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Research on Reservoir Optimal Operation Based on Price Forecast under the Condition of Power Market

Author: HuangSiXia
Tutor: XueXiaoJie；HuangQiang
School: Xi'an University of Technology
Course: Hydrology and Water Resources
Keywords: GM (1,1) model Electricity Price Forecasting BP neural network Reservoir optimal operation POA algorithm
CLC: TV697.11
Type: Master's thesis
Year: 2009
Downloads: 29
Quote: 0
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Abstract


With the opening up of the electricity market and TOU implementation of the system, the role of hydropower reservoirs in the power system is becoming increasingly important, the simple pursuit of maximum generating capacity optimization criterion Optimization of hydropower station has been unable to meet the requirements of the development of the new era. Optimization of hydropower station under the conditions of research TOU for full use of hydropower resources, and improve the economic efficiency of the power plant, has important theoretical significance and practical value. This thesis, on the basis of previous related research in the field of the same generating capacity in the electricity market environment caused due to the difference of the level of the tariff is not easy to mean the same benefit of power generation, the traditional maximum generating capacity target reservoir optimal scheduling strategy has the largest power generation efficiency is not to hydropower, the largest generating capacity of power generation benefits model and solving case study shows that the maximum benefit of power generation model than the power generation of the largest model can bring greater economic benefits. The main content of the paper include: (1) on the basis of a lot of reading of the literature, the comprehensive review of the domestic and international hydropower optimization theories, methods and applications of the status quo, and the electricity market and electricity prices related to the field of knowledge. (2) review the meaning and methods of load forecasting based on gray theory is discussed, analysis of the gray theory is applied to predict the principle highlight GM (1,1) model, GM (1,1) improvement of the model and the residual model, and the California electricity market modeling using the above model, from September 14 2000 to September 20, 2007, load forecast. The results show that, GM (1,1) improved model and residuals model than GM (1,1) model improve the prediction accuracy. (3) the use of BP neural network to forecast the marginal price similarity search to select the inputs, and the average percentage error and the two parameters of the maximum percentage error of prediction accuracy after using correlation analysis technique input select improved results using correlation analysis technique for the prediction model inputs select predicted effect have a more significant improvement, especially on the weekend marginal price prediction accuracy enhanced to a great extent, the maximum percentage of error by reduced to 29.92% from 49.73%, the average percentage error is reduced to 12.41% from 14.34%. This shows that the correlation analysis technique to improve the prediction accuracy improved to a large extent on the selection of the input factors. (4) systematic analysis of the significance of reservoir optimization scheduling, rules and optimize the scheduling criteria, a hydropower reservoir in Pearl River, for example, the largest power generation and power generation benefits both models were established, according to the different times of tariff differences comprehensive considering the water level of the reservoir, the unit output limit, the plant discharged flow, unit characteristics curve storage capacity characteristic curve and so many constraints, the POA algorithm optimization theory to solve the optimization model, the results show that the maximum benefit of power generation for the target, the Japanese economic increase in revenue by 3.8 million, the power generation efficiency. 3.0939%.

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CLC: > Industrial Technology > Hydraulic Engineering > Water control,hydraulic structures > Reservoir Management > Reservoir operation management > Hydrological and reservoir operation
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