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In isolated power systems, frequency stability is traditionally considered to be assured by maintaining sufficient spinning reserve that is usually equal to the largest generation output of all the units in operation. However, the system security depends on the spinning reserve allocation to the units in operation and the provision of adequate emergency or fast reserve that need be available in the first crucial seconds after a loss of generation event. The system emergency reserve is usually much lower than that expected if only the unit droops are taken into account. Also, the generating units can be overloaded for a short time period and this capability can contribute to a faster frequency restoration. This paper presents an improved speed governor model that utilizes the short time overloading capability of the generating units. The efficient computational method described in the paper for the optimal spinning reserve allocation in isolated power systems was developed by taking into account a composite security criterion in the most economic way. Furthermore, a statistical analysis is presented in order to investigate the contribution of spinning reserve to the short-term system security. In the paper the developed method is applied to a typical isolated power system and it demonstrates its advantages. Index Terms--Frequency control, isolated power systems, power generation dispatch, power system control, power system simulation, power system stability, security, speed governor.

This paper describes a two-stage methodology that was developed for the classification of electricity customers. It is based on pattern recognition methods, such as k-means, Kohonen adaptive vector quantization, fuzzy k-means, and hierarchical clustering, which are theoretically described and properly adapted. In the first stage, typical chronological load curves of various customers are estimated using pattern recognition methods, and their results are compared using six adequacy measures. In the second stage, classification of customers is performed by the same methods and measures, together with the representative load patterns of customers being obtained from the first stage. The results of the first stage can be used for load forecasting of customers and determination of tariffs. The results of the second stage provide valuable information for electricity suppliers in competitive energy markets. The developed methodology is applied on a set of medium voltage customers of the Greek power system, and the obtained results are presented and discussed. Index Terms--Adaptive vector quantization, chronological load patterns, clustering, customer classes, fuzzy k-means, hierarchical clustering, k-means, pattern recognition.

This paper describes an efficient computational methodology that can be used for calculating the appropriate strategy for load shedding protection in autonomous power systems. It extends an existing method that is based on the sequential Monte Carlo simulation approach for comparing alternative strategies by taking into account the amount of load to be shed and the respective risk for the system stability. The extended methodology uses artificial neural networks (ANNs) for determining directly the parameters of the most appropriate load shedding protection strategy. For this purpose, the system inputs are the desirable probabilistic criteria concerning the system security or the amount of customer load interruptions. Using this methodology, the utility engineers can adopt a specific strategy that meets the respective utility criteria. The methodology was tested on a practical power system using a computer simulation for its operation, and the obtained results demonstrate its accuracy and the improved system performance. Index Terms--Frequency response, load shedding, Monte Carlo methods, neural nets, power system protection, simulation.

The objective of this paper is to present a new methodology for midterm energy forecasting. The proposed model is an adaptive artificial neural network (ANN), which properly transforms the input variables to differences or relative differences, in order to predict energy values not included in the training set. The ANN parameters, such as the finally used input variables, the number of neurons, initial values, and time periods of momentum term and training rate, are simultaneously selected by an optimization process. Another characteristic of the model is the use of a minimal training set of patterns. Results from an extensive analysis conducted by the developed method for the Greek power system and for different categories of customers are compared to those obtained from the application of standard regression methods. Index Terms--Adaptive artificial neural network (ANN), energy forecasting, optimization of ANN parameters.

This paper presents a computational method using the Monte-Carlo simulation approach for the calculation of the settings of load shedding underfrequency relays and the selection of appropriate spinning reserve policy in autonomous power systems. The method can be used for comparing alternative strategies by taking into account the amount of load to be shed and the corresponding risk for system stability. This method was applied on a system, that is based on the autonomous power system of the Greek island of Crete and the obtained results are presented and discussed. Index Terms--Autonomous power systems, load shedding, Monte-Carlo simulation, spinning reserve, underfrequency protection.

Electric power systems are increasingly involved in the competitive electrical energy market where several players have access to the transmission networks even though in a regulated way. The purpose of this paper is to present an improved computational method and the associated computer program RACTRS that can be used for the reliability and cost analysis of power transmission networks that accommodate transmission transactions. This is a method that is based on a Monte-Carlo sequential simulation approach and gives an increased emphasis in the realistic and efficient representation of the real design and operational characteristics and practices applied by the electric utilities today. For this purpose, the operating aspects of wheeling transactions are integrated into the developed method together with a technique for tracing the power flows through the meshed transmission networks. An extended set of appropriate reliability and cost indices is calculated for each load-point of interest, the entire system and each wheeling transaction under consideration. The analysis of a system based on the IEEE-RTS is also included for a number of studies representing the various operating and design characteristics. Index Terms--Competitive market, Monte-Carlo simulation, power transmission networks, reliability, transmission transactions, wheeling.

Presents an improved method for assessing the overall reliability performance of composite generation and transmission power systems that contain non-utility generating (NUG) facilities; Operating features and reliability considerations of NUG facilities; Monte-Carlo simulation approach as used in estimating the reliability indices of a power systems; Computational method use in the study; Reliability assessment studies; Conclusions.