Behind-the-meter EMS demand charge minimization
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Solution Overview
Problem
Conventional behind-the-meter energy storage management systems face challenges in reducing monthly demand charges and extending battery lifetime, as they often lead to battery degradation due to improper utilization and inaccurate demand threshold selection, which results in shortened battery life and increased operational costs.
Innovation Solution
A method and system for controlling behind-the-meter energy storage management systems that determine an optimal monthly demand charge threshold by clustering daily load profiles and using a real-time controller to adjust charging and discharging profiles, thereby minimizing demand charges and extending battery life through battery-optimized operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the EMS maintains sufficient energy in the battery to meet unexpected large power demands, then demand charge reduction is achieved, but the average battery State-of-Charge increases causing degradation in battery capacity and performance
Solution Approach 1:
The patent implements a dynamic demand threshold that adapts to seasonal variations and historical load patterns. The threshold is adjusted monthly based on clustering analysis of daily load profiles, allowing the system to respond optimally to changing conditions rather than using a static threshold. This dynamic adjustment prevents overcharging the battery during periods when high thresholds would cause degradation, while still providing demand charge reduction when needed.
Solution Approach 2:
The system changes the operational parameters of the battery by controlling the State-of-Charge to remain below a calculated threshold. The threshold itself is a parameter that is continuously optimized based on load profile analysis. By changing these parameters dynamically rather than maintaining fixed values, the system achieves demand charge reduction without causing battery degradation from sustained high State-of-Charge conditions.
2Reliability
If a high demand threshold is set to reduce demand charges, then more demand charge savings are achieved, but the battery State-of-Charge increases leading to faster degradation
Solution Approach 1:
The system performs preliminary analysis of historical load profiles and seasonal patterns before setting the demand threshold. By clustering daily load profiles and analyzing historical data in advance, the system determines an optimal threshold before the billing period begins. This preliminary action ensures the threshold is set appropriately to achieve demand charge savings without unnecessarily high values that would cause battery degradation.
Solution Approach 2:
The system uses feedback from historical performance data and load profile analysis to continuously optimize the demand threshold. By monitoring actual demand charge savings and battery State-of-Charge levels, the system adjusts the threshold in subsequent periods to maintain optimal performance. This feedback mechanism prevents the threshold from being set too high, which would cause battery degradation, while still achieving adequate demand charge reduction.
3Reliability
If the battery is used to meet peak power demands, then demand charge reduction is achieved, but the frequent charging and discharging cycles accelerate battery degradation
Solution Approach 1:
The patent introduces a demand threshold as an intermediary parameter that mediates between demand charge reduction goals and battery protection. Rather than directly controlling charging/discharging based on every peak demand event, the threshold acts as a filter that only triggers battery discharge when truly necessary. This intermediary mechanism reduces unnecessary charge cycles while still achieving demand charge reduction, thereby extending battery cycle life.
Solution Approach 2:
The system applies partial action by not responding to every peak demand event with battery discharge. Instead, it selectively responds only to peaks that exceed the optimized threshold, leaving smaller peaks to be handled by other means or accepted as unavoidable. This partial response strategy reduces the total number of charge cycles compared to responding to all peaks, thereby extending battery cycle life while still achieving significant demand charge reduction.
Data Source
AI summary
Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month. A mean demand threshold for the dominant group is selected as the monthly demand threshold, and continuous battery-optimized demand charge minimized EMS operations are provided based on the monthly demand threshold using a real-time controller configured for overriding the optimal charging/discharging profiles when a monthly demand threshold violation is detected.