Power Grid Planning Decomposition for Reliability-Constrained Optimization
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Solution Overview
Problem
The complexity of power grid planning is exacerbated by the integration of variable renewable energy sources (VREs), leading to inefficiencies in optimisation calculations due to numerous reliability issues and uncertainties, which hinder effective grid planning.
Innovation Solution
A power grid planning support method and system that employs a decomposition approach, allowing for the breaking down of optimisation problems into sub-problems, along with constraint generation and reinforcement learning, to facilitate robust solution finding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimisation calculations are applied to power grid planning considering reliability criteria and VRE integration, then solution quality improves, but computational complexity and convergence difficulty worsen
Solution Approach 1:
The patent segments the complex optimisation problem into distinct components: objective function (cost minimisation), constraint functions (power flow equations, reliability criteria, asset capacity limits), and variable definitions. This segmentation allows each component to be developed and validated independently, reducing overall system complexity while maintaining comprehensive reliability considerations.
Solution Approach 2:
The patent introduces an intermediary computational layer that mediates between the optimisation model and the power grid system. This intermediary handles the complex calculations through structured algorithms, separating the mathematical complexity from the physical grid planning process, thereby improving reliability assessments without directly increasing grid complexity.
2Reliability
If comprehensive constraint functions are included in the optimisation model, then solution robustness improves, but computational time and convergence difficulty worsen
Solution Approach 1:
The patent performs preliminary actions by pre-defining and validating constraint functions before executing the main optimisation calculation. Power flow equations, reliability criteria, and asset capacity constraints are prepared in advance with proper formulations, allowing the main calculation to proceed more efficiently without sacrificing solution robustness.
Solution Approach 2:
The patent implements dynamic constraint application where constraint functions are actively evaluated and adjusted during the optimisation process. The system dynamically balances between maintaining comprehensive constraints for robustness and adjusting computational intensity based on convergence progress, thereby managing computational time effectively.
3Adaptability or versatility
If multiple scenarios are contemplated for uncertainties in grid planning, then planning completeness improves, but optimisation calculation efficiency worsens
Solution Approach 1:
The patent creates a universal optimisation framework that can handle multiple scenarios and uncertainty types through a single integrated model. The objective function and constraint functions are designed to accommodate various planning scenarios (different VRE penetration levels, demand patterns, and reliability criteria) without requiring separate calculation systems, thereby maintaining both completeness and efficiency.
Data Source
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AI summary
A power grid planning support method and system. The method comprises steps of: (i) receiving an objective function, the objective function representing a quantity or parameter associated with a power grid; (ii) receiving one or more constraint functions, the or each constraint function representing constraints on the power grid, the objective function and constraint function(s) together representing a model of the power grid; (iii) receiving input data related to the power grid; (iv) executing an optimisation calculation defined by the model of the power grid and the input data; and (v) displaying a result of the optimisation calculation.