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Reference Number NIA_SPT_1504
Title Managing uncertainty in future load-related investment
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 ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
SP Energy Networks
Award Type Network Innovation Allowance
Funding Source ENA Smarter Networks
Start Date 01 February 2016
End Date 01 February 2019
Duration 36 months
Total Grant Value £300,000
Industrial Sectors Power
Region Scotland
Programme Network Innovation Allowance
 
Investigators Principal Investigator Project Contact , SP Energy Networks (100.000%)
Web Site http://www.smarternetworks.org/project/NIA_SPT_1504
Objectives To make use of statistical relationships between weather/time of day/season and the demand/output of individual and aggregated LCTs and underlying demand. To provide the capability to model the uptake of LCTs with respect to customer type etc. To identify and model a suitable exemplar network for test purposes. To construct and demonstrate a framework to sample uncertain factors influencing use of a network and simulate network performance under each of a large number of instances of those variations to produce a database of input-linked outcomes To develop methods of interrogating and analysing the database to identify intervention priorities and the underlying network, demand and LCT conditions which trigger them To apply the models, framework and methodology to the selected exemplar network to identify weak points and points of pressure, assess overall intervention need and urgency, identify suitable interventions. Compare results with existing Business as Usual (BAU) planning methods. The development of a production quality software tool is not an objective of this project. In order that investment risk is reduced, the development of such a tool will be considered under a subsequent/parallel project once the learning derived from this project is sufficiently developed.
Abstract Difficulty is already being experienced in dealing effectively with uncertainty in demand and wind generation output in network planning and operation. PV generation is also becoming significant in some areas, and is demonstrably adding to the level of uncertainty in assessing network capability and adequacy. The expected increase in uptake of electric vehicles, heat pumps and other low carbon technologies will further increase the level of uncertainty which is encountered in both planning and operational timescales. Without action to directly model and analyse these uncertainties, two risks may arise in relation to planning and operation of the network: Over-conservatism in which the network is reinforced to ensure that it will have a higher power transfer capability than turns out to be necessary. This results in overinvestment in assets which are not fully utilised in providing the required level of service and reliability. Under-investment, such that the ability of the network to accommodate future patterns of generation and demand is significantly limited by network constraints with an associated impact on (a) facilitation of connection of generation and (b) reliability in meeting demand. Existing models and techniques used to analyse the variability and correlations of demand and generation (and hence their impact on network flows and voltages) are not sufficient to deal adequately with the scale of adoption of these new technologies. There is therefore a need for a new and more rigorous approach, making use of probabilistic representations of the behaviour of existing and new generation sources and demand types, both individually and in aggregate. Furthermore, methods are needed to exploit these models to allow the need for and benefits of traditional and novel interventions (such as ‘smarter’ network control and operation) to be assessed and compared. Given adequate information obtained from a suitable software tool and pertaining to a number of credible scenarios, it should be possible for a network planner to identify an appropriate network investment strategy to mitigate the risks outlined above Given suitable models of the time variation of both demand and generation output, their relationship with number of installations and correlations between them, the overall approach will then be to use simulation techniques to examine a large range of scenarios of possible future operating conditions and demands on the network. These system operation scenarios will be constructed by sampling from the statistical models, collectively and consistently so as to represent a coherent picture of uptake of technologies of interest, and of the behaviour of those technologies as well as of traditional loads. The use of robust models and sampling techniques will ensure that the resulting database of network operating states and external conditions is statistically representative of situations likely to arise. The database can be interpreted, analysed and mined using statistical techniques to identify: The most likely violations of network operating constraints, their nature, e.g. thermal or voltage, and their severity Common factors underlying violations of constraints Impacts in terms of Customer Interruptions (CI) and Customer Minutes Lost (CML)Priority network locations for intervention, and the nature and cause of the problem to be solved Trigger points for intervention in terms of load growth or Low Carbon Technologies (LCT) uptake, observed network behaviour, etc. Learning from previous IFI, LCNF, NIC and NIA work, as well as wider international experience can then be applied to suggest the most suitable intervention (including traditional reinforcement) to relieve the identified and forecast problemsNote : Project Documents may be available via the ENA Smarter Networks Portal using the Website link above
Publications (none)
Final Report (none)
Added to Database 12/09/18