Charging Demand Research Articles

Multistep Multiagent Reinforcement Learning for Optimal Energy Schedule Strategy of Charging Stations in Smart Grid.

An efficient energy scheduling strategy of a charging station is crucial for stabilizing the electricity market and accommodating the charging demand of electric vehicles (EVs). Most of the existing studies on energy scheduling strategies fail to coordinate the process of energy purchasing and distribution and, thus, cannot balance the energy supply and demand. Besides, the existence of multiple charging stations in a complex scenario makes it difficult to develop a unified schedule strategy for different charging stations. In order to solve these problems, we propose a multiagent reinforcement learning (MARL) method to learn the optimal energy purchasing strategy and an online heuristic dispatching scheme to develop a energy distribution strategy in this article. Unlike the traditional scheduling methods, the two proposed strategies are coordinated with each other in both temporal and spatial dimensions to develop the unified energy scheduling strategy for charging stations. Specifically, the proposed MARL method combines the multiagent deep deterministic policy gradient (MADDPG) principles for learning purchasing strategy and a long short-term memory (LSTM) neural network for predicting the charging demand of EVs. Moreover, a multistep reward function is developed to accelerate the learning process. The proposed method is verified by comprehensive simulation experiments based on real data of the electricity market in Chicago. The experiment results show that the proposed method can achieve better performance than other state-of-the-art energy scheduling methods in the charging market in terms of the economic profits and users' satisfaction ratio.

An Efficient and Secure Trading Framework for Shared Charging Service Based on Multiple Consortium Blockchains

While electric vehicles are proliferating rapidly, the number of charging piles is only 1/16 of the number of electric vehicles, showing big tension between small supply of public charging piles and intense charging demand of electric vehicles. To alleviate the tension, it is highly desired to incorporate existing idle private charging piles into a shared charging service. However, existing solutions do not well implement the system due to some non-negligible issues, like poor consensus performance or lack of verification for outputs from blockchain networks. In the paper, we propose an efficient and secure trading framework built atop multiple consortium blockchains. It devises a novel node voting mechanism so as to improve consensus efficiency. Meanwhile, it employs a publicly verifiable design based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> Boneh-Lynn-Shacham (BLS) threshold signature technique, so as to enable entities outside the blockchain to verify the consistency between the output results with the valid consensus. Besides these novel system-level designs, it also introduces a fair and robust trading strategy running by smart contracts on the platform. Furthermore, we implemented a proof-of-concept system and conducted extensive experiments. The results show that the overall latency across chains is reduced by about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$26\%$</tex-math></inline-formula> with the node voting mechanism and the transactions fees are comparable to recent popular cross-chain applications.

A Bi-Level Optimization and Scheduling Strategy for Charging Stations Considering Battery Degradation

This paper proposes a bi-level optimization scheduling strategy for integrated photovoltaic (PV) and energy storage systems (ESS) to meet electric vehicle (EV) charging demands while reducing charging costs. First, a battery degradation cost model is developed in order to convert the long-term costs into short-term costs for real-time operation. The upper layer of ESS and power grid operation strategies are obtained by minimizing costs associated with battery degradation and distribution grid costs. The lower layer considers the PV uncertainty and the error caused by the upper layer operation strategy, and obtains the lower layer operation strategy by adding a penalty function to minimize fluctuations in power. Second, the author proposes a global optimization algorithm that combines Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) in order to solve the above-mentioned models, effectively combining the global search feature of PSO with the local search capability of SQP. Finally, the bi-level optimization scheduling strategy is obtained by solving the model through the algorithm. Simulation results verify the practicality of the scheduling strategy and the effectiveness of the proposed algorithm.

Review of the Impact of Electric Vehicle Charging on Distribution Networks

Abstract: Electric vehicles (EVs) have gained significant popularity, leading to concerns about their impact on distribution networks and the integration of renewable energy sources.In response, researchers have proposed various strategies and methodologies. These include adaptive charging coordination strategies that aim to minimize the influence of EV chargingon distribution networks while considering uncertainties in renewable energy generation and charging demands. Optimizationbased approaches have been employed to balance loads, alleviate network congestion, and coordinate EV charging schedules with renewable energy generation and distribution network demand. The studies have also highlighted potential issues such as distribution system overloading, voltage stability problems, and the need to account for different charging infrastructure scenarios and standards. To address these challenges, control strategies integrating EV charging and battery energy storage systems (BESS) have been proposed. These strategies aim to reduce peak power demands, optimize the utilization of renewable energy sources, and shift charging loads away from peak periods. How- ever, there are still gaps to be addressed, including scalability forlarge-scale deployment, user convenience, and economic viability.Further research is needed to explore these areas and consider the impact of user behavior, smart grid technologies, and cost- effectiveness in accommodating EV charging and plug-in hybrid electric vehicles (PHEVs) in residential distribution grids

Peak Shaving Mechanism Employing a Battery Storage System (BSS) and Solar Forecasting

Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.

Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network

Short-term Electric Vehicle (EV) charging demand prediction is an essential task in the fields of smart grid and intelligent transportation systems, as understanding the spatiotemporal distribution of charging demand over the next few hours could help operators of charging stations and the grid to take measures (e.g., dynamic pricing) in response to varying charging demand. This study proposed a heterogeneous spatial–temporal graph convolutional network to predict the EV charging demand at different spatial and temporal resolutions. Specifically, we first learned the spatial correlations between charging regions by constructing heterogeneous graphs, i.e., a geographic graph and a demand graph. Then, we used graph convolutional layers and gated recurrent units to extract spatio-temporal features in the observations. Further, we designed a region-specific prediction module that grouped regions based on graph embedding and point of interest (POI) data for prediction. We used a large real-world GPS dataset which contained over 76,000 private EVs in Beijing for model training and validation. The results showed that, compared with recently popular spatio-temporal prediction methods, the proposed model had superior prediction accuracy and steady performance at different scales of regions. In addition, we conducted ablation studies and hyperparameter sensitivity tests. The results suggested that incorporating the demand graph and geographic graph could help improve model performance.

Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors.

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

Charging management and pricing strategy of electric vehicle charging station based on mean field game theory

AbstractAs an intermediary between the power grid and the electric vehicles (EVs) in the charging station, the charging pile promotes the exchange of electric energy between the power grid and EV group and also brings benefits to the charging station. It is difficult to accurately analyze the detailed energy trading behavior of a large number of charging piles with the power grid and EV group. Based on the theoretical framework of mean field game (MFG), this paper considers the battery degradation and charging efficiency taking into account the charging demand of EVs, the charging control problem of energy storage charging piles is proposed to achieve the goal of minimizing the cost of the charging station. In addition to modeling the interaction between the charging station and power grid and EVs as a finite‐time dynamic game problem, optimal decentralized energy scheduling control strategies are formulated for charging piles, and by introducing the mean field term, the optimal pricing strategy for power trading between the charging station and EVs is obtained under the mean field equilibrium condition. An iterative algorithm is proposed to solve the charging control problem, and the rationality of the problem and the effectiveness of the algorithm are verified by numerical simulation.

Performance Evaluation of a Scaled-Up Membraneless Organic-Based Hybrid Flow Battery

This article presents an evaluation of the performance of a membrane-less organic-based flow battery using low-cost active materials, zinc and benzoquinone, which was scaled up to 1600 cm2, resulting in one of the largest of its type reported in the literature. The charge–discharge cycling of the battery was compared at different sizes and current densities, and its performance was evaluated under various mass transport and operating conditions. The results showed that the round-trip coulombic and voltage efficiencies were over 90% and 85%, respectively, for the laboratory-scale (1 cm2 electrode) cell, but these performances tended to deteriorate with the scaled-up (1600 cm2 electrode) cell due to inadequate mass transfer and sediment coverage of quinone, as well as the formation of a passivation film on the zinc anode. Despite this, the scaled-up batteries exhibited high coulombic and voltage efficiencies of up to 99% and 68.5%, respectively, at a current density of 10 mA cm−2. The capital cost of this system is also estimated to be several times lower than those of commercially available all-vanadium flow batteries and zinc bromide flow batteries for demand charge management applications.

Mind the gap: Modelling difference between censored and uncensored electric vehicle charging demand

Electric vehicle charging demand models, with charging records as input, will inherently be biased toward the supply of available chargers. These models often fail to account for demand lost from occupied charging stations and competitors. The lost demand suggests that the actual demand is likely higher than the charging records reflect, i.e., the true demand is latent (unobserved), and the observations are censored. As a result, machine learning models that rely on these observed records for forecasting charging demand may be limited in their application in future infrastructure expansion and supply management, as they do not estimate the true demand for charging. We propose using censorship-aware models to model charging demand to address this limitation. These models incorporate censorship in their loss functions and learn the true latent demand distribution from observed charging records. We study how occupied charging stations and competing services censor demand using GPS trajectories from cars in Copenhagen, Denmark. We find that censorship occurs up to 61% of the time in some areas of the city. We use the observed charging demand from our study to estimate the true demand and find that censorship-aware models provide better prediction and uncertainty estimation of actual demand than censorship-unaware models. We suggest that future charging models based on charging records should account for censoring to expand the application areas of machine learning models in supply management and infrastructure expansion.

Incorporation of desulfurization wastewater to achieve ultra-low energy consumption in electrochemical lead recycling

Cleaner and more cost-effective battery recycling techniques are still in demand for improving battery's sustainability. Herein, a novel electrochemical spent lead-acid battery recycling approach with ultra-low energy consumption is proposed in this work, which is achieved via coprocessing with desulfurization wastewater. Desulfurization wastewater (containing sulfite ion, SO32−) is used as reducing agent for lead paste leaching process and electrolyte for the subsequent solid-state electrolysis process respectively, while the wastewater can be effectively treated. Morphology and crystal structure characterizations indicate the lead paste leached by simulated desulfurization wastewater is mainly composed of lead suboxide (Pb2O). Electrochemical analyses reveal that charge demand for cathodic electro-reducing desulfurization wastewater leached lead paste into metallic lead is significantly lower than original lead paste. Furthermore, sulfite ion is oxidated on anode during the solid-state electrolysis process, thereby decreasing the anodic potential compared to the traditional oxygen evolution reaction. Thus, the introduction of desulfurization wastewater significantly reduces the overall energy consumption of the solid-state electrolysis by influencing both of the anodic and cathodic processes. The energy consumption to recycle a ton of lead is 169.8–230.5 kWh under optimized operation conditions, which is only 21.2–51.8% of the traditional electrochemical lead recycling process, while 1.6–1.8 m3 desulfurization wastewater with 50,000 mg L−1 chemical oxygen demand can be treated. Therefore, the co-processing of spent lead-acid battery and desulfurization wastewater is proven to be a win-win waste disposal strategy.

Charging Station Location and Sizing for Electric Vehicles Under Congestion

This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .

Dynamic pricing for fast charging stations with deep reinforcement learning

With the rapid development of electric vehicles (EVs) and charging infrastructures, the unbalanced utilization rate of fast charging stations (FCSTs) and the long waiting time for charging have aroused considerable attention. The incurred low operation profit of FCSTs and low satisfaction of EVs impose difficulties on the further development of EV industry. Existing literature ignored the influence of real-time charging price changes on traffic flow variation and EV charging determination during the dynamic price regulating process. This paper focuses on solving these crucial issues in the dynamic pricing for FCSTs with deep reinforcement learning (DRL). Firstly, considering the spatial–temporal interactions of different roads, a traffic flow prediction model is proposed based on the LSTM combined with the GNN-FiLM. Then, the Origin-Destination (OD) estimation is used to estimate the charging requirements of EVs based on the predicted traffic flow, and a charging demand prediction method for FCSTs is developed by converting the EV satisfaction into economic costs with different dimensions. Then, the vehicle–road learning environment is built with the Markov decision process (MDP), and a dynamic pricing strategy based on the Deep Deterministic Policy Gradient (DDPG) learning is proposed to achieve the optimal charging prices of FCSTs with maximum operation profit. Moreover, during the learning process, the real-time charging price is renewed based on the predicted charging demand, and the future charging demand is further predicted under the renewed charging price until the optimal price is achieved. Finally, simulation results validate that the proposed dynamic pricing strategy effectively improves the profit of FCSTs, alleviates the road congestion, and improves the users’ satisfaction.

Optimal Electric Vehicle Fleet Charging Management with a Frequency Regulation Service

Electric vehicles are able to provide immediate power through the vehicle-to-grid function, and they can adjust their charging power level when in the grid-to-vehicle mode. This allows them to provide ancillary services such as frequency control. Their batteries differ from conventional energy storage systems in that the owner’s energy requirement constraint must be met when the vehicles participate in a frequency control system. An optimization problem was defined by considering both the owner satisfaction and frequency control performance. The main contribution of the proposed paper, compared to the literature, are (1) to keep the total available energy stored in the batteries connected to a charging station in an optimal region that favors the frequency regulation capability of the station and the proposed QoS and (2) to consider the optimal region bounded by the efficiency thresholds of the charger to allow for maximum regulation power. The problem is expressed as a multi-criteria optimization problem with time-dependent references. The paper presents an energy management strategy for frequency control, describes a concept of an optimal time-dependent state of charge for electric vehicle charging demands, and considers the power dependence of the electric vehicle charger efficiency. Finally, the simulation results are presented via Matlab/Simulink to prove the effectiveness of the proposed algorithm.

The impact of electric vehicle charging infrastructure on the energy demand of a city

With the increased market penetration of electric vehicles (EVs), energy consumption is growing at a quicker rate. Electric vehicles are slowly but surely gaining a foothold in India’s rapidly expanding market. With more EVs on the road, there will be more demand for charging and more energy use in some places, which can put a lot of stress on the power grid. In the absence of sufficient planning, there is a risk of power system interruption and failure. Therefore, the need for energy in cities will shift in light of the advent of EVs in the next decade or two. Accordingly, Surat has been chosen as the research region for the planning of charging infrastructure in the current study. After reviewing the literature, primary data was acquired to estimate citizen travel demands. Vehicle registration statistics were compiled from the Regional Transport Office (RTO) for the purpose of EV forecasting. After collecting data, the EV population and demand for public charging stations by 2025 and 2030 were calculated. This study aims to meet the public charging demand in the city of Surat by analyzing the electric vehicle growth rate and vehicle forecast. This anticipated data was then utilized to compute the energy demand consumption for Surat city by calculating vehicle kilometers traveled. The demand for public charging comes out to be 42.4 MWh/day in 2025 and 358.05 MWh/day in 2030, respectively. The study’s objective is to determine energy demand per region in order to better understand grid planning due to the imbalance in electricity demand.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.

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.

The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region

The synergetic benefit of air quality improvements together with greenhouse gas (GHG) mitigations from fleet electrification can be maximized if the power used for electric vehicles (EVs) is from renewables. The worth-noting mismatch between renewable power generation and EV fleet charging demand requires appropriate coordination strategy. Here, we analyze the environmental benefits from increased EVs penetration in Beijing-Tianjin-Hebei (BTH) regions by integrating various scenarios of fleet electrification with coordinated charging strategies to examine the air quality improvement and GHG abatement. The study found that fleet electrification could bring substantial reduction on urban PM2.5 in BTH, especially in December by 0.8 ± 0.5 μg/m3. The coordinated charging strategy could further improve the air quality in BTH, albeit smaller than that of fleet electrification itself. PM2.5 reduction benefit from EV adoption could be significantly more pronouncing when ammonia emission reduction was considered, by more than 0.3 μg/m3 in both December and July, validating the great significance of vehicle NH3 emission control and the necessity of prioritizing the electrification of high ammonia emitting fleet. The outcome of this study helps to formulate the effective on-road transportation pollutant abatement strategies and offers technological support for policy makers to conduct more sensible sequence of future fleet electrification process.

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.