Battery Charge Control for Demand Charge and Response Management
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Solution Overview
Problem
Behind the meter energy management systems struggle to optimize battery charge levels to maximize power demand savings due to competing optimizations between demand charges, time-of-use charges, and demand response rewards, often failing to account for greater savings in other factors.
Innovation Solution
A method and system that predict demand charge thresholds based on historical load, using a multi-layer power demand management controller to optimize battery charge levels by concurrently managing demand charge rates, time-of-use rates, and demand response rewards, with a rolling time horizon optimizer and real-time controller to determine load reduction capability factors and control battery charge levels accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If BTM-EMS optimizes for demand charge reduction, then demand charge savings are improved, but time-of-use charge savings and demand response rewards are worsened due to competing optimizations
Solution Approach 1:
The patent combines multiple optimization objectives (demand charge reduction, time-of-use charge reduction, and demand response reward maximization) into a unified multi-objective optimization framework. The system simultaneously considers all three rate factors and determines battery charge/discharge schedules that optimize the combined financial outcome across all factors, rather than optimizing each factor separately.
Solution Approach 2:
The optimization system is designed to handle multiple rate structures and optimization goals within a single unified framework. The multi-objective optimizer can adapt to different utility rate structures (demand charges, time-of-use rates, demand response programs) and simultaneously optimize for multiple financial benefits, making the system universally applicable to various utility pricing schemes.
2Loss of energy
If battery charge levels are optimized for peak demand reduction, then demand charge rates are reduced, but demand response reward opportunities are missed
Solution Approach 1:
The system dynamically adjusts battery charge/discharge schedules based on real-time and forecasted conditions, including predicted peak demand events and demand response reward opportunities. The multi-objective optimizer continuously re-evaluates the optimal battery operation strategy, balancing peak demand reduction with demand response participation based on current grid conditions and utility rate structures.
Solution Approach 2:
The system uses load forecasting and demand prediction to proactively plan battery charge/discharge schedules in advance. By predicting future peak demand events and demand response reward opportunities, the system can pre-charge batteries during off-peak periods to ensure adequate energy availability when both peak demand reduction and demand response rewards are simultaneously beneficial.
3Loss of energy
If multiple optimization objectives are pursued simultaneously, then overall financial savings are improved, but system complexity increases
Solution Approach 1:
The optimization system is structured in hierarchical layers with different time horizons. The monthly layer handles long-term demand charge optimization, the daily layer manages day-ahead scheduling considering time-of-use rates, and the real-time layer executes immediate battery control actions. This segmentation allows complex multi-objective optimization to be broken down into manageable sub-problems that can be solved sequentially.
Solution Approach 2:
The system introduces a temporal dimension to the optimization by implementing multi-layer optimization across different time horizons (monthly, daily, real-time). This transforms the complex multi-objective optimization problem into a series of simpler optimizations across time layers, where each layer operates with appropriate forecast accuracy and computational requirements for its time scale.
Data Source
AI summary
Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.