IEA Wind TCP

Providing transparency in the cost elements of wind projects among all participating countries will result in a better understanding of the cost drivers of wind technology and the reasons for differences among participating countries.

In the first phase of the task, participants used a common model to estimate the Levelized Cost of Energy (LCOE) for typical land-based wind projects in their respective countries.

In the second phase, a common template for representing the population of land-based wind technology, cost, and performance trends was developed. A report describing these trends and estimating the cost of energy over the period from 2008 to 2012 was produced. In the 2015 extension, participants will continue to build on past efforts.

Offshore wind technology has been installed by some participating countries, and others are exploring the potential uses of this technology. A representative baseline offshore wind plant description was developed in the recent phase of the task. This baseline provides an opportunity for two types of analysis to better understand and define cost drivers for offshore wind energy:

1. A common cash flow model, which explores the impact of market and policy aspects of offshore wind in each country relative to the baseline technology description
2. Component models (e.g., electrical infrastructure, operations, and maintenance) under development by various countries, can be compared and improved through collaborative efforts relative to the baseline technology description

Based on insights gained through sensitivity analysis and model comparisons, Task 26 will identify characteristics of offshore wind cost models that could be of use to a range of stakeholders, including decision-makers.

Estimates of future cost and performance for wind technology are important for analyses of the potential for wind energy to meet national targets for carbon emission reductions or renewable electricity generation. Learning curves are one method for assessing the effect of technology development, manufacturing efficiency improvement, and economy of scale. Component level cost and scaling relationships can also be used to estimate future technology development pathways.

Engineering models can isolate theoretical improvements associated with individual technical changes, e.g., larger rotors. These models can also project theoretical cost and energy production from future technology advances. Expert elicitation provides a quantifiable means for assessing a range of expert perspectives on the future cost of energy. All projections of future wind energy costs can be informed by an analysis of historical trends that capture both technology and market-related influences.

Repowering is becoming an increasingly relevant issue in the wind energy landscape, as significant capacities of wind turbines are reaching the end of their technical/economic lifetimes across Europe and the United States. Due to repowering being an emerging trend, there is little research on the topic to date. Achieving a better level of understanding regarding technical, performance, and economic impacts of repowering would therefore be a value-adding contribution to the currently existing knowledge base and provide critical input to informed decision-making on the part of policy-makers, investors, etc.

High shares of wind pose fundamental challenges to the current market setup. The higher the share of prioritized wind power, the lower the share of market-based dispatchable power, which affects the merit order of the supply and lowers the power prices in general.

Variable wind energy supply may also create hours with very high prices in cases with little wind and high electricity demand, or even negative prices in hours with high wind and low demand. Even though these extreme prices are only observed in a minor number of hours per year it challenges the current market and price mechanisms.

A possible scenario setup for further investigating the value of wind power within the Task could be aimed at exploring the effects of applying different turbine technologies, thereby combining the knowledge on technology cost and trends from the wind task with an outlook on how technology costs compare to the value of wind power. Is the extra cost of low cut-in wind speed wind turbines beneficial from a system perspective? In this way, added value compared to other scenario analyses would be a closer look at technology characteristics, as well as wind speed and resource modeling.

A possible scenario setup could also be to reach a certain desired level of wind power generation by different paths with respect to applied technology and regional distribution. The results can be illustrated in terms of key parameters, such as total system costs and the electricity price wind plants receive in the electricity market compared to average market prices.

Electric sector models will be used to explore these questions. The primary analysis will be conducted using Balmorel, an economic and technical partial equilibrium model that simulates the power system and least-cost dispatch. Supplemental analysis of relevant regions may be conducted using e.g. PLEXOS, a production cost model widely used by the electric industry.