Energy Storage Pool Capacity Limits for Exception-Based Control
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Solution Overview
Problem
Existing energy storage systems in telecommunications networks face challenges in efficiently managing capacity limits during exceptional situations such as dynamically changing pricing, load fluctuations, frequency balancing, and weather conditions, leading to increased wear and suboptimal operation.
Innovation Solution
A computer-implemented method for controlling a pool of energy storages by detecting exception events, selecting appropriate energy storages, and temporarily adjusting capacity limits to accommodate these events, including generating and scheduling adjustments based on collected data from various information sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If energy storages are used for optimization purposes (balancing electric grid, energy trading), then energy efficiency and cost optimization are improved, but reliability of backup energy supply deteriorates because capacity limits are exceeded during exceptional situations
Solution Approach 1:
The system dynamically adjusts capacity limits based on operational context. During normal operations, energy storages can be used for optimization purposes with higher capacity utilization. During exceptional situations (outages, emergencies), the system automatically enforces stricter capacity limits to ensure backup requirements are met. This dynamic adjustment resolves the contradiction by making the capacity limits adaptive rather than static.
Solution Approach 2:
The system changes the parameter of capacity limits based on the operational state. By monitoring exceptional situations and operational conditions, the system modifies the capacity limit parameters to balance optimization activities against backup reliability requirements, allowing the same energy storage system to serve dual purposes under different conditions.
2Reliability
If capacity limits are strictly enforced for backup purposes, then reliability of backup energy supply is improved, but energy optimization opportunities are lost during exceptional situations
Solution Approach 1:
The system transitions from static capacity limits to dynamic capacity limits that respond to operational conditions. During exceptional situations, the system temporarily relaxes or adjusts capacity limits to allow energy optimization activities, while maintaining adequate backup capacity. This dynamic approach prevents loss of optimization opportunities while ensuring backup reliability is not compromised.
Solution Approach 2:
The system performs preliminary assessment of exceptional situations and pre-adjusts capacity limits accordingly. By detecting exceptional situations early and proactively modifying capacity limits before optimization activities commence, the system ensures both backup reliability and optimization efficiency are maintained without conflict.
3Loss of energy
If energy storages are continuously used for optimization purposes, then energy cost savings are improved, but wear and longevity of energy storage systems deteriorate
Solution Approach 1:
The system adjusts operational parameters (capacity limits, charge/discharge rates) based on the state of the energy storage system. When exceptional situations arise or when optimization activities cause excessive wear, the system modifies these parameters to reduce stress on the batteries, thereby extending their lifespan while still allowing optimization activities during normal conditions.
Solution Approach 2:
The system converts the potential harm of excessive wear from continuous optimization operations into a benefit by implementing exception-based control. Exceptional situations trigger adjustments that protect the energy storage system, and the system leverages normal operations for cost-effective optimization while using the exception handling mechanism to prevent long-term degradation, thus turning a potential weakness into a strength.
4Adaptability or versatility
If manual control of energy storages is used during exceptional situations, then operational flexibility is improved, but response time and efficiency deteriorate
Solution Approach 1:
The system implements self-service control mechanisms that automatically detect exceptional situations and adjust energy storage operations without requiring manual intervention. The automated system monitors operational conditions, identifies exceptional situations, and modifies capacity limits and optimization activities in real-time, maintaining operational flexibility while eliminating the time delay associated with manual control processes.
Solution Approach 2:
The system employs feedback loops that continuously monitor operational status and automatically adjust control parameters in response to exceptional situations. This closed-loop control provides the flexibility of adaptive response while maintaining fast response times through automated feedback-driven adjustments, eliminating the need for slow manual decision-making processes.
Data Source
AI summary
A computer implemented method for controlling a pool of energy storages. The energy storages of the pool have capacity limits defining how the energy storages are intended to be used. The method is performed by detecting an exception event; selecting one or more energy storages associated with the detected exception event; and temporarily adjusting at least one capacity limit of the selected energy storages in accordance with the detected exception event.