A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector

Stavrakas, Vassilis; Flamos, Alexandros

doi:10.1016/j.enconman.2019.112339

Cited by 104 publications

(29 citation statements)

References 83 publications

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“…In a highly electrified economy with high shares of variable solar and wind electricity systems, reducing systemic mismatches between the generation and energy demand assets in an efficient manner requires investments in smart energy management systems. Energy management systems consist of two main categories: (a) supply-side devices from the electric utility-side used to manage the fluctuation of the load demand such as substations, and (b) the demand-side management devices used to manage energy consumption and meet the available power from the generation side [25][26][27][28]. Substations encompass transformers, switchgear, and protection, control and automation systems, and connect parts of the electric grid that operate at different voltage levels and managing these multidirectional power flows while ensuring reliability and security is critical as the share of decentralized and renewable energy increases.…”

Section: Leveraging Digitalization and Business Model Innovations Formentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

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The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

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Section: Leveraging Digitalization and Business Model Innovations Formentioning

confidence: 99%

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Nyangon¹

2021

Sustainable Energy Investment - Technical, Market and Policy Innovations to Address Risk

View full text Add to dashboard Cite

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“…Furthermore, the transitions research agenda should acknowledge the need to explore game-changing business models and novel regulatory frameworks that can monetise and maximise the value of technological capability so as to engage citizens and incentivise changes at the household level. In this respect, modelling frameworks can help explore the benefits of different technological configurations towards energy autonomy [82] , as well as the ways in which envisaged innovations can be adopted by and diffused into households of different profiles [83] . From a modelling perspective, research can also delve into policy instruments and market models that target low-carbon investment or purchasing decisions [84] based on consumer preferences, explore policies that go beyond market models [85] , as well as simulate real-life behaviour and modal shifts [86] , [87] .…”

Section: Disaggregated Understanding Of the Diffusion Of Social Innovmentioning

confidence: 99%

The desirability of transitions in demand: Incorporating behavioural and societal transformations into energy modelling

Νίκας

Lieu

Şorman

et al. 2020

Energy Research & Social Science

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Quantitative systems modelling in support of climate policy has tended to focus more on the supply side in assessing interactions among technology, economy, environment, policy and society. By contrast, the demand side is usually underrepresented, often emphasising technological options for energy efficiency improvements. In this perspective, we argue that scientific support to climate action is not only about exploring capacity of “what”, in terms of policy and outcome, but also about assessing feasibility and desirability, in terms of “when”, “where” and especially for “whom”. Without the necessary behavioural and societal transformations, the world faces an inadequate response to the climate crisis challenge. This could result from poor uptake of low-carbon technologies, continued high-carbon intensive lifestyles, or economy-wide rebound effects. For this reason, we propose a framing for a holistic and transdisciplinary perspective on the role of human choices and behaviours in influencing the low-carbon transition, starting from the desires of individuals and communities, and analysing how these interact with the energy and economic landscape, leading to systemic change at the macro-level. In making a case for a political ecology agenda, we expand our scope, from comprehending the role of societal acceptance and uptake of end-use technologies, to co-developing knowledge with citizens from non-mainstream and marginalised communities, and to defining the modelling requirements to assess the decarbonisation potential of shifting lifestyle patterns in climate change and action.

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“…Both, energy and fund savings could be reached with more efficient time management, which is a valuable feature in our present economic system. Different control options are possible within the smart home concept and enable an efficient integration of renewable energy technologies in homes ( Stavrakas and Flamos, 2020 ), and their efficient balancing (efficient supply and demand).…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Nižetić

Šolić

López-de-Ipiña

et al. 2020

Journal of Cleaner Production

576

165

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The rapid development and implementation of smart and IoT (Internet of Things) based technologies have allowed for various possibilities in technological advancements for different aspects of life. The main goal of IoT technologies is to simplify processes in different fields, to ensure a better efficiency of systems (technologies or specific processes) and finally to improve life quality. Sustainability has become a key issue for population where the dynamic development of IoT technologies is bringing different useful benefits, but this fast development must be carefully monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and ensure the smart utilization of limited global resources. Significant research efforts are needed in the previous sense to carefully investigate the pros and cons of IoT technologies. This review editorial is partially directed on the research contributions presented at the 4th International Conference on Smart and Sustainable Technologies held in Split and Bol, Croatia, in 2019 (SpliTech 2019) as well as on recent findings from literature. The SpliTech2019 conference was a valuable event that successfully linked different engineering professions, industrial experts and finally researchers from academia. The focus of the conference was directed towards key conference tracks such as Smart City, Energy/Environment, e-Health and Engineering Modelling. The research presented and discussed at the SpliTech2019 conference helped to understand the complex and intertwined effects of IoT technologies on societies and their potential effects on sustainability in general. Various application areas of IoT technologies were discussed as well as the progress made. Four main topical areas were discussed in the herein editorial, i.e. latest advancements in the further fields: (i) IoT technologies in Sustainable Energy and Environment, (ii) IoT enabled Smart City, (iii) E-health – Ambient assisted living systems (iv) IoT technologies in Transportation and Low Carbon Products. The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.

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A modular high-resolution demand-side management model to quantify benefits of demand-flexibility in the residential sector

Cited by 104 publications

References 83 publications

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

The desirability of transitions in demand: Incorporating behavioural and societal transformations into energy modelling

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

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