HVAC Temperature Setpoint Scheduling to Reduce Peak Electrical Demand
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Solution Overview
Problem
Commercial and institutional facilities face high electrical power bills due to peak demand charges, which can be half or more of their total electrical power bill, making it desirable to reduce peak electrical demand costs without requiring direct control over HVAC system equipment.
Innovation Solution
A method that generates a baseline electrical demand profile using historical and weather data, identifies peak demand values, and implements a policy with temperature setpoint adjustments across pre-cooling, drift, and curtailment periods to manage peak demand by changing temperature setpoints, thereby reducing the load on cooling equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If direct control over HVAC system equipment is implemented to reduce peak demand, then peak electrical demand can be reduced, but device complexity and difficulty of operation increase
Solution Approach 1:
The patent introduces a policy engine as an intermediary layer between the building owner and the HVAC system. Instead of directly controlling complex HVAC equipment, the policy engine generates simplified temperature setpoint schedules that indirectly control the HVAC system's peak demand. This mediator translates high-level policy intentions into actionable control parameters without requiring the building owner to understand HVAC system complexity.
Solution Approach 2:
The HVAC system is enabled to self-regulate peak demand through automated policy execution. The system monitors electrical demand, automatically generates appropriate temperature setpoint adjustments, and implements control actions without requiring external intervention or complex user configuration. This self-service capability reduces both device complexity and operational difficulty while achieving peak demand reduction.
2Measurement precision
If direct control over HVAC compressors is implemented, then peak demand control precision improves, but ease of operation deteriorates
Solution Approach 1:
The policy engine serves as an intermediary that translates simple policy inputs into precise control actions. Building owners specify high-level objectives (e.g., reduce peak demand by 10%), and the policy engine automatically generates detailed temperature setpoint schedules with precise timing and magnitude adjustments. This intermediary layer maintains operational simplicity while achieving precise peak demand control through automated optimization.
Solution Approach 2:
The system achieves precise peak demand control by dynamically adjusting temperature setpoint parameters rather than directly controlling compressor operations. The policy engine modifies setpoint temperature, duration, and timing parameters to precisely control when and how much peak demand is reduced, without requiring direct intervention in complex HVAC control parameters. This parameter-based approach maintains ease of operation while achieving precise demand management.
Data Source
AI summary
A method for reducing electrical costs relating to conditioning air in a building with an air handling unit, the method comprising: identifying a starting hour and a corresponding starting temperature; identifying an ending hour and a corresponding ending temperature; identifying a plurality of time increments between the starting hour and the ending hour; for each of the plurality of time increments, identifying a plurality of temperature setpoint nodes; determining a least cost pathway from the starting temperature at the start hour to the ending temperature at the ending hour across the plurality of temperature setpoint nodes; publishing a temperature setpoint schedule for each time increment based on the temperature setpoint nodes included in the least cost pathway; and operating the air handling unit from the starting hour to the ending hour based on the published temperature setpoint schedule.