Participatory Data Analysis: A New Method for Investigating Human Energy Practices

This paper presents a novel data-driven method to investigate the interdependence between technology design and human energy practices. The method – called Participatory Data – makes use of fine-grained energy data collected via smart meters and smart plugs, and behaviour visualisation during home visits to spark self-reflection among householders

Interactive Demand Shifting: in the Context of Emerging Energy Technologies

We are entering a key time period for home energy. While ubiquitous computing takes an increasing space in our daily life, emerging energy technologies including local generation and electric vehicle are leaving the stage of pioneer's users to become more common. This research highlights the potential of new energy behavior supported by digital tools in the context of domestic solar electricity generation

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Using Participatory Data Analysis to Understand Social Constraints and Opportunities of Electricity Demand-Shifting

International audienceThis paper reports results from an empirical study that explored the potential of interactive electricity Demand-Shifting – a particular form of behavior change where electricity consumption is shifted towards times of the day when production is at its highest – in the context of residential solar electricity generation. We conducted an in-the-wild user study with eighteen households over a period of six months, combining electricity data from smart meters and smart plugs with in-depth interviews to explore laundry routines and washing machine usage for each household. The study highlights that, although washing machines can be considered to be "shiftable appliances", the specific ways in which each household goes about shifting varies considerably. Furthermore, there is a clear need for ICTs to support people by analyzing current context and future plans to automate electricity demand-shifting and synchronization of both laundry and other appliances. Such support will help them maximize the self-consumption of micro-generated electricity while balancing this with environmental concerns, financial benefits and day-to-day issues relating to convenience. Index Terms— Microgeneration, demand-shifting, in-home study, energy management

Interactive Demand Shifting in the context of Domestic Micro-Generation

The combination of ubiquitous computing and emerging energy technologies is radically changing the home energy landscape. Domestic micro-generation, dominated by solar photovoltaic, is increasing at a rapid pace. This represents an opportunity for creating and altering energy behaviours. However, these transformations generate new challenges that we call the domestic energy gap: domestic electricity consumption and microgeneration are out of sync. Micro-generation is mainly uncontrollable production relying on weather while domestic energy consumption tends to happen mostly during the evening. This thesis focuses on understanding and supporting new domestic practices in the context of domestic solar electricity generation, looking at ‘Demand-Shifting’. Specifically, we look at how can digital tools leverage Demand-Shifting practices in the context of domestic micro-generation? Relying on a mixed-method approach, we provide a qualitative and quantitative answer with the collaboration of 38 participating households in several field studies including two spanning more than eight months. Through a deep investigation of laundry and electric mobility routines in the context of domestic micro-generation, we emphasised a natural engagement into Demand-Shifting which appeared as a complex and time-consuming task for participants which was not visible when we analysed their quantitative data. We revealed this complexity through Participatory Data Analyses, a method we designed to analyse the data in collaboration with the participating householders. This provided us with a comprehensive view of the relationship between domestic micro-generation and daily routines. Finally, we highlight the need for timely and contextual support through the deployment of interventions in-the-wild. Building on discussions of our findings in perspective of the literature, we propose a conceptual framework to support domestic interactive Demand-Shifting

Effective demand response gathering and deployment in smart grids for intensive renewable integration using aggregation and machine learning

Tesis por compendio de publicaciones.[EN] Distributed generation, namely renewables-based technologies, have emerged as a crucial component in the transition to mitigate the effects of climate change, providing a decentralized approach to electricity production. However, the volatile behavior of distributed generation has created new challenges in maintaining system balance and reliability. In this context, the demand response concept and corresponding programs arise giving the local energy communities prominence. In demand response concept, it is expected an empowerment of the consumer in the electricity sector. This has a significant impact on grid operations and brings complex interactions due to the volatile behavior, privacy concerns, and lack of consumer knowledge in the energy market context. For this, aggregators play a crucial role addressing these challenges. It is crucial to develop tools that allow the aggregators helping consumers to make informed decisions, maximize the benefits of their flexibility resources, and contribute to the overall success of grid operations. This thesis, through innovative solutions and resorting to artificial intelligence models, addresses the integration of renewables, promoting fair participation among all demand response providers. The thesis ultimately results in an innovative decision support system - MAESTRO, the Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO is composed by a set of diversified models that together contribute for handling the complexity of managing energy communities with distributed generation resources, demand response providers, energy storage systems and electric vehicles. This PhD thesis comprises a comprehensive analysis of state-of-the-art techniques, system design and development, experimental results, and key findings. In this research were published twenty-six scientific papers, in both international journals and conference proceedings. Contributions to international projects and Portuguese projects was accomplished. [ES] La generación distribuida, en particular las tecnologías basadas en energías renovables, se ha convertido en un componente crucial en la transición para mitigar los efectos del cambio climático, al proporcionar un enfoque descentralizado para la producción de electricidad. Sin embargo, el comportamiento volátil de la generación distribuida ha generado nuevos desafíos para mantener el equilibrio y la confiabilidad del sistema. En este contexto, surge el concepto de respuesta de la demanda y los programas correspondientes, otorgando prominencia a las comunidades energéticas locales. En el concepto de "respuesta a la demanda" (DR por sus siglas en inglés), se espera un empoderamiento del consumidor en el sector eléctrico. Esto tiene un impacto significativo en la operación de la red y genera interacciones complejas debido al comportamiento volátil, las preocupaciones de privacidad y la falta de conocimiento del consumidor en el contexto del mercado energético. Para esto, los agregadores desempeñan un papel crucial al abordar estos desafíos. Es fundamental desarrollar herramientas que permitan a los agregadores ayudar a los consumidores a tomar decisiones informadas, maximizar los beneficios de sus recursos de flexibilidad y contribuir al éxito general de las operaciones de la red. Esta tesis, a través de soluciones innovadoras y utilizando modelos de inteligencia artificial, aborda la integración de energías renovables, promoviendo una participación justa entre todos los proveedores de respuesta de la demanda. La tesis resulta en última instancia en un sistema de apoyo a la toma de decisiones innovador: MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. MAESTRO está compuesto por un conjunto de modelos diversificados que contribuyen juntos para manejar la complejidad de la gestión de comunidades energéticas con recursos de generación distribuida, proveedores de respuesta de la demanda, sistemas de almacenamiento de energía y vehículos eléctricos. Esta tesis de doctorado comprende un análisis exhaustivo de las técnicas de vanguardia, el diseño y desarrollo del sistema, los resultados experimentales y los hallazgos clave. En esta investigación se publicaron veintiséis artículos científicos, tanto en revistas internacionales como en actas de conferencias. Se lograron contribuciones a proyectos internacionales y proyectos portugueses. [POR] A produção distribuída, nomeadamente as tecnologias baseadas em energias renováveis, emergiram como um componente crucial na transição para mitigar os efeitos das alterações climáticas, proporcionando uma abordagem descentralizada à produção de eletricidade. No entanto, o comportamento volátil da geração distribuída criou desafios na manutenção do equilíbrio e da fiabilidade do sistema. Nesse contexto, surge o conceito de resposta à procura e os programas correspondentes, conferindo proeminência às comunidades energéticas locais. No conceito de resposta à procura, espera-se um empoderamento do consumidor no setor elétrico. Isso tem um impacto significativo nas operações da rede e gera interações complexas devido ao comportamento volátil, preocupações com a privacidade e falta de conhecimento dos consumidores no contexto do mercado energético. Para isso, os agregadores desempenham um papel crucial ao lidar com esses desafios. É fundamental desenvolver ferramentas que permitam aos agregadores ajudar os consumidores a tomar decisões informadas, maximizar os benefícios de seus recursos de flexibilidade e contribuir para o sucesso global das operações da rede. Esta tese de doutoramento, através de soluções inovadoras e recorrendo a modelos de inteligência artificial, aborda a integração de energias renováveis, promovendo uma participação justa entre todos os fornecedores de resposta à procura. A tese resulta, em última instância, num sistema inovador de apoio à tomada de decisões - MAESTRO, Machine learning Assisted Energy System management Tool for Renewable integration using demand respOnse. A ferramenta MAESTRO é composta por um conjunto de modelos diversificados que, em conjunto, contribuem para lidar com a complexidade da gestão de comunidades energéticas com recursos de geração distribuída, fornecedores de resposta à procura, sistemas de armazenamento de energia e veículos elétricos. Esta tese de doutoramento abrange uma análise abrangente de técnicas de ponta, design e desenvolvimento do sistema, resultados experimentais e descobertas-chave. Nesta pesquisa, foram publicados vinte e seis artigos científicos, tanto em revistas internacionais como em atas de conferências. Foram realizadas contribuições para projetos internacionais e projetos portugueses