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Reference Number NIA_ENWL001
Title Demand Scenarios with Electric Heat and Commercial Capacity Options
Status Completed
Energy Categories OTHER POWER and STORAGE TECHNOLOGIES(Electricity transmission and distribution) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%;
ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
Electricity North West Limited
Award Type Network Innovation Allowance
Funding Source ENA Smarter Networks
Start Date 01 April 2015
End Date 01 October 2016
Duration 18 months
Total Grant Value £500,000
Industrial Sectors Power
Region North West
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , Electricity North West Limited (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_ENWL001
Objectives The financial benefits of the project will come from ensuring that load-related investment is well justified, and in particular by identifying where the Capacity to Customers DSM technique or customer interventions beyond the meter can be used to avoid or defer load-related investment. This will be done by more accurately and credibly representing current and future load, to minimise load-related expenditure to deliver only the justified capacity. It will also provide the foundation for future use of commercial solutions where these can be shown to provide an overall cost benefit. These commercial solutions also offer the opportunity to release capacity more quickly than traditional network solutions (customer service benefit), and with lower environmental impact e. g. reducing electricity demand, avoiding embedded carbon associated with new network assets. It is also expected that the project will streamline analysis of demand in the planning process, allowing our engineers to take a more sophisticated view of current and future demand, without an increase in planning engineer resource. A. Load Scenarios with Electric Heat Appropriate methods implemented to correct observed past Grid & Primary peak demand for weather effects and distributed generation contribution, balancing accuracy with cost. Enhanced quantification of impact of growth of electric heating in 2022 and 2030 on the Electricity North West network under different scenarios - using analysis of different types of heat pumps in different housing types. High-level analysis of a provisional scenario to 2050. Quantification of how other electricity value chain players’ influence of electric heating operation will affect this impact (either reducing the impact and / or increasing the impact at different times) in 2022 and 2030, with high level view to 2050. Revised tools and methods available to generate credible Grid & Primary and secondary networks peak load scenarios by asset to 2030, reflecting the scale and sources of uncertainty in demand, with scenarios used for internal and external business requirements. B. Commercial Capacity Options Created a ‘Real options’ decision approach with supporting Excel tool(s), supported by the University of Manchester’s analysis, which uses demand scenarios to make an economic assessment of whether to use the Capacity to Customers post-fault DSR method versus traditional reinforcement. Identification, initial assessment and ranking of ways that Electricity North West can mitigate (other than reinforcing the network) the impact that electric heating will have on their network (focusing on the customer side of the meter). Internal / external dissemination of results from both aspects of project is detailed in the ‘potential for new learning’ section.
Abstract There is significant uncertainty around the timescale and location of future changes in peak electricity demand on distribution network assets. Factors contributing to that uncertainty include economic changes, energy efficiency, alterations in customer behaviour (such as peak relative to average behaviour, response to smart metering and increased use of air conditioning), and adoption of low carbon technologies such as distributed generation, electric vehicle charging and heat pumps. Alongside future uncertainty, volatilities in past peak demand can make it difficult to understand the past baseline e. g. related to economic activity, generator output and weather-dependence, alongside measurement uncertainties. Heat pumps offer huge potential for carbon savings once electricity is decarbonised. But we consider rising non-diverse electricity demand from heat pumps is the most significant and uncertain long-term (from RIIO-ED2 onwards) effect on demand on the distribution networks. This is due to limited adoption so far, and uncertainty about future incentives for deployment and during operation. DNOs need to make assumptions about the timescales and location of demand growth so they can invest efficiently in network capacity. Existing methods of demand analysis and forecast do not capture and address this multi-faceted uncertainty in a structured way. So we think there is a need to reassess and improve how we understand uncertain electricity demand, particularly how this is affected by electric heat. Secondly given that uncertainty, further analysis is required around how to make decisions about investing for capacity, including assessment of the commercial options to release network capacity, which may be cheaper and quicker to deliver than technical solutions on the network. This project builds on the previous IFI projects “Demand Forecasts and Real Options” and “Load Allocation”, the First Tier LCNF project “Low Voltage Network Solutions”, the Second Tier LCNF project “Capacity to Customers” and our internally funded “Power Saver Challenge” and “Demand and Generation Dashboard” projects. It first involves Development and Demonstration of better Technical approaches to estimating current and future load by distribution network asset, reflecting the associated uncertainties in load. It will progress from a best-view forecast based on past observed demand, to a set of scenarios based on a corrected version of past demand. These load scenarios are then the foundation for assessing two Commercial solutions to capacity problems. The project will Develop and Demonstrate a ‘Real Options’ approach to assessing when to use our ‘Capacity to Customers’ demand side management (DSM) technique versus various scales of traditional Grid & Primary reinforcement. The project will also do enabling Research on identifying and prioritising potential Commercial non-network solutions to address secondary networks constraints associated with uptake of domestic heat pumps.Note : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 09/08/18