Projects
Projects: Projects for Investigator |
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| Reference Number | NIA_NGSO0029 | |
| Title | Applications of convex optimisation to enhance National Grids NOA process | |
| Status | Completed | |
| Energy Categories | Other Cross-Cutting Technologies or Research(Energy system analysis) 20%; Other Power and Storage Technologies(Electricity transmission and distribution) 80%; |
|
| Research Types | Applied Research and Development 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics) 50%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 50%; |
|
| UKERC Cross Cutting Characterisation | Systems Analysis related to energy R&D (Other Systems Analysis) 100% | |
| Principal Investigator |
Project Contact No email address given National Grid plc |
|
| Award Type | Network Innovation Allowance | |
| Funding Source | Ofgem | |
| Start Date | 01 October 2019 | |
| End Date | 01 April 2021 | |
| Duration | ENA months | |
| Total Grant Value | £300,000 | |
| 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/NIA_NGSO0029 |
|
| Objectives | The aim of this project is to develop new tools using new advancements in mathematical and computational techniques to enable us to assess more scenarios and backgrounds and check whether they comply with SQSS and other industry codes. We will develop and test convex optimisation models and machine learning algorithms that adequately represent voltage and reactive power in the system. The key project outputs are provided as follows: A convex optimisation model of the ET system to assess reactive power requirements A clustering algorithm to group and classify expected future operational scenarios in the GB system Pilot testing of the proposed model on a part of the GB network Application of the clustering algorithm on the scenarios generated using Future Energy Scenarios and Electricity Ten Year Statement data Full scale test of the proposed model on representative network in a size of GB network Recommendations for integrating the proposed model into the NOA process This approach will help us better understand the future needs for reactive power to (1) decrease the operational costs to maintain voltage compliance, (2) help decarbonization by reducing the number of conventional units needed for voltage support, and (3) make better investment decisions for reactive power resources. The NOA recommends investment options to achieve a sustainable, economic and efficient future electricity grid. Boundary Capabilities (BCs) are the basis for the NOA process. These BCs are focused on the bulk transfer of power across the network and therefore predominantly address thermal constraint issues. The energy supply industry is rapidly evolving and the level of uncertainty is increasing due to high renewable penetration. Due to these changes, we have witnessed increased costs for managing voltage and reactive power in our network. To understand the reactive power and voltage issues, detailed modelling of year-around operational conditions is required. Through this project, we are going to develop a capability that will allow us to perform year-round voltage assessment (reactive power requirements). The project aims to: Create a standard pipeline of data flow between NG ESO data format and the prototype OPF Where necessary, enhance the prototype OPF to include a reasonable representation of relevant steady-state components models in PowerFactory. Create a prototype tool to quantify future reactive power requirements against a very large range of scenarios in planning time-scale that capture uncertainties going forwards. To develop an appropriate convex optimization method for inclusion in an AC-OPF model of the GB electricity system that permits assessment of the transmission systems ability to meet voltage and reactive power requirements. Test the model on a representative network in size of GB high-voltage electricity system and validate results using current tools and techniques. Provide advice on updates for the NOA process. | |
| Abstract | The aim of this project is to develop new tools using new advancements in mathematical and computational techniques to enable us to assess more scenarios and backgrounds and check whether they comply with SQSS and other industry codes. We will develop and test convex optimisation models and machine learning algorithms that adequately represent voltage and reactive power in the system. | |
| Publications | (none) |
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| Final Report | (none) |
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| Added to Database | 09/11/22 | |
