Distributed Energy Resource Control With Nested MPC Horizons
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing power distribution networks rely on centralized control, which can lead to grid vulnerabilities and inefficiencies, particularly in managing distributed energy resources (DERs) like microgrids.
Innovation Solution
A decentralized algorithmic approach for DER coordination, using model predictive control (MPC) routines and asset models to optimize power exchange within DER systems, allowing each asset manager to independently solve optimization trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized control is used to manage distributed energy resources, then coordination and control capability are improved, but grid vulnerability and inefficiency increase
Solution Approach 1:
The patent divides the centralized control system into multiple distributed asset managers, each independently managing specific DERs. This segmentation allows local decision-making while maintaining system-wide coordination, reducing grid vulnerability by eliminating single-point control failures.
Solution Approach 2:
The patent introduces a hierarchical control dimension with multiple prediction horizons (long-term, medium-term, short-term) operating simultaneously. This multi-dimensional approach enables coordination at different time scales while distributing computational tasks, improving both control capability and system reliability.
2Ease of operation
If a single long prediction horizon is used for optimization, then system-wide coordination is improved, but computational burden increases
Solution Approach 1:
The patent segments the single long prediction horizon into multiple nested prediction horizons (long-term, medium-term, short-term), each solved independently by different asset managers. This divides the computational burden while maintaining system-wide coordination through constraint coupling.
Solution Approach 2:
The patent implements nested optimization where short-term trajectories are constrained by medium-term trajectories, which are in turn constrained by long-term trajectories. This nested structure allows coordination at multiple scales while distributing computational complexity across different time horizons.
3Productivity
If multiple asset managers operate independently with different prediction horizons, then computational efficiency is improved, but coordination difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where asset managers exchange trajectory constraints and boundary conditions. Each asset manager uses feedback from others to adjust its local optimization, ensuring coordination while maintaining independent computational efficiency.
Solution Approach 2:
The patent uses parameter coupling through shared constraints (e.g., power balance, energy storage limits) that link different prediction horizons. By changing and coordinating key parameters across asset managers, the system achieves both computational efficiency and proper coordination.
Data Source
AI summary
An asset manager is configured to control distribution of power within an aggregated distributed energy resources system. The asset manager is configured to solve a given asset model that models a real asset. The asset controller is configured to optimize a setpoint of the asset by determining a first trajectory over the course of a first prediction horizon and a second trajectory over the course of a second prediction horizon that is temporally shorter than the first prediction horizon. The trajectories are determined by minimizing a cost function associated with the DER model or a DERs system model. The first prediction horizon has a first temporal length and a first plurality of set points. The second prediction horizon has a second temporal length and a second plurality of set points. The asset controller is configured to constrain the second trajectory based on the first plurality of set points.