کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
704279 | 1460879 | 2016 | 10 صفحه PDF | دانلود رایگان |
• We assess the impact of demand response (DR) on capacity planning of transformers.
• Existing switches type for load transfer is also appropriately considered.
• We offer an optimization model that considers all costs associated with transformers.
• Substantial savings can be obtained by using DR during planning of transformers.
• Cost savings are higher when DR is employed along with network automation.
The inclusion of smart grid features such as demand response (DR) and network automation for capacity planning of substation transformers may provide substantial monetary savings. This paper proposes an optimization model for quantification of the savings in capacity management of substation transformers over long-run. The proposed model incorporates the DR as a resource to decrease the outage cost during contingencies while considering existing switching types for load transfer between substations. The model provides optimal selection and scheduling of multistage transformer installations and their refurbishments by considering all the costs associated with them including investment, losses, maintenance, reliability, and the salvage value. For a realistic study, numerical value of the savings in transformers’ cost is calculated for a typical Finnish two-transformer primary distribution substation planning over a period of forty years. Case studies are performed based on situations encountered by utilities and type of load transfer switching (manual and remote) between substations. A sensitivity analysis based on DR penetration and load curtailment (LC) cost is also performed. The results indicate that substantial monetary benefits can be obtained in substation transformers’ cost by utilities through employing DR. The benefit of DR is superior for cases where it is used in combination with remote switching of load transfer between neighbouring substations (NSS).
Journal: Electric Power Systems Research - Volume 134, May 2016, Pages 176–185