Representative Days for Expansion Decisions in Power Systems
Short-term uncertainty needs to be properly modeled when analyzing a planning problem in a power system. Since the use of all available historical data may lead to problems of computational intractability, clustering algorithms may be applied in order to reduce the computational effort without compr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/2/335 |
| _version_ | 1798041828824449024 |
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| author | Álvaro García-Cerezo Luis Baringo Raquel García-Bertrand |
| author_facet | Álvaro García-Cerezo Luis Baringo Raquel García-Bertrand |
| author_sort | Álvaro García-Cerezo |
| collection | DOAJ |
| description | Short-term uncertainty needs to be properly modeled when analyzing a planning problem in a power system. Since the use of all available historical data may lead to problems of computational intractability, clustering algorithms may be applied in order to reduce the computational effort without compromising accurate representation of historical data. In this paper, we propose a modified version of the traditional K-means method, seeking to represent the maximum and minimum values of input data, namely, electricity demand and renewable production in several locations of a power system. Extreme values of these parameters must be represented as they are high-impact decisions that are taken with respect to expansion and operation. The method proposed is based on the K-means algorithm, which represents the correlation between demand and wind-power production. The chronology of historical data, which influences the performance of some technologies, is characterized through representative days, each made up of 24 operating conditions. A realistic case study, applying representative days, analyzes the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System. Results show that the proposed method is preferable to the traditional K-means technique. |
| first_indexed | 2024-04-11T22:27:02Z |
| format | Article |
| id | doaj.art-f0128b955db64260af2ec115a3f5840d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:27:02Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f0128b955db64260af2ec115a3f5840d2022-12-22T03:59:38ZengMDPI AGEnergies1996-10732020-01-0113233510.3390/en13020335en13020335Representative Days for Expansion Decisions in Power SystemsÁlvaro García-Cerezo0Luis Baringo1Raquel García-Bertrand2Escuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, SpainEscuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, SpainEscuela Técnica Superior de Ingeniería Industrial, Universidad de Castilla-La Mancha, Campus Universitario s/n, 13071 Ciudad Real, SpainShort-term uncertainty needs to be properly modeled when analyzing a planning problem in a power system. Since the use of all available historical data may lead to problems of computational intractability, clustering algorithms may be applied in order to reduce the computational effort without compromising accurate representation of historical data. In this paper, we propose a modified version of the traditional K-means method, seeking to represent the maximum and minimum values of input data, namely, electricity demand and renewable production in several locations of a power system. Extreme values of these parameters must be represented as they are high-impact decisions that are taken with respect to expansion and operation. The method proposed is based on the K-means algorithm, which represents the correlation between demand and wind-power production. The chronology of historical data, which influences the performance of some technologies, is characterized through representative days, each made up of 24 operating conditions. A realistic case study, applying representative days, analyzes the generation and transmission expansion planning of the IEEE 24-bus Reliability Test System. Results show that the proposed method is preferable to the traditional K-means technique.https://www.mdpi.com/1996-1073/13/2/335clustering methodgeneration and transmission expansion planningrenewable productionstorage |
| spellingShingle | Álvaro García-Cerezo Luis Baringo Raquel García-Bertrand Representative Days for Expansion Decisions in Power Systems Energies clustering method generation and transmission expansion planning renewable production storage |
| title | Representative Days for Expansion Decisions in Power Systems |
| title_full | Representative Days for Expansion Decisions in Power Systems |
| title_fullStr | Representative Days for Expansion Decisions in Power Systems |
| title_full_unstemmed | Representative Days for Expansion Decisions in Power Systems |
| title_short | Representative Days for Expansion Decisions in Power Systems |
| title_sort | representative days for expansion decisions in power systems |
| topic | clustering method generation and transmission expansion planning renewable production storage |
| url | https://www.mdpi.com/1996-1073/13/2/335 |
| work_keys_str_mv | AT alvarogarciacerezo representativedaysforexpansiondecisionsinpowersystems AT luisbaringo representativedaysforexpansiondecisionsinpowersystems AT raquelgarciabertrand representativedaysforexpansiondecisionsinpowersystems |