京都大学 / 新制・課程博士 / 博士(工学) / 甲第23203号 / 工博第4847号 / 新制||工||1757(附属図書館) / 京都大学大学院工学研究科電気工学専攻 / (主査)教授 引原 隆士, 教授 土居 伸二, 教授 梅野 健 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The objective of this work is to design and implement an architectural framework for a web-based demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeowners' preferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a web-based HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed web-based HEM system, which makes it possible for residential customers to participate in demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood. As one of the demand response (DR)-enabling technologies, the web services-based DR communication has been developed to enable households without smart meters or advanced metering infrastructure (AMI) to participate in a DR event via the HEM system. This implies that an electric utility can send a DR signal via a web services-enabled HEM system, and appropriate appliances can be controlled within each home based on homeowner preferences. The interoperability with other systems, such as utility systems, third-party Home Area Network (HAN) systems, etc., is also taken into account in the design of the proposed web services-based HEM system. That is, it is designed to allow interaction with authorized third-party systems by means of web services, which are collectively an interface for machine-to-machine interaction. This work also designs and implements device organization and interface for end-use appliances utilizing ZigBee Device Profile and Smart Energy Profile. Development of the Home Area Network (HAN) of appliances and the HAN Coordinator has been performed using a ZigBee network. Analyses of security risks for a web-based HEM system and their mitigation strategies have been discussed as well. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / Heat, cooling, and ventilation units are major energy consumers for commercial buildings, they can consume as much as 50% of the total annual power usage of a building. Coherent management of an air handling system’s energy is a key factor of reducing the energy costs and CO2 emissions that are associated with the demand for ventilating and conditioning the air in a building. The issue is that buildings are frequently over ventilated as a full assessment of the air handling unit (AHU) data is not evaluated by building operators. According to ASHRAE standards there are three key parameters that control indoor air quality (IAQ); these are the temperature, humidity, and CO2. Commonly occupancy setpoints implemented by building operators are focused on temperature and humidity control while neglecting the CO2 levels and their impact. While this may seem insignificant additional data proves to be important and can assist with energy management. Additionally, it can develop awareness of implementable procedures which conserve energy. Furthermore, data is not monitored in regard to the continuous assessment of the energy consumption with respect to analysis of opportunities to implement energy saving control strategies. By using these standards as a guide an AHUs energy can be managed more effectively by measuring the data and assessing the outputs compared to the standard. Previous research has shown that up to 75% savings for the ventilation fan energy is achievable when taking into account ASHRAE ventilation standards and controlling outside air ventilation, however, this research has omitted investigating the savings for other energy consumers associated with AHU’s operation. In order to assess the demand, it is required that the CO2 levels of the occupied zones be measured, and the outdoor air ventilation rate be adjusted based on real-time demand. The goal of the research is to assess the number of CO¬2 sensors needed to accurately measure demand-based needs for ventilation and determine an algorithm that will help building operators assess the energy savings by implementing demand-controlled ventilation (DCV) procedures. The scope of this research is to identify what sensors at minimum are required to collect the most pertinent data for implementation of a comprehensive energy saving algorithms and assess the impact on energy consumption of AHUs when demand-controlled ventilation procedures are implemented.

<p>    </p> <p>Technological advances and gaming have assisted users in becoming energy-efficient or raising awareness about energy efficiency. However, these games typically take place in schools and workplaces. Low-income households, which spend a larger percentage of income on utilities compared to average income households, exhibit greater sensitivity to energy disturbances. Despite this, there has been limited research on applying these technologies in low-income households. </p> <p><br></p> <p>The dissertation addresses the research gap concerning motivating low-income households to adopt new technologies focused on implementing energy-efficient HVAC behaviors. To achieve this objective, a gamification approach is employed, integrating a competitive social game into a cloud-based application named MySmartE. This application offers personalized eco-feedback and enables voice commands using Amazon Alexa. The game is deployed in two multi-residential low-income household communities located in Indiana. The collected data from field studies is analyzed to explore various aspects, including community interactions during the gaming seasons, technology adoption, and factors influencing participation in the social game. The findings reveal a positive correlation between increased gaming interac- tions and the adoption of MySmartE technology within these communities, underscoring the potential of gamification and technology to effectively engage low-income households in adopting energy-efficient practices. </p>

<p>An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power systems. Decentralized power generation, in particular, is emerging as one of the most effective and promising tools in addressing these concerns.</p> <p>Microgrids are small-scale electricity grids with elements of load, generation and storage. Microgrids have emerged as an essential building block of a future smart grid, and an enabling technology for distributed power generation and control. This thesis presents an optimization-based approach for the design and control of energy management systems (EMS) for electric microgrids. A linear programming formulation of power/energy management is proposed to minimize energy cost for a microgrid with energy storage and renewable energy generation, by taking advantage of time-of-use (TOU) pricing. The thesis also addresses the issue of sizing of the battery storage and solar power generation capacity by formulating and solving a mixed integer linear programming (MILP) problem. The aim of the optimization is to minimize the combined capital and electricity usage cost subject to applicable physical constraints. Several case scenarios are analyzed for grid-connected microgrids in residential, commercial and industrial settings, as well as a case of an islanded microgrid intended for a remote community.</p> <p>Finally, the thesis investigates circuit level control of a microgrid with EMS. A finite state machine based control logic is proposed that enables outage ride through and smooth transition between islanded and grid connected operation. Simulation results are provided to demonstrate the effectiveness of the proposed controller under various possible scenarios.</p> / Master of Applied Science (MASc)

Commercial buildings consume more than 40% of the total energy consumption in the United States. Almost 90% of these buildings are small- and medium-sized buildings that do not have a Building Energy Management (BEM) system. The reasons behind this are – lack of awareness, unavailability of inexpensive packaged solutions, and disincentive to invest in a BEM system if the tenant is not the owner. Several open source tools and technologies have emerged recently that can be used for building automation and energy management. However, none of these systems is turnkey and deployment ready. They also lack consistent and intuitive navigation, security, and performance required for a BEM system. The overall project - of which this thesis research is a part - addresses the design and implementation of an open source secure web based user platform to monitor, schedule, control, and perform functions needed for a BEM system serving small and medium-size buildings. The focus of this work are: principles of intuitive graphical user interface design, abstracting device functions into a comprehensive data model, identifying threats and vulnerabilities, and implementing a security framework for the web platform. Monitor and control solutions for devices such as load controllers and sensors are abstracted and their decentralized control strategies are proposed and implemented using an open source robust scalable user platform accessible locally and remotely. The user platform is open-source, scalable, provides role-based access, dynamic, and modular in design. The comprehensive data model includes a user management model, device model, session model, and a scheduling model. The data model is designed to be flexible, robust and can be extended for any new device type. Security risks are analyzed using a threat model to identify security goals. The proposed security framework includes user authentication, device approval, role-based access, secure information exchange protocols, and web platform security. Performance of the user interface platform is evaluated for responsiveness in different screen sizes, page response times, throughput, and the performance of client side entities. / Master of Science

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D.

Vi lever i en värld av klimatförändringar där det ständigt söks efter lösningar för att minska utsläppen av växthusgaser. IMO har infört SEEMP:en med målet att reducera utsläppen från världshandelsflottan och därmed öka dess energieffektivitet. Med denna bakgrund har syftet varit att undersöka hur svenska tankrederier implementerat och tillämpat SEEMP:en samt kartlägga åsikter kring den. En kvalitativ metod har använts för att på djupet undersöka rederiernas tillvägagångssätt. Undersökningen omfattar fyra semistrukturerade intervjuer med totalt sex personer ifrån två rederier där både ombord- och landanställda finns representerade. Resultatet visade att rederierna implementerat SEEMP:en efter IMO:s riktlinjer, i samråd med sitt klassningssällskap, där rederierna också har försökt ta tillvara möjligheten att energieffektivisera sin organisation. Båda rederierna såg positivt på införandet av SEEMP:en och menade att den borde införts tidigare. Det framkommer dock faktorer som anses motverka SEEMP:ens fulla potential, bland annat att strukturen i branschen måste ändras för att kunna applicera SEEMP:en fullt ut. / We live in a world of climate change where finding a solution to reducing the emission of GHG is a continuous pursuit. To reduce the emission from global shipping IMO has adopted the SEEMP consequently making the shipping industry more energy efficient. With this in mind the purpose of this study has been to review the implementation and usage of the SEEMP among Swedish shipping companies trading oil. Furthermore, the views of the involved parties with regard to the SEEMP were also investigated. In order to deeply analyze the companies´ approach to the SEEMP a qualitative research method has been used. The study consists of four semi-structured interviews with sex persons from two shipping companies including both onshore and ship-based personnel. The result shows that the implementation has been carried out in consultation with their own classification societies and with the IMO guidelines in mind. They have also tried to take the opportunity to make their organization more energy efficient. Both companies welcomed the adoption of the SEEMP and were of the opinion that it shouldhave been introduced at an earlier stage. However, some barriers to the SEEMP have been found, for example the operational structure of the shipping industry.

L’intégration des énergies renouvelables produites par un bâtiment et les réseaux de fourniture, qui sont amenés à proposer des tarifications et des puissances disponibles variables au cours de la journée, entraînent une grande variabilité de la disponibilité de l’énergie. Mais les besoins des utilisateurs ne sont pas forcément en accord avec cette disponibilité. La gestion de l’énergie consiste alors à faire en sorte que les moments de consommation des installations coïncident avec les moments où celle-ci est disponible. Notre objectif a été de proposer une stratégie de commande prédictive, distribuée et hiérarchisée, pour gérer efficacement l’énergie de l’habitat. Les aspects prédictifs de notre approche permettent d’anticiper les besoins et les variations de la tarification énergétique. L’aspect distribué va permettre d’assurer la modularité de la structure de commande, pour pouvoir intégrer différents usages et différentes technologies de manière simple et sans faire exploser la combinatoire du problème d’optimisation résultant. / Electrical system is under a hard constraint: production and consumption must be equal. The production has to integrate non-controllable energy resources and to consider variability of local productions. While buildings are one of the most important energy consumers, the emergence of information and communication technologies (ICT) in the building integrates them in smart-grid as important consumer-actor players. Indeed, they have at their disposal various storage capacities: thermal storage, hot-water tank and also electrical battery. In our work we develop an hierarchical and distributed Building Energy Management Systems based on model predictive control in order to enable to shift, to reduce or even to store energy according to grid informations. The anticipation enables to plan the energy consumption in order to optimize the operating cost values, while the hierarchical architecture enables to treat the high resolution problem complexity and the distributed aspect enables to ensure the control modularity bringing adaptability to the controller.

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.