Recentemente, muitas mudanças vêm ocorrendo na forma como a energia elétrica é gerada e distribuída, criando com isso oportunidades de utilizar os recursos disponíveis in loco de forma mais eficiente, atuando juntamente com sistemas centralizados convencionais, para o atendimento das necessidades energéticas. Neste contexto, as minirredes de energia são consideradas pontos chaves para melhorar a confiabilidade e a qualidade da energia, aumentar a eficiência do sistema elétrico como um todo, viabilizar aos consumidores finais a possibilidade de uma certa independência da rede e uma participação mais ativa no mercado de energia elétrica. No entanto, a aplicação de tais topologias ainda não superou todas as barreiras para que todos os benefícios possam ser apreciados. Há ainda perguntas a serem respondidas sobre como lidar com as especificidades e os aspectos operacionais de minirredes, tanto em operação normal quanto em operação anômala, que são fundamentais para a sustentabilidade de longo prazo dos sistemas. Este trabalho discute aspectos relacionados à operação de minirredes, tanto em sistemas isolados quanto em sistemas conectados à rede da concessionária. No caso de sistemas isolados, a abordagem leva em consideração os riscos associados aos projetos voltados para eletrificação rural e discute questões não restritas ao projeto inicial, mas também à operação destes tipos de sistemas. No caso de sistemas conectados, são discutidas tendências relacionadas às topologias de utilização destes sistemas para operação conjunta com a rede elétrica. Um algoritmo para controle de minirredes em diferentes níveis hierárquicos é proposto. Este algoritmo serve como base para o controle da plataforma de interação de agentes distribuídos, montada no Laboratório de Sistemas Fotovoltaicos da Universidade de São Paulo para testes em equipamentos reais e para a formação de recursos humanos. / Recently, many changes are taking place in the way energy is generated and distributed, thus creating opportunities to utilize the resources available on site more efficiently, working along with conventional centralized systems, to meet energy needs. In this context, mini-grids are considered key points to improve the reliability and power quality, increase the efficiency of the electric system as a whole, enable end consumers to have a certain independence from the grid and a more active participation in the electricity market. However, the application of such topologies has not yet overcome all barriers so that all their benefits can be appreciated. There are still questions to be answered on how to deal with the specificities and operational aspects of micro-grids in both normal and anomalous operation, which are fundamental to the long-term sustainability of power systems. This work discusses aspects related to micro-grids in both isolated and grid-connected operation modes. In the case of isolated systems, the approach takes into account the risks associated with rural electrification projects and discusses issues not restricted to the design, but also to the operation of these types of systems. In the case of grid-connected systems, trends related to the topologies to be used for joint operation with the power grid are discussed. An algorithm to control micro-grids at different hierarchical levels is proposed. This algorithm serves as a basis for the control of the platform for the interaction of distributed agents assembled at the Laboratório de Sistemas Fotovoltaicos of the Universidade de São Paulo to be used for tests on real equipment and for capacity building.

Redes elétricas desempenham um papel fundamental no que tange à distribuição de energia elétrica. Entretanto, as redes elétricas convencionais são muito antigas, apresentando uma série de deficiências e inseguranças. Neste cenário surgem as redes elétricas inteligentes, mais conhecidas como smart grids. Smart grids são uma evolução para as redes elétricas tradicionais, apresentando como grande diferencial a presença intensiva de tecnologia de ponta para monitorar cada elemento que a compõe. Uma das principais características de smart grids é seu fluxo bidirecional de energia e informação, que permite a qualquer elemento tanto consumir quanto fornecer energia para a rede, seja um aerogerador ou mesmo uma residência. Tal característica vai de encontro à necessidade de se distribuir a produção energética, tornando-a mais robusta e tolerante a falhas. Uma tecnologia que surgiu em meio ao desenvolvimento de smart grids denomina-se Veículo-Para-Rede (V2G, do inglês Vehicle-To-Grid). Através de sessões V2G, veículos elétricos (EVs, em inglês electric vehicles) podem vender a energia de suas baterias para a rede, obtendo lucro com este procedimento. Existem duas vantagens nesta tecnologia. Por um lado, proprietários de EVs podem obter lucro com a venda de energia, reduzindo os custos de se manter seu veículo. Por outro lado, a rede como um todo se beneficia, pois as baterias podem ser utilizadas para aumentar a estabilidade da rede. Entretanto, para que estas vantagens sejam expressivas, é necessário utilizar-se de mecanismos para aumentar a eficiência do processo V2G, uma vez que baterias são muito caras. Uma alternativa que tem sido muito explorada é a formação de coalizões entre os EVs. A proposta deste trabalho é utilizar informações sobre o domínio de smart grids de modo a impor restrições no processo de formação de coalizões de EVs, visando à redução do espaço de busca de estruturas de coalizão. Especificamente, estabelece-se a distância máxima que pode haver entre dois EVs de uma mesma coalizão, através da qual é possível identificar e podar porções inválidas do espaço de busca. Para tanto, é proposto o algoritmo CPCSG, capaz de identificar restrições entre os EVs e de podar o espaço de busca. A abordagem proposta pode ser utilizada em conjunto com algoritmos de geração de estruturas de coalizão para torná-los mais rápidos e eficientes. Com base em experimentos, percebe-se que a abordagem proposta proporciona um ganho notável de desempenho e uma redução expressiva no uso de memória em relação a outros algoritmos para geração de estruturas de coalizão. Em geral, quanto mais restritiva a rede e quanto maior o número de agentes, maior será o percentual do espaço de busca passível de ser podado. Resultados mostram, ainda, que quando comparada com outros algoritmos de geração de estruturas de coalizão, a técnica proposta chega a superar o tempo dos demais em diversas ordens de magnitude. / Electric grids play a key role in the energy distribution process. However, conventional grids are very old, which causes the onset of weaknesses and uncertainties. In such a scenario the smart grid concept arises. Smart grids are an evolution to the ageing electric grids, whose major breakthrough is the intensive use of technology to monitor every element that comprises it. One of the main features of smart grids is its bi-directional flow of electricity and information, which allows any element to consume and even supply energy to the grid, regardless of being a wind turbine or even a residence. Such a characteristic meets the need to make the energy production more distributed, making it more robust and fault tolerant. Amidst the development of smart grids emerged the concept of Vehicle-To-Grid (V2G). Through V2G sessions, electric vehicles (EVs) can sell the surplus energy of their batteries to the grid, making a profit. Two advantages arise from this technology. First, EVs’ owners can make a profit from the sale of energy, reducing their vehicles’ maintenance cost. Second, the network as a whole is benefited as batteries could be used to increase the network stability. However, in order to benefit from such advantages, it is necessary the use mechanisms to increase the efficiency of the V2G process, since batteries are very expensive. One way that has been explored is the coalition formation among EVs. The proposal of this work is to use smart grids’ domain information to impose constraints on the coalition formation process in order to reduce the coalition structures’ search space. Specifically, we define a maximum distance that can exist between two EVs of a given coalition, through which it is possible to identify and prune invalid portions of the search space. To this end, we propose the CPCSG algorithm, which has the capability of identifying constraints among EVs and pruning the search space. The proposed approach can be used together with coalition structure generation algorithms to make them faster and more efficient. Based on experiments, it can be seen that our approach provides a noticeable performance gain and a significant memory usage reduction compared to other coalition structure generation algorithms. In general, the more restrictive the grid and the greater the number of agents, the greater the percentage of the search space that can be pruned. Results also show that when compared with other coalition structure generation algorithms, the proposed technique is able to overcome the other in time by several orders of magnitude.

Os consumidores residenciais de energia elétrica no Brasil pagam um preço constante pela mesma em qualquer horário do dia, a despeito da variação constante nos custos de oferta. Isto não é economicamente eficiente. Para se atingir esta eficiência a implantação de uma tarifa inteligente se faz necessária, questão mais factível com o advento das redes inteligentes. Este trabalho busca antever se este desenvolvimento é custo efetivo ou não. Em primeiro lugar, os conceitos de redes inteligentes e de medidores avançados são apresentados. Em segundo lugar, são apresentados os conceitos de resposta da demanda e se demonstra porque o preço da eletricidade, para o consumidor final, deve ser maior na ponta do que fora da ponta. Por fim, se busca fazer uma análise custo benefício de um projeto hipotético de Infraestrutura de Leitura Avançada, desenvolvido por uma distribuidora de energia da região Centro Oeste do Brasil, a partir do estudo de cenários. Esse projeto hipotético ocorre num horizonte de dez anos, entre 2014 e 2023. O primeiro passo foi o desenvolvimento de campanhas de medição entre os anos de 2012 e 2013. Usando os dados aí obtidos, duas curvas de carga horárias foram desenvolvidas, uma para os dias úteis e a outra para finais de semana e feriados. O horário de pico é entre as 19 e as 22 horas nos dias úteis, e das 18 as 23 horas nos finais de semana e feriados. O custo da oferta e o consumo total de eletricidade foram obtidos, respectivamente, no Operador Nacional do Sistema e na Agência Nacional de Energia Elétrica. Os resultados obtidos em 15 experimentos prévios foram usados para estimar as hipotéticas elasticidades preço e elasticidades de substituição. Duas modalidades tarifárias foram testadas nos cenários: Tarifa Pelo Horário de Uso e Tarifa Pelo Horário de Uso com Preço de Pico Crítico. Os resultados obtidos ficaram aquém dos conceitualmente previstos. Uma análise é feita para tentar entender a razão desta resposta. / Residential customers in Brazil pay a constant price throughout the day, despite the large time variation in costs of supply. It is not economically efficient. It is necessary to set it to costumers with smart rates, and this possibility is getting closer from the development of smart grids. This work aims understand in advance if this deployment is cost-effective or not. Firstly, the concepts of Smart Grids, AMR (Automatic Meter Reading) and AMI (Advanced Metering Infrastructure) are presented. Secondly, concepts of demand response are described, and there is a demonstration of the reasons why electricity peak prices must be higher than off-peak prices. Thirdly, we seek to make a cost-benefit analysis for a hypothetical AMI project installation to residential customers, served by a utility in the Middle West of Brazil, under some potential scenarios. This hypothetical project runs in a ten year horizon (2014-2023). The first step was to perform measurement campaigns in 2012 and 2013. Using the data obtained, two residential hourly load curves were developed, one for weekdays and another for weekends and holidays. Peak time occurs between 7 and 10 PM in weekdays, and from 6 to 11 PM on weekends and holidays. The cost of supply and total consumption in the residential segment were obtained, respectively, from the Brazilian National System Operator (ONS) and Electric Energy Agency (ANEEL). The results obtained in fifteen previous experiments were used to estimate hypotheticals price elasticity and elasticity of substitution. Two types of rates were tested in scenarios: TOU and TOU with CPP. The results were lower than expected. An analysis is made to try to understand the reasons for this answer.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

Diese Dissertation widmet sich der Erarbeitung verschiedener Managementstrategien für ein leistungsflussoptimierendes Energiemanagement von dezentralen Energieversorgungssystemen in zukünftigen Niederspannungsnetzstrukturen. Als dezentrales Energieversorgungssystem ist dabei eine beliebige Kombination von Erzeuger-, Verbraucher- und Speichereinheiten zu sehen. Die Optimierung des Leistungsflusses auf Niederspannungsebene wird durch zwei Managementansätze untersucht. In einem verlustoptimierten Managementansatz stehen die bekannten Leitverluste, verursacht durch die wirksamen Leitungsresistanzen, im Fokus der Betrachtung. Durch einen zweiten Ansatz, dem clusterbasierten Managementansatz, wird nochmals eine Fokussierung auf die wirksamen Leitungsresistanzen durch eine Cluster-Bildung von Hausanschlüsen erreicht, welche nur durch geringe wirksame Leitungsresistanzen voneinander getrennt sind. Anhand realitätsnaher Netzabbildungen sowie unterschiedlicher Erzeuger- und Verbrauchersituationen wird der Einfluss eines solchen Energiemanagements auf die Integration dezentraler Erzeuger und der Leitverluste gezeigt sowie die Möglichkeit einer netzoptimierten Betriebsweise untersucht. / This thesis includes two different options to manage the situation of consumption and supply in a low-voltage grid. On the one hand the energy management is based on the optimization of the losses in the low-voltage grid caused by the resistance of the branchs. On the other hand the resistance between consumption and supply is the optimized parameter. This is implemented with a clustering of the whole low-voltage grid in groups of households. Based on realistic models of low-voltage grids and different situations of consumption and supply the two management strategies are tested. These tests shows the influence on the losses, the integration of distributed suppliers and the controlled power flow to the medium-voltage grid.

Dans le cadre des débats sur la transition énergétique, l'implémentation des Smart Grids est avancée comme une part de la solution pour répondre à la fois aux questions climatiques et aux enjeux énergétiques. Les modèles de prospective constituent des outils d'aide à la décision permettant d'orienter les trajectoires énergétiques afin de satisfaire aux futures demandes en tenant compte de contraintes environnementales et techniques. Ces modèles historiques, caractérisés principalement par l'adéquation de l'offre à la demande doivent désormais évoluer pour intégrer les développements futurs attendus du système électrique. Dans ce travail, nous avons implémenté dans une approche de long terme différentes fonctionnalités propres aux Smart Grids (gestion de la demande, stockage, énergies renouvelables). Cette approche nous permet d'évaluer les bénéfices liés à chacune d'entre elles séparément, ou bien conjointement au travers de bouquets de solutions, tenant ainsi compte des possibles interactions entre ces fonctionnalités. Nous avons également intégré un indicateur reflétant le niveau de fiabilité du système électrique dans notre modèle. Ce paramètre supplémentaire permet de contraindre les futurs systèmes électriques afin qu'ils garantissent un niveau de service en terme de fourniture électrique identique à celui des systèmes existants. Cette étude est illustrée par le cas de l'île de la Réunion, qui s'est donné pour objectif de produire d'ici 2030 son électricité à partir de sources uniquement renouvelables, et pour qui les fonctionnalités de Smart Grids pourraient constituer un levier intéressant.

Världens energibehov förväntas ökar samtidigt som miljökraven blir allt hårdare. För att komma till rätta med klimatförändringarna och utsläppen av växthusgaser måste användningen av fossila bränslen minska samtidigt som energieffektiviseringar och förnybara energikällor måste öka. En större andel intermittenta förnybara energikällor på elmarknaden medför utmaningar. Finns det inget elbehov då det exempelvis blåser eller när solen skiner går den producerade elen förlorad, detta leder till att produktion och konsumtion av elektricitet måste ske samtidigt. För att förnybar energi ska kunna expandera men också effektiviseras måste samhället utveckla smarta elnät. Det finns olika uppfattningar vad som krävs för att skapa smarta elnät men elektrisk energilagersystem återkommer ofta i litteraturen. Det finns forskare som anser att satsning på intermittenta förnybara energikällor inte är ett alternativ om inte energin går att lagra. Compressed air energy storage är ett energilager som använder komprimerad luft för att lagra energin tills det finns ett behov. Industrin i Sverige står för drygt en tredjedel av den totala energianvändningen. Över 90 % av tillverkningsindustrin använder tryckluft. Det finns stora och små förbrukare av tryckluft beroende på användningsområde.  I denna studie kommer en internationell nulägesbeskrivning ges i utvecklingen av smarta elnät med fokus på elektriska energilagersystem. Syftet är att studien ska vara ett diskussionsunderlag, en informationsbärare och idéskapare. Den internationella nulägesbeskrivningen baseras på studiebesök, litteratursammanställning samt intervjuer. Resultatet från den internationella nulägesbeskrivningen visar att intresset för elektriska energilagersystem ökar då det är en central del i utvecklingen av smarta elnät.  Mellan 2011-2013 ökade investeringarna i elektriska energilager med 521 %. En anledning till denna ökning är den internationella trenden med microgrids och mindre decentraliserade kraftverk. Med ökad efterfrågan på energilagringssystem kommer nya energilagringssystem skapas och befintliga system utvecklas. Syftet med studien är även att undersöka om energilager är en lösning till hållbar tryckluftsförsörjning inom industrin. Målet är att dimensionera ett luftningssystem bestående av vindkraftverk och energilager, med en viss volym och maxtryck, för en stor- och liten tryckluftsförbrukare. I studien kommer även kostnadsbesparingen för den stora förbrukaren optimeras genom arbitrage. Dimensioneringen görs utifrån simuleringar i Simulink och optimering görs i MATLAB. Dimensionerat luftningssystemet för den stora tryckluftsförbrukaren består av ett vindkraftverk, ett energilager på 200 m3 med maxtryck på 10 bar. Täckningsgraden, det vill säga andelen av luftbehovet som kan täckas med vindkraft tillsammans med ett energilager, är 26 % för det dimensionerade luftningssystemet. Resultatet ger då 48 % mindre energiförbrukning, cirka 1,2 miljoner kronor i kostnadsbesparing och en miljövinning motsvarande 532 ton CO2-ekvivalenter. Kostnadsbesparing, då el köps via arbitrage, för den stora förbrukaren optimeras till maximalt 1,2 miljoner kronor. Generatorn har då en verkningsgrad på 85 % och kompressorn 90 %. Dimensionerat luftningssystemet för den mindre tryckluftsförbrukaren består av en vindsnurra, ett energilager på 20 m3 med maxtryck på 30 bar. Täckningsgraden, det vill säga andelen av luftbehovet som kan täckas med vindsnurra tillsammans med ett energilager, är 61 % för det dimensionerade luftningssystemet. Resultatet ger då 93 % mindre energiförbrukning, cirka 26 tusen kronor i kostnadsbesparing och en miljövinningen motsvarande 10,7 ton CO2-ekvivalenter. Skillnaden mellan en vindsnurra och ett vindkraftverk är att vindsnurran inte producerar el utan använder rörelseenergin direkt. Ett system bestående av energilager som drivs av energi från vinden lämpar sig bättre för ett mindre tryckluftsbehov där det går att nå upp i högre täckningsgrad. Övergången till smarta elnät är nödvändigt för att tillgodose alla aspekter av hållbar utveckling. Det är ingen del av smarta elnät som är viktigare än någon annan. En hållbar tryckluftanvändning inom industrin är en del av smarta elnät och för att göra det möjligt har energilager en avgörande roll. Nulägesbeskrivningen visar att det i dagsläget finns ett ökat intresse för EES internationellt men att det inte finns ett EES som ensamt kommer lösa integrationen av förnybar energi. Tekniken för energilagring finns idag och växer imorgon. / The world’s energy demand is expected to increase and at the same time the environmental requirements are becoming stricter. To deal with the climate change and the greenhouse gas emissions, the use of fossil fuel need to decrease, while the energy efficiency and renewable energy production must increase. A greater share of intermittent renewable energy on the electricity market entails challenges. If there is no need for electricity when the wind is blowing or when the sun is shining the electricity is lost, this leads to production and consumption of electricity must occur simultaneously. To expand the renewable energy and make it more efficient, society must develop a smart grid. There are different opinions about what it takes to create smart grids, but electrical energy storage, EES, reappears frequently in the literature. There are even scientists who believe that investment in intermittent renewable energy sources is not an option unless energy can be stored. Compressed air energy storage is a technique that uses compressed air to store energy until there is a demand.   The Swedish industry accounts for over a third of total energy consumption in the country. Over 90 % of the all manufacturing industry uses compressed air. There are big and small users of compressed air depending on the industry.  In this study, an international status description is given in the development of smart grids with a focus on electrical energy storage systems. The aim of this study is to be an information carrier that creates discussion and new ideas. The international status description is based on field visits, literature surveys and interviews. The results from the international status description shows that interest in electric energy storage systems is increasing since it is a central part in the development of smart grids. Between 2011 and 2013 the investments increased in electrical energy storage with 521 %. One reason for this increase is the international trend of micro grids and small decentralized power plants. With the increased demand for energy storage, new energy storage systems are created and existing systems evolve. The purpose of the study is also to examine if energy storage is a solution for a sustainable supply of compressed air in the industry. The goal is to design a compressed air system consisting of wind turbines and energy storage with a certain volume and maximum pressure, for a large and a small compressed air consumer. The study will also determine the cost saving for the big users is an optimized through arbitrage. The design is based on simulations in Simulink and the optimization is done in MATLAB. The selected compressed air system for the large consumer is based on one wind turbine, energy storage of 200 m3 with a maximum pressure of 10 bar. The coverage ratio, i.e. the proportion of the air need that is covered by wind energy with energy storage, is 26 %. An investment in this system would give reduced energy consumption by 48 % leading to a cost reduction of about 1.2 million SEK and a reduced environmental impact equivalent to 532 tons of CO2-equivalents. The generator then has an efficiency of 85 %, and the compressor has 90 %. The selected compressed air system for the smaller consumer achieves a coverage rate of 61 % with the following dimensions; one windmill, energy storage of 20 m3 and maximum pressure of 30 bar. An investment in this system would give a reduced energy consumption by 93 %, leading to a cost reduction of about 26 000 SEK and a reduced environmental impact equivalent to 10.7 ton of CO2 equivalents. The difference between a windmill and a wind turbine is that the windmill does not produce electricity instead it uses kinetic energy directly. A system consisting of energy storage driven by energy from the wind is more suited for smaller air requirements where it is possible to achieve greater coverage. The transition to smart grids is necessary to be able to meet all aspects of sustainable development. There is no part of smart grids that is more important. Sustainable use of compressed air in industry is a part of smart grids and to make it possible energy storage is crucial. The international status description shows that there is a growing international interest in EES but there isn’t one EES alone that will solve the integration of renewable energy. The techniques for energy storage are existing today and are growing tomorrow.

Electricity networks are undergoing a period of rapid change and transformation, with increased penetration levels of renewable-based distributed generation, and new influences on electricity end-use patterns from demand-manageable loads and micro-generation. This creates a number of new challenges for the delivery of a reliable supply of electrical energy. The main aim of this PhD research is to provide a methodology for a more detailed and accurate assessment of the effects of wind-based distributed generation (DG) and demand side management (DSM) on transmission network operation. In addition, the work investigates the potential for co-ordinated implementation and control of DG and DSM to improve overall system performance. A significant amount of previous literature on network integration of DG and DSM resources has focused on the effects at the distribution level, where their impact is direct and often easily observed. However, as penetration levels increase, DG and DSM will have a growing influence on the operation and management of the bulk transmission system. Modelling and analysis of the impact of embedded and highly-dispersed DG and DSM resources at transmission voltage levels will present a significant challenge for transmission network operators in the future. Accordingly, this thesis presents a number of new approaches and methodologies allowing for a more accurate modelling and aggregation of DG and DSM resources in power system studies. The correct representation of input wind energy resources is essential for accurate estimation of power and energy outputs of wind-based DG. A novel modelling approach for a simple and accurate representation of the statistical and temporal characteristics of the wind energy resources is presented in the thesis. An "all-scale" approach to modelling and aggregation of wind-based generation is proposed, which is specifically intended for assessing the impact of embedded wind generation on the steady state performance of transmission systems. The methodology allows to include in the analysis wind-based generation at all scales and all levels of implementation, from micro and small LV-connected units, through medium-size wind plants connected at MV, up to large HV-connected wind farms. The thesis also presents an assessment of the potential for DSM in the UK residential and commercial sectors, based on the analysis and decomposition of measured demands at system bulk supply points into the corresponding load types. Using a section of the Scottish transmission network as a case study, a number of DG and DSM scenarios are investigated in detail. These results demonstrate the importance of accurately modelling the interactions between the supply system and various DG and DSM schemes, and show that the aggregated effects of highly-distributed DG and DSM resources can have significant impacts on the operation of the bulk transmission system.

The European power sector is facing significant challenges related to investments in grid infrastructure and generation capacity. The continued deployment of intermittent renewables also puts pressure on current grid conditions. Smart grids is seen as a cost-efficient way to overcome these challenges through a more efficient use of current capacity. Demand response is a corner-stone in smart grid development,  and is implemented to introduce flexibility on the demand side. Most demand response programs have used dynamic pricing to incentivize consumers to shift consumption from peak to off-peak hours. In Stockholm Royal Seaport, where a sustainable energy system is envisioned, it has been proposed that dynamic pricing should be complemented with an indicator depicting carbon intensity of purchased electricity. This indicator is based on average emissions, which is one of two fundamental perspectives on assessing environmental impacts of electricity consumption.  The aim of this study was to evaluate whether the approach used to quantify carbon intensity in Stockholm Royal Seaport is appropriate in the context of demand response. To achieve this, a literature review has been conducted regarding potential benefits of demand response, power system dynamics and carbon dioxide allocation methods. A quantitative analysis has also been conducted, where the signal proposed for Stockholm Royal Seaport has been modeled under different timeframes. The results show that the CO2-signal in Stockholm Royal Seaport is constructed in such a way that it is largely affected by hydro generation, which in turn makes it correlate negatively with price. As a result, the CO2-signal would counteract many of the predicted long-term benefits of demand response. Furthermore it seems unlikely that the signal would result in significant short-term emission reductions, since hydro generally is used to balance supply and demand in the Swedish and Nordic systems.  Based on the literature review, it was concluded that marginal emissions would be a more appropriate environmental indicator than average emissions. However, it remains a difficulty to construct a day-ahead control signal based on this perspective because of system complexity and lack of data. Historical marginal carbon intensity was nevertheless modeled in this study using a linear regression model. The results indicate that price itself might be a sufficient indicator of marginal emissions. Finally, a model for a signal based on prognoses of intermittent renewable generation is proposed, where the rationale is that consumers should decrease consumption during hours of low renewable generation. This signal was modeled using data on renewable generation from Denmark since corresponding data in Sweden is not yet available. Results show that it would be possible to construct a rather accurate control signal in this way. There are also reasons to believe that demand response based on this type of signal would result in long-term environmental benefits. / Den europeiska energisektorn står inför stora utmaningar, bland annat i form av investeringsbehov i nätinfrastruktur och produktionskapacitet för att säkra framtida leveranssäkerhet. Den fortsatta utbyggnaden av intermittent förnybar kraftproduktion ställer också nya krav på nätet och på aktörernas flexibilitet. Smarta nät ses som ett kostnadseffektivt sätt för att övervinna dessa utmaningar genom en mer effektiv användning av nuvarande kapacitet. En viktig del i detta är efterfrågerespons, som syftar till att minska belastningen på nätet under höglasttimmar genom att i högre grad än tidigare involvera konsumenten. De flesta initiativ inom efterfrågerespons har använt dynamisk prissättning för att uppmuntra konsumenter att flytta konsumtion från höglast- till låglasttimmar. I Norra Djurgårdsstaden, där visionen är att bygga ett hållbart och mer flexibelt energisystem, har det föreslagits att dynamisk prissättning bör kompletteras med en indikator som visar den inköpta elens koldioxidintensitet. Denna indikator är baserad på medelel, vilket är ett av två fundamentala sätt att miljövärdera el. Syftet med denna studie var att utvärdera om den metod som används för att kvantifiera koldioxidintensiteten i Norra Djurgårdsstaden är lämplig i samband med efterfrågerespons. För att uppnå detta har en litteraturstudie genomförts gällande potentiella fördelar med efterfrågerespons, hur kraftsystemet fungerar samt olika metoder för att miljövärdera el. En kvantitativ analys har också genomförts, där CO2-signalen i Norra Djurgårdsstaden har modellerats utifrån olika tidsperspektiv. Resultaten visar att CO2-signalen i Norra Djurgårdsstaden är konstruerad på ett sådant sätt att den till stor del påverkas av vattenkraftens produktionsvariationer, vilket i sin tur gör att signalen generellt rör sig i motsatt riktning mot prissignalen. Resultatet av detta är att CO2-signalen motverkar många av de långsiktiga fördelarna med efterfrågestyrning. Dessutom ter det sig osannolikt att signalen skulle leda till signifikanta utsläppsminskningar på kort sikt, eftersom lasten i Sverige främst balanseras av variationer i vattenkraft. Utifrån litteraturstudien kan man dra slutsatsen att marginalelens koldioxidintensitet skulle vara en lämpligare miljöindikator än genomsnittliga utsläpp i samband med efterfrågestyrning. Det är dock svårt att i praktiken konstruera en styrsignal baserat på detta perspektiv på grund av systemets komplexitet och brist på data. Historiska marginella utsläpp modellerades emellertid med hjälp av linjär regression. Resultaten från detta indikerade att priset kan vara en tillräcklig indikator även för variationerna i koldioxidintensitet utifrån ett marginalperspektiv. Slutligen föreslås en modell för en signal baserad på dagenföreprognoser om intermittent förnybar produktion, där budskapet skulle vara att användaren minskar sin konsumtion under timmar med låg förnybar produktion. Denna signal modellerades med hjälp av uppgifter om förnybar produktion från Danmark eftersom motsvarande uppgifter om Svensk produktion inte finns tillgängliga ännu. Resultaten visar att det skulle vara möjligt att konstruera en relativt träffsäker styrsignal på detta sätt. Det finns också skäl att tro att efterfrågerespons baserat på denna typ av signal skulle leda till miljömässiga fördelar på längre sikt.

Smart Grid has emerged a very important concept in modern power systems. The integration of different loads such as residential, commercial and industrial into the smart grid and their optimal operation has a significant effect on the system's reliability, stability, peak power demand and energy price. This work presents the mathematical modeling of a Centralized Temperature Control System (CTCS) of a Multi-Unit Residential Building (MURB) and its optimal operation considering electricity prices and weather variations. The model considers comfort levels, preference settings and activity of residents in different units of the building to determine the optimal operation schedules of the CTCS, minimizing its total energy consumption cost. Multi-objective operation of the MURB is also investigated when residents in different units have conflicting interests, and the impact of such conflicting preferences on the operation of CTCS is analyzed. A case-study on optimal energy management of a single unit house considering net-metering is also presented. The proposed CTCS model is a Mixed Integer Non Linear Programing (MINLP) model, where some of the constraints are linearized to reduce the computational complexity arising from the non-linearity, for real-time applications. The model is studied for various customers' preferences using a realistic MURB model. Simulation results show that significant cost savings can be achieved using the proposed mathematical model.