Business-Driven Energy Storage Control for Backup Power Priority
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Solution Overview
Problem
Existing energy management systems fail to address the unique business-critical functions of facilities like restaurants, particularly in managing backup energy and voltage fluctuations, lacking the ability to prioritize energy storage based on business needs and ensuring uninterrupted operations.
Innovation Solution
A system that utilizes predictive analytics, machine learning, and artificial intelligence to prioritize energy storage and discharge based on business-critical functions, allocating energy reserves for key tasks while optimizing energy savings, and managing various energy sources to ensure availability and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If energy storage is optimized for maximum energy savings, then energy costs are reduced, but business-critical functions may suffer from energy shortages
Solution Approach 1:
The system segments energy storage into priority-based allocations, dividing the battery capacity into reserved portions for critical functions and available portions for cost-saving opportunities. This segmentation allows simultaneous optimization of both reliability and energy cost by preventing over-discharge during critical periods while capturing savings during non-critical periods.
Solution Approach 2:
The system dynamically changes the discharge rate parameter based on predicted business needs and energy prices. By adjusting discharge rates in response to changing conditions, the system can maximize energy savings when business impact is low while ensuring adequate power availability when business operations are critical, thus resolving the contradiction between cost reduction and reliability.
2Reliability
If energy storage capacity is increased to ensure business continuity, then reliability is improved, but energy savings opportunities are reduced
Solution Approach 1:
The system performs preliminary actions by predicting future energy needs and prices to determine optimal charging and discharging strategies in advance. This predictive capability allows the system to prepare energy reserves for anticipated critical periods while capturing savings during predicted low-demand or low-price periods, thus achieving both reliability and cost efficiency without requiring excessive storage capacity.
3Loss of energy
If energy management follows traditional peak demand reduction methods, then energy costs are reduced, but business-critical operations may be impacted
Solution Approach 1:
The system dynamically adjusts energy discharge based on real-time and predicted business conditions rather than following static peak demand reduction strategies. By continuously adapting discharge rates to match actual business needs and energy price signals, the system avoids impacting critical operations while still achieving cost savings through intelligent load management during non-critical periods.
Data Source
AI summary
A system and method of energy resource reservation for an energy storage system based on business requirements is presented. Energy reservations are created and prioritized based on both energy savings potential and business continuity needs.