Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Adaptive V2G peak shaving and smart charging control for grid integration of PEVs.

The stochastic nature of plug-in electric vehicle (PEV) driving behavior and distribution grid load profile make it challenging to control vehicle-grid integration in a mutually beneficial way. This article proposes a new adaptive control strategy that manages PEV charging/discharging for peak shaving and load leveling in a distribution grid. For accurate and high fidelity transportation mobility modeling, real vehicle driving test data are collected from the field. Considering the estimated total required PEV battery charging energy, the vehicle-to-grid capabilities of PEVs, and the forecasted non-PEV base load, a reference operating point for the grid is estimated. This reference operating point is updated once at the end of peak hours to guarantee a full final state-of-charge to each PEV. Proposed method provides cost-efficient operation for the utility grid, utmost user convenience free from range anxiety, and ease of implementation at the charging station nodes. It is tested on a real residential transformer, which serves approximately one thousand customers, under various PEV penetration levels and charging scenarios. Performance is assessed in terms of mean-square-error and peak shaving index. Results are compared with those of various reference operating point choices and shown to be superior

A fast and efficient coordinated vehicle-to-grid discharging control scheme for peak shaving in power distribution system.

This study focuses on the potential role of plug-in electric vehicles (PEVs) as a distributed energy storage unit to provide peak demand minimization in power distribution systems. Vehicle-to-grid (V2G) power and currently available information transfer technology enables utility companies to use this stored energy. The V2G process is first formulated as an optimal control problem. Then, a two-stage V2G discharging control scheme is proposed. In the first stage, a desired level for peak shaving and duration for V2G service are determined off-line based on forecasted loading profile and PEV mobility model. In the second stage, the discharging rates of PEVs are dynamically adjusted in real time by considering the actual grid load and the characteristics of PEVs connected to the grid. The optimal and proposed V2G algorithms are tested using a real residential distribution transformer and PEV mobility data collected from field with different battery and charger ratings for heuristic user case scenarios. The peak shaving performance is assessed in terms of peak shaving index and peak load reduction. Proposed solution is shown to be competitive with the optimal solution while avoiding high computational loads. The impact of the V2G management strategy on the system loading at night is also analyzed by implementing an off-line charging scheduling algorithm

Smart Vehicle to Grid Interface Project: Electromobility Management System Architecture and Field Test Results

This paper presents and discusses the electromobility management system developed in the context of the SMARTV2G project, enabling the automatic control of plug-in electric vehicles' (PEVs') charging processes. The paper describes the architecture and the software/hardware components of the electromobility management system. The focus is put in particular on the implementation of a centralized demand side management control algorithm, which allows remote real time control of the charging stations in the field, according to preferences and constraints expressed by all the actors involved (in particular the distribution system operator and the PEV users). The results of the field tests are reported and discussed, highlighting critical issues raised from the field experience.Comment: To appear in IEEE International Electric Vehicle Conference (IEEE IEVC 2014

Optimized charging control method for plug-in electric vehicles in LV distribution networks

207 p.Title: Optimized charging control method for plug-in electric vehicles in low voltage distributionnetworksKeywords: plug-in electric vehicles, smart charging, V2G, distribution networks, smart grids, multiobjectiveoptimization, demand side management, voltage unbalances, DIgSILENT PowerFactory[EN] This thesis proposes a new methodology to integrate plug-in electric vehicles in low voltagedistribution networks. Charging a significant number of plug-in electric vehicles will lead to severalimpacts in low voltage distribution networks such as increase of energy losses, overloads of linesand distribution transformers, voltage drops and unbalances, etc. These impacts will dependlargely on the charging control method used. Furthermore, there can be a conflict of interestsbetween electric vehicle users and electric utilities. In this context, this thesis proposes a newmethodology to efficiently integrate plug-in electric vehicles and, at the same time, it reducescharging costs for electric vehicle users. This new methodology is based on a multi-objectiveoptimization which objective functions are minimizing load variance and charging costs. Inaddition, an improvement has been proposed to coordinate the charging of multiple PEVs in orderto reduce voltage drops and unbalances. Furthermore, the proposed solution has beenimplemented in a decentralized architecture which provides several advantages. Aspects such asusers¿ privacy, reliability and scalability are improved compared to centralized controlarchitectures. A real distribution network located in Borup (Denmark) has been used as model totest the effectiveness of the proposed methodology. Simulation results show that the newmethodology improves load factor, limits energy losses, reduces charging costs and limits voltagedrops and unbalances. Considering all these aspects, the proposed methodology improves theintegration of plug-in electric vehicles in low voltage distribution networks.[SP] La presente tesis doctoral propone una nueva metodología para integrar los vehículoseléctricos enchufables en las redes de baja tensión. La carga de un número significativo devehículos eléctricos producirá varios impactos en las redes de baja tensión como son el aumentode pérdidas, la sobrecarga de líneas y transformadores, caídas de tensión, desequilibrios detensión, etc. Estos impactos dependerán en gran medida del método de control de carga utilizado.Además, puede existir un conflicto de intereses entre los usuarios de vehículos eléctricos y lascompañías distribuidores de electricidad. En este contexto, la presente tesis propone una nuevametodología para integrar eficientemente los vehículos eléctricos enchufables y, al mismo tiempo,reducir los costes de carga. Esta metodología está basada en una optimización multiobjetivo cuyasfunciones objetivo son la minimización de la varianza de la carga y de los costes de carga.Asimismo, se introduce una mejora para coordinar la carga de los vehículos eléctricos enchufablescon el objeto de reducir los desequilibrios y las caídas de tensión. Igualmente, la soluciónpropuesta ha sido implementada en una arquitectura descentralizada que proporciona una seriede mejoras adicionales. Aspectos como la privacidad de los usuarios, la fiabilidad y la modularidadson mejorados respecto a soluciones con arquitecturas centralizadas. Un modelo de una red dedistribución real, localizada en el municipio de Borup (Dinamarca), ha sido utilizado paracomprobar la eficacia de la metodología propuesta. Los resultados obtenidos en las simulacionesdemuestran que la nueva metodología mejora el factor de carga, limita las pérdidas de energía,reduce los costes de carga y limita los desequilibrios y caídas de tensión. Teniendo en cuenta todosestos aspectos, la metodología propuesta mejora la integración de los vehículos eléctricosenchufables en las redes de distribución de baja tensión

Using information processing techniques to forecast, schedule, and deliver sustainable energy to electric vehicles

As the number of electric vehicles on the road increases, current power grid infrastructure will not be able to handle the additional load. Some approaches in the area of Smart Grid research attempt to mitigate this, but those approaches alone will not be sufficient. Those approaches and traditional solution of increased power production can result in an insufficient and imbalanced power grid. It can lead to transformer blowouts, blackouts and blown fuses, etc. The proposed solution will supplement the ``Smart Grid\u27\u27 to create a more sustainable power grid. To solve or mitigate the magnitude of the problem, measures can be taken that depend on weather forecast models. For instance, wind and solar forecasts can be used to create first order Markov chain models that will help predict the availability of additional power at certain times. These models will be used in conjunction with the information processing layer and bidirectional signal processing components of electric vehicle charging systems, to schedule the amount of energy transferred per time interval at various times. The research was divided into three distinct components: (1) Renewable Energy Supply Forecast Model, (2) Energy Demand Forecast from PEVs, and (3) Renewable Energy Resource Estimation. For the first component, power data from a local wind turbine, and weather forecast data from NOAA were used to develop a wind energy forecast model, using a first order Markov chain model as the foundation. In the second component, additional macro energy demand from PEVs in the Greater Rochester Area was forecasted by simulating concurrent driving routes. In the third component, historical data from renewable energy sources was analyzed to estimate the renewable resources needed to offset the energy demand from PEVs. The results from these models and components can be used in the smart grid applications for scheduling and delivering energy. Several solutions are discussed to mitigate the problem of overloading transformers, lack of energy supply, and higher utility costs

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Optimal power tracking for autonomous demand side management of electric vehicles

Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.publishedVersio

Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Optimal power tracking for autonomous demand side management of electric vehicles

publishedVersio