Cascaded HVAC Energy Control Using Pre-Cooling and Demand Limiting
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Solution Overview
Problem
Building management systems (BMS) lack integration with smart grid components and data, limiting their ability to efficiently manage energy consumption and reduce costs, especially during peak usage times.
Innovation Solution
A method and system for controlling HVAC power consumption using a feedback controller that adjusts operations based on energy use setpoints, integrating with smart grid data to optimize energy use and reduce demand through demand limiting strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If demand limiting is utilized to reduce energy costs during peak usage times, then energy costs are reduced, but building temperature control may be compromised
Solution Approach 1:
The system performs pre-cooling of the building before demand limiting periods begin, storing thermal energy in the building structure (walls, floors, furniture). This preliminary action allows the building to maintain comfortable temperatures during peak periods without active HVAC operation, resolving the contradiction between reducing energy costs and maintaining temperature control.
Solution Approach 2:
The system continuously monitors building temperature, thermal mass status, and weather forecasts to dynamically adjust pre-cooling strategies and demand limiting operations. This feedback mechanism ensures temperature constraints are maintained while optimizing energy cost reductions during peak periods.
2Use of energy by moving object
If HVAC system operation is reduced during demand limiting periods, then energy consumption is reduced, but temperature constraints may be violated
Solution Approach 1:
The system pre-cools the building and charges thermal mass before demand limiting periods, creating a thermal buffer that allows HVAC reduction without immediately violating temperature constraints. This preliminary energy storage action enables reliable temperature maintenance with reduced HVAC operation.
Solution Approach 2:
The system dynamically adjusts temperature setpoints and HVAC operating parameters based on thermal mass status, weather forecasts, and predicted demand limiting periods. By changing operational parameters rather than simply reducing capacity, the system maintains reliability while reducing energy consumption.
3Use of energy by stationary object
If building pre-cooling is performed to enable demand limiting, then energy cost savings are achieved, but additional energy is consumed during pre-cooling periods
Solution Approach 1:
The system uses weather forecasts, thermal mass monitoring, and historical data to optimize pre-cooling timing and intensity. By providing feedback on actual versus predicted conditions, the system adjusts pre-cooling strategies to minimize additional energy consumption while maximizing overall cost savings during peak periods.
Solution Approach 2:
The system dynamically changes pre-cooling parameters (temperature setpoints, duration, intensity) based on thermal mass status, outdoor temperature predictions, and grid pricing signals. This optimization ensures that pre-cooling energy consumption is minimized while achieving the necessary thermal storage for cost-effective demand limiting.
Data Source
AI summary
A system includes a processing circuit configured to provide an energy use setpoint based on energy data including an energy characteristic and subject to a constraint based on a variable condition of a building. The processing circuit is configured to estimate a value for the energy use setpoint that will result in the variable condition of the building satisfying the constraint and provide a control signal for equipment based on a difference between the energy use setpoint and a measured energy use.