Power Distribution Management Apparatus for Voltage Stability
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Solution Overview
Problem
The existing power distribution systems are not equipped to handle reverse energy flows from distributed energy resources, leading to voltage fluctuations beyond the tolerance range, which can affect the efficient distribution of power to load equipment.
Innovation Solution
A power distribution management apparatus that stores history information on switch states and environmental attributes, allowing for the calculation of similarity between specified attribute information and historical data to extract patterns of switch states that can mitigate voltage fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If distributed energy resources are installed at customer premises to generate power, then power generation capability is improved, but reverse flow causes voltage fluctuations beyond tolerance range
Solution Approach 1:
The system performs preliminary actions by calculating voltage drops and predicting power flows before actual power distribution occurs. The management apparatus computes required switch patterns in advance based on forecasted power generation and consumption, ensuring voltage remains within tolerance range even when reverse flow occurs from distributed energy resources.
Solution Approach 2:
The system dynamically adjusts switch patterns in the power distribution system based on real-time or forecasted conditions. The management apparatus changes switch states adaptively to accommodate varying power generation from distributed energy resources and changing load demands, maintaining voltage stability despite fluctuating power flows including reverse flow conditions.
2Reliability
If switch patterns are adjusted to maintain voltage within tolerance range, then voltage stability is improved, but complexity of power distribution control increases
Solution Approach 1:
The management apparatus performs self-service by automatically calculating voltage drops, determining optimal switch patterns, and controlling power distribution without requiring manual intervention. The system uses automated algorithms to analyze power flow conditions and adjust switch states independently, reducing the need for complex human-operated control systems while maintaining voltage stability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual power generation, consumption, and voltage conditions, then using this information to adjust switch patterns. The management apparatus compares forecasted conditions with actual measurements and refines its control decisions accordingly, simplifying the control process through adaptive learning rather than requiring overly complex predetermined control logic.
3Productivity
If voltage is adjusted by switching in the high voltage system, then power distribution efficiency is improved, but inability to handle reverse flow causes power beyond tolerance range to flow
Solution Approach 1:
The system applies segmentation by dividing the power distribution system into controllable segments using switches at various levels (high voltage and low voltage). The management apparatus independently controls different segments and their corresponding switches, allowing localized adjustment of power flow to handle reverse flow conditions while maintaining overall distribution efficiency. This segmented control enables granular management of voltage and power flow in each distribution zone.
Data Source
AI summary
A power distribution management apparatus includes: a memory configured to store history information including a pattern of open and closed states of switches that switch a path that supplies power between a substation and load equipment of a customer, and attribute information related to an environment where the power is supplied; and a processor configured to execute a process. The process includes: upon acceptance of specification of the attribute information related to the environment, calculating a degree of similarity between the specified attribute information and attribute information included in the history information; and extracting a pattern of the open and closed states of switches whose degree of similarity satisfies a predetermined condition among the history information.