• Energy Research

This paper investigates three stochastic modelling procedures for generating N (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of HMM for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.

Integrating renewable energy technologies into a decentralised smart grid presents the ‘Duck Curve’ challenge — the disparity between peak demand and solar photovoltaic (PV) yield. Smart grid operators still lack an effective solution to this problem, resulting in the need to maintain standby fossil fuel-fired plants. The COVID-19 pandemic-induced lockdowns necessitated a shift to remote work (work-from-home) and home-based education. The primary objective of this study was to explore mitigating strategies for the duck curve challenge by investigating this notable shift in behaviour by examining the effect of remote work and education on grid and decentralised solar PV electricity use in 100 households with battery energy storage in the southwest of the UK. This study examined 1-min granular grid electricity and decentralised solar energy consumption data for April–August 2019 and 2020. The findings revealed statistically significant disparities in energy demand. Notably, there was a 1.4—10% decrease in average electricity consumption from April to August 2020 (during and following the lockdown) compared to the corresponding months of 2019. Furthermore, household grid electricity consumption was reduced by 24—25%, while self-consumption from solar PV systems increased by 7—8% during the lockdown in April and May 2020 compared to 2019. This increase in self-consumption was particularly prominent in the morning and afternoon, possibly attributed to the growing prevalence of work-from-home and home-based education. The dynamic shifts in energy consumption patterns emphasised the role of decentralised solar PV energy in meeting the evolving needs of households during unprecedented societal changes. Additionally, remote work might unlock decentralised solar PV's potential in resolving the ‘Duck Curve’, urging further investigation into the implications for energy infrastructure and policy development.

This paper examines the major challenges associated with evaluating energy demand in the residential building sector in an integrated energy system modelling environment. Three established modelling fields are examined to generate a framework for assessing the impact of energy policy: energy system models, building stock models and dynamic building simulation. A set of profound challenges emerge when attempting to integrate such models, due to distinct differences in their intended applications, operational scales, formulations and computational implementations. Detailed discussions are provided on the integration of temporally refined energy demand, based on thermodynamic processes and socio-technical effects which may stem from new policy. A detailed framework is discussed for generating aggregate residential demands, in terms of space heating demand, domestic hot water demand, and lighting, appliance and consumer electronics demand. The framework incorporates a pathway for interpreting the effects of changes in household behaviour resulting from prospective policy measures. When long-term planning exercises are carried out using this framework, the cyclic effects between behavioural change and policy implementation are also considered. This work focused specifically on the United Kingdom energy system, however parallels can be drawn with other countries, in particular those with a mature privatised system, dominated by space heating concerns.

Abstract The energy assessment of single buildings and of larger areas of built environment, although exhibiting similarities in terms of technique, have in the past often used different approaches to energy modelling. The growing availability of empirical data and the capability of building modelling software has, more recently, allowed these differences to be reduced. This paper demonstrates, across two very different case-studies in UK and India, that techniques for community energy modelling can be used in a way that maintains detail in energy demand characteristics, thus helping to bridge the gap between detailed building assessment and higher-level energy system modelling. However, understanding the portability of such techniques requires an understanding of energy characteristics that can be specific to a geographic area. This study documents these important differences and proposes a more transferrable approach to detailed community energy modelling.

Projected climate change is likely to have a significant impact on a range of energy systems. When a building is the centre of that system, a changing climate will affect the energy system in several ways. Firstly, the energy demand of the building will be altered. Taken across the entire building stock, and placed in context of technological and behavioural changes over the same timescale, this can have implications for important parameters such as peak demand and load factors of energy requirement. The performance of demand-side, distribution/transmission and supply-side technologies can also alter as a result of changing temperatures. With such uncertainty, a flexible approach is required for ensuring that this whole energy system is robust for a wide range of future scenarios. Therefore, building design must have a standardised and systematic approach for integrating climate change into the overall energy assessment of a building (or buildings), understanding the implications for the larger energy network. Based on the work of the Low Carbon Futures (LCF) and Adaptation and Resilience In Energy Systems (ARIES) projects, this paper overviews some of the risks that might be linked to a changing climate in relation to provision and use of energy in buildings. The UK is used as a case-study but the outputs are demonstrated to be of relevance, and the tools applicable, to other countries.

Abstract The Low Carbon Futures project, funded by the Adaptation and Resilience in a Changing Climate (ARCC) Programme, has the objective of using the latest UK climate projections (UKCP’09) to assess overheating in a range of domestic and non-domestic buildings. As these climate projections are probabilistic in nature, and dynamic building simulation is being used by the project to assess building performance, the information produced is vast. To understand how to filter this data into a useable tool that can interact with current building practices, the project has commissioned a range of focus groups to obtain practitioner feedback. These focus groups provide guidance on how buildings are currently designed with respect to overheating but also how future overheating risk assessments, incorporating probabilistic climate projections, might be carried out. This paper describes the assimilation of all this research into a coherent building simulation methodology that could be used by building practitioners to assess future overheating risks of a range of buildings, and provide guidance for applying adaptation solutions to prevent defined comfort thresholds being exceeded.

Abstract Climate change could substantially impact the performance of the buildings in providing thermal comfort to occupants. Recently launched UK climate projections (UKCP09), clearly indicate that all areas of the UK will get warmer in future with the possibility of more frequent and severe extreme events, such as heat waves. This study, as part of the Low Carbon Futures (LCF) Project, explores the consequent risk of overheating and the vulnerability of a building to extreme events. A simple statistical model proposed by the LCF project elsewhere has been employed to emulate the outputs of the dynamic building simulator (ESP-r) which cannot feasibly be used itself with thousands of available probabilistic climate database. Impact of climate change on the daily external and internal temperature profiles has been illustrated by means of 3D plots over the entire overheating period (May–October) and over 3000 equally probable future climates. Frequency of extreme heat events in changing climate and its impact on overheating issues for a virtual case study domestic house has been analyzed. Results are presented relative to a baseline climate (1961–1990) for three future timelines (2030s, 2050s, and 2080s) and three emission scenarios (Low, Medium, and High).

Abstract As our need for energy information of buildings evolves, and the tools and methods at our disposal increase in scale and complexity, it is perhaps reasonable to expect a similar level of change in the way energy in buildings is assessed within national energy compliance frameworks. By comparing the available opportunities for building energy modelling with the current methodologies underlying Energy Performance Certificates, this study proposes future directions for standardised energy assessment of residential buildings and the impact this could have on different facets of energy policy. In carrying out this exercise, a number of criteria are proposed that could be used to appraise methodologies that align with future requirements of energy assessment, with two potential candidates for future energy assessment considered as part of this appraisal. An argument is thus proposed for better aligning future forms of standardised energy assessment with directions and requirements of future low-carbon energy policy.