Multiobjective dynamic load-dispatching method for optimal operation of power systems

A new practical method is proposed which gives a quasi-optimal solution of the dynamic load-dispatching problem formulated by a multiconstraint multiobjective optimization problem. Here, the multiple constraints are rate reserve constraints and power flow ones, and the multiple objectives are fuel cost and CO₂ emission. The proposed method is an integration of the following three techniques: (1) “Group Dispatch Scheme,” which is developed by the authors, is used to satisfy a violated constraint easily. This scheme consists of three steps. The first step is to classify all generators into two groups. One is the group which should be more loaded to satisfy the violated constraint, and the other is the should-be less loaded group. The second step is to add some load to the former group, and to subtract the same load from the latter. The third step is to dispatch each group load to each groups generators, respectively; (2) the dynamic load-dispatching algorithm is used which provides, in practical time, quasi-optimal generation trajectories; (3) the conventional weighting parametric method is used to obtain a Pareto optimal solution of multiobjective problems. The effectiveness of the method is clarified by a computer simulation on the actual power system of Kansai Electric Power Company.     

Intelligent scheduling with tabu search: An application to jobs with linear delay penalties and sequence-dependent setup costs and times

In this article we study thetabu search (TS) method in an application for solving an important class of scheduling problems. Tabu search is characterized by integrating artificial intelligence and optimization principles, with particular emphasis on exploiting flexible memory structures, to yield a highly effective solution procedure. We first discuss the problem of minimizing the sum of the setup costs and linear delay penalties when N jobs, arriving at time zero, are to be scheduled for sequential processing on a continuously available machine. A prototype TS method is developed for this problem using the common approach of exchanging the position of two jobs to transform one schedule into another. A more powerful method is then developed that employs insert moves in combination with swap moves to search the solution space. This method and the best parameters found for it during the preliminary experimentation with the prototype procedure are used to obtain solutions to a more complex problem that considers setup times in addition to setup costs. In this case, our procedure succeeded in finding optimal solutions to all problems for which these solutions are known and a better solution to a larger problem for which optimizing procedures exceeded a specified time limit (branch and bound) or reached a memory overflow (branch and bound/dynamic programming) before normal termination. These experiments confirm not only the effectiveness but also the robustness of the TS method, in terms of the solution quality obtained with a common set of parameter choices for two related but different problems.     

Optimizing a multiserver queuing system with a variable number of servers

A multiserver Poisson queuing system with losses, with a variable number of servers, and an additive quality functional is investigated. All major characteristic of the system are obtained in an explicit form. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 92–101, July–August 2007.     

Fast, large-scale model predictive control by partial enumeration

Partial enumeration (PE) is presented as a method for treating large, linear model predictive control applications that are out of reach with available MPC methods. PE uses both a table storage method and online optimization to achieve this goal. Versions of PE are shown to be closed-loop stable. PE is applied to an industrial example with more than 250 states, 32 inputs, and a 25-sample control horizon. The performance is less than 0.01% suboptimal, with average speedup factors in the range of 80-220, and worst-case speedups in the range of 4.9-39.2, compared to an existing MPC method. Small tables with only 25-200 entries were used to obtain this performance, while full enumeration is intractable for this example.     

Metaheuristic downhill simplex method in combinatorial optimization

The concept of combinatorial objects is formalized. It allows strict definition of a combinatorial optimization problem (COP). An efficient metaheuristic method to solve COPs (H-method) is considered. It includes stochastic local search algorithms as a built-in procedure. A parallel implementation of the H-method is set forth and analyzed. The results from a numerical experiment and solution of some well-known COPs on personal computers and on the SKIT cluster supercomputer are presented. The study was supported by INTAS (Project 06-1000017-8909). __________ Translated from Kibernetika i Sistemnyi Analiz, No. 6, pp. 70–79, November–December 2007.     

Robust control of polytopic systems by convex optimization

Robust control synthesis of linear time-invariant SISO polytopic systems is investigated using the polynomial approach. A convex set of all stabilizing controllers for a polytopic system is given over an infinite-dimensional space. A finite-dimensional approximation of this set is obtained using the orthonormal basis functions and represented by a set of LMIs thanks to the KYP lemma. Then, an LMI based convex optimization problem for robust pole placement with sensitivity function shaping in two- and infinity-norm is proposed. The simulation results show the effectiveness of the proposed method.     

Information cut for clustering using a gradient descent approach

We introduce a new graph cut for clustering which we call the Information Cut. It is derived using Parzen windowing to estimate an information theoretic distance measure between probability density functions. We propose to optimize the Information Cut using a gradient descent-based approach. Our algorithm has several advantages compared to many other graph-based methods in terms of determining an appropriate affinity measure, computational complexity, memory requirements and coping with different data scales. We show that our method may produce clustering and image segmentation results comparable or better than the state-of-the art graph-based methods.     

Search for the upper bound of Bayesian estimates of the parameter in an exponential failure model with two known quantiles of an a priori distribution function

A fast exact algorithm of searching for the upper bound of Bayesian estimates for the parameter of the exponential distribution under the condition that an a priori distribution belongs to the class of all distribution functions with two equal quantiles. This problem arises in analyzing the sensitivity of Bayesian estimates to the choice of an a priori distribution in an exponential failure model. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 90–102, January–February 2007.     

工业发酵的生物学属性对发酵过程最优控制的制约与对策

从工业发酵过程计算机控制的历史和现状出发，探讨了工业发酵过程的生物学属性对发酵过程最优化控制的制约，以及解决这个难题的对策。在论证了工业发酵具有明显的生物学属性的基础上，阐明了发酵的生物学原理及其在发酵过程最优化控制中应用的重要性。     

基于网格的电大网络教育资源整合模式

建设优质的电大网络教育资源及其服务是电大远程教育长远发展的一大重要课题，通过对电大网络教育资源及其服务现状的调查，文章提出利用网格技术实现电大网络教育资源的整合优化模式，分别从网格系统框架、基本原则、组织形式、整合模式等方面阐述电大网格系统的建设，各电大网格节点统一规划、分工协作，在网络教育理论指导下或进行资源开发，或着重开发网格服务．实现电大网络教育资源的全面共享。