Variability in large‐scale wind power generation

Kiviluoma, Juha; Holttinen, Hannele; Weir, David; Scharff, Richard; Söder, Lennart; Menemenlis, Nickie; Cutululis, Nicolaos Antonio; Lopez, Irene Danti; Lannoye, Eamonn; Estanqueiro, Ana; Gómez‐Lázaro, Emilio; Zhang, Qin; Bai, Jing; Wan, Yih-huei; Milligan, Michael

doi:10.1002/we.1942

Cited by 50 publications

(48 citation statements)

References 11 publications

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“…This is less than the distance (725 km) at which Giebel found an exponential decay in the cross‐correlation of wind energy production. Kiviluoma et al also demonstrated for several power systems that the distance between wind power farms can explain the variability in wind power production.…”

Section: Resultssupporting

confidence: 61%

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

et al. 2017

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In this work we use the mean-variance portfolio optimization, using as input the power derived from the wind and density results of meteorological model simulations (ERA-Interim reanalysis), to minimize the variability of the wind power produced in a large region. The methodology involves selecting the placement of the wind farms on a high spatial resolution grid. We used the EU-28 region to check the method and perform sensitivity tests. We studied the influence of the ratio between the total installed power of the whole domain (P t ) and the maximum power that can be installed per cell (P mi ) on the variability of wind power yield. The results show that the reliability of the electrical system improves when P mi grows and worsens when P t grows. A quadratic fit relates the variability of the system and the aforementioned ratio. The optimization procedure tends to select groups of terrain cells where wind farms should be installed. These groups grow when more energy production is demanded of the system, but they roughly maintain their location. There is some evidence that in a larger region greater system reliability could be achieved. Most of the selected cells have either a high or a low capacity factor and those with the latter are crucial in enhancing system reliability. KEYWORDS electricity, Europe, MVP, planning, variability, wind energy 1 | INTRODUCTION In many countries, there has been a rapid development of wind energy. While a small amount of wind energy can be incorporated into the electrical system without difficulty, higher amounts of it generate problems in predicting how much energy has to be produced by other sources, mainly coal, oil, open-cycle gas turbines, combined-cycle gas turbines, combustible renewables and waste, and hydropower. 1 In addition, other undesirable consequences, such as difficulties for the grid or increased costs of secondary services, have already been identified and estimated. 2-4 While wind power prediction, mainly based on persistence and atmospheric models' predictions, helps in ameliorating the problem, a nonnegligible degree of uncertainty about the wind power that will be added to the electrical system still exists. It leads to instabilities in the electrical network and therefore limits the number of wind farms that it is desirable to have installed in a region. To compensate for that instability, a number of flexible resources (eg, dispatchable power plants and demand-side management) whose power output should be equal to the variability are required to cover it. 5 Therefore, to increase the amount of clean energy from wind power that is provided to the electrical network, either the number of flexible resources must also grow or the wind power variability must be reduced. The installation and maintenance of flexible resources increases the overall cost of energy. From the work of Roques et al 6 and Drake and Hubacek, 7 we know that variability of wind energy can be reduced by combining the power output of separate wind farms or countries in a powerful electrical...

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Section: Resultssupporting

confidence: 61%

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

et al. 2017

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“…The geographical distribution of installed VRE generation has a significant effect on the probability distribution of the aggregate VRE generation (Kiviluoma et al, ; Koivisto, Ekström, et al, ). In the analyzed scenarios, a notable increase in geographical distribution takes place from 2014 until 2020; however, after 2020 the overall distribution does not change significantly (Koivisto et al, ).…”

Section: Case 1: Simulating Large‐scale Wind and Solar Pv Generation mentioning

confidence: 99%

Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Koivisto

Das

Guo

et al. 2018

WIREs Energy & Environment

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The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large-scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis.

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“…Providing ancillary services also from renewable power plants can contribute to an overall decrease in system costs. These achievable savings have been estimated to be in the order of a few percentages (Kiviluoma, Azevedo, Holttinen, Altiparmakis, & Cutululis, ). However, focusing on this main goal can be challenging, with involved stakeholders actively working to achieve the lowest cost (or highest ancillary service market revenue) for themselves, at a higher total cost for society.…”

Section: Discussionmentioning

confidence: 99%

Wind power within European grid codes: Evolution, status and outlook

Vrana

Flynn

Gómez‐Lázaro

et al. 2018

WIREs Energy & Environment

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Grid codes are technical specifications that define the requirements for any facility connected to electricity grids. Wind power plants are increasingly facing system stability support requirements similar to conventional power stations, which is to some extent unavoidable, as the share of wind power in the generation mix is growing. The adaptation process of grid codes for wind power plants is not yet complete, and grid codes are expected to evolve further in the future. ENTSO‐E is the umbrella organization for European TSOs, seen by many as a leader in terms of requirements sophistication. A current development by ENTSO‐E aims to develop a uniform grid code framework for Europe. The new European codes leave many key aspects unspecified, referring instead to regulation by the relevant TSO, but they do provide a positive and encouraging step in the right direction. The present document is largely based on the definitions and provisions set out by ENTSO‐E. The main European grid code requirements are outlined here, including also HVDC connections and DC‐connected power park modules. The focus is on requirements that are considered particularly relevant for large wind power plants. Afterwards, an outlook and discussion on possible future requirements is provided. This review has been written by members of IEA Wind Task 25, but it does not represent an official viewpoint of the IEA. This article is categorized under: Wind Power > Systems and Infrastructure

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Variability in large‐scale wind power generation

Cited by 50 publications

References 11 publications

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

Using wind velocity estimated from a reanalysis to minimize the variability of aggregated wind farm production over Europe

Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Wind power within European grid codes: Evolution, status and outlook

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