Kaifeng ZHANG, Ying WANG, Kun YUAN, Yahui QIAO, Xuemei DAI, Jian GENG, Qianqian LIU, Yonghui LIU

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 thesmoothing effect from the prospect of output variability. Specifically, the aggregated output variability of a wind farmcluster may be significantly lower than that of an independent wind farm, and this phenomenon is referred to as thevariability smoothing effect. In order to quantitatively analyze the variability smoothing effect, this paper introduces theconcept of variability costs and evaluates the variability costs of each wind farm and overall wind farm cluster based onan optimal scheduling model. It is found that the variability cost of a wind farm cluster as a whole is lower than the sumof variability costs of all wind farms. Moreover, the difference between wind farm cluster variability cost and the sum ofvariability costs of each wind farm is termed the variability smoothing benefit. Meanwhile, the Shapley value method isdeployed to equitably allocate the variability smoothing benefits of the wind farm cluster. The results indicate that thecombined wind farms have the additional benefits of reducing variability costs as well as encouraging the integration oflarge scale wind farms.

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[1] Astariz S, Iglesias G. Output power smoothing and reduced downtime period by combined wind and wave energy farms. Energy 2016; 97: 69-81.
[2] Santos-Alamillos FJ, Pozo-Vazquez D, Ruiz-Arias JA, Lara-Fanego V, Tovar-Pescador J. A methodology for evaluating the spatial variability of wind energy resources: application to assess the potential contribution of wind energy to base load power. Renew Energy 2014; 69: 147-156.
[3] Roy S. Impact of short duration wind variations on output of a pitch angle controlled turbine. IEEE T Sustain Energy 2012; 3: 566-575.
[4] Gholami M, Fathi SH, Milimonfared J, Chen Z, Deng F. The effect of turbulence and wake on the power fluctuation in the wind farms. In: IEEE 2017 Iranian Conference on Electrical Engineering; 2–4 May 2017; Tehran, Iran: IEEE. pp. 1148-1153.
[5] Archer C, Simao H, Kempton W, Powell WB, Dvorak MJ. The challenge of integrating offshore wind power in the U.S. electric grid. Part 1: wind forecast error. Renew Energy 2017; 103: 346-360.
[6] Nastaran B, Roger G. Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks. Appl Energy 2017; 93: 269-380.
[7] Guizzi G, Iacovella H, Manno M. Intermittent non-dispatchable renewable generation and reserve requirements: historical analysis and preliminary evaluations on the Italian electric grid. In: 69th Conference of the ItalianThermal-Engineering-Association; 10–13 September 2014; Milan, Italy: pp. 339-344.
[8] Biyun CH, Suifeng W, Yongjun Z, Ping H. Wind power prediction model considering smoothing effects. In: IEEE PES Asia-Pacific Power and Energy Engineering Conference; 8–11 December 2013; Hong Kong, China: IEEE. pp. 1-4.
[9] Wevita P, Holttinen H, Weir D, Scharff R, Soder L, Menemenlis N. Variability in large scale wind power generation. Wind Energy 2016; 19: 1649-1665.
[10] Ekstrom J, Koivisto M, Mellin I, Millar J, Lehtonen M. A statistical model for hourly large-scale wind and photovoltaic generation in new locations. IEEE T Sustain Energy 2017; 99: 1-1.
[11] Akdag SA, Onder G. Alternative Moment Method for wind energy potential and turbine energy output estimation. Renew Energy 2018, 120: 69-77.
[12] Wevita P, Matsuhashi R, Yoshioka T. Smoothing effect of distributed wind farms and its impact on output fluctuation. In: 2013 International Conference on Renewable Energy Research and Applications; 20–23 October 2013; Madrid, Spain: IEEE. pp. 938-943.
[13] Huang J, Lu X, Mcelroy MB. Meteorologically defined limits to reduction in the variability of outputs from a coupled wind farm system in the Central US. Renew Energy 2014; 62: 331-340.
[14] Jie S, Lee W, Liu X. Generation scheduling optimization of wind-energy storage system based on wind power output fluctuation features. In: 2017 IEEE/IAS 53rd Industrial and Commercial Power System Technical Conference; 6–11 May 2017; Niagara Falls, Canada: IEEE. pp. 1-7.
[15] Fertig E, Apt J, Jaramillo P, Katzenstein W. The effect of long-distance interconnection on wind power variability. Environ Res Lett 2012; 7: 453-475.
[16] Hirth L. The market value of variable renewables, the effect of solar wind power variability on their relative price. Energy Econ 2013; 38: 218-236.
[17] Milligan M, Ela E, Lew D, Corbus D, Wan YH, Hodge BM, Kirby B. Operational analysis and methods for wind integration studies. IEEE T Sustain Energy 2012; 3: 777-783.
[18] Hirth L, Ueckerdt F, Edenhofer O. Integration costs revisited - an economic framework for wind and solar variability. Renew Energy 2015; 74: 925-939.
[19] Ueckerdt F, Brecha R, Luderer G, Sullivan P, Schimid E, Bauer N, Bottger D, Pietzcker R. Representing power sector variability and the integration of variable renewables in long-term energy-economy models using residual load duration curves. Energy 2015; 90: 1799-1814.
[20] Reichenberg L, Hedenus F, Odenberger M, Johnsson F. The marginal system LCOE of variable renewables – evaluating high penetration levels of wind and solar in Europe. Energy 2018; 152: 914-924.
[21] Changsheng CH, Kaifeng ZH, Jinwei W, Tingwei W, Yaxian ZH, Bike X. Balancing cost analysis of large-scale integrated wind power. In: 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications; 29–30 September 2014; Ottawa, Canada; IEEE: pp. 1388-1391.
[22] Arabali A, Ghofrani M, Etezadi-Amoli M, Fadali MS, Baghzouz Y. Genetic-Algorithm-Based Optimization Approach for Energy Management. IEEE T Power Del 2013; 28: 162-170.
[23] Palma-Behnke R, Benavides C, Lanas F, Severino B, Reyes L, Llanos J, Sáez D. A microgrid energy management system based on the rolling horizon strategy. IEEE T Smart Grid 2013; 4: 996-1006.
[24] Wang J, Zhong H, Lai X, Xia Q, Shu C, Kang C. Distributed real-time demand response based on Lagrangian multiplier optimal selection approach. Appl Energy 2017; 190: 949-959.
[25] Hargreaves JJ, Hobbs BF. Commitment and dispatch with uncertain wind generation by dynamic programming. IEEE T Sustain Energy 2012; 3: 724-734.
[26] Morales-España G, Latorre JM, Ramos A. Tight and compact MILP formulation for the thermal unit commitment problem. IEEE T Power Syst 2013; 28: 4897-4908.
[27] Li B, Roche R, Miraoui A. Microgrid sizing with combined evolutionary algorithm and MILP unit commitment. Appl Energy 2017; 188: 547-562.
[28] Silvente J, Papageorgiou LG. An MILP formulation for the optimal management of microgrids with task interruptions. Appl Energy 2017; 206: 1131-1146.
[29] Good N, Mancarella P. Flexibility in multi-energy communities with electrical and thermal storage: a stochastic, robust approach for multi-service demand response. IEEE T Smart Grid 2017; 99: 1-1.
[30] Hwang YA, Wand BS. A matrix approach to the associated consistency with respect to the equal allocation of non-separable costs. Oper Res Lett 2016; 44: 826-830.
[31] Sharma S, Abhyankar AR. Loss allocation of radial distribution system using Shapley value: a sequential approach. Int J Electr Power Energy Syst 2017; 88: 33-41.