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An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements
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IET Energy Syst Integration - 2024 - Ayo - An integrated planning framework for optimal power generation portfolio.pdf | Published version (early view) | 1.83 MB | Adobe PDF | View/Open |
Title: | An integrated planning framework for optimal power generation portfolio including frequency and reserve requirements |
Authors: | Ayo, O Falugi, P Strbac, G |
Item Type: | Journal Article |
Abstract: | Electricity system decarbonisation poses several challenges to network stability and supply security, given renewables' intermittency and possible reduction of system inertia. This manuscript presents a novel integrated system framework to determine optimal generation investments for addressing decarbonisation challenges and achieving cost-effective electricity systems while ensuring frequency stability and reserve requirements are met at the operational level in a net-zero system. The novel planning framework is a mixed-integer bilinear programming problem accurately modelling clustered variables for the on/off status of generation units and seconds-timescale frequency requirements at an operational and planning level. The benefits of the decision framework and effects of dispatch decisions in a year are illustrated using the Great Britain case study. The results provide optimal trade-offs and cost-effective investment portfolios for including detailed modelling of unit-commitment and frequency stability constraints versus not including them in the planning model. Making investment decisions for a net-zero electricity system without these constraints can lead to very high system costs due to significant demand curtailment. Although the model's computation burden was increased by these constraints, complexity was managed by formulating them tightly and compactly. Non-convex quadratic nadir constraints were efficiently solvable to global optimality by applying McCormick relaxations and branching techniques in an advanced solver. |
Issue Date: | 18-Jun-2024 |
Date of Acceptance: | 22-May-2024 |
URI: | http://hdl.handle.net/10044/1/113418 |
DOI: | 10.1049/esi2.12152 |
ISSN: | 2516-8401 |
Publisher: | Wiley |
Journal / Book Title: | IET Energy Systems Integration |
Copyright Statement: | © 2024 The Author(s). IET Energy Systems Integration published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Tianjin University. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Publication Status: | Published online |
Online Publication Date: | 2024-06-18 |
Appears in Collections: | Grantham Institute for Climate Change Faculty of Natural Sciences Faculty of Engineering |
This item is licensed under a Creative Commons License