Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration

Framework to Develop Time- and Voltage-Dependent Building Load Profiles Using Polynomial Load Models

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building\u27s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models are typically voltage-invariant. This paper proposes a novel framework to transform these voltage-invariant building load models into fully time- and voltage-dependent load profiles using available data on the voltage sensitivity of individual load components. While a voltage-dependent building model could theoretically be generated from static load models of every component in a building, this approach faces two challenges: first, load models representing all load components are impractical to develop for all possible load component types; second, building energy consumption is never measured or modeled at the individual component level. The proposed framework compiles available component data in the form of static ZIP load model parameters, and maps them into the end use categories utilized by standard building modeling programs. The voltage sensitivity of each end use category is then bounded by the extrema of the component models within it. This framework is applied to a load profile case study representing the aggregate U.S. residential building stock. In addition to the minimum/ maximum conditions, a load profile based on typical load composition and weighted ZIP parameters is generated for the same building stock. The results show that for a 10% drop in voltage, using the least sensitive ZIP parameters, active power is expected to be 3% to 14% lower than nominal, depending on the season and time of day. Using the most sensitive ZIP parameters, the active power is expected to be 9% to 20% lower than nominal, also depending on the season and time of day

The Impact of PEV User Charging Behavior in Building Public Charging Infrastructure

Plug-in electric vehicles (PEVs) play a significant role in the development of green cities since they generate less pollution than conventional vehicles. To promote PEV adoption and mitigate range anxiety, charging infrastructure should be deployed at strategic locations that are readily accessible to the public. Nebraska is working on the expansion of charging infrastructure around the state; however, stakeholders face several difficulties in trying to minimize irregular charging behaviors. Most electric vehicle users plug in and leave their vehicles for an extended time at public parking lots designated for PEVs. Some users even leave their vehicles for longer than 24 hours. Prolonged idle time is a concern for other PEV users who need to charge their vehicles to complete their planned trip. This thesis proposes several well-known regression methods to predict the idle time to help policymakers minimize the impact of irregular charging behaviors. In addition, PEV user charging behavior has a significant influence on the distribution network and its reliability. In addition, to increase efficiency in management of the electric grid, this thesis also proposes several well-known regression methods to predict the energy consumption of a charging session. The performance of different regression methods for predicting the idle time as well as energy consumption are characterized using established statistical metrics. Adviser: Mahmoud Alahma

Plug-in Electric Vehicle Charging Demand Prediction Using Artificial Intelligence

Climate change has been a serious issue around the world for a long time, and innumerable resolutions have been offered to decrease the issues caused by global warming . In the outcome of the Paris Agreement of 2015, each country was required to decrease the emission levels in a dynamic action to oppose climate change . Most countries started to reduce the emissions in their transportation division by encouraging people to use electric vehicles instead of conventional vehicles . Many challenges appear due to the variation in Plug-in Electric Vehicle (PEV) user charging behavior as well as battery sizes. Limited information is available about the effect of charging behavior on the distribution network and its reliability at public charging stations in any given area. Generally, monitoring the energy consumption has become one of the most important factors in green and micro grids; therefore. The main objective of this research is to assess the feasibility of predicting the energy demand of a charging session, using only information available at the start of charging. This prediction could help to efficiently manage the electric grid. Consequently, machine-learning methods will be applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach will be validated using a real data collected from different household charging stations installed in 480 homes in Omaha, NE, USA. The performance of different regression methods for predicting the charging demand will be characterized using established statistical metrics. Accurate prediction of session charging demand has many possible applications, including scheduling, grid stability, and smart grid integration

Analysis of PEV User Charging Behavior at Household Charging Stations, Omaha Case Study

While the increase in EV use is a positive step towards embracing green technology, the heightened energy demands resulting from this rapid growth present major challenges to local energy grid load management. For context, the new EVs being deployed store approximately 100 kWh, about four times the daily electricity use of the average household in the U.S. Current local distribution grids do not have the capacity to accommodate these massively increased loads. For this reason, it is important that local utilities have a full understanding of the charging demand within a given grid. The main objective of this research is to deepen the understanding of charging behavior at the household level using real data. Specifically, data from existing 417 residential Level-2 charging stations, located in Omaha, Nebraska, USA, are collected and analyzed. The results show a clear pattern in user behavior for the starting time of sessions, as well as the connection duration, charging duration, and subsequent energy demand

Data-Driven Framework for Predicting and Scheduling Household Charging of EVS

The increasing prevalence of EV charging poses challenges for power grid stability and quality due to high charging load demands. Without effective energy management strategies for EV charging, the simultaneous power demand from numerous EVs can strain the electric grid, impacting power quality and the wholesale electricity market. To address these challenges, this dissertation presents a comprehensive framework comprising five critical tasks: analyzing EV charging behavior, optimizing charging schedules, developing predictive models, analyzing aggregated impacts, and evaluating implications of predicted user behavior on scheduling. By examining EV charging behavior at household and public charging stations, this study aims to understand patterns and variations in charging sessions. The framework introduces a centralized scheduling approach for household charging stations to reduce peak demand and costs, relying on accurate knowledge of EV charging behavior. Machine learning and linear regression models are utilized to predict session charging parameters, with Random Forest models outperforming other methods, yet uncertainties persist in the predictions. The study also investigates aggregate demand and connectivity of multiple EV users, revealing the potential to predict aggregate trends by incorporating session predictions. Evaluating the day-ahead scheduling framework implemented with predicted charging data using actual data highlights challenges in meeting user demand due to prediction errors. This research provides valuable insights into EV charging behavior, emphasizing the significance of accurate data for strategic scheduling. While machine learning models show potential in predicting EV charging behavior, limited correlation between session variables and available information at plugin is observed. Challenges arise from errors in session predictions when scheduling EV charging for a group of users, suggesting the need for a more decentralized approach

Data-Driven Framework for Predicting and Scheduling Household Charging of EVs

The increasing prevalence of EV charging poses challenges for power grid stability and quality due to high charging load demands. Without effective energy management strategies for EV charging, the simultaneous power demand from numerous EVs can strain the electric grid, impacting power quality and the wholesale electricity market. To address these challenges, this dissertation presents a comprehensive framework comprising five critical tasks: analyzing EV charging behavior, optimizing charging schedules, developing predictive models, analyzing aggregated impacts, and evaluating implications of predicted user behavior on scheduling. By examining EV charging behavior at household and public charging stations, this study aims to understand patterns and variations in charging sessions. The framework introduces a centralized scheduling approach for household charging stations to reduce peak demand and costs, relying on accurate knowledge of EV charging behavior. Machine learning and linear regression models are utilized to predict session charging parameters, with Random Forest models outperforming other methods, yet uncertainties persist in the predictions. The study also investigates aggregate demand and connectivity of multiple EV users, revealing the potential to predict aggregate trends by incorporating session predictions. Evaluating the day-ahead scheduling framework implemented with predicted charging data using actual data highlights challenges in meeting user demand due to prediction errors. This research provides valuable insights into EV charging behavior, emphasizing the significance of accurate data for strategic scheduling. While machine learning models show potential in predicting EV charging behavior, limited correlation between session variables and available information at plug-in is observed. Challenges arise from errors in session predictions when scheduling EV charging for a group of users, suggesting the need for a more decentralized approach. Advisor: Mahmoud Alahma

Incentivizing Electric Vehicle Adoption Through State and Federal Policies: Reviewing influential policies

All-electric vehicles (EVs), battery-powered EVs (BEVs), and plug-in hybrid EVs (PHEVS) are gaining market share and increasing in popularity with the buying public because the battery range (longer) and cost (lower) have reached sweet spots, the charging infrastructure is more robust, and concern with global cli­mate change is high. In 2013, only 100,000 EVs were sold in the United States, but by 2022, approximately 800,000 have been purchased. A similar growth is seen in EV sup­ply equipment (EVSE), i.e., EV charging stations, with 19,742 documented EV charging station locations in the United States in 2013 to 50,054 documented EV charging station locations, with approximately 130,000 ports, by the end of 2022. As impressive as this growth is, the eightfold increase in EV sales is inconsistent across individual states. While geography and the states’ differing physical and demo­graphic conditions play a role, state and federal EV and EVSE policies play an outsized role in the differences among the states. While the federal government can enact nationwide policies to increase EV adoption in the United States, it is also up to each state to incentivize EV adoption through their own policies and initiatives in their legislation. As of August 2021, 44 states had some form of charging infra­structure incentives, and 32 states had some form of EV incentives. (The nomenclature used in each state’s legisla­tion changes in reference to EVs with the most common being EVs/BEVs referring to EVs powered by an electric battery and HEVs referring to vehicles that can utilize more than one form of onboard energy. However, some states, such as Nebraska, refer to EVs and HEVs as alterna­tive fuel vehicles, and California refers to EVs as zero-emis­sion vehicles. For simplicity, EVs will be used to refer to any of these vehicle classifications.) This article intends to shed light on the federal and state policies that most influence uniform and nationwide EV adoption—or the lack thereof. The first section, “LRTPs,” is about long-range transportation plans (LRTPs), prepared by each state’s transportation department, that include EV promotion in future planning. The second section, “Motor Fuel Tax and Vehicle Registration Fees,” is about EV regis­tration fees, a legislative action to mitigate motor fuel tax reduction from increased EV usage. The third section, “Federal, State, and Utility Incentives,” is about federal, state, and utility incentives to support the adoption and deployment of EVs and EVSE

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration