Existing literature on planning for electric vehicle charging stations (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users’ average wait time and distance to charge costs and minimize the DSVS index. To simplify the problem, we use the super-efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multi-objective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.

An economic feasibility study of vehicle-to-grid (V2G) frequency regulation is performed in consideration of battery wear. Usually, a transaction for frequency regulation is made in terms of power capacity while the battery-wear proceeds in proportion to the absolute amount of energy transferred. In order to relate the two quantities, we first estimate the amount of transferred energy in terms of contracted power capacity, and hence regulation income, by analyzing actual regulation signals and transactions. On the other hand, the amount of transferrable energy during the life cycle of a battery is estimated analyzing some pervasive specifications for electric vehicle (EV) batteries. The expected V2G income is then estimated and compared with battery prices to judge the economic feasibility of V2G regulation. In the latter part of the paper, the assessment result is validated with actual cycle life data of an EV battery cell. As a result, it is concluded that the estimated profit exceeds current market price of EV batteries, indicating that V2G regulation is an economically feasible service.

As a new type of transportation, electric vehicles (EV) can effectively adjust the supply and demand balance of power systems using their vehicle-to-grid (V2G) characteristics. To better promote the participation of EV resources in the energy market and interact with power systems, we propose a novel framework of an electric vehicle aggregator (EVA) that can aggregate schedulable EVs within its jurisdiction to provide auxiliary services for the power grid. Due to EV charging behavior’s uncertain nature, we employ a probability mass function (PMF) based model to provide more accurate forecasts of future EV behaviors. To reduce EVA operation costs and maximize the travel utility for EV users participating in this service, we develop an EVA optimization schedule model that combines a day-ahead optimization schedule and real-time optimization schedule. Finally, we create three case studies to verify the results of the proposed method. Matlab is used to simulate and analyze each case study concerning uncoordinated charging, coordinated charging while considering day-ahead optimization schedules, and an ensemble of coordinated charging activities that consider the day-ahead optimization schedule and real-time optimization schedule. Through comparative analysis, it is verified that the proposed strategy can effectively reduce EVAs’ operating costs and meet the travel requirements of EV users. The impact of different degrees of error of EV plug-out information on the proposed method is also analyzed.

The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR) market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM), to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

A demand side management technique is deployed along with battery energy-storage systems (BESS) to lower the electricity cost by mitigating the peak load of a building. Most of the existing methods rely on manual operation of the BESS, or even an elaborate building energy-management system resorting to a deterministic method that is susceptible to unforeseen growth in demand. In this study, we propose a real-time optimal operating strategy for BESS based on density demand forecast and stochastic optimization. This method takes into consideration uncertainties in demand when accounting for an optimal BESS schedule, making it robust compared to the deterministic case. The proposed method is verified and tested against existing algorithms. Data obtained from a real site in South Korea is used for verification and testing. The results show that the proposed method is effective, even for the cases where the forecasted demand deviates from the observed demand.

Owing to the increasing use of electric vehicles (EVs), the demand for lithium-ion (Li-ion) batteries is rising. In this light, an essential factor governing the safety and efficiency of electric vehicles is the proper diagnosis of battery errors. In this article, we address the detection of battery problems by using the intraclass correlation coefficient (ICC) method and the order of cell voltages to enhance EV performance. Furthermore, we propose a framework for diagnosing problems with battery packs, which could be used to detect abnormal behavior. The proposed method calculates ICC values based on the terminal voltages extracted from a caravan battery pack. These ICC values are then used to determine whether the battery has a defect. In addition, the order of cell voltages is used to analyze the causes of faults. Furthermore, we conducted experiments to investigate and evaluate battery cell faults in EVs. The experimental results indicate that the proposed approach can be used to detect battery cell faults accurately.