Distribution Of Charging Demand Research Articles

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. The results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Charging Station Siting and Sizing Considering Uncertainty in Electric Vehicle Charging Demand Distribution

Charging Station Siting and Sizing Considering Uncertainty in Electric Vehicle Charging Demand Distribution

Network equilibrium of battery electric vehicles considering drivers’ resting behavior

Driving fatigue cost is a major component of vehicle drivers’ travel costs in an intercity or regional network. The charging behavior of electric vehicle (EV) drivers, which is generally synchronized with drivers’ resting behavior, can contribute to the mitigation of drivers’ fatigue, especially after a prolonged driving period. Overlooking the impact of driving fatigue on the travel cost may overestimate the side-effects of the charging behavior for EV drivers, and result in biased flow and charging demand distribution. In this study, by considering the fatigue as part of the travel cost, we make the first attempt to investigate the impact of EV drivers’ charging and resting behaviors on their fatigue cost, and thus their travel plans and resultant flow distribution across the network. To this end, a novel network equilibrium modeling framework is first developed to capture the interaction among EV drivers’ travel plans, which specify the routing, recharging, and resting plans on a general intercity or regional network where charging stations and rest stops are deployed. When traveling between their origins and destinations, EV drivers are assumed to determine their travel plans to minimize their individual travel cost composed of driving time, rest time, charging cost, and fatigue cost, while preventing their batteries from being exhausted. The equilibrium model is then formulated as a variational inequality and transformed into a nonlinear optimization problem. An efficient solution algorithm integrating column generation and Benders decomposition approach is proposed to solve the established optimization problem. Numerical examples are presented to demonstrate the performance of the proposed models and solution algorithm. Numerical results validate that considering the impact of driving fatigue on the travel cost emphasizes the need for en-route charging for EV drivers with long-distance trips, and has an appreciable impact on the network flow and charging demand distribution. In addition, large-sized batteries and fast chargers may not necessarily reduce drivers’ travel costs for long-distance travel since charging behavior can be synchronized with drivers’ resting behavior and thus contribute to the mitigation of drivers’ fatigue.

Fast-charging station location problem: A two-phase approach with mathematical program with equilibrium constraints considering charging choice behaviour

Electric vehicles (EVs) produce zero emissions, which makes them an essential solution for reducing air pollution in urban areas. Fast-charging station (FCS) planning is crucial for ensuring a successful transition to EVs. Therefore, to address the FCS location problem (FCSLP) for EVs in urban areas, a two-phase approach consisting of data processing and model optimization is proposed. During data processing, the spatiotemporal charging demand distribution, existing FCSs, and potential locations for new FCSs are sequentially identified. The results are then used in the model optimization phase, where a spatiotemporal mathematical program with equilibrium constraints (MPEC) model is developed to minimize the total social cost (TSC). The MPEC model integrates the maximal coverage location model and charging equilibrium model, which is formulated as a variational inequality to capture the charging choice behaviour of the EVs within the FCS service radius. To solve the MPEC model, a modified genetic algorithm is developed, in which the charging equilibrium model is iteratively solved. Finally, a case study is conducted in a practical area in Shenzhen, China. The results indicate that a trade-off exists between the TSC and the system service level concerning the optimal FCS locations under a specific FCS service radius. Moreover, the FCS service radius significantly influences EV drivers’ charging choice behaviour and FCS planning.

Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model

Abstract In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations.

Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence

This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure.

Research on electric vehicle load forecasting based on travel data

Due to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric vehicle charging station based on travel data. Firstly, the didi trip data is processed and mined to get the trip matrix and other information. Then, the electric vehicle charging load forecasting model is established based on the established unit mileage power consumption model and charging model, and the charging demand distribution information is predicted by Monte Carlo method. Finally, the simulation analysis is carried out based on the trip data of some areas of a city, which shows the effectiveness of the established model feasibility.

Optimal planning of charging station based on discrete distribution of charging demand

The continuous growth in the number of electric vehicles (EVs) places higher demands on public charging station (PCS) planning. This study proposes a PCS locating and sizing method based on the discrete distribution of EV charging demand. First, the distribution of EV charging demand is predicted by the distribution of gas station sales, functional areas, and population density. Next, a method of determining the PCS capacity based on the distribution of the charging demand is given. Then, the PCS locating and sizing model with the goal of minimising the total social cost is established, and the particle swarm optimisation algorithm is used to solve the problem. Finally, the method is verified by a case in Beijing. The results show that after obtaining several convenient parameters and using a simple algorithm calculation, this method can effectively reduce the construction and operation costs of PCSs and give full play to the social benefits of PCSs.

A trajectory-based energy consumption estimation method considering battery degradation for an urban electric vehicle network

As a cost-effective and environmental-friendly transport means, electric vehicle (EV) has received widespread attention in the recent decade. The increasing market share and wider adoption of EV in transportation systems also bring about the energy use issues. In this paper, we propose a method to estimate network-wide EV energy consumption by taking into account battery degradation. We first derive unit energy consumption functions with respect to the battery degradation rate by quadratic regression method using EV operational data in the US. We then put forward an easy-to-implement and tangible method to estimate EV network energy consumption based on EV trajectories and the derived unit energy consumption function. In detail, EVs’ battery degradation rates are assumed to follow a given and known probability distribution and daily travel mileage of each EV is derived from EV trajectories. Based on these, we can get the expected total energy consumption in the network. We also propose a prorated assignment approach to determine the expected energy consumed by EVs through each EV charging point based on the estimated temporal-spatial charging demand distribution from EVs’ trajectories. A case study in Singapore is demonstrated in the end and the importance of incorporating battery degradation is highlighted. The results reveal that overlooking battery degradation would lead to more than 10% of estimation error in energy consumption estimation.

Charging demand for electric vehicle based on stochastic analysis of trip chain

With its popularisation, electric vehicle (EV) has been part of the important load of smart grid. The analysis on charging demands of EVs is the basis of the study of influences on power grid and charging infrastructures planning. Charging demand calculation is based on the precise analysis on the driving law of users. This study proposes a method based on the stochastic simulation of trip chain to solve the existing problem. First, the concept and characteristic variables (trip start time, driving time, parking duration, driving distance and trip purpose) of trip chain are introduced. Second, the probability distribution models of these variables are established considering their correlative relationships. Third, using Monte Carlo method, the complete trip chains are simulated, and then the spatial–temporal distributions of charging demands are calculated. Finally, based on the National household trip survey data, and according to the proposed method, charging demands in different areas are studied. The results show that the proposed method can accurately simulate the driving law of users and reflect spatial–temporal distribution characteristics of charging demands, and different charging scenarios will lead to different forms of charging demand distribution, which will exert different impacts on power system.