Informing Choices for Meeting China’s Energy Challenges
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Even perfectly competitive wind sectors that maximize private profit would not operate at maximum theoretical capacity, as improving efficiencies beyond a certain point in multiple dimensions e. Institutional constraints compound this and as we posit in section 7 are likely to be a primary explanation for the differences between the US and Chinese systems. While previous studies have examined subsets of the drivers contributing to lowered capacity factors or have relied on aggregated data see table 1 for a summary , to our knowledge, no study has explored the full range of drivers using wind farm-level data in a manner that reveals the relative importance of each driver.
As a result of this gap in the literature, there is limited systematic understanding of the drivers of the Chinese wind generation shortfall—an understanding that is critical for assessing the future costs and benefits of various policies that could be put in place to address the shortfall.
Further, as an early-moving developing country in installing wind power, learning from China's experience would be valuable for other developing countries anticipating an expansion of their domestic wind power sectors. Using this framework, we quantify the impact of the following six drivers that explain the performance of each country's wind sector:. These six drivers are described in table 1. Table 1. Factors contributing to the gap between potential and actual wind power generation, listing previous assessments, definitions, and mechanisms.
See section SM. To quantify the impacts of the factors that contribute to the shortfall between technical potential and observed wind power generation in China and the United States, we compile a uniquely detailed dataset of individual turbines and wind farms.
This dataset includes high-resolution hourly wind speed, turbine models, installation information, and power generation for each existing wind farm in both countries between and , the latest year for which micro-level Chinese data is available. We believe this dataset to be the first of its kind in its comprehensiveness and micro-level information. Our dataset allows us to take a unique bottom-up approach to calculating an estimate of technical potential. Beginning with the observed data, we first correct for inconsistencies in the timing of reported data 7.
Then we sequentially impose a set of technical assumptions to arrive at our technical potential. These imposed assumptions allow us to quantify six driving factors that explain the gap between the technical potential capacity factor and observed capacity factor. Previous 'top-down' approaches to estimating technical potential begin with assessments of natural wind resources and layer assumptions of imperfect wind extraction technologies, including taking capacity factors from other countries e.
McElroy et al In contrast, our approach to estimating technical potential begins with a 'bottom-up,' data-rich characterization of actual performance and sequentially connects observations with theoretically optimal configurations to estimate the technical potential in terms of capacity factor.
We are able to systematically explore a wide range of explanatory factors using historical data to inform what would need to change to make it possible to reach the higher capacity factors assumed in forward-looking analyses. Figure 2. The shortfall in average capacity factors in the cumulative installed wind power base in China from to attributed to seven factors: installation-date correction, grid-connection delay, curtailment, operational efficiency shortfall, turbine model shortfall, tower height shortfall, and siting shortfall.
The siting shortfalls shown in different shades of blue indicate average capacity factors Chinese wind farms could achieve if they were installed in sites in the same province with better wind resources. The X th siting percentile areas show the average capacity factor that could be achieved if all wind turbines were installed in the X th percentile of sites within the same province. We adopt the convention of using the 75th percentile of within-province site quality to define technical potential, but display the 70th, 75th, 80th, and 90th percentiles as well.
Our estimate of technical potential capacity factors begins with observations and then adds the following layers of assumptions:. The average capacity factor of Chinese wind farms installed between —, if calculated based on reported annual electricity generation and end-of-year installed capacity, is Over this period, there has been a slight upward trend from However, this average capacity factor, which is often cited in the literature Yang et al , Li , Williams and Kahrl , does not take into account the fact that new installations are installed continuously over the course of a year and are therefore available for power generation for only a portion of the year.
We calculate that the corrected capacity factor for the period —, accounting for the commissioning dates of wind farms, is In the early portion of this period — , when wind capacity doubled every year and most installations came online during the later months of the year, this correction is very significant: e. Importantly, with this installation date correction, the apparent upward trend of average capacity factors in the reported data is reversed: while uncorrected figures indicate an improvement in capacity factors, we find that actual capacity factors decreased from The correction for installation-date falls sharply over time because new capacity additions represent an increasingly smaller share of cumulative capacity.
The slight downward trend in actual capacity factors suggests that despite significant government and industry efforts, average wind farm performance in China has not measurably improved between and Figure 2 shows reported capacity factors and actual capacity factors that account for mid-year installations.
Figure 3. Breakdown of the generation shortfall between reported values and technical potential in China and the United States from their installed capacities in , displayed as percentage points of lost capacity factor. Our estimates suggest that over the period —, the fleet of Chinese wind farms produced only Expressed in terms of capacity factors, actual average capacity factors for — range from Expressed in terms of total generation, China's installed base of wind farms produced Here we discuss country-level drivers of the shortfall in generation.
Prospects for wind energy in China have significant regional variation He and Kammen and province-level differences in each of the gaps are discussed in section SM. We find that factors related to project design rather than wind farm operation, are the most important drivers of the shortfall of actual generation from technically achievable levels figure 3 top. In , the three project design gaps together contribute to an aggregate shortfall in capacity factor of Factors relating to the uptake of wind electricity by the grid—grid connection delays and curtailment—were responsible for 4.
The shortfall attributable to other operational inefficiencies was responsible for a 4. Over time, there are only modest relative changes to each of the driving factors of the gap between technical potential and actual generation figure 2.
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These trends indicate that the impact of the changing policies and economics for wind plant installation and construction have not induced meaningful changes in aggregate performance outcomes. Grid-connection delays decrease only slightly in absolute terms despite the fact that new installations make up an ever-decreasing share of the total.
This is because the average time to connect a wind farm to the grid, estimated here based on the difference between installed and grid-connected generation capacity in government reports, has significantly outpaced the rate of new capacity additions, increasing from an average of 31 days in to over days in Further, more recent data indicates an upward trend in curtailment in the years after our study period NEA The project design factors siting, turbine choice, and tower height are roughly constant in aggregate, with a slight decrease in the gap attributable to turbine model choice indicating improved selection of technically efficient turbines and a slight increase in the gap attributable to site selection indicating that sites selected in later years have had marginally worse wind resources.
The United States provides a useful benchmark for the Chinese wind sector's performance. While the US and Chinese contexts vary in many important ways, the US system still provides empirical performance benchmarks for what may be feasible in countries with different political and economic incentives and infrastructure systems.
Comparing differences in performance between the US and Chinese systems may provide practicable insight beyond the quantification of the gaps in section 5 and identification for priorities to bring the performance of the Chinese wind sector to the benchmarks used in the technical potential studies reviewed in section SM. Figure 3 shows the breakdown of the shortfall in wind power generation in the United States and China in relative to their technical potentials.
The United States produced Over the period —, the range for this figure in the United States was Turbine model selection is the largest cumulative gap in both countries; a reduction of 9.
As we discuss in the next section and in SM. Siting also plays an important role in both countries, but is a more important driver in China 6. Lost generation from grid connection delays and curtailment are the largest differences between the two countries, attributable for, respectively, 4.
In absolute terms, the United States produced more power from its wind fleet in Despite increasing awareness of the underperformance of the Chinese wind sector Liu , The Economist , Bloomberg News , Lu et al , we show that the Chinese wind sector's overall performance has not improved in a measurable way relative to its technical potential or international benchmarks. Our analysis and interviews with experts in the Chinese wind industry suggest two possible explanations for this underperformance, highlighting key areas for future empirical research. First, policy efforts by the Chinese government to grow the wind sector over the past ten years have focused primarily on creating incentives to install new wind turbines rather than incentivize generation , leading to project design decisions namely site selection, turbine model selection, and hub height to be insufficiently valued and evaluated.
Second, recent efforts by the Chinese government to increase capacity factors have primarily focused on factors related to the uptake of wind electricity, especially improving grid integration and grid management.caconzuteter.gq
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Reducing curtailment and grid-connection delays will be critical to improve the performance of existing turbines and are a major point of departure between the United States and Chinese wind sectors. However, our results suggest that these measures will have to be complemented by policies to address turbine siting and technology choices to increase the sector's performance relative to the levels seen in developed countries. Gaps related to decisions about turbine siting and technology choices turbine model and hub height have received much less attention in the literature and in policy debates than grid connection delays and curtailment e.
Unlike the factors related to the uptake of electricity from wind, turbine siting and technology choices are the decisions made in the design stage of new wind projects.
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Therefore, as the Chinese wind sector has rapidly increased capacity installations, suboptimal site selection and turbine selection has created inefficiencies that are locked-in for the lifetime of new wind farms, typically 20 or more years. The comparison with the United States suggests that China has a larger shortfall in siting and has not managed to improve turbine model choices over time in the same way as the United States has see section SM.
Taken together, these findings imply that besides improving grid connection and reducing curtailment, in order to avoid locking in further inefficiencies in the sector, more attention needs to be paid to addressing the incentives and constraints affecting farm siting and technology choice. Here we turn to specific measures that could reduce siting and turbine-selection-related inefficiencies. Siting choices for new wind farms in China are driven by trade-offs between land availability and prices developers are required to lease land for wind farms from local governments , wind resource quality and the availability of such knowledge , proximity of the site to the electric grid and service roads this affects prospects for timely grid connection , and the level of available feed-in tariffs to guarantee revenue.
The many factors that affect site selection can lead to development in sites with poorer wind resources, particularly in provinces with limited infrastructure. Further, there is limited information about wind resource quality in many prospective sites.
Comprehensive, multi-year wind measurements prior to project development are uncommon in China. Neither the provincial governments who approve new wind projects nor the state-owned banks who provide loans regularly require detailed evaluation of a prospective site's wind resources. Instead, because of incentives that have historically incentivized wind capacity development over actual generation, developers often rely on short-term wind measurements or measurements of nearby weather stations, and thus often do not collect reliable information about the quality of the wind resource in their proposed sites.
The wind farm siting gap could be reduced by increasing access to multi-year wind measurements in specific locations. Such assessments could also be required or otherwise incentivized. For example, state-owned banks could require detailed wind resource assessments before tendering project finance loans, as is common practice for commercial and public banks that finance wind projects in Europe and the United States. Developers and provincial-level policy makers should consider the procedures of obtaining land leases at the provincial level so that sites with higher wind quality are favored for the construction of new wind farms.
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A project developer's turbine model choice, in principle, is primarily a trade-off between a turbine model's cost and performance. Following the abolishment of local content requirements that gave preference to domestic turbines, there are few existing formal incentives to select economically suboptimal turbines. However, trends in the turbine model gap figure SM. For example, developers and turbine manufacturers commonly have long-term personal relationships and may even operate under the same parent company Gosens and Lu , Lin and Purra , Tang and Popp These formal and informal relationships distort what could otherwise be a more market-driven choice and helps explain part of the turbine model gap found in the Chinese system.
However, the turbine model gap is also a large factor in explaining the gap between technical and actual capacity factors in the US wind system where turbine model choices are made under more competitive economic conditions. Yet the drivers of the turbine model gap in the United States and China may be somewhat different.
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While wind capacity has been growing rapidly in both countries, the growth rate has been higher in China, and as such, wind farms in the United States are relatively older than China. Weighted by capacity, as of , turbines in the US wind system were 5. Therefore, as our data shows, in the US system, the turbine model gap is partly attributable to older, smaller turbines continuing to operate. Older turbines have locked in some degree of inefficiency in the US wind sector simply due to less efficient technologies being available at the time of installation.
The Chinese turbine model gap is likely to be influenced to a greater extent by non-economic considerations, such as preferential treatment for domestic manufacturers, some of which are not subject to international quality standards. While newly installed turbines in China were actually more efficient than newly installed US turbines from —, the most recent three years of data — , show that the turbine efficiency gap has been increasing, rather than decreasing, in China, while it has decreased in the US. Several measures could help reduce the Chinese wind turbine gap.
He has also worked with J. What follows is a list of specific career steps you can take to gain the above. Most people should pursue a combination depending on their existing expertise, and personal fit. It would be great to have more Chinese people involved in the effective altruism community, and for more existing community members to learn about China. We list more ways to get involved here.
Beyond this, it would be ideal to spend time working or interning in one of the relevant organisations.
See a list of organisations and a guide to getting jobs at them in our career review. It might be possible to get funding for people with knowledge of China to do placements in Western organisations. For people especially focused on China, there is also a WeChat group for the Chinese-speaking community with around members, and local meet ups in Beijing, Shanghai, Hong Kong and Taipei. If you contact us at the end of this post, we can make introductions to these groups.