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A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network
Thesis (MEng)--Stellenbosch University, 2020.
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| Format: | Thesis |
| Language: | English |
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Stellenbosch : Stellenbosch University
2020
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| _version_ | 1867613970837274624 |
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| access_status_str | Open Access |
| author | Waswa, Lewis |
| author2 | Bekker, Bernard |
| author_browse | Bekker, Bernard Waswa, Lewis |
| author_facet | Bekker, Bernard Waswa, Lewis |
| author_sort | Waswa, Lewis |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2020. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/108062 |
| institution | Stellenbosch University (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:44:36.436Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/108062 A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network Waswa, Lewis Bekker, Bernard Chihota, Justice Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. Electric networks -- Planning Electric power distribution -- Estimates Solar power plants -- Production control Smart power grids UCTD Thesis (MEng)--Stellenbosch University, 2020. ENGLISH ABSTRACT: Increased solar photovoltaic (PV) installation on to the grid has led to increased technical challenges in electricity network operations. These challenges mainly stem from the design structure of the grid, which only allows unidirectional power flow. This results in several challenges including violation of voltage limits, tripping of network protection systems and distribution line overloads among other issues. These challenges are mainly restricted to the distribution networks, as most solar PV small-scale embedded generators (PV SSEGs) are connected to the distribution networks, whose conditions are, in most cases, not remotely monitored. This results in increased challenges experienced by the networks in terms of network planning, distribution network operations, maintenance, regulation and grid control. To manage these challenges, the distribution operator needs to estimate the total capacity of solar PV installed on the distribution network, in addition to how much of that capacity is embedded in the network’s net demand, which is important in determining the condition of the network at any particular time. Several methods have been used to estimate the capacity of solar PV SSEGs installed in an area. Most studies apply remote sensing and computer vision algorithms to count the number of solar PV panels found in an area. Analysis of these studies indicate that the results obtained cannot be used in determining the condition of the network as they only determine the capacity of solar PV in an area. Secondly, disaggregation studies have largely been used to quantify the installed solar PV capacity embedded in the net demand of a feeder or network. These methods assume a multi-variable approach which requires multiple inputs that are not readily available. This study introduces a novel probabilistic method that applies Monte Carlo methods to quantify the solar PV SSEGs embedded in the net demand of a low voltage feeder. Historical demand, net demand and the solar PV output is used to determine the solar PV capacity embedded in the net demand of a feeder. The accuracy of the method is tested using simulated net demand and actual measured net demand metered from households connected on carefully selected feeders. Results demonstrate that the method performs well where the historical demand and the net metered demand are obtained from similar customer classes. Therefore, it is concluded that it is possible to estimate the capacity of solar PV SSEGs embedded in the net demand obtained from a feeder by analysing and comparing the net demand of that feeder and the historical demand of a similar customer class feeder. AFRIKAANSE OPSOMMING: Verhoogde sonkrag-fotovoltaïese (FV) installasies op die netwerk het gelei tot verhoogde tegniese uitdagings in elektrisiteitsnetwerkbedrywighede. Hierdie uitdagings spruit hoofsaaklik uit die ontwerpstruktuur van die netwerk, wat slegs eenrigtingskrag moontlik maak. Dit lei tot verskeie uitdagings, insluitend die oortreding van spanningsbeperkings, die uitklop van netwerkbeskermingstelsels en oorlading van verspreidingslyne, onder andere. Hierdie uitdagings is hoofsaaklik beperk tot die verspreidingsnetwerke, aangesien die meeste kleinskaalse ingeboude kragopwekkers op die sonkrag (FV KSIK's) aan die verspreidingsnetwerke gekoppel is, waarvan die toestande in die meeste gevalle nie op afstand gemonitor word nie. Dit lei tot verhoogde uitdagings wat die netwerke ervaar ten opsigte van netwerkbeplanning, verspreidingsnetwerkbedrywighede, instandhouding, regulering en netwerkbeheer. Om hierdie uitdagings te hanteer, moet die verspreidingsoperateur die totale kapasiteit van sonkrag-FV wat op die verspreidingsnetwerk geïnstalleer is, skat, benewens hoeveel van die kapasiteit ingebed is in die netto aanvraag van die netwerk, wat belangrik is om die toestand van die netwerk te bepaal op enige spesifieke tyd. Verskeie metodes is gebruik om die kapasiteit te bereken vir PVsonkrag-KSIK's vir sonkrag in 'n gebied. Die meeste studies gebruik algoritmes vir afstandwaarneming en rekenaarvisie om die aantal sonkrag-FV-panele in 'n gebied te tel. Analise van hierdie studies dui daarop dat die resultate wat verkry is nie gebruik kan word om die toestand van die netwerk te bepaal nie, aangesien dit slegs die kapasiteit van sonkrag-FV in 'n gebied bepaal. Tweedens is verdeeldheidstudies grootliks gebruik om die geïnstalleerde sonkrag-FV-kapasiteit wat in die netto vraag van 'n voerder of netwerk ingebed is, te kwantifiseer. Hierdie metodes veronderstel 'n multi-veranderlike benadering wat veelvuldige insette benodig wat nie geredelik beskikbaar is nie. Hierdie studie stel 'n nuwe waarskynlikheidsmetode bekend wat die Monte Carlometodes toepas om die KSIK-sonkrag-PV-sonkrag te bepaal wat ingebed is in die netto aanvraag van 'n laespanning-voerder. Historiese aanvraag, netto aanvraag en die sonkrag-FV-uitset word gebruik om die sonkrag-FV-kapasiteit wat in die netto aanvraag van 'n voerder ingebed is, te bepaal. Die akkuraatheid van die metode word getoets met behulp van gesimuleerde netto aanvraag en werklike gemete netto aanvraag gemeet van huishoudings wat op noukeurig geselekteerde voerkrale gekoppel is. Resultate demonstreer dat die metode goed presteer waar die historiese aanvraag en die netto gemeet aanvraag van soortgelyke kliënteklasse verkry word. Daarom word die gevolgtrekking gemaak dat dit moontlik is om die kapasiteit te bepaal van FV-sonkrag wat ingebed is in die netto aanvraag wat van 'n voerder verkry word, deur die netto aanvraag van die voerder te analiseer en te vergelyk met die historiese aanvraag van 'n soortgelyke klantklas-voerder. Masters 2020-02-26T05:59:57Z 2020-04-28T12:17:07Z 2020-02-26T05:59:57Z 2020-04-28T12:17:07Z 2020-03 Thesis http://hdl.handle.net/10019.1/108062 en Stellenbosch University xix, 114 leaves : illustrations (some color) application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Electric networks -- Planning Electric power distribution -- Estimates Solar power plants -- Production control Smart power grids UCTD Waswa, Lewis A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title | A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title_full | A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title_fullStr | A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title_full_unstemmed | A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title_short | A probabilistic estimation of the capacity of solar PV SSEGs installed on a LV feeder network |
| title_sort | probabilistic estimation of the capacity of solar pv ssegs installed on a lv feeder network |
| topic | Electric networks -- Planning Electric power distribution -- Estimates Solar power plants -- Production control Smart power grids UCTD |
| url | http://hdl.handle.net/10019.1/108062 |
| work_keys_str_mv | AT waswalewis aprobabilisticestimationofthecapacityofsolarpvssegsinstalledonalvfeedernetwork AT waswalewis probabilisticestimationofthecapacityofsolarpvssegsinstalledonalvfeedernetwork |