Shin-Yeu Lin

An ordinal optimization theory-based algorithm for solving the optimal power flow problem with discrete control variables

The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal optimization theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard optimization problems, the ordinal optimization theory, in contrast to heuristic methods, guarantee to provide a top n% solution among all with probability more than 0.95. The approach of our ordinal optimization theory-based algorithm consists of three stages. First, select heuristically a large set of candidate solutions. Then, use a simplified model to select a subset of most promising solutions. Finally, evaluate the candidate promising-solutions of the reduced subset using the exact model. We have demonstrated the computational efficiency of our algorithm and the quality of the obtained solution by comparing with the competing methods and the conventional approach through simulations.

Multi-year multi-case optimal VAR planning

An integrated methodology for long-term VAr planning is presented that results in determining the timing (year), the location, and the amount of VAr compensation. The system security and investment and operating economics are taken into account. The proposed methodology is an integration of the Newton-OPF with the generalized Benders decomposition (GBD). The total problem is decomposed into two levels: master and slave. The master level deals with the investment decision of installing discretized new VAr devices. The slave level deals with operating the existing controllers, in conjunction with the new devices solved in the master level, to maintain system feasibility and to reduce MW losses. The overall solution methodology contains numerous extensions to the basic theory. Tests performed on actual Taiwan power system data have been encouraging. Sample results are presented. >

A distributed state estimator for electric power systems

The author develops a theoretically robust and computationally efficient distributed state estimator to solve the weighted least square state estimation problem by using distributed computation. This distributed state estimator is used in decentralized control and executes in a data communication network that is assumed to be topologically the same as and physically in parallel with the power network. Several attractive satellite functions can be obtained which include: (1) reduction of the time-skew problem; (2) freedom from the power network topological error; (3) easy identification of the unobservable states; and (4) bad data detection and identification. The computational complexity of this distributed state estimator was analyzed. This state estimator was simulated on several cases of the IEEE 30-bus system. The numerical accuracy of the simulation results is satisfactory, and the estimated computation time including the communication delay demonstrates the excellent computational performance of the distributed state estimator. >

Universal alignment probability revisited

In this note, we quantify and validate the representativeness of the uniformly sampled set N for the search space Θ and the use of universal alignment probability (UAP) curves.

A new dual-type method used in solving optimal power flow problems

In the framework of the sequential quadratic programming (SQP) method for optimal power flow (OPF) problems, the authors propose a new dual-type method for solving the QP subproblems induced in the SQP method. Their method achieves some attractive features; it is computationally efficient and numerically stable. The computational formulae of their method are simple, concise and easily programmed. The authors have tested their method for OPF problems on several power systems, including a 2500-bus system.

Distributed Optimal Power Flow With Discrete Control Variables of Large Distributed Power Systems

In this paper, we propose a distributed algorithm to solve the yet explored distributed optimal power flow problem with discrete control variables of large distributed power systems. The proposed algorithm consists of two distinguished features: 1) a distributed algorithm for solving continuous distributed optimal power flow to serve as a core technique in the framework of ordinal optimization (OO) strategy, and 2) implementing the OO strategy in a distributed power system to select a good enough discrete control variable solution. We have tested the proposed algorithm for several cases on the IEEE 118-bus and Tai Power 244-bus systems using a 4-PC network. The test results demonstrate the validity, robustness, and excellent computational efficiency of the proposed distributed algorithm in getting a good enough feasible solution.

A hardware implementable two-level parallel computing algorithm for general minimum-time control

A hardware implementable two-level parallel computing algorithm for general minimum-time control is proposed. The minimum-time control problem for a continuous-time system is discretized and transformed into a parameter optimization problem which is large dimensional and nonseparable. The proposed two-level algorithm decomposes this parameter optimization problem into a master-slave problem. The master problem is easily solved by a one-dimensional gradient method, and the slave problem is solved by a parallel computing method which combines recursive quadratic programming with the dual method. The convergence of this iterative two-level parallel computing algorithm under some conditions is proved. On the basis of the VLSI array processor technology, a dedicated hardware computing architecture for realizing this algorithm is presented. The corresponding time complexity, is also analyzed. Simulation of practical problems shows that the algorithm is well suited for real-time application of minimum-time control. >

Evolutionary algorithm assisted by surrogate model in the framework of ordinal optimization and optimal computing budget allocation

This work proposes an evolutionary algorithm (EA) that is assisted by a surrogate model in the framework of ordinal optimization (OO) and optimal computing budget allocation (OCBA) for use in solving the real-time combinatorial stochastic simulation optimization problem with a huge discrete solution space. For real-time applications, an off-line trained artificial neural network (ANN) is utilized as the surrogate model. EA, assisted by the trained ANN, is applied to the problem of interest to obtain a subset of good enough solutions, S. Also for real-time application, the OCBA technique is used to find the best solution in S, and this is the obtained good enough solution. Most importantly, a systematic procedure is provided for evaluating the performance of the proposed algorithm by estimating the distance of the obtained good enough solution from the optimal solution. The proposed algorithm is applied to a hotel booking limit (HBL) problem, which is a combinatorial stochastic simulation optimization problem. Extensive simulations are performed to demonstrate the computational efficiency of the proposed algorithm and the systematic performance evaluation procedure is applied to the HBL problem to quantify the goodness of the obtained good enough solution.

Memetic Algorithm for Real-Time Combinatorial Stochastic Simulation Optimization Problems With Performance Analysis

A three-phase memetic algorithm (MA) is proposed to find a suboptimal solution for real-time combinatorial stochastic simulation optimization (CSSO) problems with large discrete solution space. In phase 1, a genetic algorithm assisted by an offline global surrogate model is applied to find N good diversified solutions. In phase 2, a probabilistic local search method integrated with an online surrogate model is used to search for the approximate corresponding local optimum of each of the N solutions resulted from phase 1. In phase 3, the optimal computing budget allocation technique is employed to simulate and identify the best solution among the N local optima from phase 2. The proposed MA is applied to an assemble-to-order problem, which is a real-world CSSO problem. Extensive simulations were performed to demonstrate its superior performance, and results showed that the obtained solution is within 1% of the true optimum with a probability of 99%. We also provide a rigorous analysis to evaluate the performance of the proposed MA.

Application of an Ordinal Optimization Algorithm to the Wafer Testing Process

In this correspondence, we have formulated a stochastic optimization problem to find the optimal threshold values to reduce the overkills of dies under a tolerable retest level in wafer testing process. The problem is a hard optimization problem with a huge solution space. We propose an ordinal optimization theory-based two-level algorithm to solve for a vector of good enough threshold values and compare with those obtained by others using a set of 521 real test wafers. The test results confirm the feature of controlling the retest level in our formulation, and the pairs of overkills and retests resulted from our approach are almost Pareto optimal. In addition, our approach spends only 6.05 min in total in a Pentium IV personal computer to obtain the good enough threshold values