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

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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. This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure
A block diagram of the main parts of CorRES. Simulated available power generation means the generation before, for example, curtailment
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Frequency control during severe storm. (a) Deviation of total wind power in DK1 (western Denmark), (b) change in power imported from Germany, (c) generation imbalance in DK1 system, and (d) system frequency
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Simulated actual wind generation (with HWSD control), hour‐ahead (HA) prediction and the prediction error in Western Denmark during the studied storm case
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Probability of wind power imbalance more than 3,000 MW for CE in 2020 and 2030
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Flowchart for estimating adequacy of reserves for handling wind power imbalance (Das, Litong‐Palima, et al., ). SCADA is supervisory control and data acquisition
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The estimated PDF of the first difference of aggregate load and residual load for the 2014, baseline 2050 and modified 2050 scenarios (using 2012 meteorological and load data). The PDF estimation is carried out using standard kernel estimation in Matlab (MathWorks, n.d.)
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The aggregate load time series of 2012 and the aggregate variable renewable energy (VRE) generation of the 2050 scenario for the analyzed Nordic and Baltic countries. The VRE generation is simulated for the VRE installation scenario 2050 using historical meteorological year of 2012
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Average autocorrelation functions (ACFs) of the different variable renewable energy (VRE) generation types of all the analyzed areas, and ACF of the aggregate VRE generation in the 2014 scenario. All ACFs are estimated from the 5 years of simulated VRE generation data from CorRES
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The analyzed countries in case 1. The analyzed regions are (approximately) the Nord Pool bidding areas (Nord Pool, ), except for Finland, which is divided into two areas. © EuroGeographics for the administrative boundaries; regions are combined of the EU nomenclature of territorial units for statistics (NUTS) classification
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