Optimal portfolio-theory-based allocation of wind power: Taking into account cross-border transmission-capacity constraints

Rombauts, Yannick; Delarue, Erik; D’haeseleer, William

doi:10.1016/j.renene.2011.02.010

Cited by 53 publications

(23 citation statements)

References 7 publications

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“…These results are also documented in Table 1 for the specific choice α = 0.8 of the renewable mix. The mismatch has been truncated according to (18). The full (K = 30) balancing variance Var( B) is fully reproduced with the K = 6 principal components.…”

Section: Discussion: Contribution Of Principal Mismatch Patterns To Tmentioning

confidence: 99%

“…Table 1. Various infrastructure measures as a function of the truncation parameter K used for the PCA approximation (18). Table 1 lists also the other, more realistic infrastructure measures introduced in Section 2.2.…”

Section: Discussion: Contribution Of Principal Mismatch Patterns To Tmentioning

confidence: 99%

“…Wind power generation is correlated up to a length scale of about 500 km [4,[14][15][16]. Approaches based on spatial correlations, such as the optimal portfolio theory [17][18][19] and the copula method [20], are used for systemic resource assessments of renewables and for analysis of national and continental power grids.…”

Section: Introductionmentioning

confidence: 99%

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Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network

et al. 2017

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Due to its spatio-temporal variability, the mismatch between the weather and demand patterns challenges the design of highly renewable energy systems. A principal component analysis is applied to a simplified networked European electricity system with a high share of wind and solar power generation. It reveals a small number of important mismatch patterns, which explain most of the system's required backup and transmission infrastructure. Whereas the first principal component is already able to reproduce most of the temporal mismatch variability for a solar dominated system, a few more principal components are needed for a wind dominated system. Due to its monopole structure the first principal component causes most of the system's backup infrastructure. The next few principal components have a dipole structure and dominate the transmission infrastructure of the renewable electricity network.

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Section: Discussion: Contribution Of Principal Mismatch Patterns To Tmentioning

confidence: 99%

Section: Discussion: Contribution Of Principal Mismatch Patterns To Tmentioning

confidence: 99%

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Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network

et al. 2017

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“…In the field of renewable energy research, MVP has been widely used in the context of wind power planning [40][41][42][43][44]. For example, Rombauts et al [43] constructed portfolios for allocating wind farms across different countries to reduce the variability of hourly wind power production.…”

Section: Mean-variance Portfolio Theorymentioning

confidence: 99%

“…For example, Rombauts et al [43] constructed portfolios for allocating wind farms across different countries to reduce the variability of hourly wind power production. Similarly, Roques et al [44] analyzed the geographic diversification of wind farms within Europe to smooth out the fluctuations in wind power production and to reduce the associated costs of such systems.…”

Section: Mean-variance Portfolio Theorymentioning

confidence: 99%

PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment

2015

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Geographic aggregation of wind power-an optimization methodology for avoiding low outputs

et al. 2016

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This work investigates macro-geographic allocation as a means to improve the performance of aggregated wind power output. The focus is on the spatial smoothing effect so as to avoid periods of low output. The work applies multiobjective optimization, in which two measures of aggregated wind power output variation are minimized, whereas the average output is maximized. The results show that it is possible to allocate wind power so that the frequency of low outputs is substantially reduced, while maintaining the average output at around 30% of nameplate capacity, as compared with the corresponding output of 20% for the present allocation system. We conclude that in a future, fully electrically integrated Europe, geographic allocation can substantially reduce instances of low aggregate output, while impairing little on capacity factor and at the same time providing reduction in of short-term jumps in output. Copyright © 2016 John Wiley & Sons, Ltd. KEYWORDS wind integration; wind power allocation; variation measures for wind power; smoothing effect of wind power Correspondence

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Optimal portfolio-theory-based allocation of wind power: Taking into account cross-border transmission-capacity constraints

Cited by 53 publications

References 7 publications

Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network

Principal Mismatch Patterns Across a Simplified Highly Renewable European Electricity Network

PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment

Geographic aggregation of wind power-an optimization methodology for avoiding low outputs

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