Scheduling Risk Quantification for New Energy Grids
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional power system analysis methods fail to effectively assess scheduling risks due to the increased uncertainty and volatility introduced by new energy sources like wind and solar power, which complicates grid operation and planning.
Innovation Solution
A method and apparatus for quantifying and assessing scheduling risks by establishing a scene set considering new energy disturbances, solving a rescheduling optimization model, and calculating risk indices such as wind and light abandonment, load shedding, backup shortages, and line power flow risks, using an objective function that minimizes operation and regulation costs while adhering to power balance, network security, and plant output constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional power system analysis methods are used, then the analysis process is simple, but they fail to effectively assess scheduling risks caused by new energy uncertainty and volatility
Solution Approach 1:
The patent segments the risk assessment process into multiple distinct modules: scene set construction module that generates multiple possible scenarios, rescheduling optimization model that evaluates each scene, and risk index calculation module that quantifies different risk dimensions. This segmentation allows comprehensive risk assessment while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent performs preliminary actions by pre-establishing the scene set considering new energy disturbance characteristics before actual risk assessment. The scene set construction module pre-generates multiple possible operational scenarios based on historical data and uncertainty analysis, so that when real-time assessment is needed, the computational burden is reduced and accuracy is improved.
2Adaptability or versatility
If new energy sources are continuously added to the power grid, then carbon peaking and carbon neutrality goals are advanced, but uncertainty and volatility in the power grid increase significantly
Solution Approach 1:
The patent implements feedback mechanisms through the risk quantification and assessment process. The risk index calculation module continuously evaluates scheduling risks based on actual operational data and scene comparisons, providing feedback information that can be used to adjust scheduling plans and improve grid reliability while accommodating new energy sources.
Solution Approach 2:
The patent applies beforehand cushioning by establishing multiple scenario sets that include adverse conditions and uncertainties. The rescheduling optimization model pre-evaluates how the power system would respond to various disturbance scenarios, allowing operators to prepare contingency plans and cushion against potential reliability issues before they occur.
3Productivity
If the installed capacity of new energy system increases, then carbon neutrality progress is made, but the risk of safe operation increases significantly
Solution Approach 1:
The patent applies dynamics by creating a dynamic risk assessment system that adapts to changing new energy generation capacities. The scene set construction and rescheduling optimization model can be reconfigured as new energy capacity increases, allowing the system to dynamically evaluate emerging risks and adjust scheduling strategies to maintain safe operation.
Data Source
AI summary
A method for quantifying and assessing a scheduling risk considering source load fluctuation and rescheduling, and an apparatus for quantifying and assessing a scheduling risk considering source load fluctuation and rescheduling are provided. The method includes establishing a scene set considering a new energy disturbance; establishing and solving a rescheduling optimization model considering the new energy disturbance in each scene based on a preset scheduling plan according to the scene set, so as to obtain a solving result of the rescheduling optimization model; and calculating a risk quantifying and assessing result corresponding to the preset scheduling plan according to the solving result of the rescheduling optimization model.