New strategies for the massive introduction of electric vehicles in the operation and planning of Smart Power Systems

En el contexto actual, donde el calentamiento climático es cada vez más importante, existe la necesidad de limitar el consumo de combustibles fósiles. De esta manera, el transporte es uno de los sectores en los que más se están generando cambios en cuanto a la sostenibilidad. El vehículo eléctrico aparece como una solución para este cambio paulatino ya que no contamina localmente y su balance energético es muy eficiente. Así, se han propuesto diferentes programas para el crecimiento del vehículo eléctrico en el parque automotor. Sin embargo, el cambio de vehículos de gasolina por vehículos eléctricos genera desafíos en varios aspectos, como el impacto que ocasiona en la red eléctrica una implantación masiva: caídas de tensión, pérdidas de potencia, problemas con la calidad de la electricidad, inversiones importantes, etc. Se han planteado algunas soluciones en la parte operativa, pero muchas de ellas no han tomado en cuenta la flexibilidad de los usuarios, lo cual es muy importante para la adopción de vehículos eléctricos. De igual manera, en muchas ocasiones, en la literatura se asumen valores para ciertas variables (estado de carga, recorrido, tipo de batería, etc) que pueden cambiar según el comportamiento de cada usuario, lo que modificaría las previsiones realizadas. Finalmente, pocos trabajos han estudiado el impacto de lo vehículos eléctricos en redes eléctricas cuya gestión energética es más complicada debido a su aislamiento de una macrored y con alta penetración de energías renovables, como lo son las microredes. En este marco, esta tesis propone un enfoque novedoso en cuanto a la participación de los usuarios de vehículos eléctricos en la operación y planificación de diferentes sistemas eléctricos de potencia. Esta trata de algunos aspectos principales: disminución de costos de carga, participación en servicios de regulación, aprovechamiento de energía renovable, así como la planificación de generación de una microred incorporando vehículos eléctricos. En una primera parte, se presenta un análisis del vehículo eléctrico y su interacción en sistemas de potencia. De igual manera, se presentan los trabajos de investigación relacionados sobre la temática. En base al análisis de dichos trabajos, esta tesis propone una nueva metodología para optimizar la carga de los vehículos eléctricos. Se propone la participación de un nuevo agente del mercado eléctrico, el Agregador de vehículos eléctricos. Tendrá que gestionar la carga de dichos vehículos en una importante zona, coordinar con el operador de la red para evitar fallos y minimizar los costos de carga. De igual manera, se considera la diferente flexibilidad de los usuarios ya qu podrán escoger una tarifa que se adapte a su disponibilidad en espera y pagar el precio por aquello. La metodología ha sido aplicada a un caso de estudio a la red de Quito, Ecuador. Se propone también la participación en servicios de regulación, necesitando esta vez de usuarios que sean más flexibles al dejar su vehículo conectado a la red. Se considera las tarifas de la parte anterior para realizar dicho estudio. De igual manera, se aplicó al caso de estudio de la red de Quito, Ecuador. Con el crecimiento de las energías renovables, como solar y eólica, la gestión de la electricidad se vuelve más compleja. Con vistas a utilizar el exceso de energía renovable, se propone una tarifa de electricidad que permita al agregador de cargar los diferentes vehículos, tomando en cuenta precios bajos en periodos en donde la energía renovable esté en exceso. Finalmente, se plantea a planificación de generación de una microred que incluya la introducción masiva de vehículos eléctricos. Se aplicó al caso de las islas de Santa Cruz y Baltra, Galápagos, Ecuador, estudiando el impacto en los costos y en el medio ambiente de nueva generación y considerando la variación del precio del diésel debido a su incertidumbre.In the current context, where global warming is growing progressively, it is fundamental to limit fossil fuels consumption. Hence, transportation is one of the sectors in which several changes are occurring considering the sustainability. The Electric Vehicle appears as a new solution for this gradual change; it does not pollute locally and its energy's balance is very efficient. So, different programs have been proposed for the growth of electric vehicles in the automotive market. Nevertheless, the change from internal combustion vehicles to electric vehicles generates challenges in several aspects, such as the impact in the electric grid of a massive introduction of electric vehicles: voltage drops, power losses, quality of electricity issues, important investments, among others. Several solutions in operation have been formulated, but most of them do not consider the flexibility of users, which is a significant criterion for the electric vehicle acquisition. Moreover, in several works of the literature, many variables are assumed (stateof- charge, routes, type of battery, etc), which can vary significantly depending on the user, so also the results. Finally, few works have studied the impact of electric vehicles in very complex power systems, as the ones that are isolated from a macrogrid and because of significant penetration of renewable energy sources, such as microgrids. In this context, this thesis proposes a novel approach to the participation of the electric vehicle users in operation and planning of different electric power systems. This thesis is intended to cover various topics: charging costs decrease, regulation services participation, use of an excess of renewable energy, and the power generation planning of a microgrid considering the introduction of electric vehicles. In a first part, an analysis of the electric vehicle and its interaction with power systems is presented. Additionally, the principal works on the topic are summarized. Based on the analysis of these works, this thesis proposes a new methodology for optimizing the charge of electric vehicles. The participation of a new agent of the electricity market, the electric vehicle aggregator, is proposed. It has the ability to manage the charge of the electric vehicles in a zone with significant size, to coordinate with the grid operator in order to avoid troubles and to minimize charging costs. Furthermore, the different flexibility of electric vehicle users is considered because they will choose an EV customer choice product (CCP) that is adapted to their waiting needs and to the cost they can pay. The methodology has been applied to a case study in the grid of Quito, Ecuador. The participation in regulation services has been also considered to discuss this participation in Ancillary services. The CCPs from the part before are considered for performing such study but assuming more involvement from the electric vehicle users. The case study of Quito, Ecuador, was also studied. With the growth of renewable energies, such as solar and wind, the electricity management becomes more complicated. In order to use the excess of renewable energy, an EV charging mechanism for the aggregator is proposed, based on low prices when the renewable energy is in excess. Finally, a power generation planning for a microgrid is proposed, considering the massive introduction of electric vehicles. The case of the Santa Cruz and Baltra islands, Galapagos, Ecuador are studied to determine its costs and environmental impacts, based on diesel costs sensitivity studies to account for its uncertainty.En el context actual, on l'escalfament climàtic és cada vegada més important, hi ha la necessitat de limitar el consum de combustibles fòssils. El transport és un dels sectors en els quals més s'estan generant canvis pel que fa a la sostenibilitat. El vehicle elèctric apareix com una solució per a aquest canvi gradual ja que no contamina localment i el seu balanç energètic és molt eficient. Així, s'han proposat diferents programes per al creixement del vehicle elèctric al parc automotor. No obstant això, el canvi de vehicles de gasolina per vehicles elèctrics generen desafiaments en diversos aspectes, com son l'impacte que ocasiona a la xarxa elèctrica una implantació massiva: caigudes de tensió, pèrdues de potència, problemes amb la qualitat de l'electricitat, inversions importants, disminució de la vida útil dels transformadors, etc. S'han plantejat algunes solucions a la part operativa, però moltes d'elles no han tingut en compte la flexibilitat dels usuaris, la qual cosa és molt important per a l'adopció de vehicles elèctrics. De la mateixa manera, en moltes ocasions, en la literatura s'assumeixen valors per certes variables (estat de càrrega, recorregut, tipus de bateria, etc.) que poden canviar segons el comportament de cada usuari, el que modificaria les previsions realitzades. Finalment indicar que pocs treballs han estudiat l'impacte del que vehicles elèctrics en xarxes elèctriques on la gestió energètica és més complicada a causa del seu aïllament d'una macroxarxa i amb alta penetració d'energies renovables, com ho són les microxarxes. En aquest marc, aquesta tesi proposa un enfocament nou pel que fa a la participació dels usuaris de vehicles elèctrics en l'operació i planificació de diferents sistemes elèctrics de potència. Aquesta tracta alguns aspectes principals: disminució de costos de càrrega, participació en serveis de regulació, aprofitament d'energia renovable, així com la planificació de generació d'una microxarxa incorporant vehicles elèctrics. En una primera part, es presenta una anàlisi del vehicle elèctric i la seva interacció en sistemes de potència. De la mateixa manera, es presenten els treballs de recerca relacionats sobre la temàtica. En base a l'anàlisi d'aquests treballs, aquesta tesi proposa una nova metodologia per optimitzar la càrrega dels vehicles elèctrics. Es proposa la participació d'un nou agent del mercat elèctric, el Agregador de vehicles elèctrics. Haurà de gestionar la càrrega d'aquests vehicles en una important zona, coordinar amb l'operador de la xarxa per evitar fallades i minimitzar els costos de càrrega. De la mateixa manera es considera la diferent flexibilitat dels usuaris ja que podran escollir una tarifa que s'adapti a la seva disponibilitat en espera i pagar el preu per allò. La metodologia ha estat aplicat a un cas d'estudi a la xarxa de Quito, Equador. Es proposa també la participació en serveis de regulació, necessitant aquest cop d'usuaris que siguin més flexibles en deixar el seu vehicle connectat a la xarxa. Es consideren les tarifes de la part anterior per a realitzar dit estudi. De la mateixa manera, es va aplicar al cas d'estudi de la xarxa de Quito, Equador. Amb el creixement de les energies renovables, com solar i eòlica, la gestió de l'electricitat es torna més complexa. Amb vista a utilitzar l'excés d'energia renovable, es proposa un tarifa d'electricitat que permeti a l'agregador de carregar els diferents vehicles, especialment en períodes on l'energia renovable estigui en excés. Finalment, es planteja la planificació de generació d'una microxarxa que inclogui la introducció massiva de vehicles elèctrics. En concret, es va aplicar al cas de la illes de Santa Cruz i Baltra, Galápagos, Equador, estudiant l'impacte de la nova generació en els costos i en el medi ambient i considerant la variació del preu del dièsel, causa de la seva incertesa.Clairand Gómez, JM. (2018). New strategies for the massive introduction of electric vehicles in the operation and planning of Smart Power Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/110971TESI

Optimization of Aggregators Energy Resources considering Local Markets and Electric Vehicle Penetration

O sector elétrico tem vindo a evoluir ao longo do tempo. Esta situação deve-se ao facto de surgirem novas metodologias para lidarem com a elevada penetração dos recursos energéticos distribuídos (RED), principalmente veículos elétricos (VEs). Neste caso, a gestão dos recursos energéticos tornou-se mais proeminente devido aos avanços tecnológicos que estão a ocorrer, principalmente no contexto das redes inteligentes. Este facto torna-se importante, devido à incerteza decorrente deste tipo de recursos. Para resolver problemas que envolvem variabilidade, os métodos baseados na inteligência computacional estão a se tornar os mais adequados devido à sua fácil implementação e baixo esforço computacional, mais precisamente para o caso tratado na tese, algoritmos de computação evolucionária (CE). Este tipo de algoritmo tenta imitar o comportamento observado na natureza. Ao contrário dos métodos determinísticos, a CEé tolerante à incerteza; ou seja, é adequado para resolver problemas relacionados com os sistemas energéticos. Estes sistemas são geralmente de grandes dimensões, com um número crescente de variáveis e restrições. Aqui a IC permite obter uma solução quase ótima em tempo computacional aceitável com baixos requisitos de memória. O principal objetivo deste trabalho foi propor um modelo para a programação dos recursos energéticos dos recursos dedicados para o contexto intradiário, para a hora seguinte, partindo inicialmente da programação feita para o dia seguinte, ou seja, 24 horas para o dia seguinte. Esta programação é feita por cada agregador (no total cinco) através de meta-heurísticas, com o objetivo de minimizar os custos ou maximizar os lucros. Estes agregadores estão inseridos numa cidade inteligente com uma rede de distribuição de 13 barramentos com elevada penetração de RED, principalmente energia renovável e VEs (2000 VEs são considerados nas simulações). Para modelar a incerteza associada ao RED e aos preços de mercado, vários cenários são gerados através da simulação de Monte Carlo usando as funções de distribuição de probabilidade de erros de previsão, neste caso a função de distribuição normal para o dia seguinte. No que toca à incerteza no modelo para a hora seguinte, múltiplos cenários são gerados a partir do cenário com maior probabilidade do dia seguinte. Neste trabalho, os mercados locais de eletricidade são também utilizados como estratégia para satisfazer a equação do balanço energético onde os agregadores vão para vender o excesso de energia ou comprar para satisfazer o consumo. Múltiplas metaheurísticas de última geração são usadas para fazer este escalonamento, nomeadamente Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with Normal-Cauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Os resultados mostram que o modelo proposto é eficaz para os múltiplos agregadores com variações de custo na sua maioria abaixo dos 5% em relação ao dia seguinte, exceto para o agregador e de VEs. É também aplicado um teste Wilcoxon para comparar o desempenho do algoritmo CUMDANCauchy++ com as restantes meta-heurísticas. O CUMDANCauchy++ mostra resultados competitivos tendo melhor performance que todos os algoritmos para todos os agregadores exceto o DEEDA que apresenta resultados semelhantes. Uma estratégia de aversão ao risco é implementada para um agregador no contexto do dia seguinte para se obter uma solução mais segura e robusta. Os resultados mostram um aumento de quase 4% no investimento, mas uma redução de até 14% para o custo dos piores cenários.The electrical sector has been evolving. This situation is because new methodologies emerge to deal with the high penetration of distributed energy resources (DER), mainly electric vehicles (EVs). In this case, energy resource management has become increasingly prominent due to the technological advances that are taking place, mainly in the context of smart grids. This factor becomes essential due to the uncertainty of this type of resource. To solve problems involving variability, methods based on computational intelligence (CI) are becoming the most suitable because of their easy implementation and low computational effort, more precisely for the case treated in this thesis, evolutionary computation (EC) algorithms. This type of algorithm tries to mimic behavior observed in nature. Unlike deterministic methods, the EC is tolerant of uncertainty, and thus it is suitable for solving problems related to energy systems. These systems are usually of high dimensions, with an increased number of variables and restrictions. Here the CI allows obtaining a near-optimal solution in good computational time with low memory requirements. This work's main objective is to propose a model for the energy resource scheduling of the dedicated resources for the intraday context, for the our-ahead, starting initially from the scheduling done for the day ahead, that is, 24 hours for the next day. This scheduling is done by each aggregator (in total five) through metaheuristics to minimize the costs or maximize the profits. These aggregators are inserted in a smart city with a distribution network of 13 buses with a high penetration of DER, mainly renewable energy and EVs (2000 EVs are considered in the simulations). Several scenarios are generated through Monte Carlo Simulation using the forecast errors' probability distribution functions, the normal distribution function for the day-ahead to model the uncertainty associated with DER and market prices. Multiple scenarios are developed through the highest probability scenario from the day-ahead when it comes to intraday uncertainty. In this work, local electricity markets are used as a mechanism to satisfy the energy balance equation where each aggregator can sell the excess of energy or buy more to meet the demand. Several recent and modern metaheuristics are used to solve the proposed problems in the thesis, namely Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with NormalCauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators. The metaheuristics present satisfactory results and mostly less than 5% variation in costs from the day-ahead except for the EV aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining metaheuristics. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results. A risk aversion strategy is implemented for an aggregator in the day-ahead context to get a safer and more robust solution. Results show an increase of nearly 4% in day-ahead cost but a reduction of up to 14% of worst scenario cost

A Bayesian real-time electric vehicle charging strategy for mitigating renewable energy fluctuations

A novel pricing and scheduling mechanism is proposed here for Plug-in electric vehicles (PEVs) charging/discharging to track and synchronize with a renewable power generation pattern. Moreover, the proposed mechanism can be used in the demand-side management and ancillary service applications, respectively for the peak shaving and frequency regulation responding. We design a fully distributed stochastic optimization mechanism using Bayesian pure strategic repeated game by which the PEVs optimally schedule their demands. We also use a mixed Bayesian-diffusion Kalman filtering strategy for the customers to collaboratively estimate and track the stochastic price and regulation signals for the upcoming scheduling window. In the proposed paper all the characteristics of the PEVs, as well as the uncertainty about their deriving patterns are considered. As our framework converges to an equilibrium even with incomplete information, is agent-based, and the agents share the information only with their optional neighbors, it is scale-free, robust, and secure

Motion Hub, the implementation of an integrated end-to-end journey planner

© AET 2018 and contributorsThe term “eMobility” and been brought into use partly to encourage use of electric vehicles but more especially to focus on the transformation from electric vehicles as products to electrified personal transport as a service. Under the wider umbrella of Mobility-as-a-Service (MaaS) this has accompanied the growth of car clubs in general. The Motion Hub project has taken this concept a step further to include not just the car journey but the end-to-end journey. The booking of multifaceted journeys is well established in the leisure and business travel industries, where flights, car hire and hotels are regularly booked with a single transaction on a website. To complete an end-to-end scenario Motion Hub provides integration of public transport with electric vehicle and electric bike use. Building on a previous InnovateUK funded project that reviewed the feasibility of an integrated journey management system, the Motion Hub project has brought together a Car Club, a University, and EV infrastructure company, a bicycle hire company with electric bicycle capabilities and a municipality to implement a scheme and test it on the ground. At the heart of the project has been the development of a website that integrates the public transport booking with the hire of electric vehicles or bicycles. Taking the implementation to a fully working system accessible to members of the public presents a number of significant challenges. This paper identifies those challenges, details the progress and success of the Motion Hub and sets out the lessons learnt about end-to-end travel. The project was fortunate to have as its municipal partner the Council of a sizeable South East England town, Southend-on-Sea. With a population of 174,800 residents with good road, rail and air links there is considerable traffic in and out of the town. The Council has already shown its commitment to sustainable transport. In the previous six years it had installed a number of electric vehicle charging points for use by the public and latterly had trialled car club activity. An early challenge in the project was the location of physical infrastructure in an already crowded municipal space in order to provide the local ‘spokes’ of the system. In addition to its existing charging points, Southend now has four locations where electric cars can be hired, five where electric bikes are available and the local resources to maintain these assets. Combining a number of web-based services and amalgamating their financial transactions is relatively straightforward. However, introducing the potential for public transport ticketing as well raises additional security, scale and financial constraints. The project has engaged with major players and regulators across the public transport industry.Peer reviewe

Electricity powering combustion: hydrogen engines

Hydrogen is ameans to chemically store energy. It can be used to buffer energy in a society increasingly relying on renewable but intermittent energy or as an energy vector for sustainable transportation. It is also attractive for its potential to power vehicles with (near-) zero tailpipe emissions. The use of hydrogen as an energy carrier for transport applications is mostly associated with fuel cells. However, hydrogen can also be used in an internal combustion engine (ICE). When converted to or designed for hydrogen operation, an ICE can attain high power output, high efficiency and ultra low emissions. Also, because of the possibility of bi-fuel operation, the hydrogen engine can act as an accelerator for building up a hydrogen infrastructure. The properties of hydrogen are quite different from the presently used hydrocarbon fuels, which is reflected in the design and operation of a hydrogen fueled ICE (H2ICE). These characteristics also result in more flexibility in engine control strategies and thus more routes for engine optimization. This article describes the most characteristic features of H2ICEs, the current state of H2ICE research and demonstration, and the future prospects