A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service
Abstract As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response perform...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-12-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.13067 |
| _version_ | 1797384262622642176 |
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| author | Yunfeng Ma Chao Zhang Bangkun Ding Zengqiang Mi |
| author_facet | Yunfeng Ma Chao Zhang Bangkun Ding Zengqiang Mi |
| author_sort | Yunfeng Ma |
| collection | DOAJ |
| description | Abstract As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method. |
| first_indexed | 2024-03-08T21:32:55Z |
| format | Article |
| id | doaj.art-262fd7db8a8344bbb081615da4748865 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-08T21:32:55Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-262fd7db8a8344bbb081615da47488652023-12-21T04:27:03ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952023-12-0117245498551010.1049/gtd2.13067A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following serviceYunfeng Ma0Chao Zhang1Bangkun Ding2Zengqiang Mi3School of Electrical and Engineering North China Electric Power University Beijing ChinaDepartment of Electrical and Engineering University of Manchester Manchester UKSchool of Electrical and Engineering North China Electric Power University Beijing ChinaSchool of Electrical and Engineering North China Electric Power University Beijing ChinaAbstract As renewable power generation increases in distribution networks, the real‐time power balance is becoming a tough challenge. Unlike simple peak‐load shedding or demand turn‐down scenarios, generation following (GF) requires persistent and precise control due to the temporal response performance of controlled resources. This motivates a comprehensive control design considering the temporal response limits and execution performance of air conditioner clusters (ACCs) when providing GF. Accordingly, this paper proposes a self‐constraint model predictive control (SMPC) that properly allocates the generation following task among different ACCs, consisting of three main parts: response rehearsal, distributed consistency‐based power allocation, and real‐time task execution. Specifically, the rehearsal knowledge of ACCs is evaluated by introducing model predictive control (MPC) to track power signals with different values and thus obtain prior factors, including the upward/downward limits and control cost function. On this basis, the coherence of the incremental response costs of different clusters is achieved to allocate the GF signals. Once the optimised following signals (OFS) are obtained, a real‐time MPC for generation following task execution is employed, where the OFS are used as reference and the upward/downward limits are used as constraints. Simulations are conducted to verify the feasibility and effectiveness of the proposed method.https://doi.org/10.1049/gtd2.13067demand side managementancillary servicegeneration followingair conditioner clusters |
| spellingShingle | Yunfeng Ma Chao Zhang Bangkun Ding Zengqiang Mi A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service IET Generation, Transmission & Distribution demand side management ancillary service generation following air conditioner clusters |
| title | A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service |
| title_full | A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service |
| title_fullStr | A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service |
| title_full_unstemmed | A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service |
| title_short | A self‐constraint model predictive control method via air conditioner clusters for min‐level generation following service |
| title_sort | self constraint model predictive control method via air conditioner clusters for min level generation following service |
| topic | demand side management ancillary service generation following air conditioner clusters |
| url | https://doi.org/10.1049/gtd2.13067 |
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