Óptima repuesta a la demanda para estaciones de carga de vehículos eléctricos con alta incertidumbre considerando el perfil de voltaje en la red de distribución.

This document shows an analysis of the behavior of the voltage profile in the distribution network by the insertion of charge of different Electric Vehicles (EVs) in different periods of time and amount of load, it is analyzed the voltage profiles since they are an indicator of The reliability of an electrical system. Based on several recharge scenarios, it is possible to determine the percentage of affectation to the current distribution network and whether the network can supply the load of the EVs. By means of a heuristic based on the Hungarian algorithm it is possible to estimate the behavior of the demand of the distribution networks and the variation of the voltage profiles, and it is possible to discriminate the optimum hours for the recharge of the batteries of the EVs. The analysis is associated with the prediction of the maximum power that a four-bay charging station can have at different times, considering slow, fast and super fast load, each of them with different level of affectation to the network, also looking for the Correct load management of EVs to increase the efficiency, quality and reliability of distribution networks.Este documento muestra un análisis del comportamiento del perfil de voltaje en la red de distribución por la inserción de carga de distintos Vehículos Eléctricos (EVs) en diferentes periodos de tiempo y cantidad de carga, se analiza los perfiles de voltaje ya que son un indicador de la confiabilidad de un sistema eléctrico. Basándose en varios escenarios de recarga, es posible determinar el porcentaje de afectación a la red de distribución actual y si la red puede abastecer la carga de los EVs. Mediante una heurística en base al algoritmo Hungariano es posible estimar el comportamiento de la demanda de las redes de distribución y la variación de los perfiles de voltaje, logrando discernir cuáles son las horas óptimas para realizar la recarga de baterías de los EVs. El análisis se asocia con la predicción de la potencia máxima que puede poseer una estación de carga de cuatro bahías en diferentes horas, considerando carga lenta, rápida y súper rápida, cada una de estas con diferente nivel de afectación a la red, buscando también la correcta gestión de carga de EVs para aumentar la eficiencia, calidad y confiabilidad de las redes de distribución

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Otimização Robusta de Recursos Energéticos em Edifícios utilizando MetaHeurística

A utilização da produção distribuída é vista como uma solução para responder aos desafios energéticos e ambientais dos dias de hoje, existindo cada vez maior integração destas unidades de produção de pequena escala nos edifícios. Com o aumento da produção distribuída por fontes de energia renováveis, como é o caso da eólica e fotovoltaica, são introduzidas novas dificuldades no escalonamento de recursos, isto devido às incertezas das condições atmosféricas que levam a erros de previsão. Dito isto, as ferramentas de gestão de recursos energéticos em edifícios têm que ser capazes de modelar este comportamento incerto. A principal contribuição desta dissertação foca-se no desenvolvimento de uma metodologia capaz de resolver o problema da gestão de recursos energéticos em edifícios para o dia seguinte, considerando as incertezas associadas à produção de energia das unidades fotovoltaicas e eólicas. Para modelar esta incerteza foi incorporado um modelo de otimização robusta no multi-objective particle swarm optimization. A otimização robusta numa meta-heurísticas representa uma importante contribuição desta dissertação, tendo em conta a escassez de trabalhos que abordam esta temática na literatura atual, especialmente na área da gestão de edifícios. Este tipo de abordagem permite obter uma solução mais conservadora, a melhor solução considerando os piores cenários. O problema proposto nesta tese considera dois objetivos conflituantes, a maximização dos lucros e minimização das emissões de CO2. Outras contribuições relevantes são os modelos de negócios considerados, nomeadamente, o facto do edifico poder em cada período comprar energia a diferentes comercializadores de energia e o uso do vehicle-to-building, onde o veículo pode fornecer energia ao edifício, e ainda o uso de sistemas de armazenamento. Adicionalmente, foi proposto um modelo inovador de gestão da procura, que considera o preço diário da potência de pico e um incentivo para a minimizar. É apresentado um caso de estudo de um edifício real de Portugal, de forma a verificar a viabilidade do algoritmo robusto implementado.The use of distributed generation is seen as one of the possible solutions to answer today’s energy and environmental challenges with an increasing integration of this small scale production units in buildings. With the increasing of distributed generation from renewable energy sources, such as wind and photovoltaic, new difficulties are introduced in resources scheduling, due to the uncertainties from weather conditions that lead to forecast errors. That said, the energy resource management tools in buildings need to be able to model this uncertain behaviour. The main contribution of this thesis focuses on the development of a methodology to solve the day-ahead energy resource management problem in buildings, considering the uncertainties associated with the energy production from photovoltaic and wind units. To model this uncertainty a robust optimization was incorporated in multi-objective particle swarm optimization. The robust optimization applied to a meta-heuristic, taking into account the scarcity of studies addressing this subject, especially in the area of building management, is an important contribution of this work. This approach allows a more conservative solution, the best solution considering the worst-case scenarios. The proposed problem in this thesis considers two conflicting objectives, maximizing profits and minimizing CO2 emissions. Other relevant contributions are the business models considered, namely the fact that the building can buy energy from different external suppliers in each period, the use of vehicle to-building, in which the electric vehicle can supply energy to the building, and the use of storage systems. In addition, an innovative demand response model has been proposed, which considers a daily peak power pricing and an incentive to minimize it. A case study is presented using a real building facility from Portugal, in order to verify the feasibility of the robust algorithm implemented