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| Reference Number | NIA2_NGESO063 | |
| Title | Causal Analysis of Balancing Costs | |
| Status | Started | |
| Energy Categories | Other Cross-Cutting Technologies or Research (Energy Models) 50%; Other Cross-Cutting Technologies or Research (Energy Economics) 50%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | SOCIAL SCIENCES (Economics and Econometrics) 50%; ENGINEERING AND TECHNOLOGY (Electrical and Electronic Engineering) 50%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 100% | |
| Principal Investigator |
Project Contact No email address given National Grid plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 April 2024 | |
| End Date | 31 October 2025 | |
| Duration | ENA months | |
| Total Grant Value | £330,000 | |
| Industrial Sectors | Power | |
| Region | London | |
| Programme | Network Innovation Allowance | |
| Investigators | Principal Investigator | Project Contact , National Grid plc (100.000%) |
| Industrial Collaborator | Project Contact , National Grid plc (0.000%) |
|
| Web Site | https://smarter.energynetworks.org/projects/NIA2_NGESO063 |
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| Objectives | This project will seek to quantify the probability that certain conditions will lead to high balancing costs, and will consider the Balancing Mechanism, balancing services, and energy trading as a resulting cost. The research is open and investigative in identifying the factors and conditions leading to these high-costs, and it is one of the objectives to identify the most impactful factors that result in high costs. The project will be delivered in 3 key work packages:WP 1 Research The initial phase of the project will include a comprehensive overview of all major influences on balancing costs. This will cover at least six different factors and will likely include observations into wind generation, constraint management, outage optimisation, and interconnector swings. A methodology based on semi-parametric modelling and causal discovery will be developed based on these influences to quantify the probability that certain conditions lead to certain cost outcomes. An initial review will be used to prioritise which influences have the greatest impact, and which the ESO have the greatest control of in terms of mitigations.WP 2(a) In depth analysisThe most impactful factor from phase 1 that the ESO has control over mitigating will be prioritised for a more detailed causal and statistical analysis to produce a more granular output of methodologies. This phase will outline the methodology to identify conditions that lead to this cause being particularly influential on balancing costs.WP 2(b) Prototype tool (Stage gate will be present prior to starting this phase.) The methodology developed in earlier phases will be used to produce a prototype tool for the control room that can identify the probability of high balancing cost outcomes and inform the control room decisions on how they can best mitigate these conditions. The threshold for what is considered as a high-cost day will be reviewed continuously throughout the project. In line with the ENA"s ENIP document, the risk rating is scored Low:TRL steps = 1 (2 TRL step)Cost = 1 (<£500k)Suppliers = 1 (1 supplier)Data assumptions = 2Total = 5 The tools used to balance the system are extremely complex with many different behaviours that contribute to balancing costs. For example, improvements to Balancing Services Use of System (BSUoS) forecasts have been made, but these do not deal with fine-grained influences on balancing costs. Similarly, the effect of interconnectors on balancing costs have been identified but these are not yet fully understood. The aim of this project is to use the wealth of data being collected at system and market levels to comprehensively quantify the probability that certain conditions lead to the greatest balancing costs. The project will consider the Balancing Mechanism, balancing services, and energy trading throughout the causal analysis on balancing costs.Previous work has shown how semi-parametric modelling may allow for the modelling and predicting unscheduled flows over the EU; this project will build upon this research. The project will also look to build a prototype tool that can be used by the control room to identify the probability of a high balancing cost outcome Develop a methodology for identifying the most probable factors that impact balancing costs.Identify a prioritised factor and identify the key influences on this prioritised factor when considering balancing costs.Develop a tool using open-source code that can identify the probability of a high balancing cost outcome when operating the system. | |
| Abstract | Balancing costs have been increasing significantly over the past three years and are forecast to increase even further out to 2030. There are many factors which influence increasing balancing costs. Identifying what system conditions lead to higher-cost outcomes, and which of these have the most significant effect, is vital to improve control room decisions and to ensure the ESO"s balancing cost reduction strategy is fit for purpose. This project will deliver a method to quantify the probability that certain conditions will lead to high balancing costs, and a more detailed causal and statistical analysis will then be completed for the most impactful factors identified. If successful, the methodology will be used to produce a prototype tool that can identify the probability of high balancing costs outcomes and inform the control room on how to best mitigate these conditions. | |
| Data | No related datasets |
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| Projects | No related projects |
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| Publications | No related publications |
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| Added to Database | 02/10/24 | |
