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Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services
Oosthuizen, T. 2025. Optimised Geospatial Model for Renewable Resource Allocation to Minimise Dependence on Costly Ancillary Services. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/aa0b392c-5a02-4a02-b1bb-49dd0609a398
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613995990515712 |
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| access_status_str | Open Access |
| author | Oosthuizen, Theunis |
| author2 | Van Staden, Chantelle Y. |
| author_browse | Oosthuizen, Theunis Van Staden, Chantelle Y. |
| author_facet | Van Staden, Chantelle Y. Oosthuizen, Theunis |
| author_sort | Oosthuizen, Theunis |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Oosthuizen, T. 2025. Optimised Geospatial Model for Renewable Resource Allocation to Minimise Dependence on Costly Ancillary Services. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/aa0b392c-5a02-4a02-b1bb-49dd0609a398 |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/132523 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:45:00.328Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/132523 Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services Oosthuizen, Theunis Van Staden, Chantelle Y. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Renewable energy sources -- Management Smart power grids Reliability (Engineering) Electric power systems -- Load dispatching UCTD Oosthuizen, T. 2025. Optimised Geospatial Model for Renewable Resource Allocation to Minimise Dependence on Costly Ancillary Services. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/aa0b392c-5a02-4a02-b1bb-49dd0609a398 Thesis (MEng)--Stellenbosch University, 2025. ENGLISH ABSTRACT: The shift towards renewable energy sources in electric power systems presents unique challenges and opportunities for grid stability, economic performance, and environmental sustainability. Traditional power systems rely on dispatchable fossil-fuel-based generation to balance electricity supply with fluctuating demand. However, as renewable energy penetration increases, particularly from variable sources such as wind and solar, the resulting variability and uncertainty in net load complicates the balancing process. This research investigates methods to improve grid flexibility and reliability under high renewable integration, focusing on strategies to manage the system’s ramping capability, which is essential for addressing rapid changes in net load and avoiding scarcity events. A multi-disciplinary approach, combining advanced data analysis, machine learningbased clustering, and optimisation techniques, is employed to identify optimal siting locations. High-resolution wind and solar data provide the basis for clustering regions with similar renewable energy patterns. Through time of use feature extraction and negative mapping of environmentally sensitive zones, the framework identifies viable sites for renewable projects while respecting ecological constraints. Clustering enables a representative view of wind and solar patterns across the Western Cape, which can inform the analysis of renewable resource complementarity that can support grid reliability objectives at a provincial level. The results show that optimising ramping capabilities and incorporating robust system flexibility measures can mitigate the frequency and severity of operational shortages, particularly when future renewables are integrated. This approach improves economic efficiency by reducing dependency on high-cost, carbon-intensive power sources during peak demand intervals. Furthermore, the proposed integration framework informs optimal absorption of renewable energy, helping the system to avoid the reliability challenges associated with increased resource power generation. This resource allocation framework offers valuable insights for renewable energy planning and can be used as a model for other regions in South Africa and beyond that face similar challenges in renewable energy integration. By balancing renewable capacity with grid flexibility requirements, this framework enables the strategic allocation of resources to optimise economic performance and maintain grid reliability. The findings contribute to South Africa’s renewable energy landscape and provide actionable recommendations for future renewable energy infrastructure planning. AFRIKAANSE OPSOMMING: Die verskuiwing na hernubare energiebronne in elektriese kragstelsels bied unieke uitdagings en geleenthede vir netwerkstabiliteit, ekonomiese prestasie en omgewingsvolhoubaarheid. Tradisionele kragstelsels maak staat op versendbare fossielbrandstof-gebaseerde opwekking om elektrisiteitsvoorsiening met wisselende vraag te balanseer. Soos die penetrasie van hernubare energie egter toeneem, veral van veranderlike bronne soos wind en sonkrag, bemoeilik die gevolglike wisselvalligheid en onsekerheid in netto lading die balanseringsproses. Hierdie navorsing ondersoek metodes om netwerkbuigsaamheid en betroubaarheid onder ho¨e hernubare integrasie te verbeter, en fokus op strategie¨e om die stelsel se opritvermo¨e te bestuur, wat noodsaaklik is om vinnige veranderinge in netto krag aan te spreek en skaarsheidsgebeurtenisse te vermy. ’n Multi-dissiplinˆere benadering, wat gevorderde data-analise, masjienleer-gebaseerde groepering en optimeringstegnieke kombineer, word aangewend om optimale liggingsliggings te identifiseer. Ho¨e-resolusie wind- en sonkrag data verskaf die basis vir groepering van streke met soortgelyke hernubare energiepatrone. Deur gebruik te maak van onttrekking en negatiewe eliminasie van omgewings sensitiewe sones, identifiseer die raamwerk lewensvatbare terreine vir hernubare projekte terwyl ekologiese beperkings gerespekteer word. Groepering maak ’n verteenwoordigende siening van wind- en sonpatrone regoor die Wes-Kaap moontlik, wat die ontleding van hernubare hulpbronkomplementariteit kan verskaf wat netwerk-betroubaarheidsdoelwitte op ’n provinsiale vlak kan ondersteun. Die resultate toon dat die optimalisering van opritvermo¨ens en die insluiting van stelselbuigsaamheidsmaatre¨els die frekwensie en erns van bedryfstekorte kan versag, veral wanneer toekomstige hernubare energie ge¨ıntegreer word. Hierdie benadering verbeter ekonomiese doeltreffendheid deur die afhanklikheid van ho¨ekoste, koolstofintensiewe kragbronne tydens spitsvraagintervalle te verminder. Verder verskaf die voorgestelde integrasieraamwerk optimale absorpsie van hernubare energie, wat die stelsel help om die betroubaarheidsuitdagings wat verband hou met verhoogde hulpbronkragopwekking te vermy. Hierdie hulpbrontoewysingsraamwerk bied waardevolle insigte vir hernubare energiebeplanning en kan as ’n model gebruik word vir ander streke in Suid-Afrika wat soortgelyke uitdagings in hernubare energie-integrasie in die gesig staar. Deur hernubare kapasiteit met netwerkbuigsaamheidsvereistes te balanseer, maak hierdie raamwerk die strategiese toewysing van hulpbronne moontlik om ekonomiese prestasie te optimaliseer en netwerkbetroubaarheid te handhaaf. Die bevindinge dra by tot Suid-Afrika se hernubare-energie-landskap en verskaf uitvoerbare aanbevelings vir toekomstige hernubareenergie- infrastruktuurbeplanning. Masters 2025-06-10T12:33:11Z 2025-06-10T12:33:11Z 2025-03 Thesis https://scholar.sun.ac.za/handle/10019.1/132523 en Stellenbosch University xvi, 126 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Renewable energy sources -- Management Smart power grids Reliability (Engineering) Electric power systems -- Load dispatching UCTD Oosthuizen, Theunis Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title | Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title_full | Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title_fullStr | Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title_full_unstemmed | Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title_short | Optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| title_sort | optimised geospatial model for renewable resource allocation to minimise dependence on costly ancillary services |
| topic | Renewable energy sources -- Management Smart power grids Reliability (Engineering) Electric power systems -- Load dispatching UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/132523 |
| work_keys_str_mv | AT oosthuizentheunis optimisedgeospatialmodelforrenewableresourceallocationtominimisedependenceoncostlyancillaryservices |