The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

Being located in subtropical area, the weather in Taiwan is constantly hot and humid which imposes huge cooling load on buildings. Especially, the economic booms in Taiwan further boosted power demand, and worsened the power shortage situation. Dr. H.T. Lin and Dr. K.H. Yang had conducted systematic research since mid-1980s, which constructed a solid ground in this field in Taiwan. Among these results, the ENVLOAD index has become legal binding since 1997 while the PACS index is now under investigation. However, it is in short of analysis and full-scale experimental investigation on energy use of hotels in Taiwan. Therefore, the establishment of the EUI and DUI indexes in Taiwan is the goal of this study. A simplified calculation method has been established in analyzing the energy use and demand use of hotels in Taiwan, by normalizing experimental data from full-scale tests. The result can be drawn accurately based on a few terms, which are available from daily building operations such as occupancy, and is thus practically straightforward and easy to use. In addition, the accuracy was validated by experiments performed and data collected through information technology with Internet access in 4 different forms, which yielded successful results. It is anticipated that the calculation methodology developed in this study on EUI and DUI, and the experimental validation would provide a foundation for the establishment of hotel building energy codes in Taiwan in the future.

The deployment of Renewable Energy (RE)-based generation has experienced a sustained global growth in the recent decades, driven by many countries' interest in reducing greenhouse gas emissions and dependence on fossil fuel for electricity generation. This trend is also observed in remote off-grid systems (isolated microgrids), where local communities, in an attempt to reduce fossil fuel dependency and associated economic and environmental costs, and to increase availability of electricity, are favouring the installation of RE-based generation. This practice has posed several challenges to the operation of such systems, due to the intermittent and hard-to-predict nature of RE sources. In particular, this thesis addresses the problem of reliable and economic dispatch of isolated microgrids, also known as the energy management problem, considering the uncertain nature of those RE sources, as well as loads. Isolated microgrids feature characteristics similar to those of distribution systems, in terms of unbalanced power flows, significant voltage drops and high power losses. For this reason, detailed three-phase mathematical models of the microgrid system and components are presented here, in order to account for the impact of unbalanced system conditions on the optimal operation of the microgrid. Also, simplified three-phase models of Distributed Energy Resources (DERs) are developed to reduce the level of complexity in small units that have limited impact on the optimal operation of the system, thus reducing the number of equations and variables of the problem. The proposed mathematical models are then used to formulate a novel energy management problem for isolated microgrids, as a deterministic, multi-period, Mixed-Integer Nonlinear Programming (MINLP) problem. The multi-period formulation allows for a proper management of energy storage resources and multi-period constraints associated with the commitment decisions of DERs. In order to obtain solutions of the energy management problem in reasonable computational times for real-time, realistic applications, and to address the uncertainty issues, the proposed MINLP formulation is decomposed into a Mixed-Integer Linear Programming (MILP) problem, and a Nonlinear programming (NLP) problem, in the context of a Model Predictive Control (MPC) approach. The MILP formulation determines the unit commitment decisions of DERs using a simplified model of the network, whereas the NLP formulation calculates the detailed three-phase dispatch of the units, knowing the commitment status. A feedback signal is generated by the NLP if additional units are required to correct reactive power problems in the microgrid, triggering a new calculation MINLP problem. The proposed decomposition and calculation routines are used to design a new deterministic Energy Management System (EMS) based on the MPC approach to handle uncertainties; hence, the proposed deterministic EMS is able to handle multi-period constraints, and account for the impact of future system conditions in the current operation of the microgrid. In the proposed methodology, uncertainty associated with the load and RE-based generation is indirectly considered in the EMS by continuously updating the optimal dispatch solution (with a given time-step), based on the most updated information available from suitable forecasting systems. For a more direct modelling of uncertainty in the problem formulation, the MILP part of the energy management problem is re-formulated as a two-stage Stochastic Programming (SP) problem. The proposed novel SP formulation considers that uncertainty can be properly modelled using a finite set of scenarios, which are generated using both a statistical ensembles scenario generation technique and historical data. Using the proposed SP formulation of the MILP problem, the deterministic EMS design is adjusted to produce a novel stochastic EMS. The proposed EMS design is tested in a large, realistic, medium-voltage isolated microgrid test system. For the deterministic case, the results demonstrate the important connection between the microgrid's imbalance, reactive power requirements and optimal dispatch, justifying the need for detailed three-phase models for EMS applications in isolated microgrids. For the stochastic studies, the results show the advantages of using a stochastic MILP formulation to account for uncertainties associated with RE sources, and optimally accommodate system reserves. The computational times in all simulated cases show the feasibility of applying the proposed techniques to real-time, autonomous dispatch of isolated microgrids with variable RE sources.

abstract: The electric power system is monitored via an extensive network of sensors in tandem with data processing algorithms, i.e., an intelligent cyber layer, that enables continual observation and control of the physical system to ensure reliable operations. This data collection and processing system is vulnerable to cyber-attacks that impact the system operation status and lead to serious physical consequences, including systematic problems and failures. This dissertation studies the physical consequences of unobservable false data injection (FDI) attacks wherein the attacker maliciously changes supervisory control and data acquisition (SCADA) or phasor measurement unit (PMU) measurements, on the electric power system. In this context, the dissertation is divided into three parts, in which the first two parts focus on FDI attacks on SCADA and the last part focuses on FDI attacks on PMUs. The first part studies the physical consequences of FDI attacks on SCADA measurements designed with limited system information. The attacker is assumed to have perfect knowledge inside a sub-network of the entire system. Two classes of attacks with different assumptions on the attacker's knowledge outside of the sub-network are introduced. In particular, for the second class of attacks, the attacker is assumed to have no information outside of the attack sub-network, but can perform multiple linear regression to learn the relationship between the external network and the attack sub-network with historical data. To determine the worst possible consequences of both classes of attacks, a bi-level optimization problem wherein the first level models the attacker's goal and the second level models the system response is introduced. The second part of the dissertation concentrates on analyzing the vulnerability of systems to FDI attacks from the perspective of the system. To this end, an off-line vulnerability analysis framework is proposed to identify the subsets of the test system that are more prone to FDI attacks. The third part studies the vulnerability of PMUs to FDI attacks. Two classes of more sophisticated FDI attacks that capture the temporal correlation of PMU data are introduced. Such attacks are designed with a convex optimization problem and can always bypass both the bad data detector and the low-rank decomposition (LD) detector. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

A principal motivação para o surgimento do conceito de Smart Grid é a otimização do uso das redes de energia através da inserção de novas tecnologias de medição, automação e telecomunicações. A implementação desta complexa infra-estrutura produz ganhos em confiabilidade, eficiência e segurança operacional. Além disso, este sistema tem como principais objetivos promover a geração distribuída e a tarifa diferenciada de energia para usuários residenciais, provendo ferramentas para a participação dos consumidores no gerenciamento global do fornecimento de energia. Considerando também o uso de dispositivos de armazenamento de energia, o usuário pode optar por vender ou armazenar energia sempre que lhe for conveniente, reduzindo a sua conta de energia ou, quando a geração exceder a demanda de energia, lucrando através da venda deste excesso. Esta pesquisa propõe um Sistema Inteligente de Suporte à Decisão baseado em técnicas de aprendizado por reforço como uma solução para o problema de decisão sequencial referente ao gerenciamento de energia de uma Smart Home. Resultados obtidos mostram um ganho significativo na recompensa financeira a longo prazo através do uso de uma política obtida pela aplicação do algoritmo Q-Learning, que é um algoritmo de aprendizado por reforço on-line, e do algoritmo Fitted Q-Iteration, que utiliza uma abordagem diferenciada de aprendizado por reforço ao extrair uma política através de um lote fixo de transições adquiridas do ambiente. Os resultados mostram que a aplicação da técnica de aprendizado por reforço em lote é indicada para problemas reais, quando é necessário obter uma política de forma rápida e eficaz dispondo de uma pequena quantidade de dados para caracterização do problema estudado. / The main motivation for the emergence of the Smart Grid concept is the optimization of power grid use by inserting new measurement, automation and telecommunication technologies into it. The implementation of this complex infrastructure also produces gains in reliability, efficiency and operational safety. Besides, it has as main goals to encourage distributed power generation and to implement a differentiated power rate for residential users, providing tools for them to participate in the power grid supply management. Considering also the use of energy storage devices, the user can sell or store the power generated whenever it is convenient, reducing the electricity bill or, when the power generation exceeds the power demand, make profit by selling the surplus in the energy market. This research proposes an Intelligent Decision Support System as a solution to the sequential decision-making problem of residential energy management based on reinforcement learning techniques. Results show a significant financial gain in the long term by using a policy obtained applying the algorithm Q-Learning, which is an on-line Reinforcement Learning algorithm, and the algorithm Fitted Q-Iteration, which uses a different reinforcement learning approach called Batch Reinforcement Learning. This method extracts a policy from a fixed batch of transitions acquired from the environment. The results show that the application of Batch Reinforcement Learning techniques is suitable for real problems, when it is necessary to obtain a fast and effective policy considering a small set of data available to study and solve the proposed problem.

This thesis focuses on energy management and implementation of ISO 16001:2010. The first two chapters describe DEZA a.s., its operations and activities. The third chapter explains the requirements of ISO 16001:2010 and in the fourth chapter, the process of implementation of these requirements in operation is described.The last chapter describes the requirements of ISO 50001:2012 and compares them with the requirements of ISO 16001:2010.

In this thesis, a physics-based single particle modeling is presented to analyze a proposed direct coupled hybrid energy storage modules using lithium-ion battery and ultracapacitor. Firstly, a state of the art for the energy storage system in the electrified vehicles are summarized. Several energy storage elements including lead-acid battery, nickel-metal hydride battery, lithium-ion battery, ultracapacitor, and lithium-ion capacitor are reviewed. Requirements of the energy storage systems in electric, hybrid electric, and plug-in hybrid electric vehicles are generalized. Typical hybrid energy storage system topologies are also reviewed. Moreover, these energy storage elements and hybrid energy storage system topologies are compared to the requirements of the energy storage systems in terms of specific power and specific energy. Secondly, the performance of different battery balancing topologies, including line shunting, ring shunting, synchronous flyback, multi-winding, and dissipative shunting are analyzed based on a linear programming methodology. As a traction battery in an electric or plug-in electric vehicle, high voltage lithium-ion packs are typically configured in a modular fashion, therefore, the analysis considers the balancing topologies at module level and cell level and focuses on minimum balancing time, minimum plug-in charge time, minimum energy loss, and component counts of every balancing topology for the entire battery pack. Thirdly, different modeling techniques for the lithium-ion battery and ultracapacitor are presented. One of the main contributions of this thesis is the development of a physics-based single particle modeling embedded with a solid-electrolyte interface growth model for a lithium-ion battery in battery management system. This development considers the numerical solution of diffusion equation, cell level quantities, parametrization method, effects of number of shells in a spherical particle, SOC-SOH estimation algorithms, and aging effects. The accuracy of the modeling is validated by experimental results of a Panasonic NCR18650A lithium-ion battery cell. Fourthly, the physics-based modeling is applied to analyze the performance of a proposed direct coupled hybrid energy storage module topology based on the Panasonic NCR18650A lithium-ion battery and Maxwell BCAP0350 ultracapacitor. There are many ways to directly connect battery cells and ultracapacitor cells in a module which would influence the performance of the module. The results show that a module has 9 cells in a battery string and 14 cells in an ultracapacitor string can obtain the highest power capability and utilize the most of the energy in an ultracapacitor. More ultracapacitor strings connected in parallel would increase the power density but reduce the energy density. Moreover, the simulation and experimental results indicate that the direct coupled hybrid modules can extend the operating range and slow the capacity fade of lithium-ion battery. An SOC-SOH estimation algorithm for the hybrid module is also developed based on the physics-based modeling. Finally, a pack design methodology is proposed to meet U.S. Advanced Battery Consortium LLC PHEV-40, power-assist, and 48V HEV performance targets for the battery packs or the proposed direct coupled topologies. In order to explore replacement tradeoffs between the battery and ultracapacitor, a case study of the direct coupled topologies is presented. From the case study, ultracapacitors enhance the power capability for short term pulse power and marginally reduce the cost of an entire energy storage system. Moreover, the hybrid module topologies can keep a relatively long all-electric range when the batteries degrade. / Dissertation / Doctor of Philosophy (PhD)

No description available.

Résumé : Depuis la fin du XXème siècle, l’augmentation du prix du pétrole brut et les problématiques environnementales poussent l’industrie automobile à développer des technologies plus économes en carburant et générant moins d’émissions de gaz à effet de serre. Parmi ces technologies, les véhicules électriques hybrides constituent une solution viable et performante. En alliant un moteur électrique et un moteur à combustion, ces véhicules possèdent un fort potentiel de réduction de la consommation de carburant sans sacrifier son autonomie. La présence de deux moteurs et de deux sources d’énergie requiert un contrôleur, appelé système de gestion d’énergie, responsable de la commande simultanée des deux moteurs. Les performances du véhicule en matière de consommation dépendent en partie de la conception de ce contrôleur. Les véhicules électriques hybrides rechargeables, plus récents que leur équivalent non rechargeable, se distinguent par l’ajout d’un chargeur interne permettant la recharge de la batterie pendant l’arrêt du véhicule et par conséquent la décharge de celle-ci au cours d’un trajet. Cette particularité ajoute un degré de complexité pour ce qui est de la conception du système de gestion d’énergie. Dans cette thèse, nous proposons un modèle complet du véhicule dédié à la conception du contrôleur. Nous étudions ensuite la dépendance de la commande optimale des deux moteurs par rapport au profil de vitesse suivi au cours d’un trajet ainsi qu’à la quantité d’énergie électrique disponible au début d’un trajet. Cela nous amène à proposer une technique d’auto-apprentissage visant l’amélioration de la stratégie de gestion d’énergie en exploitant un certain nombre de données enregistrées sur les trajets antérieurs. La technique proposée permet l’adaptation de la stratégie de contrôle vis-à-vis du trajet en cours en se basant sur une pseudo-prédiction de la totalité du profil de vitesse. Nous évaluerons les performances de la technique proposée en matière de consommation de carburant en la comparant avec une stratégie optimale bénéficiant de la connaissance exacte du profil de vitesse ainsi qu’avec une stratégie de base utilisée couramment dans l’industrie. // Abstract : Since the end of the XXth century, the increase in crude oil price and the environmental concerns lead the automotive industry to develop technologies that can improve fuel savings and decrease greenhouse gases emissions. Among these technologies, the hybrid electric vehicles stand as a reliable and efficient solution. By combining an electrical motor and an internal combustion engine, these vehicles can bring a noticeable improvement in terms of fuel consumption without sacrificing the vehicle autonomy. The two motors and the two energy storage systems require a control unit, called energy management system, which is responsible for the command decision of both motors. The vehicle performances in terms of fuel consumption greatly depend on this control unit. The plug-in hybrid electric vehicles are a more recent technology compared to their non plug-in counterparts. They have an extra internal battery charger that allows the battery to be charged during OFF state, implying a possible discharge during a trip. This particularity adds complexity when it comes to the design of the energy management system. In this thesis, a complete vehicle model is proposed and used for the design of the controller. A study is then carried out to show the dependence between the optimal control of the motors and the speed profile followed during a trip as well as the available electrical energy at the beginning of a trip. According to this study, a self-learning optimization technique that aims at improving the energy management strategy by exploiting some driving data recorded on previous trips is proposed. The technique allows the adaptation of the control strategy to the current trip based on a pseudo-prediction of the total speed profile. Fuel consumption performances for the proposed technique will be evaluated by comparing it with an optimal control strategy that benefits from the exact a priori knowledge of the speed profile as well as a basic strategy commonly used in industry.

As Ações de Eficiência Energética encontram grandes barreiras para sua implantação. Um dos motivos pode estar na falta de conhecimento do tomador de decisão que, para evitar o custo inicial mais elevado de um equipamento eficiente, opta por instalar um equipamento mais barato, mas que consequentemente consome mais energia e aumenta os dispêndios com a eletricidade. Os sistemas de gestão de energia visam demonstrar a origem das despesas relacionadas ao consumo de energia elétrica, conscientizando os usuários acerca de tais custos. Muitos usuários não enxergam a possibilidade de economia de energia e de dinheiro, ao investir em equipamentos mais eficientes. Muitos consideram as faturas de energia como despesas fixas, logo, sem exigência de acompanhamento. Fato não compatível com os dias atuais. Ao identificar o consumo desagregado de energia da instalação, os usuários poderão avaliar os impactos de suas atividades em relação ao consumo de energia, assim com seu custo nas faturas de energia. A medição direta dos equipamentos reproduz o valor mais preciso do consumo desagregado. Entretanto, para muitas instalações esta prática é inviável, pois seus circuitos são compartilhados por diversos tipos de equipamentos e os custos de aquisição, implantação e leitura dos medidores podem se tornar proibitivos. É possível obter o valor do consumo desagregado por inspeção da instalação, que consiste no levantamento das características elétricas dos equipamentos, suas respectivas potências e períodos de utilização. Esse método, no entanto não é tão preciso na análise do consumo desagregado, pois envolve uma série de estimativas acerca da utilização dos equipamentos, que nem sempre são acertadas. Visando contornar estas situações, as técnicas de monitoramento não intrusivo de carga passaram a buscar na curva de carga as assinaturas elétricas dos equipamentos, para identificar seus períodos de funcionamento e assim obter o consumo desagregado. / The energy efficiency programs face huge difficulties to be deployed. The reason may be the lack of knowledge about total costs in acquires less efficient devices, which is cheaper, though the increases in energy bills eliminate this initial economy. Thereby, the Energy Management Systems aims to demonstrate the relation between the user\"s behavior and the electric power consumption. Many managers consider the electric bill as a fixed cost, without require tracking its origin. This means waste of energy and money. Analyzing the facility by sectors may improve the understanding about the costs in electricity and the knowledge about the disaggregated energy consumption, though is not always an easy issue to be obtained. Monitoring each equipment provides the exactly amount of energy is used in that system. However the costs of acquirement, implementation and monitoring these meters may become prohibitively. This way, the researches about nonintrusive load monitoring aim to demonstrate where the energy is being used and how it can be minimized.