Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Smart operation of transformers for sustainable electric vehicles integration and model predictive control for energy monitoring and management

The energy transmission and distribution systems existing today are stillsignificantly dependent on transformers,despite beingmore efficient and sustainable than those of decadesago. However, a large numberof power transformers alongwith other infrastructures have been in service for decades and are considered to be in their final ageing stage. Anymalfunction in the transformerscouldaffect the reliability of the entire electric network and alsohave greateconomic impact on the system.Concernsregardingurban air pollution, climate change, and the dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to explore alternatives to conventional fossil-fuelled internal combustion engine vehicles. One such alternative is the introduction of electric vehicles. A broad implementation of such mean of transportation could signify a drastic reduction in greenhouse gases emissions and could consequently form a compelling argument for the global efforts of meeting the emission reduction targets. In this thesis the topic of a high penetration of electric vehicles and their possible integration in insular networksis discussed. Subsequently, smart grid solutions with enabling technologies such as energy management systems and smart meters promote the vision of smart households, which also allows for active demand side in the residential sector.However, shifting loads simultaneously to lower price periods is likely to put extra stress on distribution system assets such as distribution transformers. Especially, additional new types of loads/appliances such as electric vehicles can introduce even more uncertaintyon the operation of these assets, which is an issue that needs special attention. Additionally, in order to improve the energy consumption efficiencyin a household, home energy management systems are alsoaddressed. A considerable number ofmethodologies developed are tested in severalcasestudies in order to answer the risen questions.Os sistemas de transmissão e distribuição de energia existentes hoje em dia sãosignificativamente dependentes dos transformadores, pese embora sejammais eficientes e sustentáveis do que os das décadas passadas. No entanto, uma grande parte dos transformadores ao nível dadistribuição, juntamente com outras infraestruturassubjacentes, estão em serviço há décadas e encontram-se nafasefinal do ciclo devida. Qualquer defeito no funcionamento dos transformadorespode afetara fiabilidadede toda a redeelétrica, para além de terum grande impactoeconómico no sistema.Os efeitos nefastos associadosàpoluição do arem centro urbanos, asmudançasclimáticasea dependência de fontes de energiafósseis têm levado os decisores políticos e os investigadores aexplorar alternativas para os veículos convencionais de combustão interna. Uma alternativa é a introdução de veículos elétricos. Umaampla implementação de tal meio de transporte poderia significar uma redução drástica dos gases de efeito de estufa e poderiareforçar os esforços globais para ocumprimento das metas de redução de emissõesde poluentes na atmosfera.Nesta tese é abordado o tema da elevada penetração dos veículos elétricose a sua eventual integração numarede elétricainsular. Posteriormente, são abordadas soluções de redeselétricasinteligentes com tecnologias específicas, tais como sistemas de gestão de energia e contadores inteligentes que promovamo paradigmadas casas inteligentes, que também permitem a gestão da procura ativano sector residencial.No entanto, deslastrando significativamente as cargaspara beneficiar de preçosmais reduzidosé suscetíveldecolocarconstrangimentosadicionaissobre os sistemas de distribuição, especialmentesobre ostransformadores.Osnovos tipos de cargas tais como os veículos elétricospodem introduzir ainda mais incertezassobre a operação desses ativos, sendo uma questão que suscitaespecial importância. Além disso, com ointuitode melhorar a eficiência do consumo de energia numa habitação, a gestão inteligente daenergia é um assunto que também éabordadonesta tese. Uma pletora de metodologias é desenvolvida e testadaemvários casos de estudos, a fim de responder às questões anteriormente levantadas

The Future European Energy System

This open access book analyzes the transition toward a low-carbon energy system in Europe under the aspects of flexibility and technological progress. By covering the main energy sectors – including the industry, residential, tertiary and transport sector as well as the heating and electricity sector – the analysis assesses flexibility requirements in a cross-sectoral energy system with high shares of renewable energies. The contributing authors – all European energy experts – apply models and tools from various research fields, including techno-economic learning, fundamental energy system modeling, and environmental and social life cycle as well as health impact assessment, to develop an innovative and comprehensive energy models system (EMS). Moreover, the contributions examine renewable penetrations and their contributions to climate change mitigation, and the impacts of available technologies on the energy system. Given its scope, the book appeals to researchers studying energy systems and markets, professionals and policymakers of the energy industry and readers interested in the transformation to a low-carbon energy system in Europe

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030

As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world's energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country's 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China

Multi-objective network planning for the integration of electric vehicles as responsive demands

The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.]The integration of electric vehicles (EVs) into distribution networks presents substantial challenges to Distribution Network Operators (DNOs) internationally. In the 12 months from November 2017, EV registrations in Great Britain have increased by ~22% [A.1], though it is noted that EVs account for only 6% of all UK vehicle registrations [A.1] in 2018. With the UK Government announcement in 2017 [A.2] that "by 2040 there will be an end to the sale of all conventional petrol and diesel cars and vans", the penetration of EVs will require to - unless a new technology emerges - grow exponentially over the next 10 to 20 years towards 100% penetration by 2050. However, the increasing penetration of EVs can provide to the system multiple benefits and assist in mitigating issues; if EV integration is optimally planned using a suitable method. The managed charging of multiple EVs can assist in better utilising power generated by intermittent renewables, which will provide substantial benefits such as peak shifting, deferred reinforcement costs and the reduced requirement for imported energy to support the network at times of need.;Accurately assessing the impact that EVs will have on distribution networks is critical to DNOs [A.3]. In particular, the aim of this thesis is to identify the optimal location, battery size, charger power output and operational envelope for multiple EVs when used as responsive demands in high voltage/low voltage (HV/LV) distribution networks. Societal benefits can include reduced or deferred asset investment costs; reduced technical losses and increasing the utilisation of renewable generation [A.3]. System benefits must be accounted for and can support and inform planning and operational decisions - such as asset investment and network reinforcement. Coordinated smart charging of multiple EVs can assist in managing peaks in the demand curve and increase the utilisation of intermittent renewables. Unmanaged EV charging at times of peak demand would require the DNO to invest in reinforcement solutions to ensure the required additional capacity is made available. However, one approach is to cluster EV charging in periods when the base load would otherwise be low, to lessen the need for asset reinforcement as EV charging during the period of peak demand would be avoided.;Time periods for charging EVs (dependent on the chosen objectives) will be identified and then correlated to times when renewable generation availability is high and when base demand is low. The use of the presented network planning tool will identify EV charging strategies that can be applied to multiple EVs (based on the chosen objectives and with respect to constraints) whilst optimising the type, number and location on a specific modelled network. The planning framework utilises the Strength Pareto Evolutionary Algorithm 2 (SPEA2); the use of this algorithm will ensure that the network constraints are not breached and that multiple objectives are included in the analyses. This thesis investigates the impact that the inclusion of multiple EVs (when used as responsive demands); will have on the HV distribution network when the additional EV load is smartly scheduled to meet specific objectives and to correspond with the availability of intermittent renewables. The ultimate aim of this planning approach is to offer DNOs low cost solutions to multiobjective problems relating to EV integration and operation. [References A1-A3 for Abstract available p. XV of thesis.

Aspects of Electric Vehicles and Demand Response in Electricity Grids

The growing global energy demand combined with limited resources of fossil energy (especially crude oil), climate change and other environmental issues, the energy system has faced significant challenges. There is significant pressure to diversify the energy sources towards more sustainable choices and to increase energy efficiency. Shifting gradually from the use of fossil fuels to use of renewable energy sources is a long way to go, and to make it economically feasible requires a significant amount of will, effort and innovation.This thesis deals with two parts of the energy system: the electrical energy system and the energy system of road transportation. A “smart” electrical energy system of the future includes the flexibility of electricity demand, i.e. demand response (DR), enabled by different types of incentives and offering many potential advantages. A road transportation system of the future can include significant amount of electric vehicles (full electric vehicles – full EVs and plug-in hybrid electric vehicles – PHEVs) as part of the vehicle fleet. These vehicles could also participate in the operation of an electrical power system. This thesis discusses electric vehicles and demand response in smart grid context.The most important results and findings of the thesis are the following. In Finland, PHEVs could offer a significant proportion or even most of the benefits of EVs even with a quite modest charging infrastructure, and simultaneously the most severe obstacles of full EVs could be avoided or at least mitigated. In this thesis, a flexible methodology for modeling PHEV charging load using National Travel Survey data has been developed. Statistical PHEV charging load models, taking into account modeled statistical distributions of the loads, have been used by two different real DNOs in their network information systems to assess the impacts of EVs on distribution network planning in urban networks. It seems that high amounts of EVs fit well into Finnish distribution networks, but in certain cases demand response of electric vehicles would be reasonable. Electric vehicles, some DR actions and other changes in electricity use can increase peak powers in distribution networks. New distribution tariffs have been developed and simulated in a real distribution network with the purpose of encouraging small electricity customers towards peak load restriction. It seems that these kinds of tariffs would be efficient in restricting the increase of peak powers of spot price based DR, although is seems to be hard to decrease the present peak powers very much in the distribution networks. Different general DR and smart charging concepts have been sketched, and a practical local customer-site peak load control management algorithm of an EV charging station group has been developed as a tool to realize demand response of a group of electric vehicles

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed