Optimal Load Scheduling Research Articles

Short-Term Load Forecasting Using Soft Computing Techniques

Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.

Peak Load Management in Electrolytic Process Industries

Electrolytic process, employed for manufacturing basic chemicals like caustic soda and chlorine, is highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the load curve. Load management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for load management for electrolytic process industries. The formulation utilizes mixed integer nonlinear programming (MINLP) technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the loads, satisfying the industry constraints. The case study of a typical caustic-chlorine plant shows that a reduction of about 19% in the peak demand with a corresponding saving of about 3.9% in the electricity cost is possible with the optimal load scheduling under TOU tariff.

Short-term electric load forecasting based on a neural fuzzy network

Electric load forecasting is essential to improve the reliability of the ac power line data network and provide optimal load scheduling in an intelligent home system. In this paper, a short-term load forecasting realized by a neural fuzzy network (NFN) and a modified genetic algorithm (GA) is proposed. It can forecast the hourly load accurately with respect to different day types and weather information. By introducing new genetic operators, the modified GA performs better than the traditional GA under some benchmark test functions. The optimal network structure can be found by the modified GA when switches in the links of the network are introduced. The membership functions and the number of rules of the NFN can be obtained automatically. Results for a short-term load forecasting will be given.

Probabilistic load scheduling with priorities in distributed computing systems

Scheduling of processing loads play an important role in the performance of a distributed computing system/network. This is more important when the distributed computing system consists of many heterogeneous processors, with different processing capabilities. The scheduling strategies should also take into account the priorities given to the loads, when there is more than one type of jobs utilizing the system. A methodology is presented to obtain optimal load scheduling strategies, when there are two type of loads in a heterogeneous distributed computing system. In this paper a distributed computing system/network in which two type of loads (or jobs) namely local loads and global loads arrive is considered. The local loads originate at the individual processors/nodes in the network and have to be processed at the same processors. The global loads originate at the central scheduler and have to be distributed among the processors in the system. The individual processors in the system assign different priorities to local and global loads. A priority queuing optimization model of this situation is formulated as a nonlinear programming problem and a solution methodology is presented. The arrival rate of the global and local loads are assumed to follow Poisson distribution and the service time of the global and local loads at the processors follow a general or exponential service time distribution. Both the situations are analyzed. The objective of this study is to minimize the response time of the global loads in the network.

Sensory Systems and Intelligent Control for Electric Power Plants

The power industry in the US and Europe is going through a major transition. The change that the telecommunication industry experienced in the early 80's as a result of government deregulation is analogous to what is now facing the utilities. Government deregulation, open transmission access, presence of independent power producers (IPPs), and increased attention to the global air pollution have introduced major challenges to the utility industry. Smart sensors and intelligent control are believed to play a very important role in the future operation and survival of the electric power industry. Development of real-time optimal load scheduling, on-line optimal power rate determination, signature analysis, and pattern recognition for major load identification, active control for increased power quality, optimal demand-side management, closed loop combustion process control for pollution reduction, etc., are examples of control system application areas that have potential for major economic impacts in the power industry. Development of new sensors and sensory systems, e.g., real-time SO2 measurement in generation control or gas density sensor for high power transformer, that would provide information about die state of the system can be used for preventative maintenance as needed rather than as scheduled. Thus, advanced control and sensory systems can provide the technological advancement required for power industry in this highly competitive era. In this regard, the Electric Power Research Institute (KPRI) along with major utilities and the National Science Foundation (NSF) have initiated several programs both in industry and in universities in the areas of smart sensory systems and intelligent control. The main objective of this panel is to bring together industry representatives as well as university researchers involved in these initiatives to discuss:•State-of-the-art in control of electric power systems;•Role of smart sensors and intelligent control in the new utility business environment;•Industry needs and identification of future research directions.

Optimal load balancing and scheduling in a distributed computer system

Optimal load balancing and scheduling in a distributed computer system