Hybrid Power System Stability Analysis via Continuous Dynamics Modeling
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Solution Overview
Problem
Centralized control centers in power systems face challenges in handling large data volumes from smart buildings and distributed energy sources, leading to operational complexities and privacy concerns, which complicate dynamic stability analysis in hybrid systems with iterative discrete decision making architectures.
Innovation Solution
The approach involves modeling power systems with multiple control agents using continuous dynamics to represent discrete decision making processes, allowing for stability analysis and validation through nonlinear time-domain simulation, thereby addressing the dynamic stability of hybrid systems by integrating power system dynamics into the decision-making process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If centralized control centers collect all measurements and data from smart buildings and distributed energy sources, then operational control capability is improved, but system complexity and computational burden increase
Solution Approach 1:
The patent divides the centralized control architecture into multiple distributed control agents that operate autonomously at different levels (building level, community level, utility level). Each control agent handles local decision-making independently, segmenting the monolithic centralized system into modular units that reduce overall system complexity while maintaining operational control capability.
2Loss of information
If all data is communicated from the utility grid to retail and residential users, then information availability is improved, but privacy concerns and data security risks increase
Solution Approach 1:
The patent implements local quality by allowing different levels of data access and processing at different hierarchical levels. Local control agents process and analyze data locally, making decisions based on local conditions without requiring all data to be centralized or shared system-wide. This preserves privacy by limiting data exposure to only what is necessary at each local level while maintaining information availability for operational decisions.
3Adaptability or versatility
If iterative discrete decision making processes are implemented in power systems, then adaptability to distributed energy sources is improved, but dynamic stability analysis becomes more difficult
Solution Approach 1:
The patent introduces an intermediary continuous dynamic model that bridges the discrete decision-making processes and the continuous power system dynamics. This intermediary model allows for stability analysis by translating the discrete iterative decisions into a continuous framework that can be analyzed using conventional stability tools, thereby reducing analysis complexity while preserving adaptability.
Data Source
AI summary
Various examples are provided that are related to coupling dynamics for, e.g., power systems with iterative discrete decision making architectures. In one example, a method includes determining an output power adjustment using a frequency difference associated with a generator of a first area of a power system and price signals corresponding to power generation in the first area and in a second area coupled to the first area by a tie-line; and providing a power command based upon the output power adjustment to a control system of the generator. In another example, a power system control system includes first and second agents configured to control power generation of a first area and a second area of a power system, respectively. The second agent can control power generation of the second area using frequency differences of generators in the second area and price signals of the first and second areas.