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This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. Index Terms--Machine learning, meteorology, nonstationarity, wind generation forecasting.

This correspondence paper focuses on the problem of exponential stability for neural networks with a time-varying delay. The relationship among the time-varying delay, its upper bound, and their difference is taken into account. As a result, an improved linear-matrix- inequality-based delay-dependent exponential stability criterion is obtained without ignoring any terms in the derivative of Lyapunov-Krasovskii functional. Two numerical examples are given to demonstrate its effectiveness. Index Terms--Exponential stability, linear matrix inequality (LMI), neural networks, time-varying delay.

This paper deals with the problem of exponential stability for a class of discrete-time recurrent neural networks with time-varying delay by employing an improved free-weighting matrix approach. The relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, a new and less conservative delay-dependent stability criterion is obtained without ignoring any useful terms on the difference of a Lyapunov function, which is expressed in terms of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness of the proposed techniques. Index Terms--Discrete-time recurrent neural networks, time-varying delay, delay-dependent stability, Lyapunov function, linear matrix inequalities (LMIs).

This paper presents a method to evaluate the economic value of transmission loss based on the share proportion principle and the sensitivity analysis. We first formulate an optimal power flow problem to derive the operation conditions, the nodal prices of each bus, and the transmission prices between two buses and then trace the transmission loss during operation based on the share proportion principle of the graph theory. The sensitivity analysis has been used to identify the wheeling path from each generator to any load, and the value of transmission loss is also evaluated. The proposed method is applied to the IEEE 30-bus standard system, which confirms our theoretical results. The proposed method can be used not only to improve the efficient usage of the power grid and congestion management but also to provide valuable economic signals for the generation or transmission investment. Index Terms--Optimal power flow, sensitivity analysis, share proportion principle, transmission loss, wheeling path.

This paper presents a new interior point quadratic programming algorithm which can solve power system optimization problems with significantly less computational efforts. The proposed algorithm has the following two special features. First, it is based on the path-following interior point algorithm whose search direction is the Newton direction, and therefore the algorithm has quadratic convergence. In the second place, it solves directly a symmetric indefinite system and thus the algorithm avoids the formation of [A[D.sup.-1][A.sup.T]] and as a result generates fewer fill-ins than the case of factorizing the positive definite system matrix for large scale systems. This has brought about a profound speed-up. Since the formula of the interior point method have been deduced more generally, the proposed algorithm can start from either a feasible (interior point) or an infeasible point (non-interior point). Numerical results on the IEEE test systems and a Japanese 344 bus system have verified that the proposed algorithm possesses enough robustness and needs significantly less solution time compared with already reported applications of the interior point method.

This paper presents a flexible approach to a coordinated control of voltage and reactive power in order to enhance voltage security of an electric power system. The control strategy is expressed by simple rules, which measure the proximity of system state to certain operating conditions, and utilize linear equations to obtain the effective control models. The desired control actions are determined by considering several criteria at the same time. The procedure has been applied to a model system in order to verify its effectiveness. The simulation results show the advantages of this approach over conventional expert systems for voltage-reactive power control.

This paper presents an interior point algorithm for hydrothermal optimal power flow problems (HTOPF) which is derived from the perturbed KKT conditions of the primal problem. Moreover, the algorithm is extended successfully to solve approximate HTOPF problems (A-HTOPF) to find a suboptimal solution with much less execution time. For large scale systems, A-HTOPF can reduce CPU time by half and can guarantee more than 99% accuracy in most cases. By properly exchanging rows and columns of a correction equation, the reduced equation with novel 4T x 4T block diagonal submatrices can be derived, where T is the number of time interval. Its topological structure is identical to that of the nodal admittance matrix, thus enabling an efficient algorithm. Numerical tests have been executed on six test systems of up to 1047 buses for HTOPF over 72 time intervals. The computational burden of the maximum test system is equal to that of 75 384 (1047 x 72) bus OPF problem. The simulation results have verified that the proposed algorithm possesses good convergence property within reasonable execution time, and hence, the algorithm is quite promising for large scale applications. Index Terms--Cascade hydroplant, Centering direction, Hydrothermal power system, Interior point method, Optimal power flow.