Intelligent optimal scheduling method for wind and solar energy storage grid-connected power generation

• Main Authors: LI BAOQUAN, LUO HONG, YANG XIAOCHU, LIU KAI, LUO JIANCHUN, RAN HONG, YANG JIE
• Format: Patent
• Language: English
• Published: 14.01.2015
• Subjects: CALCULATING; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; COMPUTING; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRICITY; GENERATION; PHYSICS; SYSTEMS FOR STORING ELECTRIC ENERGY; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention provides an intelligent optimal scheduling method for wind and solar energy storage grid-connected power generation. The intelligent optimal scheduling method comprises the steps that (1) historical wind electricity data and solar electricity data are collected, and wind electricity and solar electricity output prediction error distribution before a day and in a day at different time scales is obtained; (2), wind electricity and solar electricity predicted output values are obtained according to meteorological data (illumination and temperatures) with an existing wind electricity output predicting system and a solar electricity output predicting system; (3), a wind electricity output scene value and a solar electricity output scene value are determined through the step (1) and the step (2); (4), an intelligent wind and solar energy storage gird-connected power generation scheduling model is established, wherein the intelligent wind and solar energy storage gird-connected power generation scheduling model comprises a scheduling optimized objective function model and a scheduling constraint condition model, and the scheduling optimized objective function model comprises a day-ahead scheduling model, a intraday scheduling model and a real-time scheduling model; (5), a first wind and solar energy storage power generation day-ahead combined scheduling value is obtained through optimization; (6), a wind and solar energy storage power generation intraday combined scheduling value, a wind energy storage power generation intraday scheduling value and a solar energy storage power generation intraday scheduling value are obtained; (7) a real-time wind and solar energy storage scheduling value is obtained.