Steps for a Complete Wind Integration Study

Holttinen, Hannele; O’Malley, Mark; Dillon, Jody; Flynn, Damian; Keane, Andrew; Abildgaard, Hans; Söder, Lennart

doi:10.1109/hicss.2013.497

Cited by 18 publications

(20 citation statements)

References 15 publications

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“…4 Inspired by [15] we use the term "integration options" as an umbrella term for all technologies that reduce integration costs. The alternative term "flexibility options" can be used as in [16] or [17]. ∶= ̅̅̅̅̅̅̅̅ + ∆.…”

Section: System Lcoe and Integration Costsmentioning

confidence: 99%

“…For steps toward a complete integration study that includes modeling balancing and grid costs see [17].…”

Section: Model Description and Literature Estimatesmentioning

confidence: 99%

“…Integration studies usually operationalize this definition by estimating different cost components from bottom up, like "grid costs", "balancing costs" and "adequacy costs" ( [15], [17], [19], [21], [22], also see section 2.3). They assume that these components add up to total integration costs even though it is not clear if that is exhaustive.…”

Section: A Mathematical Definition Of Integration Costsmentioning

confidence: 99%

“…Integration cost studies typically decompose integration costs into three cost components, balancing costs, grid costs and adequacy costs ( [15], [17], [19], [21], [22]) (see Figure 3, left bar).…”

Section: Implications For Decomposing Integration Costsmentioning

confidence: 99%

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System LCOE: What are the Costs of Variable Renewables?

et al. 2013

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-Levelized costs of electricity (LCOE) are a common metric for comparing power generating technologies. However, there is criticism particularly towards evaluating variable renewables like wind and solar PV power based on LCOE because it ignores variability and integration costs. We propose a new metric System LCOE that accounts for integration and generation costs. For this purpose we develop a new mathematical definition of integration costs that directly relates to economic theory. As a result System LCOE allow the economic comparison of generating technologies and deriving optimal quantities in particular for VRE. To demonstrate the new concept we quantify System LCOE from a simple power system model and literature values. We find that at high wind shares integration costs can be in the same range as generation costs of wind power and conventional plants in particular due to a cost component "profile costs" captured by the new definition. Integration costs increase with growing wind shares and might become an economic barrier to deploying VRE at high shares. System LCOE help understanding and resolving the challenge of integrating VRE and can guide research and policy makers in realizing a cost-efficient transformation towards an energy system with potentially high shares of variable renewables.

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Section: System Lcoe and Integration Costsmentioning

confidence: 99%

“…For steps toward a complete integration study that includes modeling balancing and grid costs see [17].…”

Section: Model Description and Literature Estimatesmentioning

confidence: 99%

Section: A Mathematical Definition Of Integration Costsmentioning

confidence: 99%

Section: Implications For Decomposing Integration Costsmentioning

confidence: 99%

See 2 more Smart Citations

System LCOE: What are the Costs of Variable Renewables?

et al. 2013

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“…As a result of the increasing use of RES, some operational challenges must be kept in mind in order to not affect the system stability. One of these challenges is the analysis and advances of grid rules or technical code requirements regarding the improvement of dynamic support to the grid, and an adequate frequency response provision [3].…”

Section: Introductionmentioning

confidence: 99%

Wind power impact on power system frequency response

Chamorro

Ghandhari

Eriksson

2013

2013 North American Power Symposium (NAPS)

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-The use of high power electronics in the large scale integration of wind power in the transmission and distribution systems can affect the system inertia response and the ability to recover frequency stability after large disturbances. Different approaches have been presented to show the system dynamic behaviour, and to quantify the wind power impact on the system inertial and frequency response. This paper gives a short overview of studies performed regarding the system inertia issues under high penetrations of wind power. Also, it presents the results of a case study to show how the system inertia can be affected by high penetrations of wind power.

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Simulating intra‐hourly wind power fluctuations on a power system level

2016

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In wind integration studies, sub-hourly, load synchronous wind data are often preferable. These datasets can be generated by a hybrid approach, combining hourly measurements or output from meteorological models with a stochastic simulation of the high-frequency fluctuations. This paper presents a method for simulating aggregated intra-hourly wind power fluctuations for a power system, taking into account the time-varying volatility seen in measurements. Some key elements in the modelling were transformations to stationarity, the use of frequency domain techniques including a search for appropriate phase angles and an adjustment of the resulting time series in order to get correct hourly means. Generation data from Denmark and Germany with 5 and 15 min temporal resolution were used for training models. It is shown that the distribution and non-stationarity of simulated deviations from hourly means closely follow those of measurements. Power spectral densities and step change distributions agree well. Of particular importance is that the results are good also when the training and objective power systems are not the same. The computational cost is low in comparison with other approaches for generating high-frequency data. NOMENCLATUREVariables in continuous time are denoted with the time argument in parentheses, while discrete time is indicated with subscripts. Additional subscripts indicate whether the time series are simulations or measurements used for validation of the model performance, e.g., P t,sim and x t,val . Subscript obj is used for variables that are common for the objective power system (i.e. both simulation and validation). No additional subscript indicates training data or when the variable is discussed in more general terms. The first-order and second-order derivatives are denoted with prim and bis, mean values with a vertical bar and absolute values with horizontal bars.

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Steps for a Complete Wind Integration Study

Cited by 18 publications

References 15 publications

System LCOE: What are the Costs of Variable Renewables?

System LCOE: What are the Costs of Variable Renewables?

Wind power impact on power system frequency response

Simulating intra‐hourly wind power fluctuations on a power system level

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