Charging Price Research Articles

Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria

Microgrids can integrate variable renewable energy sources into the energy system by controlling flexible assets locally. However, as the energy system is dynamic, an effective microgrid controller must be able to receive feedback from the system in real-time, plan ahead and take into account the active electricity tariff, to maximize the benefits to the operator. These requirements motivate the use of optimization-based control methods, such as Model Predictive Control to optimally dispatch flexible assets in microgrids. However, the major bottleneck to achieve maximum benefits with these methods is their predictive accuracy. This paper addresses this bottleneck by developing a novel multi-step forecasting method for a Model Predictive Control framework. The presented methods are applied to a real test-bed of a renewable energy community in Austria, where its operational costs and CO2 emissions are benchmarked with those of a rule-based control strategy for Flat, Time-of-Use, Demand Charge and variable energy price tariffs. In addition, the impact of forecast errors and electric battery capacity on energy community operational savings are examined. The key results indicate that the proposed controller can outperform a rule-based dispatch strategy by 24.7% in operational costs and by 8.4% in CO2 emissions through optimal operation of flexibilities if it has perfect foresight. However, if the controller is deployed in a realistic environment, where forecasts for electrical load and PV generation are required, the same savings are reduced to 3.3% for cost and 7.3% for CO2, respectively. In such environments, the proposed controller performs best in highly dynamic tariffs such as Time-of-Use and Real-time pricing rates, achieving real cost savings of up to 6.3%. These results show that the profitability of optimization-based control of microgrids is threatened by forecast errors. This motivates future research on control strategies that compensate for forecast errors in real-world operation and more accurate forecasting methods.

Profit improvement strategy of electric vehicle charging stations sharing carbon trading revenue

With the development of electric vehicles (EVs), a large number of electric vehicle charging stations (CSs) have been rapidly rolled out to meet the charging demand of EVs. However, high construction costs and long payback periods motivate investigations to improve the profits of CSs. Considering the profit improvement of CSs and carbon emission reductions, this paper first proposes a carbon revenue model for CSs to participate in the carbon trading market. A charging price strategy is proposed to share the carbon revenue with EV users to reduce the charging cost of users, increase the charging income of CSs, and reduce carbon emissions. By describing the EV users' response to the charging price based on the fuzzy theory, this paper establishes the charging behavior model of EV users and solves the profit optimization of the dynamic charging price model by particle swarm optimization algorithm. Finally, the results of the simulation case demonstrate the effectiveness of the proposed strategy. A sensitivity analysis of various grid power purchase prices illustrates the difference between the fixed and dynamic charging price methods. The dynamic charging price method is superior, and under the power purchase price of 0.5 yuan/kWh, it can lower EV user charging costs by 16.25%, improve CS profits by 30.09%, and reduce carbon emissions by 74.68%.

Economic Viability of Dynamic Wireless Charging Technology for Private EVs

Dynamic wireless charging (DWC), which enables charging, while electric vehicles (EVs) are in motion, is an attractive charging way. However, additional installed power tracks underneath the lane bring a significant concern to its economic viability. This article proposes a comprehensive framework to evaluate the economic viability of the DWC lane for private EVs. The investment of local power support with renewable energy integration and energy storage is also considered in the DWC system. Charging choices of private EVs among multiple charging providers are modeled to estimate the electricity demand of the DWC system. The grid impact of the DWC system is studied via a multibus ac network. Moreover, an optimization policy is proposed to maximize the DWC provider’s profit and minimize the grid impact by adjusting the charging price and electricity procurement at each horizon. It is found that the payback period of the DWC system with the proposed optimization policy is shortened by 25% compared with the fixed charging price strategy. Even with a tight grid impact limit, the payback period will not be significantly longer under the proposed optimization policy. When the efficiency of the DWC lane increases to 90% and the cost reduces to 50%, the payback period is shortened by 19% and 22%, respectively.

Economic Operation Strategy of an EV Parking Lot with Vehicle-to-Grid and Renewable Energy Integration

The economic operation of an electric vehicle (EV) parking lot under different cases are explored in the paper. The parking lot is equipped with EV charging stations with a vehicle-to-grid (V2G) function, renewable energy sources (RESs), and energy storage system (ESS). An optimisation problem is formulated to maximise the profit of the parking lot from EV charging and feed-in energy to the grid under various charging modes while considering the uncertain factors, ESS degradation, and diverse EV parking conditions. The electricity market price, solar radiation and wind speed are considered as uncertain factors, and the scenred toolbox of MATLAB is used to generate scenarios. Based on the parking time of different EVs, the model classifies the EVs entering the charging station and dynamically determines the charging price according to their charging demand through a linear price-demand relationship. The efficacy of the proposed model is verified by the comparison with two other models under three different cases. It is shown that the proposed model gains the most profit based on the proposed V2G services and dynamic charging price.

Reducing Urban Traffic Congestion via Charging Price

Traffic jams are one of the major transportation problems. The United States spends USD billions to mitigate the problem, and not always with good outcomes. This problem increases and has effects on sustainable transport, such as life quality, pollution, perishables, and costs. Large cities reduce traffic jams through congestion charges. This paper aims to reduce urban traffic congestion by estimating the charge through a multivariable model. It studies the main jammed areas in Santiago, Chile. The data came from published surveys. The model evaluation included Fisher multiple regression (F) and the determination coefficient (R2). These validations showed that the model is statistically significant. They also showed that the parameter estimation was good. Finally, this model contributes to improving the Sustainable Development Goals, such as SDG 3, SDG 11, and SDG 13, which may be successfully applied to Santiago City, as well as to any city worldwide.

Variation in Cost of Echocardiography Within and Across United States Hospitals

While transthoracic echocardiography (TTE) is responsible for more Medicare spending than any other cardiovascular imaging procedure, little is known about its commercial cost footprint. The 2021 Hospital Price Transparency Final Rule mandated that U.S. hospitals publish their insurer-negotiated and self-pay prices for services. This study sought to characterize and assess factors contributing to variation in TTE prices. We used a commercial database containing hospital-disclosed prices to characterize variation in TTE prices within and across hospitals. We linked these price data to hospital and regional characteristics using Medicare Facility IDs. A total of 1,949 hospitals reported commercial prices. Among reporting hospitals, median commercial and self-pay prices were 2.93 and 3.06 times greater than the median Medicare price ($1,313 and $1,422, respectively, vs $464). Within hospitals, the 90th percentile payer-negotiated rate was 2.78 (interquartile range, 1.80-5.09) times the 10th percentile rate (within-center ratio). Across hospitals within the same hospital referral region, the median price at the 90th percentile hospital was 2.47 (interquartile range, 1.69-3.75) times that at the 10th percentile hospital (across-center ratio). On univariate analysis, for-profit (P=.04), teaching (P<.01), investor-owned (P<.01), and higher-rated hospitals (P<.01) charged higher prices, whereas rural referral centers (P=.01) and disproportionate share hospitals (P<.01) charged less. On multivariate analysis, the association between these characteristics and TTE prices persisted, except for investor ownership and rural referral centers. Self-pay and commercial TTE prices were higher than Medicare prices and varied significantly within and across hospitals. For-profit, teaching, and higher-rated hospitals had higher prices, in contrast to DSH hospitals. A better understanding of the relationship between this cost variation and quality of care is critical given the impact of cost on health care access and affordability.

Electric Vehicle Charging Pricing Design for Agent-Based Traffic Microsimulation

Over the past few years, electric vehicles (EVs) have become a popular mode of individual transportation due to their significant benefits over internal combustion engine vehicles, resulting in significant growth in their penetration rate. However, the current flat electricity charging pricing and rapid diffusion of EVs may impose challenges to both transportation systems and power grids. It is well-accepted that the implementation of charging pricing schemes is a promising solution for changing and controlling EV users’ charging behavior. Nevertheless, in most research studies that have examined pricing schemes, the charging logic is defined in a way that agents will charge their vehicles while performing their daily activities. This will make the application of pricing schemes unfeasible, as agents will not have the freedom to unplug their EVs whenever they want, to reduce their billing costs. Accordingly, this paper provides an agent and activity-based framework for charging pricing schemes by decoupling activity and charging start and end times. Following this, three different charging pricing schemes have been introduced, including time of use (TOU), non-linear and zonal pricing and tested on the Montreal scenario.

Pricing for Electric Vehicle Charging Stations Based on the Responsiveness of Demand

Electric vehicles (EVs) have the promising potential to be effective in mitigating greenhouse gas emissions in the transportation sector. Hence, the penetration of EVs in some countries experiences steady growth. However, the proliferation of EVs can cause negative impacts on the power distribution network (PDN) and the transportation network (TN). Thus, in this paper, a dynamic pricing strategy for electric vehicle charging stations (EVCSs) is proposed to facilitate EVCSs to earn higher profits while alleviating the potential negative impacts on both PDN and TN. First, a pricing strategy, considering the competition effect established based on the TN, is formulated to facilitate the EVCS to attract the defined competitive charging demand. Second, a two-step approach is proposed to mathematically formulate the responsiveness of demand towards the charging price. Third, EV user behaviors are incorporated based on both an admission control scheme and a queueing model to further adjust the charging demand. We have conducted simulations to verify the effectiveness of the pricing strategy in a PDN and TN coupled system, which contains approximately 2000 EVs. Results show that the proposed pricing strategy can facilitate the EVCS to gain 14.2% more net charging profit on average compared with the other three cases. Moreover, the operational stability of the EVCS can be ensured because the proposed pricing strategy can result in the least queueing length volatility with moderate profit variance compared with the other three cases. Furthermore, the proposed pricing strategy can achieve spatial load shifting by incentivizing EVs to alter their station-selection decisions to avoid possible power congestion in the electricity network.

AI-Empowered Game Theoretic-Enabled Dynamic Electric Vehicles Charging Price Scheme in Smart City

AI-Empowered Game Theoretic-Enabled Dynamic Electric Vehicles Charging Price Scheme in Smart City

Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA.

A novel approach for steelmaking scheduling with self-generation under real-time and demand charge tariffs

It is difficult for energy-intensive enterprises equipped with self-generation to incentivize consumers to shift load to low-demand periods through time-based pricing. This paper addresses a scheduling problem of steel plant equipped with self-generation under real-time electricity prices. Considering the possibility of centralized load shifting resulting in excessive loads for some periods, we also consider the demand charge pricing. A continuous-time mixed-integer linear programming model based on event expression and generalized disjunctive programming is proposed. As industrial scale problems cannot be solved by some commercial solvers in an acceptable time, a SPEA2 algorithm is tailored. Effective encoding based on decomposition-and-offsetting mechanism and decoding based on backward-and-rules are used to generate the initial population and scheduling scheme, respectively. Meanwhile, crossover and mutation operators incorporating conflict resolution strategies can strike a balance between exploring and exploiting to produce high-quality solutions. Numerical experiments indicate that the proposed approach has superiority in performance and efficiency.

An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City

The world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in today’s transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time (CWT), average charging price (CP), and charging failure rate (CFR) of EVs.

A novel modified tunicate swarm algorithm for optimal charge scheduling and pricing of electric vehicles with user priority‐based charging station selection

SummaryIn recent days, electric vehicles (EVs) are seen as a viable transportation alternative by industry people to minimize the greenhouse gas emissions problems of regular fuel vehicles. While EVs' performance is limited due to their restricted battery power, charging wait for service, and high resource costs, they are still a viable option. To enhance the EV's performance, this work introduced an optimized priority‐based charge scheduling with dynamic pricing model. For this, a novel modified tunicate swarm algorithm method, which is the enhanced form of the standard tunicate swarm algorithm, is proposed. When an EV arrives at the charging station, the EV user is permitted to choose the type of charging based on his/her requirements. In general, when charging EVs in public stations or during peak hours, customers must be patient and wait for significant periods of time. The introduced model ignores this negative effect. EV users have also mentioned the issue of controlled charging rates as a key barrier. This is a difficult task, but it is accomplished in this study, which optimizes total charge costs. Finally, the results acquired are discussed under overall cost and energy consumption.

A contract-based energy harvesting mechanism in UAV communication network

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

Multi-agent reinforcement mechanism design for dynamic pricing-based demand response in charging network

Dynamic pricing for the charging network needs to consider the interdependency of charging stations’ decision-making, and acquire user’s demand information and their price sensitivity on demands. Most existing pricing schemes assume users consistently follow stable and inferrable patterns, while it remains crucial to explore how they may be influenced by historic prices in a charging market, their private utility functions, and the best demand in response. To integrate the strategic user-station interactions and uncertainties into the network dynamic pricing, this paper proposes a multi-agent reinforcement mechanism design framework to simultaneously determine the optimal charging prices for multiple charging stations over a period considering power output limits, unexpected arriving requests, and undetermined charging demands of self-interested users who aim to maximize their own utility. Specifically, the station–user interaction is modelled as a mechanism design problem, and station–station cooperation is captured by the Markov game and solved by multi-agent deep deterministic policy gradient. The objective is to maximize the long-term network revenue considering the social welfare of all users. We evaluate our framework through an experimental study, and the results demonstrate that our approach outperforms the non-cooperative deep deterministic policy gradient algorithm and time-of-use pricing scheme.

Promoting electric vehicles: Reducing charging inconvenience and price via station and consumer subsidies

Environmental and energy independence concerns lead to government subsidies for electric vehicles (EVs). Operational decisions for a government are (i) to incentivize EV ownership by a direct consumer subsidy, a station subsidy that reduces charging inconvenience, or by both subsidies; and (ii) to minimize subsidy expenditure or to maximize EV adoption level. We model the interactions between the government and the charging supplier as a Stackelberg game and study the optimal structure of subsidies by incorporating charging inconvenience. We prove that this inconvenience is decreasing convex in the number of stations. In the expenditure minimization case, the optimal policy depends on the government adoption target and the charging station construction cost. If the adoption target is below a threshold that depends on the construction cost, the government provides pure consumer subsidy or no subsidy; otherwise, a combination of consumer and station subsidies is optimal. As the construction cost increases, the charger builds fewer stations, regardless of the subsidy type. We establish that expenditure minimization and adoption maximization yield the same subsidy policy if the charging inconvenience is linear. In a real‐life case, we find numerically that a station subsidy alone is optimal if the construction cost is not low but the adoption target is low. Besides, a long driving range reduces the need for subsidies significantly if the construction cost is high, whereas a long charging time necessitates high expenditure allocated mostly to a station subsidy.

A Distributed Coordination Method for Coupled Traffic-Power Network Equilibrium Incorporating Behavioral Theory

The increasing penetration of electric vehicles (EVs) gradually evolves the urban transportation network (UTN) and power distribution network (PDN) from being independent to being coupled. To analyze the impact of drivers' behavior on the coupled network, and reveal the relationship between the two networks, a coupled traffic-distribution (CTD) model is proposed, in which the total traveling cost of UTN and the power service cost of PDN is minimized. In the CTD model, A stochastic user equilibrium traffic assignment problem (SUE-TAP) with elastic traffic demand and discrete path selection of drivers is formulated to capture the traffic flow distribution comprised of EVs and gasoline vehicles (GVs). An alternative current optimal power flow (ACOPF) model of PDN is utilized to provide the optimal charging price and scheduling plan. Besides, the SUE-TAP with nonlinear functions is difficult to solve, a novel piecewise linear approximation method is presented to deal with the nonlinear items of the SUE-TAP caused by drivers' traveling cost and traveling tendency incorporating perception. Incorporating the behavioral theory, a distributed coordination method derived from optimal condition decomposition is proposed for the coupled traffic-power network equilibrium. The proposed distributed scheme is examined using a coupled network, which consists of a 20-road UTN and a 33-bus PDN. Numerical results demonstrate the effectiveness and practicability of the proposed model and the distributed coordination method.

Integrating ride-sourcing with electric vehicle charging under mixed fleets and differentiated services

This paper proposes a novel nested two-sided market framework to investigate integrated ride-sourcing (RS) and electric vehicle (EV) charging services considering mixed electric and gasoline vehicles (GVs). In the proposed integrated scheme, a monopoly RS platform builds or purchases charging piles to provide RS and private EV drivers with additional differentiated services (i.e., different charging prices and priority) besides public charging piles. Both RS and private drivers with different charging pile ownership are considered. The complex endogenous correlations between RS and charging services are formulated. The mathematical properties of drivers' charging behavior, passengers' mode choices, and platform's operation strategies are analytically derived, which is closely related to the differentiated priority of drivers. The conditions for judging the profitability of the RS and charging business under profit maximization strategies are obtained. We examine and compare the potential impact of introducing integrated services with two strategies and identify the sufficient conditions to boost total profit by providing the integrated services. It is rigorously proved that the integrated services benefit passengers and specific driver groups, while it may be harmful to the environment under a high RS electrification level. The Pareto-efficient frontiers indicate that the charging price for RS drivers in the social optimum regime is higher than that in the profit maximization regime. The analytical results gain managerial insights into platform operations and government regulation of integrated RS and charging services in the RS market under mixed fleets of EVs and GVs.

Dynamic Pricing Strategy of Charging Station Based on Traffic Assignment Simulation

The number of electric vehicles is increasing rapidly worldwide, leading to increasing demand for charging. This will negatively impact the grid. Therefore, it is essential to relieve the power grid operation pressure by changing the charging behaviour of users. In this paper, the charging behaviour of electric vehicles was guided by price instruments to maintain grid balance This paper uses travel simulation to establish the relationship between travel demand and electricity prices. The results were evaluated through the amount of grid voltage drop and network loss. Furthermore, we used the differential evolutionary algorithm to calculate the optimal operation status of the grid, which contains the minimal network loss and maximal voltage drop at different charging stations and the charging price. Finally, the effectiveness of the mechanism proposed in this paper was compared with other simulation examples. The results showed that the pricing strategy could guide users’ charging choices and maintain the grid load balancing. The simulation results show that the average bus voltage increases by 1.26% and 6.59%, respectively, under different requirements.

A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.