This thesis focusses on three specific areas of integrating wind energy with power systems: 1) technical modeling of wind generators for power flow analysis, 2) probabilistic modeling of wind generators for planning studies, and 3) economic modeling for integration of wind energy in electricity markets. Wind generator output is a function of wind speed and 3-phase terminal voltages. Complete nonlinear three-phase models of wind generators are accurate but are computationally cumbersome and unsuitable for power flow analysis purposes. Intelligent models of wind generators are proposed for their accurate representation and use in power flow analysis algorithms. The main advantages of these intelligent models of wind generators are their mathematical simplicity, computational speed and numerical accuracy when the generators are connected to unbalanced three-phase distribution systems. These proposed intelligent models of wind generators were tested with the three-phase, unbalanced, IEEE 37-bus test system. The results show that the intelligent models of wind generators are computationally ten times faster than exact nonlinear models. In addition, simplicity of the proposed intelligent models of wind generators allows easy integration into commercial software such as PSS®E and PSS®SINCAL.
In the second study, a probabilistic model of wind generators was integrated with algorithm for distribution system analysis. The proposed probabilistic power flow analysis method for distribution systems takes into account the stochastic nature of wind generation and forecasted bus-wise peak load. Probability distribution functions for bus voltages are reconstructed. The proposed method is tested on a modified 70-bus distribution system and the results are reported. Thirdly, an economic integration model for wind generators with electricity markets is proposed. The proposed model is in the form of a Wind Generators Cooperative (WGC). This proposed model overcomes challenges posed by uncertainty and intermittency of wind generation. The proposed cooperative model maximizes returns for wind generators by minimizing the effect of uncertainty by smoothing effect and using pumped-hydro facilities. A case study with actual data from Ontario (Canada) was completed. Analyses clearly demonstrate that the WGC increases returns to wind generators and reduces their exposure to uncertainty.
A Sensitivity-Based Three-Phase Weather-Dependent Power Flow Algorithm for Networks with Local Controllers—Supplementary
