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Downloaded 09/26/21 12:40 PM UTC DECEMBER 2008 CASSOLAETAL. 3099 Optimization of the Regional Spatial Distribution of Wind Power Plants to Minimize the Variability of Wind Energy Input into Power Supply Systems FEDERICO CASSOLA,MASSIMILIANO BURLANDO,MARTA ANTONELLI, AND CORRADO F. RATTO Department of Physics, University of Genoa, Genoa, Italy, and National Consortium of Universities for Physics of Atmospheres and Hydrospheres (CINFAI), Toronto, Ontario, Canada (Manuscript received 1 October 2007, in final form 15 February 2008) ABSTRACT In contrast to conventional power generation, wind energy is not a controllable resource because of its stochastic nature, and the cumulative energy input of several wind power plants into the electric grid may cause undesired fluctuations in the power system. To mitigate this effect, the authors propose a procedure to calculate the optimal allocation of wind power plants over an extended territory to obtain a low temporal variability without penalizing too much the overall wind energy input into the power system. The procedure has been tested over Corsica (France), the fourth largest island in the Mediterranean Basin. The regional power supply system of Corsica could be sensitive to large fluctuations in power generation like wind power swings caused by the wind intermittency. The proposed methodology is based on the analysis of wind measurements from 10 anemometric stations located along the shoreline of the island, where most of the population resides, in a reasonably even distribution. First the territory of Corsica has been preliminarily subdivided into three anemological regions through a cluster analysis of the wind data, and the optimal spatial distribution of wind power plants among these regions has been calculated. Subsequently, the 10 areas around each station have been considered independent anemological regions, and the procedure to calculate the optimal distribution of wind power plants has been further refined to evaluate the improve- ments related to this more resolved spatial scale of analysis. 1. Introduction not without consequences for many power systems yet. In contrast to conventional power generation, where After the Kyoto conference on global climate change energy input can be scheduled and regulated to be con- in 1997, the worldwide on- and offshore capacity of sistent with the national power supply system (PSS), grid-connected wind power plants has increased expo- wind energy is indeed not a controllable resource be- nentially. According to the Global Wind Energy Coun- cause of its stochastic nature. cil Report (2006), 2006 was another record year for the On the local scale, the control system for a single wind energy market, with installations of 15 197 MW, wind power plant is usually designed just to regulate the which has brought the total installed wind energy energy output of both the overall wind farm and indi- capacity to 74 223 MW. In terms of new installed ca- vidual wind turbines to optimize the wind farm dynamic pacity in 2006, the United States continued to lead. performance (Steinbuch et al. 1988; Chinchilla et al. Nevertheless, Europe still remains the market leader 2005). At this scale, the interest is primarily focused on with 48 545 MW of installed capacity, representing 65% the evaluation of the maximum wind turbine efficiency of the global total. so as to extract as much energy as possible (Mosetti et From a technical point of view, at present, wind en- al. 1994; Milligan and Factor 2000). ergy is often conveniently integrated into regional elec- On the regional or national scale, the cumulative en- tricity supply systems, but its intermittent character is ergy input of the overall wind power plants may cause noticeable input fluctuations in the power system. In- deed, the intermittency of wind is directly transmitted Corresponding author address: Federico Cassola, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 into the power supply system and this dramatically re- Genoa, Italy. duces the marketing value of wind energy (Milligan and E-mail: [email protected] Porter 2005). At an operational level, the actual chal- DOI: 10.1175/2008JAMC1886.1 © 2008 American Meteorological Society Unauthenticated | Downloaded 09/26/21 12:40 PM UTC JAMC1886 3100 JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY VOLUME 47 lenge is to develop accurate models to perform wind After stating the advantages of interconnected wind power forecasting to predict the overall energy input farms with respect to an individual wind farm in terms into the power system (Persaud et al. 2003). For ex- of base load power supply and reduction of the vari- ample, the “development of a next-generation wind re- ability of wind energy production (see also Kahn 1979; source forecasting system for the large-scale integration Archer and Jacobson 2003; Simonsen and Stevens of onshore and offshore wind farms” (ANEMOS) 2004), in the present paper we suggest a possible meth- project (Kariniotakis et al. 2006) focuses on forecasting odology to answer the specific question about which the wind resource available for wind power plants up to wind power distribution maximizes the base load and two days ahead through physical and statistical predic- minimizes the variability. Therefore, following the idea tion models (Giebel et al. 2006; Sánchez 2006; Madsen that the spatial distribution of wind power plants on a et al. 2005). The outcome of the ANEMOS project is regional scale could be optimized to guarantee the expected to increase the wind energy integration minimum temporal variability without penalizing too through an optimized management of the risk related much the overall wind energy input into the power sys- to the intermittent nature of wind generation. tem, we propose a procedure to calculate the optimal Provided that short-term wind power prediction is a allocation of fractions of wind power through the mini- primary requirement for the efficient integration of mization of either the wind energy variability or the wind energy in power systems and electricity markets, it ratio between energy variability and energy input into is also of particular interest to the wind power industry the PSS. On behalf of Agence De l’Environment et de to develop standard procedures to find the best way to la Maîtrise de l’Energie (ADEME) and Collectivité distribute wind-generating capacity among several Territorial de Corse, this procedure has been applied to sites, to control the stability of the overall wind energy Corsica (France), the fourth largest island in the Medi- input into the power system. For example, Pantaleo et terranean Basin, and is based on two steps: al. (2003) noted that the high concentration of the Ital- • wind measurements at 10 m above ground level ian wind energy resource in few areas of southern re- (AGL) are converted into wind power output at gions could cause grid integration difficulties, like over- higher levels; loading and regulation problems. Archer and Jacobson • the aforementioned minimization is performed to (2007) suggested interconnecting wind farms as a pos- calculate the optimal distribution of a fixed number sible solution to improve wind power reliability by re- of wind power plants among zones with different ducing wind energy fluctuations on the power system. anemological regimes. They show how by linking a certain number of wind farms together, the overall performance of intercon- We anticipate that, in the procedure, the definition of nected systems might improve substantially when com- zones similar from the wind climatology point of view is pared with that of any individual wind farm. The ad- essential to calculate the final optimal spatial distribu- vantages concern both supplying base load power as tion of power plants. These zones can be characterized well as reducing deliverable power swings caused by by individual anemological stations, if enough repre- wind intermittency. Their idea is that while wind speed sentative of the surrounding territory, as well as by clus- could be calm at a given location, it will be higher some- ters of stations. In this paper we shall analyze both where else, so that the wind energy production of the cases, first by considering three large regions and then interconnected system is more regular and constant as 10 smaller regions. the number of interconnected wind farms increases. In The present document is organized as follows: in sec- particular, Archer and Jacobson (2007), starting from tion 2 a short description of the territory under study 19 measurement sites, analyzed the performances of and of available wind measurements is reported. Re- all the possible combinations of k sites (with k ϭ 1, 3, 7, sults from a cluster analysis of these anemological 11, 15, 19). In so doing, more than 130 000 different data, performed to identify the different anemological spatial wind power distributions were taken into ac- regions of Corsica (Burlando et al. 2008) within which count, as each site is considered to have a single wind the total wind power should be distributed, are also turbine. For instance, when they analyzed the advan- briefly presented. In section 3, measurements are trans- tages of connecting 11 stations (k ϭ 11) over 19, they formed into wind power and the procedure is verified studied all the possible combinations (75 582) of 11 for one station through comparison with the wind en- sites among the 19 of interest. However, since they re- ergy produced by a wind farm already installed in the ported results averaged over all the combinations, the area under study. Section 4 focuses on the methodology question about which combination performs best still for the minimization of wind power variability onto the remains open. power system. Conclusions are drawn in section 5. Unauthenticated | Downloaded 09/26/21 12:40 PM UTC DECEMBER 2008 CASSOLAETAL. 3101 2. Territory, measurements, and anemology of eter of the island, they could induce fluctuations on Corsica the power supply system with a typical half-day cycle.
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