Building Thermal Mass Grid Integration Optimization
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Solution Overview
Problem
Current building optimization systems are limited in their ability to address the inefficiencies in energy consumption and generation, particularly in large commercial buildings, as they fail to integrate with the dynamic and complex electric grid operations and markets, leading to suboptimal energy use and inefficiencies.
Innovation Solution
A system that uses a building model to predict behavior based on future conditions and an optimization algorithm to determine control inputs, interacting with building automation systems to manage energy use and generation, considering various environmental and building dynamics to optimize energy efficiency and grid integration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If building optimization systems minimize end-use consumption at the retail meter, then building energy costs are reduced, but the systems fail to account for grid-level inefficiencies and complexity in electric system operations
Solution Approach 1:
The patent merges building-level energy optimization with grid-level operations by integrating the building control system with electric system operations and markets. The optimization system considers both end-use consumption and grid conditions, combining previously separate optimization layers into a unified system that operates across the building-grid interface.
Solution Approach 2:
The optimization system performs multiple functions simultaneously: it minimizes building energy costs, accounts for grid-level inefficiencies, responds to varying electricity prices, and integrates with electric system operations. This multi-functional approach allows a single system to address both building-specific and grid-wide objectives.
2Reliability
If conventional demand response technology curtails customer demand during grid stress, then grid reliability is maintained, but efficiency potentials on the supply side remain unlocked
Solution Approach 1:
The system performs preliminary actions by pre-cooling buildings during off-peak hours when electricity prices are low and grid stress is minimal. This anticipatory approach stores thermal energy in building mass, allowing the building to maintain comfortable temperatures during peak demand periods without active cooling, thereby reducing peak demand before grid stress occurs.
Solution Approach 2:
The optimization system dynamically adjusts building operations based on real-time and forecasted grid conditions, electricity prices, and weather patterns. Rather than static demand response curtailment, the system continuously adapts control strategies to optimize both building comfort and grid efficiency under varying conditions.
3Use of energy by moving object
If building control systems optimize for lowest energy expense at the meter, then building operating costs are minimized, but they cannot respond to varying values of kWh produced from different generating resources
Solution Approach 1:
The system incorporates feedback loops that continuously monitor electricity prices, grid conditions, and building responses. This feedback enables the optimization algorithm to learn from past performance and adjust control strategies in real-time, responding to varying values of kWh produced from different generating resources while minimizing building energy expenses.
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 and peak demand by shifting energy use to lower price hours, harnessing building thermal mass to stabilize grid operations, and providing grid services, thereby enhancing energy efficiency and reducing environmental impact.
Implementation Method 1
harnessing building thermal mass to stabilize grid operations
Data Source
AI summary
Buildings or facilities containing energy consuming or energy generating devices may be optimized for efficient energy usage and distribution. Energy consumption or generation by a building or components may be controlled by a system comprising a building model for predicting behavior of the building given predicted future conditions and possible control inputs. An optimization component running an optimization algorithm in conjunction with the building model may evaluate the predicted building behavior in accordance with at least one criterion and determine a desired set of control inputs. Commercial building thermal mass may be harnessed to continuously and optimally integrate large commercial building HVAC operations with electric grid operations and markets in large metropolitan areas. The service may be deployed using scalable, automated, web-based technology.