State-of-the-art scenario exercises in the energy and environment fields argue for combining qualitative storylines with quantitative modelling. This paper proposes an approach for linking a highly detailed storyline with multiple, diverse models. This approach is illustrated through a cross-scale study of the UK power system transition until 2050. The storyline, called Central Co-ordination, is linked with insights from six power system models and two appraisal techniques. First, the storyline is ‘translated’ into harmonised assumptions on power system targets for the models. Then, a new concept called the landscape of models is introduced. This landscape helps to map the key fields of expertise of individual models, including their temporal, spatial and disciplinary foci. The storyline is then assessed based on the cross-scale modelling results. While the storyline is important for transmitting information about governance and the choices of key actors, many targets aspired in it are inconsistent with modelling results. The storyline overestimates demand reduction levels, uptake of marine renewables and irreplaceability of carbon capture and storage. It underestimates the supply–demand balancing challenge, the need for back-up capacity and the role of nuclear power and interconnectors with Europe. Thus, iteratively linking storylines and models is key

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/) This is an extended and updated version of a paper originally presented\ud at the 10th Conference on Sustainable Development of Energy, Water\ud and Environment Systems (SDEWES 2015) held in Dubrovnik, Croatia over\ud the period 27 September – 2 October 2015 (denoted then as paper\ud SDEWES2015.00656)

Generation-integrated energy storage (GIES) systems store energy at some point along the transformation between the primary energy form and electricity. Instances exist already in natural hydro power, biomass generation, wave power, and concentrated solar power. GIES systems have been proposed for wind, nuclear power and they arise naturally in photocatalysis systems that are in development. GIES systems can compare very favourably in both performance and total cost against equivalent non-integrated systems comprising both generation and storage. Despite this, they have not hitherto been recognised as a discrete class of systems. Consequently policy decisions affecting development or demonstration projects and policy approaches concerning low-carbon generation are not fully informed. This paper highlights that policy structures exist militating against the development and introduction of GIES systems-probably to the detriment of overall system good

Energy Management Systems involve monitoring of loads, control, and providing recommendations to reduce demand or energy costs. Energy Disaggregation works on monitoring loads non-intrusively, by having a single smart meter at the entry point to perform the task with machine learning techniques. Training of the machine learning model is an important step and may require historical sub-metered data of the appliances. In this paper, an Energy Demand model is used to generate the training data and alleviate the need for historical data of the appliance. The model is optimized for the Indian scenario based on the representation of appliances and active occupancy. The other important contribution of the present work is the use of Internet of Things (IoT) Devices to feed observable states to the Disaggregation model to improve efficiency. A selectively enabled Factorial Hidden Markov Model is utilized in which states of IoT control relays are presented to the model. The platform developed, enables both monitoring and control of appliances and provides insight into the overall user energy consumption and its breakdown at the appliance level.