Changming Ji

Short-term scheduling of cascade reservoirs using an immune algorithm-based particle swarm optimization

This paper presents a new approach for short-term hydropower scheduling of reservoirs using an immune algorithm-based particle swarm optimization (IA-PSO). IA-PSO is employed by coupling the immune information processing mechanism with the particle swarm optimization algorithm in order to achieve a better global solution with less computational effort. With the IA-PSO technique, the hydro-electrical optimization model of reservoirs is formulated as a high-dimensional, dynamic, nonlinear and stochastic global optimization problem of a multi-reservoir hydropower system. The purpose of the proposed methodology is to maximize total hydropower production. Here it is applied to a reservoir system on the Qingjiang River, in the Yangtze watershed, that consists of two reservoirs. The results are compared with the results obtained through conventional operation method, the dynamic programming and the standard PSO algorithm. From the comparative results, it is found that the IA-PSO approach provides the most globally optimum solution at a faster convergence speed.

Research and Application of Multidimensional Dynamic Programming in Cascade Reservoirs Based on Multilayer Nested Structure

AbstractThe multidimensional dynamic programming (MDP) algorithm is a traditional method used to solve cascade reservoir operation optimization (CROO) problems, but the high dimensionality called the curse of dimensionalitycannot be ignored. In order to alleviate this problem, this paper proposes a new MDP algorithm named multilayer nested multidimensional dynamic programming (MNDP), which is based on a multilayered, nested structure. MNDP is mainly used to deal with computer memory space and computation complexity problems of MDP in CROO, and its recursive equation of reverse recursion calculation and specific calculation steps are presented in detail. This paper takes the cascade reservoirs of the Li Xianjiang River in China as an example to solve the CROO problem with the proposed MNDP. By comparing with the dynamic programming with successive approximations (DPSA) method, MNDP presents better performance in terms of power generation and the assurance rate in wet, normal, dry, and average years. The gl...

Application of immune algorithm-based particle swarm optimization for optimized load distribution among cascade hydropower stations

The immune algorithm-based particle swarm optimization (IA-PSO), which is proposed by involving the immune information processing mechanism into the original particle swarm optimal algorithm, improves the ability to find the globally excellent result and the convergence speed with its special concentration selection mechanism and immune vaccination. Based on analyzing the model of load distribution among cascade hydropower stations and the traits of IA-PSO, the corresponding mathematical description and the solution procedure made with IA-PSO are given in detail. The result demonstrates that IA-PSO can achieve both a superior load distribution scheme and a higher convergence precision as compared to PSO, and will hopefully be applied to solving more extensive optimization problems.

Study of Mid and Long-term Runoff Forecast Based on Back-propagation Neural Network

Based on back-propagation (BP) neural network algorithm, by analyzing the data of Dan jiangkou reservoir many years historical runoff series in chronological order and introducing frequency factor, the neural network on mid and long-term runoff forecast has been established. And furthermore, the model has been applied to forecast and analyze the month runoff process of Dan jiangkou reservoir. The case study indicates that the forecasting accuracy of the model has been improved by introducing frequency factor. At the same time, the practical applicability of the model for mid and long-term runoff forecast is verified as well. So, this paper provides a new idea to mid and long-term runoff forecast of reservoirs.

Wavelet analysis-based projection pursuit autoregression model and its application in the runoff forecasting of Li Xiangjiang basin

ABSTRACT The wavelet analysis technique was combined in this study with the projection pursuit autoregression (PPAR) model, and a new mid- and long-term runoff forecasting model, the wavelet analysis-based PPAR (PPAR-WA) is proposed, which realizes runoff forecasting from the perspective of the internal mechanism of a sequence. The runoff forecasting of the leading hydropower station in the Li Xianjiang cascade reservoirs in China was carried out to test the performance of the proposed model, and the accuracy and stability of the forecasting results were evaluated and analysed. The results show that the average relative error of the forecasting period can reach 9.6%, and the best relative error is less than 5% in some years. In addition, compared with PPAR, a back-propagation neural network and autoregression moving average model through three evaluation indexes, the results of PPAR-WA have higher accuracy and stronger stability. So, it has a certain value of popularization and application.

Study of the Scheduling Rules of Short-term Power Generation Dispatching for Single Hydropower Station

Short-term optimal operation of hydropower station is a high-dimensional, multi-constraint and complex system problem. In this paper, Liyuan hydropower station located in the middle reaches of Jinsha River is cited as an example, and the mathematical model of optimal power generation dispatching has been established. We obtain a large amount of optimal scheduling processes by using the progressive optimality algorithm (POA). Then, the corresponding scheduling rules for single hydropower station which can take advantage of runoff prediction results are derived. A case indicates that the scheduling rules can be effectively used to guide the actual operation run of Liyuan hydropower station.