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Assessing the benefits of capacity payment, feed-in-tariff and time-of-use programme on long-term renewable energy sources integration

Title
Assessing the benefits of capacity payment, feed-in-tariff and time-of-use programme on long-term renewable energy sources integration
Type
Article in International Scientific Journal
Year
2019
Authors
Javadi, MS
(Author)
Other
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Nezhad, AE
(Author)
Other
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Shafie Khah, M
(Author)
Other
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Siano, P
(Author)
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Journal
Title: IET Smart GridImported from Authenticus Search for Journal Publications
Vol. 2
Pages: 602-611
Publisher: Wiley-Blackwell
Indexing
Other information
Authenticus ID: P-00R-R7W
Abstract (EN): Recently, demand response programmes (DRPs) have captured great attention in electric power systems. DRPs such as time-of-use (ToU) programme can be efficiently employed in the power system planning to reform the long-term behaviour of the load demands. The composite generation expansion planning (GEP) and transmission expansion planning (TEP) known as composite GEP¿TEP is of high significance in power systems to meet the future load demand of the system and also integrate renewable energy sources (RESs). In this regard, this study presents a dynamic optimisation framework for the composite GEP¿TEP problem taking into consideration the ToU programme and also, the incentive-based and supportive programmes. Accordingly, the performances of the capacity payment and feed-in tariff mechanisms and the ToU programme in integrating RESs and reducing the total cost have been evaluated in this study. The problem has been formulated and solved as a standard two-stage mixed-integer linear programming model aimed at minimising the total costs. In this model, the ToU programme is applied and the results are fed into the expansion planning problem as the input. The proposed framework is simulated on the IEEE Reliability Test System to verify the effectiveness of the model and discuss the results obtained from implementing the mentioned mechanisms to support the RESs integration.
Language: English
Type (Professor's evaluation): Scientific
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