Supervisory Controller Dynamic Voltage Thresholds Renewable Energy
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Solution Overview
Problem
Current systems for managing renewable energy in radio communications systems lack flexibility and efficiency, as they rely on fixed voltage thresholds for battery charging and dump load control, leading to energy wastage and oversized equipment, and do not effectively utilize weather data or battery state for optimal power management, nor do they provide redundancy or remote control capabilities for load prioritization and backup generator operation.
Innovation Solution
A Supervisory System Controller (SSC) that dynamically adjusts voltage thresholds based on battery state and weather forecasts to optimize energy transfer, allows remote control of load disconnection and generator operation, and provides redundancy through multiple controllers and battery strings to ensure reliable power supply.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed voltage thresholds are used for battery charging and dump load control, then the control system is simple, but energy is wasted and equipment must be oversized
Solution Approach 1:
The patent implements dynamic voltage thresholds that adjust based on battery state of charge, temperature, and weather forecasts. Instead of fixed thresholds, the system continuously adapts control parameters to optimize energy capture and storage, preventing energy waste while maintaining manageable control complexity through automated algorithms.
Solution Approach 2:
The system incorporates feedback loops that monitor battery state, weather conditions, and energy generation in real-time. This feedback enables the controller to dynamically adjust voltage thresholds and dump load operations, optimizing energy efficiency without requiring excessively complex control architecture.
2Device complexity
If fixed voltage thresholds are used for battery charging and dump load control, then the control system is simple, but equipment must be oversized
Solution Approach 1:
By implementing dynamic voltage thresholds that adapt to battery state and weather conditions, the system maximizes energy capture efficiency. This allows for right-sized equipment selection without excessive capacity, as the intelligent control compensates for variable generation conditions, reducing the need for oversized generators and battery banks.
3Reliability
If renewable energy generation is increased to ensure continuous power, then power availability is improved, but system cost and size increase
Solution Approach 1:
The system uses weather forecast data to predict future energy generation and proactively adjusts battery charging and load management strategies. By preparing in advance for expected generation patterns, the system ensures power availability without requiring excessive generation capacity or storage, optimizing the balance between reliability and system size.
4Loss of energy
If dynamic voltage threshold adjustment is implemented, then energy efficiency is improved, but control system complexity increases
Solution Approach 1:
The patent employs feedback-based control that automatically adjusts voltage thresholds based on monitored parameters including battery state of charge, temperature, and weather forecasts. This automated feedback mechanism achieves high energy efficiency without requiring complex manual intervention or excessively sophisticated control architecture, as the system self-regulates based on real-time conditions.
5Productivity
If weather data is integrated for energy prediction, then power management optimization is improved, but system complexity increases
Solution Approach 1:
The system integrates weather forecast data to predict future energy generation patterns and proactively optimizes battery charging and load management. This preliminary action based on forecasted conditions enables efficient power management without requiring complex real-time decision-making, as the system prepares optimal strategies in advance based on predicted weather and generation patterns.
Data Source
AI summary
A supervisory system controller for controlling and monitoring the generation of electrical energy from renewable sources and management methods for the storage of energy so generated and interconnecting the energy-generating elements, storage and load. The supervisory system controller operates to maximum the power transfer from a wind turbine to a battery by automatically varying the threshold levels at which turbine dump loads are switched based on system inputs and measurements. The method conserves generator fuel by delaying a scheduled generator maintenance running period such that it occurs when renewable energy availability is predicted to be low and battery is in a reduced state of charge. Further modifications and management methods are also provided.