• Energy Research
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Abstract Localisation of energy technologies and policies is increasing the need for high-resolution spatial and temporal energy demand simulation modelling, which goes beyond annual and national scale. Increasing the temporal resolution is crucial for demand side management modelling or for the simulation of load profile changes due to the installation of new technologies such as heat pumps. Increasing the spatial resolution enables regional energy planning and capturing the spatial dynamics of drivers of energy demand. Yet regional and local energy grids are interconnected with national and continental networks, so to capture multi-scale effects, high resolution is required everywhere. A high-resolution bottom-up engineering energy demand simulation model is introduced, which projects energy demands both for a high spatial and high temporal scale and enables spatial explicit simulation of model parameters. The model is applied for exploring implications of the electrification of heat by a large-scale uptake of heat pumps for water and space heating in the United Kingdom and to simulate heat pump related demand side management opportunities. We simulate a change in peak electricity heating load of −0.4 t– 21.5 GW for 50% heat pump uptake for space heating demands across different scenarios resulting in an increase of total peak electricity demand of 3.3–31.2 GW (6.3–59.8%). The simulation results show considerable regional differences in change of electricity load factors (−17.2–8.4%) and peak electricity demands (−9.9–206.1%). The potential to reduce national electricity peak load with managed heat pump load profiles for heating is simulated to be 0.2–5.8 GW (0.4–11.1%). These results exemplify the importance of discussing heat-pump induced change in peak electricity demands within a scenario context. Including different drivers in energy demands and their variability considerably affects the scale of anticipated electricity peak demand.

Energy efficiency has long been considered a key component of an industrial company's competitive repertoire. However, despite the potential benefits of adopting so-called energy efficiency measures, their uptake in such companies remains low. In response, this study proposes a framework aimed at supporting key decision-makers in undertaking a thorough assessment of energy efficiency measures. This involves, on the one hand, providing a complete characterization of a general industrial energy efficiency measure and, on the other, identifying the multiple impacts stemming from its adoption based on a novel performance measurement system that encompasses sustainability features and is defined at the shop floor level. Once theoretically validated through literature, the framework is empirically tested with a heterogeneous sample of Italian companies. The preliminary results demonstrate the framework's ability to thoroughly assess energy efficiency measures, highlighting characteristics and impacts that are sometimes considered more critical than energy saving by industrial decision-makers and therefore able to guide the outcome of the adoption decision.

Abstract With renewable energy sources growing, solar power generation is becoming ever more popular around the world, so forecasting and scenario analysis of Solar Photovoltaic production is beneficial for grid operators and investors. In this paper, we introduce a novel combination of a Mycielski–Markov model, standard regime switching, and jump diffusion models to generate 1-minute Global Horizontal Irradiance time series over any time scale. It can simulate different scenarios of solar irradiance in the future after being trained on empirical data. We verify our model using statistical tests to compare our simulations with those from an observed time-series in Mauritius. The resulting model is able to generate simulations retaining the statistical properties of the data. Further, we find the proposed calibration process to be robust, and identified that splitting the day into 16 periods to be perfect balance to counter overfitting. The proposed model has the potential to better understand the effects of including large scale Solar Photovoltaic generation into an energy network, value future investments, or even allow for a cost–benefit analysis of subsidies.

Energy Service Companies (ESCOs) are key players in the provision of energy services, hence they are considered strategic means to foster energy efficiency across countries. Nonetheless, despite the undergoing radical trans-formation within the ESCOs industry -especially the increasing tendency to bundle services, recent research capable to capture such evolution and development, in terms of services, technologies, and markets targeted by ESCOs is scarce. The present study aims at fulfilling the outlined research gaps by developing a novel framework for the classification and characterisation of services offered by ESCOs, as well as technologies and markets addressed. Services are characterised into nine categories, namely: preliminary analysis; project assessment; project contracting; project financing; project technology management and project supervision; energy pro-curement, management of incentives and regulations, and other services. The developed framework is then preliminary tested by applying it to a sample of eight Italian ESCOs to assess its completeness and usefulness. Results show the capability of the framework to adequately detail and map clusters of services-technologies -markets addressed by ESCOs, also identifying areas for future business opportunities for ESCOs themselves. The study concludes with recommendations for industry and policymaking on future efforts to promote energy services, as well as future research in this area.

There is an urgent need for all industries to go beyond the existing set of policies against greenhouse gas (GHG) emissions to combat climate change. As the major producer (19%) of GHG, the construction industry needs to optimise its carbon footprint (CF) decision-making processes to provide better management solutions against global warming. Identification and evaluation of the key factors that affect CF decision-making are critical yet limited. Based on our knowledge, no existing study compares the different characteristics of construction projects’ CF management approaches through the whole life cycle phases (including assessment, optimisation, and comparison) to enable CF-related knowledge transfer between the building and infrastructure sectors. To meet these knowledge gaps, this paper reviewed 1,465 studies related to infrastructure and building CF projects and selected 31 studies to evaluate academic researchers’ contributions toward the different CF decision-making stages for the building and infrastructure projects’ whole lifecycle. The paper systematically reviewed and assessed the existing information in five key CF decision-making stages: project drafting stage, methodology choosing stage, data collection stage, CF analysis stage, and CF visualisation stage. A generalised CF decision-making framework is also developed to inform future CF management. The limitations of the existing studies and the benefits of the developed framework were summarised. This study identifies the key information required from the perspective of CF management decision-makers and contributes a universal CF decision-making framework for state-of-the-art CF management practice.