Real-time transmission transfer capability assessment in a deregulated bulk power market

Herbert Hung-Kwong Yan

doi:10.17918/00008339

The transmission systems in the U.S. are interconnected. The enactment of the U.S. Energy Policy Act of 1992 established a new "open access" environment that has spurred bulk power marketing activities among electric utilities in the interconnected network. In bulk power marketing, a common practice is to assume that the power deliveries will flow through predetermined paths, although the actual amount and direction of transfers are controlled by the law of physics. This practice has created extraneous power flows, known as "parallel flows," on transmission systems. Depending on system conditions, these "parallel flows" can cause or aggravate transmission constraints in systems external to the transacting systems. With the proliferation of regional power transfers, it is obvious that one needs to obtain additional operating data, such as bus voltages and active power flows (megawatts), in the external systems in order to assess adequately the transmission transfer capability. This thesis presents the theory and algorithms of a method for estimating bus voltages and active power flows using available operating data in a control center. The theory states that the summation of voltage phase angle differences, taken in the same direction in a loop, is zero. The main contribution of this thesis is that it gives a new approach in estimating the operating state of a power system, thereby providing additional operating data which are otherwise not available in a control center. This method can therefore be used to estimate operating states in areas of the external systems that are electrically close to the internal system modeled in the control center. An illustration is provided using a 17-bus system. As this method can be easily implemented in the online computer system, the method is applicable in the real-time environment. This thesis also presents an approach using, in addition to the above method, artificial neural networks for real-time security assessment. The artificial neural networks are used as a security/insecurity predictor and as an estimator of post-contingency line flows and voltages. The overall approach for real-time transmission transfer capability assessment is presented as an enhancement to a typical Energy Management System to overcome some of the deficiencies in the current techniques.

Real-time transmission transfer capability assessment in a deregulated bulk power market

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Real-time transmission transfer capability assessment in a deregulated bulk power market

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