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With the advent of wind power generation on worldwide power systems, many operators and researchers are analyzing the impacts that higher future amounts may have on system operations. Many of the tools are analyzing longer term impacts on the steady-state operations of power systems and are primarily using cost as a metric. They are also using tools that are often inflexible to accommodating different market designs or operational structures. In this paper a model was developed to mimic operator behavior using a combination of security-constrained unit commitment, security-constrained economic dispatch, and automatic generation control programs. New metrics are used to compare reliability in terms of energy imbalance for different systems or different market and operational structures at very high time resolution. Finally an example application of the tool and results for a test system are shown.

During the past few years, there has been significant interest in alternative ways to manage power systems over a larger effective electrical footprint. Large regional transmission organizations in the Eastern Interconnection have effectively consolidated balancing areas, achieving significant economies of scale that result in a reduction in required reserves. Conversely, in the Western Interconnection there are many balancing areas, which will result in challenges if there is significant wind and solar energy development in the region. A recent proposal to the Western Electricity Coordinating Council suggests a regional energy imbalance service (EIS). To evaluate this EIS, a number of analyses are in process or are planned. This paper describes one part of an analysis of the EIS's implication on operating reserves under several alternative scenarios of the market footprint and participation. We improve on the operating reserves method utilized in the Eastern Wind Integration and Transmission Study and apply this modified approach to data from the Western Wind and Solar Integration Study.

Wind forecasting is an important consideration in integrating large amounts of wind power into the electricity grid. The wind power forecast error distribution assumed can have a large impact on the confidence intervals produced in wind power forecasting. In this work we examine the shape of the persistence model error distribution for ten different wind plants in the ERCOT system over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution. The shape of the distribution is found to change significantly with the length of the forecasting timescale. The Cauchy distribution is proposed as a model distribution for the forecast errors and model parameters are fitted. Finally, the differences in confidence intervals obtained using the Cauchy distribution and the normal distribution are compared.