Energy Storage System Optimizing Power via Time-of-Use Profiles
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Solution Overview
Problem
Existing energy storage systems (ESS) operate in a binary fashion, failing to dynamically optimize energy storage and consumption based on time-of-use (TOU) rates, leading to underutilization of battery capacity and increased energy costs due to inadequate handling of sudden changes in consumption habits.
Innovation Solution
The ESS receives inputs to maximize battery charge and adjust power demand, using charge and discharge profiles to set target state of charge for each time period, considering variable energy prices and sources like photovoltaic power, and anticipates off-peak consumption to optimize energy use during high-rate time windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the ESS operates in a binary fashion (charge or discharge based on current power balance), then the system is simple to operate, but the battery capacity is underutilized and energy costs increase
Solution Approach 1:
The ESS control system transitions from a static binary decision-making approach to a dynamic multi-factor optimization system. The controller continuously evaluates multiple variables including forecasted power production, forecasted power consumption, current battery state of charge, and time-of-use rate structures to dynamically adjust charging and discharging decisions. This dynamic approach maximizes battery utilization while responding to changing conditions throughout the day.
Solution Approach 2:
The system performs preliminary actions by forecasting future power production and consumption patterns, and pre-planning optimal charging and discharging schedules. The controller uses forecasted data to anticipate future energy needs and grid conditions, allowing the ESS to charge during optimal periods before high-rate time windows and discharge in advance of peak demand periods, thereby avoiding expensive grid power while maintaining operational simplicity.
2Loss of energy
If the ESS charges during low-rate time windows and discharges during high-rate time windows, then energy costs are reduced, but the system fails to respond to sudden changes in consumption habits
Solution Approach 1:
The ESS control system incorporates continuous feedback loops that monitor actual power production, actual power consumption, and battery state of charge in real-time. This feedback is combined with forecasted data to dynamically adjust charging and discharging decisions. When sudden changes in consumption habits are detected, the system responds by modifying its operational strategy, such as accelerating discharge rates to meet unexpected demand or adjusting charge schedules based on updated forecasts, thereby maintaining both cost efficiency and adaptability.
3Productivity
If the ESS uses forecasted power production and consumption data, then energy management is optimized, but the system complexity increases
Solution Approach 1:
The ESS control system performs self-service by autonomously processing forecasted power production and consumption data, along with real-time sensor data, to generate optimal charging and discharging schedules without requiring complex external control infrastructure. The controller integrates multiple data sources, evaluates various operational scenarios, and automatically implements the optimal strategy, thereby achieving high energy management efficiency while keeping the control architecture relatively simple and self-contained.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach maximizes stored energy for high-rate periods, reduces grid energy consumption costs, and optimizes energy management according to tiered time-based criteria, enhancing efficiency and reducing overall energy expenses for users and utility grids.
Implementation Method 1
PV energy systems are frequently connected to an energy storage system (ESS), typically a direct current (DC) battery
Implementation Method 2
generation of electricity by photovoltaic (PV) cells
Data Source
AI summary
Systems and methods of improving storage and consumption of electricity according to time-based tiered criteria are disclosed. An energy storage system controlled by a processor is optionally connected to a utility power grid, a photovoltaic (PV) power source, and/or electrical loads. Time-of-use (TOU) rates (or similar tiered criteria) are input into the processor, which sets charge and discharge profiles according to the criteria, the arrangement of time windows, and the user's preferences. Energy is stored or discharged according to these profiles. Additionally, the energy storage system may record the production and consumption patterns of the user over time, and use this information to modify the profiles for enhanced performance by allowing discharge during non-peak windows. Benefits include reduced electrical cost to the user, reduced strain on the utility power grid during peak consumption hours, and enhanced performance with regard to any other criteria input into the processor.