Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network
Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1486478/full |
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| author | Tianshou Li Qing Xu Weiwu Li Xinying Wang Zhengying Liu |
| author_facet | Tianshou Li Qing Xu Weiwu Li Xinying Wang Zhengying Liu |
| author_sort | Tianshou Li |
| collection | DOAJ |
| description | Currently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects. |
| format | Article |
| id | doaj-art-1471e298fb2247b086306de7f2fa7d38 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-1471e298fb2247b086306de7f2fa7d382025-01-03T04:13:52ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-01-011210.3389/fenrg.2024.14864781486478Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution networkTianshou LiQing XuWeiwu LiXinying WangZhengying LiuCurrently, the time-of-use pricing model for electricity focuses on a single objective, often overlooking various factors that influence electricity costs. This oversight can lead to significant disparities in peak and off-peak electricity usage within the distribution network following optimization. Therefore, a new time of using electricity price optimization method is proposed that takes into account the losses of distributed photovoltaic access to the distribution network. Considering the topology structure of the distribution network after the integration of distributed photovoltaic, this paper calculates the comprehensive losses generated by the operation of the distribution network. Also, this paper constructs a time of use electricity price optimization mathematical model with the objectives of minimizing network loss, minimizing load variance, minimizing peak valley difference of equivalent load, and maximizing user satisfaction. And refer to the basic requirements for electricity pricing in the distribution network, set a series of constraints for optimizing electricity prices. Applying an improved imperialist competition algorithm this paper integrates Tent chaotic reverse learning to solve a multi-objective optimization model and obtain an optimized time of use electricity pricing plan. The experimental results show that after the implementation of this optimization method, the peak valley difference of the daily power load curve of the distribution network is only 350 MW, demonstrating superior peak shaving and valley filling effects.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1486478/fulldistributed photovoltaicsnetwork losstime of use electricity pricemulti objective optimizationimprove imperialist competition algorithmspeak valley difference |
| spellingShingle | Tianshou Li Qing Xu Weiwu Li Xinying Wang Zhengying Liu Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network Frontiers in Energy Research distributed photovoltaics network loss time of use electricity price multi objective optimization improve imperialist competition algorithms peak valley difference |
| title | Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| title_full | Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| title_fullStr | Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| title_full_unstemmed | Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| title_short | Optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| title_sort | optimization method of time of use electricity price considering losses in distributed photovoltaic access distribution network |
| topic | distributed photovoltaics network loss time of use electricity price multi objective optimization improve imperialist competition algorithms peak valley difference |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1486478/full |
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