Adaptive Energy Storage Control for Peak Demand Reduction
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Solution Overview
Problem
Conventional energy management systems face challenges in reliably managing intermittent energy sources and peak demand, leading to inefficiencies and increased costs due to forecasting errors and misalignment between energy generation and demand.
Innovation Solution
An energy management system with a system control unit and device control unit that adaptively determines and adjusts the charge/discharge profile of an energy storage device based on forecasted and actual energy generation and demand models, using advanced algorithms to optimize energy utilization and reduce peak demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional energy management systems use scheduling capability to forecast variable energy generation and demand, then energy optimization is improved, but forecasting accuracy deteriorates due to large errors from weather patterns and aggregated small errors
Solution Approach 1:
The system performs preliminary actions by charging the energy storage device during off-peak hours when energy is cheaper and more abundant, and discharging during peak demand periods. This advance preparation allows the system to optimize energy costs and reduce peak demand charges without relying solely on accurate real-time forecasting.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor actual energy generation and consumption patterns, comparing them against forecasts. This feedback loop enables the system to learn from forecasting errors and adjust future scheduling decisions, improving energy optimization over time despite initial forecasting inaccuracies.
2Reliability
If energy storage devices are used to compensate for misalignment of energy generation and demand, then energy reliability is improved, but device complexity increases due to integration of control systems and forecasting models
Solution Approach 1:
The energy storage device serves multiple functions simultaneously: it stores energy for later use, smooths out generation-demand misalignment, provides backup power, and enables participation in demand response programs. This multi-functionality justifies the added complexity by delivering diverse benefits from a single integrated system.
Solution Approach 2:
The control system acts as an intermediary between the variable energy source, energy storage device, and load. It manages the complexity of coordinating these components, handling forecasting, scheduling, and real-time control, thereby enabling reliable energy management without requiring direct complex interactions between all system elements.
3Ease of operation
If conventional energy management systems operate energy storage devices based on forecasted schedules, then operational simplicity is improved, but adaptability deteriorates when forecast errors occur
Solution Approach 1:
The system transitions from static, pre-determined schedules to dynamic, real-time control. The energy storage device operates based on current system conditions, generation availability, and demand patterns rather than rigid forecasts. This dynamic operation maintains simplicity through automated control while achieving high adaptability to changing conditions.
Solution Approach 2:
The control system automatically adjusts energy storage operations based on real-time data without requiring manual intervention. It self-corrects for forecast errors by monitoring actual generation and consumption patterns and adapting its schedule accordingly, maintaining operational simplicity while achieving adaptability through autonomous decision-making.
Data Source
AI summary
Systems, methods and apparatuses are provided for reducing peak energy demand and to smooth intermittent energy profiles from onsite variable energy sources and loads. Some embodiments use system level and device level analysis and optimization to adaptively adjust the operation of a behind the meter energy storage (BMES) to smooth out energy generation variabilities and follow a reference load signal, including at short time resolutions.