Optimal open-circuit voltage (OCV) model for improved electric vehicle battery state-of-charge in V2G services

Abstract: Electric vehicles (EVs) with voltage-to-grid (V2G) capability are useful in augmenting grid capability to handle high energy demand of end users during peak periods. We propose a hybrid state-of-charge (SOC) battery model with aggregator to optimize battery charging and maintain grid stability during peak periods. The proposed SOC model leverages the advantages of three well-known previously proposed battery models namely: Shepherd, Unnewehr and Nernst models. The proposed hybrid model is a combination of the merits of the three specified empirical Lithium-ion battery models to optimize slow charging. This will enhance battery performance by improving its depth-of-discharge profile. This results in enhanced V2G capability and longer driving time for EV owners. Battery parameters used in the simulation are for Nissan Leaf 2019 EV. The proposed SOC model parameters are used to optimize a two-objective function which is used by the aggregator to maximize benefits to both EV owners and DSO. Multi-objective genetic algorithm (MOGA) is used to optimize the objective function because of its ability to obtain non-dominated solutions while still maintaining diversity of the solutions. From simulation results, proposed OCV model improves battery SOC by 10% after V2G operating period (2 p.m.) compared to a case without the model. Also, proposed model earns aggregator $445 and$45 more for voltage and frequency regulation services, respectively. Voltage stability of all 5 considered grid buses of the IEEE 33-node system remains at 0.9–1.0 p.u

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

Optimal Energy and Reserve Market Management in Renewable Microgrid-PEVs Parking Lot Systems: V2G, Demand Response and Sustainability Costs

Vehicle-to-grid (V2G) technology heralds great promise as a demand-side resource to contribute to more efficient grid management and promote the use of decentralized renewable energy. In this light, we propose a new optimization model for the sustainable energy and reserve market management in renewable-driven microgrid (RMG) plug-in electric vehicles (PEVs) parking lot systems. The RMG is composed of a hybrid photovoltaic/wind/hydrogen energy and storage system, along with local dispatchable generation units and bidirectional grid connection. The RMG is coupled to a smart PEVs parking lot, which is equipped with grid-to-vehicle (G2V) and V2G technologies allowing for not only PEVs aggregation and control but also optimal allocation of energy resources. Time-of-use (TOU) prices are considered in a demand response program (DRP) to enhance both economic and environmental performances by encouraging end-users to shift their energy demands from peak to off-peak time periods. Additionally, the model accounts for an economic incentive to PEVs owners to compensate for battery degradation. The integrated system eco-efficiency is evaluated through the application of the novel life cycle assessment-based Eco-cost indicator. The resulting mixed-integer linear programming model to minimize sustainability costs is implemented in GAMS and solved to global optimality. Different case studies are performed to demonstrate the effectiveness of the proposed modelling approach. Energy analyses results reveal that the optimal G2V-V2G operation, allied to TOU prices in a DRP, and reserve market management can reduce around 42% the energy and environmental costs of the RMG-PEVs parking lot system

A Comprehensive Assessment of Vehicle-to-Grid Systems and Their Impact to the Sustainability of Current Energy and Water Nexus

This dissertation aims to explore the feasibility of incorporating electric vehicles into the electric power grid and develop a comprehensive assessment framework to predict and evaluate the life cycle environmental, economic and social impact of the integration of Vehicle-to-Grid systems and the transportation-water-energy nexus. Based on the fact that electric vehicles of different classes have been widely adopted by both fleet operators and individual car owners, the following questions are investigated: 1. Will the life cycle environmental impacts due to vehicle operation be reduced? 2. Will the implementation of Vehicle-to-Grid systems bring environmental and economic benefits? 3. Will there be any form of air emission impact if large amounts of electric vehicles are adopted in a short time? 4. What is the role of the Vehicle-to-Grid system in the transportation-water-energy nexus? To answer these questions: First, the life cycle environmental impacts of medium-duty trucks in commercial delivery fleets are analyzed. Second, the operation mechanism of Vehicle-to-Grid technologies in association with charging and discharging of electric vehicles is researched. Third, the feasible Vehicle-to-Grid system is further studied taking into consideration the spatial and temporal variance as well as other uncertainties within the system. Then, a comparison of greenhouse gas emission mitigation of the Vehicle-to-Grid system and the additional emissions caused by electric vehicle charging through marginal electricity is analyzed. Finally, the impact of the Vehicle-to-Grid system in the transportation-water-energy nexus, and the underlying environmental, economic and social relationships are simulated through system dynamic modeling. The results provide holistic evaluations and spatial and temporal projections of electric vehicles, Vehicle-to-Grid systems, wind power integration, and the transportation-water-energy nexus

State-of-the-Art Assessment of Smart Charging and Vehicle 2 Grid services

Electro-mobility – especially when coupled smartly with a decarbonised grid and also renewable distributed local energy generation, has an imperative role to play in reducing CO2 emissions and mitigating the effects of climate change. In parallel, the regulatory framework continues to set new and challenging targets for greenhouse gas emissions and urban air pollution. • EVs can help to achieve environmental targets because they are beneficial in terms of reduced GHG emissions although the magnitude of emission reduction really depends on the carbon intensity of the national energy mix, zero air pollution, reduced noise, higher energy efficiency and capable of integration with the electric grid, as discussed in Chapter 1. • Scenarios to limit global warming have been developed based on the Paris Agreement on Climate Change, and these set the EV deployment targets or ambitions mentioned in Chapter 2. • Currently there is a considerable surge in electric cars purchasing with countries such as China, the USA, Norway, The Netherlands, France, the UK and Sweden leading the way with an EV market share over 1%. • To enable the achievement of these targets, charging infrastructures need to be deployed in parallel: there are four modes according to IEC 61851, as presented in Chapter 2.1.4. • The targets for SEEV4City project are as follow: o Increase energy autonomy in SEEV4-City sites by 25%, as compared to the baseline case. o Reduce greenhouse gas emissions by 150 Tonnes annually and change to zero emission kilometres in the SEEV4-City Operational Pilots. o Avoid grid related investments (100 million Euros in 10 years) by introducing large scale adoption of smart charging and storage services and make existing electrical grids compatible with an increase in electro mobility and local renewable energy production. • The afore-mentioned objectives are achieved by applying Smart Charging (SC) and Vehicle to Grid (V2G) technologies within Operational Pilots at different levels: o Household. o Street. o Neighbourhood. o City. • SEEV4City aims to develop the concept of 'Vehicle4Energy Services' into a number of sustainable business models to integrate electric vehicles and renewable energy within a Sustainable Urban Mobility and Energy Plan (SUMEP), as introduced in Chapter 1. With this aim in mind, this project fills the gaps left by previous or currently running projects, as reviewed in Chapter 6. • The business models will be developed according to the boundaries of the six Operational Pilots, which involve a disparate number of stakeholders which will be considered within them. • Within every scale, the relevant project objectives need to be satisfied and a study is made on the Public, Social and Private Economics of Smart Charging and V2G. • In order to accomplish this work, a variety of aspects need to be investigated: o Chapter 3 provides details about revenue streams and costs for business models and Economics of Smart Charging and V2G. o Chapter 4 focuses on the definition of Energy Autonomy, the variables and the economy behind it; o Chapter 5 talks about the impacts of EV charging on the grid, how to mitigate them and offers solutions to defer grid investments; o Chapter 7 introduces a number of relevant business models and considers the Economics of Smart Charging and V2G; o Chapter 8 discusses policy frameworks, and gives insight into CO2 emissions and air pollution; o Chapter 9 defines the Data Collection approach that will be interfaced with the models; o Chapter 10 discusses the Energy model and the simulation platforms that may be used for project implementation

Smart PEV Charging Station Operation and Design Considering Distribution System Impact

Penetration of plug-in electric vehicles (PEVs) into the market is expected to be large in the near future. Also, as stated by the Ontario Ministry of Transportation, the province is investing $20 million from Ontario's Green Investment Fund to build nearly 500 electric vehicle charging stations (EVCSs) at over 250 locations in Ontario by 2017. Therefore, estimating PEV charging demand at an EVCS with their complex charging behavior, their impact on the power grid, and the optimal design of EVCS need be investigated. This thesis first presents a queuing analysis based method for modeling the 24-hour charging load profile of EVCSs. The queuing model considers the arrival of PEVs as a non-homogeneous Poisson process with different arrival rates over the day; considering customer convenience and charging price as the factors that influence the hourly arrival rate of vehicles at the EVCS. One of the main contributions of the thesis is to model the PEV service time considering the state-of-charge of the battery and the effect of the battery charging behavior. The impact of PEV load models on distribution systems is studied for a deterministic case, and the impact of uncertainties is examined using the stochastic optimal power flow and Model Predictive Control approaches. The thesis presents a novel mathematical model for representing the total charging load at an EVCS in terms of controllable parameters; the load model developed using a queuing model followed by a neural network (NN). The queuing model constructs a data set of PEV charging parameters which are input to the NN to determine the controllable EVCS load model. The smart EVCS load is a function of the number of PEVs charging simultaneously, total charging current, arrival rate, and time; and various class of PEVs. The EVCS load is integrated within a distribution operations framework to determine the optimal operation and smart charging schedules of the EVCS. Objective functions from the perspective of the local distribution company (LDC) and EVCS owner are considered for studies. The performance of a smart EVCS vis-à-vis an uncontrolled EVCS is examined to emphasize the demand response (DR) contributions of a smart EVCS and its integration into distribution operations. Finally, the thesis presents the optimal design of an EVCS with the goal of minimizing the life-cycle cost, while taking into account environmental emissions. Different supply options such as renewable energy technology based and diesel generation, with realistic inputs on their physical, operating and economic characteristics are considered, in order to arrive at the optimal design of EVCS. The charging demand of the EVCS is estimated considering real drive data. Analysis is also carried out to compare the economics of a grid-connected EVCS with an isolated EVCS and the optimal break-even distance is determined. Also, the EVCS is assumed to be connected to the grid as a smart energy hub based on different supply options

Microgrid Energy Management

In IEEE Standards, a Microgrid is defined as a group of interconnected loads and distributed energy resources with clearly defined electrical boundaries, which acts as a single controllable entity with respect to the grid and can connect and disconnect from the grid to enable it to operate in both grid-connected or island modes. This Special Issue focuses on innovative strategies for the management of the Microgrids and, in response to the call for papers, six high-quality papers were accepted for publication. Consistent with the instructions in the call for papers and with the feedback received from the reviewers, four papers dealt with different types of supervisory energy management systems of Microgrids (i.e., adaptive neuro-fuzzy wavelet-based controls, cost-efficient power-sharing techniques, and two-level hierarchical energy management systems); the proposed energy management systems are of quite general purpose and aim to reduce energy usages and monetary costs. In the last two papers, the authors concentrate their research efforts on the management of specific cases, i.e., Microgrids with electric vehicle charging stations and for all-electric ships

optimizing the operation of energy storage using a non linear lithium ion battery degradation model

Abstract Given their technological and market maturity, lithium-ion batteries are increasingly being considered and used in grid applications to provide a host of services such as frequency regulation, peak shaving, etc. Charging and discharging these batteries causes degradation in their performance. Lack of data on degradation processes combined with requirement of fast computation have led to over-simplified models of battery degradation. In this work, the recent experimental evidence that demonstrates that degradation in lithium-ion batteries is non-linearly dependent on the operating conditions is incorporated. Experimental aging data of a commercial battery have been used to develop a scheduling model applicable to the time constraints of a market model. A decomposition technique that enables the developed model to give near-optimal results for longer time horizons is also proposed