HVAC Energy Management Using Pricing and Comfort Models
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Solution Overview
Problem
Increasing energy demands and variability in energy sources, such as photovoltaic and wind power, challenge the consistency and reliability of energy supply, necessitating efficient management systems to optimize energy usage and costs.
Innovation Solution
An energy management system that includes a controller receiving inputs like time, current energy generation, stored energy, pricing, outdoor temperature, and user preferences to optimize HVAC operations by determining the optimal temperature set point that balances energy cost and comfort using a discomfort-cost trade-off function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If programmable thermostats and automatic timers are used to reduce energy consumption, then energy usage is reduced, but system complexity increases
Solution Approach 1:
The patent combines multiple energy management functions (thermostat control, timer control, renewable energy monitoring, and cost optimization) into a single integrated energy management system. The controller consolidates these previously separate programmable devices into one unified system that manages HVAC operations while monitoring renewable energy sources and pricing signals, reducing the number of separate devices needed while maintaining or improving energy efficiency.
Solution Approach 2:
The energy management controller performs multiple functions simultaneously: it acts as a thermostat, timer, renewable energy monitor, and cost optimization engine. This multi-functional approach eliminates the need for separate dedicated devices for each function, reducing system complexity while achieving comprehensive energy management including load shifting, peak shaving, and optimal HVAC operation based on renewable availability and pricing.
2Reliability
If renewable energy sources are added to generate and store energy, then energy reliability is improved, but system complexity and cost increase
Solution Approach 1:
The system continuously monitors renewable energy generation output and uses this feedback to dynamically adjust HVAC operation. The controller receives real-time data on available renewable energy and automatically modulates heating and cooling loads to match supply availability, maximizing self-consumption of renewable energy and reducing reliance on grid power without requiring complex manual intervention or additional sophisticated storage systems.
Solution Approach 2:
The energy management system dynamically adjusts HVAC setpoints and operation schedules based on real-time renewable energy availability and electricity pricing conditions. Rather than using fixed programmable schedules, the system continuously adapts its control strategy to match varying renewable generation patterns and price signals, optimizing energy usage without requiring oversized storage capacity or complex manual reconfiguration.
3Ease of operation
If HVAC systems operate at optimal comfort temperatures continuously, then thermal comfort is maintained, but energy consumption increases
Solution Approach 1:
The system performs preliminary conditioning of spaces before occupancy or peak demand periods by pre-heating or pre-cooling rooms during times when renewable energy is abundant or electricity prices are low. This advance preparation allows the HVAC system to reduce or suspend operation during high-cost or low-renewable periods while maintaining comfort, effectively shifting load to more favorable conditions without compromising thermal comfort when needed.
Solution Approach 2:
The energy management system dynamically changes HVAC operational parameters including setpoints, run schedules, and equipment staging based on real-time conditions. Rather than maintaining fixed optimal comfort temperatures continuously, the system adjusts temperature setpoints within acceptable comfort ranges during different periods, modulating HVAC operation to match renewable availability and pricing while maintaining comfort through adaptive parameter changes rather than continuous full-capacity operation.
Data Source
AI summary
An energy management system includes a controller that receives a signal representative of current electrical generation. A storage source stores energy from the current electrical generation associated with the controller. The controller predicts the amount of storage of energy in said storage source over time, receives pricing information, includes a model of a temperature system of a building, and includes a comfort model of a user. The controller selects a desirable temperature based upon the current electrical generation, the stored energy, the pricing information, the building model, and the comfort model.