Deep analysis of wind variability and smoothing effect in Moroccan wind farms

Abstract

The Moroccan energy strategy has set new targets of reaching 52% of installed renewable energy capacity by 2030, through the development of renewable energy, including solar and wind energy. The massive use of renewables leads the country to deal with its intermittency and improve its integration in the grid. The offset between wind farms generation in different regions is essential for quantifying the reduction of intermittency when we chose wind farms sites. This offset, the smoothing effect, is analyzed deeply in this article to evaluate different compensation zones. Six wind farms with wind power variability and their potential impact on the capacity reserve are presented. The site's power was generated from reel hourly data during 12 months. Results show that energy fluctuation depends on typical wind regimes and the capacity of farms. North wind farms (280 MW) produce 50% of the maximum during 43% of time and south wind farms produce it only 17% of time. The first farm's power consistently spreads more than south farms, but also more smoothed. The average intermittency reduced as farm capacity increased due to the turbine smoothing effect and the reserve requirements increased on average. The behavior of south wind farms is more correlated, then the backup needed will be less.

Evaluation of the reserve capacity in a grid supplied by intermittent energy sources

“…Such additional costs will have no impact on the profitability of wind farms in Morocco because these wind farms have a significant benefit margin due to the high potential of wind resources (already achieved $US30/MWh for large‐scale project). Regarding wind projects developed by the National Electricity Office, this paper shows that considering smoothing effect during the phase of site selection has a significant and positive impact on the energy requirement needed to balance because compensation among different climate zones contributes significantly to the supply–demand, as announced by Choukri et al [13]. ‘Regarding the actual limited installed capacity, a heavy smoothing effect has not yet been reached in Morocco.…”

“…5 shows that the wind farms of the north produce more than 20% of their nominal power at 70% of the time while producing more than 70% of their nominal power only for 10% of the time. As for the total output of all wind farms, 70% of the rated power is generated for 25% of the time, proving a significant improvement over existing wind farms [13], while over 20% of the rated power is generated at 75% of the time.…”

“…50% (from 15% of the consumption to 7.5%) [12]. In Morocco, a previous paper shows that for the existing wind farms ‘the behaviour of a wind farm is nearly smoothened by the other wind farms through the smoothing effect’ between south and north in Morocco [13], we will analyse this behaviour for planned and future Moroccan wind farms and then we will focus on the backup needed (capacity reserve) to optimise the system. By this optimisation, the ‘National Electricity Company’ will allow to develop more wind farms because the backup needed will be less if the smoothing effect is taken into consideration.…”

Wind power variability during the passage of cold fronts across South Africa

“…Understanding this variability and power ramps could assist network operators in unit commitment, load scheduling and short-term maintenance planning. There is a significant body of literature considering the nature and effects of wind power variability (Kiviluoma et al, 2016; Choukri et al, 2017;Kalverla et al, 2017;Monforti et al, 2016;Sørensen et al, 2018;Thapar et al, 2011). This literature may be divided into studies that were conducted using real wind power data (Kiviluoma et al, 2016;Choukri et al, 2017) and those using meteorological data.…”

“…There is a significant body of literature considering the nature and effects of wind power variability (Kiviluoma et al, 2016; Choukri et al, 2017;Kalverla et al, 2017;Monforti et al, 2016;Sørensen et al, 2018;Thapar et al, 2011). This literature may be divided into studies that were conducted using real wind power data (Kiviluoma et al, 2016;Choukri et al, 2017) and those using meteorological data. The latter may in turn be divided between studies using actual observational data (Kalverla et al, 2017) and studies using outputs from numerical weather prediction (Monforti et al, 2014;Sørensen, Heunis, et al, 2018) as proxies for generation data.…”