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Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support
Thesis (PhD)--Stellenbosch University, 2022.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2022
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| _version_ | 1867613830728646656 |
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| access_status_str | Open Access |
| author | Van Staden, Chantelle |
| author2 | Vermeulen, Johan |
| author_browse | Van Staden, Chantelle Vermeulen, Johan |
| author_facet | Vermeulen, Johan Van Staden, Chantelle |
| author_sort | Van Staden, Chantelle |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)--Stellenbosch University, 2022. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/124682 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:42:21.587Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/124682 Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support Van Staden, Chantelle Vermeulen, Johan Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Optimal grid support UCTD Renewable energy sources Geospatial data Solar power plants Thesis (PhD)--Stellenbosch University, 2022. ENGLISH ABSTRACT: South Africa has displayed a unique energy supply profile over recent years, where the ability to consistently meet the energy demand has been constrained by physical limitations of the current energy supply infrastructure. The inadequate supply infrastructure results in countrywide loadshedding events, where total energy supply within high demand periods cannot be met. Low-grade coal, poorly maintained power plants and the impending decommissioning of existing thermal plants adds to the country’s energy supply deficit. Inadequate supply in high demand periods typically requires response from expensive on demand dispatch units, which are often non-renewable resources. This also equates to a decrease in grid supply stability. It is expected that optimised geospatial capacity allocation of new build wind and solar plants can assist in addressing the generation capacity constraints in the medium to longer term future. The framework proposed in this study favours a cascaded optimisation strategy, whereby the residual load profile is optimised statistically to reduce the requirements of ancillary services to complement baseload generation. In support of a reliable future energy supply scenario with high penetration of renewable energy, the optimisation framework proposed in this work represents a probabilistic risk-based approach that seeks to minimise the number of events where high residual load values require ancillary service interventions to maintain power balance. In this approach, renewable energy resource features are categorised in terms of the statistical properties of the spatiotemporal wind and solar power profiles for a given set of daily and seasonal Time-of-Use periods. In this context, it is recognised that the resource characteristics and grid impact of wind and solar generation profiles can be interpreted with reference to the daily and seasonal cycles exhibited by the demand profiles, wherein some Time-of-Use periods are more important than others. Apart from the benefit of assigning renewable energy capacities to spatial regions rather than specific coordinates, clustering reduces the dimensions of input data sets dramatically. This reduces the dimensionality of the multi-variable optimisation search space, which translates to reduced risk of local minima and reduced computational cost. The proposed framework has been implemented for a number of baseline case studies and optimisation case studies. It is concluded that the framework is highly flexible in the sense that the formulation of the minimum and maximum allocation constraints allow application for real-world scenarios where capacity allocation constraints apply on a regional level. Overall, the optimisation framework provides a robust method for the geospatial capacity allocation of wind and solar resources. The framework employs a robust way of handling constraint scenarios when considering multiple highly granular resource clusters. AFRIKAANSE OPSOMMING: Suid-Afrika het die afgelope jare 'n unieke energie-voorsienings-profiel getoon, waar die vermoë om konsekwent aan die energievraag te voldoen deur fisiese beperkings van die huidige energievoorsienings-infrastruktuur. Die onvoldoende voorsienings-infrastruktuur lei tot landswye beurtkraggebeurtenisse, waar die totale energie voorraad tydens hoë aanvraag periodes nie nagekom kan word nie. Laegraadse steenkool, swak onderhoud op kragsentrales en die naderende afskakel van bestaande termiese aanlegte dra by tot die land se tekort aan energie-voorsiening. ʼn Onvoldoende aanbod tydens hoë-aanvraag-periodes vereis tipies ʼn onmiddellike reaksie vanaf die kragopwekker, waar duurder intydse elektrisiteits-eenhede opgewek moet word. Hierdie eenhede is gewoonlik afkomstig vanaf niehernubare hulpbronne en plaas addisionele druk op krag-stelsel-stabiliteit. Daar word verwag dat die beperkings op opwekkings-kapasiteit, in die medium- tot langtermyn toekoms, aangespreek kan word deur die geoptimaliseerde georuimtelike-kapasiteits-toewysing van nuwe winden sonkrag-aanlegte. Die raamwerk wat in hierdie studie voorgestel word, bevoordeel 'n kaskadeoptimeringstrategie, waardeur die oorblywende-lasprofiel statisties geoptimaliseer word om die vereistes van bykomende dienste te verminder om basislading-opwekking aan te vul. Ter ondersteuning van 'n betroubare toekomstige energie-voorsienings-scenario met 'n hoë penetrasie van hernubare energie, verteenwoordig die voorgestelde optimaliserings-raamwerk 'n risiko-gebaseerde waarskynlikheids-benadering wat poog om die aantal gebeurtenisse te minimaliseer waar hoë oorblywende laswaardes aanvullende diens-ingryping vereis om die kragbalans te handhaaf. In hierdie benadering word hernubare-energie-hulpbron-kenmerke gekategoriseer. Dit word gedoen volgens die statistiese eienskappe van die tydruimtelike wind- en sonkragprofiele, vir 'n gegewe stel daaglikse en seisoenale tyd-van-gebruik periodes. In hierdie konteks word erken dat die hulpbron-kenmerke van winden sonkragkragstelsels se opwekkings-profiele geïnterpreteer kan word met verwysing na die daaglikse en seisoenale siklusse, soos vertoon deur die aanvraag-profiel. In hierdie aanvraag-profiel is daar ook sommige tyd-van-gebruik periodes wat belangriker is as ander. Afgesien van die voordeel om hernubare energie-vermoëns aan ruimtelike streke toe te ken, eerder as spesifieke koördinate, verminder die groepering van die insetdatastel-afmetings dramaties. Dit verminder die dimensionaliteit van die multiveranderlike optimaliserings-soekruimte, wat neerkom op ʼn verminderde risiko van plaaslike minima en berekenings-koste. Die voorgestelde raamwerk is geïmplementeer vir 'n aantal basislyn-scenarios en optimaliseringsgevallestudies. Daar word tot die gevolgtrekking gekom dat die raamwerk hoogs buigsaam is rakende die formulering van die minimum en maksimum toekennings-beperkings-toepassing, soos toegelaat vir werklike scenarios waar kapasiteits-toekennings-beperkings op 'n streeksvlak geld. In die algemeen bied die optimaliseringsraamwerk 'n robuuste metode vir die georuimtelike-kapasiteits toewysing van wind- en sonkragbronne. Die raamwerk gebruik 'n robuuste manier om beperkingscenarios te hanteer wanneer verskeie hoogs korrelvormige hulpbrongroeperings oorweeg word. Doctoral 2022-03-10T06:32:08Z 2022-04-29T09:26:12Z 2022-03-10T06:32:08Z 2022-04-29T09:26:12Z 2022-04 Thesis http://hdl.handle.net/10019.1/124682 en_ZA Stellenbosch University 152 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Optimal grid support UCTD Renewable energy sources Geospatial data Solar power plants Van Staden, Chantelle Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title | Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title_full | Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title_fullStr | Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title_full_unstemmed | Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title_short | Geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| title_sort | geospatial capacity allocation framework of wind and solar renewable generation for optimal grid support |
| topic | Optimal grid support UCTD Renewable energy sources Geospatial data Solar power plants |
| url | http://hdl.handle.net/10019.1/124682 |
| work_keys_str_mv | AT vanstadenchantelle geospatialcapacityallocationframeworkofwindandsolarrenewablegenerationforoptimalgridsupport |