The variability inherent in wind power production will require increased flexibility in the power system, when a significant amount of load is covered with wind power. Standard deviation (σ) of variability in load and net load (load net of wind) has been used when estimating the effect of wind power on the short term reserves of the power system. This method is straightforward and easy to use when data on wind power and load exist. In this paper, the use of standard deviation as a measure of reserve requirement is studied. The confidence level given by ±3–6 times σ is compared to other means of deriving the extra reserve requirements over different operating time scales. Also taking into account the total variability of load and wind generation and only the unpredicted part of the variability of load and wind is compared. Using an exceedence level can provide an alternative approach to confidence level by standard deviation that provides the same level of risk. The results from US indicate that the number of σ that result in 99% exceedence in load following time scale is between 2.3–2.5 and the number of σ for 99.7% exceedence is 3.4. For regulation time scale the number of σ for 99.7 % exceedence is 5.6. The results from the Nordic countries indicate that the number of σ should be increased by 67–100% if better load predictability is taken into account (combining wind variability with load forecast errors).

During the summer of 2003, the state of Arizona took delivery of a set of high-resolution wind energy maps that were developed with a meso-scale wind energy model coupled with wind data. The geographical information system data supplied with the wind maps was used to create a wind resource inventory that included wind energy potential, proximity to transmission lines, and land ownership. Four diverse sites were selected for further study, one predominantly class 3, one predominantly class 4, one predominantly class 5, and one predominantly class 6. At each site, the capacity factor was determined, the seasonal influence was observed, and the real levelized cost of energy in 2005 dollars determined. As the wind class varied from 6 to 3 the levelized cost of energy ranged from 4.22 to 6.00 cents per kWh. These results do not include the production tax credit or the renewable energy production incentive, do include adjustments for elevation, losses, and inflation, and are considered conservative. This paper documents the findings of the wind mapping process, describes the method and results of evaluating the most promising sites for wind development, and presents the cost of energy results.

No abstract

In May of 2004, the IEA Wind Implementing Agreement (IA) established R&D Task 24, “Integration of Wind and Hydropower Systems.” Australia, Canada, Finland, Norway, Sweden, Switzerland, and the United States joined Task 24 with the goal of collaborating in the study of wind integration in a variety of electrical system configurations (load, generation, and transmission); hydro system configurations and characteristics; and market and operational configurations. Representing these countries were utilities and research organizations with the intent to understand the potential for and limiting factors in integrating wind into systems with hydropower. Case studies that analyze the feasibility, benefits, detriments, and costs of specific wind-hydro integration projects were the mechanism through which the goals of the task were addressed. The purpose of this article is to summarize the framework within which these studies were performed, and to present the key results and the general conclusions of the Task.

A process was developed to evaluate the economic benefits from constructing and operating a wind energy project. The process uses an economic input/output analysis in conjunction with Monte Carlo simulation. Process results estimate the number of jobs and amount of spending that will occur in the analysis region because of the construction and operation of a wind energy project. Results from the proposed process may be used to garner community and governmental support for projects. The National Renewable Energy Laboratory jobs, Economic Development and Impacts (JEDI) model, developed specifically for wind energy projects, is used. As there is uncertainty in some of the required input parameters, the Monte Carlo simulation allows the input parameters to be entered as a range. The results of the JEDI model with the Monte Carlo simulation analysis produce a distribution for jobs, salaries and wages, and economic output during construction and operations. The results of the Monte Carlo simulation also provide a sensitivity analysis for each of the JEDI outputs. Two northern Arizona counties, Coconino County and Navajo County, were analysed to demonstrate the process. a tool used frequently in regional economic impact studies that estimates the number of jobs and the amount of money that will circulate in the local economy because of the direct and indirect spending from a project. Results from our process will interest local government offi cials, wind developers, renewable energy advocates and utility regulators. We demonstrate the process using two northern Arizona counties.The proposed process is unique because it adds Monte Carlo simulation techniques to the Jobs and Economic Development Impact (JEDI) economic I/O analysis allowing the analyst to incorporate project parameter uncertainty into the model. This is useful for several reasons. First, the process allows project costs and parameters to be estimated using a range of values instead of single values. When an economic impact study is conducted, a wind energy project is generally hypothetical or in the early stages of development. Therefore, there is uncertainty or a lack of knowledge about the project parameters and costs needed to perform the study. The analyst may not have enough information to accurately estimate a cost. For instance, without the Monte Carlo simulation, construction cost per megawatt must be estimated with a single value that represents the most likely or expected construction cost (i.e. $1320 per megawatt). The likelihood that the construction cost will be $1320 per megawatt is very small. However, the analyst may be fairly certain that the construction cost will fall in a range $1300-$1600. Using the Monte Carlo simulation, the analyst can use this range in the analysis. As the project becomes more defi ned, the analyst may be able to improve the estimate and narrow the range (i.e. $1300-$1400 per megawatt).Second, the output from the process; number of jobs, wages and salaries; and economic activity or value of the project in the loca...