, we then propose an online decentralized algorithm with low complexity which utilizes effective heuristics to forward each EV to the most profitable CS in a smart manner. Results of simulations on the IEEE 37 bus distribution network verify that the proposed algorithm improves the social welfare by about 30% on average with respect to an alternative scheduling strategy under the equal participation of EVs in charging and discharging operations. Considering the best-case performance where only EV profit maximization is concerned, our solution also achieves upto 20% improvement in flatting the final electricity load. Furthermore, the results reveal the existence of an optimal number of CSs and an optimal vehicle-to-grid penetration threshold for which the overall profit can be maximized. Our findings serve as guidelines for V2G system designers in smart city scenarios to plan a cost-effective strategy for large-scale EVs distributed energy management

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

An improved methodology for the hierarchical coordination of PEV Charging

This paper proposes an improved methodology for the hierarchical coordination of daily Plug-in Electric Vehicle (PEV) charging. The aim is to limit the power supplied by the primary distribution transformer (PDT) while minimizing the energy costs of the aggregators. This methodology consists of an iterative optimization of the total aggregated power at the PDT level, considering the local power constraints of the aggregators and the PEVs with a reduced number of decision variables and constraints which only depend on the number of time intervals. Moreover, it defines the energy boundaries of the optimization problem in each iteration through a proposed method for simulating early charging and delayed charging, considering the power constraints of the aggregators. Otherwise, it evenly distributes the total power among the aggregators, and the local power of each aggregator among the PEVs, maximizing the feasible region of the optimization problem. The proposed methodology is applied to two case studies. The uncertainties related to the charging scenarios are considered by means of Monte-Carlo simulations. The results obtained show that the total power profile is effectively limited, while the profits of the aggregators are not significantly affected by the coordinated approach that is expected to be performed by the Distribution System Operator (DSO). Additionally, to demonstrate the reduction of the impact of PEV charging on the distribution system, the voltage profile, the transformer loss of life and the power and energy losses are reported.Fil: Sanchez, Angel Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Coria Pantano, Gustavo Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Romero Quete, Andrés Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan. Instituto de Energía Eléctrica. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Energía Eléctrica; ArgentinaFil: Rivera, Sergio Raúl. Universidad Nacional de Colombia; Colombi

Smart Charging for Electric Vehicle Aggregators considering Users Preferences

(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works[EN] Most of the road transportation currently depends on fossil fuels, which result in significant environmental and health issues. This is being addressed with the deployment of electric vehicles. However, a massive penetration will lead to new technical and economic challenges for power systems. This paper proposes a novel way to account for the effect of this new load and to minimize the negative impacts by providing new tools for the agent responsible of managing the EV charge in some area (EV aggregator). The proposed method allows EV charging at the lowest cost while complying with technical constraints required by Distribution System Operator (DSO) and Transmission System Operator (TSO). Moreover, EV users are able to choose among different customer choice products (CCPs) that meets their needs in terms of charging time. A case study in the city of Quito (Ecuador) is analyzed in the paper where the advantages of the proposed coordinated charging method are quantified. The model presents cost benefits compared to uncoordinated charging while complying with technical constraints. Additionally, the savings using the presented model are at least 5% higher than uncoordinated charging, and can reach more than 50% at best.Clairand-Gómez, J.; Rodríguez-García, J.; Álvarez, C. (2018). Smart Charging for Electric Vehicle Aggregators considering Users Preferences. IEEE Access. 6:1-12. https://doi.org/10.1109/ACCESS.2018.2872725S112

Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

[EN] The increase in global electricity consumption has made energy efficiency a priority for governments. Consequently, there has been a focus on the efficient integration of a massive penetration of electric vehicles (EVs) into energy markets. This study presents an assessment of various strategies for EV aggregators. In this analysis, the smart charging methodology proposed in a previous study is considered. The smart charging technique employs charging power rate modulation and considers user preferences. To adopt several strategies, this study simulates the effect of these actions in a case study of a distribution system from the city of Quito, Ecuador. Different actions are simulated, and the EV aggregator costs and technical conditions are evaluated.All the authors wish to thank Manuel Alcázar Ortega and José Francisco Carbonell Carretero from Universitat Politècnica de València for their contributions to this paper. We also thank the Ministry of Electricity and Renewable Energy of Ecuador (MEER) and Empresa Eléctrica Quito (EEQ) for providing important information for this study.Clairand-Gómez, J.; Rodríguez-García, J.; Álvarez, C. (2020). Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging. Journal of Modern Power Systems and Clean Energy (Online). 8(2):356-366. https://doi.org/10.35833/MPCE.2018.000840S3563668

A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.info:eu-repo/semantics/publishedVersio

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns