- Open Access
Climate change impacts on Ontario wind power resource
© Yao et al.; licensee Springer. 2012
Received: 18 April 2012
Accepted: 14 August 2012
Published: 14 August 2012
The availability and reliability of wind power depend largely on current and future climate conditions, which may vary in the context of climate change. A high resolution regional climate model (PRECIS) was used for dynamic downscaling of the future wind speed over Ontario. The changes of wind power density and power production were further investigated through case studies.
The spatial pattern and the magnitude of wind speed from PRECIS simulation, Wind Energy Atlas and the observation indicated a successful climate simulation. Climate modelling indicated that there would be a decrease of up to 5% in wind speed over southern Ontario from present to the period of 2071–2100. It was showed in the case studies that the changes of wind power production were not in proportion to the changes of average wind speed, due to the variations of wind speed distribution.
The decrease of projected wind speed would be more intense in A2 than in B2 scenario, showing statistically significant differences in the grid cell mean wind speed. The changes of wind power production may not be in proportion to the changes of average wind speed. It would be reasonable to develop onshore or offshore wind energy industry around Georgian Bay and James Bay, considering the projected increasing wind speeds within these areas.
Around the world, the effects of climate change are already upon us. Many countries have included renewable energy systems in their future energy plans so that they can produce reliable and environmentally friendly energy. Compared to the environmental effects of traditional energy sources, the environmental effects of wind power are relatively minor (Dvorak et al., ; Migoya et al., ). Wind power consumes no fuel, and emits no air pollution. However, it’s advisable to realize that wind energy is based on natural forces, which means it’s highly spatial correlated, and is also variable over time. Moreover, the availability and reliability of wind power depend greatly on current and future climate conditions, which also may vary in light of possible global climate change (Cai et al., ; Huang & Qin, ; Lin et al., ). Ontario is at the forefront of wind energy in Canada. According to Canadian Wind Energy Association, Ontario takes up 36% of current wind power generated in the country, and its capacity will continue to increase dramatically in the future. In terms of regional energy planning, there is an urgent need to refine global climate change impacts down to regional level in order to understand its trend and longer-term effect to avoid energy safety and economical risks.
It is widely recognized that the increasing of greenhouse gas (GHG) emission in atmosphere has effects on global climate. A range of GHG emission scenarios have been developed in the IPCC Special Report on Emissions Scenarios that reflect a number of different ways in which the world might develop and the consequences for population, economic growth, energy use and technology (Nakicenovic et al., ). To estimate the effect that these emissions have on the global climate, global climate models (GCMs) are employed (Smithson, ). GCMs describe important physical elements and processes in the atmosphere, oceans and land surface that make up the climate system. One disadvantage of GCMs is their scale, which is typically a few hundred kilometers in resolution. In order to link the impacts of climate change to practical problems such as local wind power availability, we need to predict changes on much finer scales. The GCM modeling results are required to be downscaled to higher resolution. Generally, there are two types of downscaling techniques. One is statistical downscaling. The other one is dynamic downscaling. The dynamic one is through the use of regional climate models (RCMs), which have the potential to improve the representation of the climate information which is important for assessing a region’s vulnerability to climate change. PRECIS (Providing Regional Climates for Impacts Studies) is a regional modeling system that can be run over any area of the globe to provide regional climate information for impacts studies. Boundary conditions are required at the limits of the model’s domain to provide the meteorological forcing for the RCM.
In the past decade, much attention in the climate change researches has been focused on the potential impacts on temperature and precipitation. Recently, a growing number of studies have looked at potential impacts on renewable energy resources, and on wind power (Sailor et al., ). In particular, it was found that wind power potential throughout Finland might increase by 2–10% under conditions of climate change Using GCM output from the Hadley (Venäläinen et al., ). Pryor (Pryor et al., [2005a]) has found that annual wind power potential over Northern Europe under the IPCC A2 and B2 scenarios was highly dependent on the boundary conditions used in Rossby Centre coupled Regional Climate Model (RCAO). Using empirical downscaling of five GCMs for 46 stations over Northern Europe, it’s shown a slight decrease in mean wind speeds under a 2080–2100 climate projection (Pryor et al., [2005b]). Breslow & Sailor explored climate change implications for wind power in California and Texas using neural network-based downscaling (Breslow & Sailor, ). In the recent research using statistically downscaling tools (Sailor et al., ), the summertime wind speeds in the Northwest U.S. may decrease by 5–10% which suggests a 40% reduction in summertime generation potential. RCM was also used in Brazil to find out that the wind power potential in Brazil would not be jeopardized in the future due to possible new climate conditions (Breslow & Sailor, ). However, within all these researches, uncertainty remains a bottleneck. The models, methods and scenarios used are so sophisticated that the massive uncertainty is immeasurable, which leads to a relatively low credibility of the findings. There is also a lack of studies on the substantial impacts of changing wind speed on the actual wind power production.
Therefore the objective of this research is to: a) determine whether the wind speed over Ontario generated by PRECIS using boundary conditions supplied from HadAM3H GCM during the control period (1961–1990) exhibit reasonable and realistic features as other reanalysis data products. b) determine if there are substantial differences between near-surface wind speed calculated for the control run versus a prognostic period (2071–2100) for either of two IPCC emission scenarios (A2 and B2). c) investigate the detailed impacts on wind power utilization as the result of the changes of the wind speed.
Study area and data acquisition
The territory of Ontario is often broken into two regions, Northern Ontario and Southern Ontario. The great majority of population (94%, i.e. 36.3% of the population of Canada) and arable land in Ontario is located in the south, which contrasts with its relatively small land area in comparison to the north. Ontario’s existing installed generation capacity includes nuclear, coal, oil, gas, hydroelectric, wood and waste-fuelled generation, which results in a total installed capacity of approximately 35485 MW. The wind energy only constitutes about 3% of the total capacity, while this percentage is 30% in Denmark. The total installed wind power capacity is 1208 megawatts, or 2.7 percent of Ontario’s total installed capacity. Ontario has 36% of Canada’s installed capacity for wind-generated electricity. The Ontario Power Authority’s (OPA) Integrated Power System Plan had called for 4600 MW of wind energy by 2020. In 2009, Ontario’s new Green Energy Act came into force with a feed-in-tariff and (FIT) new transmission investments that make it likely that this target will be exceeded. Approximately 8000 MW of wind energy projects have submitted applications for FIT contracts. Currently, 2500 MW of capacity will be able to connect to the grid, but transmission upgrades are planned to allow the connection of significantly more capacity over the next few years (Pereira de Lucena et al., ). With such a fast growing rate of wind power, there is a growing need for a long-term outlook of the wind speed variation.
Climate change projections
PRECIS is a high resolution climate model that covers a limited area of the globe, typically 5000 × 5000 km, with a typical horizontal resolution of 50 km. The Hadley Centre’s current version of the PRECIS (HadRM3P) is based on HadAM3H, an improved version of the atmospheric component of the latest Hadley Centre coupled AOGCM. HadRM3P has been used with horizontal resolutions of 50 and 25 km with 19 levels in the atmosphere (from the surface to 30 km in the stratosphere) and four levels in the soil. This study was based on PRECIS 1.8.2.
In order to investigate the state of the regional climate, the length of the simulation should be at least 10 years to give a reasonable idea of the mean climate change. However, a 30-year experiment is preferable to better determine changes in higher order statistics. This is particularly important for the analysis of aspects of climate variability, such as distributions of daily wind speed or climate extremes. Therefore, in this study, the simulation length was set as 30 years (1961–1990 for baseline and 2071–2100 for projection). It is important to run RCMs to generate more than one of the future climate scenarios implied by different emissions scenarios in order to start to quantify the “emissions” uncertainty in the predictions. For PRECIS, boundary data was available from two 31 year integrations of the HadAM3P atmosphere-only atmosphere global model. One of them provides a simulation of the climate of 2070–2100 under A2 emission scenario and the other is associated with B2 emission scenario.
Reanalyzed climate data
To provide independent assessment of the PRECIS output during the control period, the reanalyzed Canadian Wind Energy Atlas was used. This data set was produced by statistical-dynamical downscaling method. The method consists of using large scale long term atmospheric data and their statistical properties to run a mesoscale model and post-process its output in order to get a small scale picture of atmospheric motion. The NCAR/NCEP reanalysis (Frey-Buness et al., ) was chosen as its data base. The simulations are performed with the Mesoscale Compressible Community (MC2) model (Kalnay et al., ) to provide 5 km resolution output. This data set was considered as the representative of statistical wind properties for the period of 1958 to 2000.
Observed climate data
To analyze the historical weather condition and to evaluate the performance of PRECIS control run, observed weather data was collected from Environmental Canada. Twelve weather stations at major cities in Ontario were selected. The data was downloaded from National Climate Data and Information Archive on Environment Canada website (Benoit et al., ). This archive, operated and maintained by Environment Canada, contains official climate and weather observations for Canada. Climate elements, such as temperature, precipitation, relative humidity, atmospheric pressure, wind speed, wind direction, visibility, cloud types, cloud heights and amounts, soil temperature, evaporation, solar radiation and sunshine as well as occurrences of thunderstorms, hail, fog or other weather phenomena are warehoused in a digital database.
where P/A (W/m2) is the power density, ρ is the air density (kg/m3) and u is the wind speed (m/s).
where k is the shape factor, specified by the user. The shape factor will typically range from 1 to 3. For a given average wind speed, a lower shape factor indicates a relatively wide distribution of wind speeds around the average while a higher shape factor indicates a relatively narrow distribution of wind speeds around the average. A lower shape factor will normally lead to a higher energy production for a given average wind speed.
The actual output of a wind turbine is related directly to its: a) start-up speed: the speed at which the rotor and blade assembly begins to rotate, b) cut-in speed: the minimum wind speed at which the wind turbine will generate usable power, c) rated speed: the minimum wind speed at which the wind turbine will generate its designated rated power, d) cut-out speed: the safety speed which protects the wind turbine from damage.
where xi and xo is the cut-in and cut-off speed of the turbine; p(x) is the power curve of a specific turbine.
Results and discussion
Validation of climate model
Changes of wind speed
Once the baseline simulation was validated, it’s reasonable to assume that the used climate model was able to reflect the meteorological processes which it was designed to capture. Therefore, by comparing the projected climate under various scenarios and the baseline, the possible changes of the future climate can be investigated.
The grid differences of wind speed between A2, B2 projection and baseline ranged from −0.65 m/s to 0.82 m/s and −0.55 m/s to 0.37 m/s, respectively. The Mean Absolute Difference (MAD) and its standard deviation were 0.016 m/s and 0.212 m/s under A2 scenario, and 0.002 m/s and 0.122 m/s under B2 scenario. Although the MAD was small, the changes of wind speed required detailed interpretation by referring to the spatial distribution, which was not homogeneous considering the relatively high standard deviation.
Impacts on wind power resource
Figure 6 and Figure 7 also shows the major wind farms in Ontario, it could be inferred that most of the turbines would still be in the suitable zone where the average wind speed exceeds 6 m/s. However, as can be seen from the figures, most of Ontario’s major wind farms were built in the Great Lakes region, where the wind speed would likely to be lower, in which case the wind farms might be facing a decreasing power production. As can be calculated by Eq. (2), the wind energy density would drop down dramatically as wind speed decreases, because its value is proportional to the third power of the wind speed. Take Erie Shores Wind Farm as an example, it is located at northern shoreline of Lake Eire, installed in 2006 with 66 GE 1.5 MW Turbines and total installed Capacity of 99 MW. According to the projected results, the average wind speed in 2071–2100 would decrease by 4.9% and 3.2% under A2 and B2 scenarios respectively. That equaled to a significant wind power density deduction of 14.0% and 9.3% under A2 and B2 scenarios.
Variations (%) of wind speed, power density and power production at studied locations under A2 (B2) scenario in 2070
James Bay, onshore
James Bay, offshore
Georgian Bay, onshore
Georgian Bay, offshore
Currently, many wind farms around Georgian Bay has already been built and the nearby transmission lines also provide support for the development of wind energy. It’s reasonable to consider Georgian Bay’s shoreline as a potential hotspot for wind energy in the climate change context. As for James Bay, the wind energy potential there is abundant and would keep increasing in the future projections. There is also transmission line connecting hydropower stations at Abitibi River and Mattagami River. But the extreme weather in this region, the cold weather specially, must be solved technically before initiate any wind farm project. In a cold climate, icing represents one of the biggest challenges to the operation of wind turbines. Wind turbines must be able to sustain at least limited icing without incurring damage that would prevent normal operation.
The above finding also suggests offshore wind farm as an alternative regarding the increasing wind energy potential above the water surfaces over Georgian Bay and James Bay. While offshore wind energy is undergoing rather rapid global growth, Canada does not yet have any offshore wind facilities installed. However, the potential resources for this technology in Canada are amply at present and in the projected future. Although offshore wind turbines can be more costly to install and operate, they offer several distinct advantages over their onshore counterparts: (1) in general, they can be installed closer to coastal urban load centers, where most electrical energy demand exists, (2) offshore winds are faster and more consistent at lower vertical heights due to the reduced surface roughness over the ocean (Manwell et al., ), and (3) offshore turbines and components are not limited by roadway shipping constraints, so higher capacity turbines can be installed.
The results corresponding to the objectives are as follows: a) The spatial pattern of wind speed was similar between PRECIS simulation (1961–1990) and Canadian Wind Energy Atlas. The mean absolute difference of the mean wind speed between PRECIS simulation, Wind Energy Atlas and the observed data were 0.73 and 0.84 m/s respectively, indicating a successful climate simulation. b) The PRECIS simulation (2071–2100) suggested decreasing wind speed over the populated southern Ontario relative to the baseline period. These changes would be more intense in the A2 than in B2 scenario, and lead to statistically significant differences in the grid cell mean wind speed. c) Based on case studies around Erie Shores, James Bay and Georgian Bay, it was showed that the changes of wind power production may not be in proportion to the changes of average wind speed, due to the possible variations of wind speed distribution. It would be reasonable to develop onshore or offshore wind energy industry around Georgian Bay and James Bay, considering the projected increasing wind speeds within these areas.
Although it would provide more valuable information, the analysis of temporal variations of wind speed was not conducted due to limited computational capacity. It should also be noted that the results of this work depend fundamentally on the quality of the climate projections on which it is based. Improvements could be made through a more thorough analysis, including wind speed seasonality, and projections from different GCM configurations and downscaling methods. These could aid in coping with some of the uncertainties and add useful information about the vulnerability of wind power.
This research was supported by the Program for Innovative Research Team (IRT1127), the MOE Key Project Program (311013), the Natural Science and Engineering Research Council of Canada, and the Major Project Program of the Natural Sciences Foundation (51190095).
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