Sparse Corrective SCOPF Model for Power Grid Contingency Management
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Solution Overview
Problem
Current methods for determining power output levels in electric power systems are inefficient in handling contingencies, leading to excessive post-contingency rescheduling and computational complexity, especially in large-scale power grids.
Innovation Solution
A sparse, corrective security-constrained optimal power flow (SCOPF) model using l1-regularization and Alternating Direction Multiplier Method (ADMM) is implemented to reduce the number of post-contingency rescheduling operations by inducing sparsity in power output adjustments, allowing for efficient and parallelizable computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional security-constrained optimal power flow (SCOPF) methods are used to handle contingencies, then grid reliability is maintained, but the number of post-contingency rescheduling operations increases and computational complexity increases
Solution Approach 1:
The patent applies l1-regularization to the SCOPF formulation, transforming the objective function to include a sparsity-inducing penalty term. This parameter change in the optimization formulation reduces the number of non-zero elements in the corrective action vector, thereby reducing post-contingency rescheduling operations while maintaining feasibility and grid reliability
Solution Approach 2:
The patent employs the Alternating Direction Multiplier Method (ADMM) to decompose the large-scale SCOPF problem into smaller sub-problems that can be solved in parallel. This segmentation of the computational task reduces the overall computational complexity and enables efficient handling of large-scale power systems with multiple contingencies
2Reliability
If traditional SCOPF methods are used to handle contingencies, then grid reliability is maintained, but the number of post-contingency rescheduling operations increases
Solution Approach 1:
By modifying the objective function to include l1-regularization, the patent changes the optimization parameters to favor sparse solutions. This parameter change directly reduces the number of post-contingency rescheduling operations needed to restore grid reliability after contingencies, as the regularization penalizes non-zero corrective actions
3Productivity
If sparse optimization with l1-regularization is applied to reduce post-contingency rescheduling, then the number of post-contingency actions is reduced, but computational complexity increases
Solution Approach 1:
The patent uses ADMM to segment the computationally intensive sparse SCOPF problem into manageable sub-problems. By decomposing the optimization into iterative steps that can be parallelized, the method makes the sparse formulation computationally tractable while maintaining the benefit of reduced post-contingency rescheduling operations
4Loss of energy
If sparse C-SCOPF model is implemented to reduce post-contingency actions, then operational costs are reduced, but the complexity of the optimization model increases
Solution Approach 1:
The patent modifies the SCOPF model by adding an l1-regularization term to the objective function, which encourages sparsity in the corrective actions. This parameter change leads to fewer post-contingency rescheduling operations and lower operational costs, despite the increased mathematical complexity of the optimization formulation
Data Source
AI summary
A method for determining a generation schedule with contingency constraints for controlling power output levels for a plurality of generators in an electric power system including determining a measure of a sparse, corrective model (C), security-constrained optimal power flow (SCOPF), which reduces a number of post-contingency rescheduling operations for each of a plurality of contingencies, and adjusting a power output level of at least one of the plurality of generators according to the measure of the sparse C-SCOPF upon detecting a contingency in the electric power system.