A Stochastic Resource-Sharing Network for Electric Vehicle Charging

We consider a distribution grid used to charge electric vehicles such that voltage drops stay bounded. We model this as a class of resource-sharing networks, known as bandwidth-sharing networks in the communication network literature. We focus on resource-sharing networks that are driven by a class of greedy control rules that can be implemented in a decentralized fashion. For a large number of such control rules, we can characterize the performance of the system by a fluid approximation. This leads to a set of dynamic equations that take into account the stochastic behavior of EVs. We show that the invariant point of these equations is unique and can be computed by solving a specific ACOPF problem, which admits an exact convex relaxation. We illustrate our findings with a case study using the SCE 47-bus network and several special cases that allow for explicit computations.Comment: 13 pages, 8 figure

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

Active and Reactive Power Control of Flexible Loads for Distribution-Level Grid Services

Electric vehicle (EV) charging/discharging can take place in any P-Q quadrants, which means EVs could provide reactive power at any state-of-charge (SOC). This dissertation shows four-quadrant operation of EVs and aggregation of EVs for support of grid operations. First, this work develops hierarchical coordination frameworks to optimally manage active and reactive power dispatch of number of spatially distributed EVs incorporating distribution grid level constraints. This work demonstrates benefits of coordinated dispatch of active and reactive power from EVs using a 33-node distribution feeder with large number of EVs (more than 5,000). Case studies demonstrate that, in constrained distribution grids, coordinated charging reduces the average cost of EV charging if the charging takes place at non-unity power factor mode compared to unity power factor. Similarly, the results also demonstrate that distribution grids can accommodate charging of increased number of EVs if EV charging takes place at non-unity power factor mode compared to the unity power factor. Next, this work utilizes detailed EV battery model that could be leveraged for its four-quadrant operations. Then, the developed work coordinates the operations of EVs and distribution feeder to support voltage profile on the grid in real time. The grid level problem is devised as a distribution optimal power flow model to compute voltage regulation signal to dispatch active/reactive power set points of individual EVs. The efficacy of the developed models are demonstrated by using a LV secondary feeder, where EVs\u27 operating in all four quadrants are shown to compensate the feeder voltage fluctuations caused by daily time varying residential loads, while honoring other operational constraints of the feeder. Furthermore, a novel grid application, called virtual power plant (VPP), is developed. Traditional nonlinear power flow problems are nonconvex, hence, time consuming to solve. In order to be used in real time simulation in VPP, an efficient linearized optimal power flow model is developed. This linearization method is used to solve a 534-bus power system with 3 VPPs in real-time. This work also implements VPP scheduling in real-time using OPAL-RT\u27s simulator in hardware-in-the-loop (HIL), where the loads are emulated using micro-controller devices

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources

Optimal Online Charging Coordination of Plug in Electric Vehicles in Unbalanced Grids for Ancillary Voltage Support

This PhD thesis will propose an optimal online charge control through genetic algorithm for G2V coordination of PEVs (OL-C-TP) in unbalanced systems. Moreover the algorithm will be extended to also include V2G coordination and offer ancillary voltage support (OL-CD-TPQ) by considering two different methods based on the utility time-of-day prices for exporting reactive power and droop controller for decentralized exporting of reactive power. Then the performance of OL-CD-TPQ by switching PEVs in three phase unbalanced networks is improved

A Modelling Tool for Distribution Networks to Demonstrate Smart Grid Solutions

The increased deployment of low carbon technologies in power distribution networks, particularly at the distribution level, is expected to cause significant problems to network operation unless existing networks are appropriately adapted and actively controlled as part of a smart grid. This paper describes the development of a modelling tool to examine Smart Grid solutions to a number of issues affecting low voltage power distribution networks. Use of the tool in the context of transformer overload, line overvoltage, active load control, Grid storage and Black Start analysis is examined

On-Line Optimal Charging Coordination of Plug-In Electric Vehicles in Smart Grid Environment

This PhD research proposes a new objective function for optimal on-line PEV coordination. A new enhanced on-line coordinated charging using coordinated aggregated particle swarm particle optimization (OLCC-CAPSO) has been used to solve the PEV coordination objective objection and associated constraints. The objective function provides a chance for all PEVs to start charging as quickly as possible, while customer satisfaction function is being optimized subject to network criteria including voltage profiles, generator and distribution transformer ratings