Optimization of a wind farm by coupled actuator disk and mesoscale models to mitigate neighboring wind farm wake interference from repowering perspective

Khan, Mehtab Ahmad; Javed, Adeel; Shakir, Sehar; Syed, Abdul Haseeb

doi:10.1016/j.apenergy.2021.117229

Cited by 19 publications

(10 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research shows that the topic, despite being new in the academic universe, presents recent evolution, with 16 studies between the years 2018 and 2022 [15,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], which represents 61.5% of the total studies published on the topic, with 3.9% in 2018 [38], 3.9% in 2019 [39], 23.10% in 2020 [15,[40][41][42][43][44], 23.10% in 2021 [45][46][47][48][49][50] and 7.7% in 2022 [51,52]. The complete distribution of publications per year regarding the topic is presented in Figure 3.…”

Section: Classification/systematization Of Publicationsmentioning

confidence: 99%

Factors Influencing the Decision-Making Process at the End-of-Life Cycle of Onshore Wind Farms: A Systematic Review

Agra Neto,

González,

Castro

et al. 2024

Energies

View full text Add to dashboard Cite

It is observed that the number of onshore wind farms that reach the end of their service life is continually increasing. The decision-making process that defines the future of the farm is a challenge for the owners. This systematic review aimed to identify which factors influence the decision-making process at the end-of-life cycle of onshore wind farms. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, a research strategy was developed and used the Scopus, Web of Science and EMBASE databases. Initially, 2767 articles were identified, but, after double-blind screening, 26 articles were analyzed in full. The scarcity of studies on this topic and little elucidation are limitations of this review. The results include (i) a systematization of six options for decision making, (ii) thirteen factors influencing the decision-making process associated with categories of external factors (logistics and infrastructure aspects, regulatory aspects and public policies, national energy guidelines, the technological development of the sector); and internal factors (economic/financial, operational and environmental aspects). It is concluded that most of the publications consist of simulations and theoretical studies highlighting a bottleneck in experiences and feasible data to support decisions at the end of service life. It is highlighted that most of the studies showed that partial decommissioning with partial repowering, as well as total decommissioning, were the most feasible options for the end-of-life cycle, with aspects related to public policies and regulatory aspects, as well as environmental, operational and economic/financial aspects, being the most influential, especially due to the wake effect, operation and maintenance costs (OPEX) and the protection of guarantees and incentives for operation in a new operating cycle.

show abstract

Section: Classification/systematization Of Publicationsmentioning

confidence: 99%

Factors Influencing the Decision-Making Process at the End-of-Life Cycle of Onshore Wind Farms: A Systematic Review

Agra Neto,

González,

Castro

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…A wake region exists downstream of an operating wind turbine and is characterised by lower wind speed and higher turbulence intensity. As a result, reduced power output and more severe load fatigue will happen to the turbines operating in the wake region [2,3]. In commercial wind farms, many wind turbines are usually concentrated in a limited area, which aggravates the power loss of the wind farm caused by wake interaction.…”

Section: Introductionmentioning

confidence: 99%

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Deng

Huo

et al. 2023

Energies

View full text Add to dashboard Cite

In recent years, a major focus on wind farm wake control is to maximise the production of wind farms. To improve the power generation efficiency of wind farms through wake regulation, this study investigates yaw optimisation for wind farm production maximisation from the perspective of time-varying wakes. To this end, we first deduce a simplified dynamic wake model according to the momentum conservation theory and backward difference method. The accuracy of the proposed model is verified by simulation comparisons. Then, the time lag of wake propagation and its impact on wind farm production maximisation through wake meandering is analysed. On this basis, a yaw optimisation method for increasing wind farm energy capture is presented. This optimisation method uses the proposed dynamic wake model for wind farm prediction. The results indicate that the optimisation period is critical to the effect of the optimisation method on wind farm energy capture.

show abstract

“…With regard to the technology of wind turbines, there are two main types which are offshore and onshore wind generation. Because of the low cost of maintenance and installation of onshore wind farms by comparison with offshore wind farms, onshore generation represents nearly 96% of the total global wind energy [5]. According to the application type, WECS is mainly classified into two categories, standalone and grid-connected WECS.…”

Section: Introductionmentioning

confidence: 99%

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Mostafa

El-Hay

Elkholy

2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

Wind energy is an effective and promising renewable energy source to produce electrical energy. Wind energy conversion systems (WECS) have been developing on a wide scale worldwide. The expansion of wind energy demand tends to produce high-quality output power in terms of grid integration. Due to the intermittent nature of wind energy, great challenges are found regarding WECS modeling, control, and grid integration. This paper introduces a comprehensive review of WECS and their grid-interface systems based on soft computing methods. To achieve this aim, more than 300 articles are organised and only 160 papers are presented in this review. This is intended to cover a broad range of topics concerning the configurations of WECS, electrical generators, and various topologies of power converters used for control and grid integration. Furthermore, international grid codes for wind energy integration with electric grids, particularly frequency, power factor, and low voltage ride through (LVRT) capability are investigated. The major controller approaches and topologies for grid and generator converters are discussed. Different aspects of modern control of WECS are introduced either for grid-side or generator-side. Moreover, control strategies for maximum power point tracking methods are compared along with methods of frequency control. This review paper introduces a comprehensive and a useful summery for the recent work in literature regarding WECS. Detailed modelling, control, and grid integration along with comparisons and discussion are introduced.

show abstract

Optimization of a wind farm by coupled actuator disk and mesoscale models to mitigate neighboring wind farm wake interference from repowering perspective

Cited by 19 publications

References 51 publications

Factors Influencing the Decision-Making Process at the End-of-Life Cycle of Onshore Wind Farms: A Systematic Review

Factors Influencing the Decision-Making Process at the End-of-Life Cycle of Onshore Wind Farms: A Systematic Review

Yaw Optimisation for Wind Farm Production Maximisation Based on a Dynamic Wake Model

Recent Trends in Wind Energy Conversion System with Grid Integration Based on Soft Computing Methods: Comprehensive Review, Comparisons and Insights

Contact Info

Product

Resources

About