Hydro energy management optimization in a deregulated electricity market

When electricity prices were regulated, hydropower optimization often considered only the inflow uncertainty. In a deregulated electricity market, price uncertainty must be also considered in addition to inflow uncertainty. This makes the operation problem more challenging due to inclusion of the objective of minimizing risk. It also makes the objective function nonlinear while the estimation of risk is problematic. For dealing with uncertainty, a set of finite scenarios of inflow or price sequences may form the evolution of information over the stages that are used in optimization algorithms. Such implicit optimization methods can be seen as an extension of deterministic optimization. A disadvantage is the number of scenarios may grow exponentially in multi-stage optimization problems, even with only a few branches at each stage. An explicit method, denoted as the Fletcher-Ponnambalam model (FP), has been recently developed for the first and second moments of the storage state distributions in terms of moments of the inflow distributions. This method provides statistical information on the nature of random behaviour of the system state variables without any discretization and hence suitable for multireservoir problems. Also not considering scenarios makes it computationally inexpensive, as there is little growth to the size of the original problem. In this paper, we introduce the price uncertainty into the FP model; our results indicate that the method could achieve optimum policy considering also the reduction of risk, using the second moment information. Our study is for medium term operations of a single reservoir. The results are compared with corresponding results from simulation and where possible, with the well-known Benders' Decomposition method (BD).     

Process factor optimization for controlling pull-down defects in iron castings

Defects in castings lead to non-conformities and affect productivity. Pull-down is a kind of defect occurring in castings. Several factors contribute to pull-down defects. Among those, pouring temperature, carbon equivalent and gating system are more significant. In the first stage, a set of process factors that were contributing to pull-down defect were identified. The identified factors were analyzed using ‘Design of Experiments’ approach. ‘Signal-to-noise’ ratio was estimated. Robust design factor values were estimated from the ‘signal-to-noise’ calculations. ANOVA analysis was done for robust design factor values. The interaction effects within factors were studied and the results were validated using ‘Fisher's test’. In the second stage, optimized factor values were adopted in practical runs. It was identified that the optimized values had improved the acceptance percentage from 86.22% to 96.17%. The improved acceptance percentage had enhanced productivity of the foundry.     

A meta-heuristic approach to single machine scheduling problems

A new meta-heuristic evolutionary algorithm, named a memetic algorithm, for solving single machine total weighted tardiness scheduling problems is presented in this paper. Scheduling problems are proved to be NP-hard (Non-deterministic polynomial-time hard) types of problems and they are not easily or exactly solved for larger sizes. Therefore, application of the meta-heuristic technique to solve such NP hard problems is pursued by many researchers. The memetic algorithm is a marriage between population-based global searches with local improvement for each individual. The algorithm is tested with benchmark problems available in the OR (operations research) library. The results of the proposed algorithm are compared with the best available results and were found to be nearer to optimal. The memetic algorithm performs better than the heuristics like earliest due date and modified due date.     

Estimation of Optimum Genetic Control Parameters for Job Shop Scheduling

Genetic algorithms (GA) have demonstrated considerable success in providing good solutions to many non-polynomial hard optimization problems. GAs are applied for identifying efficient solutions for a set of numerical optimization problems. Job shop scheduling (JSS) has earned a reputation for being difficult to solve. Many workers have used various values of genetic parameters. This paper attempts to tune the control parameters for efficiency, that are used to acceleate the genetic algorithm (applied to JSS) to converge on an optimal solution. The genetic parameters, namely, number of generations, probability of crossover, probability of mutation, are optimized relating to the size of problems. The results are validated in job shop scheduling problems. The results indicate that by using an appropriate range of parameters, the genetic algorithm is able to find an optimal solution faster. RID=" ID=" <E5>Correspondence and offprint requests to</E5>: Dr S. G. Ponnambalam, Department of Production Engineering, Regional Engineering College, Tiruchirapalli, 620 015, India. E-mail: pons&commat;rect.ernet.in     

Long-term uncertainty evaluation of pool electricity markets

Pool electricity markets are cleared under the strong assumption of having a perfectly known future; in real life, this is anything but true. The inability to predict the random parameters of the supply and the demand function introduces risk into the market clearing process. Therefore, the main interest is to minimize such risk by means of a trade-off of the mean and the variance of the social cost function. This paper considers random variations on the levels and on the slopes of the quadratic supply and demand functions. Correlation is considered between the corresponding coefficients of the supply and demand curves. By means of the mean–variance Markowitz theory, the risk introduced by these random variations is analyzed. A comprehensive analysis on the effects that the mean–variance Markowitz theory has on the nodal spot prices and on the point-elasticities of the supply and demand curves is made. The non-linear optimization model presented in this paper is validated through a three-, a six-, and a 21-node system.     

Variable gain wideband LNA in 130 nm SiGe HBT technology with stepped impedance micro-strip open circuited stubs for minimum transmission phase variation for WiGig phased array receivers

Journal of Computational Electronics - This paper presents a 130nm SiGe HBT process variable gain low noise amplifier (VGLNA) with low phase variation that can be used in phased array systems. The...