In this paper, the transmission planning state-of-the-art, which was obtained from the review of the most interesting models found in the international technical literature, is presented. The classification of publications was made, keeping in mind the solution methods, the treatment of the planning horizon, and the consideration of the new competitive schemes in the power sector. A discussion about the available tools for development of transmission planning models is also included.

Classification of publications and models on transmission expansion planning

http://ntur.lib.ntu.edu.tw/bitstream/246246/176291/1/34.pdf

Adaptive neuro-fuzzy inference system for prediction of water level in reservoir

The data of four networks that can be used in carrying out comparative studies with methods for transmission network expansion planning are given. These networks are of various types and different levels of complexity. The main mathematical formulations used in transmission expansion studies-transportation models, hybrid models, DC power flow models, and disjunctive models are also summarised and compared. The main algorithm families are reviewed-both analytical, combinatorial and heuristic approaches. Optimal solutions are not yet known for some of the four networks when more accurate models (e.g. the DC model) are used to represent the power flow equations-the state of the art with regard to this is also summarised. This should serve as a challenge to authors searching for new, more efficient methods.

Test systems and mathematical models for transmission network expansion planning

The recent increased number of black outs in the power system has been largely due to growing competition and deregulation among the power industry. Power systems are complex nonlinear systems and often exhibit low frequency electro-mechanical oscillations due to insufficient damping caused by severe operating conditions. This needs an advanced modeling and control techniques for effective control of power plants. In case of hydroelectric plant the hydro turbine is a non-linear, non-stationary multivariable system whose characteristics vary significantly with the unpredictable load on it and this presents a difficulty in designing an efficient and reliable controller. A conservatively designed control fails to perform as expected. Keeping this in mind, hydro plant control is an application area with an interesting set of problems for control engineering people. Mainly some of these problems focus towards regulation of turbine with large load variation in the power system. These problems have not been adequately solved and continue to pose challenges to the control community. In this review paper, the authors have tried to broadly categorize the research work done so far on the basis of hydro plant model development and its controller design under different sections. A substantial number of relevant research papers can be found on the plant modeling, design aspects of control methodologies and their performance study.

A review on hydropower plant models and control

A greedy randomized adaptive search procedure (GRASP) is a heuristic method that has shown to be very powerful in solving combinatorial problems. In this paper we apply GRASP to solve the transmission network expansion problem. This procedure is an expert iterative sampling technique that has two phases for each iteration. The first, construction phase, finds a feasible solution for the problem. The second phase, a local search, seeks for improvements on construction phase solution by a local search. The best solution over all GRASP iterations is chosen as the result.

A Greedy Randomized Adaptive Search Procedure for Transmission Expansion Planning

A new method is presented for long-range transmission network expansion planning, based on the decomposition principle. The overall transmission expansion planning task is divided into two problems, the first one dealing with investments, and the second with operations. The investment problem is specified as the minimum cost problem of network programming, leading to the decomposition into the model of minimum load curtailment and the proposed model of the (local) marginal network. The operation problem is solved by applying the Monte Carlo simulation, with suitable control strategies and additional reliability constraints. The final result is the software package, verified on test examples, as well as on the real transmission network of the power pool of eastern Yugoslavia. >

A new decomposition based method for optimal expansion planning of large transmission networks

This paper presents an attempt of nonlinear, multivariable control of low-head hydropower plants, by using adaptive-network based fuzzy inference system (ANFIS). The new design technique enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near optimal manner. The controller has flexibility for accepting more sensory information, with the main goal to improve the generator unit transients, by adjusting the exciter input, the wicket gate and runner blade positions. The developed ANFIS controller whose control signals are adjusted by using incomplete on-line measurements, can offer better damping effects to generator oscillations over a wide range of operating conditions, than conventional controllers. Digital simulations of hydropower plant equipped with low-head Kaplan turbine are performed and the comparisons of conventional excitation-governor control, state-feedback optimal control and ANFIS based output feedback control are presented. To demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired neuro-fuzzy controller, the controller has been implemented on a complex high-order nonlinear hydrogenerator model.

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

The paper is devoted to an optimal solution of the investment model within overall transmission network expansion planning. The proposed investment model is defined as the static, minimum-cost linear-programming problem. Adequate rearrangement of this model achieves its decomposition into two interrelated subproblems. The first subproblem deals with the solution of initial power flows encompassing security aspects only, and the second subproblem solves superimposed power flows taking into account the economy of the problem as well. The minimum-load curtailment model is used for the solution of the first subproblem, and the proposed model of linear-programming marginal network is used for the solution of the second subproblem. The proposed investment model is incorporated into the newly developed, very flexible procedure for overall transmission network expansion planning, which enables the application of different expansion planning concepts.

Linear-programming-based decomposition method for optimal planning of transmission network investments

Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

This paper proposes the application of artificial neural networks to real-time optimal generation dispatch of thermal power plant units. The approach can take into account operational requirements and power network losses. The proposed economic dispatch uses an artificial neural network (ANN) for the generation of penalty factors, depending on the input generator powers and identified system load change. Then, a few additional iterations are performed within an iterative computation procedure for the solution of coordination equations, by using reference-bus penalty-factors derived from Newton-Raphson load flow. A coordination technique for environmental and economic dispatch of pure thermal power systems, based on neural net theory for simplified solution algorithms and an improved man-machine interface is introduced. Numerical results on two test examples show that the proposed algorithm can efficiently and accurately develop optimal and feasible generator output trajectories by applying neural net forecasts of power system load patterns.

Milan S. Ćalović

Papers

A new decomposition based method for optimal expansion planning of large transmission networks

Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

Linear-programming-based decomposition method for optimal planning of transmission network investments

Coordinated stabilizing control for the exciter and governor loops using fuzzy set theory and neural nets

Neural-net based real-time economic dispatch for thermal power plants