Simulating wind power forecast error distributions for spatially aggregated wind power plants

Miettinen, Jorma K.; Holttinen, Hannele

doi:10.1002/we.2410

Cited by 17 publications

(8 citation statements)

References 29 publications

(39 reference statements)

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“…In Olauson (2018), generation is simulated from reanalyses, and up to 1-weekahead synthetic forecasts are based on meteorological "reforecasts" and some statistical post-processing. Miettinen et al (2020) proposed a method for estimating distributions of forecast errors. It is based on the area size and dispersion of WPPs in the area.…”

Section: Forecast Error Data Simulationsmentioning

confidence: 99%

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

Holttinen¹,

Kiviluoma²,

Helistö³

et al. 2021

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Section: Forecast Error Data Simulationsmentioning

confidence: 99%

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

Holttinen¹,

Kiviluoma²,

Helistö³

et al. 2021

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“…The power factor has been increased by increasing wind speed as illustrated in Figure (7). It then decreases at higher wind speeds although this feature is desirable as it ultimately regulates power output automatically and prevents over-speed [16,17].…”

Section: Resultsmentioning

confidence: 99%

The design, construction and performance analysis of a portable vertical axis wind turbine (VAWT) generator

Jasim¹,

Salman²,

Ashawi³

2022

Tikrit J. Pure Sci.

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The increase demand for electric power, high consumption and little power station capacity has caused to reduce supplying hours of electricity in Iraq. Furthermore high bills prices especially in rural areas and cities faraway from electricity-generating power stations. In addition to the increase in pollution due to the use of fossil fuel sources. All those obstacles have led to the search for cheap, clean and renewable energy sources. In this research, wind turbine model operates at a low wind speed and high efficiency was achieved. The model was located and studied in Tikrit city where wind power need to be consumed. Here, the vertical turbine with an area of 0.49 m2 was designed and then it was placed at least 8 meters above the ground without any disturbances within 100 meters in any direction. The system was observed to start rotating with wind speed of 1.5 m/s. However, the best turbine cycles per minute were 68 was obtained at wind speed of 6.6 m/s and the system torque was 60 N.m. Finally, 75 watts of power was produced from a multipurpose generator that was designed to run a single battery. and was used (Neodymium magnet), and also operated at low speed.

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“…Thus, zonal flexibility needs assessment is more immune to uncertainty. The aggregation of uncertain parameters leads to the reduction in variance of uncertainty is a well known concept [54]. Table I shows that a significant amount of ramp up and ramp down flexibility is needed for DN, with network issues less than 10% of the time.…”

Section: A System Descriptionmentioning

confidence: 99%

Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network

Hashmi¹,

Koirala²,

Ergun³

et al. 2022

Preprint

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For market-based procurement of low voltage (LV) flexibility, DSOs identify the amount of flexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avoid over or under procurement of flexibility in the presence of uncertainty. To this end, we propose a scenario-based robust chance-constrained (CC) day-ahead flexibility needs assessment (FNA) framework. The CC level is analogous to the risk DSO is willing to take in flexibility planning. Multi-period optimal power flow is performed to calculate the amount of flexibility needed to avoid network issues. Flexibility is defined in terms of nodal power ramp-up and ramp-down and cumulative energy needs over a full day for each node. Future uncertainties are considered as multiple scenarios generated using multivariate Gaussian distribution and Cholesky decomposition. These scenarios are utilized to solve the flexibility needs assessment optimal power flow (FNA-OPF) problem. Zonal clustering of an LV feeder is performed using electrical distance as a measure and spatial partitioning. The FNA tool calculates ramp-up and ramp-down flexibility's power and energy requirements. Energy and power needs are often valued differently in many energy markets. We identify the marginal value of flexibility associated with energy and power needs separately. From numerical results for an LV feeder, it is observed that zonal flexibility needs assessment is more immune to uncertainty than nodal flexibility needs, making it more useful for DSOs to evaluate day-ahead flexibility procurement. We also propose a Pareto optimal mechanism for selecting CC level to reduce flexibility needs while reducing DN congestion.

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Simulating wind power forecast error distributions for spatially aggregated wind power plants

Cited by 17 publications

References 29 publications

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

The design, construction and performance analysis of a portable vertical axis wind turbine (VAWT) generator

Chance constrained day-ahead robust flexibility needs assessment for low voltage distribution network

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