Study on variability smoothing benefits of wind farm cluster

Smoothing effect is an important characteristic of large scale wind power. In this paper we analyze the smoothing effect from the prospect of output variability. Specifically, the aggregated output variability of a wind farm cluster may be significantly lower than that of an independent wind farm, and this phenomenon is referred to as the variability smoothing effect. In order to quantitatively analyze the variability smoothing effect, this paper introduces the concept of variability costs and evaluates the variability costs of each wind farm and overall wind farm cluster based on an optimal scheduling model. It is found that the variability cost of a wind farm cluster as a whole is lower than the sum of variability costs of all wind farms. Moreover, the difference between wind farm cluster variability cost and the sum of variability costs of each wind farm is termed the variability smoothing benefit. Meanwhile, the Shapley value method is deployed to equitably allocate the variability smoothing benefits of the wind farm cluster. The results indicate that the combined wind farms have the additional benefits of reducing variability costs as well as encouraging the integration of large scale wind farms.

[1]  Willett Kempton,et al.  The challenge of integrating offshore wind power in the U.S. electric grid. Part I: Wind forecast error , 2017 .

[2]  Abdellatif Miraoui,et al.  Microgrid sizing with combined evolutionary algorithm and MILP unit commitment , 2017 .

[3]  Matti Lehtonen,et al.  A Statistical Model for Hourly Large-Scale Wind and Photovoltaic Generation in New Locations , 2017, IEEE Transactions on Sustainable Energy.

[4]  Michael Milligan,et al.  Variability in large-scale wind power generation , 2016 .

[5]  Roger Ghanem,et al.  Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks , 2017 .

[6]  Filip Johnsson,et al.  The marginal system LCOE of variable renewables – Evaluating high penetration levels of wind and solar in Europe , 2018 .

[7]  S. Roy,et al.  Impact of Short Duration Wind Variations on Output of a Pitch Angle Controlled Turbine , 2012, IEEE Transactions on Sustainable Energy.

[8]  J. Milimonfared,et al.  The effect of turbulence and wake on the power fluctuation in the wind farms , 2017, 2017 Iranian Conference on Electrical Engineering (ICEE).

[9]  S. Akdağ,et al.  Alternative Moment Method for wind energy potential and turbine energy output estimation , 2018 .

[10]  Xiaofei Liu,et al.  Generation scheduling optimization of Wind-Energy Storage System based on wind power output fluctuation features , 2017, I&CPS 2017.

[11]  Zhang Yongjun,et al.  Wind power prediction model considering smoothing effects , 2013, 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[12]  Abhijit R. Abhyankar,et al.  Loss allocation of radial distribution system using Shapley value: A sequential approach , 2017 .

[13]  P. Jaramillo,et al.  The effect of long-distance interconnection on wind power variability , 2012 .

[14]  Ryuji Matsuhashi,et al.  Smoothing effect of distributed wind farms and its impact on output fluctuation , 2013, 2013 International Conference on Renewable Energy Research and Applications (ICRERA).

[15]  Robert J. Brecha,et al.  Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves , 2015 .

[16]  Yan-An Hwang,et al.  A matrix approach to the associated consistency with respect to the equal allocation of non-separable costs , 2016, Oper. Res. Lett..

[17]  Y. Baghzouz,et al.  Genetic-Algorithm-Based Optimization Approach for Energy Management , 2013, IEEE Transactions on Power Delivery.

[18]  G. L. Guizzi,et al.  Intermittent non-dispatchable renewable generation and reserve requirements: historical analysis and preliminary evaluations on the Italian electric grid , 2015 .

[19]  Pierluigi Mancarella,et al.  Flexibility in Multi-Energy Communities With Electrical and Thermal Storage: A Stochastic, Robust Approach for Multi-Service Demand Response , 2019, IEEE Transactions on Smart Grid.

[20]  C. Kang,et al.  Distributed real-time demand response based on Lagrangian multiplier optimal selection approach ☆ , 2017 .

[21]  Rodrigo Palma-Behnke,et al.  A Microgrid Energy Management System Based on the Rolling Horizon Strategy , 2013, IEEE Transactions on Smart Grid.

[22]  L. Papageorgiou,et al.  An MILP formulation for the optimal management of microgrids with task interruptions , 2017 .

[23]  B. F. Hobbs,et al.  Commitment and Dispatch With Uncertain Wind Generation by Dynamic Programming , 2012, IEEE Transactions on Sustainable Energy.

[24]  Lion Hirth,et al.  The effect of solar wind power variability on their relative price , 2013 .

[25]  David Pozo-Vázquez,et al.  A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power , 2014 .

[26]  Xi Lu,et al.  Meteorologically Defined Limits to Reduction in the Variability of Outputs from a Coupled Wind Farm System in the Central US , 2013 .

[27]  B. Kirby,et al.  Operational Analysis and Methods for Wind Integration Studies , 2012, IEEE Transactions on Sustainable Energy.

[28]  Jesús María Latorre Canteli,et al.  Tight and compact MILP formulation for the thermal unit commitment problem , 2013 .

[29]  Gregorio Iglesias,et al.  Output power smoothing and reduced downtime period by combined wind and wave energy farms , 2016 .

[30]  Kaifeng Zhang,et al.  Balancing cost analysis of large-scale integrated wind power , 2014, 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA).

[31]  O. Edenhofer,et al.  Integration costs revisited – An economic framework for wind and solar variability ☆ , 2015 .