Assessment Model For Power System Flexibility Adequacy At Multiple Time Scales

As renewable energy sources become an important part of the power system,the generation mix and load characteristics of the power system have changed significantly.The traditional thermal-based power system is no longer able to meet the growing demand for renewable energy access,and therefore requires more flexible operation methods.At the same time,with the gradual opening of the electricity market and increased competition,the operation of the power system requires better utilisation of different types of resources and real-time scheduling and planning on different time scales to improve economy and reliability.Therefore,it is important to study the flexibility adequacy of the power system to ensure its sustainable development.The main objective of the study is to design a flexibility adequacy assessment model for power systems at multiple time scales.The current division of flexibility into single time periods can hinder the development of flexibility resources at multiple time scales,and the existing flexibility analysis methods cannot adapt to the new characteristics of the flexibility needs of high percentage scenery power systems at different time scales.Therefore,the power load in the power system is first predicted based on a long and short-term memory neural network,while a wind power model and a photovoltaic power model are established to predict the scenic power output.The net load curve of the power system is obtained by combining the power load data with the scenic power output data.The net load curve is then decomposed by an ensemble empirical modal decomposition algorithm to generate three fluctuating variables,high frequency,medium frequency and low frequency,corresponding to three time scales,short term,medium term and long term,respectively,and then determine the flexibility demand under different time scales.Finally,the flexibility adequacy at multiple time scales is assessed based on the constructed flexibility adequacy evaluation index.The research methodology draws extensively on interdisciplinary theoretical approaches,involving not only theories and methods related to power systems,but also the intersection of information science,mathematical morphology and other disciplines,and is innovative and comprehensive to a certain extent.At the same time,the research focuses on combining theoretical research on basic scientific problems with practical engineering case analysis.On the basis of constructing models,it uses the accumulated actual data of power systems to carry out case analysis,so as to better understand the operation of power systems and explore potential problems.Compared with existing results,the innovation of the study is mainly in two aspects.Firstly,it combines long and short-term memory neural network algorithms,ensemble empirical modal decomposition and waveform recognition to perform morphological decomposition of multi-time-scale flexibility demand and investigate the difficulties in calculating flexibility demand for high proportional scenery power systems.Secondly,a power system flexibility adequacy assessment method is constructed to overcome the bottleneck of multi-timescale flexibility adequacy assessment considering net load uncertainty,which has certain practical value and application prospects.The proposed model for assessing the flexibility adequacy of power systems at multiple time scales is valuable in practical applications and can provide an important reference for the planning,design and operation of power systems,and can also provide effective support for the development of new power systems.