In the paper [PS1], strategies are proposed to reconfigure the feeder in distribution systems by using ANNs with mapping ability. ANNs determine the appropriate system topology that reduces the power loss according to the variation of load pattern. The control strategy can be easily obtained on the basis of the system topology which is provided by ANNs. ANNs are designed in two groups. The first group estimates the proper load level from the load data of each zone. The second determines the appropriate system topology from the input load level. Several programs with the training set builder are developed for the design, the training, and the accuracy test of ANNs. The performance of ANNs designed is evaluated on the test distribution system. ANNs were implemented in FORTRAN language and trained on a 386 PC.
An improved method for achieving the economic dispatch of Taiwan power system (TPS) using an ANN is proposed in the paper [PS2]. The ANN is constructed according to the BP learning rule. The developed ANN was used to plan the optimal dispatch of each generating unit by training data. The data are derived by using the Lagrange multipliers method to analyze economic dispatch problems of TPS. To demonstrate the effectiveness of the proposed approach, the economic dispatch of 21 thermal units in Taiwan was performed for a 24 hours schedule. Numerical results show that the system production cost was minimal and the time taken on processing the economic dispatch problems was reduced. Hence, the ANN will be a valuable tool to assist system dispatchers in handling the online economic dispatch of TPS.
The paper [PS3] presents a new method for the economic load dispatch in power pools using ANN. The paper assumes that the power pool is modeled as one large company and implements single area approach for economic dispatch in power pools. The proposed method can be used for offline study or online for operations, planning, and real time economic dispatch. The ANN is simulated to solve the problem by a new approach called the clamped state variable method. The authors conclude that the new approach is very simple from the problem formulation point of view. The computer time for implementing the algorithm is much less than other schemes used for complete circuit simulation. The results obtained by the proposed method are presented and discussed in the paper.
An approach based on ANNs is proposed [PS4] for the scheduling of hydroelectric generations. The purpose of hydroelectric generation scheduling is to figure out the optimal amounts of generated powers for the hydro units in the system for the next N (N=24 in the work) hours in the future. Input data include system hourly loads and the natural in flow of each reservoir. In the proposed ANN approach, a clustering ANN is first developed to identify those days with similar hourly load patterns and natural inflows. These days with similar load patterns and inflows are said to be of the same group. A total of four groups are used in the work. Then a multilayer feedforward ANN is developed for each group to reach a preliminary generation schedule for the hydro units. Since some practical constraints may be violated in the preliminary schedule, a heuristic rule based search algorithm is developed to reach a feasible suboptimal schedule which satisfies all practical constraints. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of 10 hydro plants. It is concluded that the proposed approach is very effective in reaching proper hydro generation schedules. Moreover, the proposed approach is much faster than conventional dynamic programming approach.
In the paper [PS5], a combination of Hopfield Tank type, and Chua-Lin type ANNs is applied to solve simultaneously the unit commitment and the associated economic unit dispatch problems. The approach is based on imbedding the various constraints in a generalized energy function, and then defining the network dynamics in such a way that the generalized energy function is a Lyapunov function of the ANN. The novel feature of the proposed approach is that the nonlinear programming and the combinatorial optimization problems are solved simultaneously by one network. An illustrative example is also presented.
A method for solving a power generation scheduling problem in an electric power system is presented [PS6]. The objective is to determine the hourly start-up/shut-down schedules of all generators so that forecasted hourly power demands per day may be met and total operating costs, the sum of setup and fuel costs for a given day, may be minimized. The problem may be formulated as a large-scale combinatorial optimization problem which includes 0-1 variables representing the start-up/shut-down of generators and continuous variables representing the power outputs. Until now, the Lagrangian relaxation method has been studied as it appeared to be the most practical method for obtaining an approximate solution to the problem. The efficiency of this method, however, depends on how the Lagrange multipliers are determined. It is proposed that the Lagrange multipliers be estimated by utilizing the ANN and results determined from examination of the possibility of applying the BP algorithm to pattern recognitions which presume the relationship between power demand pattern and Lagrange multipliers are reported.
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