Concurrent SCUC Optimization for Faster Day-Ahead Grid Dispatch
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Solution Overview
Problem
Current methods for solving Security Constrained Unit Commitment (SCUC) problems in day-ahead market clearing processes are inefficient, as they struggle to find optimal solutions within the given time constraints due to the complexity and size of the Mixed Integer Programming (MIP) problems, leading to increased solution times as the number of variables and constraints grows.
Innovation Solution
The development of a High Performance Power-Grid Optimization (HIPPO) tool that employs distributed, scalable algorithms leveraging high-performance computing resources, allowing concurrent execution of computing nodes or threads to share solutions and hints, and utilizing algorithms like Branch-and-Bound, Relaxation Induced Neighborhood Search, and Alternating Direction Method of Multipliers to efficiently solve SCUC problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If off-the-shelf MIP solvers are used to solve SCUC problems, then solution accuracy is maintained, but solution time increases exponentially with problem size
Solution Approach 1:
The patent segments the large-scale SCUC MIP problem into smaller subproblems that can be solved in parallel using distributed computing nodes. By dividing the problem space and solving independent or loosely-coupled subproblems concurrently, the overall solution time is reduced while maintaining solution quality through coordination mechanisms.
Solution Approach 2:
The patent introduces a new dimension of parallelism by deploying multiple computing nodes that execute solving algorithms simultaneously. This transforms the single-threaded sequential solving process into a multi-dimensional parallel processing architecture, enabling exponential speedup for large-scale SCUC problems.
2Adaptability or versatility
If the number of distributed resources and continuous variables increases in SCUC models, then model comprehensiveness improves, but solution time increases
Solution Approach 1:
The patent segments the comprehensive SCUC model with numerous distributed resources and continuous variables into manageable subproblems that can be processed in parallel. This segmentation allows the system to handle increased model comprehensiveness without proportionally increasing solution time, as multiple computing nodes work simultaneously on different portions of the problem.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the SCUC model to identify independent subproblems and prepare them for parallel execution. This includes setting up the distributed computing environment, partitioning variables and constraints, and initializing solving algorithms before the main optimization process begins.
3Measurement precision
If SCUC model density increases with more non-zero coefficients, then problem representation accuracy improves, but solver performance deteriorates
Solution Approach 1:
The patent segments the dense SCUC model into subproblems that can be solved in parallel, reducing the computational burden on individual solvers. By distributing the processing of dense model components across multiple computing nodes, the system maintains accurate problem representation while improving overall solver performance through parallel execution.
Solution Approach 2:
The patent addresses solver performance deterioration by introducing parallel computing dimensions. Instead of relying on a single solver to handle the dense model, multiple solvers work simultaneously on different aspects of the problem, transforming the performance bottleneck into a scalable parallel processing architecture.
Data Source
AI summary
Apparatus and methods are disclosed for solving Mixed Integer Programming (MIP) problems, such as Security Constrained Unit Commitment (SCUC) problems used by power grid authorities to perform day-ahead market clearing. In certain examples, a plurality of threads of a software tool implementing a concurrent optimizer can be executed concurrently and sequentially to generate new solutions to a SCUC problem for an upcoming planning horizon. Data can be shared among the concurrently executing threads, such as intermediate/incumbent solutions and hints regarding the fixing of variables and constraints to reduce the size of the SCUC problem. In some examples, the threads are seeded with historical solutions from prior planning horizons. The software tool can select a best solution from the solutions generated by the threads, and determine dispatch instructions for a device coupled to the power grid for the upcoming planning horizon based at least in part on the selected solution.