Browsing by Subject "Wind power"

Now showing 1 - 4 of 4
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    A decision theoretic framework for reliability-based optimal wind turbine selection
    (Elsevier, 2022-05) Eryilmaz, S.; Navarro, J.; Estadística e Investigación Operativa
    The problem of choosing the optimal wind turbine for a specific site is of special importance in the design process of wind farm. Manifestly, the selection of the optimal wind turbine should depend on a certain criteria. In this paper, optimal wind turbine selection is studied in terms of the capacity factor of wind turbine generator and the Expected Energy not Supplied which is one of the most commonly used reliability indices for power systems. The latter one considers the load profile of the system and is suitable to compare different wind farm compositions while the former one completely ignores the load profile of the system. This paper presents general theoretical results that are helpful to compare performance of wind turbines and wind farms without data collection and further numerical assessment. In particular, the conditions on wind turbine characteristics and availability values of wind turbines are determined to compare wind turbines and wind farms in terms of the capacity factor and Expected Energy not Supplied.
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    Future changes, or lack thereof, in the temporal variability of the combined wind-plus-solar power production in Europe
    (2019) Jerez, Sonia; Tobin, Isabelle; Turco, Marco; Jiménez Guerrero, Pedro; Vautard, Robert; Montávez, Juan Pedro; Física
    Here we present the first assessment of climate change impacts on the temporal variability of the joint production of wind and solar photovoltaic (PV) power across Europe. For that we adopted regional and continental perspectives (assuming a single European electricity grid), considered several temporal frequencies (from daily to annual), used state-of-the-art regional climate projections together with a climate-production model, and assumed a future massive deployment of wind and PV power installations. Results support that the spatio-temporal complementarity between the wind and solar resources helps to minimize the temporal variability of the combined production under both present (1971–2000) and future (2070–2099) climate conditions similarly. Thus the projected changes are overall negligible (well below ±5%). However, an additional assessment of theoretical upper/bottom bounds for these changes indicated significant potential increases in the stability of the joint production ranging from 5 to 25% across regions, 15% at the continental scale. This would be subordinated to the feasibility of reaching, with the future deployment strategies, individual wind and PV power production series with a perfect temporal anticorrelation. These results may encourage stakeholders to take holistically optimized decisions.
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    The CLIMIX model: a tool to create and evaluate spatially-resolved scenarios of photovoltaic and wind power development
    (Elsevier, 2015-02) Jerez, Sonia; Thais, Françoise; Tobin, Isabelle; Wild, Martin; Colette, Augustin; Yiou, Pascal; Vautard, Robert; Física
    Renewable energies arise as part of both economic development plans and mitigation strategies aimed at abating climate change. Contrariwise, most renewable energies are potentially vulnerable to climate change, which could affect in particular solar and wind power. Proper evaluations of this two-way climate–renewable energy relationship require detailed information of the geographical location of the renewable energy fleets. However, this information is usually provided as total amounts installed per administrative region, especially with respect to future planned installations. To help overcome this limiting issue, the objective of this contribution was to develop the so-called CLIMIX model: a tool that performs a realistic spatial allocation of given amounts of both photovoltaic (PV) and wind power installed capacities and evaluates the energy generated under varying climate conditions. This is done over a regular grid so that the created scenarios can be directly used in conjunction with outputs of climate models. First, we used the 0.44° resolution grid defined for the EURO-CORDEX project and applied the CLIMIX model to spatially allocate total amounts of both unreported 2012 and future 2020 PV and wind power installations in Europe at the country level. Second, we performed a validation exercise using the various options for estimating PV and wind power production under the created scenarios that are included in the model. The results revealed an acceptable agreement between the estimated and the recorded power production values in every European country. Lastly, we estimated increases in power production derived from the future deployment of new renewable units, often obtaining non-direct relationships. This latter further emphasizes the need of accurate spatially-resolved PV and wind power scenarios in order to perform reliable estimations of power production.
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    Time-scale and extent at which large-scale circulation modes determine the wind and solar potential in the Iberian Peninsula
    (2013) Jerez, Sonia; Machado Trigo, Ricardo; Física
    The North Atlantic Oscillation (NAO), the East Atlantic (EA) and the Scandinavian (SCAND) modes are the three main large-scale circulation patterns driving the climate variability of the Iberian Peninsula. This study assesses their influence in terms of solar (photovoltaic) and wind power generation potential (SP and WP) and evaluates their skill as predictors. For that we use a hindcast regional climate simulation to retrieve the primary meteorological variables involved, surface solar radiation and wind speed. First we identify that the maximum influence of the various modes occurs on the interannual variations of the monthly mean SP and WP series, being generally more relevant in winter. Second we find that in this time-scale and season, SP (WP) varies up to 30% (40%) with respect to the mean climatology between years with opposite phases of the modes, although the strength and the spatial distribution of the signals differ from one month to another. Last, the skill of a multi-linear regression model (MLRM), built using the NAO, EA and SCAND indices, to reconstruct the original wintertime monthly series of SP and WP was investigated. The reconstructed series (when the MLRM is calibrated for each month individually) correlate with the original ones up to 0.8 at the interannual time-scale. Besides, when the modeled series for each individual month are merged to construct an October-to-March monthly series, and after removing the annual cycle in order to account for monthly anomalies, these correlate 0.65 (0.55) with the original SP (WP) series in average. These values remain fairly stable when the calibration and reconstruction periods differ, thus supporting up to a point the predictive potential of the method at the time-scale assessed here.