Dynamic Demand Threshold Optimization for Battery Lifetime
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Solution Overview
Problem
Conventional energy management systems fail to effectively reduce peak demand charges while maximizing the usable lifetime of energy storage systems, as they often rely on fixed thresholds that lead to battery degradation and increased costs.
Innovation Solution
A method that determines a dynamic monthly demand threshold using mixed-integer linear programming to minimize peak demand charges and extend the battery's lifetime by optimizing the split between power drawn from the electrical grid and the energy storage system, based on load profiles and demand charge rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If a fixed threshold is set for user load and the energy storage system discharges when the load exceeds this threshold, then peak demand charges are reduced, but the battery capacity diminishes and the battery's useful lifetime is shortened
Solution Approach 1:
The patent applies dynamics by transitioning from a fixed threshold to a dynamic, adaptive threshold that changes based on battery state of charge, time of day, and predicted load patterns. The threshold is continuously adjusted to optimize both peak demand charge reduction and battery lifetime extension, allowing the system to respond flexibly to varying conditions rather than following a rigid fixed threshold approach.
Solution Approach 2:
The patent implements parameter changes by modifying the discharge threshold parameter dynamically based on multiple factors including battery state of charge, time of day, seasonal variations, and predicted load patterns. This allows the system to adapt the discharge behavior to current conditions, optimizing the balance between reducing peak charges and preserving battery lifetime through intelligent parameter adjustment.
2Loss of energy
If the energy storage system maintains a high state of charge with large battery throughput, then peak demand charges are minimized, but the battery capacity diminishes rapidly
Solution Approach 1:
The patent applies partial action by discharging the battery only when necessary and beneficial, rather than maintaining constantly high throughput. The system intelligently determines when discharge should occur based on predicted peak periods and current state of charge, avoiding unnecessary discharge cycles that would degrade battery capacity while still achieving sufficient peak charge reduction.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring battery state of charge, discharge cycle history, and performance metrics to adjust discharge behavior. This feedback loop allows the system to learn from past performance and optimize future discharge decisions, balancing peak charge reduction with battery capacity preservation through adaptive control based on real-time and historical data.
Data Source
AI summary
Systems and methods for power management include determining a demand threshold by solving an optimization problem that minimizes peak demand charges and maximizes a usable lifetime for a power storage system. Power is provided to a load from an electrical grid when the load is below the demand threshold and from a combination of the electrical grid and the power storage system when the load is above the demand threshold.