Distributed Iterative Optimization for Power Grid Load Tracking
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Solution Overview
Problem
Traditional approaches are computationally infeasible for solving large-scale optimization problems of power tracking for aggregated loads, especially with hundreds of thousands or millions of loads, as they fail to handle local constraints effectively and achieve aggregated power tracking performance.
Innovation Solution
A distributed iterative optimization method is employed, where each distributed flexibility resource node solves a local optimization problem with a global Lagrange multiplier calculated at an aggregation level, iteratively adjusting local power consumptions and control variables to track a commanded power profile, using a distributed computation scheme that converges to an optimal solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional centralized optimization methods are used to solve large-scale power tracking problems, then aggregated power tracking performance can be achieved, but computational complexity becomes infeasible for hundreds of thousands or millions of loads
Solution Approach 1:
The patent divides the centralized optimization problem into distributed local optimization problems solved by individual load controllers. Each controller independently optimizes its local power consumption based on local constraints and a global Lagrange multiplier, eliminating the need for computationally infeasible centralized optimization of millions of loads while maintaining aggregated power tracking performance.
Solution Approach 2:
The patent introduces a global Lagrange multiplier as an additional dimension that coordinates distributed local optimizations. This multiplier acts as a pricing signal that aligns individual load optimizations with the global power tracking objective, transforming the infeasible centralized problem into a feasible distributed problem without losing optimality.
2Productivity
If distributed control is implemented to reduce computational complexity, then scalability improves, but coordination among loads becomes more difficult
Solution Approach 1:
The patent implements feedback through the global Lagrange multiplier that is updated based on the aggregated power tracking error and broadcast to all load controllers. This feedback mechanism enables automatic coordination among distributed loads, allowing the system to scale to millions of loads while maintaining coherent aggregated power tracking without complex direct coordination between individual loads.
3Adaptability or versatility
If load-side control is used to accommodate renewable power generation uncertainties, then flexibility increases, but control coordination across distributed loads becomes more complex
Solution Approach 1:
The patent creates a universal control framework where the same distributed optimization algorithm with Lagrange multiplier coordination can handle multiple objectives including renewable energy accommodation, power tracking, and local constraint satisfaction. This multi-functional approach enables flexible adaptation to renewable uncertainties while avoiding the need for separate complex coordination mechanisms for each control objective.
Data Source
AI summary
The technology described herein is generally directed towards a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes that operates iteratively until a commanded power profile is produced by aggregated loads. The technology uses a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using a global Lagrange multiplier that is based upon information from each other node. The global Lagrange multiplier is determined at an aggregation level using load-specific information that is obtained in a condensed form (e.g., a scalar) from each node at each iteration. The global Lagrange multiplier is broadcasted to the nodes for each new iteration. The technology provides an iterative, distributed solution to the network optimization problem of power tracking of aggregated loads.