9 research outputs found

    The implementation of energy sharing using a system of systems approach

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    There is an increasing demand for renewable energy and consumers need more procurement options to meet their needs. Energy sharing provides a peer-to-peer (P2P) marketplace where prosumer electricity is redistributed to fellow energy-sharing community participants. This redistribution of prosumer electricity provides consumers with additional electricity suppliers, while also decreasing the load on the utility company. Though significant progress has been made regarding research and implementation of energy sharing, there is still room for growth when evaluating energy-sharing communities and defining appropriate community coordination based on end-user needs. The first contribution in this work identified nine characteristics of energy-sharing communities as a decentralized complex adaptive system of systems (DCASoS). Considering each characteristic before determining community coordination is vital to ensure ample participation within the energy-sharing community. The second contribution was the exploration of a two-stage stochastic programming model as an alternative to the classic energy distribution business model. The third contribution compares three behavioral theories to identify the best fitting model to predict interest in participating in an energysharing community. This research provides companies with foundational knowledge to develop an energy-sharing community that both fulfills end-user satisfaction and increases robustness of electricity distribution business models --Abstract, page iv

    PROFILING - CONCEPTS AND APPLICATIONS

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    Profiling is an approach to put a label or a set of labels on a subject, considering the characteristics of this subject. The New Oxford American Dictionary defines profiling as: “recording and analysis of a person’s psychological and behavioral characteristics, so as to assess or predict his/her capabilities in a certain sphere or to assist in identifying a particular subgroup of people”. This research extends this definition towards things demonstrating that many methods used for profiling of people may be applied for a different type of subjects, namely things. The goal of this research concerns proposing methods for discovery of profiles of users and things with application of Data Science methods. The profiles are utilized in vertical and 2 horizontal scenarios and concern such domains as smart grid and telecommunication (vertical scenarios), and support provided both for the needs of authorization and personalization (horizontal usage).:The thesis consists of eight chapters including an introduction and a summary. First chapter describes motivation for work that was carried out for the last 8 years together with discussion on its importance both for research and business practice. The motivation for this work is much broader and emerges also from business importance of profiling and personalization. The introduction summarizes major research directions, provides research questions, goals and supplementary objectives addressed in the thesis. Research methodology is also described, showing impact of methodological aspects on the work undertaken. Chapter 2 provides introduction to the notion of profiling. The definition of profiling is introduced. Here, also a relation of a user profile to an identity is discussed. The papers included in this chapter show not only how broadly a profile may be understood, but also how a profile may be constructed considering different data sources. Profiling methods are introduced in Chapter 3. This chapter refers to the notion of a profile developed using the BFI-44 personality test and outcomes of a survey related to color preferences of people with a specific personality. Moreover, insights into profiling of relations between people are provided, with a focus on quality of a relation emerging from contacts between two entities. Chapters from 4 to 7 present different scenarios that benefit from application of profiling methods. Chapter 4 starts with introducing the notion of a public utility company that in the thesis is discussed using examples from smart grid and telecommunication. Then, in chapter 4 follows a description of research results regarding profiling for the smart grid, focusing on a profile of a prosumer and forecasting demand and production of the electric energy in the smart grid what can be influenced e.g. by weather or profiles of appliances. Chapter 5 presents application of profiling techniques in the field of telecommunication. Besides presenting profiling methods based on telecommunication data, in particular on Call Detail Records, also scenarios and issues related to privacy and trust are addressed. Chapter 6 and Chapter 7 target at horizontal applications of profiling that may be of benefit for multiple domains. Chapter 6 concerns profiling for authentication using un-typical data sources such as Call Detail Records or data from a mobile phone describing the user behavior. Besides proposing methods, also limitations are discussed. In addition, as a side research effect a methodology for evaluation of authentication methods is proposed. Chapter 7 concerns personalization and consists of two diverse parts. Firstly, behavioral profiles to change interface and behavior of the system are proposed and applied. The performance of solutions personalizing content either locally or on the server is studied. Then, profiles of customers of shopping centers are created based on paths identified using Call Detail Records. The analysis demonstrates that the data that is collected for one purpose, may significantly influence other business scenarios. Chapter 8 summarizes the research results achieved by the author of this document. It presents contribution over state of the art as well as some insights into the future work planned

    Smart Grid Enabling Low Carbon Future Power Systems Towards Prosumers Era

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    In efforts to meet the targets of carbon emissions reduction in power systems, policy makers formulate measures for facilitating the integration of renewable energy sources and demand side carbon mitigation. Smart grid provides an opportunity for bidirectional communication among policy makers, generators and consumers. With the help of smart meters, increasing number of consumers is able to produce, store, and consume energy, giving them the new role of prosumers. This thesis aims to address how smart grid enables prosumers to be appropriately integrated into energy markets for decarbonising power systems. This thesis firstly proposes a Stackelberg game-theoretic model for dynamic negotiation of policy measures and determining optimal power profiles of generators and consumers in day-ahead market. Simulation results show that the proposed model is capable of saving electricity bills, reducing carbon emissions, and increasing the penetration of renewable energy sources. Secondly, a data-driven prosumer-centric energy scheduling tool is developed by using learning approaches to reduce computational complexity from model-based optimisation. This scheduling tool exploits convolutional neural networks to extract prosumption patterns, and uses scenarios to analyse possible variations of uncertainties caused by the intermittency of renewable energy sources and flexible demand. Case studies confirm that the proposed scheduling tool can accurately predict optimal scheduling decisions under various system scales and uncertain scenarios. Thirdly, a blockchain-based peer-to-peer trading framework is designed to trade energy and carbon allowance. The bidding/selling prices of individual prosumers can directly incentivise the reshaping of prosumption behaviours. Case studies demonstrate the execution of smart contract on the Ethereum blockchain and testify that the proposed trading framework outperforms the centralised trading and aggregator-based trading in terms of regional energy balance and reducing carbon emissions caused by long-distance transmissions

    Load forecast on a Micro Grid level through Machine Learning algorithms

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    As Micro Redes constituem um sector em crescimento da indústria energética, representando uma mudança de paradigma, desde as remotas centrais de geração até à produção mais localizada e distribuída. A capacidade de isolamento das principais redes elétricas e atuar de forma independente tornam as Micro Redes em sistemas resilientes, capazes de conduzir operações flexíveis em paralelo com a prestação de serviços que tornam a rede mais competitiva. Como tal, as Micro Redes fornecem energia limpa eficiente de baixo custo, aprimoram a coordenação dos ativos e melhoram a operação e estabilidade da rede regional de eletricidade, através da capacidade de resposta dinâmica aos recursos energéticos. Para isso, necessitam de uma coordenação de gestão inteligente que equilibre todas as tecnologias ao seu dispor. Daqui surge a necessidade de recorrer a modelos de previsão de carga e de produção robustos e de confiança, que interligam a alocação dos recursos da rede perante as necessidades emergentes. Sendo assim, foi desenvolvida a metodologia HALOFMI, que tem como principal objetivo a criação de um modelo de previsão de carga para 24 horas. A metodologia desenvolvida é constituída, numa primeira fase, por uma abordagem híbrida de multinível para a criação e escolha de atributos, que alimenta uma rede neuronal (Multi-Layer Perceptron) sujeita a um ajuste de híper-parâmetros. Posto isto, numa segunda fase são testados dois modos de aplicação e gestão de dados para a Micro Rede. A metodologia desenvolvida é aplicada em dois casos de estudo: o primeiro é composto por perfis de carga agregados correspondentes a dados de clientes em Baixa Tensão Normal e de Unidades de Produção e Autoconsumo (UPAC). Este caso de estudo apresenta-se como um perfil de carga elétrica regular e com contornos muito suaves. O segundo caso de estudo diz respeito a uma ilha turística e representa um perfil irregular de carga, com variações bruscas e difíceis de prever e apresenta um desafio maior em termos de previsão a 24-horas A partir dos resultados obtidos, é avaliado o impacto da integração de uma seleção recursiva inteligente de atributos, seguido por uma viabilização do processo de redução da dimensão de dados para o operador da Micro Rede, e por fim uma comparação de estimadores usados no modelo de previsão, através de medidores de erros na performance do algoritmo.Micro Grids constitute a growing sector of the energetic industry, representing a paradigm shift from the central power generation plans to a more distributed generation. The capacity to work isolated from the main electric grid make the MG resilient system, capable of conducting flexible operations while providing services that make the network more competitive. Additionally, Micro Grids supply clean and efficient low-cost energy, enhance the flexible assets coordination and improve the operation and stability of the of the local electric grid, through the capability of providing a dynamic response to the energetic resources. For that, it is required an intelligent coordination which balances all the available technologies. With this, rises the need to integrate accurate and robust load and production forecasting models into the MG management platform, thus allowing a more precise coordination of the flexible resource according to the emerging demand needs. For these reasons, the HALOFMI methodology was developed, which focus on the creation of a precise 24-hour load forecast model. This methodology includes firstly, a hybrid multi-level approach for the creation and selection of features. Then, these inputs are fed to a Neural Network (Multi-Layer Perceptron) with hyper-parameters tuning. In a second phase, two ways of data operation are compared and assessed, which results in the viability of the network operating with a reduced number of training days without compromising the model's performance. Such process is attained through a sliding window application. Furthermore, the developed methodology is applied in two case studies, both with 15-minute timesteps: the first one is composed by aggregated load profiles of Standard Low Voltage clients, including production and self-consumption units. This case study presents regular and very smooth load profile curves. The second case study concerns a touristic island and represents an irregular load curve with high granularity with abrupt variations. From the attained results, it is evaluated the impact of integrating a recursive intelligent feature selection routine, followed by an assessment on the sliding window application and at last, a comparison on the errors coming from different estimators for the model, through several well-defined performance metrics

    To repair or not to repair: an investigation of the factors influencing prosumer repair propensity

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    The Circular Economy is a sustainable system of production and consumption that aims to minimise waste, emissions and energy leakages through a closed system. Repair is one of the strategies to facilitate the transition towards the Circular Economy (CE). Repair is also a behaviour that the prosumer can adopt and enact in collaboration with other citizens in society. The inclination to repair, called explicitly in this thesis, ‘the prosumer’s propensity to repair’ is influenced and affected by a myriad of complex and inter-related elements. Those elements need to be further investigated to support the actors involved in the transition towards the CE to develop interventions to support the prosumer to repair further. The investigation was set in an extreme case scenario by choosing to engage with individuals who, by their affiliation with pro-environmental organisations, appear to be more inclined to engage with pro-environmental behaviours. The investigation focuses on them trying to repair small electrical items because there have been conflicting results in previous research, on the extent to which environmental concerns influence Prosumer Repair Propensity. The literature explores Repair as an activitiy to be embraced by prosumers and strategy to facilitate the transition towards a CE. The review discusses the role, and capacity of the prosumers in to both produce and consume value to support the transition. The literature explores the role of Design in developing solutions to extend product lifespan and to influence prosumer behaviour. The literature in the field of Design for Sustainable Behaviour emphasises the significance of investigating prosumer behaviour before developing interventions. As a result, the literature reviews past research on the factors influencing Prosumer Repair Propensity and behavioural frameworks that could support the investigation. The Theory of Trying and the Transtheoretical model of Change are selected as tools for understanding prosumer repair behaviour as a multi-dimensional object.Two studies are undertaken to meet the research objectives set out for the investigation. Two hundred and eight respondents completed a survey for the Survey study. For the Prosumer study, 10 participants completed a survey, a semi-structured interview and two video-elicitation exercises. The analysis and integration of the findings from the Survey study and Prosumer study helped to consolidate five sets of findings. The first set of findings presents the range of factors influencing Prosumer Repair Propensity; it includes the most significant factors to support the engagement with repair practice and the most critical factors to successfully repair an item. The second set of findings examines the attitudes towards trying to repair. The third set of findings presents the processes of change affecting the prosumer to repair. The fourth and fifth set of findings presents the repair process and considers how the factors and attitudes influence the prosumer at different stages of the repair process. From the findings, suggestions are given on the prosumer actions that can be adopted to support the transition towards the CE. The thesis finally closes on the limitations of the research project and recommends areas for further research to support the prosumer to change.</div

    Facilitating the Growth of Local Energy Communities

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    Energietransitie staat hoog op de agenda. Op de route naar een duurzamer energiesysteem zien we de laatste jaren een grote toename van het aantal lokale energie communities. Deze energie communities streven naar duurzaamheid door middel van het stimuleren van energiebesparingen en het realiseren van lokale duurzame energieopwekking, samen met en voor de deelnemende leden. Echter veel van deze prosumer communities missen de kennis en expertise om strategische beslissingen te kunnen nemen die noodzakelijk zijn om hun ambitieuze doelstellingen te behalen. In dit onderzoek, gebaseerd op een ontwerpgerichte onderzoeksmethodiek, zijn case studies uitgevoerd om het groeipad van prosumer communities te exploreren en te begrijpen. Dit leidde tot het ontwerp van een decision enhancement environment: de prosumer community shopping mall. Deze shopping mall biedt een rijke verzameling aan diensten, kennis en expertise. Prosumer communities, en hun leden, worden geleidelijk door de shopping mall gevoerd, waarbij gaandeweg die diensten worden aangeboden die behoren bij de ontwikkelingsfase waarin de communities verkeren. De dienstenaanbieders in de mall maken gebruik van richtlijnen en standaard bouwblokken om hun diensten samen te stellen, zodat de kwaliteit van dienstverlening en de interoperabiliteit van het dienstenaanbod in de mall worden gegarandeerd. Als regisseur en beheerder van genoemde shopping mall faciliteiten speelt de aanbieder van de mall een belangrijke rol.Meerdere prosumer communities en experts uit het energiewerkveld hebben de prosumer community shopping mall geëvalueerd. Zij concludeerden dat de shopping mall een bruikbaar en gebruiksvriendelijk concept is dat prosumer communities ondersteunt in hun groeipad

    Development of a community-based framework to manage prosumers in smart grid

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    This dissertation proposes frameworks to address key research challenges of managing prosumers in smart grid in the form of prosumer community groups (PCGs) namely PCG definition and characterization, new prosumer recruitment, multiple goals management and mutual goal definition and member assessment and ranking. The existing literature has very little to offer, by way of either reviewing the related concepts of PCGs or resolving the associated challenges, making our work novel within the research field

    Small-Scale Smart Grid Construction and Analysis

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    The smart grid (SG) is a commonly used catch-phrase in the energy industry yet there is no universally accepted definition. The objectives and most useful concepts have been investigated extensively in economic, environmental and engineering research by applying statistical knowledge and established theories to develop simulations without constructing physical models. In this study, a small-scale version (SSSG) is constructed to physically represent these ideas so they can be evaluated. Results of construction show data acquisition three times more expensive than the grid itself although mainly due to the incapability to downsize 70% of data acquisition costs to small-scale. Experimentation on the fully assembled grid exposes the limitations of low cost modified sine wave power, significant enough to recommend pure sine wave investment in future SSSG iterations. Findings can be projected to full-size SG at a ratio of 1:10, based on the appliance representing average US household peak daily load. However this exposes disproportionalities in the SSSG compared with previous SG investigations and recommended changes for future iterations are established to remedy this issue. Also discussed are other ideas investigated in the literature and their suitability for SSSG incorporation. It is highly recommended to develop a user-friendly bidirectional charger to more accurately represent vehicle-to-grid (V2G) infrastructure. Smart homes, BEV swap stations and pumped hydroelectric storage can also be researched on future iterations of the SSSG
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