Multi-Objective Optimization of EV Charging for Cost and Loss Minimization Under TOU Tariff

Electric vehicle (EV) smart charging, which regulates the charging rates of EVs in response to the availability of surplus solar photovoltaics (PV) power or the electricity prices, can effectively enhance the local power demand–supply balance and help improve the PV power local utilization. In this regard, researchers have developed two categories of EV charging controls: (i) B2V or G2V ((i.e., building-to-vehicle or grid-to-vehicle) power flow in which the EV can only be charged; and (ii) B2V2B or G2V2G (i.e., building to vehicle to building or grid-vehicle-to-grid) in which the vehicles can be both charged and discharged. The frequent charging/discharging could potentially accelerate the EV battery degradation, which might make such applications not economical. However, systematic investigation has rarely been conducted for the impact of various EV usage and charging factors (including the EV charging strategy, different EV charging forms, EV charging limits, and commuting distance) on the power balancing performances and EV battery cycling degradation. As a result, the EV owners may not be willing to join the smart charging demand response due to the concerns of accelerated battery degradation, and this hinders the applications of EVs in the power regulation in the future energy system. This chapter aims to investigate the effect of different ways of using EVs on the demand response performances and the EV battery degradation. A parametric study considering a set of different scenarios combining various EV charging forms, EV charging limits and commuting distances will be conducted in Sweden. A smart charging control method of the EV will be developed, which can optimize the EV charging and discharging rates to minimize the grid interactions. A degradation model, which can evaluate the EV battery degradation due to charging/discharging cycling, will be constructed to investigate the EV battery degradation under typical scenarios. The performances of each scenario will be analyzed and compared to draw conclusions. The study results can help improve researchers’ understanding of the impacts of smart EV charging in the building community performances. The obtained impacts on the battery degradation can also support decision makers in selecting suitable EV charging and usage strategies.

A temporal and spatial electric vehicle charging optimization scheme with DSO-EVA coordination framework

A Blockchain Based Electric Vehicle Smart Charging System with Flexibility

Considering the increasing integration of distributed renewable energies (DREs), the active distribution network (ADN) is facing challenges to maintain generation-load balance. Meanwhile, since the electric vehicles (EVs) are usually charged together in time and space, the EV charging is typical impulsive load and may further aggravate power imbalance. A bi-level EV charging strategy is proposed to reduce the adverse impacts of DRE power fluctuation and EV power impulse on ADN. In the outer level, a multi-objective optimization model is provided based on the predicted DRE power. With the objectives of minimizing net power fluctuations and EV charging power changes, the optimal Pareto solution set is obtained through the multi-objective particle swarm optimization algorithm, on which a basic EV charging scheme in the large time scale is determined. Then, in the inner level, the wavelet decomposition is utilized to extract the high-frequency power prediction errors. According to the high-frequency components and the state of charge of EVs, the charging scheme is adjusted in a small-time scale.

A synergistic dispatch model is proposed for dispatching electric vehicles (EVs) charging, which provides interruptible load (IL) service. EV users take part in IL service through biding. Dispatch plan is made according to the IL demand and the EV users' biding, and the marginal price is formed. The EVs bid lower than the marginal price are stopped charging, and the EV users are compensated according to their bids. The implementation process is illustrated by a simulation analysis of a power grid study case, and the effectiveness of the proposed method was verified. This paper explores the business model and technology architecture of EV charging providing IL service based on the market mechanism, and provides the basis for using the EV charge to provide IL service.

Electric vehicles (EVs) are part of the solution to achieve global carbon emissions reduction targets, and the number of EVs is increasing worldwide. Increased demand for EV charging can challenge the grid capacity of power distribution systems. Smart charging is therefore becoming an increasingly important topic, and availability of high-grade EV charging data is needed for analysing and modelling of EV charging and related energy flexibility. This study provides a set of methodologies for transforming real-world and commonly available EV charging data into easy-to-use EV charging datasets necessary for conducting a range of different EV studies. More than 35,000 residential charging sessions are analysed. The datasets include realistic predictions of battery capacities, charging power, and plug-in State-of-Charge (SoC) for each of the EVs, along with plug-in/plug-out times, and energy charged. Finally, we analyse how residential charging behaviour is affected by EV battery capacity and charging power. The results show a considerable potential for shifting residential EV charging in time, especially from afternoon/evenings to night-time. Such shifting of charging loads can reduce the grid burden resulting from residential EV charging. The potential for a single EV user to shift EV charging in time increases with higher EV charging power, more frequent connections, and longer connection times. The proposed methods provide the basis for assessing current and future EV charging behaviour, data-driven energy flexibility characterization, analysis, and modelling of EV charging loads and EV integration into power grids.

Electric vehicles (EVs) have become increasingly popular because they are highly efficient and sustainable. However, EVs have intensive electric loads. Their penetrations into the power system pose significant challenges to the operation and control of the power distribution system, such as a voltage drop or transformer overloading. Therefore, grid operators need to prepare for high-level EV penetration into the power system. This study proposes data-driven, parameterized, individual, and aggregated EV charging models to predict EV charging loads in the urban residential sector. Actual EV charging profiles in Saskatchewan, Canada, were analyzed to understand the characteristics of EV charging. A location-based algorithm was developed to identify residential EV charging from raw data. The residential EV charging data were then used to tune the EV charging model parameters, including battery capacity, charging power level, start charging time, daily EV charging energy, and the initial state of charge (SOC). These parameters were modeled by random variables using statistic methods, such as the Burr distribution, the uniform distribution, and the inverse transformation methods. The Monte Carlo method was used for EV charging aggregation. The simulation results show that the proposed models are valid, accurate, and robust. The EV charging models can predict the EV charging loads in various future scenarios, such as different EV numbers, initial SOC, charging levels, and EV types (e.g., electric trucks). The EV charging models can be embedded into load flow studies to evaluate the impact of EV penetration on the power distribution systems, e.g., sustained under voltage, line loss, and transformer overloading. Although the proposed EV charging models are based on Saskatchewan’s situation, the model parameters can be tuned using other actual data so that the proposed model can be widely applied in different cities or countries.

This article explores the potential impacts of integrating electric vehicles (EVs) and variable renewable energy (VRE) on power system operation. EVs and VRE are integrated in a production cost model with a 5 min time resolution and multiple planning horizons to deduce the effects of variable generation and EV charging on system operating costs, EV charging costs, dispatch stacks, reserves and VRE curtailment. EV penetration scenarios of the light-duty vehicle fleet of 10%, 20%, and 30% are considered in the RTS-GMLC test system, and VRE penetration is 34% of annual energy consumption. The impacts of EVs are investigated during the annual peak in the summer and during the four weeks of the year in which high VRE and low loads lead to overgeneration. Uncoordinated and coordinated EV charging scenarios are considered. In the uncoordinated scenario, charging is undertaken at the convenience of the EV owners, modeled using data from the Idaho National Laboratory’s EV Project. Coordinated charging uses an “aggregator” model, wherein EV charging is scheduled to minimize operating costs while meeting the daily charging requirements subject to EV availability and charging constraints. The results show that at each EV penetration level, the uncoordinated charging costs were higher than the coordinated charging costs. During a high-VRE, low-load week, with uncoordinated EV charging at 30% penetration (3% energy penetration), the peak load increased by as much as 27%. Using coordinated charging, the EV load shifts to hours with low prices, coincident with either low load, high VRE, or both. Furthermore, coordinated charging substantially reduces the curtailment of PV by as much as nine times during the low-load seasons, and the curtailment of wind generation by more than half during the summer peak season, compared to the scenarios with no EVs and uncoordinated EV charging. Using a production cost model with multiple planning cycles, load and VRE forecasts, and a “look ahead” period during scheduling and dispatching units was crucial in creating and utilizing the flexibility of coordinated EV charging.

The transition to sustainable transportation has fueled the need for innovative electric vehicle (EV) charging solutions. Building Integrated Photovoltaics (BIPV) systems have emerged as a promising technology that combines renewable energy generation with the infra-structure of buildings. This paper comprehensively reviews the BIPV system for EV charging, focusing on its technology, application, and performance. The review identifies the gaps in the existing literature, emphasizing the need for a thorough examination of BIPV systems in the context of EV charging. A detailed review of BIPV technology and its application in EV charging is presented, covering aspects such as the generation of solar cell technology, BIPV system installation, design options and influencing factors. Furthermore, the review examines the performance of BIPV systems for EV charging, focusing on energy, economic, and environmental parameters and their comparison with previous studies. Additionally, the paper explores current trends in energy management for BIPV and EV charging, highlighting the need for effective integration and recommending strategies to optimize energy utilization. Combining BIPV with EV charging provides a promising approach to power EV chargers, enhances building energy efficiency, optimizes the building space, reduces energy losses, and decreases grid dependence. Utilizing BIPV-generated electricity for EV charging provides electricity and fuel savings, offers financial incentives, and increases the market value of the building infrastructure. It significantly lowers greenhouse gas emissions associated with grid and vehicle emissions. It creates a closed-loop circular economic system where energy is produced, consumed, and stored within the building. The paper underscores the importance of effective integration between Building Integrated Photovoltaics (BIPV) and Electric Vehicle (EV) charging, emphasizing the necessity of innovative grid technologies, energy storage solutions, and demand-response energy management strategies to overcome diverse challenges. Overall, the study contributes to the knowledge of BIPV systems for EV charging by presenting practical energy management, effectiveness and sustainability implications. It serves as a valuable resource for researchers, practitioners, and policymakers working towards sustainable transportation and energy systems.

The number of Electric vehicle (EV) users is expected to increase in the future. The driving profile of EV users is unpredictable, necessitating the design of charging scheduling protocols for EV charging stations servicing multiple EVs. A large EV charging load affects the grid in terms of peak load demand. Electric vehicle charging stations with solar panels can help to reduce the grid impact of EV charging events. With reference to the increasing number of EVs, new technology needs to be developed for charging station and management to create a stable system for users, and electric utilities. The load of a total EV charge can affect the grid, degrading quality and system stability. In this paper, a charging station scheduling strategy is proposed based on the game theoretic approach. In the proposed strategy, with respect to the grid load demand minimization, charging stations have scheduled EV charging times to prevent sudden peak load on the grid the proposed game theory strategy is sudden peak load on the grid. The proposed game theory strategy is defined on the basis of priority so that both grid operators and EV users can maximize their profit by setting priorities for charging and discharging. This work provides a strategy for grid peak load minimization.

Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

A market-based real-time algorithm for congestion alleviation incorporating EV demand response in active distribution networks

Mid- and long-term strategy based on electric vehicle charging unpredictability and ownership estimation

Reducing Emissions and Costs with Vehicle-to-Grid