Looking forward

The future state of the electricity industry always differs from the scenarios ana­lyzed in large-scale integration studies. Given this fact, studies should focus more on quantifying the relative effect of particular changes in operating policy or technologies than on seeking to precisely quantify the economic or reliability effect of a particular penetration scenario. For example, a conclusion that using fast-ramping storage will reduce ancillary service costs by 10% is likely to be more useful than one that says that the ancillary service costs will be $1.52/MWh for scenario X...

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Analysis of transmission system investments

Several of the studies suggested the need for substantial transmission expansion in order to facilitate high-renewables-penetration scenarios. While some transmis­sion expansion is certainly warranted, large-scale expansion of the bulk trans­mission network is costly and will face substantial siting barriers. Thus there is, in our opinion, a need for creative thinking about how to most effectively use existing transmission resources, with perhaps a minimum amount of network expansion, to facilitate high-renewables scenarios. As an example of the type of analysis that is needed, Hoppock and Patino-Echeverri (2010) compared the levelized cost of energy from distant, high-capacity-factor wind sites to energy from near, lower-capacity-factor sites...

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Power system reliability modeling

Power system reliability modeling is important to wind integration studies, particu­larly given the potential for long periods of low wind and the need to accurately compensate wind plants for their contribution to system adequacy. Regarding the computation of capacity credits for wind, most of the studies used some variant of the ELCC method from Keane and colleagues (2011), but with only 2 or 3 years of data. As suggested in Hasche and colleagues (2011), accurate estimates of ELCC require 5 or more years of data because substantial interannual variations in wind resources are possible. Going forward, capacity credit calculations in wind integra­tion studies should be based on at least 5 years of wind data.

A majority of the studies reviewed used GE MARS for reliability modeling...

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Production cost simulation (PCS)

The technology for power system PCS has improved substantially across the studies in this review. While some early studies either did not include PCS (e. g., GE Energy, 2008) or did not include accurate transmission system models (e. g.,WindDS in U. S. DOE, 2008), the most recent studies used detailed PCS models that modeled unit commitment, generator ramp rate constraints, and transmission limits. Given that these costs can be important contributors to wind integration costs (Mount et al.,

2012) , including these details is important. Since most PCS is performed on hourly data, the fine time-scale data challenges described in Sections 17.5.1 and 17.5.2 are unlikely to have a significant effect on PCS results.

Going forward, there is a need to improve the ways in which uncertainty and wind...

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Statistical modeling and balancing reserves

Almost all of the studies in this review used net load step-change statistics to esti­mate the need for additional balancing (regulation and load-following) reserves. Most of the studies implicitly or explicitly assumed that load and wind are uncor­related and that the data fit Gaussian statistical models, neither of which is accurate. Methods such as the one proposed in Charles River Associates (2010) that use the magnitude of low-probability ramping events rather than standard deviations are likely to produce balancing resource estimates that more accurately predict what will be needed to maintain system reliability...

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Discussion and conclusions

We have reviewed 11 large North American wind integration studies. This review highlights a number of areas in which wind integration studies have evolved to provide valuable insight, as well as a few areas in which improvements in methods and additional research are needed to facilitate greater insight from future studies. We now summarize our observations and conclusions from this review.

17.5.1 Wind data sources

The quality of the wind data used for wind integration studies has improved sub­stantially over time. Almost all of the wind integration studies used data from meso- scale numerical weather prediction models. Early studies used models with a 10-km resolution, whereas the most recent models used 2-km spatial and 10-minute tem­poral resolution. As shown in Section 17...

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Suggested system changes and operating practices

The effect of wind integration depends heavily on the system in which it is being integrated. These studies made numerous recommendations that could reduce the cost of wind integration. First, several of the studies concluded that larger balancing authority areas were better suited to managing wind variability than smaller balancing authorities (Charles River Associates, 2010; EnerNex Corporation, 2006, 2011; GE Energy, 2008, 2010). Large balancing areas reduced variability in net load and provided a large pool of generating units from which to manage deviations from forecasted net load. Balancing area consolidation is discussed in Chapter 5 .

Second, wind benefits from systems with greater flexibility in the generating portfolio, as more startups and shorter cycles are required to accommo...

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Wind forecast accuracy

Improved wind forecasting techniques also mitigate the difficulty of incorporating wind into the power system (see Chapter 9). Several studies provided estimates of the benefits of perfect wind forecast relative to state-of-the-art wind forecast and found significant operational improvements with improved forecasting (EnerNex Corporation, 2011; GE Energy, 2005a, 2010). EWITS determined that perfect, day – ahead wind forecast would reduce the cost of wind generation by between $2.26 and $2.84/MWh. WWSIS concluded that perfect wind forecast would decrease WECC operating cost by $1 to 2/MWh of wind energy relative to current state-of-the-art forecasts and prevent the need for any wind curtailment (GE Energy, 2010). The NYSERDA study found perfect wind forecasts to be worth approximately $1...

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