Iranian Journal of science and Technology (B: Engineering)
Volume:31 Issue: 6, December 2007

A new approach for optimal reserve determination and cost allocation in the pool-based and disaggregated market model using a well-being framework is presented in this paper. In the proposed method, customers have the chance to specify the risk level which they are willing to accept. Firstly, the energy market is cleared, and afterwards the reserve market is cleared such that the required risk levels of different customers are satisfied. Then, according to the required risk levels and the provided reserve, the cost of reserve is allocated between the different customers with different reliability requirements. For fairly managing the shortage between different customers in the real time operation by the system operator, the Interruption Factor is introduced for the first time in this paper. Although from the economic point of view reliability is a public good, using the concept of the interruption factor, different customers with different reliability requirements can be differentiated. Finally, the proposed method is applied to the IEEE-RTS to examine the applicability and effectiveness of the proposed method.

In a deregulated electric power system in which a competitive electricity market can influence system reliability, system analysts are rapidly recognizing that they cannot ignore market risks. This paper first proposes an analytical probabilistic model for the reliable evaluation of competitive electricity markets and then develops a methodology for incorporating the market reliability problem into composite power system reliability studies. The market reliability is evaluated using the Markov state space diagram. Since the market is a continuously operated system, the concept of absorbing states is applied to it in order to evaluate reliability. The market states are identified using market performance indices and the transition rates are calculated using historical data. The key point in the proposed method is the concept that the reliability level of a restructured electric power system can be calculated using the availability of the composite power system and the reliability of the electricity market. To illustrate an interesting feature of the proposed methodology, two case studies are carried out over a test system.

This paper proposes a simultaneous approach to manage congestion, evaluate congestion cost, and to allocate this cost among consumers in a short-run market model. The proposed method consists of an iterative algorithm to search and apply a sequence of the best feasible bilateral power exchange among the generators that would optimally reduce and completely remove the congestion. The best exchanges are selected using sensitivity analysis and the AC load flow is used to apply the selected exchange on the system. Congestion cost is calculated using the change in generation cost and divided among the overloaded lines regarding the change in power flow of these lines, after applying the selected exchange. Following each exchange, the costs are allocated to consumers based on the generalized load distribution factors, which are calculated on the system operating condition. The effectiveness of the proposed approach is illustrated in two case studies on the standard IEEE 14 and 118-bus systems, and the results discussed and compared with the other methods in the paper.

Application of the nonlinear H¥ identification method to identify a synchronous generator model is investigated in this paper. The linear H¥ identification method has been well established in the literature for robust modeling despite noise and system uncertainities. Since many practical systems such as synchronous generators are nonlinear, linear models identified for particular operating conditions do not perform well for other operating conditions. To overcome this shortcoming, the linear H¥ identification method has been modified to cover some nonlinearities of the systems such as saturation in synchronous machines. The derived proposed algorithm is then applied to a seventh order nonlinear model of a synchronous machine with saturation effect. In this study, the field voltage is considered as the input and the active output power and the terminal voltage are considered as the outputs of the synchronous machine. Simulation results show good accuracy of the identified models.

This paper discusses the design and implementation of an inductorless differential VCO with a maximum oscillation frequency of 20 GHz, in a 47 GHz SiGe process technology. The VCO is based on a full-wave rectification frequency-doubling technique, applied to a half rate differential single-stage feedback oscillator. It also benefits from a new circuit phenomenon named hereinafter Self Injection Locking (SIL). The implemented VCO has an area of 0.5 mm2 and features a remarkably high ratio of VCO frequency to process fT. Based on measurement results, the VCO consumes a DC power of less than 165 mW and exhibits a phase noise of -96 dBc/Hz at 1 MHz offset.

Based on the wavelet transform and fuzzy set theory, we present a fuzzy wavelet network (FWN) for approximating feedback linearization control input. Each fuzzy rule corresponds to a sub-wavelet neural network (sub-WNN) consisting of wavelets with a specified dilation value. The degree of contribution of each sub-WNN can be controlled flexibly. The constructed rules used to approximate the control signal in which the mathematical model of the system under control is unknown can be adjusted by learning the translation parameters of the selected wavelets and determining the shape of Gaussian membership functions of a fuzzy system. The proposed FWN shows good approximation accuracy and fast convergence. Finally a nonlinear inverted pendulum system is applied to verify the effectiveness and ability of the proposed network.

In this paper, a new effective and computationally reduced method for congestion control in high speed dynamic computer networks is introduced. The controller is designed using the well-known predictive functional control (PFC) scheme and an ARMarkov model representation that considers the system delay explicitly. Use of the multi-step-ahead predictive ARMarkov model structure within the PFC results in a simple algebraic control law that does not require recursive model output computation in the so-called prediction horizon performed in the other Model Predictive Controllers (MPC). This combination not only reduces the required computational load, but the accumulative error due to the model uncertainties decrease considerably. Packet-level simulations based on ns-2 are provided to show good performance of ARM-PFC in a large variety of topology and traffic mixtures for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay and jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the proposed method with respect to the well-known AQM methods such as RED, PI, and REM, which are also simulated for comparison.