Electric Power Allocation in a Network of Fast Charging Stations

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked by the closest charging station. Overall, the paper addresses key issues related to the efficient operation of a network of charging stations.Comment: Published in IEEE Journal on Selected Areas in Communications July 201

Essays On Stochastic Programming In Service Operations Management

Deterministic mathematical modeling is a branch of optimization that deals with decision making in real-world problems. While deterministic models assume that data and parameters are known, these numbers are often unknown in the real world applications.The presence of uncertainty in decision making can make the optimal solution of a deterministic model infeasible or sub-optimal. On the other hand, stochastic programming approach assumes that parameters and coefficients are unknown and only their probability distribution can be estimated. Although stochastic programming could include uncertainties in objective function and/or constraints, we only study problems that the goal of stochastic programming is to maximize (minimize) the expectation of the objective function of random variables. Stochastic programming has a wide range of application in manufacturing production planning, machine scheduling, dairy farm expansion planning, asset liability management, traffic management, and automobile dealership inventory management that involve uncertainty in decision making. One limitation of stochastic programming is that considering uncertainty in mathematical modeling often leads to a large-scale programming problem. The most widely used stochastic programming model is two-stage stochastic programming. In this model, first-stage decision variables are determined before observing the realization of uncertainties and second-stage decision variables are selected after exposing first-stage variables into the uncertainties. The goal is to determine the value of first-stage decisions in a way to maximize (minimize) the expected value of second-stage objective function. 1.1 Motivation for Designing Community-Aware Charging Network for Electric Vehicles Electric vehicles (EVs) are attracting more and more attentions these days due to increase concern about global warming and future shortage of fossil fuels. These vehicles have potential to reduce greenhouse gas emissions, improve public health condition by reducing air pollution and improving sustainability, and addressing diversication of transportation energy feedstock. Governments and policy makers have proposed two types of policy incentives in order to encourage consumers to buy an EV: direct incentives and indirect incentives. Direct incentives are those that have direct monetary value to consumers and include purchase subsidies, license tax/fee reductions, Electric Vehicle Supply Equipment (EVSE) financing, free electricity, free parking and emission test exemptions. On the other hand, indirect incentives are the ones that do not have direct monetary value and consist of high-occupancy vehicle access, emissions testing exemption time savings, and public charger availability. Lack of access to public charging network is considered to be a major barrier in adoption of EVs [38]. Access to public charging infrastructure will provide confidence for EV owners to drive longer distances without going out of charge and encourage EV ownership in the community. The current challenge for policy makers and city planners in installing public charging infrastructure is determining the location of these charging service stations, number of required stations and level of charging since the technology is still in its infancy and the installation cost is high. Since recharging of EV battery takes more time than refueling conventional vehicles, parking lots and garages are considered as potential locations for installing charging stations. The aim of this research is to develop a mathematical programming model to find the optimal locations with potentially high utilization rate for installing community-aware public EV charging infrastructure in order to improve accessibility to charging service and community livability metrics. In designing such charging network, uncertainties such as EV market share, state of battery charge at the time of arrival, driver’s willingness to charge EV away from home, arrival time to final destination, driver’s activity duration (parking duration), and driver’s walking distance preference play major role. Incorporating these uncertainties in the model, we propose a two-stage stochastic programming approach to determine the location and capacity of public EV charging network in a community. 1.2 Motivation for Managing Access to Care at Primary Care Clinics Patient access to care along with healthcare efficiency and quality of service are dimensions of health system performance measurement [1]. Improving access to primary care is a major step of having a high-performing health care system. However, many patients are struggling to get an in-time appointment with their own primary care provider (PCP). Even two years aer health insurance coverage was expanded, new patients have to wait 82% longer to get an internal-medicine appointment. A national survey shows that percentage of patients that need urgent care and could not get an appointment increased from 53% to 57% between 2006 and 2011 [30]. This delay may negatively impact health status of patients and may even lead to death. Patients that cannot get an appointment with their PCP may seek care with other providers or in emergency departments which will decrease continuity of care and increase total cost of health system. The main issue behind access problem is the imbalance between provider capacity and patient demand. While provider panel size is already large, the shortage in primary care providers and increasing number of patients mean that providers have to increase their panel size and serve more patients which will potentially lead to lower access to primary care. The ratio of adult primary care providers to population is expected to drop by 9% between 2005 and 2020 [12]. Moreover, patient flow analysis can increase efficiency of healthcare system and quality of health service by increasing patient and provider satisfaction through better resource allocation and utilization [39]. Effective resource allocation will smooth patient ow and reduce waste which will in turn results in better access to care. One way to control patient flow in clinic is managing appointment supply through appointment scheduling system. A well-designed appointment scheduling system can decrease appointment delay and waiting time in clinic for patients and idle time and/or overtime for physicians at the same time and increase their satisfaction. Appointment scheduling requires to make a balance between patient needs and facility resources [13]. The purpose of this study is to gain a better understanding for managing access to care in primary care outpatient clinics through operations management research. As a result of this under standing, we develop appointment scheduling models using two-stage stochastic programming to improve access while maintaining high levels of provider capacity utilization and improving patient flow in clinic by leveraging uncertainties in patient demand, patient no-show and provider service time variability

Development of a power monitoring and control system to provide demand side management of electric vehicle charging activity.

Due to the recent inflow of Electric Vehicles (EVs) to the automobile market, new concerns have risen with respect to the additional electrical load and the resultant effects on an overloaded electric grid. Either for convenience purposes or possibly necessity due to limited electric range on EVs, some EV owners may desire to charge their EV while at work in addition to charging at home. These forward-thinking daytime charging providers are typically Commercial and Industrial (C&I) electric ratepayers, or other large electric consumers which constitute the majority of businesses, shopping centers, academic campuses and manufacturing facilities. Increased electricity consumption due to EV charging activity results in higher electricity costs due to differences in the billing structures between residential and C&I electric ratepayers. Therefore, it is beneficial to the EVSE charging provider to minimize charging activity around peak demand periods which would result in lower electrical costs overall. A solution is developed that can provide this control without creating a nuisance to electric vehicle owners since EV charging demand is somewhat inelastic due to range anxiety. The primary objective of the research detailed in this dissertation is to develop a novel demand side management system for monitoring the peak demand of commercial time-of-day electric ratepayers that cost effectively predicts and controls electric vehicle charging during peak demand periods. This objective is achieved, therefore confirming the hypothesis that such a system can provide cost and demand benefits to forward-thinking commercial electric ratepayers that provide daytime charging capabilities. This work proposes and evaluates a novel Power Monitoring and Control System (PMCS) that can be implemented at C&I EV charging locations to minimize or eliminate the negative impacts of charging electric vehicles at the workplace in C&I environments. Operation of the PMCS begins by forecasting electrical demand in advance of every 15 minute demand interval throughout the day. The forecast is generated using an artificial neural network and a number of input data streams. Electrical demand has been shown to correlate well with weather data such as temperature and dew point. Therefore, using those measurements along with a date and time stamp, and historical electrical demand measurements, a highly accurate forecast for the following 15-minute demand interval was achieved. From that forecast, the number of EV charging stations that may be active, without the chance of creating new electrical demand peaks, is calculated. Finally, the forecast is then used to properly schedule EV charging activity so that electrical demand peaks can be avoided but charging activity is maximized. The avoidance of charging activity at or near peaks in electrical demand results in lower total electric costs associated with the charging process. The final design was implemented in an EV charging testbed at the University of Louisville and data was collected to verify the operation and performance of the PMCS. With a properly designed scheduling and prioritization control algorithm, increases in electrical demand and associated costs are limited to the error in the forecasting algorithm used for predicting electrical demand levels. The final design of the forecasting algorithm results in a mean absolute percent error of 0.02% to 0.08% in the electrical demand forecast. This corresponds to approximately 3 to 10 kVA of error in electrical demand. Taking this error into account, total cost of charging several EVs is reduced by nearly 90%. Furthermore, for scenarios where there are several more electric vehicles requiring charge than there are charging stations available, several scheduling algorithms are presented in an attempt to minimize the total processing time required for completing all charging transactions

A Survey on Coordinated Charging Methods for Electric Vehicles

Electric vehicles (EVs) is regarded as one of the most effective ways to reduce oil and gas use. EVs (electric vehicles) have many advantages over ICEVs (internal combustion engine vehicles), including zero pollution, little noise, and exceptional energy efficiency. Even though an EV is known to have a three times higher fuel efficiency than an ICEV, the driving range is often significantly lower because batteries have a lower energy density than gasoline or diesel. Over the next few decades, it is anticipated that the number of electric vehicles will increase significantly due to concerns about pollution and technological advancements in the sector. Utilizing a variety of energy sources will boost energy security while reducing emissions and fuel usage. A paradigm shift has been observed with the switch from internal combustion to electric car technology. For electric vehicles to become widely used, a charging infrastructure must be developed. However, there is a cap on the amount of electricity that can be used to charge the vehicles in a charging station. Rearranging charging times, specifically charging coordination can help optimize the distribution of the available power among the vehicles. In this paper, a review of the various coordinated charging methods has been presented. A detailed comparison of the methods has been done

Market based intelligent charging system for electric vehicles

The existing electrical infrastructure is very unlikely to expand overnight. Therefore, a smart solution is certainly needed to integrate the additional load which electric vehicles (EV) bring to the network. The aim of the thesis is to study the electricity market, different intelligences related to electric vehicle charging and establish an algorithm that produces an optimized charging schedule for electric vehicles. The algorithm ensures a cost profit for user and takes part in demand response by shifting the timing of charging loads based on energy prices. The core intelligences integrated to the EV charging system in the thesis are cost optimization, peak shaving and load shifting. The algorithm follows the hourly unit cost related to the energy consumption and distribution fee in order to find the cheapest time slot for charging operation. It allocates as high charging power as possible to the cheapest time slots and then start selecting the expensive time slots until the battery is charged to desired state of charge. Along this process, the algorithm continuously calculates the maximum charging power available after other house-hold usage. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Distribution unit prices include time-of-use pricing for day and nighttime energy use in addition to the fixed fuse-based fee. By following these unit prices, the algorithm shifts the load from high demand to low demand hours in order to minimize the total costs. The algorithm is implemented in MATLAB and tested through a case study on different type of Finnish detached houses. Detached houses with different load profile data are used as input for charging a 75 kWh EV with a 10 kW and 7.5 kW charger in different cases, where the other inputs remain same for all the test cases. The Elspot area price of Finland for 2018 added with 0.3 cents/kWh margin and 24% VAT are used as energy prices. Different day and night-time distribution prices are applied depending on the consumption. The simulation results are compared to regular EV charging, where the charging operation starts right after the EV is plugged in and finishes charging within shortest time. The results from the simulation are investigated from user’s and grid’s point of view. From user’s perspective, all the charging events with intelligent charging have costs savings over regular charging. The monetary profit is higher for higher charger rating (10 kW). In cases where the household usage is low, the proportional profit is high. From grid point of view, over 99% of the load gets shifted to night-time for 10 kW charger cases. For the 7.5kW charger, the amount of shifted load is over 97%, which is a little lower than 10 kW charger cases because of longer charging time. The findings of the case study validate the use of smart charging algorithm in order to ensure cost savings for the user

Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality