Resource Documents: Economics (187 items)
Documents presented here are not the product of nor are they necessarily endorsed by National Wind Watch. These resource documents are provided to assist anyone wishing to research the issue of industrial wind power and the impacts of its development. The information should be evaluated by each reader to come to their own conclusions about the many areas of debate.
The market value of variable renewables: The effect of solar and wind power variability on their relative price
Author: Hirth, Lion
Abstract – This paper provides a comprehensive discussion of the market value of variable renewable energy (VRE). The inherent variability of wind speeds and solar radiation affects the price that VRE generators receive on the market (market value). During wind and sunny times the additional electricity supply reduces the prices. Because the drop is larger with more installed capacity, the market value of VRE falls with higher penetration rate. This study aims to develop a better understanding how the market value with penetration, and how policies and prices affect the market value. Quantitative evidence is derived from a review of published studies, regression analysis of market data, and the calibrated model of the European electricity market EMMA. We find the value of wind power to fall from 110 percent of the average power price to 50-80 percent as wind penetration increases from zero to 30 percent of total electricity consumption. For solar power, similarly low values levels are reached already at 15 percent penetration. Hence, competitive large-scale renewables deployment will be more difficult to accomplish than many anticipate.
- The variability of solar and wind power affects their market value.
- The market value of variable renewables falls with higher penetration rates.
- We quantify the reduction with market data, numerical modeling, and a lit review.
- At 30% penetration, wind power is worth only 50-80% of a constant power source.
Lion Hirth, Vattenfall and Potsdam-Institute for Climate Impact Research
Energy Policy 2013; 38: 218–236. doi: 10.1016/j.eneco.2013.02.004
Download original document: “The market value of variable renewables: The effect of solar and wind power variability on their relative price”
Author: Plummer, James; Frank, Charles; and Michaels, Robert
We compare three technologies that produce electricity in the United States: wind, solar, and combined-cycle gas turbines (CCGT). We use the 2016 electric utility database compiled by the U.S. Energy Information Administration (EIA). That database has the advantage of being based on a census of U.S. power plants rather than sampling, as well as excluding any subsidies received by the power plants.
We show the cost savings achieved when there is a shift between coal-fired generation and generation by wind, solar, or CCGTs, where costs include both capital and operating costs. The net cost reduction per tonne of CO₂ reduction is $4,340 for a shift from coal to wind, −$98,826 (a cost increase rather than a cost decrease) for a shift from coal to solar, and a $251,920 decrease for a shift from coal to CCGT.
When the net emissions from switching away from coal are considered, the net cost savings for each tonne of emissions avoided is $1.27 for a switch from coal to wind, −$44.11 (a net cost increase) for a switch from coal to solar, and a savings of $50.72 for a switch from coal to CCGT. The differentials between the savings from a switch to wind or solar and a switch to CCGT is a measure of the “dead weight economic loss involved in switching from coal to either form of “renewables” instead of switching from coal to CCGT.
This research concludes that CCGT is the only “economic” choice from the perspective of benefit-cost analysis.
- Following Joskow, we do separate analyses for peak and off-peak generation.
- This study borrows heavily from a 2014 Brookings Working Paper by Charles R. Frank, “The Net Benefits of Low and No-Carbon Electricity Technologies.” However, we use updated 2016 data.
- The basic data for this study is the annual census of electricity generation conducted by the EIA of the U.S. Department of Energy.
- One advantage of using the EIA data is that it measures the costs of electricity production on a “real resource cost basis.” That is, the data do not incorporate the large U.S. government subsidies paid to the owner/operators of U.S. wind and solar electricity plants.
- The federal subsidy to solar energy is 30% of capital cost. The federal “production tax credit” (PTC) for wind was $.023 per kWh in 2016, but has complex annual yearly inflation adjustments.
OTHER BASIC ASSUMPTIONS
- A new low-carbon (wind, solar, or CCGT) plant replaces a coal plant off-peak and a simple cycle gas turbine on-peak.
- The price of natural gas is the average price paid by electric utilities.
- The cost of capital is 7.5%.
- The emissions from a new CCGT plant are grossed up to account for fugitive from the production and transport of natural gas.
- We include “balancing and cycling costs.” These are the extra cost that electric utilities incur to accommodate the intermittent nature of wind and solar.
THE CONCEPT OF “DECARBONIZATION EFFICIENCY”
Decarbonization cost is the differential cost of producing a MW year of electricity via coal plants and three other technologies – wind, solar, and CCGTs – divided by the differential CO₂ emissions (measured in tonnes per year).
Total net cost savings in 2016 of switching from coal to …
- Wind: $4,340 per MW-year
- Solar: $98,826 per MW-year
- CCGT: $237,684 per MW-year
Tonnes of CO₂ emissions per MW-year avoided by switching from coal to …
- Wind: 3,418
- Solar: 2,241
- CCGT: 4,686
Net cost savings per tonne of emissions avoided
- Wind: $1.27
- Solar: −$44.11
- CCGT: $50.72
DEAD WEIGHT ECONOMIC LOSS …
Of a decision to switch from coal to wind instead of to CCGT:
- $49.45 per tonne of emissions avoided
Of a decision to switch from coal to solar instead of to CCGT:
- $94.83 per tonne of emissions avoided
Conclusion: Switching to either wind or solar instead of to CCGT involves a dead weight economic loss. However, the dead weight economic loss is twice as great for a switch to solar instead of a switch to wind.
A SCENARIO OF DECARBONIZATION
In recent years, U.S. CO₂ emissions have been about 5.8 billion tonnes per year.
Suppose a goal of reducing those emissions by 10% or about 580 million tonnes.
As shown before, substitution of wind for coal results in a cost savings of $1.27 per tonne of CO₂ reduction, or $0.74 billion in this decarbonization scenario.
As shown before, substitution of solar for coal results in extra costs of $44.11 per tonne of CO₂ reduction, or $25.58 billion if all the investment was in solar.
However, if all the investment were done in CCGT, then the total cost savings would be $29.42 billion. So, the cost savings are larger when all the investment is in CCGT. The differences in cost savings are the amount of “dead weight economic loss” from investing in wind or solar instead of CCGTs.
These equations could be turned around to calculate, for a given fixed outlay of costs, what would be the “foregone CO₂ emissions opportunity” from investing in wind or solar instead of CCGT.
OTHER ALLEGED “SIDE BENEFITS OR COSTS” OF RENEWABLES
Job creation. Many of the jobs created by renewables are at the installation or capital goods production stages. The inherent capital intensivity of renewables limit their job creation potential.
Infant industry learning. This was a label invented by Argentine economist Raul Prebisch to argue for tariff protection of industry in less developed countries. However, those tariffs often led to “soft industries” that became dependent on the tariffs and did not focus on increased efficiency. A higher gain results from investing in specialized R&D activity.
Siting issues. Renewables progress over time from more favorable wind and solar sites to sites that involve higher cost per kWh produced, a classic example of “diminishing economic returns.” CCGTs are smaller physical plants, which can be sited close to natural gas supply or end-use electricity customers.
BROADER ISSUES OF RENEWABLES VS. CCGTs
Should CCGT be eligible to receive federal tax credits analogous to the current federal tax subsidies to wind and solar? No. This would be doubling down on a bad federal policy. CCGT does not need subsidies. They can out compete wind and solar on their own.
The states mainly follow a policy of “renewables mandates” placed on regulated utilities. The utilities don’t resist these mandates very hard because the system of a fixed return on “utility rate base” largely eliminates the incentives to lower costs via investment in CCGTs. This pattern is a classic example of political “confusion of ends and means.” If the goal of electricity policy at the state level is reducing CO₂ emissions, then the state should not intervene to put CCGTs at a disadvantage.
James L. Plummer, President, Climate Economics Foundation
Charles R. Frank, Senior Non-resident Fellow, Brookings Institution
Robert R. Michaels, California State University Fullerton
[presented at the 35th United States Association for Energy Economics/International Association for Energy Economics Conference, November 12–15, 2017, Houston]
Author: Gordon, David
1. In the course of public debate on contentious topics, especially when large sums of money and politics are involved, ‘evidence’ is often collateral damage. Statistics are more often than not used, as the old joke has it, as a drunk uses a lamp-post: for support not for illumination.
2. This paper is the product of frustration and dismay at the misuse of evidence, particularly statistical evidence, by a powerful pro-wind lobby to create a confused, unbalanced and complacent picture of the possible impact of the growth of onshore wind electricity generation in Scotland on tourism and recreation, particularly mountainlinked tourism and recreation. Hyperbole by opponents of wind energy in the face of this well-organised and well-connected lobby is understandable, but equally fails to illuminate.
3. Proponents of wind farms would have us believe that tourism impacts are negligible. Opponents would have us believe that the destruction of tourism in Scotland is nigh. Neither position is at all tenable. The real position is much more subtle and complex. That is an uncomfortable message for all sides in a polarised debate.
4. This paper is an independently-written attempt to assess, as objectively as possible, what is really known about the possible impact of wind farms upon mountain-linked tourism and recreation within Scotland. This is set in the context of tourism in general, not least because there is no data specifically on mountaineering other than that produced by Mountaineering Scotland itself. It is foregrounded by a brief setting out of my personal and Mountaineering Scotland’s positions so that readers can judge whether these have biased my interpretation of the available evidence.
The key findings are:
5. There is no simple answer to the question of whether wind farms affect tourism (or recreation). It depends on
- the characteristics of the proposed development, both individually and as part of regional and national patterns;
- the nature of the local tourism offer and market, and that of competitors; and
- the characteristics of local tourists.
6. The hypothesis that best fits the available, far from perfect, data is that wind farms do have an effect on tourism but the effect is experienced predominantly in areas where large built structures are dissonant with expectations of desired attributes such as wildness or panoramic natural vistas, and where a high proportion of visitors come from the 25% of tourists in Scotland who are particularly drawn by the quality of upland and natural landscapes, with mountaineering visitors prominent amongst these. In much of Scotland, and for most tourists, wind farms are no serious threat to tourism: the nature of the local tourism offer, and good siting of wind farms, mean they can co-exist.
7. The main adverse effect of wind farms on tourism, thus far, is displacement within Scotland from areas perceived as ‘spoilt’ to areas seen as still retaining the desired sense of naturalness. The GCU Moffat Centre study, relied upon by developers and the Scottish Government, estimated the likely level of tourism displacement across Scotland by wind farms to be around 1-2%. The estimates in the present paper range up to 5%. This difference is modest given the five-fold increase in onshore wind farm capacity in Scotland between the data points for the two studies (2007 & 2015).
8. Tourism in Scotland is not thriving, with standard indicators of tourism volume in 2016, the latest available consistent data, still below pre-2008 levels. Positive media coverage of a ‘thriving’ tourism sector, typically based on statistically selective press releases, is seldom supported by the full figures. In a competitive world, it is foolish to put at risk any segment of Scotland’s tourism market.
9. Five per cent of Scottish tourism spend would be £250m. This is well within the range of fluctuation seen in national tourist spend from year to year and therefore undetectable, even if it was all lost to Scotland and not simply displaced within Scotland. Since the true figure could well be smaller, attempting to find evidence in national or regional tourism statistics of the effect of any particular change is almost certainly futile. It is statistically illiterate to think the lack of detection of a modest effect in volatile regional and national tourism statistics is evidence of no effect.
10. But any effect of wind farms will be much less visible in routine statistics because the income is not lost to the national tourism economy but displaced and relocated within Scotland. Even the lowest level estimated – 1% or £35m – would have a marked impact if concentrated in a limited number of places. It is still doubtful if such an effect could be detected in routine statistics since much tourism economic activity does not feature in statistics (e.g. many tourism business are below the VAT registration level) and it is such activity that might be most likely to be affected by a local drop in visitors.
11. BiGGAR Economics has attempted to look at impact in the vicinity of a general cohort of wind farms and has found no effect. Setting aside several methodological concerns about this study, the sample included only one wind farm in an area where a tourism effect would be predicted based on the conclusions of the present paper. The postconstruction outcome data for this wind farm was confounded by continuing wind farm construction locally, making it impossible to separate any tourism effect from the effect of construction worker accommodation and expenditure.
12. The evidence on wind farms and tourism in Scotland relates to the present pattern of development consented under a rigorous planning system. Mountaineering Scotland does not agree with all planning decisions, but the process is certainly exacting. This makes it difficult to assess impact on mountaineering or wild land tourism empirically because few wind farms that might be expected to have an adverse effect have been consented and most are not yet built. Insofar as Mountaineering Scotland objections can be used to identify planning applications in areas important for mountaineering and related tourism, there have been only eight wind farm consents in such areas and only two were operational by 2016. When wind farms are refused planning permission in mountain or wild land areas the reasons given are typically landscape and visual, but an unrecognised side-effect has been to limit potential for tourism impacts.
13. Despite the clearly inadequate nature of the present evidence base on wind farms and tourism, the Scottish Government remains content with reviews of old research with almost no primary research later than 2008, despite the substantially changed context. That the government and its agencies have little interest in commissioning research to better define and understand the interaction between specific segments of the tourism market and wind farms is to be regretted and serves the public interest poorly.
14. Strategic and local planning decisions on the extent and pattern of wind farm development in Scotland should take better account of the potential for adverse impact in areas important for landscape-dependent tourism, and safeguard sufficient such areas in each part of Scotland. It is not enough to protect only those landscapes within the small number of National Parks and National Scenic Areas.
Published by Mountaineering Scotland, November 2017
Download original document: “Wind farms and tourism in Scotland: A review with a focus on mountaineering and landscape”
Author: Wind Energy Update
- O&M costs for wind power are double or triple the figures originally projected; they are particularly high in the U.S.
- There’s a −21% change in wind farm return on investment. This underperformance of wind assets is most likely attributable to both differences in power production and O&M costs over original estimates.
- $0.027/kWh, or €0.019/kWh, is the average values of O&M costs obtained from report surveys. This compares to early estimates by one of the world’s dominant turbine suppliers of $0.005/kWh.
- A significant amount of R&D is currently going into gearbox reliability. Many gearboxes, designed for a 20-year life, are failing after 6 to 8 years of operation.
- Data suggests that O&M challenges for wind turbines peaked in 2007/2008.
- At 2 cents/kWh, O&M costs are roughly equal to the federal production tax credit offered in the U.S. as a subsidy to make wind energy competitive.
- Engineers are still scratching their heads when it comes to gearboxes. Even though gearboxes are certified to operate for 20 years, none of them on today’s market lasts more than 8 years.
- 66% – the percentage of offshore O+M costs that are caused by unscheduled corrective maintenance
- 2-6 times higher – offshore wind turbines O+M costs compared with on-shore
- 10% – the loss in revenue due to the effect of spattered debris accumulation on the blade’s leading edge
- €100,000 to €300,000 per year – the costs of keeping offshore turbines online vs. an allocation of €45,000 per turbine for onshore wind
As part of our research into failure rates, costs and downtime on US Wind Farms, we have built a model which estimates lifetime costs of scheduled maintenance for a wind farm in the US. The input data used to build this data pack is a 210MW wind farm made up of 105 2MW turbines of 80 metres in height. The tables below show component risk factors, periodic maintenance costs, failure scenarios and supply chain factors [all costs USD]. CMS [complete monitoring system] options play an increasingly important role in both mean time to repair and the time between failures. As a result they have a large impact on costs. These are also taken into account. Finally the data pack provides major component lifetime O&M cost for the wind farm.
Scheduled maintenance cost—
Frequency per year: 2
Cost per action per turbine: $6,000
Reduced cost: $5,100
Lifetime cost per farm: $21,420,000
Component risk factors—
|Components||Replacement cost||Failure rate (%), failures per 100 parts by year 20||Total failures in 20 years (total farm)||Average downtime per failure, days||Average downtime losses per faiure||Total downtime losses for the rest of the||Labor cost per failure||Crane cost per failure|
Supply chain risk factors—
|Spare in stock / No spare||Distance to manufacturing facility (if no spare available)|
|Available / No spare||Lead time, days||Close / Medium / Remote||Time for transportation, days|
CMS factors (per turbine)—
|Capital Sensor Cost (including installation) per turbine||Annual cost (O&M) per turbine||Detectability||Efficiency|
|Monitoring type||Cost||Reduced cost (economies of scale)||Fixed cost||Reduced cost (economies of scale)|
|Lifetime maintenance cost for the farm||Lifetime maintenance cost assuming secondary damage||Lifetime maintenance and CMS operation cost for the farm||Monitoring type|
We have also looked at failure rates across different turbine technology types and designs. The graph below shows major component failure rates for all types of turbines in our dataset during the first ten years of operations. Different failure modes have different repair times, ultimately leading to different costs.