Power Grid Resource Allocation With Parallel Primal-Dual Convergence
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Solution Overview
Problem
Solving large-scale security constrained unit commitment in power grids is computationally intensive and time-consuming due to its complexity and huge dimensions.
Innovation Solution
A power management system employing a parallel asynchronous collaborative primal dual solver that uses multiple primal and dual solvers with different initial conditions to generate resource allocation schedules by comparing upper and lower bounds, reducing computational time and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional unit commitment methods are used to solve security constrained unit commitment problems, then solution accuracy is maintained, but computational time and complexity increase significantly
Solution Approach 1:
The patent divides the complex security constrained unit commitment problem into separate primal and dual subproblems that can be solved independently and iteratively. The primal problem handles feasibility while the dual problem handles optimality, allowing parallel computation and reducing overall solution time while maintaining accuracy through iterative convergence.
Solution Approach 2:
The patent employs warm-start strategies where solutions from previous iterations or similar problems are used as initial conditions for the current problem. This preliminary action reduces the number of iterations needed to converge, significantly cutting computational time while preserving solution accuracy through refined iterative optimization.
2Reliability
If multiple convergence paths with different initial conditions are explored, then solution reliability improves, but computational complexity increases
Solution Approach 1:
The patent merges multiple convergence paths by running parallel primal and dual solvers with different initial conditions, then combining their results through iterative coordination. This merging approach maintains solution reliability by exploring multiple paths while managing complexity through structured integration and convergence criteria.
Solution Approach 2:
The patent creates multiple copies of the solver with different initial conditions to explore various convergence paths simultaneously. These copied solvers operate in parallel and their results are coordinated, improving reliability through diverse exploration while managing complexity through efficient resource utilization and convergence monitoring.
3Reliability
If security constraints are included in unit commitment, then grid security is improved, but operational flexibility and solution speed decrease
Solution Approach 1:
The patent segments security constraints into the dual problem formulation, separating them from the primal feasibility checks. This allows security constraints to be rigorously enforced through dual variables and Lagrangian relaxation while maintaining solution speed through efficient primal-dual iterative optimization that avoids repeated full-constraint evaluations.
Solution Approach 2:
The patent transforms security constraints into parameter adjustments through Lagrangian multipliers and dual variables. By changing the representation of constraints from hard constraints to soft constraints with penalty parameters, the system maintains grid security requirements while improving solution speed through more flexible optimization that avoids infeasibility rejections.
Data Source
AI summary
Embodiments of the disclosure includes operating a power grid, including: generating, by a power management system of the power grid, a power grid resource allocation profile indicative of an operation of the power grid constrained by operational information of the power grid; generating a difference between a value of upper bounds from a plurality of obtained convergence paths and a value of lower bounds from the obtained convergence paths, the obtained convergence paths being based on a plurality of different initial conditions for the generated power grid resource allocation profile; and generating a resource allocation schedule for power grid resources operating within the power grid if the generated difference is smaller than a pre-determined threshold, the resource allocation schedule corresponding to a convergence path associated with the value of the upper bounds, the resource allocation schedule being configured to be received at the power grid resources.