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In competitive electric energy markets, the power generation dispatch optimization is one of the most important missions among generation companies-how to respond to the markets, dispatch their units, and maximize profits. This paper proposes an approach to incorporate power contracts, which include call and put options, forward contracts, and reliability must-run contracts, into multi-area unit commitment and economic dispatch solutions. The proposed solution algorithm is based on adaptive Lagrangian relaxation, unit decommitment, and lambda-iteration methods. The problem formulation consists of three stages: 1) the incorporation of the power contracts, 2) the multi-area unit commitment, and 3) the multi-area economic dispatch. The proposed algorithm has been successfully implemented, and its testing results on modified IEEE test cases are promising.

The development of wind generation has rapidly progressed over the last decade. With the advance in wind turbine technologies, wind energy has become competitive with other fuel-based generation resources. The fluctuation of wind, however, makes it difficult to optimize the use of wind power generation. Current practice ignores the possible available capacity of the wind generation during the unit commitment scheduling. This may cause operation issues and waste usable capacity when the installation of the wind generation increases. An accurate wind capacity forecasting is essential for efficient wind energy and capacity dispatching. To ensure the system reliability, one also has to consider the forecast uncertainty when integrating the wind capacity into generation planning. This paper discusses the development of an Artificial Neural Network based wind forecast model with the consideration of wind generation uncertainty by using probabilistic concept of confidence interval. The data from a wind farm located in the Southern Oklahoma is used for this study.Keywords-Short-term wind power forecast, Artificial Neural Network, Wind forecast uncertainty, Confidence level L INTRODUCTION W ind energy has gained an extensive interest and become one of the most mature renewable energy alternatives to the conventional fuel-based power generation. The development of wind electricity generation has rapidly progressed over the last decade, largely driven by the public concern about global warming, limited fuel resources, as well as the provision of federal production tax credit. The record shows that wind power generation has expanded with annual rate of 25 percent since 1990 and has a great potential to be realized in many regions of the US [I].Despite various benefits of the wind power, an integration of wind generation into the electric grid is difficult to manage. The main challenge associates with its variability and unpredictability, The power generated from a wind usually fluctuates rapidly, which imposes difficulties in both operation and planning.Because of intennittent nature of the resource, utilities traditionally discount any contribution from interconnected wind resource during unit commitment scheduling, Savings can be realized from the reduction of the thennal plant operation if part of wind capacity can be counted as a 'finn' energy resource. Besides, most utilities utilize 'slow-start' unit such as nuclear plant or coal-fired plant to serve major portion of the load because of their low generation cost. It will be more economical to have these units online for a certain period of time since they require lengthy shut down and restart procedure, However, a system with too much wind generation can be a penalty to these units. For example , additional wind energy may push the output of the base units lower than their minimum operating o· 7803·9020·2/05/$20.00 ©200S IEEE level during off-peak that forces utility to involuntary shut down the unites) or give away their excess energy, In contrast, the under-commit of...

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