Electric Vehicles Charging Cost Research Articles

Economic and low-carbon dispatch of industrial integrated energy system with EV load based on Stackelberg game framework

Industrial integrated energy systems (IESs) and electric vehicles (EVs) provide new solutions for addressing the increasing challenges of the energy crisis and environmental pollution. With the increasing number of EVs and smart charging stations in industrial IES, the uncoordinated charging load of EVs imposes significant pressure on IES. Therefore, a well-designed dispatch scheme is crucial for reducing the economic cost for both parties, alleviating the energy supply pressure on IES, and promoting the development of a low-carbon society. To this end, given the load characteristics of EVs in industrial IES, we propose a dispatch framework based on the Stackelberg game theory, which includes the leader and the follower. The leader IES is responsible for formulating both unit dispatch and demand response plans, as well as determining the charging pricing for the smart charging station. The follower smart charging station optimizes EVs charging power by minimizing the charging cost in order to protect the interest of EV owners. Additionally, we introduce the carbon emission flow model into charging station pricing to shift the responsibility for carbon emissions from the generation side to the EV load side. Considering that the two-layer game model is difficult to solve, we use the Karush–Kuhn–Tucker condition and duality theorem to transform it into an equivalent single-layer optimization problem, which is easily solved. Simulation results demonstrate that the proposed game framework effectively reduces the economic cost of IES and the charging cost of EVs, alleviates the pressure from charging load, and reduces the carbon emissions of industrial IES.

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

AbstractIntegrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

Electric vehicle charging scheduling based on improved bald eagle algorithm

With the continuous promotion of electric vehicles (EVs), charging fees for EVs has gradually become a hot issue. Pointing at the defects of the bald eagle algorithm, such as slow rate of convergence and poor solving accuracy, the bald eagle search algorithm based on opposition learning and Gaussian variation (GBES) is proposed. The improved bald eagle algorithm has the characteristics of fast convergence speed and strong optimization ability. The model of EV charging cost minimum scheduling is optimized by the GBES, and its effectiveness is verified by simulation experiments.

Aggregator pricing and electric vehicles charging strategy based on a two-layer deep learning model

In response to the increasing number of EVs(electric vehicles) being charged in a disorderly manner, this paper proposes a two-layer deep learning model based on aggregator pricing and EV user charging strategy. Firstly, the relationship linking the power grid, aggregator, and EVs is defined. Secondly, the pricing of the aggregator and the allocation of EV charging stations are divided into two stages. In the first stage, the aggregator sets the tariffs and sends them to the users, and the users receive the charging strategy. In the second stage, the aggregator adjusts the tariff on the basis of the charging strategy provided by the EVs and the actual situation of different charging stations and then releases it. Next, a two-layer deep learning model is built to solve the final pricing and EV charging strategy model, and the concept of humanity is introduced. The upper layer is the charging decision layer, which is solved by deep reinforcement learning. The lower layer is the charging station selection layer, which is solved by deep Q-learning. The method proposed in this paper can effectively improve the profits of the aggregator and reduce the charging cost of EVs.

A Review on the Performance of Aggregator of EV for Power Market

Abstract: This paper presents a review on the performance of aggregator of EV for power market. The EV aggregator is defined as an interface entity that aggregates the EVs to act as dispersed energy resources, enabling them to support the V2G mechanism. There are various methods to optimize the performance of electric vehical aggregator for power market. This paper analyzes the bidding strategy problem of an aggregator managing a set of electric vehicles. The electric vehicle aggregator determines the bidding decisions in the market that minimize the charging costs of electric vehicles and, at the same time, meets the driving requirements of electric vehicle users. It will take into account the uncertainty in both market prices and driving needs using a set of scenarios, which allows formulating the problem using a stochastic programming approach. The impact of the bidding decisions of the electric vehicle aggregator on market prices is also addressed in the decision-making tool. A number of illustrative examples explain how to formulate and solve this type of problems.

Electric vehicle charging scheduling control strategy for the large-scale scenario with non-cooperative game-based multi-agent reinforcement learning

With the popularity of electric vehicles (EVs), electric vehicle charging scheduling control in the complex urban environment has become a hot research issue, especially the use of multi-agent reinforcement learning (MARL) to solve game problems in the process of EV scheduling. In a complex and unstable environment involving high-speed dynamic changes of vehicles, the traditional methods have poor learning effects. In this paper, a multi-agent charge scheduling control framework with the cooperative vehicle infrastructure system (CVIS) is proposed to solve the game problems between electric vehicle charging stations (EVCSs) and EVs. To achieve effective control of electric vehicle charging stations in large-scale road networks, a new multi-agent A2C algorithm based on a non-cooperative game (NCG-MA2C) is proposed. In the proposed NCG-MA2C algorithm, the state representation is based on the K-nearest neighbor multi-head attention mechanism, the action definition is based on the proposed adaptive service price adjustment strategy, and spatio-temporal discount joint reward stable learning convergence. The results show that the proposed algorithm has good performance in increasing the efficiency of EVCSs while reducing EV charging cost compared with the fixed strategy, the greedy strategy, the independent Q learning (IQL) algorithm, the multi-agent Q-learning algorithm (MAIQL), and the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The NCG-MA2C algorithm has strong extensibility and validity in the complex urban environment.

Comparison of mono and bifacial modules for building integration and electric vehicle charging: A case study in Sweden

Future energy systems have a major difficulty in ensuring a reliable supply of electricity without affecting the environment, and numerous innovative renewables-based solutions are being introduced to meet this issue. This paper proposes a building photovoltaic (PV) system design for residential and electric vehicle (EV) charging demand and evaluates the techno-economic and environmental performance of the system in Orebro, Sweden, as it aims to become net zero carbon economy by 2045. Literature review shows that, although many studies exist, most of them did not fully consider the techno-economic and environmental aspects of PV systems for residential and EV charging loads in the chosen location. Two different PV technologies monofacial and bifacial monocrystalline panel in three different roof slopes 15°,30° and 45° has been analyzed to find the optimized system that can meet a typical house's annual energy demand. Economic indicators such as cumulative cash flow, levelized cost of electricity (LCOE), payback period and cost of EV charging have been evaluated for the PV system without discount and with discount which affects the system's profitability. PVSyst software was used to simulate the system for energy generation. Results have shown that the bifacial PV system performed better in energy generation, which is approximately 10% higher than the monofacial panel. However, in terms of economics, Case 6, a bifacial PV system with a roof angle of 45°, shows the lowest payback period of 7.3 years. In contrast, monofacial PV system with roof slope of 30° showed LCOE of 0.8988 Swedish Krona per kilowatt hour (SEK/kWh), EV charging cost of 0.1471 Swedish Krona per kilometer (SEK/km). Environmental parameters such as greenhouse gases (GHG) reduction have been analyzed. Results showed that GHG savings due to EV was higher than PV plant as Sweden's grid emission factor is very low due to less dependency on fossil fuels. The significance of this study will enable us to understand the performance of PV systems in Swedish aspect and methods can be extended to other countries for meeting location-specific energy demand.

Roof integrated photovoltaic for electric vehicle charging towards net zero residential buildings in Australia

The residential building sector & transportation sector has received significant attention in recent years due to carbon emissions affecting the livelihoods of humans. Electric vehicles (EVs) are poised to play a vital role in reducing dependency on non-renewable fuels and the transportation sector's environmental implications. Building integrated Photovoltaics (BIPV), on the other hand, will be able to cut down the energy requirements and contribute to building sustainability. This paper evaluates the energy, economic, and environmental performances of BIPV with EV charging system systems for a building in Canberra, Australia. BIPV with EV charging can assist in fulfilling building energy needs, charging electric vehicles, and reducing grid reliance. Different load scenarios were studied to optimize system size. Different panel layouts with orientation have been studied for the 5.0kWp BIPV system, each module rating 355kWp. The annual energy output of the BIPV system was found to vary between 8.56 MWh for layout one and 7.08MWh for layout 3. It was found that the payback back of the proposed BIPV system is 4.46 years, LCOE of 0.074AUD/kWh and EV charging cost of 0.95AUD/100 km. The BIPV with EV charging system could save approximately 160,198 kgCO2e in GHG emissions over its lifespan.

Coordinated Scheduling for Multimicrogrid Systems Considering Mobile Energy Storage Characteristics of Electric Vehicles

Because of the rapid development of electric vehicles (EVs), the energy management of multimicrogrid (MMG) systems has attracted considerable research attention. The objective of this study is to coordinate scheduling performance for MMG systems under large-scale EV operations. To address the problem that the calculation time increases exponentially with the scale of EVs, a clustering algorithm was proposed to speed up the solving efficiency of the coordinated scheduling of MMG systems. To address the problem of the departure and arrival times of clustering EV crossover among various MGs, the proposed clustering algorithm set the boundary of the parking period of clustering EVs by maximizing their controllable time in various MGs. The coordinated scheduling strategy of MMG systems was executed considering the charging cost of EVs, the optimization of transmission power curves, and the absorption of renewable energy. The simulation results revealed that by fully utilizing the mobile energy storage characteristics of EVs, the performance of MMG systems can be maximized. Meanwhile, the computing efficiency of coordinated scheduling can be considerably improved in the case of large-scale EVs integrated into MMG systems by using the proposed clustering algorithm.

Maximizing solar-powered charging and discharging of electric vehicles in residential and parking areas: A case study in optimal scheduling

The expectation of a huge number of Electric Vehicles (EVs) into the market creates multiple technical problems. The power system is at risk due to the unsecured and unreliable operation resulting from unplanned charging and discharging. The unplanned EV charging and discharging change the load profile of the electric grid. Hence, it is required to develop an effective scheduling scheme for charging and discharging EVs. This research focuses on the minimization of the charging cost of a large number of Electric Vehicles (EVs) in a residential area and parking station. When a huge number of EVs are aggregated in the residential and parking stations, charging and discharging power should be a significant and viable contributor to the power grid. V2G (Vehicle-to-Grid) technology offers additional potential benefits to improve power quality and also system reliability. EVs can also supply G2V (Grid-to-Vehicle) power through the discharge of the battery, which benefits both EV owners and electric utilities. It is very difficult to schedule the configurations of EV charging and discharging in an optimized way so as to decrease the charging cost of EVs. The usage of renewable energy sources like solar Photovoltaic (PV) power, reduces the burden on the grid. The solar PV systems are likely to have significant surplus power during the daytime. In this research, optimal charging and discharging scheduling schemes for EVs are developed and the proposed work is carried out under the MATLAB environment with and without Photovoltaic (PV) systems. The realistic load data is collected from Transmission Corporation of Andhra Pradesh (APTRANSCO), India and the solar data is taken from the Indian Solar Resource Data website. The effectiveness of the scheduling scheme is rated according to its capability to reduce the total cost. Extensive simulation studies have been performed on the distribution system under consideration and the results have been presented. The results demonstrate that the charging cost of EVs can be reduced significantly with the use of solar PV power.

Optimization of Orderly Charging/Discharging Strategy for Electric Vehicles Considering Incremental Cost of V2G

The integration of electric vehicles (EV) into demand-side response holds significant potential, which not only helps to reduce the operational expense of EVs, but also presents a viable strategy for the grid peak regulation. In this paper, the load simulation model of EVs is proposed based on the probability distribution of charging/discharging behaviours of different types of EV users from statistical data. On this basis, considering the incentive effect of hourly electricity price, an orderly charging/discharging optimization model is proposed to consider EV charging cost, discharge income and incremental battery loss cost caused by EV participating in the vehicle-to-grid (V2G) mode. This model provides a more accurate estimation on the incremental costs and market arbitrage benefits of EVs in V2G mode on the basis of conventional transportation utilization, so as to better optimize the operation strategy of the EVs for demand response. Numerical results show that in the context of tariff of usage (TOU), the orderly charge and discharge model reduces the operating cost of EV users, and can better exploit the potential of EV energy storage batteries in power grid peak regulation.

Optimal Management of Energy Communities Hosting a Fleet of Electric Vehicles

In this paper, we study the problem of managing an energy community hosting a fleet of electric vehicles for rent. On the day ahead, service requests for electric vehicles are submitted to the community. Then, the optimal request-to-vehicle assignment has to be found, as well as the optimal charging schedule of vehicle batteries. A suitable model is presented and included in an existing energy community architecture. The overall community management problem is formulated as a bi-level model, featuring two nested optimization problems. The optimal request-to-vehicle assignment requires the solution of a mixed-integer linear program. To reduce the computational complexity, a heuristic solution to the assignment problem is presented. Numerical results show that participation in the community grants a remarkable reduction in the electric vehicle charging cost. The adoption of the heuristic assignment solution provides a dramatic reduction in the computation time required to solve the bi-level model. At the same time, the level of suboptimality introduced appears to be negligible, being less than 1% in most of the considered cases.

A Deep RL-Based Algorithm for Coordinated Charging of Electric Vehicles

The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area.

Constrained large-scale real-time EV scheduling based on recurrent deep reinforcement learning

The rapid growth of electric vehicles (EVs) is an unstoppable worldwide development trend. An optimal charging strategy for large-scale EVs is able to deal with the randomness of EVs charging and satisfy charging demands of users while ensuring safe and economic operation of the power system. The current centralized and model-based methods failed to overcome the randomness charging problem of the large-scale EVs. Thus, the paper proposes a decentralized approach based on model-free deep reinforcement learning (DRL) to determine the optimal strategy for reducing EVs charging cost considering power limit of the charging station (CS), users' charging demands and fair charging fees. First, a decentralized framework and a dynamic energy boundary (DEB) model of single EV which discretizes the charging demand are proposed. Second, the problem as a Markov Decision Process (MDP) with unknown transition probability is formulated. Moreover, the recurrent deep deterministic policy gradient (RDDPG) based approach is proposed to determine the charging strategy for all charging piles in the CS. Finally, digital simulation studies are conducted to demonstrate the effectiveness of the proposed approach in charging cost reduction and fair charging fees. In addition, the RDDPG-based approach has great scalability which can apply a small-scale model to solve a large-scale problem without being retrained.

Techno-economic modelling for energy cost optimisation of households with electric vehicles and renewable sources under export limits

With the proliferation of electric vehicles (EVs), EV charging cost will become an integral part of household energy cost. This research proposes a novel household energy cost optimisation method for grid-connected homes with EV under power export constraints. It addresses the limitations of previous studies by incorporating more realistic variable EV charging characteristics, power export limits, degradation of battery energy storage (BES) and battery salvage revenue into a comprehensive techno-economic energy system model. Cost optimisation results are presented for four system configurations using relatively new time-of-use (ToU) tariff and real load and photovoltaic (PV) generation data for South Australian households. Sensitivity analysis for annual energy cost (AEC) is conducted by varying daily household load demand, PV/BES capacity, power export limits and PV/BES cost. Power flow and peak demand analyses are presented to the impacts of PV, BES and EV on household demand. Results show that PV with BES and EV is the most economical configuration for individual households, where the AEC can be reduced by up to 39.6% compared to a normal household without EV, PV and BES. The BES can effectively reduce household power and energy demands during peak periods by up to 80.4% and 89.1%, respectively.

Distributed Electric Vehicle Charging Mechanism: A Game-Theoretical Approach

To reduce the overloading of the power system due to the increasing use of electric vehicles (EVs), the power system operators employ dynamic cost plans depending on the number of EVs being charged. In this situation, the charging cost of an EV can be reduced by considering other EVs’ charging strategies and avoiding simultaneous charging. In this article, we develop a distributed EV charging mechanism (D-EVCM) where each EV possesses some information, such as the charging probabilities of other EVs, electricity price, current energy level, and expected departure time. Based on the information, an EV periodically decides whether to charge its battery in a distributed manner. To minimize the average charging cost of EV while avoiding the situation where an EV departs from a charging station with insufficient energy level, a constrained stochastic game model is formulated and its solution is obtained using a best response dynamics-based algorithm. The evaluation results show that D-EVCM can reduce the average charging cost over 48% with a sufficient low battery outage probability (e.g., 0.01%) compared to a threshold-based charging scheme.

Routing and Charging Facility Location for EVs Under Nodal Pricing of Electricity: A Bilevel Model Solved Using Special Ordered Set

We consider the problem of identifying optimal location of electric vehicle (EV) charging stations, while accounting for (i) route optimization and (ii) charging cost optimization by the EV fleets, where the electricity price is obtained endogenously by an optimal power flow (OPF) model. We solve the problem using a bi-objective bilevel programming framework with the objectives being one of minimising travel time and the other of minimising EV charging cost. The upper level problem consists of the facility location and the transportation model and the lower level problem consists of the OPF model. After reformulating this computational hard problem as a mathematical program with equilibrium constraints (MPEC), we solve the problem using a special ordered sets-type 1 (SOS1)-based approach. We record the significant improvement in speed by our method, as opposed to the standard Big-M approach. Finally, we apply the technique to the Sioux Falls transportation network with the IEEE 14-bus electricity network embedded on it. We observe that solutions through our models results in as much 37% lower operating costs for the EVs.

Day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles

Abstract The day-ahead and real-time congestion scheduling method for distribution network with multiple access to electric vehicle charging piles is studied to effectively solve the day-ahead and real-time congestion scheduling problem of distribution network. The charge adjustment strategy of charge and discharge service fee is established to realize the double response regulation between the distribution system’s scheduling organization and the charging pile operator; considering the adjustment strategy of charging service fee, a day-ahead congestion scheduling model is established with the goal of minimizing the charging cost of electric vehicles; based on the day-ahead congestion scheduling, a real-time congestion scheduling model is established to minimize the regional power fluctuation; the day-ahead and real-time congestion scheduling model is solved by differential evolution algorithm, and the optimal scheduling scheme is obtained. Experiments show that this method can reduce the line load rate of distribution network, avoid re-congestion, reduce the congestion scheduling cost and improve the security and economy of power grid operation.

Grid reactive voltage regulation and cost optimization for electric vehicle penetration in power network

Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.