Finite Time Power Control for Smart Grid Distributed Systems
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Solution Overview
Problem
Existing methods for power sharing among distributed energy resources (DERs) in a power grid are either inaccurate due to lack of consideration for other DER capacities or suffer from communication overhead and asymptotic convergence, failing to determine the exact power generation within a finite number of communication steps.
Innovation Solution
A method where each DER determines its power contribution by exchanging and accumulating portions of the total demand and capability within a fixed number of communication steps, using encoding and decoding operations to ensure accurate determination, and generating power as a product of the total demand and the ratio of its capability to the network's capability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods using local measurements at each DER are used to calculate power output, then the calculation is simple, but the accuracy is poor because they do not consider the capacities of other DERs
Solution Approach 1:
The patent segments the global power distribution problem into local iterative updates. Each DER only needs to exchange information with its immediate neighbors in the network, breaking down the complex global optimization into simpler local operations that converge to the optimal solution.
Solution Approach 2:
The patent implements a feedback mechanism where each DER continuously receives power capacity information from neighboring DERs and adjusts its own power output accordingly. This iterative feedback process allows the system to converge to an accurate power distribution that considers all DER capacities without requiring centralized control.
2Measurement precision
If full communication between DERs or with a centralized entity is implemented to specify power of each DER, then the power distribution accuracy is improved, but the communication overhead increases and the system becomes too slow for some situations
Solution Approach 1:
The patent divides the communication network into local neighbor relationships rather than requiring full mesh communication or centralized control. Each DER only communicates with its immediate neighbors, significantly reducing the total number of communication channels and messages required while still achieving accurate power distribution through iterative convergence.
Solution Approach 2:
The patent uses partial communication (only with neighbors) rather than full communication, yet achieves sufficient accuracy through iterative refinement. The system performs just enough communication steps to converge to the optimal solution without unnecessary additional communication overhead.
3Loss of time
If partial communication between each DER and its neighbors is used, then the communication overhead is reduced, but the convergence is asymptotic and each DER never achieves the exact value of the power to be generated
Solution Approach 1:
The patent employs periodic iterative communication rounds where each DER exchanges updated power capacity information with its neighbors at regular intervals. This periodic action allows the system to progressively refine the power distribution estimates and converge to the exact optimal solution in a finite number of steps, overcoming the asymptotic limitation.
Solution Approach 2:
The patent implements refined feedback mechanisms where each DER uses the received information from neighbors to update its power output calculation. The feedback loop continues iteratively until convergence criteria are met, ensuring that each DER achieves the exact power generation value rather than merely approaching it asymptotically.
4Ease of operation
If a centralized entity specifies and dispatches power commands to each DER, then the power distribution control is simplified, but the communication overhead increases and the system scalability is reduced
Solution Approach 1:
The patent enables each DER to autonomously determine its own power output by exchanging information with its neighbors and applying the iterative algorithm locally. This self-service approach eliminates the need for a centralized controller to calculate and dispatch commands to each DER, distributing the computational burden and reducing communication overhead while maintaining control simplicity at the device level.
Solution Approach 2:
The patent segments the centralized control function into distributed local decision-making at each DER. Instead of one centralized entity managing all power distribution calculations, each DER independently performs local optimization based on neighbor information, reducing the complexity of the communication infrastructure while maintaining effective power distribution control.
Data Source
AI summary
A distributed energy resource (DER) exchanges with each neighboring DER portions of the total demand for power accumulated by the DER and each neighboring DER and portions of a total capability of the network to generate the total power accumulated by the DER and each neighboring DER before each communication step. The DER updates the portion of the total demand for power and total capability accumulated by the DER using the portions of the total demand and the portions of the total capability received from the neighboring DERs. After the fixed number of communication steps, the DER accumulates the total demand for power and the total capability of the network and generates an amount of the power as a product of the total demand for the power and a ratio of a maximum capability of the DER to generate the power and the total capability of the network.