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Renewable Energy in an Unpredictable and Changing Climate
operation of hydropower plants. Snowpack acts as a natural reservoir Wind speed varies significantly across different heights. Projections
during the winter, and as such early snowmelt can affect the seasonal of wind speed made by GCMs are usually made for a 10m height, which
operation of hydropower systems by increasing river flow in the does not correspond to the hub height of a wind turbine (above 50m).
spring and reducing it in the summer. If the built reservoirs are not There are some methods that can be used for extrapolating wind speeds
designed to cope with the earlier increased flow, energy can be wasted at different heights. Nevertheless, some variables can affect the vertical
through spillovers. wind profile; converting wind speed to a higher height should account
for this. For example, using the common logarithmic extrapolation, the
Wind Power Generation roughness of the terrain is a key parameter.
The roughness can, in
Wind power, owing to the lack of a reservoir, cannot store energy or turn, vary with the kind of vegetation cover. Given that climate change
regularise output, although some technologies, such as pumped can have a significant impact on ecosystems,
climate impact on
storage water reservoirs, can be used for that purpose. Therefore, the vegetation patterns can be relevant for impact assessment of wind
natural wind speed’s hourly, seasonal and annual variability has a power generation on a large scale (e.g. wind atlases) as well.
significant impact on the amount of power generated. This renders
wind power extremely susceptible to climate change.
River flows and wind speeds are
Although wind speed variability plays a crucial role in wind power
regarded as stationary stochastic
operation, GCM results do not provide enough information about wind
speed variability at a fine spatial resolution. Therefore, wind speed and its variables, which may not be the
distribution in large-scale projections from GCMs are not suitable for
case under a global climate
impact analysis, raising the need to use downscaling techniques.
For a set of spatially downscaled GCM results, assessing the impact of
climate change on wind power production can be performed for average
or by applying projected percentage variations over
This method has the advantage of reducing the Even though wind power is likely to be more vulnerable to negative impact
climate modelling bias, assuming that projections for present and from climate change than hydro, wind power systems have a smaller life-
future conditions are biased in the same way.
However, these impact span, which makes them more adaptable in the long term. The decision
studies assume that the frequency distribution of wind speed will to build a dam entails not only high capital costs but also a stationary
remain unchanged, which may not be the case. Given the importance structure with a longer physical and economic life-span. In this context,
of wind speed variability for wind power analysis, it is also important to wind power climate impact studies should focus on the total exploitable
have information about the frequency distribution of wind speed. The wind resource, indicating the future availability of power generation and
relationship between wind speed and wind energy density is not identifying/prioritising areas for site-specific viability assessments.
Wind speeds that are below average yield much less power,
while speeds that are much above the average can overstress turbine Concluding Remarks
and affect the cut-out speed control. Thus, alterations in In a global climate change environment, past experience of climatic
wind speed frequency distribution can affect the optimal match conditions should be used with care for future planning and operation
of renewable energy systems based on hydro and wind. River flows and
wind speeds are regarded as stationary stochastic variables, which may
Because of its dependence on
not be the case under a global climate change scenario. Climate
conditions influence the technical specifications of renewable energy
climate conditions, renewable
technologies (size of reservoir, number of turbines, etc.; wind turbine
energy is most susceptible to
model, rated power, etc.). Changing those conditions will alter the
output from a set that has been optimised for a different climate
condition. Therefore, the planning and operation of energy systems will
have to adapt to a new, more uncertain environment. Therefore, an
important adaptation measure is improving meteorological databases
between the power availability from natural resources and the power for current climate conditions so as to reduce the uncertainty of
curve of wind turbines. These alterations can range from changes on sectoral climate impact modelling and build the means to evaluate the
the Weibull distribution scale and shape parameters to changes on the extent of actual climate changes as they occur.
choice of the most suitable distribution function that mirrors the actual
distribution of hourly wind speeds. While the majority of energy impact studies use mean values of GCM
runs as inputs for representing future climate conditions, the
Therefore, besides arriving at a smaller spatial grid, it is important that probability of extreme climate events is not necessarily proportional to
downscaling techniques provide information on how wind speed changes in mean values.
Future energy climate impact assessments
distribution might change over time as a result of the different climate should try to develop a methodological framework for incorporating
forcings projected by GCMs. Very few methodologies have the possible impacts of extreme climate events on renewable energy.
accomplished that, although some
have made some progress in
statistically downscaling the frequency distribution of wind speed using Finally, energy system integration and diversification is a key aspect of
GCM-produced variables as predictors. adaptation.
Few studies have taken a broader look at the energy
MODERN ENERGY REVIEW VOLUME 1