PEV Charging Infrastructure Integration into Smart Grid

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute to the reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. With the increasing popularity of PEVs, public electric-vehicle charging infrastructure (EVCI) becomes indispensable to meet the PEV user requirements. EVCI can consist of various types of charging technologies, offering multiple charging services for PEV users. Proper integration of the charging infrastructure into smart grid is key to promote widespread adoption of PEVs. Planning and operation of EVCI are technically challenging, since PEVs are characterized by their limited driving range, long charging duration, and high charging power, in addition to the randomness in driving patterns and charging decisions of PEV users. EVCI planning involves both the siting and capacity planning of charging facilities. Charging facility siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. Capacity planning of charging facilities must ensure a satisfactory charging service for PEV users in addition to a reliable operation of the power grid. During the operation of EVCI, price-based coordination mechanisms can be leveraged to dynamically preserve the quality-of-service (QoS) requirements of charging facilities and ensure the profitability of the charging service. This research is to investigate and develop solutions for integrating the EVCI into the smart grid. It consists of three research topics: First, we investigate PEV charging infrastructure siting. We propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. In the proposed model, we consider transportation network dynamics and congestion, in addition to different characteristics and usage patterns of each charging facility type. Second, we propose a QoS aware capacity planning of EVCI. The proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. Lastly, we investigate the coordination of PEV charging demands. We develop a differentiated pricing mechanism for a multiservice EVCI using deep reinforcement learning (RL). The proposed framework enhances the performance of charging facilities by motivating PEV users to avoid over-usage of particular service classes. Since customer-side information is stochastic, non-stationary, and expensive to collect at scale, the proposed pricing mechanism utilizes the model-free deep RL approach. In the proposed RL approach, deep neural networks are trained to determine a pricing policy while interacting with the dynamically changing environment. The neural networks take the current EVCI state as input and generate pricing signals that coordinate the anticipated PEV charging demand

Location analysis of electric vehicle charging stations for maximum capacity and coverage

Electric vehicle charging facility location is a critical component of long-term strategic planning. Integration of electric vehicles into mainstream adoption has unique characteristics as it requires a careful investigation of both electric and transportation networks. In this paper, we provide an overview of recent approaches in location analyses of electric vehicle charging infrastructures. We review approaches from classical operations research for fast and slow charging stations. Sample formulations along with case studies are presented to provide insights. We discuss that classical methods are appropriate to address the coverage of charging networks which is defined as average time or distance to reach a charging station when needed. On the other hand, calculating required capacity, defined as the individual charging resources at each node, is still an open research topic. In the final part, we present stochastic facility location theory that uses queuing and other probabilistic approaches

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Data-Driven Behavior Analysis and Implications in Plug-in Electric Vehicle Policy Studies

The adoption of plug-in electric vehicles (PEVs) is considered to be a potential solution to reduce transportation-related emissions. People’s vehicle choice and driving behavior will have important implications for the realized emissions reductions from PEVs. Therefore, PEV-related policy studies require good understanding of human behavior. Traditional approaches to analyze travel behavior are mostly to build analytic models based on assumptions because of the limited accuracy and information of data. With the development of sensor technology, there are more methods than ever to collect accurate and informative behavioral data, so the crucial consideration is how to creatively use these data to better understand people’s behavior. This dissertation proposed some data-driven approaches to simulate behavior and provided a discussion of the implications for three PEV-related topics.The first study explored the potential of greenhouse gas (GHG) reductions that can be achieved with adoption of PEVs in California by simulating vehicles’ emissions based on tracing data. It was found that assigning the right model of PEVs to drivers can help to reduce annual GHG emissions by 65%, compared to everyone driving a Toyota Corolla.The second study presented a tool to evaluate the spatial distribution of fast charging demand and to assess how much a charger in a certain location would be used based on travel diary. Scenario analysis illustrated that en-route fast charging demand will shift from primarily inside metro areas to long distance corridors outside metro areas as the battery size increases.The third study estimated the value of Clean Air Vehicle (CAV) decals by simulating the frequency of PEV owners’ access to high occupancy vehicle/toll (HOV/T) lanes based on survey data. The results indicated that the CAV Decals Program is one of the most attractive incentive policies, but there is spatial heterogeneity of CAV decal value across different regions

Stochastic planning for active distribution networks hosting fast charging stations

With the advent of electric vehicles (EVs), charging infrastructure needs to become more available and electricity providers must build additional power generation capacity to support the grid. In siting and sizing of fast charging stations (FCSs), both the distribution network constraints, as well as the traffic network limitations, must be considered because FCSs exist on both levels. Moreover, the siting and sizing of wind-powered distributed generation (WPDG) is a solution to gradually decarbonizing the grid; therefore, reducing our carbon footprint. In addition to providing capacity, they also have other benefits in the distribution network such as reducing transmission losses. In this thesis, a new framework is proposed which successfully implements a novel scoring technique to rate the attractiveness of FCS candidate locations thus, determining the expected FCS demand in each candidate location and uses WPDGs to support that load. A study has been conducted to compare the suitability of industrial-scale turbines versus micro-wind turbines in an urban area. A method for selecting candidate locations for the later has been developed. A stochastic program is proposed to account for the non-deterministic elements of the problem including generic loads, residential electric vehicle loads, FCS loads, and wind speed where they are accounted for collectively using a method called convolution. This comes hand-in-hand with a mixed-integer non-linear programming model that sites and sizes both FCSs and WPDGs with an objective of maximizing profits to incentivize investments. A list of novel constraints has been introduced that connect the traffic network to the power network. The problem is modeled from the perspective of electric utilities but also considers the perspectives of the urban planners and potential investors. A case study was implemented showing how the scoring technique works and the results show that the math model considered all the parameters and respected all the constraints delivering a holistic set of decisions to site and size both FCSs and micro WPDGs in an urban area

Planning Model for Implementing Electric Vehicle Charging Infrastructure in Distribution System

Plug-in electric vehicles (PEVs) are growing in popularity in developed countries in an attempt to overcome the problems of pollution, depleting natural oil and fossil fuel reserves and rising petrol costs. In addition, automotive industries are facing increasing community pressure and governmental regulations to reduce emissions and adopt cleaner, more sustainable technologies such as PEVs. However, accepting this new technology depends primarily on the economic aspects for individuals and the development of adequate PEV technologies. The reliability and dependability of the new vehicles (PEVs) are considered the main public concerns due to range anxiety. The limited driving range of PEVs makes public charging a requirement for long-distance trips, and therefore, the availability of convenient and fast charging infrastructure is a crucial factor in bolstering the adoption of PEVs. The goal of the work presented in this thesis was to address the challenges associated with implementing electric vehicle fast charging stations (FCSs) in distribution system. Installing electric vehicle charging infrastructure without planning (free entry) can cause some complications that affect the FCS network performance negatively. First, the number of charging stations with the free entry can be less or more than the required charging facilities, which leads to either waste resources by overestimating the number of PEVs or disturb the drivers’ convenience by underestimate the number of PEVs. In addition, it is likely that high traffic areas are selected to locate charging stations; accordingly, other areas could have a lack of charging facilities, which will have a negative impact on the ability of PEVs to travel in the whole transportation network. Moreover, concentrating charging stations in specific areas can increase both the risk of local overloads and the business competition from technical and economic perspectives respectively. Technically, electrical utilities require that the extra load of adopting PEV demand on the power system be managed. Utilities strive for the implementation of FCSs to follow existing electrical standards in order to maintain a reliable and robust electrical system. Economically, the low PEV penetration level at the early adoption stage makes high competition market less attractive for investors; however, regulated market can manage the distance between charging stations in order to enhance the potential profit of the market. As a means of facilitating the deployment of FCSs, this thesis presents a comprehensive planning model for implementing plug-in electric vehicle charging infrastructure. The plan consists of four main steps: estimating number of PEVs as well as the number of required charging facilities in the network; selecting the strategic points in transportation network to be FCS target locations; investigating the maximum capability of distribution system current structure to accommodate PEV loads; and developing an economical staging model for installing PEV charging stations. The development of the comprehensive planning begins with estimating the PEV market share. This objective is achieved using a forecasting model for PEV market sales that includes the parameters influencing PEV market sales. After estimating the PEV market size, a new charging station allocation approach is developed based on a Trip Success Ratio (TSR) to enhance PEV drivers’ convenience. The proposed allocation approach improves PEV drivers’ accessibility to charging stations by choosing target locations in transportation network that increase the possibility of completing PEVs trips successfully. This model takes into consideration variations in driving behaviors, battery capacities, States of Charge (SOC), and trip classes. The estimation of PEV penetration level and the target locations of charging stations obtained from the previous two steps are utilized to investigate the capability of existing distribution systems to serve PEV demand. The Optimal Power Flow (OPF) model is utilized to determine the maximum PEV penetration level that the existing electrical system can serve with minimum system enhancement, which makes it suitable for practical implementation even at the early adoption rates. After that, the determination of charging station size, number of chargers and charger installation time are addressed in order to meet the forecasted public PEV demand with the minimum associated cost. This part of the work led to the development of an optimization methodology for determining the optimal economical staging plan for installing FCSs. The proposed staging plan utilizes the forecasted PEV sales to produce the public PEV charging demand by considering the traffic flow in the transportation network, and the public PEV charging demand is distributed between the FCSs based on the traffic flow ratio considering distribution system margins of PEV penetration level. Then, the least-cost fast chargers that satisfy the quality of service requirements in terms of waiting and processing times are selected to match the public PEV demand. The proposed planning model is capable to provide an extensive economic assessment of FCS projects by including PEV demand, price markup, and different market structure models. The presented staging plan model is also capable to give investors the opportunity to make a proper trade-off between overall annual cost and the convenience of PEV charging, as well as the proper pricing for public charging services.