Genetic fuzzy schedules for charging electric vehicles

This work tackles the problem of scheduling the charging of electric vehicles in a real-world charging station subject to a set of physical constraints, with the goal of minimising the total tardiness with respect to a desired departure date given for each vehicle. We model a variant of the problem that incorporates uncertainty in the charging times using fuzzy numbers. As solving method, we propose a genetic algorithm with tailor-made operators, in particular, a new chromosome evaluation method based on generating schedules from a priority vector. Finally, an experimental study avails the proposed genetic algorithm both in terms of algorithm convergence and quality of the obtained solutions.Acknowledgements. This work was supported by the Spanish Government [Grant Nos.TIN2016-79190-R, MTM2014-55262-P]

Detection of Lying Electrical Vehicles in Charging Coordination Application Using Deep Learning

The simultaneous charging of many electric vehicles (EVs) stresses the distribution system and may cause grid instability in severe cases. The best way to avoid this problem is by charging coordination. The idea is that the EVs should report data (such as state-of-charge (SoC) of the battery) to run a mechanism to prioritize the charging requests and select the EVs that should charge during this time slot and defer other requests to future time slots. However, EVs may lie and send false data to receive high charging priority illegally. In this paper, we first study this attack to evaluate the gains of the lying EVs and how their behavior impacts the honest EVs and the performance of charging coordination mechanism. Our evaluations indicate that lying EVs have a greater chance to get charged comparing to honest EVs and they degrade the performance of the charging coordination mechanism. Then, an anomaly based detector that is using deep neural networks (DNN) is devised to identify the lying EVs. To do that, we first create an honest dataset for charging coordination application using real driving traces and information revealed by EV manufacturers, and then we also propose a number of attacks to create malicious data. We trained and evaluated two models, which are the multi-layer perceptron (MLP) and the gated recurrent unit (GRU) using this dataset and the GRU detector gives better results. Our evaluations indicate that our detector can detect lying EVs with high accuracy and low false positive rate

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings

Multi objective optimization in charge management of micro grid based multistory carpark

Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.Web of Science117art. no. 179

A Practical Approach for Coordination of Plugged- In Electric Vehicles To Improve Performance and Power Quality of Smart Grid

This PhD research is undertaken by supplications including 14 peer-reviewed published articles over seven years research at Curtin University. This study focuses on a real-time Plugged-in Electric Vehicle charging coordination with the inclusion of Electric Vehicle battery charger harmonics in Smart Grid and future Microgrids with incorporation of Renewable Energy Resources. This strategy addresses utilities concerns of grid power quality and performance with the application of SSCs dispatching, active power filters or wavelet energy

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving

LTC and Switched Shunt Capacitor Scheduling in Smart Grid with Electric Vehicles and Wind Distributed Generation Systems

Future smart grids (SGs) are expected to include distributed generations (DG), plug-in electric vehicles (PEVs) and smart appliances, as well as nonlinear industrial loads that may decrease grid efficiency and deteriorate the quality of electric power. This paper performs optimal (load tap changer)LTC and switched shunt capacitor (SSC) in SGs with nonlinear loads, wind distributed generation (WDGs) systems and PEV charging at consumers’ premises and PEV charging stations (PEV-CSs). The substantial grid energy requirements at high PEV penetrations is assumed to be partially supplied by WDGs located within the distribution network. PEV charging is performed based on a recently proposed online maximum sensitivities selection based coordination algorithm (OL-MSSCA), nonlinear loads are assumed to inject low order odd current harmonics and WDGs are treated as negative PQ loads in the employed decoupled harmonic load flow (DHLF) algorithm. Simulations are performed for the modified IEEE 23kV distribution system with three WDGs, three PEV-CSs and 22 low voltage residential networks with PEVs. Impacts of PEV coordination and WDG on the LTC/SSC scheduling outcomes including grid losses, voltage profiles and THDs are investigated