Predictive HVAC Battery Control for Grid and Green Energy
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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 incorporating HVAC equipment, green energy generation, and a predictive controller that optimizes energy consumption by defining energy components from grid, green, and battery sources, using energy pricing data to determine optimal power setpoints and temperature control signals for HVAC and battery operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If batteries are used to store energy during low-price periods and discharge during high-price periods, then energy cost is reduced, but optimization difficulty increases when green energy generation is integrated
Solution Approach 1:
The patent segments the energy sources into distinct components (grid energy, green energy, battery energy) and optimizes each separately while maintaining their interrelationships. This segmentation allows the system to handle the complexity of multiple energy sources by breaking down the optimization problem into manageable source-specific components, thereby reducing overall optimization difficulty while maintaining cost reduction benefits.
Solution Approach 2:
The system dynamically changes parameters such as energy pricing data, green energy generation forecasts, and battery state of charge to optimize energy management. By continuously adjusting these parameters based on real-time conditions, the system resolves the contradiction between reducing energy costs and managing optimization complexity through adaptive parameter modification.
2Adaptability or versatility
If green energy generation is used to supplement grid energy, then energy sustainability is improved, but optimization of battery storage and discharge becomes more difficult
Solution Approach 1:
The patent applies segmentation by treating green energy generation as a separate, identifiable component in the energy mix. This allows the system to track and optimize green energy usage independently while maintaining its integration with grid energy and battery storage, thereby preserving energy sustainability without proportionally increasing optimization difficulty.
Solution Approach 2:
The energy management system is designed with multi-functionality to handle diverse energy sources (grid, green, battery) through a unified optimization framework. This universal approach allows the system to manage multiple energy types simultaneously using consistent optimization principles, preventing complexity from escalating despite the diversity of energy sources.
3Loss of energy
If predictive control is implemented to optimize energy components, then energy cost reduction is achieved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by forecasting green energy generation and energy pricing in advance, allowing proactive optimization decisions. This predictive approach enables the system to prepare optimization strategies ahead of time, reducing actual-time computational complexity while achieving cost reduction through advance planning.
Solution Approach 2:
The patent implements feedback mechanisms that use actual energy consumption data, battery state of charge, and green energy generation to continuously refine optimization decisions. This feedback loop allows the system to learn from past performance and adjust control strategies, achieving cost reduction while managing complexity through adaptive rather than purely deterministic control.
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
The system effectively reduces energy costs by optimizing energy usage, managing peak demand, and integrating green energy sources, thereby enhancing the efficiency and cost-effectiveness of energy management in building energy systems.
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 building energy system includes HVAC equipment, green energy generation, a battery, and a predictive controller. The HVAC equipment provide heating or cooling for a building. The green energy generation collect green energy from a green energy source. The battery stores 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 discharges the stored electric energy for use in powering the HVAC equipment. The predictive controller generates a constraint that defines a total energy consumption of the HVAC equipment at each time step of an optimization period as a summation of multiple source-specific energy components and optimizes the predictive cost function subject to the constraint to determine values for each of the source-specific energy components at each time step of the optimization period.