• Energy Research
• 2016-2025close
• 13. Climate action close
• 11. Sustainability close
• Imperial College London close

Abstract Post-combustion solvent-based carbon capture is a promising technology that potentially can offset the greenhouse gas emissions from fossil-driven power generation systems. The challenge is that CO2 absorption (similar to other CCS technologies) imposes energetic penalties, and constrains the operational flexibility. In this paper, we build upon our recent contributions in the field (Sharifzadeh et al., 2016; Sharifzadeh and Shah, 2016), and study the dynamic response of such process to the electricity load changes in the power plant. The key research question is to investigate if the steady-state integrated process design and control framework applied in the previous studies, can also ensure controllability under a wide range of disturbances. The present study considers the mutual interactions between the power plant and capture process. Other features of interest include the implications of key design and operational decisions such as reboiler temperature, solvent circulation flow rate, solvent concentration and the rate of power load change or CO2 setpoint tracking for flexible process operation. The results suggest that the capture process exhibits a high degree of flexibility and the integrated design and control framework could be the key enabler for the commercialization of post-combustion solvent-based carbon capture.

Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ~0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

Les développements des systèmes de transport, les changements dans les tendances de consommation et des conditions telles que la COVID-19 ont augmenté à la fois la demande et la charge du transport de marchandises. Étant donné que diverses entreprises transportent des marchandises dans le monde entier pour évaluer la durabilité, la vitesse et la résilience des systèmes de transport de marchandises, des systèmes de mesure de la fluidité des données et du fret sont nécessaires. Dans cette étude, un modèle de prise de décision intégré est proposé pour donner la priorité aux alternatives de mesure de la fluidité du fret. Le modèle proposé est composé de deux étapes principales. Dans la première étape, la méthodologie logarithmique des poids additifs (LMAW) basée sur les normes de Dombi est utilisée pour trouver les poids des critères. Dans la deuxième phase, une évaluation étendue basée sur la méthode de la distance de la solution moyenne (EDAS) avec Dombi onction pour l'agrégation est présentée pour déterminer les résultats finaux de classement des alternatives. Trois alternatives de mesure de la fluidité du fret sont proposées, à savoir ne rien faire, intégrer les activités de fret dans Metaverse pour mesurer la fluidité et former une gouvernance mondiale des activités de fret pour mesurer la fluidité à partir des données disponibles. Treize critères, qui sont regroupés sous quatre aspects principaux à savoir la technologie, la gouvernance, l'efficacité et la durabilité environnementale, et une étude de cas au cours de laquelle un cadre de base est formé pour que les experts évaluent les alternatives compte tenu des critères utilisés dans le processus décisionnel multicritères. Les résultats de l'étude indiquent que l'intégration des activités de fret dans Metaverse pour mesurer la fluidité est l'alternative la plus avantageuse, alors que ne rien faire est la moins avantageuse. Los desarrollos en los sistemas de transporte, los cambios en las tendencias de consumismo y condiciones como el COVID-19 han aumentado tanto la demanda como la carga en el transporte de mercancías. Dado que varias empresas transportan mercancías en todo el mundo para evaluar la sostenibilidad, la velocidad y la resistencia de los sistemas de transporte de mercancías, se necesitan sistemas de medición de datos y fluidez de la carga. En este estudio, se propone un modelo integrado de toma de decisiones para priorizar las alternativas de medición de la fluidez de la carga. El modelo propuesto se compone de dos etapas principales. En la primera etapa, se utiliza la Metodología Logarítmica de Pesos Aditivos (LMAW) basada en normas Dombi para encontrar los pesos de los criterios. En la segunda fase, se presenta una Evaluación extendida basada en el método de Distancia de la Solución Media (EDAS) con Dombi unción para la agregación para determinar los resultados finales de clasificación de las alternativas. Se proponen tres alternativas de medición de la fluidez de la carga, a saber, no hacer nada, integrar las actividades de carga en Metaverse para medir la fluidez y formar una gobernanza global de las actividades de carga para medir la fluidez a través de los datos disponibles. Trece criterios, que se agrupan en cuatro aspectos principales: tecnología, gobernanza, eficiencia y sostenibilidad ambiental, y un estudio de caso en el que se forma un marco básico para que los expertos evalúen las alternativas considerando que los criterios se utilizan en el proceso de toma de decisiones multicriterio. Los resultados del estudio indican que integrar las actividades de carga en Metaverso para medir la fluidez es la alternativa más ventajosa, mientras que no hacer nada es la menos ventajosa. Developments in transportation systems, changes in consumerism trends, and conditions such as COVID-19 have increased both the demand and the load on freight transportation. Since various companies are transporting goods all over the world to evaluate the sustainability, speed, and resiliency of freight transportation systems, data and freight fluidity measurement systems are needed. In this study, an integrated decision-making model is proposed to advantage prioritize the freight fluidity measurement alternatives. The proposed model is composed of two main stages. In the first stage, the Dombi norms based Logarithmic Methodology of Additive Weights (LMAW) is used to find the weights of criteria. In the second phase, an extended Evaluation based on the Distance from Average Solution (EDAS) method with Dombi unction for aggregation is presented to determine the final ranking results of alternatives. Three freight fluidity measurement alternatives are proposed, namely doing nothing, integrating freight activities into Metaverse for measuring fluidity, and forming global governance of freight activities for measuring fluidity through available data. Thirteen criteria, which are grouped under four main aspects namely technology, governance, efficiency, and environmental sustainability, and a case study at which a ground framework is formed for the experts to evaluate the alternatives considering the criteria are used in the multi-criteria decision-making process. The results of the study indicate that integrating freight activities into Metaverse for measuring fluidity is the most advantageous alternative, whereas doing nothing is the least advantageous one. أدت التطورات في أنظمة النقل، والتغيرات في اتجاهات النزعة الاستهلاكية، وظروف مثل COVID -19 إلى زيادة الطلب والحمل على نقل البضائع. نظرًا لأن العديد من الشركات تنقل البضائع في جميع أنحاء العالم لتقييم استدامة وسرعة ومرونة أنظمة نقل البضائع، فهناك حاجة إلى أنظمة قياس سيولة البيانات والشحن. في هذه الدراسة، يُقترح نموذج متكامل لصنع القرار للاستفادة من إعطاء الأولوية لبدائل قياس سيولة الشحن. يتكون النموذج المقترح من مرحلتين رئيسيتين. في المرحلة الأولى، يتم استخدام منهجية لوغاريتمية الأوزان المضافة (LMAW) القائمة على معايير دومبي لإيجاد أوزان المعايير. في المرحلة الثانية، يتم تقديم تقييم موسع يعتمد على طريقة المسافة من الحل المتوسط (EDAS) مع Dombi unction للتجميع لتحديد نتائج الترتيب النهائي للبدائل. يتم اقتراح ثلاثة بدائل لقياس سيولة الشحن، وهي عدم القيام بأي شيء، ودمج أنشطة الشحن في ميتافيرس لقياس السيولة، وتشكيل حوكمة عالمية لأنشطة الشحن لقياس السيولة من خلال البيانات المتاحة. ثلاثة عشر معيارًا، تم تجميعها تحت أربعة جوانب رئيسية وهي التكنولوجيا والحوكمة والكفاءة والاستدامة البيئية، ودراسة حالة يتم فيها تشكيل إطار أرضي للخبراء لتقييم البدائل مع مراعاة استخدام المعايير في عملية صنع القرار متعددة المعايير. تشير نتائج الدراسة إلى أن دمج أنشطة الشحن في ميتافيرس لقياس السيولة هو البديل الأكثر فائدة، في حين أن عدم القيام بأي شيء هو البديل الأقل فائدة.

Energy demand modelling has been widely applied in various contexts, including power plant generation, building energy simulation and demand-side management. However, it is still an ongoing research topic in terms of the choice of modelling method, feature engineering for data-driven methods, the application contexts and the type of data used. In the residential sector, survey-based and meter-based approaches are categorised according to the type of input data used, i.e. the activity records from the time use survey and energy consumption from meters respectively. These two paradigms are not necessarily easy to combine, which warrants the questions of when one may be preferred over the other and whether they need to be combined despite the significant data requirements. Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There is a lack of comparative research to guide researchers and practitioners in developing energy demand modelling capability, specifically as it pertains to these issues. This study analyses three groups of test scenarios in a multi-household residential context based in the UK. Six ML algorithms (LightGBM, Random forest, ANN, SVM, KNN and LSTM), with eight sets of various influential features, at four different historical time window widths and two train-test splits were compared. An appropriate methodology was designed to capture the temporal impact of activities on energy demand and represent the overlap and interaction of activities. The results show that the combination of meter-based and survey-based energy demand models performs better in terms of modelling accuracy and robustness against sudden load variation. Particularly, integrating energy tariffs, household and individual attributes, appliance usage and general activity features can improve the energy demand model. Among the ML algorithms, LightGBM and ANN perform better than other algorithms while LSTM and SVM may not be suitable in this multi-household short monitoring context.

Abstract Cost-effectively decarbonising the power sector and household energy use using variable renewable energy will require that electricity consumption becomes much more flexible and responsive to constraints in supply and the distribution network. In recent years residential demand response (DR) has received increasing attention that has sought to answer, based on current evidence, questions about how much consumers will engage with DR. This paper critically reviews the evidence base for residential consumer engagement with DR and draws out several important limitations in it. We argue for a more action-oriented focus on developing practical strategies to enable and unlock greater loadshifting and consumer engagement with DR within a changing technology and regulatory context. A number of recommendations are put forward for accelerating UK consumer engagement with DR, presented under three broad strategies: (a) promote awareness of smart tariffs, smart meters and storage and automation behind-the-meter devices as mutually-supportive components within a common ‘DR technology cluster’; (b) deliver targeted support for adoption of electric vehicles and other storage and automation technologies; (c) enable and support informed adoption of DR-enabling products and services through ‘smarter’ digital comparison tools (DCTs), data portability, and faster, simpler switching. The interdependency between components within this DR technology cluster delivers efficiency but also poses a risk that one delayed component (e.g., smart metering) will hold-up policy and industry support for other components. The urgency of decarbonisation goals makes it necessary to push forward as many of these elements as possible rather than the pace being set by the slowest.

In spite of the benefits from thermal energy storage (TES) integration in dwellings, the penetration rate in Europe is 5%. Effective fiscal policies are necessary to accelerate deployment. However, there is currently no direct support for TES in buildings compared to support for electricity storage. This could be due to lack of evidence to support incentivisation. In this study, a novel systematic framework is developed to provide a case in support of TES incentivisation. The model determines the costs, CO2 emissions, dispatch strategy and sizes of technologies, and TES for a domestic user under policy neutral and policy intensive scenarios. The model is applied to different building types in the UK. The model is applied to a case study for a detached dwelling in the UK (floor area of 122 m2), where heat demand is satisfied by a boiler and electricity imported from the grid. Results show that under a policy neutral scenario, integrating a micro-Combined Heat and Power (CHP) reduces the primary energy demand by 11%, CO2 emissions by 21%, but with a 16 year payback. Additional benefits from TES integration can pay for the investment within the first 9 years, reducing to 3.5–6 years when the CO2 levy is accounted for. Under a policy intensive scenario (for example considering the Feed in Tariff (FIT)), primary energy demand and CO2 emissions reduce by 17 and 33% respectively with a 5 year payback. In this case, the additional benefits for TES integration can pay for the investment in TES within the first 2 years. The framework developed is a useful tool is determining the role TES in decarbonising domestic energy systems.

With diminishing fossil fuel resources and increasing environmental concerns, large-scale deployment of Renewable Energy Sources (RES) has accelerated the transition towards clean energy systems, leading to significant RES generation share in power systems worldwide. Among different RES, solar PV is receiving major focus as it is most abundant in nature compared to others, complimented by falling prices of PV technology. However, variable, intermittent and non-synchronous nature of PV power generation technology introduces several technical challenges, ranging from short-term issues, such as low inertia, frequency stability, voltage stability and small signal stability, to long-term issues, such as unit commitment and scheduling issues. Therefore, such technical issues often limit the amount of non-synchronous instantaneous power that can be securely accommodated by a grid. In this backdrop, this research work proposes a tool to estimate maximum PV penetration level that a given power system can securely accommodate for a given unit commitment interval. The proposed tool will consider voltage and frequency while estimating maximum PV power penetration of a system. The tool will be useful to a system operator in assessing grid stability and security under a given generation mix, network topology and PV penetration level. Besides estimating maximum PV penetration, the proposed tool provides useful inputs to the system operator which will allow the operator to take necessary actions to handle high PV penetration in a secure and stable manner.