Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration
Abstract An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure futu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
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| Series: | IET Smart Grid |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/stg2.12065 |
| _version_ | 1797845677820084224 |
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| author | Karanveer Bhachu Ayman Elkasrawy Bala Venkatesh |
| author_facet | Karanveer Bhachu Ayman Elkasrawy Bala Venkatesh |
| author_sort | Karanveer Bhachu |
| collection | DOAJ |
| description | Abstract An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this paper, we propose two ICA models incorporating energy‐limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimisation and considers GHG emission limits and maximises social welfare (SW). The second ICA model uses a fuzzy optimisation technique to simultaneously optimise the objectives of SW maximisation and GHG emission minimisation. Both ICA models are tested on two datasets with 10 and 338 capacity supply offers constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimisation is shown to find a better balance between maximising net SW and minimising GHG emissions, with superior reductions in GHG for minor decreases in SW. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation. |
| first_indexed | 2024-04-09T17:42:52Z |
| format | Article |
| id | doaj.art-830121376bfb453db253206093ce5360 |
| institution | Directory Open Access Journal |
| issn | 2515-2947 |
| language | English |
| last_indexed | 2024-04-09T17:42:52Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Smart Grid |
| spelling | doaj.art-830121376bfb453db253206093ce53602023-04-16T13:55:16ZengWileyIET Smart Grid2515-29472023-04-016212413510.1049/stg2.12065Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas considerationKaranveer Bhachu0Ayman Elkasrawy1Bala Venkatesh2Centre for Urban Energy Ryerson University Toronto Ontario CanadaCentre for Urban Energy Ryerson University Toronto Ontario CanadaCentre for Urban Energy Ryerson University Toronto Ontario CanadaAbstract An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this paper, we propose two ICA models incorporating energy‐limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimisation and considers GHG emission limits and maximises social welfare (SW). The second ICA model uses a fuzzy optimisation technique to simultaneously optimise the objectives of SW maximisation and GHG emission minimisation. Both ICA models are tested on two datasets with 10 and 338 capacity supply offers constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimisation is shown to find a better balance between maximising net SW and minimising GHG emissions, with superior reductions in GHG for minor decreases in SW. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.https://doi.org/10.1049/stg2.12065capacity marketfuzzy optimisationgreenhouse gasintermittent generation |
| spellingShingle | Karanveer Bhachu Ayman Elkasrawy Bala Venkatesh Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration IET Smart Grid capacity market fuzzy optimisation greenhouse gas intermittent generation |
| title | Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| title_full | Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| title_fullStr | Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| title_full_unstemmed | Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| title_short | Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| title_sort | fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration |
| topic | capacity market fuzzy optimisation greenhouse gas intermittent generation |
| url | https://doi.org/10.1049/stg2.12065 |
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