A new algorithm for on-line security constrained economic dispatch

A new approach, the constant reduced Hessian matrix (CRHM) algorithm for the on-line security constrained economic dispatch (OSED) problem, is presented in this paper. The proposed method, in which repeating the AC load flow program has been avoided, satisfies both load flow equations and security limits exactly. The computational speed and the excellent convergence of this algorithm are demonstrated by applying it to a sample system.     

Differential evolution algorithm for emission constrained economic power dispatch problem

In this paper, a differential evolution (DE) algorithm is developed to solve emission constrained economic power dispatch (ECEPD) problem. Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. The proposed algorithm attempts to reduce the production of atmospheric emissions such as sulfur oxides and nitrogen oxides, caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions as a constraint in the objective of the overall dispatching problem. A simple constraint approach to handle the system constraints is proposed. The performance of the proposed algorithm is tested on standard IEEE 30-bus system and is compared with conventional methods. The results obtained demonstrate the effectiveness of the proposed algorithm for solving the emission constrained economic power dispatch problem.     

A neural network parallel algorithm for one‐dimensional gate assignment problems

A near‐optimum parallel algorithm for solving the one‐dimensional gate assignment problem is presented in this paper, where the problem is NP‐hard and one of the most fundamental layout problems in VLSI design. The proposed system is composed of n × n processing elements based on the artificial two‐dimensional maximum neural network for (n + 2)‐gate assignment problems. Our algorithm has discovered improved solutions in the benchmark problems compared with the best existing algorithms. The proposed approach is applicable to other VLSI layout problems such as the PLA (Programmable Logic Array) folding problem. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 71–77, 1999     

一种快速求解大规模安全约束最优潮流的多核并行方法

针对传统安全约束最优潮流计算方法占用内存大、计算时间长的问题,提出了一种多核并行安全约束最优潮流算法。由正常运行状态检验预想故障状态,选取少量严重故障逐步修正正常运行状态,直到所有状态都得到满足。多核并行技术用于实现算法从高到低层次的高效率整体并行,以提高计算速度。其中,采用有向无环图并行数值分解算法实现正常运行状态修正过程的细粒度并行,并根据算法特性,直接实现预想故障状态检验过程的粗粒度并行。某省-3301节点系统(预设693个故障)等3个系统的计算结果表明:随着系统规模的扩大,所提方法占用内存不足传统方法的1%,且计算速度可快于传统方法三个数量级以上。     

Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch

This paper proposes a parallel micro genetic algorithm based on merit order loading solutions (PMGA-MOL) to solve constrained dynamic economic dispatch (DED) problems for combined cycle (CC) units with linear decreasing and decreasing staircase incremental cost (IC) functions. To minimize the synchronization overheads, the PMGA-MOL employs the load balancing and migration strategies among processors. This PMGA-MOL algorithm is implemented on the eight-processor scalable multicomputer implementation using low-cost equipment (SMILE) Beowulf cluster with a fast ethernet switch network on the generating unit system size in the range of 5–80 units over the entire dispatch periods. With different migration strategies, the proposed PMGA-MOL compromises the solution quality and speedup upper bounds for the best performance. PMGA-MOL is shown to be viable to the on-line implementation of constrained DED due to substantial generator fuel cost savings and high speedup upper bounds.     

A parallel load flow algorithm

This paper presents a parallel load flow algorithm employing the fast decoupled method. The algorithm transforms the problem's matrices into block diagonal form which is analogous to parsing the network into a number of independent clusters connected through tie lines. It modifies the clusters' matrices to account for the effect of cut lines in a manner that produces nonsingular and well-conditioned matrices. It uses existing efficient load flow programs with some modifications. The approach avoids problems associated with the parallel solution of highly sparse large linear systems of equations. Test results of standard systems and an existing 1177-bus system are included to illustrate the usefulness of the algorithm and its viability in parallel processing implementation.     

Neural networks for constrained optimization problems

This paper is concerned with utilizing neural networks and analogue circuits to solve constrained optimization problems. We propose a novel neural network architecture for solving a class of non-linear programming problems. the proposed neural network is then used, and if necessary modified, to solve minimum norm problems subject to linear constraints. Minimum norm problems have many applications in various areas, but we focus on their applications to the control of discrete dynamic processes. the applicability of the proposed neural network is demonstrated on numerical examples.     

A solution to security constrained non-convex economic dispatch problem by modified subgradient algorithm based on feasible values

A security constrained power dispatch problem with non-convex total cost rate function for a lossy electric power system is formulated. Then, an iterative solution method proposed by us and based on modified subgradient algorithm operating on feasible values (F-MSG) is used to solve it.Since all equality and inequality constraints in our nonlinear optimization model are functions of bus voltage magnitudes and phase angles, off-nominal tap settings and susceptance values of svar systems, they are taken as independent variables. Load flow equations are added to the model as equality constraints. The unit generation constraints, transmission line capacity constraints, bus voltage magnitude constraints, off-nominal tap setting constraints and svar system susceptance value constraints are added into the optimization problem as inequality constraints. Since F-MSG algorithm requires that all inequality constraints should be expressed in equality constraint form, all inequality constraints are converted into equality constraints by the method, which does not add any extra independent variable into the model and reducing the solution time because of it, before application of it to the optimization model.The proposed technique is tested on IEEE 30-bus and IEEE 57 bus test systems. The minimum total cost rates and the solution times obtained from F-MSG algorithm and from the other techniques are compared, and the outperformance of the F-MSG algorithm with respect to the other methods in each test system is demonstrated.     

A simple two stage optimization algorithm for constrained powereconomic dispatch

A simple two stage optimization algorithm is proposed and investigated for fast computation of constrained power economic dispatch control problems. The method is a simple demonstration of the hierarchical aggregation-disaggregation (HAD) concept. The algorithm first solves an aggregated problem to obtain an initial solution. This aggregated problem turns out to be classical economic dispatch formulation, and it can be solved in 1% of overall computation time. In the second stage, a linear programming method finds optimal solution which satisfies power balance constraints, generation and transmission inequality constraints and security constraints. Implementation of the algorithm for IEEE systems and EPRI Scenario systems shows that the two stage method obtains an average speedup ratio of 10.64 as compared to the classical LP-based method     

Effective transmission constrained economic dispatch for production costing

In this paper, an effective Schur complement method is presented to solve transmission‐constrained dispatch (TCD) for Monte Carlo production costing. Several techniques for improving TCD performance are also proposed by taking the advantages of TCD special problem structures. Numerical testing on a real‐life large‐scale power system is presented to illustrate the performance of these proposed methodologies. Copyright © 2015 John Wiley & Sons, Ltd.     

An effective differential harmony search algorithm for the solving non-convex economic load dispatch problems

Solving the non-convex economic load dispatch (ELD) problem with evolutionary algorithms has gained increasing research in recent years. In this paper, a differential harmony search (DHS) algorithm is proposed by combining the mechanisms of both differential evolution and harmony search. In the DHS, the pitch adjustment operation is cooperated with the different mutation operation to enhance the exploitation ability of harmony search, and both the memory consideration and the pitch adjustment are used to enhance the exploration ability of evolution search. In addition, a repair procedure and three simple selection rules are proposed for constraint handling. Numerical simulations are carried out based on different kinds of testing problems with various constraints including valve point effects, multi-fuels, ramp rate limit and prohibited operation zones. Simulation results and comparisons with the some existing algorithms demonstrate the effectiveness, efficiency and robustness of the proposed DHS algorithm. Finally, the effect of parameter setting on the DHS is investigated as well.     

基于PSO-BBO混合优化算法的动态经济调度问题

动态经济调度(Dynamic Economic Dispatch,DED)问题是电力系统运行与控制领域比较经典的多变量、非线性、强约束优化问题。为解决该问题,提出了将粒子群优化算法(Particle Swarm Optimization,PSO)和基本生物地理学优化算法(Biogeography-Based Optimization,BBO)相结合的改进生物地理学优化算法,并将该改进方法应用于一天24时段10机39节点标准算例。在考虑网损与不考虑网损两种情况下分别进行仿真分析,并将仿真结果与PSO和基本BBO算法以及参考文献中提出的六种智能算法进行对比,验证了该改进算法的有效性及在寻优能力上的提高。     

求解车间作业问题的混合遗传算法

针对遗传算法(GA)求解车间作业问题JSP(job shop problems)的早熟和收敛速度慢等问题,基于算法混合的思想,将改进的瓶颈移动算法MSB(modify shifting bottleneck)所求得的调度方案加入遗传算法解空间,参与遗传算法迭代运算,形成高性能的混合遗传算法HGA(hybrid gennetic algorithm).由于MSB所获得解的质量较高,依据遗传算法的精英保留策略,可以加快算法的收敛速度.标准实例上进行的仿真实验表明,调度结果的平均质量、最好调度的获取能力和算法稳定性方面,HGA的性能明显优于GA.     

A security constrained environmental/economic power dispatch technique using F-MSG algorithm for a power system area including limited energy supply thermal units

A security constrained non-convex environmental/economic power dispatch problem for a lossy electric power system area including limited energy supply thermal units is formulated. An iterative solution method based on modified subgradient algorithm operating on feasible values (F-MSG) and a common pseudo scaling factor for limited energy supply thermal units are used to solve it. In the proposed solution method, the F-MSG algorithm is used to solve the dispatch problem of each subinterval, while the common pseudo scaling factor is employed to adjust the amount of fuel spent by the limited energy supply thermal units during the considered operation period. We assume that limited energy supply thermal units are fueled under take-or-pay (T-O-P) agreement.The proposed dispatch technique is demonstrated on IEEE 30-bus power system with six thermal generating units having non-convex cost rate functions. Two of the generating units are selected as gas-fired limited energy supply thermal units. Pareto optimal solutions for the power system, where the constraint on the amount of fuel consumed by the limited energy supply thermal units is not considered, are calculated first. Later on, the same Pareto optimal solutions for the power system, where the fuel constraint is considered, are recalculated, and the obtained savings in the sum of optimal total fuel cost and total emission cost are presented. The dispatch problem of the first subinterval of the test system was solved previously by means of differential evolution (DE), and a hybrid method based on combination of DE and biogeography based optimization (BBO) for the best cost and the best emission cases in the literature. The results produced by these methods are compared with those of produced by the proposed method in terms of their total cost rate, emission rate and solution time values. It is demonstrated that the proposed method outperforms against the evolutionary methods mentioned in the above in terms of solution time values especially when the exact model of the test system is considered.     

水电系统短期经济运行的新方法

水电系统的短期发电量最大问题是一大型、动态、有时滞的非线性约束优化问题。针对标准遗传算法的缺陷,提出一种基于实数编码技术的混沌遗传算法用于求解此问题。该算法根据给定个体概率分布函数构造杂交算子,结合混沌和人工神经网络理论,设计了一种混沌变异算子,使算法能有效维持群体多样性,防止和克服进化中的“早熟”现象,对约束条件采用不需要设置惩罚因子的直接比较罚函数方法加以处理。仿真算例验证了该算法在提高解的精度和加快收敛速度方面都有明显改善。     

Security constrained economic dispatch using nonlinearDantzig-Wolfe decomposition

This paper proposes a new approach to the security constrained economic dispatch based on a nonlinear version of the Dantzig-Wolfe decomposition principle. The dispatch problem is formulated using truly nonlinear unit cost functions and a new, detailed representation of reserve curves. The solutions obtained by the method have the equal λ property. The proposed decomposition scheme, is more efficient as the number of generating units in the problem is increased. Numerical results obtained on three test cases are presented     

Reserve constrained economic dispatch with prohibited operatingzones

A method is described for solving the reserve constrained economic dispatch problem when some of the online generating units have prohibited operating zone(s). For a unit with prohibited zone(s), the zone(s) divide the operating region between the minimum generation limit (Pmin) and the maximum generation limit (Pmax) into disjoint convex subregions. These disjoint subregions form a nonconvex decisions space and the associated economic dispatch problem is thus a nonconvex optimization problem. As a result, the conventional Lagrangian relaxation (LR) approach (e.g. the λ-δ iterative approach) cannot be applied directly. The method proposed decomposes the nonconvex decision space into a small number of subsets such that each of the associated dispatch problems is either infeasible or one that can be directly solved via the conventional LR approach. Based on the decomposition, the optimal solution is the least costly one among all the feasible solutions of the associated dispatch problems. Examples are also given to illustrate the proposed method     

Thresholding algorithm for local and parallel processing

In this paper, we propose a thresholding algorithm. This algorithm works under a severe constraint: each pixel in a processed image must be derived from only information of its neighboring pixels. This constraint is very important for a low‐cost device such as a mobile camera, because it makes it possible to process each pixel in parallel. The proposed algorithm deals with gray‐scale images, and determines the threshold based on edge information. The proposed algorithm is represented by local and parallel image processing and has been tested using 104 scenery images. The result shows that the proposed algorithm can binarize images. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 157(2): 50–56, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20267     

Input mapping algorithm for parallel transistor structures

A new algorithm for mapping every possible input pattern of a parallel transistor structure to an equivalent set of normalized inputs (having the same starting and ending point) is introduced. This algorithm is based on the analysis of the operation of the transistor structure and can be used for the development of analytical timing models for CMOS circuits. Simulation results show a very good accuracy of the algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

A noise‐constrained algorithm for estimation over distributed networks

Much research has been devoted recently to the development of algorithms to utilize the distributed structure of an ad hoc wireless sensor network for the estimation of a certain parameter of interest. A successful solution is the algorithm called the diffusion least mean squares algorithm. The algorithm estimates the parameter of interest by employing cooperation between neighboring sensor nodes within the network. The present work derives a new algorithm by using the noise constraint that is based on and improves the diffusion least mean squares algorithm. In this work, first the derivation of the noise constraint‐based algorithm is given. Second, detailed convergence and steady‐state analyses are carried out, including analyses for the case where there is mismatch in the noise variance estimate. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios, especially the mismatch scenario. Moreover, the simulation results are found to corroborate the theoretical results very well. Copyright © 2012 John Wiley & Sons, Ltd.