Distributed Coordination and Optimisation of Network-Aware Electricity Prosumers

Electricity networks are undergoing a transformation brought on by new technologies, market pressures and environmental concerns. This includes a shift from large centralised generators to small-scale distributed generators. The dramatic cost reductions in rooftop solar PV and battery storage means that prosumers (houses and other entities that can both produce and consume electricity) will have a large role to play in future networks. How can networks be managed going forward so that they run as efficiently as possible in this new prosumer paradigm? Our vision is to treat prosumers as active participants by developing a mechanism that incentivises them to help balance power and support the network. The whole process is automated to produce a near-optimal outcome and to reduce the need for human involvement. The first step is to design an autonomous energy management system (EMS) that can optimise the local costs of each prosumer in response to network electricity prices. In particular, we investigate different optimisation strategies for an EMS in an uncertain household environment. We find that the uncertainty associated with weather, network pricing and occupant behaviour can be effectively handled using online optimisation techniques using a forward receding horizon. The next step is to coordinate the actions of many EMSs spread out across the network, in order to minimise the overall cost of supplying electricity. We propose a distributed algorithm that can efficiently coordinate a network with thousands of prosumers without violating their privacy. We experiment with a range of power flow models of varying degrees of accuracy in order to test their convergence rate, computational burden and solution quality on a suburb-sized microgrid. We find that the higher accuracy model, although non-convex, converges in a timely manner and produces near-optimal solutions. We also develop simple but effective techniques for dealing with residential shiftable loads which require discrete decisions. The final part of the problem we explore is prosumer manipulation of the coordination mechanism. The receding horizon nature of our algorithm is great for managing uncertainty, but it opens up unique opportunities for prosumers to manipulate the actions of others. We formalise this form of receding horizon manipulation and investigate the benefits manipulative agents can obtain. We find that indeed strategic agents can harm the system, but only if they are large enough and have information about the behaviour of other agents. For the rare cases where this is possible, we develop simple privacy-preserving identifiers that monitor agents and distinguish manipulation from uncertainty. Together, these components create a complete solution for the distributed coordination and optimisation of network-aware electricity prosumers

A COGNITIVE ARCHITECTURE FOR AMBIENT INTELLIGENCE

L’Ambient Intelligence (AmI) è caratterizzata dall’uso di sistemi pervasivi per monitorare l’ambiente e modificarlo secondo le esigenze degli utenti e rispettando vincoli definiti globalmente. Questi sistemi non possono prescindere da requisiti come la scalabilità e la trasparenza per l’utente. Una tecnologia che consente di raggiungere questi obiettivi è rappresentata dalle reti di sensori wireless (WSN), caratterizzate da bassi costi e bassa intrusività. Tuttavia, sebbene in grado di effettuare elaborazioni a bordo dei singoli nodi, le WSN non hanno da sole le capacità di elaborazione necessarie a supportare un sistema intelligente; d’altra parte senza questa attività di pre-elaborazione la mole di dati sensoriali può facilmente sopraffare un sistema centralizzato con un’eccessiva quantità di dettagli superflui. Questo lavoro presenta un’architettura cognitiva in grado di percepire e controllare l’ambiente di cui fa parte, basata su un nuovo approccio per l’estrazione di conoscenza a partire dai dati grezzi, attraverso livelli crescenti di astrazione. Le WSN sono utilizzate come strumento sensoriale pervasivo, le cui capacità computazionali vengono utilizzate per pre-elaborare i dati rilevati, in modo da consentire ad un sistema centralizzato intelligente di effettuare ragionamenti di alto livello. L’architettura proposta è stata utilizzata per sviluppare un testbed dotato degli strumenti hardware e software necessari allo sviluppo e alla gestione di applicazioni di AmI basate su WSN, il cui obiettivo principale sia il risparmio energetico. Per fare in modo che le applicazioni di AmI siano in grado di comunicare con il mondo esterno in maniera affidabile, per richiedere servizi ad agenti esterni, l’architettura è stata arricchita con un protocollo di gestione distribuita della reputazione. È stata inoltre sviluppata un’applicazione di esempio che sfrutta le caratteristiche del testbed, con l’obiettivo di controllare la temperatura in un ambiente lavorativo. Quest’applicazione rileva la presenza dell’utente attraverso un modulo per la fusione di dati multi-sensoriali basato su reti bayesiane, e sfrutta questa informazione in un controllore fuzzy multi-obiettivo che controlla gli attuatori sulla base delle preferenze dell’utente e del risparmio energetico.Ambient Intelligence (AmI) systems are characterized by the use of pervasive equipments for monitoring and modifying the environment according to users’ needs, and to globally defined constraints. Furthermore, such systems cannot ignore requirements about ubiquity, scalability, and transparency to the user. An enabling technology capable of accomplishing these goals is represented by Wireless Sensor Networks (WSNs), characterized by low-costs and unintrusiveness. However, although provided of in-network processing capabilities, WSNs do not exhibit processing features able to support comprehensive intelligent systems; on the other hand, without this pre-processing activities the wealth of sensory data may easily overwhelm a centralized AmI system, clogging it with superfluous details. This work proposes a cognitive architecture able to perceive, decide upon, and control the environment of which the system is part, based on a new approach to knowledge extraction from raw data, that addresses this issue at different abstraction levels. WSNs are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts in order to carry on symbolic reasoning. The aim of the reasoning is to plan a sequence of actions that will lead the environment to a state as close as possible to the users’ desires, taking into account both implicit and explicit feedbacks from the users, while considering global system-driven goals, such as energy saving. The proposed conceptual architecture was exploited to develop a testbed providing the hardware and software tools for the development and management of AmI applications based on WSNs, whose main goal is energy saving for global sustainability. In order to make the AmI system able to communicate with the external world in a reliable way, when some services are required to external agents, the architecture was enriched with a distributed reputation management protocol. A sample application exploiting the testbed features was implemented for addressing temperature control in a work environment. Knowledge about the user’s presence is obtained through a multi-sensor data fusion module based on Bayesian networks, and this information is exploited by a multi-objective fuzzy controller that operates on actuators taking into account users’ preference and energy consumption constraints

Machine-Learning-Powered Cyber-Physical Systems

In the last few years, we witnessed the revolution of the Internet of Things (IoT) paradigm and the consequent growth of Cyber-Physical Systems (CPSs). IoT devices, which include a plethora of smart interconnected sensors, actuators, and microcontrollers, have the ability to sense physical phenomena occurring in an environment and provide copious amounts of heterogeneous data about the functioning of a system. As a consequence, the large amounts of generated data represent an opportunity to adopt artificial intelligence and machine learning techniques that can be used to make informed decisions aimed at the optimization of such systems, thus enabling a variety of services and applications across multiple domains. Machine learning processes and analyses such data to generate a feedback, which represents a status the environment is in. A feedback given to the user in order to make an informed decision is called an open-loop feedback. Thus, an open-loop CPS is characterized by the lack of an actuation directed at improving the system itself. A feedback used by the system itself to actuate a change aimed at optimizing the system itself is called a closed-loop feedback. Thus, a closed-loop CPS pairs feedback based on sensing data with an actuation that impacts the system directly. In this dissertation, we propose several applications in the context of CPS. We propose open-loop CPSs designed for the early prediction, diagnosis, and persistency detection of Bovine Respiratory Disease (BRD) in dairy calves, and for gait activity recognition in horses.These works use sensor data, such as pedometers and automated feeders, to perform valuable real-field data collection. Data are then processed by a mix of state-of-the-art approaches as well as novel techniques, before being fed to machine learning algorithms for classification, which informs the user on the status of their animals. Our work further evaluates a variety of trade-offs. In the context of BRD, we adopt optimization techniques to explore the trade-offs of using sensor data as opposed to manual examination performed by domain experts. Similarly, we carry out an extensive analysis on the cost-accuracy trade-offs, which farmers can adopt to make informed decisions on their barn investments. In the context of horse gait recognition we evaluate the benefits of lighter classifications algorithms to improve energy and storage usage, and their impact on classification accuracy. With respect to closed-loop CPS we proposes an incentive-based demand response approach for Heating Ventilation and Air Conditioning (HVAC) designed for peak load reduction in the context of smart grids. Specifically, our approach uses machine learning to process power data from smart thermostats deployed in user homes, along with their personal temperature preferences. Our machine learning models predict power savings due to thermostat changes, which are then plugged into our optimization problem that uses auction theory coupled with behavioral science. This framework selects the set of users who fulfill the power saving requirement, while minimizing financial incentives paid to the users, and, as a consequence, their discomfort. Our work on BRD has been published on IEEE DCOSS 2022 and Frontiers in Animal Science. Our work on gait recognition has been published on IEEE SMARTCOMP 2019 and Elsevier PMC 2020, and our work on energy management and energy prediction has been published on IEEE PerCom 2022 and IEEE SMARTCOMP 2022. Several other works are under submission when this thesis was written, and are included in this document as well

Risks of overheating in highly insulated English houses: an investigation into the design process, comfort performance and occupant behaviour

When exploring the topic of overheating in buildings, the notion is commonly applied to future overheating, as a consequence of climate change. By contrast, this thesis is concerned with present-day overheating, as it is experienced in highly insulated houses. This can be claimed to be an unintended consequence of decarbonising the built environment, which has led to high levels of insulation and airtightness in the design of new homes in the UK. However, evidence of overheating in such homes point at possible inadequacies in the design and regulatory processes leading to highly insulated homes. Such design and processes have tended to focus only on winter comfort and carbon reduction from space heating demand. With a view of addressing the design problems leading to uncomfortably warm homes, this project is devoted to finding evidence of present-day overheating in highly insulated houses. This is pursued by an in-depth, multi case study, in which a mixed method approach to research is carried out in four (different typologies of) English houses -one of which is retrofitted while the other three were built as new. In this research, these houses have undergone longitudinal environmental monitoring and user perspective data gathering, across the four seasons of the year. In addition, in-depth semi-structured interviews with architects and designers of such houses were also carried out. A number of design factors have been found to lead to overheating, mostly resulting from a design process in which the main (physical) factors, such as control of solar gain and provision of adequate ventilation, are largely overlooked. This overlooking has, in turn, originated a potential demand for cooling, especially when no other forms of adaption are provided within the houses. Monitoring has shown that HIHs can be warmer environments: overheating was found in some instances and with different degrees of severity. However, it was also found that assessments may underestimate overheating (no consideration of vulnerable occupants throughout building lifespan). In some cases, it was found that occupants were adopting adaptive behaviour. The interview with designers revealed a generalised limitation in knowledge, where the fabric first approach adopted in low-carbon design focused on winter comfort mostly. For, the role of thermal comfort (the means to deliver it through design, as well as to achieve it by the occupants) was found to be central in HIHs, as comfort is (ought to be) delivered entirely by design. In summary, then, the research findings presented in this thesis indicate that today overheating in HIHs is the result of innovation in architecture, which requires immediate feedback from real-world research to guide regulatory bodies and designers.De Montfort University British Council-funded Global Innovation Initiative Project GII10

Improving Energy Efficiency in Commercial Buildings: Proceedings of the International Conference IEECB'06

The IEECB conference brought together all the key players from this sector, including commercial buildings’ investors and property managers, energy efficiency experts, equipment manufacturers, service providers (ESCOs, utilities, facilities management companies) and policy makers, with a view to exchange information, to learn from each other and to network. At the conference key representatives of leading organisations and companies, institutions and equipment industry presented the overall picture and give details of policies, recent advancements and examples of best practice. The wide scope of topics covered during the IEECB'06 conference included: macro/micro approaches, state-of-the-art equipment and systems (lighting, HVAC auxiliary equipment, ICT & office equipment, miscellaneous equipment, BEMS, electricity on-site production, renewable energies, etc.) and the latest advances in R&D, tools, regulation & policy, demand-side and supply-side perspectives for all branches of activity (public and private sector, the commerce and retail sectors, hotels and restaurants, banks and insurance companies, local authorities, civil services & public bodies, education, universities & laboratories, hospitals, airport and stations, etc.) We hope that the present proceedings could be a valuable contribution to disseminate information and best practices in policies, programmes and technologies to foster the penetration of highly efficient buildings in the commercial sector.JRC.H.8-Renewable energie

Managing Flexible Loads in Residential Areas

Load flexibility in households is a promising option for efficient and reliable operation of future power systems. Due to the distributed nature of residential demand, coordination mechanisms have to cope with a large number of flexible units. This thesis provides a model for demand response analysis and proposes different mechanisms for coordinating flexible loads. In particular, the potential to match intermittent output of renewable generators with electricity demand is investigated

Socio-material constructs of domestic energy demand: Household and housing practices in Pakistan

Domestic energy demand in the Global South is predicted to grow to nearly three times that of the developed nations by 2040, under rapid urbanisation, economic development and the emergence of a new, high-consuming middle-class. Current energy policy, with its largely technological template and economic focus fails to address the ways of living and patterns of demand that emerge and evolve as a result of the specific socio-material and cultural contexts that underpin how the need for energy arises and evolves. This research adopts a socio-technical perspective to explore various nexuses of practices and spatial arrangements of urban housing that have emerged, persisted and transformed over time, giving rise to unsustainable levels of electricity consumption in middle-class housing in Lahore, Pakistan. It further investigates how household practices fit within the wider system of housing practices and how this can inform low-energy interventions in house design and use. The research combines practice theories from the social sciences with architectural knowledge of spatial agency to explore the interlinked social and material structures that form domestic electricity demand. This is achieved through a mixed-methods approach including semi-structured interviews with homeowners and housing practitioners, cross-cultural comparative analysis, house case-studies, oral history narratives, environmental monitoring, spatiotemporal mapping of household practice-arrangements through time-use diaries as well a detailed review of archival documents relating to building regulations and house plans. The study highlights the significance of local socio-material and cultural context in everyday household practices and resulting electricity demands. It reveals that understanding the longitudinal dynamics of practice-arrangements and their diversity in cross-cultural contexts can help identify and prevent normalisation of unsustainable configurations that gradually become embedded in social structures and practices. It shows how a shift from outdoor to indoor activities, transformation from inward- to outward-oriented design and a spatial dispersion of practices have resulted in increased household electricity consumption. It further highlights the implications of cross-cultural transfer of technology and demand response strategies that are bound by local socio-cultural and material dynamics in the performance, bundling and synchronisation of practices. The study makes the connections between “good” and “bad” housing and household practices visible and identifies various energy transitions needed in housing practices that, through interventions in house design, can lead to less energy intensive household practice-arrangements.Cambridge Commonwealth, European and International Trus