Security-Constrained Power System Simulation Model Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current power system operation simulation technologies face challenges in efficiently calculating and modeling security-constrained unit commitment models due to the large number of decision variables and constraints, especially in large-scale power systems, leading to high computational and storage resource requirements.
Innovation Solution
A fast model generating and solving method that calculates load shifting distribution factor and generator shifting distribution factor matrices based on full power system topology, corrects these matrices according to branch on-off states, and introduces only useful security constraints to improve the efficiency of unit commitment model solving.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security constraints are included in the unit commitment model for power system operation simulation, then the evaluation comprehensiveness is improved, but the calculation and storage resource requirements increase significantly
Solution Approach 1:
The patent segments the security constraints into two categories: N-1 constraints (single element failure) and N-k constraints (multiple element failures). By dividing the constraint set, the model can prioritize computing resources on the more frequent N-1 constraints while optionally including N-k constraints, thus managing the trade-off between evaluation comprehensiveness and computational burden.
Solution Approach 2:
The patent applies different levels of security constraint detail to different parts of the power system model. Specifically, it uses detailed N-1 constraints for all branches and generators, while N-k constraints are applied selectively based on system importance and computational resources available. This local differentiation allows comprehensive evaluation where needed while conserving computational resources elsewhere.
2Adaptability or versatility
If the power system scale is increased to cover larger networks, then the simulation applicability is improved, but the number of decision variables and constraints increases, leading to higher computational complexity
Solution Approach 1:
The patent segments the security constraints into N-1 and N-k categories, allowing the model to scale by adjusting the inclusion of different constraint types. For larger power systems, the model can maintain N-1 constraints for all elements while selectively applying N-k constraints only to critical components, enabling scalable application to systems with thousands of nodes and branches.
Solution Approach 2:
The patent changes the parameter of constraint inclusion level based on system scale. For smaller systems, both N-1 and N-k constraints are fully included. For larger systems, the model adjusts by including N-1 constraints for all elements and N-k constraints only for selected critical elements, allowing the simulation to scale to larger networks while managing computational complexity.
3Measurement precision
If all security constraints are modeled in detail, then the simulation accuracy is improved, but the model solving time increases
Solution Approach 1:
The patent segments security constraints into N-1 (single element failure) and N-k (multiple element failures) categories. N-1 constraints are applied to all branches and generators to ensure basic security, while N-k constraints are applied selectively to critical elements. This segmentation ensures high simulation accuracy for common failure scenarios while reducing solving time by not modeling all possible N-k constraints in detail.
Solution Approach 2:
The patent applies different levels of constraint detail locally: N-1 constraints are applied universally to all system elements for comprehensive baseline accuracy, while N-k constraints are applied locally only to critical branches and generators where multiple failures would have significant impact. This local differentiation maintains simulation accuracy for important scenarios while reducing overall model solving time.
Data Source
AI summary
The present disclosure provides a fast model generating and solving method for security-constrained power system operation simulation, which includes: obtaining information of all branches and nodes which are involved during operation simulation time period, calculating original-node impedance matrix, load shifting distribution factor original matrix and generator shifting distribution factor original matrix of all involved branches; correcting the load shifting distribution factor original matrix and the generator shifting distribution factor original matrix according to the on-off state of branches; obtaining output of each generator unit at each time period according to no-security-constraint unit commitment model, and determining overload of each branch again; solving iteratively until no branch is overloaded, and obtaining output of each generator unit at each time period under security constraint of operation simulation for the current simulation day, performing operating simulation for the rest simulation days to obtain security-constraint operation simulation result for the whole year.