Wind power variability and power system reserves in South Africa

Sørensen, Poul Ejnar; Litong-Palima, Marisciel; Hahmann, Andrea N.; Heunis, Schalk; Ntusi, Marathon; Hansen, Jens Carsten

doi:10.17159/2413-3051/2017/v29i1a2067

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…The scheme of the general proposed methodology is presented in the Figure 1. Input data is obtained after simulating offshore power generation, at a particular site, using the software described in [6].…”

Section: Model Descriptionmentioning

confidence: 99%

Lifetime estimation and performance evaluation for offshore wind farms transmission cables

Pérez-Rúa¹,

Das²,

Cutululis³

2019

15th IET International Conference on AC and DC Power Transmission (ACDC 2019)

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A novel methodology for life estimation and performance evaluation of offshore wind farms high voltage AC export cables is presented. The method applies Dynamic Temperature Prediction (DTP) analysis using a Thermo-Electrical Equivalent model (TEE). Furthermore, it is suggested how the cable lifetime might be inferred based on the accumulated ageing effects. Afterwards, a sensitivity analysis of the seabed temperature variations is performed. Finally, a holistic procedure for calculating more accurately the electrical power losses of the cable is presented. Results show that an important increase of the total installed power, or cross-section reduction, can be achieved compared to traditional sizing methods.

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Section: Model Descriptionmentioning

confidence: 99%

Lifetime estimation and performance evaluation for offshore wind farms transmission cables

Pérez-Rúa¹,

Das²,

Cutululis³

2019

15th IET International Conference on AC and DC Power Transmission (ACDC 2019)

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“…Understanding this variability and power ramps could assist network operators in unit commitment, load scheduling and short-term maintenance planning. There is a significant body of literature considering the nature and effects of wind power variability (Kiviluoma et al, 2016; Choukri et al, 2017;Kalverla et al, 2017;Monforti et al, 2016;Sørensen et al, 2018;Thapar et al, 2011). This literature may be divided into studies that were conducted using real wind power data (Kiviluoma et al, 2016;Choukri et al, 2017) and those using meteorological data.…”

Section: Introductionmentioning

confidence: 99%

“…This literature may be divided into studies that were conducted using real wind power data (Kiviluoma et al, 2016;Choukri et al, 2017) and those using meteorological data. The latter may in turn be divided between studies using actual observational data (Kalverla et al, 2017) and studies using outputs from numerical weather prediction (Monforti et al, 2014;Sørensen, Heunis, et al, 2018) as proxies for generation data. Observed or modelled wind speeds are frequently used to estimate wind power potential through the use of wind resource assessments (Prasad et al, 2009a;Prasad et al, 2009b) and transforming wind speeds to power through turbine power curves (Thapar et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…This is especially true for studies considering the prevailing meteorological processes inherent to a specific region, highlighting the importance of conducting region-specific studies. In studies conducted by Knorr et al (2016) and Sørensen et al (2018), the variability of the wind energy resource in South Africa was assessed using the Weather Research and Forecasting Model (Skamarock et al, 2008) reanalysis data. Both studies found that power ramps will be significantly reduced by aggregated smoothing, as the number of geographically dispersed generators feeding into the grid increases over time.…”

Section: Introductionmentioning

confidence: 99%

“…Both studies found that power ramps will be significantly reduced by aggregated smoothing, as the number of geographically dispersed generators feeding into the grid increases over time. Both Knorr et al (2016) and Sørensen et al (2018) focused on wind power variability and ramps at high temporal resolutions of ten and 15 minutes respectively, but not on timescales of hours to days. Aggregated smoothing is most evident at short timescales; however, larger power ramps become evident at longer timescales due to the impact of slower-moving, synoptic-scale weather systems (Widén et al, 2015;Kiviluoma et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Wind power variability during the passage of cold fronts across South Africa

Dalton

Bekker

Kruger

2019

J. energy South. Afr.

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Wind is a naturally variable resource that fluctuates across timescales and, by the same token, the electricity generated by wind also fluctuates across timescales. At longer timescales, i.e., hours to days, synoptic-scale weather systems, notably cold fronts during South African winter months, are important instigators of strong wind conditions and variability in the wind resource. The variability of wind power production from aggregates of geographically disperse turbines for the passage of individual cold fronts over South Africa was simulated in this study. When considering wind power variability caused by synoptic-scale weather patterns, specifically cold fronts, the timescale at which analysis is conducted was found to be of great importance, as relatively small mean absolute power ramps at a ten-minute temporal resolution, order of 2-4% of simulated capacity, can result in large variations of total wind power production (at the order of 32–93% of simulated capacity) over a period of three to four days as a cold front passes. It was found that when the aggregate consists of a larger and more geographically dispersed set of turbines, as opposed to a smaller set of turbines specifically located within cold-front dominated high wind areas, variability and the mean absolute ramp rates decrease (or gets ‘smoothed’) across the timescales considered. It was finally shown that the majority of large simulated wind power ramp events observed during the winter months, especially at longer timescales, are caused by the passage of cold fronts.

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Adequacy of frequency reserves for high wind power generation

Das¹,

Litong-Palima²,

Maule³

et al. 2017

IET Renewable Power Generation

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Comments to Author : （1）This paper illustrates that power forecasts for different time scales as well as available wind power in real-time are modeled for future European wind power scenarios of 2020 and 2030. The reviewer suggests that the authors provide more details on the theoretical derivation. For example, the explanation of Figs. 5 and 7 need to be improved. The reviewer also suggests the authors include more simulation results in the Section of System Studies. For example, compare the performance of the proposed technique with the existing work.(2) Reducing the power output of wind farm will certainly decrease the economic benefit of wind farms. The corcerned discussion will be welcome.Authors sincerely thanks the editor for the feedback. Based on the feedback following changes are made:1) More theoretical derivations are included. Figure 5 and 7 are simplified for better understanding and explained in a lucid manner.2) Performance of the algorithm is compared with one of the existing methodology in the literature. This existing methodology is also discussed.3) More literature that is relevant are added. One of the existing methodology is discussed in details and performance of the algorithm is compared with this methodology.4) This paper is concerned on volume of reserve required and not on sources of reserves. Reserves can be provided from any kind of generation sources and not necessarily wind power plants. A discussion is included in this regard in the paper.Reviewer: 1Comments to the Author This paper illustrates that power forecasts for different time scales as well as available wind power in real-time are modeled for future European wind power scenarios of 2020 and 2030. The reviewer suggests that the authors provide more details on the theoretical derivation. For example, the explanation of Figs. 5 and 7 need to be improved. The reviewer also suggests the authors include more simulation results in the Section of System Studies. For example, compare the performance of the proposed technique with the existing work Authors sincerely thanks the reviewer for the review. Based on the review following changes are made: 1) More theoretical derivations are included. Figure 5 and 7 are simplified for better understanding and explained in a lucid manner.2) Performance of the algorithm is compared with one of the existing methodology in the literature. This existing methodology is also discussed. Page 1 of 34IET Review Copy Only IET Renewable Power GenerationThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page. Reviewer: 2Comments to the Author This paper researchs the reserve capacity estimation of wind farms to handle the wind power forecast error. The paper is well written and the topic is very meaningful. However, I have the following comments that may help improve this manuscript. 1) Are there any l...

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Wind power variability and power system reserves in South Africa

Cited by 7 publications

References 11 publications

Lifetime estimation and performance evaluation for offshore wind farms transmission cables

Lifetime estimation and performance evaluation for offshore wind farms transmission cables

Wind power variability during the passage of cold fronts across South Africa

Adequacy of frequency reserves for high wind power generation

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