Simulating the long term effect of asset management strategies on reliability of supply
This paper presents long-term power system reliability prognosis methods aimed for decision support in asset management and grid development. The prognosis method combines power system reliability assessment with simulation of the time development of components’ technical condition. The condition of...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525003990 |
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| author | Ivar Bjerkebæk Iver Bakken Sperstad Håkon Toftaker Gerd Kjølle |
| author_facet | Ivar Bjerkebæk Iver Bakken Sperstad Håkon Toftaker Gerd Kjølle |
| author_sort | Ivar Bjerkebæk |
| collection | DOAJ |
| description | This paper presents long-term power system reliability prognosis methods aimed for decision support in asset management and grid development. The prognosis method combines power system reliability assessment with simulation of the time development of components’ technical condition. The condition of the component population is influenced by three different factors in the model: degradation due to aging, forced replacements due to non-repairable failures, and preventive replacements. We demonstrate the prognoses by simulating and comparing a set of reinvestment strategies. The reinvestment strategies we consider are age based, condition based and risk based, where risk is quantified in terms of expected energy not supplied (EENS). In demonstrating the methodology we focus on transformers and utilize an existing transformer end-of-life model. An important secondary objective of the work is to quantify the uncertainty in the end-of-life model, and include this uncertainty in the risk prognosis. We show that although there is substantial uncertainty in the end-of-life model, the relative performance of the reinvestment strategies is easily identified. The risk based strategy is seen to outperform the age-based and condition-based strategies giving considerably lower EENS and uncertainty over time. |
| format | Article |
| id | doaj-art-f11498679a7a4e4f9a0c49cfe82c4b97 |
| institution | Matheson Library |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-f11498679a7a4e4f9a0c49cfe82c4b972025-07-13T04:53:29ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-01170110851Simulating the long term effect of asset management strategies on reliability of supplyIvar Bjerkebæk0Iver Bakken Sperstad1Håkon Toftaker2Gerd Kjølle3SINTEF Energy Research, Sem Sælands vei 11, 7034, Trondheim, Norway; Corresponding author.SINTEF Energy Research, Sem Sælands vei 11, 7034, Trondheim, NorwayStatnett, Anne Martha Kvams veg 6, 7036, Trondheim, NorwaySINTEF Energy Research, Sem Sælands vei 11, 7034, Trondheim, NorwayThis paper presents long-term power system reliability prognosis methods aimed for decision support in asset management and grid development. The prognosis method combines power system reliability assessment with simulation of the time development of components’ technical condition. The condition of the component population is influenced by three different factors in the model: degradation due to aging, forced replacements due to non-repairable failures, and preventive replacements. We demonstrate the prognoses by simulating and comparing a set of reinvestment strategies. The reinvestment strategies we consider are age based, condition based and risk based, where risk is quantified in terms of expected energy not supplied (EENS). In demonstrating the methodology we focus on transformers and utilize an existing transformer end-of-life model. An important secondary objective of the work is to quantify the uncertainty in the end-of-life model, and include this uncertainty in the risk prognosis. We show that although there is substantial uncertainty in the end-of-life model, the relative performance of the reinvestment strategies is easily identified. The risk based strategy is seen to outperform the age-based and condition-based strategies giving considerably lower EENS and uncertainty over time.http://www.sciencedirect.com/science/article/pii/S0142061525003990Power system reliabilityAsset managementPower transformersCondition monitoring |
| spellingShingle | Ivar Bjerkebæk Iver Bakken Sperstad Håkon Toftaker Gerd Kjølle Simulating the long term effect of asset management strategies on reliability of supply International Journal of Electrical Power & Energy Systems Power system reliability Asset management Power transformers Condition monitoring |
| title | Simulating the long term effect of asset management strategies on reliability of supply |
| title_full | Simulating the long term effect of asset management strategies on reliability of supply |
| title_fullStr | Simulating the long term effect of asset management strategies on reliability of supply |
| title_full_unstemmed | Simulating the long term effect of asset management strategies on reliability of supply |
| title_short | Simulating the long term effect of asset management strategies on reliability of supply |
| title_sort | simulating the long term effect of asset management strategies on reliability of supply |
| topic | Power system reliability Asset management Power transformers Condition monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525003990 |
| work_keys_str_mv | AT ivarbjerkebæk simulatingthelongtermeffectofassetmanagementstrategiesonreliabilityofsupply AT iverbakkensperstad simulatingthelongtermeffectofassetmanagementstrategiesonreliabilityofsupply AT hakontoftaker simulatingthelongtermeffectofassetmanagementstrategiesonreliabilityofsupply AT gerdkjølle simulatingthelongtermeffectofassetmanagementstrategiesonreliabilityofsupply |