Junji Kubokawa

An interior point nonlinear programming for optimal power flow problems with a novel data structure

This paper presents a new interior point nonlinear programming algorithm for optimal power flow problems (OPF) based on the perturbed KKT conditions of the primal problem. Through the concept of the centering direction, the authors extend this algorithm to classical power flow (PF) and approximate OPF problems. For the latter, CPU time can be reduced substantially. To efficiently handle functional inequality constraints, a reduced correction equation is derived, the size of which depends on that of equality constraints. A novel data structure is proposed which has been realized by rearranging the correction equation. Compared with the conventional data structure of Newton OPF, the number of fill-ins of the proposed scheme is roughly halved and CPU time is reduced by about 15% for large scale systems. The proposed algorithm includes four kinds of objective functions and two different data structures. Extensive numerical simulations on test systems that range in size from 14 to 1047 buses, have shown that the proposed method is very promising for large scale application due to its robustness and fast execution time.

A solution method of unit commitment by artificial neural networks

The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging. >

A solution of optimal power flow with multicontingency transient stability constraints

This paper presents a formulation of the multicontingency transient stability constrained optimal power flow (MC-TSCOPF) problem and proposes a method to solve it. In the MC-TSCOPF formulation, this paper introduces a modified formulation for integrating transient stability model into conventional OPF, which reduces the calculation load considerably. In our MC-TSCOPF solution, the primal-dual Newton interior point method (IPM) for nonlinear programming (NLP) is adopted. Computation results on the IEEJ WEST10 model system demonstrate the effectiveness of the presented MC-TSCOPF formulation and the efficiency of the proposed solution approach. Moreover, based on quite convincing simulation results, some phenomena occurred when considering multicontingency are elaborated.

A decoupled solution of hydro-thermal optimal power flow problem by means of interior point method and network programming

This paper presents a new decoupled model together with a very efficient coordination algorithm to solve a hydrothermal optimal power flow (HTOPF) problem over a certain time horizon. Based on the Lagrange relaxation at the level of the KKT (Karush-Kuhn-Tucker) conditions of the primal problem, the HTOPF is decomposed into thermal plant subproblems formulated as OPF and hydroplant subproblems. To solve efficiently the thermal OPF subproblems, the warm-starting scheme has been incorporated into interior point quadratic programming (IPQP). As to the hydroplant subproblems, a united network flow model is presented in which a fixed head plant is treated as a special case of a variable head plant. The hydroplant subproblem can be formulated as a minimum-cost maximum-flow problem for which unit cost functions of hydroplants are defined exactly. A proposed variant of the partitioning shortest path algorithm has brought about a great speed up in the computation of the subproblems. The validity of the proposed method has been examined by solving the IEEE test systems and a Chinese power system consisting of 13 thermal plants and 12 hydro power plants; the last system is a large size problem such that it has 107712 primal and dual variables. Simulation results obtained are quite convincing.

Large scale hydrothermal optimal power flow problems based on interior point nonlinear programming

This paper presents an interior point algorithm for hydrothermal optimal power flow problems (HTOPF) which is derived from the perturbed KKT conditions of the primal problem. Moreover, the algorithm is extended successfully to solve approximate HTOPF problems (A-HTOPF) to find a suboptimal solution with much less execution time. For large scale systems, A-HTOPF can reduce CPU time by half and can guarantee more than 99% accuracy in most cases. By properly exchanging rows and columns of a correction equation, the reduced equation with novel 4T/spl times/4T block diagonal submatrices can be derived, where T is the number of time interval. Its topological structure is identical to that of the nodal admittance matrix, thus enabling an efficient algorithm. Numerical tests have been executed on six test systems of up to 1047 buses for HTOPF over 72 time intervals. The computational burden of the maximum test system is equal to that of 75384 (1047/spl times/72) bus OPF problem. The simulation results have verified that the proposed algorithm possesses good convergence property within reasonable execution time, and hence, the algorithm is quite promising for large scale applications.

A Study on the Effect of Generation Shedding to Total Transfer Capability by Means of Transient Stability Constrained Optimal Power Flow

In nowadays deregulated market, total transfer capability (TTC) calculation, which is the basis for evaluating available transfer capability (ATC), has been becoming more significant. During the last decade, transient stability constraints have been included in the optimal power flow approach to maximize TTC. However, no previous work on investigating the effect of generation shedding action to TTC has been reported. Therefore, in this paper, we propose a TTC maximization by means of transient stability constrained optimal power flow considering the generation shedding action. Proper selection of the generator to shed is based on its Lagrange multiplier value of transient stability constraint. Our simulation results show how TTC can be increased to anticipate possible generation shedding.

A solution of dynamic available transfer capability by means of stability constrained optimal power flow

In nowadays deregulated market, available transfer capability (ATC) is a measure of the network capability for further commercial activity above the already committed uses. This work deals with the development of an interior point nonlinear programming methodology for evaluating dynamic ATC. By establishing a novel method for integrating transient stability constraints into conventional steady-state ATC problem, the dynamic ATC problem is successfully formulated as an OPF-based optimization problem. Then, an interior point nonlinear programming algorithm is used to solve the formed dynamic ATC optimization problem. The method has been implemented and tested on two IEEJ model systems (WEST10 and WEST30). In both systems, satisfactory results are obtained.

A multi-agent approach to unit commitment problems

This paper proposes a multi-agent system for solving unit commitment problems. Multi-agent is a new paradigm for developing software applications. Coordinating the behavior of autonomous agents is a key issue in agent oriented programming techniques today. In this paper, we develop a power system unit commitment application using multi-agent architecture. Our model has the following characteristics: (1) the system consists of a single facilitator agent, several generator-agents, and two kinds of mobile agent; (2) the facilitator agent is developed to act as a manager for the process by using the singleton design pattern. The mobile agent and generator agent have simple negotiation strategies; and (3) the message object is developed to communicate between agents using a KQML-like object. The proposed approach is applied to a simple system, and the results show that the multi-agent system is able to find optimal solutions for unit commitment problems.

A proposal of a supporting expert system for outage planning of electric power facilities retaining high power supply reliability

It is necessary for a power system to undertake maintenance works regularly to secure stable power supply, which inevitably cause outage of associated apparatus. This problem, referred to as the outage planning of an electric power system in this paper, has been so far made based on the knowledge and experiences of planning engineers. However, the scheduling made by the engineers is not necessarily optimal since there are no rational criterion to judge the results. Furthermore, this problem belongs to a class of combinatorial optimization problems. Therefore, we propose a supporting expert system for the outage planning retaining high power supply reliability. This paper presents some issues on the outage planning and an outline of the proposed supporting system. The concept of work unit is introduced and three security indices are utilized to properly merge several related outage work into the same group.

A solution of optimal power flow with voltage stability constraints

A competitive electricity market often requires the separation of transmission and generating services; therefore, the transmission system operator does not have an impact on short and long term generation patterns. At the same time, the transmission operator needs to accommodate various transactions and generation patterns. In the desire to accommodate market-based transactions, the transmission operator is very much interested in quantifying the various stability margins (voltage, dynamic, etc.) and in determining the level of robustness of the transmission system. In the case of voltage stability, the index of robustness used is the difference from the current operating condition to an infeasible point. Particularly relevant is the amounts of additional reactive load that can be accommodate before the system experience a voltage collapse. In this paper, the authors propose a development of OPFs based on a primal-dual nonlinear interior-point method, which can handle maximum loadability constraint under contingency state. The developed OPF can solve the optimal operating point while keeping enough loadability margin under contingency state. In order to demonstrate the advantage of the proposed methods, the OPFs are applied to several test systems under various operating conditions.