Optimal Scheduling Strategy Based On Chaos Theory And Ant Colony Algorithm In Multi-Source Water Supply System

The urban supply system develops into a vast distributed large-scale system. For the significant different demands at different time due to the different characteristic of consumers, the multi-source water system needs to be scheduled properly. The rational forecast and scheduling of water demands can bring social and economic benefits by reducing energy consumption and providing high quality of marginal consumers. There are complex environments and different kinds of water consumers. Because the water demands are changed as the development of the city, it is a dynamic constrained multi-objective optimization problem. Since it is a mixed integer nonlinear programming problem, it is necessary to bring up a paccurate forecast method and an advanced scheduling strategy.By computing the delay time and embedded dimension, the phase space of urban hourly water consumption time series is reconstructed. Chaos theory is used to study the chaotic characters, and the hourly water consumption is turning out to be a typical chaotic system. The water time series of successive users are proved to be fractal and chaotic chaining relevant by advanced tools. On this basis, the different chaotic characters of booster and leakage are compared by phase diagram and the maximum lyapunov exponent variation. Test results show that the chaotic characters are changed immediately when the booster occurs, and slow leakage can be discovered after two hours. Thus this method supplies new judgments to effectively amend water system in time and decrease the loss of water resource.After the comparison of different chaos prediction methods by training different length history data, a new prediction method is proposed named chaotic horizontal period clustering, aiming at the high short-term prediction accuracy of hourly water consumption. The horizontal time series are determined as research samples by pattern recognition with high relevancy. After reconstructing the phase space of horizontal period clustering and analyzing chaotic characteristics of typical period data, chaotic prediction model is established. On line least square support vector machine (LS-SVM) is used to forecaste the period flows. Furthermore, to track water consumptions dynamically, vertical residuals are modified by grey model prediction (GMP) after collecting real-time data. The period historical data from Xiao Shan are supplied in the normal and abnormal case study to forecast the day-ahead hourly water consumption, and prediction accuracy with different methods are compared.A Cohesion Control Ant Colony algorithm (CCAC) is used to optimize the discrete problem, by using the golden section method to set parameter, and using gathered information weights to tuning decision policy. Then the CCAC is improved (ICCAC) with continuous coding to solve global optimization problem of continuous function. Section cohesion weight is brought into the decision policy to control ant distributions; flexible search steps are used to encourage local search after ICCAC locating the most promising direction to transfer; and dynamic evaporation factor is incorporated in pheromone updating. Experimental results show that ICCAC can keep good balance between wide exploration and pheromone exploitation, is an effective method to solve continuous global optimization problems. The algorithm mentioned above has shown an execellent performance in the short time scheduling problem of multi-source water system.This paper deals with the real-time scheduling problem of numerous constraints, long time-consuming, the highly variable nodal pressure and an optimal scheduling strategy based on increment model controlling the pressure constant is proposed. Firstly, the flow parameter increment model is established by predicting the nodal real-time water demands, and the variations of nodal pressures are obtained. Then the scheduling strategy is deduced from controlling the constant monitoring pressure parameters. At last, the parameters of pumps output pressures, pumps operation status and speed changes are regarded as decision variables to be regulated, and ICCAC is used to optimize the continuous and discrete variables at a time. The test results show that the new strategy can find the optimal schedule solution quickly, and well track the nodal stream variations to keep the constant monitoring nodal pressure, thus it strengthens the real-time regulation ability of multi-source water supply system, and can reduce the energy cost effectively.