Abstract
Though the full AC power flow model can accurately represent the physical power system, the use of this model is limited in practice due to the computational complexity associated with its non-linear and non-convexity characteristics. For instance, the AC power flow model is not incorporated in the unit commitment model for practical power systems. Instead, an alternative linearized DC power flow model is widely used in today’s power system operational and planning tools. However, DC power flow model will be useless when reactive power and voltage magnitude are of concern. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a traditional LAC model and then proposes an enhanced data-driven linearized AC (DLAC) model using the regression analysis technique. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.
Highlights
The power system is one of the most complicated physical networks in the world
To solve the scalability and accuracy issues related to AC power flow linearization, a data-driven linearized AC (DLAC) power flow model is proposed in this paper
AC power flow simulation is conducted on hour 1 case to obtain the system status that is used as the training data for regression analysis: the data used for determining the coefficient for data-driven DC model are line reactance, line active power flow and phase angle difference across the line; the data used for determining the coefficients for data-driven linearized AC model include line reactance and susceptance, line active power flow and reactive power flow, phase angle difference across the line, and voltage magnitudes at two end buses
Summary
The power system is one of the most complicated physical networks in the world. Almost all electricity demands are served through power systems. The full AC power flow model, following Kirchhoff’s circuit laws and Ohm’s law, can accurately represent the physical power system This model captures all electric variables of interest, including active power, reactive power, phase angle and voltage magnitude. To solve the scalability and accuracy issues related to AC power flow linearization, a data-driven linearized AC (DLAC) power flow model is proposed in this paper This model captures all system state variables including active power, phase angle, reactive power and voltage magnitude. The proposed enhanced DLAC power flow model can (i) substantially reduce the error associated with active power flows as compared with the traditional DC power flow model; (ii) and obtain much more accurate voltage profile and significantly improve reactive power flow solutions as compared with the traditional linearized AC model.
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