Forecasting and Probabilistic Methods for Power Systems: A

Edinburgh Research Explorer Forecasting and Probabilistic Methods for Power Systems: A Review of UK Research Citation for published version: Dent, C 2015, Forecasting and Probabilistic Methods for Power Systems: A Review of UK Research. IET. <http://www.theiet.org/sectors/energy/documents/modelling-4.cfm?type=pdf> Link: Link to publication record in Edinburgh Research Explorer Document Version: Publisher's PDF, also known as Version of record General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 30. Sep. 2021 Forecasting and Probabilistic Methods for Power Systems: A Review of UK Research Chris Dent Durham University [email protected] Paper 4 of 15, Part 3: IET Special Interest Publication for the Council for Science and Technology on “Modelling Requirements of the GB Power System Resilience during the transition to Low Carbon Energy” www.theiet.org/pnjv About this report The Institution of Engineering and Technology was commissioned by the Council of Science and Technology (CST) to research the emerging challenges for modelling electricity systems and how Britain’s capabilities would need to be adapted to assess electricity system resilience as GB makes the transition to a low carbon electricity system. This project commissioned, and received, fifteen individual papers from GB-based specialists of international standing in power system modelling. The authors of the papers worked with a wide stakeholder base of network companies, academics and others, who provided review and challenge. Professor Graham Ault CEng FIET was contracted to provide technical co-ordination and drafting. The emerging conclusions were further validated by means of an industry and academic workshop sponsored by Government Office for Science. The entire project was conducted under the direction of an independent steering committee composed of senior IET Fellows, two of whom were also CST nominees. The report is composed of three parts: ■ Part 1: Main report ■ Part 2: Summary of Commissioned Papers ■ Part 3: IET Special Interest Publication – Academic & Industry Papers All three parts of this report are available from the IET website at: www.theiet.org/pnjv © The Institution of Engineering and Technology March 2015 About the IET The IET is working to engineer a better world through our mission to inspire, inform and influence the global engineering community, supporting technology innovation to meet the needs of society. It is the Professional Home for Life® for engineers and technicians, and a trusted source of Essential Engineering Intelligence®. The IET has nearly 160,000 members in 127 countries, with offices in Europe, North America, South Asia and Asia-Pacific. As engineering and technology become increasingly interdisciplinary, global and inclusive, the Institution of Engineering and Technology reflects that progression and welcomes involvement from, and communication between, all sectors of science, engineering and technology. The Institution of Engineering and Technology is registered as a Charity in England and Wales (no 211014) and Scotland (no SCO38698) Forecasting and Probabilistic Methods for Power Systems: A Review of UK Research Chris Dent Durham University [email protected] EXECUTIVE SUMMARY deployment is deemed important, a significant training or hiring programme may be necessary, as if methods are to be applied well it is critical that staff involved have Probabilistic methods have attracted renewed attention in sufficient understanding of them. power systems engineering in recent years. Most prominently, they provide the natural framework in which • Collaboration between relevant research communities to analyse the variable output from renewable sources and should be encouraged. The relevant skills are spread their integration into the system. Also, because probabilistic across multiple research communities, in addition to methods can often reflect underlying uncertainties better power systems engineering, and both academic and than deterministic approaches which have been used in industry funding schemes should be designed to bring the past, they can also support more efficient planning and together the right interdisciplinary teams where this is operation of systems. required. This paper reviews UK research capabilities in the area of • Access to the necessary data is vital for any modelling probabilistic modelling applied to power systems, based project. In areas where it is unduly challenging or on a broad survey of recent research outputs. The field is expensive for one organisation or project to produce its divided into four broad categories of planning timescale own high quality validated datasets (e.g. spatially and generation adequacy assessment, network planning, temporally disaggregated historic renewable resource forecasting and short term operation, and network data), there are great potential benefits to creating reliability analysis. The survey is based on the author’s national datasets which are broadly available to all own knowledge and experience, combined with advice relevant modelling projects. from a number of relevant researchers as to which of their own and others’ research outputs best represent current 1. BACKGROUND capabilities. While there is discussion of the context of current industry practices, the emphasis throughout Greater uncertainty in resource availability on both planning is on original research originating in the UK. and operating timescales, arising from variable output The paper ends with four broad conclusions regarding renewable generation, is a key motivation for increased future development of the field: use of probabilistic methods in power system analysis; • The UK has a number of existing centres of excellence in probability is the natural language in which to quantify probabilistic modelling applied to power systems, spread and manage such uncertainties. It is of course the case across a number of research communities including that in general the advent of variable generation only power systems, mathematical sciences and meteorology, increases uncertainty rather than introducing uncertainty and these should be encouraged. in the first place. Hence a further motivation for the introduction of probabilistic methods is the desire to plan • Even where probabilistic methods can provide major and operate systems more securely and efficiently, by benefits in planning and operating real systems, a major replacing deterministic (and usually heuristic) approaches challenge in broad deployment is that the relevant with probabilistic methods is the desire to plan and operate skills are not widespread in the industry. Where such systems more securely and efficiently, by replacing 3 Forecasting and Probabilistic Methods for Power Systems: A Review of UK Research, 2015 © The Institution of Engineering & Technology 2015 deterministic (and usually heuristic) approaches with probabilistic alternatives which directly reflect the relevant uncertainties. This paper forms part of a broader IET study on ‘Modelling Requirements to Address the Resilience of the Electricity System as it is adapted to deliver Low Carbon Transition’, which is in turn motivated by the IET Position Statement ‘Electricity Network: Handling a Shock to the System’ [1]. This highlighted the commonly discussed ‘trilemma’ of achieving simultaneously goals with respect to security of supply, affordability and the environment, with specific reference to challenges arising from renewable generation, distributed generation, new demands, demand response, and modern control and automation. While not all of these directly introduce greater uncertainty into the system (some directly introduce greater complexity), in all cases probabilistic modelling is required to assess their overall consequences for system operation across a full range of background circumstances, and may be required in determining operational approaches under forecast uncertainty. Fully probabilistic approaches are not widespread in A particular difficulty for meaningful applied renewables industrial practice, partly due to the limited number of integration studies is very limited availability of high quality people with the full set of relevant skills (which do not fall renewable resource data (where there is no widely within a traditional power engineering education syllabus). available GB equivalent of the whole system-level data There are some areas such as transmission planning and exercises carried out by the US National Renewable generation capacity adequacy assessment where a fully Energy Laboratory [5]) though there have been individual probabilistic approach is already in use, the application research activities such as those at Edinburgh University in network planning being particularly distinctive [6] (which has developed an equivalent dataset to the US internationally as discussed later. There are then areas ones using physical downscaling

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