T.S. Chung

Quantum-Inspired Evolutionary Algorithm Approach for Unit Commitment

This paper presents a novel method for solving the unit commitment (UC) problem based on quantum-inspired evolutionary algorithm (QEA). The proposed method applies QEA to handle the unit-scheduling problem and the Lambda-iteration technique to solve the economic dispatch problem. The QEA method is based on the concept and principles of quantum computing, such as quantum bits, quantum gates and superposition of states. QEA employs quantum bit representation, which has better population diversity compared with other representations used in evolutionary algorithms, and uses quantum gate to drive the population towards the best solution. The mechanism of QEA can inherently treat the balance between exploration and exploitation and also achieve better quality of solutions, even with a small population. The proposed method is applied to systems with the number of generating units in the range of 10 to 100 in a 24-hour scheduling horizon and is compared to conventional methods in the literature. Moreover, the proposed method is extended to solve a large-scale UC problem in which 100 units are scheduled over a seven-day horizon with unit ramp-rate limits considered. The application studies have demonstrated the superior performance and feasibility of the proposed algorithm.

Multi-objective transmission network planning by a hybrid GA approach with fuzzy decision analysis

In this paper, power system transmission network planning is formulated as a multi-objective mathematical optimization problem. In this context, three objectives: investment cost, reliability and environmental impact are considered in the optimization. To overcome the drawback of conventional mathematical optimization method in arriving at local optimum solution, a genetic algorithm approach is developed, from which several possible planning schemes are generated. This is followed by a fuzzy decision analysis method to select the optimum solution scheme. Case study results on a large 220 kV transmission network planning problem are presented to show the methodologys feasibility and efficiency.

Optimal active power flow incorporating FACTS devices with power flow control constraints

An improved two-stage approach to solving the new formulation of optimal power flow (OPF) problem which incorporates flexible AC transmission system (FACTS) devices and power flow control constraints is presented. This approach is shown to provide an enhanced economic solution with the use of controllable FACTS devices. The FACTS devices considered include series compensators and phase shifters. The power flow constraints are specified line flows related to FACTS devices. The proposed approach decomposes the solution of such a modified OPF problem into two stages. The first stage is a power flow control problem and the second stage is a normal OPF problem. The two are solved iteratively until a stop criterion is reached. Numerical examples are presented to show the effectiveness of the proposed approach.

Application of an innovative combined forecasting method in power system load forecasting

Abstract To fully integrate the advantages of several forecasting models and improve the accuracy of load forecast results, the application of the combined forecasting method to power system load forecasting is introduced in this paper. The evolutionary programming and fuzzy comprehensive evaluation methods are employed to deduce the weight coefficients of each model. Practical cases are studied using the two methods and tested to be feasible and effective.

A genetic algorithm approach in optimal capacitor selection with harmonic distortion considerations

This paper reports the research findings of an innovative genetic algorithm approach for optimizing shunt capacitor sizes and their placement in radical distribution systems with the consideration of harmonic distortion limit due to the presence of nonlinear power electronic devices. The algorithm is based on a genetic algorithm solution technique to minimize cost under the additional constraints of maximum limit in harmonic distortion factor. A harmonic distortion calculation is embedded in the genetic algorithm solution routine to enhance the optimal capacitor allocation solution. Results of simulation show that the approach is effective for such a discrete value optimization problem.

State estimation for power systems embedded with FACTS devices and MTDC systems by a sequential solution approach

Abstract This paper reports the development of a novel and effective approach in state estimation for power systems with flexible AC transmission system (FACTS) and multi-terminal DC (MTDC) systems, called improved sequential method. The proposed approach is sequential in nature in which the FACTS and MTDC systems without neglecting the coupling submatrices in the gain matrix, it exhibits good convergence characteristics compared to conventional techniques. The variables and measurement equations of the FACTS and MTDC systems related to the problem formulation are discussed. The effectiveness of the proposed algorithm is demonstrated in this paper with extensive testing in several test systems and the results are compared with the other state estimators.

A Tabu Search Approach to Distribution Network Reconfiguration for Loss Reduction

Distribution network reconfiguration for loss minimization is a complex, large-scale combinatorial optimization problem. A new efficient and robust algorithm for he reconfiguration of distribution networks in order to reduce the power energy losses under normal operating conditions is presented in this article. The developed algorithm is based on a tabu search (TS) approach, which is a recent member in the family of modern heuristic methods. Tabu search is used for efficiently obtaining the near-optimal solutions of combinatorial optimization problems, which makes it suitable to solve the problem of distribution network reconfiguration. The components of proposed TS-based method and effective schemes, which aim at speeding up the solution methodology, are also presented. To demonstrate the validity and effectiveness of the proposed method, three test systems with different sizes are studied. The numerical results reveal that the proposed method is promising.

Fast transient stability estimation using a novel dynamic equivalent reduction technique

A new dynamic equivalent power system (DEPS) model for fast first swing transient stability assessment is proposed in this paper. The DEPS model makes use of the phenomenon that during a fault on a large power system, there are many generators that are only lightly disturbed when compared with severely disturbed machines. The method substitutes these less disturbed machines by one equivalent generator but allows some model parameters to become time dependent. The crucial feature is that a novel method of classification of the less disturbed machines based on a Taylor series expansion which achieves a very rapid assessment of first swing critical clearing time (CCT) has been developed. This method is fundamentally different from other published dynamic equivalent techniques, such as the extended equal area criterion method and the identification of coherent generators, and both reliability and efficiency are better. With this DEPS model the efficiency of the traditional first swing transient stability analysis could be fundamentally improved. Extensive tests performed on several IEEE test systems show that the proposed model is suitable for fast transient stability assessment in large systems. >

A recursive LP-based approach for optimal capacitor allocation with cost-benefit consideration

Abstract This paper presents a new approach for minimizing line losses and finding the optimal capacitor allocation in a distribution system. The problem is formulated as a linear programming problem and is solved by a recursive process. Since this method does not need any matrix inversion, it can save computational time and memory space. The economic justification of capacitor installation is also discussed and two practical examples demonstrate the approachs effectiveness.

Optimal power flow incorporating FACTS devices and power flow control constraints

This paper presents an alternate approximating algorithm to solve the optimal power flow (OPF) problem with flexible AC transmission systems (FACTS). The proposed method integrates the power flow control needs into the OPF problem to fully use the new characteristic of FACTS devices. The FACTS devices controllable parameters are divided into three types, which are included in active power OPF (APOPF) or in reactive power OPF (RPOPF) as control variables respectively. The active and reactive power flow equations of unified power flow controllers (UPFC) are introduced into RPOPF and APOPF separately to improve the convergence property. The LP-based algorithm is used in APOPF and the successive quadratic-programming algorithm is used in RPOPF. Case studies on IEEE test systems show the effectiveness of this algorithm.