Predictive Building Energy Control for Battery Peak Shaving
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Solution Overview
Problem
Existing building energy systems face challenges in optimizing electric energy storage and discharge from batteries, especially when green energy sources are integrated, leading to inefficiencies in reducing energy costs and managing energy consumption.
Innovation Solution
A building energy system with predictive control that includes HVAC equipment, green energy generation, and a battery, where a predictive controller optimizes energy consumption by determining the optimal use of grid energy, green energy, and battery energy based on time-varying energy pricing and demand charges, using a multi-stage optimization technique to generate control signals for the equipment and battery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If battery energy storage is used to store green energy and grid energy, then energy cost reduction is improved, but optimization difficulty increases due to multiple energy sources
Solution Approach 1:
The patent segments the total energy consumption into source-specific energy components (grid energy component, green energy component, battery energy component). This segmentation allows the controller to independently optimize each component's contribution to total energy consumption, simplifying the optimization problem while achieving cost reduction through strategic energy sourcing and storage.
Solution Approach 2:
The patent dynamically changes the parameters of energy management by adjusting the weighting and prioritization of different energy sources based on pricing signals, battery state of charge, and green energy availability. The controller modifies operational parameters such as charge/discharge rates and energy procurement decisions to optimize costs under varying conditions.
2Loss of energy
If predictive control with multi-stage optimization is implemented, then energy cost optimization is improved, but computational complexity increases
Solution Approach 1:
The patent implements predictive control that performs preliminary optimization calculations based on forecasted energy prices, green energy generation, and load demands. The multi-stage optimization process pre-determines optimal energy management strategies before real-time execution, allowing the system to anticipate cost-saving opportunities and prepare control actions in advance, thereby reducing real-time computational burden while achieving superior cost optimization.
3Loss of energy
If battery discharge is used to reduce peak demand charges, then energy cost savings are improved, but battery capacity requirements increase
Solution Approach 1:
The patent applies partial action by discharging only the necessary portion of battery energy required to reduce peak demand charges, rather than depleting the entire battery capacity. The controller optimizes discharge rates to provide just enough energy during peak periods to achieve cost savings, maintaining sufficient charge levels for other operational needs and avoiding over-provisioning of battery capacity.
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 optimizes energy costs by strategically using battery storage to reduce peak demand charges and minimize energy purchases from the grid during high-price periods, while ensuring efficient energy distribution and consumption across the building energy system.
Implementation Method 1
The battery is configured to store electric energy including at least a portion of the green energy provided by the green energy generation and grid energy purchased from an energy grid and configured to discharge the stored electric energy for use in powering the HVAC equipment
Data Source
AI summary
A predictive controller for a building energy system includes one or more processing circuits configured to obtain a constraint that defines a total electric load to be served by the building energy system at each time step of a time period as a summation of multiple source-specific energy components. The source-specific energy components include a first energy component indicating a first amount of energy to obtain from a first energy source during the time step and a second energy component indicating a second amount of energy to obtain from a second energy source during the time step. The one or more processing circuits are configured to perform a predictive control process subject to the constraint to determine values of the source-specific energy components at each time step of the time period and operate equipment of the building energy system using the values of the source-specific energy components.