Czekster R. M., Morisset C., Clarke J. A., Soudjani S., Patsios C., Davison P.
February 20, 2021
Cyber‐Physical Systems (CPS) and Internet‐of‐Things (IoT) plus energy are the enabling technology of modern power systems also known as the Smart Grid (SG). A SG may consist of thousands of interconnected components communicating and exchanging data across layers that stretch beyond technical capabilities, for instance, markets and customer interactions. Cyber‐physical security is a major source of concern due to the high reliance of the SG on Information and Communication Technologies (ICT) and their widespread use. Addressing security requires developing modelling and simulation tools that approximate and replicate adversarial behaviour in the SG. These tools have in fact two simulators, one handling continuous power flows and another for capturing the discrete behaviour when communicating across CPS or IoT components. The technique of composing two models of computation in a global simulation of these coupled systems is called co‐simulation. Although there are many frameworks and tools for co‐simulation, the set of features for modelling cyber‐physical security incidents in the SG lacks thorough understanding. We present a systematic review of features and tools for co‐simulating these concerns in CPS. We also highlight and discuss research gaps with respect to the most used tools in industry and academia and comment on their relevant features.
Steadman P., Evans S., Liddiard R., Godoy-Shimizu D., Ruyssevelt P., Humphrey D.
University College London
May 20, 2020
A brief history is provided of models of energy use in the UK building stock, with the focus on the non-domestic sector. This history leads to an account of the development, since 2009, of the 3DStock method for modelling complete building stocks, both domestic and non-domestic. The paper explains how 3DStock models are built and the data sources used. Special emphasis is placed on the relationship of premises (the floorspace occupied by organisations) to buildings. Energy use may be metered at the level of premises, buildings or groups of buildings. Representing the patterns in which premises relate to buildings is therefore crucial to the modelling process, and in particular to the precise measurement of energy intensities. Applications of 3DStock models in building science and policy tools are reviewed, including the London Building Stock Model (LBSM), delivered to the Greater London Authority (GLA) in 2020. This ‘digital twin’ can be used for the monitoring, simulation and analysis of the building stock. Implications for research and policy are discussed, particularly for energy epidemiology, density, high-rise buildings, retrofit potential, energy-use intensity and benchmarking. Data are in place to extend 3DStock modelling to the whole of England and Wales.
Evans S., Godoy-Shimizu D., Humphrey D., Steadman P., Ruyssevelt P., Liddiard R.
University College London
July 1, 2020
The London Building Stock Model, commissioned by the Greater London Authority (GLA) contains detailed data on every separate domestic and non-domestic building in Greater London. It includes three dimensional information about buildings including their heights, volumes, wall areas, floor areas and the distribution of activities between different floors. These data are drawn from University College London Energy Institute’s existing 3DStock model of London. Within the model information is attached on the ages of buildings, their materials of construction, and (in some cases) their servicing systems. Energy Performance Certificates (EPCs) and Display Energy Certificates (DECs) are also attached to premises and dwellings along with gas and electricity energy consumption.
Buildings in London are responsible for over 65% of the total carbon dioxide (CO2) emissions attributed to the Greater London region in 2016. Reducing CO2 emissions that can be attributed to buildings is the key focus of this work in line with the GLA’s ambition to dramatically reduce these overall CO2 emissions; aiming to make London a zero-carbon city by 2050.
Improving the energy performance of existing buildings has to be a key strategy if the overall CO2 emissions are to be reduced. Knowing the characteristics and current energy efficiency of the building stock is the first step towards reducing direct and indirect CO2 emissions from these buildings. Collecting the data is one challenge, but making sense of these huge quantities of data is a bigger challenge. Structuring the data can help here and so for this paper we present the evaluation of energy efficiency of buildings in London using urban density to aggregate the data. Energy efficiency is measured from both EPCs and energy consumption. Some of the existing measures that might influence current energy efficiency are then shown at different levels of urban density. Finally, in order to address the improvement of the energy efficiency we quantify the ‘potential’ improvements of these buildings (according to the EPC recommendations) and hence the suitability of different retrofit solutions, again aggregated by urban density. The results show that energy use intensity decreases as urban density increases; that urban density has some influence on existing efficiency measures and finally that most of the measures of retrofit potential change with increasing urban density.
With the recent removal of the solar PV feed-in tariff on ≤4 kWp arrays, installation of domestic rooftop PV has become significantly less commercially feasible in the UK. Whilst alternative trading schemes are beginning to emerge, the adequacy of their return on investment is questionable. However, the extent to which installation may need to be subsidised has not yet been quantified. Furthermore, the extent to which increased peer-to-peer trading could contribute to network constraints, and the environmental and economic costs of managing such schemes, have not been examined extensively. In this paper, the subsidy required to encourage a rooftop PV uptake rate of 0.5% homeowners/year is determined and the costs of constraint management are calculated for various network topologies. The problems presented are solved using time series analysis, power flow simulations, and optimisation processes with Monte Carlo methods. It was found that P2P trading is the most promising post subsidy revenue stream, reducing the required feed-in tariff from £0.14 to £0.10 for systems installed in 2020. Furthermore, it was found that with P2P trading, requirement for feed-in tariff support should end by 2032. Reconductoring was the most economically effective constraint management strategy, costing £50 k to £200 k less than curtailment when applied to 29 networks over 30 years. From an environmental perspective, it was found that reconductoring had a carbon footprint 2 orders of magnitude lower than curtailment & battery storage.
Ramallo-González A.P., Eames M.E., Natarajan S., Fosas-de-Pando D., Coley, D.A.
University of Bath
March 9, 2020
Alongside a mean global rise in temperature, climate change predictions point to an increase in heat waves and an associated rise in heat-related mortality. This suggests a growing need to ensure buildings are resilient to such events. Unfortunately, there is no agreed way of doing this, and no standard set of heatwaves for scientists or engineers to use. In addition, in all cases, heat waves are defined in terms of external conditions, yet, as the Paris heat wave of 2003 showed, people die in the industrialised world from the conditions inside buildings, not those outside. In this work, we reverse engineer external temperature time series from monitored conditions within a representative set of buildings during a heat wave. This generates a general probabilistic analytical relationship between internal and external heatwaves and thereby a standard set of events for testing resilience. These heat waves are by their simplicity ideal for discussions between clients and designers, or for the setting of national building codes. In addition, they provide a new framework for the declaration of a health emergency.
Globally, a primary concern is whether green office buildings perform as promised in terms of providing better indoor environment quality (IEQ) for employees, which may affect their satisfaction and work performance. In the Middle East, although there has been renewed interest in green building design, post occupancy evaluation of performance has never been conducted to-date, and evidence of actual occupant perception in green and non-green buildings is still ambiguous. Hence, we present the first study on IEQ performance in the Middle East. We show that Jordan can be taken as a representative example and systematically compare five “green” office buildings (representing 71% of all green-certified office buildings) against eight comparable conventional office buildings (CBs). Detailed bi-lingual survey data on perceived IEQ (n = 502) and work performance are accompanied by high-resolution continuous physical measurements of air temperature + relative humidity (n = 83) and CO2 concentrations (n = 21) with periodic measurements of mean radiant temperature and air speed, covering two typical summers and one typical winter. Results show both buildings types comply with design standards for indoor CO2 levels, while thermal comfort in green buildings is better than in CBs. However, CBs have a higher overall occupant satisfaction of IEQ. Work performance measured as absolute and relative absenteeism was slightly higher in CBs, with no significant differences in relative and absolute presenteeism between the two buildings types. These findings challenge the notion that green buildings improve occupant satisfaction and work performance over CBs and suggest the need for a better understanding of the performance-satisfaction gap.
The disparity between disciplinary approaches to bioinspired innovation has created a cultural divide that is stifling to the overall advancement of the approach for sustainable societies. This paper aims to advance the effectiveness of bioinspired innovation processes for positive benefits through interdisciplinary communication by exploring the epistemological assumptions in various fields that contribute to the discipline. We propose that there is a shift in epistemological assumptions within bioinspired innovation processes at the points where biological models derived from reductionist approaches are interpreted as socially-constructed design principles, which are then realized in practical settings wrought with complexity and multiplicity. This epistemological shift from one position to another frequently leaves practitioners with erroneous assumptions due to a naturalistic fallacy. Drawing on examples in biology, we provide three recommendations to improve the clarity of the dialogue amongst interdisciplinary teams. (1) The deliberate articulation of epistemological perspectives amongst team members. (2) The application of a gradient orientation towards sustainability instead of a dichotomous orientation. (3) Ongoing dialogue and further research to develop novel epistemological approaches towards the topic. Adopting these recommendations could further advance the effectiveness of bioinspired innovation processes to positively impact social and ecological systems.
Buildings contribute a significant portion of global greenhouse gas emissions and have the potential for large-scale impact reductions. Reducing the whole-life impacts of buildings is critical for creating a net-zero carbon built environment. For this to be achieved, the whole-life carbon impacts of design decisions must be considered during the building design process. A systematic review of academic literature was conducted to assess how life cycle assessment (LCA) is incorporated at various stages of the building design process, and what improvements are needed to support net-zero carbon design. The review compiled 274 papers that were published up to the end of 2019, of which 108 were subject to detailed review following screening. The review found that LCA is generally used late in the design process, when it is too late to greatly influence the design. Incorporating LCA with either building information modelling or life cycle costing is seen to have the same challenges as undertaking a traditional LCA. Parametric methods show promise for design development, but tools and algorithms require further verification and regionalisation to be implemented throughout industry. The use of benchmarks, target values and other pre-populated information can be used to incorporate life-cycle thinking without the need to undertake a detailed LCA. The review has demonstrated that LCA continues to face barriers, in both methods and practice, preventing its ability to guide early-stage design decisions and have a large impact on the environmental performance of buildings.
Fosas D., Nikolaidou E., Roberts M., Allen S., Walker I., Coley D.
University of Bath
December 7, 2020
In most industrialized countries, the buildings sector is the largest contributor to energy consumption and associated carbon emissions. These emissions can be reduced by a combination of energy efficiency and the use of building integrated renewables. Additionally, either singularly or as a group, buildings can provide energy network services by timing their use and production of energy. Such grid-aware or grid-responsive buildings have been termed Active Buildings. The recent UK Government investment of £36m in the Active Building Centre is a demonstration that such buildings are of considerable interest. One problem with the concept, however, is that there is no clear definition of Active Buildings, nor a building code to design or research against. Here we develop and test an initial novel code, called ABCode1. It is based on the need to encourage: (i) the minimisation of energy consumption; (ii) building-integrated generation; (iii) the provision of grid services; and (iv) the minimisation of embodied carbon. For grid services, we find that a lack of a precise, quantifiable measure, or definition, of such services means that for the time being, theoretical hours of autonomy of the building is the most reasonable proxy for these services within such a code.
The Smart Grid (SG) is a Cyber-Physical System (CPS) considered a critical infrastructure divided into cyber (software) and physical (hardware) counterparts that complement each other. It is responsible for timely power provision wrapped by Information and Communication Technologies (ICT) for handling bi-directional energy flows in electric power grids. Enacting control and performance over the massive infrastructure of the SG requires convenient analysis methods. Modelling and simulation (M&S) is a performance evaluation technique used to study virtually any system by testing designs and artificially creating 'what-if' scenarios for system reasoning and advanced analysis. M&S avoids stressing the actual physical infrastructure and systems in production by addressing the problem in a purely computational perspective. Present work compiles a non-exhaustive list of tools for M&S of interest when tackling SG capabilities. Our contribution is to delineate available options for modellers when considering power systems in combination with ICT. We also show the auxiliary tools and details of most relevant solutions pointing out major features and combinations over the years.
Arnaboldi L., Czekster R. M., Morisset C., Metere R.
November 10, 2020
Cyber-Physical Systems (CPS) are present in many settings addressing a myriad of purposes. Examples are Internet-of-Things (IoT) or sensing software embedded in appliances or even specialised meters that measure and respond to electricity demands in smart grids. Due to their pervasive nature, they are usually chosen as recipients for larger scope cyber-security attacks. Those promote system-wide disruptions and are directed towards one key aspect such as confidentiality, integrity, availability or a combination of those characteristics. Our paper focuses on a particular and distressing attack where coordinated malware infected IoT units are maliciously employed to synchronously turn on or off high-wattage appliances, affecting the grid's primary control management. Our model could be extended to larger (smart) grids, Active Buildings as well as similar infrastructures. Our approach models Coordinated Load-Changing Attacks (CLCA) also referred as GridLock or BlackIoT, against a theoretical power grid, containing various types of power plants. It employs Continuous-Time Markov Chains where elements such as Power Plants and Botnets are modelled under normal or attack situations to evaluate the effect of CLCA in power reliant infrastructures. We showcase our modelling approach in the scenario of a power supplier (e.g. power plant) being targeted by a botnet. We demonstrate how our modelling approach can quantify the impact of a botnet attack and be abstracted for any CPS system involving power load management in a smart grid. Our results show that by prioritising the type of power-plants, the impact of the attack may change: in particular, we find the most impacting attack times and show how different strategies impact their success. We also find the best power generator to use depending on the current demand and strength of attack.
O’Dwyer E., Pan I., Charlesworth R., Butler S., Shah N.
Imperial College London
July 18, 2020
As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience objectives. In this paper, an energy management tool is presented that can offer optimal control, scheduling, forecasting and coordination services to energy assets across a district, enabling optimal decisions under user-defined objectives. The tool presented here can coordinate different sub-systems in a district to avoid the violation of high-level system constraints and is designed in a generic fashion to enable transferable use across different energy sectors. The work demonstrates the potential for a single open-source optimisation framework to be applied across multiple energy vectors, providing local government the opportunity to manage different assets in a coordinated fashion. This is shown through case studies that integrate low-carbon communal heating for social housing with electric vehicle charge-point management to achieve high-level system constraints and local government objectives in the borough of Greenwich, London. The paper illustrates the theoretical methodology, the software architecture and the digital twin-based testing environment underpinning the proposed approach.
In this study, the thermal performance of latent heat thermal energy storage system (LHTESS) prototype to be used in a range of thermal systems (e.g., solar water heating systems, space heating/domestic hot water applications) is designed, fabricated, and experimentally investigated. The thermal store comprised a novel horizontally oriented multitube heat exchanger in a rectangular tank (forming the shell) filled with 37.8 kg of phase change material (PCM) RT62HC with water as the working fluid. The assessment of thermal performance during charging (melting) and discharging (solidification) was conducted under controlled several operational conditions comprising the heat transfer fluid (HTF) volume flow rates and inlet temperatures. The experimental investigations reported are focused on evaluating the transient PCM average temperature distribution at different heights within the storage unit, charging/discharging time, instantaneous transient charging/discharging power, and the total cumulative thermal energy stored/released. From the experimental results, it is noticed that both melting/solidification time significantly decreased with increase HTF volume flow rate and that changing the HTF inlet temperature shows large impacts on charging time compared to changing the HTF volume flow rate. During the discharging process, the maximum power output was initially 4.48 kW for HTF volume flow rate of 1.7 L/min, decreasing to 1.0 kW after 52.3 min with 2.67 kWh of heat delivered. Based on application heat demand characteristics, required power levels and heat demand can be fulfilled by employing several stores in parallel or series.
Rodrigues L., Gillott M., Waldron J., Cameron L., Tubelo R., Shipman R., Ebbs N., Bradshaw-Smith C.
University of Nottingham
October 1, 2020
‘Community Energy’ refers to people working together to reduce and manage energy use and increase and support local energy generation. It has the potential to support the infrastructural, social and cultural changes needed to reduce the impact of climate change and increase energy security. The core part of community energy initiatives is people; therefore, successful engagement strategies are essential. SCENe (Sustainable Community Energy Networks) was a research and development project focused on community energy application in a real-world setting involving in its first phase 44 new homes built along the banks of Nottingham’s River Trent (UK). The project team adopted a variety of established and innovative engagement strategies including website and social media channels, an online platform, a physical community energy hub where meetings and workshops were held and an interactive virtual energy model could be accessed, and in-home smart voice-controlled and visual technologies. The influence of the project and the effectiveness of the engagement tools to generate behaviour changes were investigated through a survey, workshops and interviews. The results suggest that engagement with SCENe increased awareness of energy issues and supported wider participation in community initiatives.
Rodrigues L., Tubelo R., Pasos A., Gonçalves J. C. S., Wood C., Gillott M.
University of Nottingham
September 14, 2020
Airtightness refers to the amount of air leakage through a building’s envelope. This uncontrolled exchange of air between inside and outside, either infiltration or exfiltration, may lead to thermal discomfort. Nevertheless, little or no attention has been given to airtightness in some countries including Brazil. In Brazil, a range of different strategies are suitable to achieve thermal comfort depending on the several climatic regions. In those regions where winter conditions are noticeable, such as in São Paulo, airtightness is a key parameter, but it has been historically overlooked. In this work, the authors deployed the innovative Pulse test methodology to determine airtightness levels for the first time in Brazil, in the city of São Paulo. Three representative multifamily residential buildings dating from the 1970s, 1980s and 2000s were measured, and the results’ values widely ranged from 1 to 5.7 h−1, at 4 Pa. Next, dynamic building simulations were conducted using measured and representative airtightness values (converted to infiltration) to understand the contribution of this variable on the thermal comfort. The results suggested that up to 9% improvement in the thermal comfort levels could be achieved by adopting 1 h−1 as maximum infiltration, and up to 14% by adopting 0.5 h−1.
The measures to control the spread of COVID-19 are unparalleled, and this is already having an effect on Britain’s energy system. There have been massive short-term changes in the past: for instance the temporary imposition of a three-day week in the 1970s may have had an even greater overall effect, but this was due to industrial action in the coal sector affecting the supply of energy. This time, the disruption is on the demand side – the energy is still available, but the demand for it has reduced.
In 2010, Great Britain generated 75% of its electricity from coal and natural gas. But by the end of the decade*, these fossil fuels accounted for just 40%, with coal generation collapsing from the decade’s peak of 41% in 2012 to under 2% in 2019.
Data collection is a fundamental component in the study of energy and buildings. Errors and inconsistencies in the data collected from test environment can negatively influence the energy consumption modelling of a building and other control and management applications. This paper addresses the gap in the current study of missing data treatment. It presents a comparative study of eight methods for imputing missing values in building sensor data. The data set used in this study, are real data collected from our test bed, which is a living lab in the Newcastle University. When the data imputation process is completed, we used Mean Absolute Error, and Root Mean Squared Error methods to evaluate the difference between the imputed values and real values. In order to achieve more accurate and robust results, this process has been repeated 1000, and the average of 1000 simulation is demonstrated in this paper. Finally, it is concluded that it is necessary to identify the percentage of missing data before selecting the proper imputation method, in order to achieve the best result.
Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.