This master thesis in media and communication studies explores the concept of the smart home, which by various industries within communication, information and energy business alongside property developers is expected to be the model for future living, housing and infrastructure development. Departing from a theoretical framework highlighting media and infrastructure as temporal and spatial phenomena, the analysis shows how the smart home arranges and manages both means of time and space due to its saturation of information technologies in the form of sensors, applications and data visualizations. The result of the study suggests that the smart home could be understood as a logistical medium, although the temporal bias present in the expectations on future living suggests that the purpose of the smart home is to sustain a flow of logistics and capital both over space and over time; the latter in terms of sustainability.

The requirement for affordable, secure and sustainable energy production is a pressing global challenge and the production of electricity with low carbon emissions is crucial. This usually entails large quantities of renewable energy generation, which is intermittent and often highly distributed throughout the electricity supply system. One of the proposed schemes to manage such generation is the smart grid, the transition to which forms the context for this research. The aim is to investigate the effect of certain psychological and social influences on the adoption of technology necessary to enable smart grids, in order to understand the implications for effective energy policy. In particular, the case of photovoltaic (PV) system adoption in the UK is studied. Empirical data detailing PV installations registered for the Feed in Tariff is analysed in order to understand rates of adoption and how they vary across both time and space. This analysis is combined with a review of policy intervention and literature from psychology to understand drivers for adoption among householders. The results from this study are then used to inform the design of an Agent Based Model of technology adoption within the smart grid context. The decision making of householders is modelled using an algorithm based on Social Cognitive Theory. The model is used to simulate different conditions and generate adoption scenarios in order to understand the potential effects of different parameters on adoption rates. In order to combine the analysis resulting from these methods, the multi-level perspective on transition in socio-technical systems is used to understand how a transition to a smart grid could be described and how adoption of PV in the UK under the Feed in Tariff incentive fits into such a transition. The results show that whilst economic incentive policies have had success in some areas adoption is also dependent on many non-financial parameters. Simulations show that the observability of adoption and the perceived inconvenience or urgency of adoption can have dramatic effects on rates of adoption, in some cases outweighing the rational economic effects of financial incentives. The implication for smart grid related policy is that non-financial factors should be taken into account as well as the more typical financial considerations in efforts to encourage adoption of necessary enabling technology by householders. The models developed could be used in further work to examine in detail adoption of other technologies such as smart home energy management systems and the interaction between adoption rates of multiple smart technologies.

Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users’ fine-grained metering data measured by users’ Smart Meters (SMs). The SEV charging application allows EVs’ charging parameters to be changed depending on the grid’s state in return for incentives for the EV owners. However, both applications impose risks on users’ privacy. Entities having access to users’ fine-grained metering data may use such data to infer individual users’ personal habits. In addition, users’ private information such as users’/EVs’ identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users’ whereabouts, thus breach their privacy. This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner. First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users’ data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases. Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users’ fine-grained operational metering data while preserving users’ privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users’ privacy with minimum performance penalty, users’ metering data are classified and aggregated accordingly by their respective local gateways based on the users’ locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work. Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user’s accountable data, (ii) this register is updated by design at a low frequency, (iii) the user’s supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads. Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charging at home location. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user’s contracted supplier) which is anonymously signed by the user’s private key. This protocol protects the user’s identity privacy from other suppliers as well as the user’s privacy of location from its own supplier. Further, it allows the user’s contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multi-user EV charging is supported and different legitimate EV users (e.g., family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the same EV. On an application level, REVCBAMU supports fair user billing, i.e., each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers. The protocol has been analysed in terms of security and computational overheads.

In preparation for the influx of renewable energy sources that will be added to the electrical system, flexible and adaptable control schemes are necessary to accommodate the changing infrastructure. Microgrids have been gaining much attention as the main solution to the challenges of distributed and intermittent generation, but due to their low inertia, they need fast-acting control systems in order to maintain stability. Multi-Agent Systems have been proposed as dynamic control and communication frameworks. Decentralized arrangements of agents can provide resiliency and the much-desired “plug and play” behavior. This thesis describes a control system that implements droop control and the diffusion communication scheme without the need of a centralized controller to coordinate the Microgrid agents to maintain the frequency and stable operating conditions of the system. Moreover, the inter-agent communication is unaffected by changing network configurations and can achieve optimal economic dispatch through distributed optimization.

Modern power networks incorporate communications and information technology infrastructure into the electrical power system to create a smart grid in terms of control and operation. The smart grid enables real-time communication and control between consumers and utility companies allowing suppliers to optimize energy usage based on price preference and system technical issues. The smart grid design aims to provide overall power system monitoring, create protection and control strategies to maintain system performance, stability and security. This dissertation contributed to the development of a unique and novel smart grid test-bed laboratory with integrated monitoring, protection and control systems. This test-bed was used as a platform to test the smart grid operational ideas developed here. The implementation of this system in the real-time software creates an environment for studying, implementing and verifying novel control and protection schemes developed in this dissertation. Phasor measurement techniques were developed using the available Data Acquisition (DAQ) devices in order to monitor all points in the power system in real time. This provides a practical view of system parameter changes, system abnormal conditions and its stability and security information system. These developments provide valuable measurements for technical power system operators in the energy control centers. Phasor Measurement technology is an excellent solution for improving system planning, operation and energy trading in addition to enabling advanced applications in Wide Area Monitoring, Protection and Control (WAMPAC). Moreover, a virtual protection system was developed and implemented in the smart grid laboratory with integrated functionality for wide area applications. Experiments and procedures were developed in the system in order to detect the system abnormal conditions and apply proper remedies to heal the system. A design for DC microgrid was developed to integrate it to the AC system with appropriate control capability. This system represents realistic hybrid AC/DC microgrids connectivity to the AC side to study the use of such architecture in system operation to help remedy system abnormal conditions. In addition, this dissertation explored the challenges and feasibility of the implementation of real-time system analysis features in order to monitor the system security and stability measures. These indices are measured experimentally during the operation of the developed hybrid AC/DC microgrids. Furthermore, a real-time optimal power flow system was implemented to optimally manage the power sharing between AC generators and DC side resources. A study relating to real-time energy management algorithm in hybrid microgrids was performed to evaluate the effects of using energy storage resources and their use in mitigating heavy load impacts on system stability and operational security.

Monitoring and controlling smart grid assets in a timely and reliable manner is highly desired for emerging smart grid applications. Wireless Sensor Networks (WSNs) are anticipated to be widely utilized in a broad range of smart grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health care. Despite these advantages, the use of WSNs in such critical applications has brought forward a new challenge of ful lling the Quality of Service (QoS) requirements of these applications. Providing QoS support is a challenging issue due to highly resource constrained nature of sensor nodes, unreliable wireless links and harsh operation environments. In this thesis we critically investigate the problem of QoS provisioning in WSNs. We identify challenges, limitations and requirements for applying QoS provisioning for WSNs in smart grid applications. We nd that the topic of data prioritization techniques at the MAC layer to provide delay bounds in condition monitoring applications is not well developed. We develop six novel QoS schemes that provide data di erentiation and reduce the latency of high priority tra c in a smart grid context. These schemes are namely; Delay-Responsive Cross layer (DRX), Fair and Delay-aware Cross layer (FDRX), Delay-Responsive Cross layer with Linear backo (LDRX), Adaptive Realistic and Stable Model (ARSM), Adaptive Inter-cluster head Delay Control (AIDC) and QoS-aware GTS Allocation (QGA). Furthermore, we propose a new Markov-based model for IEEE 802.15.4 MAC namely, Realistic and Stable Markovbased (RSM). RSM considers actual network conditions and enhances the stability of the WSNs. We show through analytical and simulation results that all of the presented schemes reduce the end-to-end delay while maintaining good energy consumption and data delivery values.

This thesis deals with issues around the implementation of an AMM (Automated Meter Management) system within the Czech electricity distribution system. The theoretical section of the thesis provides a historical context and looks at the development of the electricity sector. The thesis then goes on to discuss issues regarding the competitiveness of distribution companies in terms of IT/ICT use. This section defines the elements of the Smart Grid concept, in particular the AMM system. The chapters dealing with the conditions for implementing the system in the Czech Republic and pilot projects abroad make up the major sources for the practical section. The goal of the practical section is to come up with a project aim for the AMM system implementation trial project in Prague. The proposed project aim is divided into a number of separate tasks whose results are obtained using a detailed analysis of available sources and the conclusions of the theoretical part of the thesis. The thesis's main benefit is in forming a basis for the AMM implementation trial project in PREdistribuce, Inc. distribution networks and providing a comprehensive overview of the development and current state of the electricity sector.

La gestion de la distribution de l’énergie électrique dans un système multi-source (véhicule hybride électrique) est primordiale. Elle permet d’augmenter les performances du système en minimisant la consommation de combustible utilisée par la source principale, tout en respectant la demande et les différentes contraintes de fonctionnement de la chaîne énergétique et de sécurité du système. Dans cette thèse, dans le cas où le profil de mission est connu, une approche combinatoire est proposée en modélisant le problème de gestion d’énergie sous la forme d’un problème d’optimisation avec satisfaction des contraintes. Celui-ci est résolu par une méthode exacte issue de la recherche opérationnelle, conduisant à des solutions optimales en des temps de calcul fortement réduits en comparaison avec ceux obtenus par l’application de la programmation dynamique ou la commande optimale. Pour éprouver la sensibilité aux perturbations, une étude de robustesse est menée sur la base de l’analyse de la solution de pire-cas d’un scénario sur des profils de mission d’un véhicule. Les cas pratiques d’utilisation imposent de ne connaître la demande du moteur électrique qu’à l’instant présent, selon le mode de conduite du chauffeur. Afin de gérer l’énergie du véhicule en temps réel, un algorithme en ligne, basé sur une approche de type floue, est développé. Pour mesurer la qualité de la solution floue obtenue, une étude de performance est réalisée (recherche de l’optimum global), en ayant recours à une optimisation hors-ligne sur des profils de mission de référence, basée sur une modélisation non linéaire du problème de gestion d’énergie. Les résultats obtenus ont permis de valider la qualité de la solution floue résultante. / Managing the distribution of electrical energy in a multi-source system (hybrid electric vehicle) is paramount. It increases the system performance by minimizing the fuel used by the primary source, while respecting demand, the differents operating constraints of the energy chain and system security. In this thesis, where the mission profile is known, a combinatorial approach is proposed by modeling the problem of energy management as an optimization problem with constraint satisfaction. The problem is solved using an exact method from operations research, leading to optimal solutions with reduced computation time in comparison with those obtained by applying dynamic programming or optimal control strategies. To test the perturbation sensitivity, robustness study is conducted, based on the analysis of the worst-case solution of the worst scenario, which can be achieved on the vehicle mission profile. In practical cases, the vehicle demand is unknown, and we have only the information about the instantaneous demand, which depends on driving style of the driver. In order to manage on line the energy of the vehicle, an on-line algorithm, based on a fuzzy approach is developed. To measure the quality of the fuzzy solution obtained, a performance study is carried out (finding the optimum solution), using an off-line optimization under reference mission profiles, based on non-linear modeling of the power management problem. The results were used to validate the quality of the resulting fuzzy solution.

Orientador: José Antônio Siqueira Dias / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-26T20:20:29Z (GMT). No. of bitstreams: 1 Tavares_MauricioLopes_D.pdf: 1729620 bytes, checksum: bd266f4180967d2a0b7dc91a0e56b872 (MD5) Previous issue date: 2014 / Resumo: Bens econômicos são classificados como rivais, não-rivais e anti-rivais. É proposta uma nova categoria: bens de rivalidade extrema (RE) para adequada classificação da eletricidade. A RE se manifesta quando o consumidor marginal demanda do sistema a última unidade de eletricidade disponível na rede o que implica no completo desligamento do sistema, mesmo que essa unidade não disponível seja infinitesimal em relação à capacidade plena, o que é uma característica única. Governos coordenam investimentos em capacidade excedente para lidar com esse fenômeno, porém essa tal capacidade extra prejudica a concorrência, a inovação e investimentos em eficiência energética e fontes alternativas. O Gerenciamento da Rivalidade Extrema (GRE) propõe tratar de maneira diferente o fenômeno da RE através de capacidades de rede inteligente para promover de maneira individualizada e organizada a desconexão de consumidores quando ocorrem situações críticas de suprimento, seguindo uma ordem que desconecta prioritariamente os consumidores menos sensíveis a interrupções. A política de Valor de Segurança do Suprimento (VSS) se baseia em declarações dos consumidores a respeito de quanto estão dispostos a pagar em eventos críticos para evitar serem desconectados. Essa política inicia desconexões individualizadas de consumidores que declararam VSS = 0 procedendo em ordem crescente de valores de VSS até que seja equilibrada a demanda e a oferta e assegurado o equilíbrio do sistema. Todos consumidores que forem preservados devem pagar o VSS declarado, cuja receita pode ser usada para remunerar consumidores que foram desconectados ou financiar investimentos em expansão de capacidade / Abstract: Economic goods are classified as rival, non-rival and anti-rivals goods. We propose a new category: extreme rivalry goods (ER) for proper classification of electricity. ER manifests itself when the marginal consumer demands the last unit of electricity available on the grid implying in a complete shutdown of the system, even though by an infinitesimal amount compared to the total capacity, which is a unique feature. Governments coordinate investments in overcapacity to deal with this with the downside of preventing competition, innovation and undermining investments in energy efficiency and alternative energy. Management of Extreme Rivalry (MER) uses a different approach to ER relying in smart grid features to promote individualized and ordered disconnection of consumers when there is a critical supply situation, following a queue that begins with consumers less sensitive to disconnections. A policy of "Value of Supply Maintenance" (VSM) is based on declarations from consumers about how much they are willing to pay during critical supply events to prevent being disconnected. The VSM method begins disconnecting consumers who informed VSM = 0 proceeding in order of increasing VSM values until the balance between generation and consumption is ensured to balance the system. All preserved consumers must pay the VSM / Doutorado / Eletrônica, Microeletrônica e Optoeletrônica / Doutor em Engenharia Elétrica

<div> <div> <div> <p>Real-time pricing (RTP) of electricity for consumers has long been argued to be crucial for realizing the many envisioned benefits of demand flexibility in a smart grid. However, many details of how to actually implement a RTP scheme are still under debate. Since most of the organized wholesale electricity markets in the US implement a two-settlement mechanism, with day-ahead electricity price forecasts guiding financial and physical transactions in the next day and real-time ex post prices settling any real-time imbalances, it is a natural idea to let consumers respond to the day-ahead prices in real-time. However, if such an idea is not controlled properly, the inherent closed-loop operation may lead consumers to all respond in the same fashion, causing large swings of real-time demand and prices, which may jeopardize system stability and increase consumers’ financial risks. </p><p><br></p> <p>To overcome the potential uncertainties and undesired demand peak caused by “selfish” behaviors by individual consumers under RTP, in this research, we develop a fully decentralized price-driven demand response (DR) approach under game- theoretical frameworks. In game theory, agents usually make decisions based on their belief about competitors’ states, which needs to maintain a large amount of knowledge and thus can be intractable and implausible for a large population. Instead, we propose using regret-based learning in games by focusing on each agent’s own history and utility received. We study two learning mechanisms: bandit learning with incomplete information feedback, and low regret learning with full information feedback. With the learning in games, we establish performance guarantees for each individual agent (i.e., regret minimization) and the overall system (i.e., bounds on price of anarchy).</p><p><br></p></div></div></div><div><div><div> <p>In addition to the game-theoretical framework for price-driven demand response, we also apply such a framework for peer-to-peer energy trading auctions. The market- based approach can better incentivize the development of distributed energy resources (DERs) on demand side. However, the complexity of double-sided auctions in an energy market and agents’ bounded rationality may invalidate many well-established theories in auction design, and consequently, hinder market development. To address these issues, we propose an automated bidding framework based on multi-armed bandit learning through repeated auctions, and is aimed to minimize each bidder’s cumulative regret. We also use such a framework to compare market outcomes of three different auction designs. </p> </div> </div> </div>