Cooperative multiagent optimization method for wind farm power delivery maximization. (15th October 2021)
- Record Type:
- Journal Article
- Title:
- Cooperative multiagent optimization method for wind farm power delivery maximization. (15th October 2021)
- Main Title:
- Cooperative multiagent optimization method for wind farm power delivery maximization
- Authors:
- Gu, Bo
Meng, Hang
Ge, Mingwei
Zhang, Hongtao
Liu, Xinyu - Abstract:
- Abstract: Reducing wake losses and improving the overall power output of wind farms have become a research focus in attempts to optimize wind farm power generation. A cooperative multiagent optimization method (CMAOM) for wind farm power delivery maximization has been proposed in this paper. In the CMAOM, a wind farm wake distribution calculation model, based on the Jensen wake model, was constructed, and each turbine was then assigned as an agent; the CMAOM was used to reduce wake losses and improve the overall wind farm power output. The agent, multiagent objective function and grid environment were defined in this study using wind turbine characteristics, and the CMAOM, including the neighborhood competition operator, mutation operator, and self-learning operator, were calibrated using wind turbine aerodynamic correlation characteristics. The Danish Horns Rev wind farm was selected as a case study, and the CMAOM and particle swarm optimization (PSO) algorithm were used to conduct analyses there. The results showed that the CMAOM proposed in this paper was more effective than the PSO algorithm and that the wind farm overall power output was increased by 7.51% for a 270° incoming wind direction and an incoming wind speed of 8.5 m/s. Highlights: A cooperative multiagent optimization method was proposed for power maximization. Agent, objective function and grid environment were defined. Competition operator, mutation operator, and self-learning operator, were calibrated. AAbstract: Reducing wake losses and improving the overall power output of wind farms have become a research focus in attempts to optimize wind farm power generation. A cooperative multiagent optimization method (CMAOM) for wind farm power delivery maximization has been proposed in this paper. In the CMAOM, a wind farm wake distribution calculation model, based on the Jensen wake model, was constructed, and each turbine was then assigned as an agent; the CMAOM was used to reduce wake losses and improve the overall wind farm power output. The agent, multiagent objective function and grid environment were defined in this study using wind turbine characteristics, and the CMAOM, including the neighborhood competition operator, mutation operator, and self-learning operator, were calibrated using wind turbine aerodynamic correlation characteristics. The Danish Horns Rev wind farm was selected as a case study, and the CMAOM and particle swarm optimization (PSO) algorithm were used to conduct analyses there. The results showed that the CMAOM proposed in this paper was more effective than the PSO algorithm and that the wind farm overall power output was increased by 7.51% for a 270° incoming wind direction and an incoming wind speed of 8.5 m/s. Highlights: A cooperative multiagent optimization method was proposed for power maximization. Agent, objective function and grid environment were defined. Competition operator, mutation operator, and self-learning operator, were calibrated. A wind farm wake calculation model, based on the Jensen wake model, was constructed. … (more)
- Is Part Of:
- Energy. Volume 233(2021)
- Journal:
- Energy
- Issue:
- Volume 233(2021)
- Issue Display:
- Volume 233, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 233
- Issue:
- 2021
- Issue Sort Value:
- 2021-0233-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-10-15
- Subjects:
- Wake losses -- Agent -- Collaborative optimization -- Wake model -- Wind farm
Power resources -- Periodicals
Power (Mechanics) -- Periodicals
Energy consumption -- Periodicals
333.7905 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.energy.2021.121076 ↗
- Languages:
- English
- ISSNs:
- 0360-5442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.445000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 17800.xml