Study On Collaborative Optimization Dispatching System For Water In Basin And Its Application

Water resources are relatively poor in China. Drought has been one of major natural disasters in China since ancient times. In order to solve the contradiction between limited water supply capability and ever-increasing water demand, scientific methods have to be found to utilize water resources with high efficiency under multi-objective constraint conditions. Scientific and reasonable methods used to allocate water source and water use, both in spatial and temporal scale, have to be explored. The objectives are to reach the goals of promoting healthy and sustainable development of society, economy and environment with the very limited water resources, and utilizing the water resources in a sustainable way.Scientific and reasonable allocation of water resources are related to two processes:the water supply and the water usage. In addition to solve the problem of water predicting in these two processes, the spatial-temporal and elements matching should be solved in priority. The collaborative theory is a optimal technology and it can be obtained the optimal scheme in a multi-objective optimization and reach the best matching in the application of water supply and the water usage. Using relevant theories and methods of synergetics as references, the structure of intelligent system was constructed on the basis of idea of collaborative optimization water dispatching in river basin. Multiple-factor simulation method and calculation for water supply and water use, and collaborative optimization algorithm have been discussed. A case study was conducted in Fuhe River Basin in Jiangxi Province. Major results and conclusions are verified by means of different methods of analysis. Major contents in this thesis are:(1) The collaborative optimization scheduling and principle for the multi-level water is established, the framework and process of a collaborative optimization of multi-level water dispatching are proposed by analyzing function requirements and design objectives of water dispatching system, In accordance with the results of analyzing the two processes of water supply and water usage in the studied area, the basis for water dispatching scheme is proposed.(2) The water requirement model of the minimum controlling for basin control section is constructed. The basin controlling section and water usage area are decided according to information from relevant hydrologic stations, precipitation stations, water intake of reservoirs and large-scale irrigation areas along the main streams and tributaries in the basin. According to the water allocation scheme, the district river water is calculated, then the water requirement of ecological environment is calculated on basis of the data of the hydrological stations measure and water function materials. After that, combining the interval inflow with river ecological water requirements, a calculation method of each control section is constructed. (3) A dynamic model of concentration and runoff based on immune clonal particle swarm optimization algorithm is proposed. The astringency of the proposed algorithm is analyzed by some functions experiments in detail and the accuration and the reliability is tested. Xin’An River model provides a calculation way for water supply, however, it is difficult to reach accuracy in practice. Appling the proposed dynamic immune clonal particle swarm algorithm to select these parameters in the Xin’An River model adaptively in different periods, the precision of simulation model of Xin’An River has greatly improved, the result shows that the calculation method is feasible.(4) Based on the theory of the data driven and the generalized regression neural network (GRNN), a dynamic long-term and short-term river runoff predictor is proposed The fitting accuracy and reliability of the monthly and10-days predicting model are tested and verified in three major monthly runoff series of hydrological station in Hongmen Reservoir Area in the Fuhe River Basin by comparing with Back Propagation Neural Network (BPNN). The experiment result shows the dynamic GRNN model have a high fitting precision and reliability.(5) The principles used in collaborative optimized water dispatching in Fuhe river basin are formulated. A model used in allocation of water amount is established. The objective function, constraint conditions, solution of model and dispatching startup conditions are determined. On the basis of water supply-demand model, different scenarios of water supply and demand in Fuhe River basin are analyzed under different conditions of water inflow. Methods used in collaborative optimized water dispatching are established. Based on the methods of collaborative optimized water dispatching in non-flooded seasons, scenarios of water dispatching under different frequencies of water supply are simulated with an aim of verifying the feasibility of the model.(6) The platform of decision supporting system used for water dispatching in non-flooded seasons is developed. This platform is constructed using calculating methods for the minimum controlling water requirement for basin control section, dynamic model to predict water supply, and collaborative optimized water dispatching model for multi-level river basin. The platform of decision supporting system used for water dispatching in non-flooded seasons was developed with C#on Visual Studio2005. The running equipment of water dispatching system and their running environment are introduced. The data flow process is also described. Technology of expert’s knowledge base used in water dispatching is studied. The process and inquiry principle of expert’s knowledge base used in water dispatching are illustrated. Also, the reliability of this technology is verified. And then, this technology is used in the formulation of water dispatching scheme.