Decarbonization of the power sector is of great importance for the transition to a sustainable and low-carbon world economy. Estimating carbon efficiency in the power sector is a key step to grasp the impact of demand-side usage changes and evaluate their potential environmental benefits. In order to quantify the environmental benefits of demand-side usage changes, Average Emission Factor (AEF) and Marginal Emission Factor (MEF) have been proposed in the electrical power sector. AEF is defined as the ratio of the total CO2 emitted in the system to the total electricity generated. It is an effective factor for reporting on CO2 emissions at system level and on an average basis, but the current AEF model lacks clarity on the factors actually affecting the estimation. MEF is defined as the incremental change in carbon emissions as a result of a change in demand. However, previous MEF assessments did not consider key technical limitations, such as ramp-rate constraint for generators and network constraints, and carbon trading mechanisms. This thesis improves the estimation for both AEF and MEF and key achievements can be summarized as: 1). A novel model of estimating AEF, with its application to GB, US and China’s electricity system. 2). Improvement on conventional MEF model by considering ramp-rate constraint in dispatch order. 3). Sensitivity studies on MEF using current fuel prices and future fuel prices. 4). A new model of estimating MEF considering both the utilization level of generators and the carbon costs when determining the dispatch order. 5). The effect of power network on MEF estimation, with a comparison of congested scenarios and non-congested scenario.

The power balance of an electrical power system is crucial to the quality of the delivered electricity as well as the security of supply. In a scenario where Swedish nuclear power plants are being phased out and replaced by renewable energy sources new constraints are added to the power balance equation since the production of many renewable energy sources, such as wind and solar power, are intermittent by nature. This leads to a situation where the currently available regulating power might have difficulties to manage the increasing frequency fluctuations in the power grid. One possible solution to the problem is to build gas turbines for the purpose of peak power generation capacity. An alternative option would be to increase customer flexibility; that is Demand Response. This master thesis investigates how the market for Demand Respond can be designed and which potential Demand Response volumes different policy programs might release. This is done through a mixed approach. Firstly, a scientific review of previously documented Demand Response experiences compares and categorizes different Demand Response programs in a typology based on the parameters cost, volume and feasibility. Subsequently an interview series with different market agents, predominantly through interviews with the Swedish energy intensive industry, identifies the existing Demand Response potential in Sweden and offers the paradigm needed to transfer the results to a future hypothetical situation. The typology of Demand Response programs and estimation of the future industrial Demand Response potential in Sweden are the main new knowledge contributions of this master thesis. The scope however is limited to the Swedish market geographically and focuses on the time horizon 2020-2050. It is also assumed that only existing technologies are likely to be implemented on a large scale over the given time horizon. The results of this master thesis suggest that a Real Time Pricing model would realize the largest potential of Demand Response and to a relatively low cost. This solution however requires actions and further development of both the pricing model and in technology. Firstly, all market agents must have free access to real time price information, something that is lacking today. Secondly, a smart grid with hourly meters is required. If policymakers consider security of supply to be more important than a low system cost, Direct Control or a continuation of the Strategic Reserve is to be preferred according to the conclusions of this report. Previous studies have placed the existing potential for industrial Demand Response in Sweden between 600 and 900 MW. This report suggests that the available volume is in the upper region of the mentioned interval already today and has potential to rise significantly in the future as industries become more aware of the concept and the transmission grid is becoming more flexible. Another driving force for increased Demand Response volumes are the increased price fluctuations which are expected as a consequence of a greater share of renewable energy sources. For the future Demand Response potential, a cost perspective is introduced and a distinction between different response durations is made. More specifically the results indicate that the potential industrial Demand Response volume will be about 1,500 MW in 2030, given a response duration time of 4 h and a spot price on 2,000 SEK/MWh. If 1,500 MW of peak generation capacity could be avoided through active Demand Side Management, it would reduce the system cost with about 350 Million SEK annually. Consequently, there is a business case for Demand Response and the issue is likely to be subject to further investigation and discussion in the future. On the long term however industrial Demand Response must be compared with other flexibility options, e.g. as import/export or energy storages but also residential Demand Response, and is in such case likely to be outcompeted due to its relatively high variable cost of providing capacity.

Radical changes to present day power systems will lead to power systems with a significant penetration of renewable energy sources and smartness, expressed in an extensive utilization of novel sensors and cyber secure Information and Communication Technology. Although these renewable energy sources prove to contribute to the reduction of CO2 emissions into the environment, its high penetration affects power system dynamic performance as a result of reduced power system inertia as well as less flexibility with regards to dispatching generation to balance future demand. These pose a threat both to the security and stability of future power systems. It is therefore very important to develop new methods through which power system security and stability can be maintained. This research investigated the development of methods through which the contributions of on-load tap changing transformers/transformer clusters could be assessed with the intent of developing real time adaptive voltage controlled demand response schemes for power systems. The development of such a scheme enables more active system components to be involved in the provision of frequency control as an ancillary service and deploys a new frequency control service with low infrastructural investment, bearing in mind that OLTC transformers are already very prevalent in power systems. In this thesis, a novel online adaptive supervisory controller for ensuring optimal dispatch of voltage-controlled demand response resources is developed. This novel controller is designed using the assessment results of OLTC transformer impacts on steady-state frequency and was tested for a variety of scenarios. To achieve the effective performance of the adaptive supervisory controller, the extensive use of statistical techniques for assessing OLTC transformer contributions to voltage controlled demand response is presented. This thesis also includes the use of unsupervised machine learning techniques for power system partitioning and the further use of statistical methods for assessing the contributions of OLTC transformer aggregates.

This PhD thesis proposes a bottom-up approach that accurately addresses the operational flexibility embedded in each part of a multi-energy system (MES). Several models which cover the simulations from replicating domestic electrified demands to power system scheduling are proposed. More specifically, a domes-tic multi-energy consumption model is firstly developed to simulate one minute resolution energy profiles of individual dwellings with the installation of prospec-tive technologies (i.e., electric heat pumps (EHPs), electric vehicles (EVs)). After-wards, a fast linear programming (LP) unit commitment (UC) model is devel-oped with the consideration of characteristics of generators and a full set of ancil-lary services (i.e., frequency response and reserves). More importantly, the fre-quency response requirements in low inertia systems are assessed with the con-sideration of three grid frequency regulations (i.e., rate of change of frequency, Nadir and quasi-steady state). Furthermore, the UC model has integrated vari-ous flexibility contributors in MES to provide ancillary and flexibility services, which include pumped hydro storages (PHSs), interconnectors, batteries and demand side resources (i.e., individual EHPs, heat networks, electrolysers). More importantly, the fast frequency response (FFR) provision from nonsynchronous resources is implemented and the demand response application of electrolysers is taken as an example to provide FFR in the UC model. By using the integrated UC model with the consideration of flexibility services provided by resources in the MES, the advantages of multi-energy operation can be clearly identified which can be used to inform system operators and policy makers to design and operate energy systems in a more economic and environment-friendly way.

On the one hand, the demand response management and dynamical pricing supported by the smart grid had started to lead to fundamentally different energy consumption behaviours; On the other hand, energy supply has gone through a dramatic new pattern due to the emergence and development of renewable energy resources. Facing these changes, this thesis investigates one of the resulting challenges, which is how to integrate the wholesale market and the retail market into one framework in order to achieve optimal balancing between demand and supply. Firstly, based on the existing mechanisms of the wholesale and retail electricity markets, a simulation tool is proposed and developed. This enables the ISO to find the best balance between supply and demand, by taking into account the different objectives of the generators, retailers and customers. Secondly, a new market mechanism based on the interval demand is proposed in order to address the challenges of the unpredictable demand due to the demand response management programs. Under the proposed new market mechanism, the corresponding approaches are investigated in order to support the retailers to find their profit-optimal pricing strategies, the generators to develop their best bidding strategies, and the ISO to identify the market clearing price function in order to best balance supply and demand. In particular: 1) For the ISO, our designed mechanism could effectively handle unpredictable demand under the dynamic retail pricing. It also enables the realisation of the goals of dynamic pricing by utilising smart meters; 2) In the retail market, we extend the smart pricing model in the current research in order to enable the retailers to find the most-profitable pricing scheme under the proposed new mechanism with the demand-based piecewise cost (i.e., market clearing price) function; 3) For the wholesale market, we developed a pricing forecasting model in order to forecast a market clearing price. Based on this model, we analysed the optimal bidding strategies for a generator under an interval demand from the ISO. Simulation results are provided in order to verify the effectiveness of the proposed approaches.

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.

Em diversos mercados de energia onde há competição, a formação de preços de energia elétrica no mercado de curto prazo decorre do equilíbrio da oferta e da demanda, onde geradores e grandes consumidores informam, em periodicidade horária ou inferior, as quantidades de energia e preços associados aos quais estão dispostos a produzir e consumir, respectivamente. No Brasil, no entanto, a demanda utilizada no modelo de formação de preço de energia elétrica no curto prazo (PLD) é considerada inelástica em relação ao preço. Por mais que se possam constatar sinais de resposta da demanda frente à volatilidade do PLD, ou ao custo com uso da rede de transmissão e distribuição no período de ponta, não há mecanismo estabelecido para que os consumidores ofertem as quantidades de energia e preços aos quais estão dispostos a reduzir seu consumo. O presente trabalho tem o objetivo de apresentar proposta de alteração no processo de formação de preço no curto prazo de modo a permitir a Oferta da Redução do Consumo (ORC) pelos consumidores industriais. A proposta parte da representação do parque termelétrico atual, que serve de base para o valor da oferta de redução do consumo, as adaptações para introdução da curva de operação para fins de consideração da redução de consumo, bem como metodologia para aferição do montante de energia efetivamente reduzido. Além disso, de modo a apresentar o potencial benefício sistêmico com a introdução da proposta, são apresentadas simulações com a cadeia de modelos de formação de preço atual tendo como base a indústria de alumínio no Brasil. Os cenários de ORC da indústria levam em consideração parâmetros econômicos que asseguram a atratividade do negócio em consonância com o benefício sistêmico de redução do custo de operação. Apresenta-se ainda simulação da operação do ano de 2015 com estimativa do potencial de ORC no Ambiente de Contratação Livre (ACL) onde se constatou reduções de até 25% no Custo Marginal de Operação (CMO) e 16% de redução despacho termelétrico. / In several competitive power markets, short term power price is the result of the balance of supply and demand represented by bid and ask prices and energy quantities. In Brazil, short term power price (PLD) calculated by Newave/Decomp price models consider price-inflexible demand, even though traces of demand response to short term power prices and demand tariffs can be identified. The purpose of this Thesis is the proposal of changes in process of power pricing allowing large energy consumers bid their price to curtail their consumption in substitution of thermal power dispatch. Topics included in proposal are: cost of installed thermal power plants in power system, industrial demand curtail and restart features, and demand response effectiveness appraisal tools. Current power price models were employed on simulations to evaluate system\'s benefits with demand response. From an industrial perspective, accounting measures were basis to convert loss of production in demand-side bidding price in order to keep business profitability. Estimate of demand side bidding potential market in Brazilian free market with simulation of system impact in 2015 with results that reached 25% of Marginal Cost reduction and 16% of Thermal Dispatch reduction.

Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experimentally demonstrate the eminent feasibility of datacenter demand response with a distributed video transcoding application and a simple distributed power controller. We also show that while some software power capping interfaces performed better than others, all the interfaces we investigated had the high dynamic range and low power variance required to achieve high quality power tracking. Our next investigation presents an empirical performance evaluation of algorithms that replace arithmetic operations with low-level bit operations for power-aware Big Data processing. Specifically, we compare two different data structures in terms of execution time and power efficiency: (a) a baseline design using arrays, and (b) a design using bit-slice indexing (BSI) and distributed BSI arithmetic. Across three different datasets and three popular queries, we show that the bit-slicing queries consistently outperform the array algorithm in both power efficiency and execution time. In the context of datacenter power shaping, this performance optimization enables additional power flexibility -- achieving the same or greater performance than the baseline approach, even under power constraints. The investigation of read-optimized index queries leads up to an experimental investigation of the tradeoffs among power constraint, query freshness, and update aggregation size in a dynamic big data environment. We compare several update strategies, presenting a bitmap update optimization that allows improved performance over both a baseline approach and an existing state-of-the-art update strategy. Performing this investigation in the context of load shaping, we show that read-only range queries can be served without performance impact under power cap, and index updates can be tuned to provide a flexible base load. This thesis concludes with a brief discussion of control implementation and summary of our findings.

La modernisation des réseaux électriques via ce que l'appelle aujourd'hui les réseaux intelligents (ou smart grids) promet des avancées pour permettre de faire face à une augmentation de la demande mondiale ainsi que pour faciliter l'intégration des ressources décentralisées. Grâce à des moyens de communication et de calcul avancés, les smart grids offrent de nouvelles possibilités pour la gestion des ressources des consommateurs finaux, y compris pour de petits éléments comme de l'électroménager. Cependant, ce type de gestion basée sur des décisions prises indépendamment peuvent causer des perturbations tels qu'un rebond de consommation, ou des instabilités sur le réseau. La prise en compte des interactions entre les décisions de gestion énergétique de différentes maisons intelligentes est donc une problématique naissante dans les smart grids. Cette thèse vise à évaluer l'impact potentiel de mécanismes de coordination entre consommateurs résidentiels au niveau de quartiers, et ce à travers trois études complémentaires. Tout d'abord, une première stratégie pour la gestion coordonnée de maisons est proposée avec l'objectif d'augmenter l'utilisation locale d'énergie renouvelable à travers la mise en place d'échanges d'énergie électrique entre voisins. Les participants reçoivent en échange une compensation financière. L'algorithme de gestion est étudié dans une configuration centralisée et une configuration décentralisée en faisant appel au concept de système multi-agents, chaque maison étant représentée par un agent. Les résultats de simulation montrent que les deux approches sont efficaces pour augmenter la consommation locale d'énergie renouvelable et réduire les coûts énergétiques journaliers des consommateurs. Bien que l'approche décentralisée retourne des résultats plus rapidement, l'approche centralisée a une meilleure performance concernant les coûts. Dans une seconde étude, deux algorithmes de gestion énergétiques à J-1 sont proposés pour un quartier résidentiel. Un modèle de tarification dynamique est utilisé, où le prix dépend de la consommation agrégée du quartier ainsi que d'une forme de tarification heures creuses-heures pleines. L'objectif est ici de concevoir un mécanisme de coordination plus avancé (par rapport au précédent), en permettant des échanges d'énergie renouvelable résiduelle au sein du quartier. La performance des algorithmes est étudiée sur une période d'une journée puis d'une année, en prenant ou non en compte les erreurs de prévision. D'après les résultats de simulation, les deux algorithmes proposés montrent de meilleurs performances que les méthodes de référence (sans contrôle, et algorithme égoïste), même en considérant les erreurs de prévision. Enfin, dans une troisième étude, l'impact de l'introduction de production photovoltaïque résidentielle sur la performance d'un agrégateur est évaluée, dans une configuration centralisée. L'agrégateur interagit avec le marché spot et le gestionnaire de réseau, de façon à proposer un nouveau modèle de tarification permettant d'influencer les consommateurs à agir sur leur consommation. Les résultats de simulation montrent quand le taux de pénétration de photovoltaïque résidentiel augmente, le profit de l'agrégateur diminue, du fait de l'autoconsommation dans le quartier. / Grid modernization through philosophies as the Smart Grid has the potential to help meet the expected world increasing demand and integrate new distributed generation resources at the same time. Using advanced communication and computing capabilities, the Smart Grid offers a new avenue of controlling end-user assets, including small units such as home appliances. However, with such strategies, decisions taken independently can cause undesired effects such as rebound peaks, contingencies, and instabilities in the network. Therefore, the interaction between the energy management actions of multiple smart homes is a challenging issue in the Smart Grid. Under this purpose, in this work, the potential of coordination mechanisms established among residential customers at the neighborhood level is evaluated through three studies. Firstly, coordinative home energy management is presented, with the aim to increase local renewable energy usage in the neighborhood area by establishing energy trading among smart homes, which are compensated by incentives. The control algorithm is realized in both centralized and decentralized manners by deploying a multi-agent system, where neighborhood entities are modeled as agents. Simulations results show that both methods are effective on increasing local renewable energy usage and decreasing the daily electricity bills of customers. However, while the decentralized approach gives results in shorter time, the centralized approach shows a better performance regarding costs. Secondly, two decentralized energy management algorithms are proposed for day-ahead energy management in the neighborhood area. A dynamic pricing model is used, where price is associated to the aggregated consumption and grid time-of-use scheme. The objective of the study is to establish a more advanced coordination mechanism (compared to previous work) with residual renewable energy is shared among smart homes. In this study, the performance of the algorithms is investigated with daily and annual analyses, with and without considering forecasting errors. According to simulations results, both coordinative control models show better performance compared to baseline and selfish (no coordination) control cases, even when considering forecasting errors. Lastly, the impact of photovoltaic systems on a residential aggregator performance (in a centralized approach) is investigated in a neighborhood area. In the proposed model, the aggregator interacts with the spot market and the utility, and proposes a novel pricing scheme to influence customers to control their loads. Simulation results show that when the penetration level of residential photovoltaics (PV) is increased, the aggregator profit decreases due to self-consumption ability with PV in the neighborhood.

Present-day economic and environmental constraints push power systems to be operated closer to their limits. A common limiting factor for power transmission is the risk of voltage instability in recent years. As the ultimate countermeasure to voltage collapse, load shedding is normally considered the last resort, when there are no other alternatives to stop an approaching voltage collapse. The requirements of a practical load shedding scheme are to prevent a power system from voltage collapse and to maximize its reliability. In order to design such a scheme, the following tasks are equally important: 1. Recognizing the approaching voltage collapse. 2. Determining the best load shedding locations. 3. Minimizing the amount of load shedding. This thesis firstly investigates the widely used undervoltage load shedding schemes (UVLS) and the single-port impedance match (SPIM) based schemes. The findings explain the difficulties faced by them. An original load shedding oriented voltage stability monitoring scheme, which involves developing a new multi-port network equivalent, is then developed. With the help of the multi-port network equivalent, the monitoring scheme can not only recognize the approaching voltage collapse in time, but also can easily rank the load buses based on their weakness. The results of ranking are consistent with those obtained from modal analysis method. This thesis then proposes a practical event-driven load shedding scheme based on the experiences learned from the schemes implemented by various utilities. The scheme involves developing a multistage method, which is to optimize the amount of load shedding. A general design procedure for the scheme is presented in the thesis. Using a real 2038 bus system as an example, the design methodology is described in detail. The methodology is expected to help power system engineers develop their own load shedding schemes. A practical emergency demand response scheme is also developed and presented in the appendix. It is aimed at choosing the proper demand response participants and minimizing the total cost while achieving a certain level of operation reserves. / Power Engineering and Power Electronics