Load flow studies are the backbone of power system analysis and design. They are necessary for planning, operation, optimal power flow and economic scheduling and power exchange between utilities. This paper describes modelling procedure and present models of system components used in performing load flow analysis. The developed models are joined together to form a system network representing an approximate Tanzanian power network model. A load flow problem is formulated using the model and a MATLAB program developed using Newton-Raphson algorithm is applied in solving the problem. Simulation results are presented and analysed. The results indicate that the voltage magnitude and voltage phase angle profiles are within the operating limits of the system; it means that the selection of system components and modelling process is appropriate and accurate. The results will form the basis of other critical power system studies of the network in the future such as power system state estimation, optimal power flow and security constrained optimal power flow studies.

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Abstract:Power system state estimation is an effective online tool for monitoring, control and for providing consistent database in energy management systems. This paper presents an algorithm for state estimation of the Tanzanian power system network using a non-quadratic state criterion. Equality and inequality constraints existing in a power system are included in formulating the estimation problem. Equality constraints are target values used in load flow analysis and are included in power system state estimation in order to restore observability to those parts of the power system network which are permanently or temporarily unobservable. Inequality constraints are limits such as minimum and maximum reactive power generation, transformer tap and phase-shift. The solution techniques used is primal-dual interior point logarithmic barrier functions to treat the inequality constraints. An algorithm is developed using the method and a program coded in MATLAB is applied in implementing the simulation. Computational issues arising in the implementation of the algorithm are presented. The simulation results demonstrate that the primal-dual logarithmic barrier interior point algorithm is a useful numerical tool to compute the state of an electrical power system network. The inequality constraints play essential role in enhancing the reliability of the estimation results. Also, it is expected that significant benefit could be gained from application of the constrained state estimation algorithm to the Tanzanian power system network.

Power system state estimation is the process of computing a reliable estimate of the system state vector composed of bus voltages' magnitudes and angles from telemetered measurements on the system. This estimate of the state vector provides the description of the system necessary for operation, security monitoring and control. Many methods are described in literature for solving the state estimation problem, the most important of which are the classical weighted least squares and the non-quadratic method. However, both showed drawbacks when it comes to application to large-scale power system networks. In this paper, a new method in the name of decomposition-coordination approach using the weighted least squares is introduced in solving the large-scale power system state estimation problem. The estimation criterion is reformulated; voltage measurement, real and reactive power injections, real and reactive power flows, and real and reactive power flows in tie-line models of a decomposed system are developed. Two level structure of solving the estimation problem is introduced. The first level solves the sub-problem using gradient procedure methods while the second level determines the interconnection variables using predictive method. The positive characteristic of the method is that the coordinator has little work of predicting interconnection variables instead of solving the state estimation problem. The method can be used to solve a multi-area state estimation using parallel or distributed processing architectures.