Abstract
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.
Highlights
Research in power systems is dominated by technological challenges in generation, transmission and distribution
3.6 Summary This Chapter reports the performance of Artificial NeuralNetwork (ANN) wind generator models and demonstrates their ready integration into popular commercial power system analysis software
Comparing accuracy of results of the power flow studies with fixed PQ models that are a function of wind speed, nonlinear accurate models and ANN models, it can be seen that ANN models give very accurate solutions that are very close to those of the nonlinear models
Summary
Research in power systems is dominated by technological challenges in generation, transmission and distribution. We present integration of ANN wind generator models with a power flow analysis algorithm and integration with commercial software such as PSS®E and PSS®SINCAL. Performance of the proposed ANN wind generator models in power flow analysis algorithms is assessed. The power flow analysis is a study wherein bus power balance equations are solved and bus voltages at all buses are determined using an algorithm such as the ladder iterative technique (see section 1.2.1). With significant integration of wind power with distribution systems, probabilistic power flow assumes importance In this Chapter uncertainty in the distribution system voltage is considered as dependent upon the uncertainty of loads and wind generation in the distribution system. This chapter proposes a cooperative model for wind generators that can be used to minimize effects of uncertainty and intermittency of wind power output while maximizing economic returns from the sale of wind energy. The WGC model collectively uses a few pumped-hydro energy storage systems to minimize uncertainty
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