Charging Demand Model Research Articles

Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

An Optimal Scheduling Strategy of a Microgrid with V2G Based on Deep Q-Learning

In recent years, the access of various distributed power sources and electric vehicles (EVs) has brought more and more randomness and uncertainty to the operation and regulation of microgrids. Therefore, an optimal scheduling strategy for microgrids with EVs based on Deep Q-learning is proposed in this paper. Firstly, a vehicle-to-grid (V2G) model considering the mobility of EVs and the randomness of user charging behavior is proposed. The charging time distribution model, charging demand model, state-of-charge (SOC) dynamic model and the model of travel location are comprehensively established, thereby realizing the construction of the mathematical model of the microgrid with EVs: it can obtain the charging/discharging situation in the EV station, so as to obtain the overall output power of the EV station. Secondly, based on Deep Q-learning, the state space and action space are set up according to the actual microgrid system, and the design of the optimal scheduling reward function is completed with the goal of economy. Finally, the calculation example results show that compared with the traditional optimization algorithm, the strategy proposed in this paper has the ability of online learning and can cope with the randomness of renewable resources better. Meanwhile, the agent with experience replay ability can be trained to complete the evolution process, so as to adapt to the nonlinear influence caused by the mobility of EVs and the periodicity of user behavior, which is feasible and superior in the field of optimal scheduling of microgrids with renewable resources and EVs.

Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.

Expansion planning of electric vehicle charging stations considering the benefits of peak‐regulation frequency modulation

The China Energy Administration has issued policies to encourage energy storage to participate in the electric auxiliary service market, which will provide ideas for electric vehicle charging stations (EVCSs) to collectively participate in peak-regulation and frequency modulation (FM). Taking into account the benefits of EVs participating in peak-regulation and FM, this paper studies expansion planning for EVCSs. First, the orderly scheduling strategy of EVs under vehicle-to-grid (V2G) technology is established in this paper. And a peak regulation method is proposed to maximize the economic benefits of charging and discharging during peak and valley periods. Second, based on the temporal and spatial prediction model of EV charging demand, the expansion planning model of EVCSs is constructed considering the benefits of peak-regulation and FM. Aiming at minimizing the comprehensive annual social cost of charging stations, the expansion site selection and capacity scheme of EVCSs are obtained. Finally, taking the 24 nodes in Xiangzhou District of Zhuhai City as an example, the location and capacity of the final annual expansion plan are obtained. And the influence of main control parameters on the results of the whole model and method is analysed in detail, the feasibility and effectiveness of the whole model and method are verified.

Emission mitigation potential from coordinated charging schemes for future private electric vehicles

Plug-in electric vehicles (EVs) are expected to synergize with low or even zero-carbon electricity towards a deep mitigation of greenhouse gas (GHG) emission and ultimately carbon neutrality. These benefits are only obtainable when EV charging maximizes the consumption of electricity generated from renewable energy sources. However, the current mismatch between renewable energy output and EV charging demand poses a substantial challenge. Rescheduling charging events, namely charging coordination, has the potential to integrate renewable electricity and tap into higher GHG emission reductions. In this study, with Beijing as the research domain, we develop a charging demand model informed by real-world usage data generated by a massive fleet of private cars, and evaluate the comprehensive impacts of various charging coordination schemes in the future with higher adoption rates of both private EVs and renewable power sources. The research finds that strategies aiming at maximizing renewable power consumption, netload valley filling, and charging cost minimization have similar climate benefits, which can reduce well-to-wheels GHG emissions by approximately 20%, cut charging cost by half, and erase 95% of the need for newly installed generation capacity compared to the benchmark scenario without coordinated charging. On the other hand, conventional charging strategies, such as the simple demand-valley-filling strategy, would increase GHG emissions. The study indicates that substantial benefits can be synergistically achieved between power and transport systems towards improved grid stability, lower fuel cost, and greater emission mitigation by leveraging EV charging flexibility at the end-user side (i.e., charging facility). Furthermore, the potential need for sophisticated charging coordination with multiple optimization objectives is brought to attention.

A scenario-based stochastic programming approach for the public charging station location problem

This paper presents an integrated framework for the optimal planning of public charging stations for plug-in electric vehicles (PEVs) in urban areas. The framework consists of two main components: (i) an out-of-home charging demand model based on an activity-based travel demand model, and (ii) a public charging station location-allocation model using a scenario-based stochastic programming (SP) approach. In order to capture the dynamic charging behaviour of PEV users, a chi-squared automatic interaction detector (CHAID)-based mixed effects decision tree is induced from multi-day activity diaries. Moreover, because the stochastic error of the micro-simulation approach brings about uncertainty, we adopted a two-stage stochastic mixed-integer programming (TSMIP) model, which measures uncertainty by means of a finite set of scenarios obtained from the derived decision rules underlying PEV charging. The proposed approach is demonstrated for the City of Eindhoven, The Netherlands, and benefits of the stochastic solution are discussed.

Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Charging stations not only provide charging service to electric vehicles (EVs), but also integrate distributed energy sources. This integration requires an appropriate planning to achieve the future sustainable distribution network. Real EV charging demand is stochastic and affected by many uncertainties, which pose challenges to the planning of a charging station. This study presents a stochastic planning model of the EV charging station with photovoltaic (PV), battery and transformer, mainly considering the uncertainties from charging demand and PV power generation. Firstly, a comprehensive EV charging demand model is established through reflecting the coupling among the EV stochastic charging behaviour, charger specifications and EV charging assignment model. Then, the planning model is formulated to minimise the total cost of the charging station, where the uncertainties arising from EV charging are addressed in various constraints. Using this model, the optimal sizing of the charging station is determined, together with the associated optimal operation strategy. Finally, the effectiveness of the proposed model is validated by multiple case studies.

Probabilistic Method for Distribution Network Electric Vehicle Hosting Capacity Assessment based on Combined Cumulants and Gram-Charlier Expansion

With the increasing penetration of electric vehicles (EVs) in distribution network (DN), it becomes crucial for distribution system operators (DSO) to determine how many EVs can be integrated into one node of DN. This paper presents a probabilistic method to evaluate distribution network EV hosting capacity based on Combined Cumulants and Gram-Charlier Expansion. First, the mathematical model of EV charging demand is established, and the Cumulants of EV and power load are derived based on Monte Carlo simulation. Then, stochastic power flow based on combined Cumulants and Gram–Charlier series expansion is used to analyze the DN EV hosting capacity. Finally, the methods are tested on IEEE 33-node DN, and the simulation results prove their validity and rapidity.

Coordinated scheduling of a gas/electricity/heat supply network considering temporal-spatial electric vehicle demands

Renewable energy-based multi-energy supply microgrids are expected to play an important role in smart cities. How to schedule such microgrids in grid-connected mode and dispatch power among sources inside the microgrids is a problem. Moreover, as electric vehicles are becoming more and more common, the charging of large numbers of vehicles is also a challenge for the utility grid. In this paper, we build a temporal-spatial electric vehicle charging demand model, which includes three parts: trip plans, duration of stay, and search for the shortest path based on the Dijkstra algorithm. Then, we build a grid-connected gas/electricity/heat microgrid and present a coordinated scheduling method for this microgrid. A day-ahead scheduling method is used to decide the role of the microgrid (i.e., operate as a load or as a generator from the point of view of the utility), a real-time rolling-horizon dispatching algorithm is used to respond to the forecasting errors and at the same time implement the real-time actual power exchange between the microgrid and the main grid. The problem is formulated as a mixed integer linear programming problem. The temporal-spatial electric vehicle charging demands model is simulated based on a 81-node transportation network, while the energy supply network is a combined IEEE-30, gas-20 and heat-14 network. The simulation results show the effectiveness of this coordinated scheduling method.

Hierarchical optimal planning approach for plug‐in electric vehicle fast charging stations based on temporal‐SoC charging demand characterisation

Fast charging stations are critical infrastructures to enable a high penetration level of plug-in electric vehicles (PEVs) into future distribution networks. The fast charging stations need to be carefully planned to meet the PEV charging demand as well as reduce costs. This study addresses this technical challenge and proposes a hierarchical planning solution for both sitting and sizing of PEV fast charging stations based on a temporal-SoC (state-of-charge) characterisation and modelling of PEV fast charging demand. The optimal sitting of fast charging stations is firstly determined to ensure minimising the total number of stations and meeting the PEV fast charging demand considering the constraints of transportation networks and the expected PEV remaining mileage. Then the sizing (number of chargers and waiting spaces) of fast charging station is optimised by the use of M/M/s/N queuing model, so as to maximise the expected profit of the operator. The proposed solution is evaluated through a set of case studies for a range of scenarios, and numerical simulation results have confirmed the effectiveness of the proposed solution.

A Novel Methodology for Charging Station Deployment

Lack of charging stations has been a main obstacle to the promotion of electric vehicles. This paper studies deploying charging stations in traffic networks considering grid constraints to balance the charging demand and grid stability. First, we propose a statistical model for charging demand. Then we combine the charging demand model with power grid constraints and give the formulation of the charging station deployment problem. Finally, we propose a theoretical solution for the problem by transforming it to a Markov Decision Process.

Agent-Based Modeling for Scale Evolution of Plug-in Electric Vehicles and Charging Demand

Scale evolution pattern recognition of plug-in electric vehicles (PEVs) and charging demand modeling are essential for various involved sectors to promote PEV proliferation and integration into power systems. Considering that the market penetration development of PEVs will drive the evolution of charging demand, an integrated dynamic method based on an agent-based modeling technology is proposed in this paper by combining scale evolution model with charging demand model to jointly detect the possible PEVs evolution patterns and long-term charging demand profiles. Heterogeneous consumers presenting different preferences in making vehicle purchase decisions and the interactions with other consumers via social dynamics are taken into consideration in the scale evolution model. After obtain the scale of PEVs by aggregating individual consumer agents purchase behavior, the driving patterns, charging behavior habits, and charging strategies are systematically incorporated into the charging demand model. Case studies demonstrate the feasibility and effectiveness of the proposed methodology by taking an urban area as an example. Furthermore, the factors that affect the market evolution of PEVs and the charging demand are also simulated and analyzed.

A Model of Charging Service Demand for the Czech Republic

The paper introduces a standalone model of electric vehicle charging demand based on large‑scale travel survey data of the Czech Republic. This demand model has been intended as a comprehensive input model for following charging infrastructure problem, where a spatial view of charging demand is usually needed. The model uses publicly available data, whose mutual incompatibility and information richness had to be overcome. The necessary data transformations are described and final data representation in the form of a mathematical graph allows the introduction of a point‑defined (vertex‑defined) charging demand model. Several drawbacks of the model are identified and their effect, as well as an application of whole model, is demonstrated on the large‑scale numerical example. Sound demand model is a cornerstone for demand‑related problems, such as general large‑scale charging infrastructure problem, which is a common issue for countries that stand at the very beginning of the electric vehicle adoption process.

A Survey on PEV Charging Infrastructure: Impact Assessment and Planning

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute in reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. Integration of PEVs into the electric power system will result in a considerable addition to electricity demand. Due to PEV mobility, this demand has a random distribution in space and time among distribution system nodes. Therefore, short term forecast of PEV charging demand is more challenging than that for conventional loads. Assessment of PEV impacts on the power system is essential to mitigate the impairments from PEV loads. Optimal planning of PEV charging infrastructure will promote the penetration rate of PEVs and minimize the negative impacts of PEVs on the electric power distribution system and transportation road network. Design of charging facilities with integrated distributed energy resources (DER) is considered a solution to alleviate strain on the grid, reduce the integration cost with the distribution network and the charging cost. In this paper, we present a comprehensive literature survey on modelling of PEV charging demand, impact assessment approaches and tools, and charging infrastructure planning. Moreover, an overview on charging facility design with integrated DER is given. Some future research directions are identified.

When Traffic Flow Meets Power Flow: On Charging Station Deployment With Budget Constraints

The lack of charging facilities has been a main obstacle to the widespread use of electric vehicles (EVs). What is worse is that existing chargers are still underutilized. Meanwhile, the grid instability caused by EV charging is becoming much more significant with increasing EV penetration. This paper studies how to size and locate charging stations in traffic networks considering grid constraints to balance the charging demand and power network stability. First, a spatiotemporal model of charging demand is proposed, and a $\text{1}-(\text{1}/e)$ approximation algorithm to maximize the charging demand is designed. We analytically prove that $\text{1}-(\text{1}/e)$ is the best bound that can be obtained in polynomial time. Then, a linearized power network model (LPNM) is proposed. Based on LPNM, a heuristic algorithm involving the grid constraints (HAG) is designed. Finally, the proposed models and algorithms are evaluated on real-world traffic networks and power networks. The relative error of the voltage deviation estimated by LPNM is about 4%. Compared with the plain demand model, adopting the spatiotemporal charging demand model improves the utilization of chargers by 5% at least. Compared with the greedy algorithm with grid constraints (GAG), HAG improves the carrying capacity of the power network by 20.7%, reduces the voltage deviation by 25%, and increases the EVs charged by 18.07%.

Electric vehicle charging demand forecasting model based on big data technologies

This paper presents a forecasting model to estimate electric vehicle charging demand based on big data technologies. Most previous studies have not considered real-world traffic distribution data and weather conditions in predicting the electric vehicle charging demand. In this paper, the historical traffic data and weather data of South Korea were used to formulate the forecasting model. The forecasting processes include a cluster analysis to classify traffic patterns, a relational analysis to identify influential factors, and a decision tree to establish classification criteria. The considered variables in this study were the charging starting time determined by the real-world traffic patterns and the initial state-of-charge of a battery. Example case studies for electric vehicle charging demand during weekdays and weekends in summer and winter were presented to show the different charging load profiles of electric vehicles in the residential and commercial sites. The presented forecasting model may allow power system engineers to anticipate electric vehicle charging demand based on historical traffic data and weather data. Therefore, the proposed electric vehicle charging demand model can be the foundation for the research on the impact of charging electric vehicles on the power system.

Probabilistic Modeling of Nodal Charging Demand Based on Spatial-Temporal Dynamics of Moving Electric Vehicles

High penetration of electric vehicles (EVs) as moving loads in power system have drawn increasing concerns about their negative impacts. Due to the spatial-temporal random dynamics of EVs, it is a challenge for identification and positioning of the space and time varying impacts. Most previous studies investigated system-wide EV charging demand based on data analysis with deterministic charging location and time. In this circumstance, this paper proposes a probabilistic model for nodal charging demand based on the spatial-temporal dynamics of moving EVs. Following the introduction to the integrated system with graph theory, a spatial-temporal model of moving EV loads is established based on random trip chain and Markov decision process (MDP). The nodal EV charging demands are derived from the charging probabilities of single and multiple EVs. The system studies show that this model is capable to assess the nodal charging demand due to the spatial-temporal distribution of moving EVs.

Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load

This paper presents a composite methodology for obtaining spatial and temporal projections of charging demand and peak-shaving potential from plug-in electric vehicles (EVs), and the associated spatio-temporal impacts on peak household electrical load. The methodology comprises a suite of models of future EV uptake, travel by households, household electricity demand and recharge/discharge of EVs at their home locations. The analysis is disaggregated to hourly time-steps over a full year, and spatially to mesh blocks comprising around 250 houses per block. The modelling suite is applied to an analysis of peak household load impacts across the state of Victoria, Australia, under nine different combinations of EV uptake and charging/discharging behaviour. The projected increase in peak household electrical loads under expected penetration rates and charging demands is less than 10 % on most high-demand days, but can be up to 15 % on a handful of days and geographic locations. Peak-load impacts under off-peak charging are mostly less than 5 %. With judicious EV discharging strategies, there is potential to shave peak loads on the highest demand days by up to 5 %.

Spatial and Temporal Model of Electric Vehicle Charging Demand

This paper presents a spatial and temporal model of electric vehicle charging demand for a rapid charging station located near a highway exit. Most previous studies have assumed a fixed charging location and fixed charging time during the off-peak hours for anticipating electric vehicle charging demand. Some other studies have based on limited charging scenarios at typical locations instead of a mathematical model. Therefore, from a distribution system perspective, electric vehicle charging demand is still unidentified quantity which may vary by space and time. In this context, this study proposes a mathematical model of electric vehicle charging demand for a rapid charging station. The mathematical model is based on the fluid dynamic traffic model and the M/M/s queueing theory. Firstly, the arrival rate of discharged vehicles at a charging station is predicted by the fluid dynamic model. Then, charging demand is forecasted by the M/M/s queueing theory with the arrival rate of discharged vehicles. This mathematical model of charging demand may allow grid's distribution planners to anticipate a charging demand profile at a charging station. A numerical example shows that the proposed model is able to capture the spatial and temporal dynamics of charging demand in a highway charging station.

Modeling of Plug-in Hybrid Electric Vehicle Charging Demand in Probabilistic Power Flow Calculations

Millions of electric vehicles (EVs), especially plug-in hybrid EVs (PHEVs), will be integrated into the power grid in the near future. Due to their large quantity and complex charging behavior, the impact of substantial PHEVs charging on the power grid needs to be investigated. Since the charging behavior of PHEVs in a certain regional transmission network or a local distribution network is determined by different uncertain factors, their overall charging demand tends to be uncertain and in this situation probabilistic power flow (PPF) can be applied to analyze the impact of PHEVs charging on the power grid. However, currently there is no suitable model of the overall charging demand of PHEVs available for PPF calculations. In this paper, a methodology of modeling the overall charging demand of PHEVs is proposed. The proposed methodology establishes a single PHEV charging demand model, and then employs queuing theory to describe the behavior of multiple PHEVs. Moreover, two applications are given, i.e., modeling the overall charging demand of PHEVs at an EV charging station and in a local residential community, respectively. Comparison between PPF calculations and Monte Carlo simulation are made on a modified IEEE 30-bus system integrated with the two demand models proposed.