Ch’i-Hsin Lin

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 computationally efficient method for nonlinear multicommodity network flow problems

In this paper, we present a new method for solving nonlinear multicommodity network flow problems with convex objective functions. This method combines a well-known projected Jacobi method and a new dual projected pseudo-quasi-Newton (DPPQN) method which solves multicommodity flow quadratic subproblems induced in the projected Jacobi method. The DPPQN method is a dual Newton-type method that differs very much from the conventional Lagrangian Newton method; our method fully exploits the structural advantages of network-type linear equality constraints to obtain a constant sparse approximate Hessian matrix with a decoupling structure and includes a novel finite-iteration successive projection and (truncated) seal algorithm to resolve the difficulty caused by coupling capacity constraints. The DPPQN method also consists of two decomposition effects, the commodity decomposition effect and the arc decomposition effect, which resolve the potential numerical difficulties caused by large dimensions. We show the convergence of our method including the convergence of the finite-iteration successive projection and (truncated) seal algorithm. Compared with the Frank–Wolfe with PARTAN algorithm in which a price-directive decomposition method is used to solve linearized multicommodity flow problems, our method is dramatically faster in terms of the CPU time on a Sparc-10 workstation at solving numerous nonlinear multicommodity network flow examples.

A parallelized DPPQN based Expert System method and implementation

In this paper, we propose a parallelized Dual Projected Pseudo Quasi-Newton (parallelized DPPQN) based Expert System method to solve a kind of distributed constrained optimization problem. The proposed parallelized DPPQN based Expert System method differs from the conventional Lagrange method by treating the inequality constraints as the domain of the original variable in the dual function and uses projection theory to process the inequality constraints. The proposed algorithm was implemented in a n+1 processors network. We also demonstrated the efficiency in solving a typical constrained weighted least squares problem in power system. The parallelized DPPQN based Expert System method associated with a projected Jacobi method can be applied to general large scale nonlinear network optimization problems in large distributed interconnected systems.

An ordinal optimization theory-based algorithm for large distributed power systems

In this paper, we propose an ordinal optimization (OO) theory-based algorithm to solve the yet to be explored distributed state estimation with continuous and discrete variables problems (DSECDP) of large distributed power systems. The proposed algorithm copes with a huge amount of computational complexity problem in large distributed systems and obtains a satisfactory solution with high probability based on the OO theory. There are two contributions made in this paper. First, we have developed an OO theory-based algorithm for DSECDP in a deregulated environment. Second, the proposed algorithm is implemented in a distributed power system to select a good enough discrete variable solution. We have tested the proposed algorithm for numerous examples on the IEEE 118-bus and 244-bus with four subsystems using a 4-PC network and compared the results with other competing approaches: Genetic Algorithm, Tabu Search, Ant Colony System and Simulated Annealing methods. The test results demonstrate the validity, robustness and excellent computational efficiency of the proposed algorithm in obtaining a good enough feasible solution.