Net load variability in Nordic countries with a highly or fully renewable power system

Olauson, Jon; Ayob, Mohd Nasir; Bergkvist, Magnus; Carpman, Nicole; Castellucci, Valeria; Goude, Anders; Lingfors, David; Waters, Rafael; Widén, Joakim

doi:10.1038/nenergy.2016.175

Cited by 106 publications

(59 citation statements)

References 36 publications

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“…Temporal correlations of wind power generation are a crucial factor to be considered for the integration of high shares of renewables [4,5,5,6,[11][12][13][14][15][17][18][19]. However, it is difficult to model these correlations and simple Markov models often fail to represent them properly [20,26,29].…”

Section: Discussionmentioning

confidence: 99%

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Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

2018

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Wind power generation exhibits a strong temporal variability, which is crucial for system integration in highly renewable power systems. Different methods exist to simulate wind power generation but they often cannot represent the crucial temporal fluctuations properly. We apply the concept of additive binary Markov chains to model a wind generation time series consisting of two states: periods of high and low wind generation. The only input parameter for this model is the empirical autocorrelation function. The two-state model is readily extended to stochastically reproduce the actual generation per period. To evaluate the additive binary Markov chain method, we introduce a coarse model of the electric power system to derive backup and storage needs. We find that the temporal correlations of wind power generation, the backup need as a function of the storage capacity, and the resting time distribution of high and low wind events for different shares of wind generation can be reconstructed.

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Section: Discussionmentioning

confidence: 99%

“…In this case, we can make use of the relation between the autocovariance function K(r) of the time series and the memory function F (r) [Eq. (6)] such that it is straightforward to deduce the stochastic model from measured data.…”

Section: A Model Of the Electric Power Systemmentioning

confidence: 99%

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

2018

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“…In one of these [61], Widén et al presents the main intermittencies and variabilities of the four sources, with a review of existing forecast methods. The standard deviation of each source according to the different time scales (frequency bands) is studied by J. Olauson et al at the level of a country [62]. The highest standard deviation rate for the different sources is related to short-term timescales for solar (<2 days), mid/short-term for wind (2 days to 2 weeks), long-term for wave (>4 months), and mid-term for tidal (2 weeks to 4 months) energies [62].…”

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

“…The standard deviation of each source according to the different time scales (frequency bands) is studied by J. Olauson et al at the level of a country [62]. The highest standard deviation rate for the different sources is related to short-term timescales for solar (<2 days), mid/short-term for wind (2 days to 2 weeks), long-term for wave (>4 months), and mid-term for tidal (2 weeks to 4 months) energies [62]. Natural cycle timescales of solar, wind, tidal, and wave resources are also discussed in the International Energy Agency report [63].…”

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

“…Recently, a review of solar and wind space-time variabilities has been conducted by K. Engeland et al but this does not include tidal and wave resources [66]. Table 1 presents the main characteristics in terms of variability and intermittency for each source, with the origin and existing methods to evaluate temporal variations according to different publications [61][62][63][64][65][66]. …”

Section: Intermittency and Variability Comparisonmentioning

confidence: 99%

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Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies

et al. 2018

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Abstract:Ocean energy holds out great potential for supplying remote maritime areas with their energy requirements, where the grid size is often small and unconnected to a continental grid. Thanks to their high maturity and competitive price, solar and wind energies are currently the most used to provide electrical energy. However, their intermittency and variability limit the power supply reliability. To solve this drawback, storage systems and Diesel generators are often used. Otherwise, among all marine renewable energies, tidal and wave energies are reaching an interesting technical level of maturity. The better predictability of these sources makes them more reliable than other alternatives. Thus, combining different renewable energy sources would reduce the intermittency and variability of the total production and so diminish the storage and genset requirements. To foster marine energy integration and new multisource system development, an up-to-date review of projects already carried out in this field is proposed. This article first presents the main characteristics of the different sources which can provide electrical energy in remote maritime areas: solar, wind, tidal, and wave energies. Then, a review of multi-source systems based on marine energies is presented, concerning not only industrial projects but also concepts and research work. Finally, the main advantages and limits are discussed.

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Spectral decomposition of regulatory thresholds for climate‐driven fluctuations in hydro‐ and wind power availability

Wörman

Bottacin‐Busolin

Zmijewski

et al. 2017

Water Resources Research

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Climate‐driven fluctuations in the runoff and potential energy of surface water are generally large in comparison to the capacity of hydropower regulation, particularly when hydropower is used to balance the electricity production from covarying renewable energy sources such as wind power. To define the bounds of reservoir storage capacity, we introduce a dedicated reservoir volume that aggregates the storage capacity of several reservoirs to handle runoff from specific watersheds. We show how the storage bounds can be related to a spectrum of the climate‐driven modes of variability in water availability and to the covariation between water and wind availability. A regional case study of the entire hydropower system in Sweden indicates that the longest regulation period possible to consider spans from a few days of individual subwatersheds up to several years, with an average limit of a couple of months. Watershed damping of the runoff substantially increases the longest considered regulation period and capacity. The high covariance found between the potential energy of the surface water and wind energy significantly reduces the longest considered regulation period when hydropower is used to balance the fluctuating wind power.

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Net load variability in Nordic countries with a highly or fully renewable power system

Cited by 106 publications

References 36 publications

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

Modeling long correlation times using additive binary Markov chains: Applications to wind generation time series

Electrical Power Supply of Remote Maritime Areas: A Review of Hybrid Systems Based on Marine Renewable Energies

Spectral decomposition of regulatory thresholds for climate‐driven fluctuations in hydro‐ and wind power availability

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