Optimization for Integration of Plug-in Hybrid Electric Vehicles into Distribution Grid

Plug-in hybrid electric vehicles (PHEVs) feature combined electric and gasoline powertrains with internal combustion engine and electric motors powered by battery packs. The battery packs of PHEVs are mostly charged during off-peaks hours at lower prices and meanwhile absorb the excess power from the grid. Similarly, the power stored in the batteries can also flow back to the electric grid in response to ease the peak load demands. With the increasing penetration and integration of PHEVs, the reliability of PHEVs is essential to overall power system reliability since the charging mechanisms of PHEVs can influence the reliability of power system. Furthermore, due to the direct integration of PHEVs into the residential distribution network, the PHEVs can work as backup batteries for power systems in case of power outage. Therefore, the reliability analysis of power systems and the ancillary services provided by PHEVs are also proposed in this thesis study. For the driving pattern of each PHEV, there are three basic elements modeled, which are the departure time, the arrival time and the daily mileage, all represented by probability density functions. Based on these basic concepts, the methodology for modeling the load demand of PHEVs is introduced. In the proposed system, both the Differential Evolution and the Particle Swarm Optimization are proposed to optimize the control strategies for power systems with integration of PHEVs. Aside from using the minimum cost as a figure of merit when designing and determining the best PHEV charging mechanism, the reliability improvement brought to the power systems by PHEVs and the extra earnings obtained by performing frequency regulation services are also quantified and taken into account. Although the reliability of power systems with PHEV penetrations has been well-studied, the adoption of the Differential Evolution algorithm for minimizing the cost of overall system is not exercised, not to mention a thorough comparative study with other common optimization algorithms. To sum up, the Differential Evolution can not only achieve multiple goals by improving the power quality, reducing the peak load, providing regulation services and minimizing the total virtual cost in this system, it can also offer better results compared with the Particle Swarm Optimization in terms of minimizing the cost

A Study of Vehicle-to-Vehicle Power Transfer Operation in V2G-Equipped Microgrid

Bidirectional vehicle-to-grid (V2G) system utilizes the batteries of parked electric-drive-vehicles to provide energy storage and backup services in a power system. Such services in a V2G-equipped microgrid system can be used as an enabler of enhancing the renewable energy source (RES) penetration by storing the energy during the surplus of RES supply and supplying the energy during the lack of RES supply. In this research, we aim at enhancing the storage capacity of V2G system by introducing a novel vehicle-to-vehicle power transfer operation that runs on the top of V2G services. The vehicle-to-vehicle (V2V) operation transfers the energy from the source vehicles (which are parked for relatively longer times) to the destination vehicles (which are parked for relatively shorter times). The depleted energy of the source vehicles is fulfilled by the surplus RES supply in the future. In this way, the destination vehicles are effectively charged by RES supply, thereby enhancing the storage capacity of the V2G system. We can also say that the V2V operation would become beneficial only when there is a sufficient amount of surplus RES supply in the future. We propose a decision rule to distinguish if a vehicle should be a source vehicle or a destination vehicle during the V2V operation. The decision rule is designed based on the two factors, namely the state-of-charge of vehicle’s battery, and the remaining time of vehicle to depart. In this research, we conduct a comprehensive study to analyze the impacts of state-of-charge and mobility pattern of vehicles on different performance metrics via simulation. The results shows that in order to achieve better performance of V2V operation, the state-of-charge of vehicle’s battery should be given more priority over the remaining time of vehicle to depart. The vehicle mobility pattern with unexpected departure greatly reduced the overall performance of the V2G system

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

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

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

Business Models for SEEV4-City Operational Pilots: From a generic SEEV4-City business model towards improved specific OP business models

This report, led by Northumbria University, provides a final analysis by project partners regarding Business Models for SEEV4-City Operational pilots. It is part of a collection of reports published by the project covering a variation of specific and cross-cutting analysis and evaluation perspectives and spans 6 operational pilots

Resource Management in Delay Tolerant Networks and Smart Grid

In recent years, significant advances have been achieved in communication networks and electric power systems. Communication networks are developed to provide services within not only well-connected network environments such as wireless local area networks, but also challenged network environments where continuous end-to-end connections can hardly be established between information sources and destinations. Delay tolerant network (DTN) is proposed to achieve this objective by utilizing a store-carry-and-forward routing scheme. However, as the network connections in DTNs are intermittent in nature, the management of network resources such as communication bandwidth and buffer storage becomes a challenging issue. On the other hand, the smart grid is to explore information and communication technologies in electric power grids to achieve electricity delivery in a more efficient and reliable way. A high penetration level of electric vehicles and renewable power generation is expected in the future smart grid. However, the randomness of electric vehicle mobility and the intermittency of renewable power generation bring new challenges to the resources management in the smart grid, such as electric power, energy storage, and communication bandwidth management. This thesis consists of two parts. In part I, we focus on the resource management in DTNs. Specifically, we investigate data dissemination and on-demand data delivery which are two of the major data services in DTNs. Two kinds of mobile nodes are considered for the two types of services which correspond to the pedestrians and high-speed train passengers, respectively. For pedestrian nodes, the roadside wireless local area networks are used as an auxiliary communication infrastructure for data service delivery. We consider a cooperative data dissemination approach with a packet pre-downloading mechanism and propose a double-loop receiver-initiated medium access control scheme to resolve the channel contention among multiple direct/relay links and exploit the predictable traffic characteristics as a result of packet pre-downloading. For high-speed train nodes, we investigate on-demand data service delivery via a cellular/infostation integrated network. The optimal resource allocation problem is formulated by taking account of the intermittent network connectivity and multi-service demands. In order to achieve efficient resource allocation with low computational complexity, the original problem is transformed into a single-machine preemptive scheduling problem and an online resource allocation algorithm is proposed. If the link from the backbone network to an infostation is a bottleneck, a service pre-downloading algorithm is also proposed to facilitate the resource allocation. In part II, we focus on resource management in the smart grid. We first investigate the optimal energy delivery for plug-in hybrid electric vehicles via vehicle-to-grid systems. A dynamic programming formulation is established by considering the bidirectional energy flow, non-stationary energy demand, battery characteristics, and time-of-use electricity price. We prove the optimality of a state-dependent double-threshold policy based on the stochastic inventory theory. A modified backward iteration algorithm is devised for practical applications, where an exponentially weighted moving average algorithm is used to estimate the statistics of vehicle mobility and energy demand. Then, we propose a decentralized economic dispatch approach for microgrids such that the optimal decision on power generation is made by each distributed generation unit locally via multiagent coordination. To avoid a slow convergence speed of multiagent coordination, we propose a heterogeneous wireless network architecture for microgrids. Two multiagent coordination schemes are proposed for the single-stage and hierarchical operation modes, respectively. The optimal number of activated cellular communication devices is obtained based on the tradeoff between communication and generation costs

Charging electric vehicles in the smart city: A survey of economy-driven approaches

International audienceElectric vehicles (EVs), as their penetration increases, do not only challenge the sustainability of the power grid but also stimulate and promote its upgrading. Indeed, EVs can actively reinforce the development of the smart grid if their charging processes are properly coordinated through two-way communications, possibly benefiting all types of actors. Because grid systems involve a large number of actors with nonaligned objectives, we focus on the economic and incentive aspects, where each actor behaves in its own interest. We indeed believe that the market structure will directly impact the actors' behaviors, and as a result, the total benefits that the presence of EVs can earn in the society, hence the need for a careful design. This survey provides an overview of economic models considering unidirectional energy flows and bidirectional energy flows, i.e., with EVs temporarily providing energy to the grid. We describe and compare the main approaches, summarize the requirements on the supporting communication systems, and propose a classification to highlight the most important results and lacks

A comprehensive study of key Electric Vehicle (EV) components, technologies, challenges, impacts, and future direction of development

Abstract: Electric vehicles (EV), including Battery Electric Vehicle (BEV), Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), Fuel Cell Electric Vehicle (FCEV), are becoming more commonplace in the transportation sector in recent times. As the present trend suggests, this mode of transport is likely to replace internal combustion engine (ICE) vehicles in the near future. Each of the main EV components has a number of technologies that are currently in use or can become prominent in the future. EVs can cause significant impacts on the environment, power system, and other related sectors. The present power system could face huge instabilities with enough EV penetration, but with proper management and coordination, EVs can be turned into a major contributor to the successful implementation of the smart grid concept. There are possibilities of immense environmental benefits as well, as the EVs can extensively reduce the greenhouse gas emissions produced by the transportation sector. However, there are some major obstacles for EVs to overcome before totally replacing ICE vehicles. This paper is focused on reviewing all the useful data available on EV configurations, battery energy sources, electrical machines, charging techniques, optimization techniques, impacts, trends, and possible directions of future developments. Its objective is to provide an overall picture of the current EV technology and ways of future development to assist in future researches in this sector