Frequency response from aggregated V2G chargers with uncertain EV connections

Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived moment-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of Misplaced &6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity

Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

DEVELOPMENT AND EVALUATION OF AN INTELLIGENT TRANSPORTATION SYSTEMS-BASED ARCHITECTURE FOR ELECTRIC VEHICLES

The rapid development of increasingly complex in-vehicle electronics now offers an unprecedented level of convenience and versatility as well as accelerates the demand for connected driving experience, which can only be achieved in a comprehensive Intelligent Transportation Systems (ITS) technology based architecture. While a number of charging and range related issues continue to impede the Electric Vehicle (EV) market growth, integrating ITS technologies with EVs has the potential to address the problems and facilitate EV operations. This dissertation presents an ITS based vehicle infrastructure communication architecture in which abundant information can be exchanged in real time through vehicle-to-vehicle and vehicle-to- infrastructure communication, so that a variety of in-vehicle applications can be built to enhance the performance of EVs. This dissertation emphasizes on developing two applications that are specifically designed for EVs. First, an Ant Colony Optimization (ACO) based routing and recharging strategy dedicated to accommodate EV trips was devised. The algorithm developed in this study seeks, in real time, the lowest cost route possible without violating the energy constraint and can quickly provide an alternate suboptimal route in the event of unexpected situations (such as traffic congestion, traffic incident and road closure). If the EV battery requires a recharge, the algorithm can be utilized to develop a charging schedule based on recharging locations, recharging cost and wait time, and to simultaneously maintain the minimum total travel time and energy consumption objectives. The author also elucidates a charge scheduling model that maximizes the net profit for each vehicle-to-grid (V2G) enabled EV owner who participates in the grid ancillary services while the energy demands for their trips can be guaranteed as well. By applying ITS technologies, the charge scheduling model can rapidly adapt to changes of variables or coefficients within the model for the purpose of developing the latest optimal charge/discharge schedule. The performance of EVs involved in the architecture was validated by a series of simulations. A roadway network in Charleston, SC was created in the simulator and a comparison between ordinary EVs and connected EVs was performed with a series of simulation experiments. Analysis revealed that the vehicle-to-vehicle and vehicle-to- infrastructure communication technology resulted in not only a reduction of the total travel time and energy consumption, but also in the reduction of the amount of the recharged electricity and corresponding cost, thus significantly relieving the concerns of range anxiety. The routing and recharging strategy also potentially allows for a reduction in the EV battery capacity, in turn reducing the cost of the energy storage system to a reasonable level. The efficiency of the charge scheduling model was validated by estimating optimal annual financial benefits and leveling the additional load from EV charging to maintain a reliable and robust power grid system. The analysis showed that the scheduling model can indeed optimize the profit which substantially offsets the annual energy cost for EV owners and that EV participants can even make a positive net profit with a higher power of the electrical circuit. In addition, the extra load distribution from the optimized EV charging operations was more balanced than that from the unmanaged EV operations. Grid operators can monitor and ease the load in real time by adjusting the prices should the load exceed the capacity. The ITS supported architecture presented in this dissertation can be used in the evolution of a new generation of EVs with new features and benefits for prospective owners. This study suggests a great promise for the integration of EVs with ITS technologies for purpose of promoting sustainable transportation system development

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

In What Ways can Electric Vehicles Assist the UK Renewable Energy Strategy?

The latest (June 2008) UK Renewable Energy Strategy (Consultation) document, has called for 30 – 35% of electricity to come from Renewable sources by 2020 As the current level in 2008 of renewable electricity production is 4% (excluding hydro) this is a challenging target. With the Government then, floating the idea of generating 32% of UK electricity by 2020 using wind, it is clearly urgent and important to look for and investigate ways of achieving this. This paper examines the possibility and possible benefits and costs of utilising electric vehicles as storage for the UK electricity grid as a means of harnessing intermittent wind power and hence of assisting with the UK Renewable Energy Strategy. Potentially, the infrastructure use described here could both not only deliver a way of permitting large scale penetrations of intermittent renewable sources in the UK electricity grid but also allow for a significant reduction in the consumption of oil based fuel by the countries’ 32m vehicles and at the same time help to deliver secure electricity from intermittent renewable sources. With input from data provided by the Met Office and by National Grid and with further input from a questionnaire designed to obtain vehicle use patterns, a mathematical model using Microsoft Excel was constructed. Using the model and sample data from 2002 Various scenarios were examined including 20% penetration of wind in 2020 and 32% penetration of wind in 2020. The possibility of 100% wind was also examined

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Demand Response Management and Control Strategies for Integrated Smart Electricity Networks

Demand Response (DR) programs are being introduced by some electricity grid operators as resource options for curtailing and reducing the demand of electricity during certain time periods for balancing supply and demand. DR is considered as a class of demand-side management programs, where utilities offer incentives to end-users to reduce their power consumption during peak periods. DR is, indeed, a promising opportunity for consumers to control their energy usage in response to electricity tariffs or other incentives from their energy suppliers. Thus, successful execution of a DR program requires the design of efficient algorithms and strategies to be used in the utility grid to motivate end-users to actively engage in residential DR. This thesis studies DR management using machine learning techniques such as Reinforcement Learning (RL), Fuzzy Logic (FL) and Neural Networks (NN) to develop a Home Energy Management System (HEMS) for customers, construct an energy customer behaviour framework, investigate the integration of Electrical Vehicles (EVs) into DR management at the home level and the provision of ancillary services to the utility grid such as Frequency Regulation (FR), and build effective pricing strategies for Peer-to-Peer (P2P) energy trading. In this thesis, we firstly proposed a new and effective algorithm for residential energy management system using Q-learning method to minimise the electricity bills and maximise the user’s satisfaction. The proposed DR algorithm aims to schedule household appliances considering dynamic electricity prices and different household power consumption patterns. Moreover, a human comfort-based control approach for HEMS has been developed to increase the user’s satisfaction as much as possible while responding to DR schemes. The simulation results presented in this Chapter showed that the proposed algorithm leads to minimising energy consumption, reducing household electricity bills, and maximising the user’s satisfaction. Secondly, with the increasing electrification of vehicles, emerging technologies such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, EVs become as distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Therefore, we proposed an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations studies are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. In addition, the potential benefits of EVs battery and V2G technology to provide grid frequency response services have also been investigated. We have designed an optimal real-time V2G control strategy for EVs to perform supplementary frequency regulation using Deep Deterministic Policy Gradient (DDPG). The main feature that distinguishes the proposed approach from previous related works is that the scheduled charging power of an individual EV is optimally tracked and adjusted in real-time to fulfil the charging demand of EV's battery at the plug-out time without using the forced charging technique to maximise the frequency regulation capacity. Finally, a Peer-to-Peer (P2P) model for energy transaction in a community microgrid has been proposed. The concept of P2P energy trading can promote the implementation of DR by providing consumers with greater control over their energy usage, incentivising them to manage their energy consumption patterns in response to changes in energy supply and demand. It also stimulates the adoption of renewable energy sources. The proposed P2P energy-sharing mechanism for a residential microgrid with price-based DR is designed to engage individual customers to participate in energy trading and ensures that not a single household would be worse off. The proposed pricing mechanism is compared with three popular P2P energy sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and Bill Sharing (BS) considering different types of peers equipped with solar Photovoltaic (PV) panels, EVs, and domestic energy storage systems. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network