T. Niimura

Multiobjective tradeoff analysis of deregulated electricity transactions

This paper analyzes the tradeoff relationships between the differing goals of power system operation and the influence of social policies, such as environmental impact minimization, upon deregulated electricity trade. An optimization procedure to reach a coordinated solution between different objectives is presented based on fuzzy interactive multiobjective optimization. Numerical examples are demonstrated on an IEEE 30 bus system. From the simulation, it has been found that the additional goals may reduce the volume of free trade of electricity, but the fuzzy multiobjective optimization can reach a good balance between conflicting goals.

Short-term electricity price modeling and forecasting using wavelets and multivariate time series

This work presents a new method to model and forecast the short-term electricity prices. The historical price and load data are first decomposed by wavelet transform, then multivariate time series is applied to model and forecast the wavelet coefficients of next day electricity price. The forecasted price is obtained by reconstructing the wavelet coefficients. The numerical examples of Pennsylvania-New Jersey-Maryland (PJM) spot market data are presented.

Transmission congestion relief by economic load management

This paper proposes an approach to congestion management in a deregulated transmission system. In heavily congested conditions, physical transmission congestion can be relieved by curtailing a small portion of nonfirm transactions. Resultant marginal cost-based electricity prices should drastically decrease. Simple and transparent indices are introduced so that both load and supplier can express their levels of acceptance with the congestion management process, and the system operator can select the most effective and desirable congestion relief measures. The proposed approach is tested on a modified IEEE 30 bus system.

Power quality control of hybrid wind power generation with battery storage using fuzzy-LQR controller

This work presents a modeling and control design for a wind-hybrid power system with a battery storage. The proposed control scheme is based on the Takagi-Sugeno fuzzy model and the linear quadratic regulator. The Takagi-Sugeno fuzzy model expresses the local dynamics of a nonlinear system through subsystems partitioned by linguistic rules. The controllers for each subsystem are designed by the linear quadratic regulator. In the simulation study, the proposed controller is compared with the proportional-integral (PI) controller. The simulation results show that the proposed controller is more effective than the PI controller against disturbances caused by wind speed and load variation. Thus, better quality of the wind-hybrid power system is achieved.

Transmission loading relief solutions for congestion management

Abstract This paper proposes an approach to transmission congestion management in a deregulated power system. Congestion in a transmission system can result in very high locational prices for electricity determined by marginal costs from optimal power flow (OPF) solutions. In heavily congested conditions, physical transmission congestion can be relieved by curtailing a small portion of non-firm transactions. Resultant marginal cost-based electricity prices should drastically decrease. Simple and transparent indices are introduced so that both load and supplier can express their levels of acceptance of the congestion management process, and the system operator can select the most effective and desirable congestion relief measures. The proposed approach is tested on a modified IEEE 30 bus system.

An intelligent controller for a remote wind-diesel power system - design and dynamic performance analysis

This paper presents an intelligent controller based on a neural network for a wind-diesel power system equipped with a stall regulated wind turbine run an induction generator. The goal for the wind-diesel power system is to design an intelligent controller to maintain a good power quality under varying wind and load conditions. On the other hand, the controller has to show acceptable closed-loop performance including stability, robustness, optimal energy, steady state, and transient performance at a permissible level of control effort. Moreover, such a controller has to be highly adaptive to various operating conditions and independent of model parameters that can be uncertain. This paper presents an inverse dynamic neural controller, which consists of an inverse dynamic neural model and generalized minimum variance control. This proposed controller is applied to design an integrated nonlinear controller to provide control input from excitation system and governor system at the same time.

Water level control of small-scale hydro-generating units by fuzzy logic

In this paper, we introduce a water level control for small-scale hydro-generating units based on fuzzy logic. The difficulty of small-scale hydro-generating systems is the small capacity of reservoir and they often operate under strict water level control by a feedback control. However, the adjustment of the feedback control is difficult because of nonlinearity and disturbance. By the application of fuzzy logic, the control strategy can be written in the IF-THEN rule form, which is highly conformable to the human logic. The conditions of the water level control are expressed by fuzzy sets, and fuzzy reasoning performs a multi-attribute decision making. Model analysis is given to demonstrate the effectiveness of the fuzzy logic control. The simulation results show that the fuzzy logic control is suitable to maintain the water level within certain limits while pursuing other operational goals at the same time.

Fuzzy time-series model of electric power consumption

In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Traditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely mathematical models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy auto-regression, and apply it to the analysis of time-series data of electric power consumption. The proposed fuzzy auto-regression model can be constructed in simpler procedure than the conventional approaches.

Transmission line model for large step size transient simulations

This paper presents a new model for the representation of very short transmission lines and cables. This model is an extension for the constant parameter line model in the electromagnetic transient programs such as the EMTP. It overcomes the limitation of using a time step size not larger than the travel time. Unlike the nominal /spl pi/-circuit line model, which is commonly used to represent short lines and cables, the new line model represents the electromagnetic transients more accurately. Simulations have been performed comparing the proposed line model with existing line models, for a case where the new line model provides more accurate results.

Machine learning approach to power system dynamic security analysis

In this paper, the authors present a pattern-learning/recognition approach for dynamic security classification using neural networks with a limited number of input data. The input is a set of data representing the precontingency power system state (voltages, angles, etc.), and the output is the possible system status (stable/unstable) after contingency. Data clustering is applied to reduce the number of input representing the cases. The reduced input data are then used to train the neural network that learns the input patterns for a possible post-contingency status. The overall accuracy of the classification is considered to be reasonable for a practical-scale power system application.