Electric Vehicle Parking Research Articles

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

Day-ahead electric vehicle charging behavior forecasting and schedulable capacity calculation for electric vehicle parking lot

The Electric Vehicle Parking Lot (EVPL) aims to address the growing demand for EV charging infrastructure. EVs connected to EVPLs have extended access times compared to those at public charging stations. Consequently, EVPL owners can aggregate the schedulable capacity of connected EVs to participate in day-ahead and ancillary service markets, thereby gaining economic benefits. However, achieving this objective is hindered by the lack of accurate day-ahead forecasts of EV charging behavior. To address this issue, this paper introduces a novel day-ahead forecasting method for EV charging behavior at EVPLs, alongside a strategy for calculating EV schedulable capacity based on these forecasts. Unlike existing methods, this paper presents a day-ahead time-of-use clustering forecasting strategy, which provides more detailed and accurate predictions of EV charging behavior, eliminating the need for numerous assumptions during schedulable capacity calculations. The study demonstrates that the proposed method, validated using actual historical data, enables precise forecasting of EV charging behavior. Furthermore, the proposed day-ahead schedulable capacity calculation strategy is shown to be both effective and practical.

A Novel Optimal Planning and Operation of Smart Cities by Simultaneously Considering Electric Vehicles, Photovoltaics, Heat Pumps, and Batteries

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

An Orderly Charging and Discharging Strategy of Electric Vehicles Based on Space–Time Distributed Load Forecasting

Given the widespread adoption of electric vehicles, their charging load is influenced not only by vehicle numbers but also by driving and parking behaviors. This paper proposes a method for forecasting electric vehicle charging load based on these behaviors, considering both spatial and temporal distribution. Initially, the parking generation rate model predicts parking demand, establishing the spatial and temporal distribution model for electric vehicle parking needs across various vehicle types and destinations. Subsequently, analyzing daily mileage and parking demand distributions of electric vehicles informs charging demand assessment. Using the Monte Carlo simulation method, large-scale electric vehicle behaviors in different spatial and temporal contexts—parking, driving, and charging—are simulated to predict charging load distributions. Optimization of electric vehicle charging and discharging enhances grid stability, cost management, charging efficiency, and user experience, supporting smart grid development. Furthermore, charging load forecasting examples under diverse scenarios validate the model’s feasibility and effectiveness.

Multi-objective economic operation of smart distribution network with renewable-flexible virtual power plants considering voltage security index

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

Security constrained unit commitment in smart energy systems: A flexibility-driven approach considering false data injection attacks in electric vehicle parking lots

In this paper, a new structure, the so-called FBSCUCFDIP-P/EV is proposed as flexibility-based security constrained unit commitment (SCUC) in the presence of false data injection (FDI) attack into the communication infrastructure of electric vehicle parking lot (EVPL). Herein, the uncertain EVPLs are integrated into the SCUC problem with the aim of reducing the operation cost. It is notable that growing integration of unspecified EVPLs can introduce novel challenges to the power system, significantly impacting its flexibility. In this study, electric vehicles are leveraged as a means to enhance system flexibility. Meanwhile, the FDI attacks in EVPLs can distort the system’s flexibility and lead to inaccurate assessments of the power system’s ability to adapt to changing conditions. In order to model the FDI attack, a bi-level optimization problem based on mixed integer linear programming is formulated. At the upper level, the impact of EVPLs on the flexibility indices of the SCUC is evaluated, and the false data injected into the EVPL is calculated at the lower level. Since both levels of the proposed FBSCUCFDIP-P/EV include discrete variables, a reformulation and decomposition technique is utilized to achieve the optimal solution. Instead, an extreme gradient boosting (XGBoost)-based machine learning method is considered to detection and correction of FDI attack. The proposed approach is tested on the IEEE 24-bus system. The simulation results initially indicate the improvement of the flexibility of the power system in proposed structure. Further, injecting false data into all available EVPLs causes to increase the system operation cost. Besides, false data leads to distorted charging and discharging scheduling of EVPLs; likewise, scheduling and commitment of power generation units also changes. Subsequently, the application of the XGBoost algorithm effectively mitigates the impact of FDI attacks, achieving a maximum accuracy of 85.41%.

A novel risk-averse optimal scheduling strategy for active distribution networks equipped with power-to-X technologies

This paper proposes a novel risk-averse approach for scheduling of an active electrical distribution network (AEDN). The network is connected to an electric vehicle parking lot (PL) and gas and hydrogen fueling stations (GHFSs). In addition, the AEDN operator is able to participate in both day-ahead and real-time markets to supply energy demands. In order to address the uncertainties of the studied network, a two-stage stochastic programming incorporating conditional value-at-risk (CVaR) is employed to model uncertainties of wind turbine and photovoltaic output, electric vehicles behavior in PL, and GHFSs energy consumption, while a robust optimization method is used to handle the uncertainty of real-time market price. According to the numerical results, the AEDN operator can decrease the risk level by up to 1.2% while the operational cost rises by 7.5%. Furthermore, the optimal use of multiple energy storage technologies and price-sensitive shiftable electric loads (PSELs) reduces the operational cost by 7.4%.

A novel stochastic framework for optimal scheduling of smart cities as an energy hub

AbstractSmart cities consist of various energy systems and services that must be optimally scheduled to improve energy efficiency and reduce operation costs. The smart city layout comprises a power distribution system, a thermal energy system, a water system, and the private and public transportation systems. Additionally, several new technologies such as reconfiguration, regenerative braking energy of the metro, etc. are considered. This study is one of the first to consider all these technologies together in a smart city. The proposed power distribution system is a grid‐connected hybrid AC–DC microgrid. The biogeography‐based optimization algorithm was utilized to seek the best solution for scheduling micro‐turbines, fuel cells, heat pumps, desalination units, energy storage systems, AC–DC converters, purchasing power from the upstream, distributed energy resources, and transferring power amongst electric vehicle parking stations and metro for the next day. Also, the reduced unscented transformation layout was used to capture the system's uncertainty. The suggested layout is implemented on an enhanced IEEE 33‐bus test system to show the efficiency of the suggested method. The results show that costs and environmental pollution are reduced. By comparing the proposed smart city with other studies, the efficiency and completeness of the proposed smart city are shown.

Real-time V2G simulator for grid system frequency and voltage regulation with fault ride-through capabilities

Electric vehicles (EVs) have become one of the promising power grid ancillary services. They can help reduce the intermittent of renewable energy sources by supporting active power and frequency control services. Additionally, bi-directional chargers included into EV batteries can supply/consume reactive power for voltage regulation. To improve voltage quality, a unique control approach is devised to offer fault ride-through. In this study, real-time charging optimization schemes based on particle swarm optimization are developed to coordinate the EVs charging/discharging power with the grid power dispatches depending on the charging priority and preferential electricity prices. Five different car user profiles including different initial state of charge and plug in state have been depicted. Different optimum issues are carried out with comprehensive simulations employing hardware in the loop via real-time simulation for a modified IEEE 14-bus test system. Where different frequency and voltage controls, management strategies, variant fault scenarios, benefits, and challenges for power system regulation with EVs are illustrated. According to the simulation results, the proposed optimized charging strategies reduce the total expenses of EV parking station by 40 % compared to other cases, hence maximizing EV owner satisfaction. The net load curve is smoothed and reduced by 11 %, particularly during peak period.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms: V2G approach for cost reduction and grid support

The adoption of Electric Vehicles (EVs) in the transportation sector is expected to grow significantly in the coming few years. While EVs offer numerous benefits, including being environmentally friendly, energy-efficient, low-noise, and can intelligently interact with smart grids through Vehicle-to-Grid (V2G) technology, their widespread adoption will increase energy demand and present challenges to grid load management. Furthermore, EV users face issues such as charging costs, charging time, access to public charging infrastructure, and more. In this article, we propose an approach utilizing metaheuristic algorithms to schedule the charging and discharging activities of EVs while parking, leveraging V2G technology with the goal of reducing the daily costs of EV users and addressing energy demand management challenges in smart grids. Four metaheuristic algorithms inspired by evolutionary and swarm concepts are applied, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO). The results obtained from the proposed approach demonstrate the feasibility of scheduling EVs charging and discharging activities to minimize EV user costs through V2G integration. This, in turn, contributes to enhancing the overall EV user experience and addressing energy demand management issues. Additionally, the results show that WOA outperformed the other algorithms in terms of convergence. This work can be further developed to create an integrated algorithm to balance the interests of both EV users and parking facility operators.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.

Comprehensive techno-economic performance assessment of PV-building-EV integrated energy system concerning V2B impacts on both building energy consumers and EV owners

Deploying photovoltaics (PVs) for buildings is an effective solution to decrease carbon emissions but it also imposes a substantial risk of energy mismatch. Battery storage can mitigate this issue to a certain extent but its capacity is limited due to safety concerns and expensive investment. Energy boundary expansion from buildings to an integrated building-transportation is another valuable attempt. However, few studies have quantified the impacts of electric vehicles (EVs) on the PV-building-EV integrated system currently. This study, therefore, conducted a comprehensive techno-economic performance assessment of this integrated system. An energy-sharing mechanism considering EV charging-discharging and PV-building energy system integration is involved in the assessment. According to this energy-sharing mechanism, feasibility studies of this integrated system without stationary battery installation were conducted. An office building integrated with the EV parking area in Chengdu is selected in this study. Results show that the system's techno-economic performance as well as EV battery degradation were comprehensively quantified from two perspectives. From the perspective of the building consumers, renewable energy penetration is enhanced at 45.1% and the reduction of levelized cost of energy (LCOE) is about 38.9%. From the perspective of EV owners, the LCOE of EV charging can decrease by 18.1%. Extra annual EV battery degradation is quantified at 5.23% and an extra compensation of 10.45 CNY for one-time participation of EV owners is calculated. Reasonable compensation for EV owners can effectively increase their motivation to participate in the energy-sharing mechanism. The comprehensive techno-economic performance assessment can provide frontier guidelines for future work.

Probabilistic Energy Management of DGs and Electric Vehicle Parking Lots in a Smart Grid considering Demand Response

In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively.

Pattern-based integration of demand flexibility in a smart community network operation

The proliferation of utilizing smart devices and electric vehicles (EVs) has engendered new opportunities for local distribution operators (LDOs) to flexibly energize these loads, given customers’ preferences. In this context, this paper proposes an integrated scheduling model allowing an LDO to efficiently incorporate and optimally reschedule potential demand flexibilities. Given the full knowledge of the LDO over the grid conditions and availability of its owned assets, we argue that an LDO can better reshape the load profiles of the customers than they could do. The proposed model couples the branch flow-based AC power flow model in distribution network with potential demand flexibilities classified into four generic types. The LDO is assumed to operate multiple solar PV plants, energy storage systems (ESS), and EV parking lots with smart charging stations. The practicality of the proposed model is investigated through two case studies implemented on the modified IEEE 33-node distribution test system. The simulation results reveal that the total operation cost of the community can be reduced by ∼10% without sacrificing customers’ comfort if the potential demand flexibilities are optimally rescheduled.

A multi-objective two-stage stochastic programming approach for wind farms and demand side provider decisions using the weighted metric method

The rapid expansion of wind-based renewable energy sources highlights the critical need to enhance reserve capacity within power systems. This research aims to develop an effective model for harnessing the combined potential of flexible demand-side resources, acting as demand-side providers (DSPs), to address the inherent unpredictability of wind power. The DSP framework comprises three demand response aggregators: the capacity market program (CAP), energy storage systems (ESSs), and plug-in electric vehicle parking lots (PEVs PLs). To achieve this, a multi-objective two-stage stochastic programming model is introduced. It not only minimizes operational costs for system operators but also maximizes returns for demand-side resource owners supervised by DSPs in the energy and reserve market environment. The problem is tackled using a mixed-integer linear programming (MILP) approach employing a weighted metric methodology. Within this framework, the Weibull probability distribution function (WPDF) is utilized to model wind power uncertainty, while the truncated Gaussian distribution function (GDF) encapsulates the uncertainty regarding electric vehicle owner behavior. The results demonstrate that employing this method for simultaneous programming of energy and reserve not only reduces system operator costs but also enhances profits for resource owners.

Single-stage vehicular fuel cell system with harmonic elimination capability to suppress distortion effects of electric vehicle parking lots

In this study, Fuel Cell Vehicles (FCVs) are implemented to reduce the negative impacts caused by electric vehicle parking lots connected to the electricity grid. The main objective of the current study is to propose a dual-function control method for grid integration of vehicular fuel cells located in FCVs to reduce the harmonic pollution emitted to the grid by other electric vehicles and connected charging stations. The proposed control method is tested on a single-stage grid connection and is provided directly via inverter without using an additional converter, in contrast to the traditional two-stage structure. A dual-function single-stage vehicular fuel cell system is designed and operated to verify the reduction of harmonic distortion problems caused by grid-connected electric vehicles in the charging process. With the proposed structure, in addition to harmonic elimination, the system contributes to the reduction of energy consumption by supplying power to the grid/electric vehicles. In the system, the vehicular fuel cell, which feeds electric vehicle parking lots ranging from 4 kW to 12.1 kW, is designed to have a maximum power of 24.57 kW. Distortion values varying between 5.76 % and 23.01 % are reduced up to 0.45 % and 1.52 % with the ability to eliminate the harmonics.

A Two-Point Estimate Method for Optimal Dispatching of Large-Scale Electric vehicles into Distribution Networks under Uncertainty

Electric vehicle (EV) charging irregularities have a massive effect on the distribution system. In recent years, research has focused on how to address the issue of large-scale charging and discharging of EVs while ensuring the grid's security. Distribution network operators should choose places where EV charging is practicable in terms of environmental and financial benefits. To establish how to best charge and discharge EVs in the distribution network, first comprehend and then determine the optimal position of EV parking lots in the distribution network. In this article, to solve this optimization problem, first the mathematical model of the problem is built, and then - according to the nonlinear features of the proposed optimization model - the second-order cone method is used to find the optimal solution with high accuracy and speed. The objective functions can accomplish more realistic objectives like lowering losses and minimizing voltage deviation changes with the charging and discharging time control technique, in addition to optimizing the technical requirements of the distribution network. The two-point estimation method of Hang is used to describe the uncertainty in the loads and capacities of solar sources in the random state, and optimization is applied to the IEEE 33-bus distribution system. The obtained results reveal that the proposed method not only meets the charging and discharging requirements of large-scale EVs, but also ensures the stability of the power grid, demonstrating the effectiveness of the proposed method in the optimal planning of EVs.

Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach

Developing a Transactive Charging Control Framework for EV Parking Lots Equipped With Battery and Photovoltaic Panels: A MILP Approach