Results: Searching for 'Energy Models'

This project aims to improve the understanding of the opportunity for and implications of moving to more integrated multi vector energy networks in the future. Future energy systems could use infrastructure very differently to how they are employed today. Several individual energy vectors - electricity, gas and hydrogen - are capable of delivering multiple services and there are other services that can be met or delivered by more than one vector or network

This Deliverable is the report summarising the work completed in Work Package 5 and provides the following:

1. A summary of economic modelling findings
2. Identification of key technical, commercial and regulatory barriers across Case Studies
3. Classification of the barriers identified, based on:
   • Impact – the scale of potential system benefit of multi vector operation
   • Risk – the extent to which these barriers are surmountable
4. Discusses the innovations that might mitigate these barriers, comprising the necessary technical capabilities, and the required regulatory and commercial frameworks.
5. Assesses the additional work for multi vector operation to achieve commercialisation at scale, comprising:
   • Investment
   • Timescales and necessary uptake rates
   • Skills gaps, required operational transformation and strategic considerations

This project aims to improve the understanding of the opportunity for and implications of moving to more integrated multi vector energy networks in the future. Future energy systems could use infrastructure very differently to how they are employed today. Several individual energy vectors - electricity, gas and hydrogen - are capable of delivering multiple services and there are other services that can be met or delivered by more than one vector or network

This study considers how greater integration between energy vectors, principally electricity, gas, heat networks and hydrogen, could lead to a more flexible and resilient energy system in the future that isable to deliver carbon reduction objectives in a more cost-effective manner. Using a Case Study approach and considering a range of over-arching energy system evolutionary pathways, the study aims to identify circumstances where a multi vector approach to energy system development and operation will lead to a better outcome than evolution of today’s largely independently operated energy networks. The study provides insights into identification of the system conditions and geographies that create opportunities for multi vector systems and the timescales over which these systems are relevant. These early insights will help to plan investment in key infrastructure that will be in place for the long term.

This report presents the analysis of each of the Case Studies:-

• Case 1: Retention of the gas network to meet peak heating loads in a future where heat decarbonisation is achieved by high electrification
• Case 2: Gas-fired Combined Heat and Power (CHP) and electric heat pumps supplying heat networks
• Case 3: Plug-in hybrid electric vehicles switching between electric and liquid fuel running modes at times of tight electricity supply margins
• Case 4: Renewable Energy Sources (RES) electricity generation to gas (hydrogen or methane) for injection into the gas system
• Case 5: Grid electrolysis to produce hydrogen for a hydrogen distribution system
• Case 6: Renewable Electricity Connection Constraint Mitigated by Domestic Thermal Demand
• Case 7: EfW Flexing Between Producing Electricity and Gas for Grid Injection

In each case, detailed simulation of the proposed multi vector and counterfactual single vector energy system configurations has been undertaken. The technical simulations inform an analysis of the resource costs associated with the multi vector solution compared to the single vector counterfactual, in order to identify cases where the multi vector solution delivers a benefit. Alongside the analysis of economic benefits, the key engineering and operational challenges associated with the multi vector configuration are introduced. The work on engineering challenges and barriers, and a consideration of potential opportunities for innovation to overcome these barriers will continue in Work Package 5 of the study.

This project aims to improve the understanding of the opportunity for and implications of moving to more integrated multi vector energy networks in the future. Future energy systems could use infrastructure very differently to how they are employed today. Several individual energy vectors - electricity, gas and hydrogen - are capable of delivering multiple services and there are other services that can be met or delivered by more than one vector or network

This Deliverable is the report summarising the work during the project overall. This study considers how greater integration between energy vectors, principally electricity, gas, heat networks and hydrogen, could lead to a more flexible and resilient energy system in the future, that is able to deliver carbon reduction objectives in a more cost-effective manner. Using a Case Study approach and considering a range of over-arching energy system evolutionary pathways, the study aims to identify circumstances where a multi vector approach to energy system development and operation will lead to a better outcome than continuation of today’s largely independently operated energy networks. The study provides insights into identification of the system conditions and geographies that create opportunities for multi vector systems and the timescales over which these systems are relevant. These early insights will help to plan investment in key infrastructure that will be in place for the long term

This briefing note introduces the UKERC Public Engagement Observatory. It highlights the importance of involving society through active and interactive forms of public participation and engagement and outlines how the Observatory is advancing this emerging state of the art, through mapping, networks and experiments.

This paper explores the sensitivity of energy system decarbonisation pathways to the role of afforestation and reduced energy demands as a means to lessen reliance on carbon dioxide removal. 

Large uncertainties surround the viability of carbon dioxide removal

The stringency of climate targets set out in the Paris Agreement has placed strong emphasis on the role of carbon dioxide removal (CDR) over this century. However, there are large uncertainties around the technical and economic viability and the sustainability of large-scale CDR options. These uncertainties have prompted further consideration of the role of bioenergy in decarbonisation pathways and the potential land-use trade-offs between energy crops and afforestation. The interest in afforestation is motivated by its potential as an alternative to large-scale bioenergy with carbon capture and storage (BECCS), with its arguably lower risk supply chains, and multiple co-benefits. Furthermore, doubt over the viability of large-scale CDR has prompted a renewed examination of the extent to which their need can be offset by lowering energy demands.

Testing the impacts of reduced demands and afforestation

A global optimisation model (TIAM-UCL) was used to examine decarbonisation pathways for the global energy system. Based on core assumptions, where energy demands follow business as usual trends and degraded land is used for energy crops, the model was unable to find a solution for a 1.5°C target. Over the period 2020-2100, the carbon budget of GtCO₂ is exceeded by 332 GtCO₂.

Scenarios where also run to examine how the least-cost decarbonisation pathway changes if i) energy demands are significantly reduced, or ii) degraded land is used for large-scale afforestation instead of energy crops. Each option on its own reduced the CO₂ budget exceedance but both were required to allow the model to meet the 1.5°C target.

Impacts on the energy system

Under the 2°C target, afforestation reduced the reliance on BECCS by 60%. Under the 1.5°C target, the system still used all of the biomass available, as the target is so ambitious. When the energy demands were lower, the effect of afforestation on biomass use was dependent on the climate target. Under the 2°C target, less biomass was used across all economic sectors, whereas under the stringent 1.5°C target, all the available wood and crop biomass was exploited, but its use shifted away from the production of liquid fuels towards use in power generation.

Lowering energy service demands had a larger effect on the energy mix than large-scale afforestation. This is because demands are lowered differently across the sectors according to their economic drivers. However, afforestation had a bigger impact on the marginal cost of climate change mitigation, as it substantially decreases the scale and pace of change required by the energy system, especially in the 2°C case.

Given its key role, afforestation should be considered more in deep decarbonisation scenarios, as should lower demand scenarios.

Lowering energy demand and introducing large-scale afforestation both present significant challenges and opportunities. Further work should focus on factors affecting the carbon sequestration potential of afforestation, along with an interdisciplinary research agenda on the scope for large scale energy demand reduction. Research on the social, technical and economic factors that affect the potential for converting abandoned agricultural land to energy crops or new forest would be beneficial. An interdisciplinary research agenda is needed that brings together techno-economic modelling and qualitative scenario development with research on the social change that could lead to large reductions in energy demand

This brief considers the potential impact that Brexit could have on meeting the UKs net zero target.

This policy brief provides a short case study of Bristol City Leap as an example to showcase a novel finance and public procurement model for delivering citywide net zero across a local authority. Alongside Project LEO in Oxfordshire, and CommuniPower in the south-east of England, this is one of three case studies of the research project 'Finance and Public Procurement for Net Zero.'

The objective of the Consumers, Vehicles and Energy Integration project is to inform UK Government and European policy and to help shape energy and automotive industry products, propositions and investment strategies. Additionally, it aims to develop an integrated set of analytical tools that models future market scenarios in order to test the impact of future policy, industry and societal choices. The project is made up of two stages:

• Stage 1 aims to characterize market and policy frameworks, business propositions, and the integrated vehicle and energy infrastructure system and technologies best suited to enabling a cost-effective UK energy system for low-carbon vehicles, using the amalgamated analytical toolset.
•  Stage 2 aims to fill knowledge gaps and validate assumptions from Stage 1 through scientifically robust research, including real world trials with privatevehicle consumers and case studies with business fleets. A mainstream consumer uptake trial will be carried out to measure attitudes to PiVs after direct experience of them, and consumer charging trials will measure mainstream consumer PiV charging behaviours and responses to managed harging options

The first project deliverable, report D1.1 “Summary of Approach, Conceptual Design and Key Research Questions”, sets out the analytical approach being taken to the identification and assessment of system options and the tool set being used. This second project deliverable, report D4.1 “Initial Analysis of Technology, Commercial and Market Building Blocks of Energy Infrastructure”, sets out the detailed components of the framework. D4.1 comprises a report and spreadsheet. The two deliverables should be read in conjunction with each other. It should be noted that both of these reports were written for the purpose of facilitating agreement regarding the details of the approach and consequently they are quite complex. Other reports later in the project will present the information in a more accessible manner for people not closely involved with the work; those laterreports are commended to the general reader as a more suitable starting point. Nevertheless, D1.1 and D4.1 are made available for completeness

The purpose of this deliverable is to provide an initial 

• ‘ First principles’ view of the key components or Building Blocks (BB) that need to be considered as part of understanding what technology/physical, actors/commercial, market/policy, and Customer Proposition structures are most effective to enable mass deployment and use of ULEVs, and their relative importance;
• Guide to structure the Analytical Framework, which will be created as part of deliverable D1.2. The aim is to focusthe frameworkon areas of highest materiality, by specifyingwhich key BBs should be considered as part of each Narrativewhich will be assessed within the Analytical Framework.

This report should be read in conjunction with the associated spreadsheet of the same title, and  D1.1 (TR1006_D1.1) Summary of approach, conceptual design and key research questions.  For each Dimension, the ‘Key focus areas for the Analytical Framework’ and the ‘Areas for further research and development’ in this document are replicated in D1.1, section 4.

ETI’s Strategy Manager Liam Lidstone presents an overview of the Consumers Vehicles and Energy Integration (CVEI) project at LCV 2017 in the “Electric Vehicles and Energy Systems: Smart Charging Projects and V2G” session. Topics covered are:

• Vehicle electrification
• The project requires a broad range of expertise
• Modelling capability
• Scenarios have been developed to test further factors
• Interim findings
• Roadmap for efficient ULEV uptake and use
• Trials will deliver further robust evidence

In summary:

• The Consumers, Vehicles and Energy Integration project is seeking to address the challenges involved in transitioning to a secure and sustainable low carbon vehicle fleet
• An integrated modelling toolset has been developed able to examine the implications for energy supply, infrastructure, vehicles, users, policy and commercial models – and with it, it is possible to test a wide range of scenarios
• Findings from several areas are already available and have been incorporated into a roadmap for delivering efficient vehicle decarbonisation
• Upcoming trials will deliver further robust evidence on how consumers respond to different charging propositions and attitudes to ULEV adoption

ETI’s Strategy Manager Liam Lidstone presents “Consumers, Vehicles and Energy Integration (CVEI) project” at the Automotive Council technology group meeting 2017. Topics covered are:

• CVEI project overview
• Project structure
• Analytical framework
• Accounting for trends and developments elsewhere
• Consumer adoption: understanding the mass-market
• Interim findings
• Dissemination of outputs

The objective of the Consumers, Vehicles and Energy Integration project is to inform UK Government and European policy and to help shape energy and automotive industry products, propositions and investment strategies. Additionally, it aims to develop an integrated set of analytical tools that models future market scenarios in order to test the impact of future policy, industry and societal choices. The project is made up of two stages:

• Stage 1 aims to characterize market and policy frameworks, business propositions, and the integrated vehicle and energy infrastructure system and technologies best suited to enabling a cost-effective UK energy system for low- carbon vehicles, using the amalgamated analytical toolset.
• Stage 2 aims to fill knowledge gaps and validate assumptions from Stage 1 through scientifically robust research, including real world trials with private vehicle consumers and case studies with business fleets. A mainstream consumer uptake trial will be carried out to measure attitudes to PiVs after direct experience of them, and consumer charging trials will measure mainstream consumer PiV charging behaviours and responses to managed charging options.

This report represents Deliverable D6.1, Fleet Study Report. Little is known about the attitudes of Light Duty fleet operators to Plug-in Vehicle (PiV) adoption or PiV use; published research has been very limited. This report is intended to inform appropriate sensitivity analyses to be conducted in the Modelling and System Analysis work package (WP7), based on in-depth qualitative insights into the processes influencing vehicle choice, and likely charging profiles, in those categories of Fleets where PiV uptake could potentially have most impact on the wider energy system. The reportsummarises the methods employed and the findings from five individual case studies, and provides: a synthesis of insights gained across the case studies considered collectively; answers to the research questions defined in the Fleet Study design; and evidence-based conclusions from the Fleet Study with regard to how the findings have advanced understanding of Fleets, their potential impact on the energy system and integration into it (based on qualitative data), and therefore what the new state-of-the-art understanding of this area is.

We welcome the opportunity to comment on the findings of the Cost of Energy Review, conducted by Professor Dieter Helm. In our response, we address most of the questions set out in the Call for Evidence from BEIS. Before turning to these specific questions, we have three general observations about the Review and the Call for Evidence.

First, whilst the review title focuses on the cost of energy, this is misleading. The terms of reference and the Review report make it clear that the main focus is electricity rather than energy in general.

This distinction is important since the data shows significant differences in the position of UK electricity and gas costs when compared to costs in other countries. There are also differences between relative costs for households and relative costs for business energy consumers. UK electricity prices are higher up the European league table than prices for gas. Electricity prices for energy intensive industries in the UK are particularly high.

Our second comment is that there are important distinctions between prices, costs and bills. Whilst much of the debate focuses on prices, the costs of energy for consumers also depends on their energy consumption. Therefore, it is also important to consider energy efficiency of buildings, appliances and industrial processes since these are a key determinant of costs.

Our third comment is that costs need to be considered for the electricity system as a whole. Whilst the separate questions in the Call for Evidence about generation, networks and retail supply are understandable, costs to consumers partly depend on interactions between these components of the electricity system. This compartmentalised approach to the evidence base could mean that some of these systemic interactions are missed.

ESME is a least-cost optimisation model designed to explore technology options for a carbon constrained energy system, subject to additional constraints around energy security, peak energy demand and more.

The main body of this document catalogues the sources used as underlying evidence for the data in the ETI’s ESME model. This information takes the form of references to the outputs of ETI technology projects and/or third party published papers. The intention is to give signposts to the underlying evidence and to briefly state which data have been used and how, without giving detailed calculations. Note that this document does not contain the final numerical values in the ESME dataset. The ESME dataset itself is not published, but is available to users of the ESME model.

The purpose of this document is to give visibility of which sources have been used to populate the ESME datasets and to show how the ESME dataset is built on a foundation of strong evidence.

The technology input data for ESME is grouped into different sections of the energy system, each shown on a separate worksheet: conversion, infrastructure, industry, buildings and transport. Other input data, including product emission factors, resource prices, availability of resources, demand for energy services are on seperate worksheets. A glossary of terms is supplied, along with reference data for currency conversions and inflation data.

Bioenergy has the potential to help secure UK energy supplies, mitigate climate change, and create significant green growth opportunities if deployed effectively. The BVCM toolkit enables us to assess the sensitivities of the system to different parameters, drawing on the best available data. The ETI is using the BVCM toolkit to help determine the role that biomass should play in achieving the UK?s energy and GHG emissions targets in 2050.

Headline insights are shown below

• Biomass combined with Carbon Capture and Storage (CCS) remains the only credible route to deliver negative emissions, necessary to meet the UK’s 2050 GHG emission reduction targets.
• Without targeted intervention and leadership, the opportunities to realise the full benefits of this negative emission potential could be missed
• Bio-hydrogen and bio-electricity are produced inpreference to biofuels and bio-methane
• Bio-heat is deployed across the UK, especially in earlier decades
• Gasification technology is a key bioenergy enabler, producing both hydrogen and syngas, and is one of the most flexible, scalable, and cost-effective bioenergy technologies
• Locational preferences for resource production are apparent: with Short Rotation Coppice Willow (SRC-W) in the west / north-west of the UK and Miscanthus in the south and east of the UK. Short Rotation Forestry (SRF) when grown is preferred in the south and east of the country, along with the collection of waste for making Refuse Derived Fuel (RDF).

This summary provides an overview of discussions and key themes arising from a modelling workshop focused on local energy system modelling and scenario tools.

This paper is the last of four to explore results from UKERCs Modelling Hub survey. It focuses on how models have underpinned decision making on the transition of the energy system.

Energy models provide the underpinning evidence to support decision makers across policy, industry and civil society, helping them to understand strategies and trade-offs in the energy transition. No single model can therefore cover all the elements required to understand the energy transition. To investigate this critical issue and gain an understanding of the energy models in use in the UK, UKERCs Energy Modelling Hub coordinated a ground-breaking survey of all UK energy models. This policy brief is the second of four exploring the models captured in the survey. In this second brief we focus on the findings that shed light on the various strengths and weaknesses of UK energy models. In considering this we focus on four key areas: How the models deal with time, in terms of temporal detail and overall time horizon How the models deal with space, in terms of geographical detail andcapturing infrastructures How the models deal with technologies, in terms of technology learning and inclusion of key mitigation options How the models deal with behaviour, in terms of consumer responses and broader societal trends

This paper is the third of four to explore results from UKERCs Modelling Hub survey. It considers how models are constructed in terms of costs, funding sources, and resulting impacts. It looks at how models are maintained in terms of costs, users and model updates. It also explores model accessibility and transparency levels.

This paper is the first of four to explore results from UKERCs Modelling Hub survey. Focusing on the UK energy modelling landscape, these initial findings detail who hosts and runs models, their methodologies and coverage, and their major outputs.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

Local Area Energy Planning is seen by the ETI and ESC as central to achieving national greenhouse gas emission reduction targets and unlockthe infrastructure investment needed to decarbonise heat.

More effective local area planning supported by objective, technology agnostic evidence can support transition in a way that enables local communities to realise the benefits and understand the costs of decarbonisation. This document supports this objective by exploring existing best practice and opportunities by way of a number of case studies.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

The Tool Kit Development Project sits within Work Package 2, and supports the Development of the EnergyPath Networks Tool. This Deliverablecomprises a report which sets out the Functional Specification for Version 2.1 of the EnergyPath Networks Tool. This report describes the functional requirements and specification which have driven the development of the tool.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This report provides detail of the methodology that has been developed to assess the economic costs and benefits of implementing a local area energy strategy and providing socio-economic indicators of a low carbon transition impact in a local area to key stakeholders. The methodology takes output data directly from EnergyPath Networks calculates indicative costs and benefits using HM Treasury (HMT) Green Book and Interdepartmental Analysts Group (IAG) guidance where appropriate.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This deliverable package comprises:

• Bidders Pack deliverable D4, EnergyPath Networks Modelling Local Energy System Designs Report, incorporating:
• Bidders Pack deliverable D2 EPN Use-Cases Limitations and Uncertainty

This report is intended to provide an accessible summary of how EPN can be used to model local energy systems. It includes context on the development of EPN (including high-level interactions with other relevant tools such as ESME) and description of the scope of EPN modelling framework including Buildings, Technologies, Transmission/Distribution, Time, Cost, Carbon and Pathways.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This Deliverable comprises a report from The purpose of this document is twofold:

• To provide a consolidated technical description ofthe EnergyPath Networks (EPN) Tool covering both its design and data architecture
• To provide a user guide for the EPN Tool.

It is designed to be a ‘living’ document accessible to an ‘expert’ user, but not the original developer, which should be updated to reflect further developments to the tool. This document reflects R2.1 of the tool. It should be noted that this document does not describe the functional requirements and specification which have driven the development of the
tool, this is described in detail in the separate EnergyPath Networks Tool Functional Specification

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This Deliverable comprises a presentation which sets out an analysis of the potential enhancements to the Energy Path Networks Toolkit which could be progressed in the Toolkit Development Project.

The objective of this report is to define the priority software limitations of the EnergyPath Networks tool R2.1 with a particular focus on the requirements of local area stakeholders, i.e. the Local Authorities and Network Operators. Some of the prioritised items are low-regret and easy to implement, others are more major development items. Our proposal is to scope these items further before implementation. The full list of limitations have been collated from views expressed by local stakeholders, the tool developers and the internal Energy Systems Catapult EnergyPath team. These items were prioritised subjectively as high, medium or low priority by the EnergyPath Networks team based on the following criteria:

1. How often studies requiring these functionality enhancements /data improvements have been requested bystakeholders.
2. How much the credibility of the model is affected by not having these improvements.

High priority items have been discussed in detail in the body of the report and shared with members of the wider SSH1 team to build consensus.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This report sets out five prioritised EPN use cases, comprising a summary of each use-case providing an accessible overview of the potential application of EPN and related capabilities. This builds on work carried out by Mark Bornhoft in Autumn 2017 to explore options for future exploitation of EPN.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

Local area energy planning enables stakeholders, led by local government, to interrogate different energy futures for an area and to develop the most promising, cost effective options for decarbonisation. For network operators, it provides a foundation for justifying and planning
network upgrades. Local area energy planning develops a shared vision as a basis for targeting investment, encouraging innovation, securing value for money, gaining public understanding and support. A small, additional investment in planning future local energy systems, using a whole-system approach, can leverage far greater savings in the capital required to improve existing or build new
energy infrastructure.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This report sets out the potential role of local area energy planning as a valuable activity that can assist with meeting the ambitious decarbonisation and housing energy performance commitments of the Clean Growth Strategy informed by the pilot studies undertaken using EnergyPath Networks as part of the Smart Systems and Heat Phase 1 Programme. It is aimed at local authorities and other local stakeholders with an emphasis on local area energy planning which reflects local conditions, challenges and priorities to enable an appropriate transition to a low carbon energy system.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

The purpose of this report is to provide an analysis of the data challenges involved in a whole system approach to local area energy planning informed by the piloting of EnergyPath Networks in three local areas. The report considers:

• The types of data that are needed, the sources from which it is available and any restrictions on use.
• The data processing required and associated costs.
• The assumptions, exclusions, limitations and value of data in supporting decision making.
• Other data that might be available and valuable for validation of modelling outputs and supporting local area energy planning in the future.
• Methods for gathering additional data and how these could be phased into local area planning

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This Deliverable comprises a report from Arup which was commissioned by the ESC to provide advice on the approach for Assessing Socio-Economic Benefits of the EnergyPath Networks Outputs. On balance, the method was found to be sound. There are questions which arise on some selected items but these do not undermine the whole.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This Guide provides advice and recommendations to Local Government and other local organisations on undertaking a new objective, evidence based and data driven whole systems approach to local area energy planning. In response to this local challenge and to support more effective local area energy planning,the Energy Technologies Institute (ETI) Smart Systems and Heat Programme has developed and piloted a local area energy planning analysis framework - EnergyPath Networks. This has involved a new, ‘whole system’ approach to planning and design of local energy systems and has been piloted with three UK local authorities.

The pilot has identified decarbonisation pathways and network choices based on the geography, buildings, energy infrastructure, energy demand, resources, urban growth plans and decarbonisation ambitions for each local area. The Guide provides the following:

• a description of the policy imperative which supports local area energy planning;
• an assessment of how this fits inwith energy planning already being undertaken by various local authorities and energy network operators across the UK;
• an explanation of the steps to be taken in adopting a whole system approach to local area energy planning outlining how local areas can follow an approach which reflects local circumstances to achieve decarbonisation goals, clean growth and other local drivers such as energy security and affordability, reflecting government advice.

A list of additional resources supporting local area energy planning is provided at the end of this guide.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This report has identified a range of policy barriers: in particular with regard to how national planning policy and guidance is expressed.As a result, it is clear that accelerating deployment of local area energy planning activity faces a number of challenges.Based upon the feedback from consultations with a number of Local Authorities, some support the principle of LAEP however, the ability to implement is further constrained by a lack of financial and staffing resources locally. There are examples where LAEP activity has been undertaken and outputs included within Development Plans but funding constraints or lack of local political support has restricted implementation and priorities.

Energy consultancy Baringa Partners were appointed to design and develop a software modelling tool to be used in the planning of cost-effective local energy systems. This software is called EnergyPath and will evolve to include a number of additional packages to inform planning, consumer insights and business metrics. Element Energy, Hitachi and University College London have worked with Baringa to develop the software with input from a range of local authorities, Western Power Distribution and Ramboll. EnergyPath will complement ETI’s national strategic energy system tool ESME which links heat, power, transport and the infrastructure that connects them. EnergyPath is a registered trade mark of the Energy Technologies Institute LLP.

This is the call for proposals for the EnergyPath project

The Tool Kit Development Project sits within Work Package 2, and supports the Development of the EnergyPath Networks Tool. This Deliverable comprises a report which sets out the Functional Specification for Version 2.1 of the EnergyPath Networks Tool. This report describes the functional requirements and specification which have driven the development of the tool.

This report contains the analysis plan and results from the first run of analysis from EPO including the design run documents for the analysis plan and the results and insights from the analysis.

This report explains how individual software modules (EPO and model components) are tested against their requirements. This is not a strategy for testing or performing analysis on the integrated model. This is explained in the “EPO Analysis Plan and Results” document.

ETI’s Strategy Analyst Alex Buckman presents “Evolution of our Energy System” at IET’s Power Academy 2017. Glasgow. This presentation covers:

• What is the ETI?
• Our energy system evolves around our choices
• Change happens fast
• Why do we need to balance?
• The tools at our disposal
• How much storage is needed?
• Vector Flexibility
• Demand Flexibility

In conclusion, we need to

• Create new energy systems, adapt existing ones and integrate them together
• Users define the energy system’s evolution, not the other way around
• Understand the implications of different scenarios so that we are prepared to evolve

The aim of this report is to consider how Local Authorities can better engage with publics as a part of the energy transition in a meaningful way and support the active participation of citizens in energy systems and the energy transition. The report has been informed by Social Science and Humanities literature as a means of recognising that the energy transition is not merely a technological transition, but a social one.

Local Authorities role in the energy transition and working with their citizens in doing so, has been recognised as crucial to paving transition paths. Material collated within this report is intended to better inform Energy Cities and its partners, Local Authorities and Municipalities, civil society groups and others interested in how citizens can be supported and encouraged to participate in energy system developments as a part of the energy transition. The findings in this report are therefore intended to directly help Local Authorities across Europe in implementing more participative approaches to their governance practices in energy systems.

Delivered as part of the Energy-PIECES project, this report was developed during a secondment with Energy Cities.

This project quantified the opportunity for Macro level Distributed Energy (DE) across the UK and accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030. The project studied energy demand such as residential accommodation, local services, hospitals, business parks and equipment, and is developing a software methodology to analyse local combinations of sites and technologies. This enabled the design of optimised distributed energy delivery solutions for these areas.

This deliverable is number 2 of 3 in Work Package 4. This report presents a summary of the results of the application of the tool and design methodology developed in D4.1 to the 20 characteristic zones of demand created in work package 2. The evaluation is carried out for all 20 characteristic zones in terms of economic performance, carbon emission, efficiency in the use of fuel and also energy security

The design methodology has been implemented in Microsoft Excel, with the add-in WhatsBest! for optimisation. The optimiser minimises the total annualised cost of the energy centre and calculates the CO₂ emissions associated with operating the energy centre and heat network. Constraints may be imposed by the user, e.g. to select or deselect certain DE systems, to achieve a given level of CO₂ emissions less than a specified limit, etc. To optimise the design and operation of the energy centre to satisfy the thermal demand of a zone takes around 5 minutes computing time on a desktop PC.

The tool has been applied to 100 different designs in the classification and clustering process of WP 2, confirming a level of robustness of the tool. In this report, results for the 20 characteristic zones are presented.

This project quantified the opportunity for Macro level Distributed Energy (DE) across the UK and accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030. The project studied energy demand such as residential accommodation, local services, hospitals, business parks and equipment, and is developing a software methodology to analyse local combinations of sites and technologies. This enabled the design of optimised distributed energy delivery solutions for these areas.

This deliverable is number 1 of 3 in Work Package 4. This report details the design methodology developed to allow the systematic design of DE solutions which satisfy the aggregated thermal demands of a ‘characteristic zone’. The design involves selecting which DE generation units (characterised in deliverable 3.2) will comprise the DE solution and specifying the best operating schedule to guarantee minimal capital and operating costs. In addition the report presents the results from the tools application to two test cases used to validate its performance - a test case representing a characteristic zone, and an existing DE solution

This project quantified the opportunity for Macro level Distributed Energy (DE) across the UK and accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030. The project studied energy demand such as residential accommodation, local services, hospitals, business parks and equipment, and is developing a software methodology to analyse local combinations of sites and technologies. This enabled the design of optimised distributed energy delivery solutions for these areas.

This deliverable is number 1 of 3 in Work Package 2. Its objective is to develop a methodology for forecasting energy demand profiles across residential and tertiary (light industry) properties in a given geographical area. In addition the report estimates the level of confidence in the methodology. The demand profiles generated form the basis for demandaggregation modelling in Work Package 4.

We believe that the methodology for the residential therrmal demand is sound and there is sufficient data such that a 10% uncertainty is reflective of the potential propagating error for the UK. While the tertiary sector data methodology is sound, there are two key issues with the datasets that may cause the larger error:

1. The registered company information databases used in the tertiary sector demand calculations contain many records that are missing the number of employee data;
2. Conversions between employee numbers and building floor area can only currently be performed at large tertiary sector groupings. The contribution of demand uncertainty from the tertiary component could be projected and tracked in the resulting demand calculations.
3. The temporal resolution of the demand is good and the method, based on energy transporter load prediction techniques, seems able to predict the main trend and features of residential thermal demand

The objective of the Distributed Energy (DE) Programme is to increase the uptake of DE through the development of integrated systems in order to reduce through-life costs, improve ease of installation and increase efficiency in the combined generation of heat and electricity. Within this programme framework the objective of the Macro DE FRP will develop and validate a software methodology to enable the design of optimised DE solutions where clusters of demand sites are linked with appropriate DE supply equipment. The project will quantify the opportunity for Macro level DE (up to 50MW) in the UK and the potential to accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030.

This deliverable is numbers 2 and 3 of 3 in Work Package 2. It builds on the approach for calculating energy demand as described in deliverable D2.0. The outcome of the work described in this deliverable is the creation of 946 zones of demand which account for 55% of the GB energy demand across 4% of its geographical area. From these 946 zones of demand, 20 ‘characteristic zones of demand’ have been identified. Between them these zones have been shown to be suitable for characterising the energy demand (both heat and electricity) for GB and will be used in the development of a benefits case for DE in GB and the identification of technology development opportunities which will facilitate the up-take of macro scale DE. These two final points will be addressed in work package 5 of the project

This project quantified the opportunity for Macro level Distributed Energy (DE) across the UK and accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030. The project studied energy demand such as residential accommodation, local services, hospitals, business parks and equipment, and is developing a software methodology to analyse local combinations of sites and technologies. This enabled the design of optimised distributed energy delivery solutions for these areas.

This deliverable is number 1 of 3 in Work Package 1. Its intention is to ensure full alignment between the project consortium and the ETI on the detailed processes to achieve the key project objectives and the assessment criteria at the start of the project. To this end it specifically covers:

• The process for achieving the project’s key objectives
• Detailed performance criteria for the assessment of DE zone approach and UK benefits, at a minimum CO₂ reduction, security of energy supply and cost of delivery
• Baseline assessment assumptions, including the target end users

This project quantified the opportunity for Macro level Distributed Energy (DE) across the UK and accelerate the development of appropriate technology by 2020 for the purposes of significant implementation by 2030. The project studied energy demand such as residential accommodation, local services, hospitals, business parks and equipment, and is developing a software methodology to analyse local combinations of sites and technologies. This enabled the design of optimised distributed energy delivery solutions for these areas.

This deliverable is number 3 of 3 in Work Package 3. Its objective is to model supply equipment improvements either already under development or which could theoretically impact the commercial viability of Macro DE. These models will be the input information for the Work Package 4 tool.

The aim of the Macro DE project is to assess the opportunity for optimising low carbon distributed energy solutions for the UK. This will be achieved by evaluating the aggregated UK energy demand, modelling the supply equipment and developing a software tool, which can use the provided supply and demand data to design characteristic energy centres.

The purpose of Work Package 3 (WP3) is to characterise the supply equipment to be used by the software tool being developed in WP4. The equipment to be modelled was agreed in Task 3.1. The commercially available supply equipment was modelled in Task 3.2. Task 3.3 was to model supply equipment improvements either already under development or which could theoretically impact the commercial viability of Macro DE. Any change in technical standards, which could help improve efficiency, was also covered as part of Task 3.3. In addition, it was decided to create models in Task 3.2 but at a larger scale due to a change made in the agreed upper boundary (from 10MWe to 50MWe) of the equipment power rating as well as some additional technologies (with currently low penetration in the UK market) as requested by the ETI.

The range of equipment modelled had previously been specified as between 100 kWe and 50 MWe. It was hoped to include 3 sizes within these limits for each type of supply equipment. However, it was not possible for all technologies investigated due to their current available scale, for example largescale CHP using CCGT tended to be larger than 50 MWe, so only two models could be included in the list.

AECOM were invited to conduct a portion of the work in Task 3.3 due to difficulty in obtaining data outside of Caterpillar’s field of influence and requiring information from Caterpillar’s competitors. Information to create the modelswas collected from manufacturers where possible but due to the commercially sensitive nature of the cost and availability or reliability data this could not always be achieved. In addition, some information was extracted from available literature for some systems, such as large scale solar heating, which have yet to be built in the UK.

The projected technical improvements (2020 – 2030) were obtained from consultation with manufacturers and experts in the industry. For the mature technologies like gas-engines, the projected technical advancement is the future technical standard set by the main players in the field. For less developed technologies like fuel cells, the projected technical advancement is the technical judgment from the manufacturer who has been contacted.

Despite the difficulties stated above, it was felt that the approach taken in Task 3.3 hasprovided the Macro Distributed Energy project with a view of the various types of technology at present and a reasonable estimate of the potential for technical improvements.

The issue: a need to better understand the models that inform demand-side energy policy-making

Under the UK Climate Change Act 2008, the government is legally bound to reduce greenhouse gas (GHG) emissions by 80% by 2050 relative to 1990 levels.

Historically, the focus of energy policy in the UK has been on supply-side policies, such as decarbonisation of electricity generation through greater use of low carbon technologies like wind and solar. Increasingly, however, demand-side energy policies are being recognised as having important contributions to make to achieving emission reduction targets, through reducing energy demand or by making energy demand more flexible and compatible with variable renewable energy sources. Such demand-side policies can seek to promote a wide range of technologies and behaviours, for example improved buildinginsulation, reduction in the use of energy intensive materials and increases in teleworking to reduce commuting.

To fully realise the potential of demand-side energy policies, it is important that they can be adequately represented in quantitative energy models, because such models play an important role in informing UK energy policy. However, we do not currently have a good understanding of how well the different energy models that inform UK government energy policy represent energy demand and demand-side energy policies.

Therefore we have undertaken a Rapid Evidence Assessment (a constrained form of systematic review) to examine the energy models that have informed energy policy documents published by the UK government between 2007 and 2017. The overarching question this review seeks to address is:

How suitableare the energy models used toinform UK government energy policy for exploring the full range of contributions that demand-side energy policies can make to climate change mitigation?

Main findings: strengths and weaknesses in existing modelling

Our Rapid Evidence Assessment reveals that the core strength of current energy modelling is the detailed representation of technologies, with many models featuring information on hundreds of potential technological options for increasing energy efficiency. Although uncertainties exist around these technological options, these models allow us to gain a coherent and realistic understanding of how different combinations of technologies could satisfy our future energy service demands under different low-carbon scenarios.

However, the modelling landscape reveals two key limitations with regard to the representationof non-technological drivers of energy demand:

• Firstly, economic, social and behavioural drivers of energy demand, such as thermostat settings, are rarely explicitly included within the models. As a result, changes in such drivers risk being overlooked as potential levers of climate change mitigation. In addition, the economic, social and behavioural assumptions that are included into the models are often not presented in a transparent manner.
• Secondly, while many of the models provide a wealth of detail on different technological options, they are not well-equipped to model how behavioural, social and economic changes impact on the use of technology. This makes it difficult to investigate how technological change comes about and how it could be steered into the right direction using different policy instruments. In many models, the uptake of different technological options is specified exogenously by the user, or is determined within the model by cost optimisation, which does not reflect the many non-cost factors influencing technology diffusion.

Key recommendations for future modelling

• Develop consistent and transparent processes for estimating and presenting the fixed inputs to energy models, such as energy service demands and socio-economic drivers.
• Quantify the potential of demand-side policies and include them as explicit options in energy models to make such policies more visible.
• Improve the representation of economic, social and behavioural processes in energy models where feasible and useful. This endeavour can build on existing research in the academic literature, which is currently not well represented in policydevelopment.
• Research further the potential implications of interactions between different drivers of energy demand and make sure they are consistent. An example of such an interaction would be the impact that a modal shift from cars to cycling would have on the growth and energy demand of the automotive industry.

This paper provides an overview of ETI’s Energy System Modelling Environment (ESME). The first objective of this paper is to describe, in general terms, what the tool does and what it isused for. This is intended to act as background reference material for anyone who has seen ESME cited in a presentation or report and wishes to find out more about the modelling work behind the headline results. The second objective of this paper is to give a more detailed guide to the modelling approach, describing how key features of the problem are represented in the model and noting someof the key assumptions and limitations of the approach. This material is more technical, and willbe particularly useful for people with knowledge of other energy models.

This publication has been produced as part of the work on the ETI’s internationally peer reviewed energy system modelling environment (ESME) - a national energy system design and planning capability that helps to identify key areas for ETI investments. ESME is also used by UK Government to underpin and inform energy policy.

This UKERC working paper reviews the literature on modelling natural gas demand and supply. This includes modelling natural gas markets in isolation, and as part of its role in the wider energy system.

This review is part of the work on a new, global gas model at the Institute for Sustainable Resources at University College London, through a UKERC PhD Studentship. The focus of the new model is on global gas production and trade, and its coupling with the TIMES Integrated Assessment model at University College London (TIAM-UCL) to represent gas demand.

Trade-offs: simplifying complexity without diverging from reality

The main section of this working paper provides a review of existing methods which model both supply chain and demand dynamics of natural gas (Part 1: recoverable volumes and corresponding costs of natural gas; Part 2: wider energy-system models; Part 3: natural gas market models). As with any modelling, it was found that there is always a trade-off between necessary simplifications, and the uncertainties and complexities which surround energy-economic-environmental systems.

In Part 1, this paper reviews a range of studies that have estimated recoverable volumes of natural gas. This includes both deterministic (e.g. a single point estimates of natural gas) and stochastic (e.g. probabilistic estimates including ranges of uncertainty) modelling methods, and the strengths and limitations of the approaches employed. The overall conclusion is that some level of probabilistic assessment is required when estimating recoverable volumes of natural gas and the cost range of extraction, particularly given the huge uncertainties inherent in the development of these resources (techno-economic, geological, environmental).

A key contribution of this review, in Part 2, is how natural gas is represented in energy system and integrated assessment models. This represents how gas supply and demand dynamics are also driven by wider developments in energy and environmental systems. Standalone natural gas models, described in Part 3, include gas market complexities. These have more disaggregated time-slices/temporal horizons in order to capture seasonality and the interaction between market agents. However, there is a trade-off between the temporal disaggregation, and the overall scope of the model. In short, the decision to take gas consumption from TIAM-UCL yields the benefit of a whole systems approach in the long-run, whilst limiting seasonal disaggregation in the short-term.

Introducing a new bottom-up global natural gas production and trade model

In section III, the paper introduces a new natural gas production and trade model, which is linked to TIAM-UCL. This linkage includes an aggregation of supply cost curves from a field-level gas volume and cost database, into the regions in TIAM-UCL. The gas model is able to account for aspects of gas markets which TIAM-UCL does not have in its architecture; e.g. fiscal regimes, take-or-pay contracts, price indexation.

Given the proprietary nature of cost data for natural gas extraction, a linear regression model was used to assign supply costs (the capital and operating expenditures required to get the gas out of the ground) to gas fields where no public information was available. This gas model aims to provide insights by quantifying various parameters which determine supply costs for individual natural gas fields, both developed and undeveloped; these include water depths, reservoir depths, the levels of hydrogen sulphide or carbon dioxide, and assumed risks to investment (e.g. due to location, political conditions, etc.).

The combination of the two models is intended to model scenarios, providing new insights into future natural gas price formation mechanics and longer-term policy developments which could alter/influence supply and demand.

This briefing paper This paper outlines the challenges for modelling local and regional energy systems and assesses the capabilities of selected UKERC energy system models, with a focus on interactions between national and local energy systems.

ETI’s Strategy Director Jo Coleman presented “Network Transition Challenges” at the Energy Institute’s energy storage 2016 - prospects for scaling up UK storage capacity conference.

• Energy networks are a core part of the energy system
• Overarching challenges for transitioning networks
• Integrating networks to optimiseacross energy vectors and deliver flexibility
• Decarbonising heat through single and multi-vector routes
• To what extent is energy storage needed?
• Using H₂ storage to maximise use of CCS investment in the power system
• How might storage be operated?
• Salt caverns for hydrogen storage in conjunction with flexible turbines
• Pumped Heat Electrical Storage

The work we have undertaken at the ETI has shown the importance of a systems approach to energy planning – sectors and their infrastructure cannot be developed in isolation. Therefore one of the first acts of the ETI was to establish ESME, our Energy System Modelling Environment, which is an internationally peer-reviewed national energy system design and planning tool.

The UK energy environment comprises a complex set of needs, technologies and choices. We first conceived ESME for our own technology investment purposes, but over the years it has developed into a powerful energy system model for the UK.

Our refinement and development of the model with input from our private and public sector membership has allowed us to develop illustrative scenarios for the UK’s energy transition out to 2050. We present here two representative scenarios of the future which each show pathways the UK can follow, but importantly highlight the changes the UK needs to consider and make to its energy infrastructure – this is unavoidable.We hope the scenarios inform and provoke debate, and progress thinking about how we power the UK in the future.

1. The UK can achieve an affordable transition to a low carbon energy system over the next 35 years. Our modelling shows abatement costs ranging from 1-2% of GDP by 2050, with potential to achieve the lower end of this range through effective planning
2. The UK must focus on developing and proving a basket of the most promising supply and demand technology options. Developing a basket of options (rather than a single system blueprint) will help to limit inevitable implementation risks
3. Key technology priorities for the UK energy system include: bioenergy, carbon capture and storage, new nuclear, offshore wind, gaseous systems, efficiency of vehicles and efficiency/heat provision for buildings
4. It is critical to focus resources in the next decade on preparing these options for wide-scale deployment. By the mid-2020s crucial decisions must be made regarding infrastructure design for the long-term
5. CCS and bioenergy are especially valuable. The most cost-effective system designs require zero or even “negative” emissions in sectors where decarbonisation is easiest, alleviating pressure in more difficult sectors
6. High levels of intermittent renewables in the power sector and large swings in energy demand can be accommodated at a cost, but this requires a systems level approach to storage technologies, including heat, hydrogen and natural gas in addition to electricity

What is the most effective way of delivering a particular bioenergy outcome in the UK, taking into account the available biomass resources, the geography of the UK, time, technology options and logistics networks?

Biomass must play a significant role in the future energy mix if the UK is to meet its GHG emissions targets cost-effectively. BVCM is a comprehensive and flexible toolkit used to understand the most effective routes from biomass to energy accounting for all end-to-end elements in the pathways: land use, biomass production (including arable crops, energy crops and forestry); imports, conversion, transport, storage, purchase, sale and disposal of resources; CCS technologies and utilisation of waste resources. The most effective route depends on the resource and technology data, combined with the objective function chosen and the constraints imposed on the system.

To the ETI’s knowledge, BVCM is the most comprehensive and flexible model for whole system optimisation of bioenergy value chains to be produced to date. It currently contains 82 different resources comprising bio-resources, wastes, intermediates, final energy vectors and co-products. There are 61 distinct technologies, at different scales with multiple modes (more than 1200 combinations in total), including: pretreatment and densification; gaseous fuel production; liquid fuel production; heat, power and combined heat and power generation; waste-to-energy; and carbon capture technologies.

Since BVCM is data-driven, it can easily be extended to include other resources or technologies by adding to the database, or modified to analyse alternative assumptions. It could also be applied to other countries simply by providing different data sets for the available land areas, yield potentials (and impacts), waste potentials and so on.

The BVCM toolkit enables us to assess the sensitivities of the system to different parameters, drawing on the best available data. The ETI is using the BVCM toolkit to help determine the role that biomass should play in achieving the UK’s energy and GHG emissions targets in 2050.

The Plug-in Vehicle Economics and Infrastructure: Economics and Carbon Benefits project is a strategic level analysis of the potential size of the market for plug-in vehicles, the total level of investment needed and the total carbon offset for the UK.

This is a detailed report on the modelling work undertaken in the Economics and Carbon Benefits project. It should be read as a supplement to the final Economics and Carbon Offset Analysis Final Report.

Results are presented in terms of

• whole life emissions from the vehicle parc (including the achievement, or otherwise, of target reductions). The whole life emissions are also subdivided into emissions associated with usage and emissions associated with production and scrappage;
• Exchequer spend and revenues;
• the sustainability of the market for PiVs; and
• the deployment and sustainabilityof public charge points

The base case scenario demonstrates a switch from gasoline towards diesel with limited uptake of PiVs; PiVs make up only 19% of the parc by 2050 and BEVs only make up 0.6%. The other themed scenarios show the effect of changes in scenario and policy variables to represent themes such as most/least favourable conditions for PiV sales, minimised emissions, green emphasis, impact of high/spiked oil prices and a variety of GDP growth assumptions. Analysis and cross-comparison of their results demonstrates which scenario/policy groupings have a significant impact.

2021 has been a landmark year for UK energy and climate policy. Plans and strategies were announced across many sectors, from offshore wind to how we heat our homes. The UK also hosted COP26 and pressed hard for greater ambition. Now that the spotlight has moved, in this Review, we consider whether plans will be adequate to deliver results.

With a focus on gas and the UK continental shelf, industrial decarbonisation, heat, mobility and the environment, we look at developments both at home and internationally and ask whether the UK is a leader in decarbonisation, and if the transition is being managed as well as it could be.

The deep decarbonisation of industry is vital if the UK is to reach net zero emissions by 2050. In this paper, we use the Net-Zero Industry Pathways (N-ZIP) model, developed by Element Energy to inform the work of the Climate Change Committee (CCC) and the Department of Business, Energy and Industrial Strategy (BEIS), to explore the impact of changing key input parameters on the industrial decarbonisation pathways to 2050. This is used to analyse the robustness of the model results that are being used to inform policy decisions. The Net-Zero Industrial Pathways (N-ZIP) model has been used to inform the sixth carbon budget advice from the CCC and the Governments Industrial Decarbonisation Strategy. N-ZIP is a spatially disaggregated, bottom-up model that contains a detailed description of the industrial activities that drive demand for energy and cause greenhouse gas (GHG) emissions. It also includes information on carbon prices, alternative fuel and technology options, and the projected future deployment of hydrogen and carbon capture and storage (CCS) infrastructure. The model then combines this information to produce a decarbonisation pathway for industry to 2050, with outputs including the level of emissions in each year and the technologies and fuels being used. The characteristics of the resulting pathways are dependent on these assumptions. Element Energy, the CCC and BEIS have undertaken some sensitivity analysis to explore how changes in key assumptions affect the results. However, given the importance of the model in informing policy on industrial decarbonisation, in this working paper we undertake further analysis to explore how the model results are affected by changing a wider range of inputs than previously studied. We find that the model results are generally robust to relatively wide variations in the input assumptions that we have explored. Key assumptions that significantly affect the results include the level of resource efficiency and energy efficiency (REEE), the costs of carbon capture and storage infrastructure and the level of carbon taxes. Assumptions about supply chain constraints also impact the profile of emissions reductions to 2050. A number of areas are also highlighted for further analysis. More work is needed to understand the trade-off between REEE and the technology and fuel options in the model and to explore potential alternatives to CCS for decarbonising sites that are not part of large industrial clusters and where the cost of CO2 transport is likely to be very high.

This report sets out seven principles of place-based net-zero policy for SMEs. Setting place-based action within context of national, international, and sector action on climate change, the report provides assessment of where place-based action can provide most added value, and address limitations in existing provision.

The primary objective of the Storage & Flexibility Modelling Project is to develop the capability to improve understanding of the future role of energy storage and the provision of system flexibility within the context of the overall energy system. This aims to provide a techno-economic evaluation of energy storage across multiple energy vectors (electricity, heat, gas and hydrogen) accounting for the different services that could be provided (frequency response or avoiding wind curtailment) and at which points in energy system (transmission, distribution, building level) they are most appropriate.

Stage 1 of the project is comprised of 3 deliverables - of which this is the first :

D1.1 Energy storage mapping report (this report) a first principles framework formapping the system technical services and benefits that storage (heat, hydrogen, gas and;electricity)and competing flexibility options could provide.

The primary objective of the Storage & Flexibility Modelling Project is to develop the capability toimprove understanding of the future role of energy storage and the provision of system flexibility within the context of the overall energy system. This aims to provide a techno-economic evaluation of energy storage across multiple energy vectors (electricity, heat, gas and hydrogen) accounting for the different services that could be provided (frequency response or avoiding wind curtailment) and at which points in energy system (transmission, distribution, building level) they are most appropriate.

Stage 1 of the project is comprised of 3 deliverables - of which this is the third :

• D1.1 Energy storage mapping report - a first principles framework for mapping ;the system technical services and benefits that storage (heat, hydrogen, gas and;electricity) and competing flexibility options could provide.
• D1.2 Assessment of the near term market potential for energy storage, over the next 5-10 years given the current market structures, with a particular focus on electricity 
• D1.3 Approach for modelling long term role of energy storage (this report) - which defines the modelling approach to analysing the longer term role for storage and other relevant flexibility options in GB from a system operator perspective.

The report defines the approach for analysing the longer term role for storage. This includes a literature review of existing approaches and a proposal for a modelling approach for analysis in Stage 2. It covers:-

• Design Requirements
• Review of Literature
• Overview of Modelling Framework
• Modules
• Data Requirements
• Scenario Framework
• Techinical and Data Architecture
• Private Investment Perspective

This project will develop energy system modelling capability to increase understanding of the role of energy storage and system flexibility in the future energy system. The modelling capability will provide a whole systems view of the different services that could be provided and at which points in the energy system they are most appropriate. Management consultancy Baringa Partners are delivering this new project to develop the capability to improve understanding with regards the future role of energy storage and the provision of cross-vector system flexibility within the context of the overall UK energy system.

The Storage and Flexibility Model is an AIMMS based model that provides a framework to allow techno-economic analysis of the role of storage and other means of energy system flexibility across multiple energy vectors at multiple levels of the energy system. This deliverable provides the project final report which summarises:

• Scenario analysis using the modelling framework and data developed during the project
• Identification and assessment of 3 case-study examples of private sector storage investment and potential generic policy options
• Wider qualitative assessment of risks/opportunities for storage deployment associated with the analysis undertaken
• The report will also describe how the scenarios have been constructed and describe the underlying model framework insofar as it is necessary to support the interpretation of its outputs.

This project will develop energy system modelling capability to increase understanding of the role of energy storage and system flexibility in the future energy system. The modelling capability will provide a whole systems view of the different services that could be provided and at which points in the energy system they are most appropriate. Management consultancy Baringa Partners are delivering this new project to develop the capability to improve understanding with regards the future role of energy storage and the provision of cross-vector system flexibility within the context of the overall UK energy system.

This report defines the modelling approach to analysing the longer term role for storage and other relevant flexibility options in Great Britain from a system operator perspective. The primary objective of the Storage Flexibility Project is to develop the capability to improve understanding of the future role of energy storage and the provision of system flexibility within the context of the overall energy system (i.e. across multiple energy vectors, points in the energy system and in provision of multiple system services, from peak shaving to frequency response or gas pressure regulation). A modelling approach for analysing the longer term role for storage and other relevant flexibility options in GB to 2050 (making use of existing ETI tools such as ESME where appropriate) has been developed during Stage 1 of the project for this report. The approach has considered findings from an extensive literature review as well as insights from two parallel Stage 1 project deliverables, a storage and flexibility requirement mapping exercise, and an assessment of nearer term energy storage potential. The development of the approach has carefully considered theimplications of a number key sources of complexity that arise as part of this type of modelling.

This study investigates how an increase in exports (a key pillar in the UK Industrial Strategy) could impact energy and industrial policy by comparing two types of energy-economy models.

Achieving the targets for reducing greenhouse gas emissions set out in the UK Climate Change Act will require a significant transformation in the UK's energy system.

At the same time, the government is pursuing a new UK Industrial Strategy, which aims to improve labour productivity, create high-quality jobs and boost exports across the UK.

The economic and the energy systems in the UK are tightly linked and so policies adopted in one area will produce spillover effects to the other.

To achieve the objectives set out in the two strategies it is therefore vital to understand how the policies in the energy system will affect economic development and vice versa.

Our study contributes to this by investigating how an increase in exports (a key pillar in the UK Industrial Strategy) could impact energy and industrial policy.

We address this question by systematically comparing the results of two types of energy-economy models of the UK, a computable general equilibrium model (CGE) and a macroeconometric (ME) model.

In both models we analyse a stimulus to demand from an increase in exports arising from a successful export strategy as motivated by the UK Industrial Strategy.

Main findings

The qualitative results of the export stimulus are similar across all models in that GDP and employment are always stimulated. In this sense, the results are reassuring for the UK’s Industrial Strategy that emphasises export promotion.

However, the models also find that total energy use and CO₂ emissions increase, and so does the energy intensity and emissions intensity of GDP.

The increase in CO₂  emissions occur because the study identifies the energy and CO₂ impacts of an export shock with other things remaining unchanged. Therefore the models do not simultaneously incorporate the UK carbon budgets or policies to support energy efficiency and decarbonisation of energy supplies.

However, our analysis reveals the likely adjustment of energy and climate policies to counteract the increase in CO₂ and energy intensity that may result from export promotion. It therefore emphasises the need to complement UK industrial policies with appropriate action on energy use and carbon emissions to meet statutory carbon targets set by the Climate Change Act (2008).

The results highlight the interdependence of the energy and economic systems. They show that there are benefits to coordinating strategic initiatives aimed at stimulating economic activity with those aimed at tackling carbon emissions, as envisaged in the UK’s Clean Growth Strategy.

ETI’s Strategy Manager Liam Lidstone presented “Transitioning to a low carbon energy system: network transition challenges” at the European Energy Research Alliance (EERA) on 24 November 2016

• Energy networks as a part of the energy system
•  Energy system scenarios
• Network transition challenges
• Integrating networks to optimise across energy vectors
• Balancing supply and demand
• Final thoughts

This report was produced at the outset of the ESME project. Written at the time of the build of the first prototype energy model, it gives a functional definition for a complete model to meet the key requirements identified by ETI. The functional definition addresses model functionality, architecture, data requirements and the implementation path. Many aspects of this functional definition were later implemented in ESME via development of the original prototype model, however not all, so this report is not a factual record of the functionality of later versions of ESME. See the modelling paper dated April 2014 instead for a record of actual model functionality

This publication has been produced as part of the work on the ETI’s internationally peer reviewed energy system modelling environment (ESME) - a national energy system design and planning capability that helps toidentify key areas for ETI investments. ESME is also used by UK Government to underpin and inform energy policy.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

The purpose of this deliverable is to explain the process of reading the multi-source raw data from the hard disks and extracting features that are useful for machine learning algorithms. The process of synchronisation of the electricity, water and HEMS data is presented. A brief description of the code developed to achieve the new data representation is provided.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

In this deliverable Bournemouth Uni presents an original approach for mining utility data usage patterns relying on a novel Deep Hierarchical Dynamic model which consists of three modules, a Deep Belief network (DBN), a hierarchical mixture model which is based on Latent Dirichlet Allocation (LDA) and a Dynamic Bayesian Network based on Hidden Markov Model (HMM), called DBN-LDA-HMM. This architecture aims at extracting topics from data while taking into account the temporal structure of the data to model the inter-topic sequential dependency. While the mathematical details of the proposed algorithm are described elsewhere, a full empirical evaluation of this pattern mining algorithm using the ETI data is discussed, highlighting its performance on various mining tasks.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

This deliverable describes the fifth task which is related to scalable inference. Specifically; it shows that the proposed inference algorithms of the previous deliverables work online. A full description of the scalable inference algorithms can be found in the previous deliverables and enclosed papers.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

The ETI collected utility meter and other data (e.g. room temperatures, humidity, and HEMS control data) from five dwellings over a period of six months. Using the collected data, work was conducted to evaluate different machine learning algorithms, research appropriate data features and calibrations thereof, and test the “art of the possible”. Thework sought not only to understand historical human activity within the building, but also to estimate probabilities of future hot water usage, occupancy and heating needs.

The described work resulted in Baringa and ASI jointly developing several Python scripts and notebooks for ETI. This document sets out to explain the purpose of the various scripts and notebooks required to rerun the analysis, the relationship between the various files and explain how to run the code. The document starts with an overview of the workflow and code, before diving into a detailed description of each section of the workflow and the corresponding code.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

The ETI collected utility meter and other data (e.g. room temperatures, humidity, and HEMS control data) from five dwellings over a period of six months. Using the collected data, work was conducted to evaluate different machine learning algorithms, research appropriate data features and calibrations thereof, and test the “art of the possible”. Thework sought not only to understand historical human activity within the building, but also to estimate probabilities of future hot water usage, occupancy and heating needs.

This report was commissioned in order to share the key learnings from the ETI High Frequency Appliance Disaggregation Analysis work carried out jointly by Baringa Partners and ASI Data Science. At a high level, the work evaluates machine learning algorithms, researches potential data features and calibrations thereof, and tests the “art of the possible” when it comes to predicting future occupancy and hot water usage from multi-vector high frequency meter data. Crucially, the work conducted not only seeks to understand historical human activity within the residency, but also to predict future needs. This report serves the purpose of sharing the key insights of the work to date as well as sharing areas of potential future work.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

The deliverable addresses the problem of prediction of hot water usage and gas using online regression models. While there was no access to the quantity of hot water consumed over time by the inhabitant, the inference can be made using the temperature of the domestic hot water flow and the central heating flow. The algorithmic steps are provided in this report, and a full description of the algorithms is given in the referred papers

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

This deliverable describes the fifth task which is related to scalable inference. Specifically; it shows that the proposed inference algorithms of the previous deliverables work online. A full description of the scalable inference algorithms can be found in the previous deliverables and enclosed papers.

The ETI commissioned the HEMS & ICT Market project to undertake an in depth study and assessment of HEMS along with what data, processes and controls andpotential additional services enabled via a linked ICT system. The project delivers key insights and findings in terms of potential future offerings and capabilities of these products along with market assessment information. The aim of the project was to characterise the existing market for HEMS and ICT systems and to quantify the market/commercial opportunities for future HEMS and ICT propositions for both consumer and business.

The High Frequency Appliance Disaggregation Analysis (HFADA) project builds upon work undertaken in the Smart Systems and Heat (SSH) programme delivered by the Energy Systems Catapult for the ETI, to refine intelligence and gain detailed smart home energy data. The project analysed in depth datafrom five homes that trialed the SSH programme’s Home Energy Management System (HEMS) to identify which appliances are present within a building and when they are in operation. The main goal of the HFADA project was to detect human behaviour patterns in order to forecast the home energy needs of people in the future. In particular the project delivered a detailed set of data mining algorithms to help identify patterns of building occupancy and energy use within domestic homes from water, gas and electricity data.

The purpose of this deliverable is to describes the second task which is related to pattern mining. Specifically, it presents an original approach for mining utility data usage patterns relying on a novel algorithm, Gaussian Latent Dirichlet Allocation (GLDA). A full empirical evaluation of the proposed algorithm using the ETI data is discussed highlighting its performance on various mining tasks.

This analysis evaluated whether a segmentation could be developed to predict the reasons why households changed their heating systems. Three different analysis attempts were made; however the best of the models only predicted 55% of the reasons for changing the boiler correctly. The method used and results are set out in this report

This deliverable provides a description of Modelling Code. This report was produced to support the analysis work done for BEIS for their input to IEA Annex 42.

The Integrated Electric Heating Project provided a modelling tool to evaluate the opportunities and challenges for electric heating to meet UK household requirements. The tool will be used to create and evaluate upgrade pathways for a small number of housing archetypes informed by detailed information gathered from dwelling participating in the recent Home Energy Management System trial.

This report contains a landscape review of domestic heat storage, produced by NEF, is included as an annex to the final report of the Integrated Electric Heat project.

We looked in detail at 13 technologies that could provide the necessary thermal storage. We considered both daily storage (energy stored and used within the a 24 hour period), and also seasonal storage (energy stored in summer to be used in winter). We also considered which technologies were appropriate for which heat sources and delivery mechanisms – like radiators, underfloor heating, or warm-air heating. We concluded that the most promising four technologies to pursue in the integrated electric heating project are:

1. Phase-change materials in a storage unit – using the Sunamp or other system to provide storage over a day or a few days.
2. A seasonal storage pit – using solar thermal energy to store energy from summer to winter.
3. Electric storage heaters – powered by excess solar PV and cheap Economy 7 electricity overnight.
4. A thermochemical system – included for its potential as a disruptive technology, which may need a buffer tank until system designs are refined

This deliverable is the final report of the Integrated Electric Heat project. It includes the methodology for developing the upgrade pathways, the upgrade pathways for five HEMS homes and an assessment of how thermal storage can flatten energy demand profiles for domestic heat. There are extensive appendices covering the modelling methodology, verification and output, as well as the home surveys and technology suitability. A landscape review of domestic heat storage, produced by NEF, is included as an annex to the main report.

The Spatial Energy Plan for Greater Manchester Combined Authority project was commissioned as part of the Energy Technologies Institute (ETI) Smart Systems and Heat Programme and undertaken through collaboration between the Greater Manchester Combined Authority and the Energy Systems Catapult. The study has consolidated the significant data and existing evidence relating to the local energy system to provide a platform for future energy planning in the region and the development of suitable policies within the emerging spatial planning framework for Greater Manchester.

This study provides a desktop review of the EnergyPath Networks (EPN) Decentralised Energy and District Heating deployment in Bury, considering the connection of non-domestic buildings to heat networks. The EPN model develops heat network deployment across cluster area, with connections to both domestic and non-domestic buildings. This report provides general recommendations on nondomestic building connection to a heat network based on their use type and size. The aim of this report is to define metrics on which the connection policy applied by the model can be tested and refined.

The Spatial Energy Plan for Greater Manchester Combined Authority project was commissioned as part of the Energy Technologies Institute (ETI) Smart Systems and Heat Programme and undertaken through collaboration between the Greater Manchester Combined Authority and the Energy Systems Catapult. The study has consolidated the significant data and existing evidence relating to the local energy system to provide a platform for future energy planning in the region and the development of suitable policies within the emerging spatial planning framework for Greater Manchester.

As part of the Smart Systems and Heat Programme the Energy Systems Catapult has undertaken a strategic high level study of the energy system in Greater Manchester, with the intention of delivering an EnergyPath Networks study for one of the local authorities in Greater Manchester. The purpose of this document is to set out the different factors which could be included in selecting that local authority and the results of initial analysis of those factors completed by the Energy Systems Catapult.

From the analysis, the report concludes that Bury has highest score for consideration factors and modelling complexity, followed by Trafford. Manchester would be very challenging to analyse, and may prove beyond the capability of the model at its current state of development.

In this report we provide a brief overview of the UKERC Whole Systems Networking Fund, before providing summaries from each of the projects awarded during the programme