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

Reichenberg, Lina; Wojciechowski, Adam; Hedenus, Fredrik; Johnsson, Filip

doi:10.1002/we.1987

Cited by 27 publications

(30 citation statements)

References 16 publications

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“…There is also a large amount of literature on the effects of geographic dispersion on aggregate wind power. Reichenberg et al [19] considered Europe as whole with an assumed rather than actual arrangement of wind farms. They showed that it was possible to optimize the layout to minimize, for example, occurrences of low aggregate power.…”

Section: Applicationsmentioning

confidence: 99%

Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

et al. 2019

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This paper describes a hierarchy of increasingly complex statistical models for wind power generation in Alberta applied to wind power production data that are publicly available. The models are based on combining spatial and temporal correlations. We apply the method of Gaussian random fields to analyze the wind power time series of the 19 existing wind farms in Alberta. Following the work of Gneiting et al., three space-time models are used: Stationary, Separability, and Full Symmetry. We build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable and symmetric, and non-separable and non-symmetric. We compare the performance of the models using kriging predictions and prediction intervals for both the existing wind farms and a new farm in Alberta. It is shown that the spatial correlation in the models captures the predominantly westerly prevailing wind direction. We use the selected model to forecast the mean and the standard deviation of the future aggregate wind power generation of Alberta and investigate new wind farm siting on the basis of reducing aggregate variability.

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Section: Applicationsmentioning

confidence: 99%

Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

et al. 2019

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“…However, for the preparation step (integral or representative days method), the demand data are normalised so that the maximum demand event is given a value of 1. The wind and solar data input is based on weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF), processed through a turbine function and a function to mimic the output of solar PV (see Reichenberg et al (2017) for a more thorough description). Solar and wind output also attain values between 0 and 1, but 1 is equivalent to the nameplate capacity.…”

Section: Datamentioning

confidence: 99%

Policy implications of downscaling the time dimension in power system planning models to represent variability in renewable output

Reichenberg

Siddiqui

Wogrin

2018

Energy

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Due to computational constraints, power system planning models are typically unable to incorporate full annual temporal resolution. In order to represent the increased variability induced by large amounts of variable renewable energy sources, two methods are investigated to reduce the time dimension: the integral approach (using typical hours based on demand and renewable output) and the representative days method (using typical days to capture annual variability). These two approaches are tested with a benchmark implementation that incorporates full time representation in order identify their suitability for assessing power systems with high renewable penetration. The integral method predicts renewable representation, it may be more applicable to one-node models, while the representative days method is suitable for multi-regional models. In order to assess power systems with increasing renewable policy targets, models should be designed to handle at least the 160 time steps needed to provide results that do not systematically overestimate the renewable capacity share.

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“…Drake and Hubacek, Roques et al, and Thomaidis et al use the total standard deviation for the wind portfolio selected. Cassola et al and Reichenberg et al use different variants of a metric measuring the total stepwise change of energy output over time. We use a similar metric, specifically the average absolute difference in residual demand between each consecutive pair of time periods, as our variability metric in one of the two model variants.…”

Section: Introductionmentioning

confidence: 99%

“…None of these metrics give proper weight to extreme changes in wind power, which, as Dowds et al point out, is the primary risk to system reliability when incorporating wind power. Recognizing the importance of extremities, Grothe and Schnieders and Reichenberg et al seek to minimize the value at risk and conditional value at risk, respectively. However, these metrics only measure the probability of low wind power output rather than capturing sudden extreme fluctuations in wind.…”

Section: Introductionmentioning

confidence: 99%

Optimizing wind farm siting to reduce power system impacts of wind variability

et al. 2019

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We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics-forward and backward greedy algorithms-to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. We find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm.Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset. KEYWORDS multiobjective optimization, siting, variability reduction INTRODUCTIONWind is widely recognized as an important resource for a sustainable energy future. In the United States, wind power accounted for 4.7% of electricity generation in 2015 and was the second most used renewable energy resource after hydropower. 1 As many countries seek to decrease their reliance on fossil fuels, wind power will become increasingly important.A well-known challenge to integrating wind power into power systems is the uncontrollability and variability of wind. Increased reliance on wind power makes power balance more difficult. To ensure system reliability, generators that can ramp up and down quickly are required to respond to fluctuations in wind power. When wind variability is high, some of these systems may be left on to meet reserve requirements, potentially negating the benefit of wind power. In this research, we compare strategies for selecting wind farm locations during the capacity planning stage of power system planning so as to minimize the burden of wind variability on the rest of the power generation system.We use residual demand to indirectly measure the impact from wind power on the rest of the power generation system. We seek to minimize both residual demand and the variability in residual demand, analyzing two variants of the variability objective in order to consider both average variability over time and sudden fluctuations in wind power. We first solve the ...

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

Cited by 27 publications

References 16 publications

Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Estimation of the Daily Variability of Aggregate Wind Power Generation in Alberta, Canada

Policy implications of downscaling the time dimension in power system planning models to represent variability in renewable output

Optimizing wind farm siting to reduce power system impacts of wind variability

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