HVAC Energy Budget Control for Peak Demand and Comfort
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Solution Overview
Problem
Building management systems (BMS) face challenges in integrating with smart grid components and data effectively, particularly in managing energy consumption and demand, leading to inefficiencies in energy use and increased costs.
Innovation Solution
A method and system that utilize a feedback controller to adjust the operation of HVAC systems based on energy use setpoints, incorporating a processing circuit with an error analyzer and error corrector to generate manipulated variables, and integrating with smart grid data for demand limiting strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If demand limiting is utilized to reduce energy costs during peak usage times, then energy costs are reduced, but building comfort and temperature control deteriorate
Solution Approach 1:
The system performs pre-cooling of the building before peak usage times by increasing HVAC operation during off-peak hours. This stores thermal energy in the building structure (walls, floors, furniture) which then passively maintains comfortable temperatures during peak periods when demand limiting is applied, thus reducing energy costs without sacrificing comfort
Solution Approach 2:
The system continuously monitors building temperature, HVAC performance, and energy consumption data, then uses this feedback to dynamically adjust pre-cooling strategies and demand limiting levels. This closed-loop control ensures optimal balance between energy cost reduction and comfort maintenance based on real-time conditions
2Productivity
If building management systems integrate with smart grid components and data, then energy management effectiveness is improved, but system complexity increases
Solution Approach 1:
The building management system is designed to perform multiple functions: traditional HVAC control, energy consumption monitoring, smart grid data integration, predictive analytics, and demand response management. By consolidating these diverse functions into a single unified platform, the system improves energy management effectiveness while avoiding the complexity that would arise from multiple separate systems
Solution Approach 2:
The system employs an intermediary layer that sits between the building's internal systems and the external smart grid infrastructure. This intermediary handles data translation, protocol conversion, and coordinated control, simplifying the integration process and reducing the complexity burden on both the building management system and smart grid components
3Loss of energy
If pre-cooling strategies are implemented before peak usage times, then energy costs are reduced during peak periods, but energy consumption increases during off-peak hours
Solution Approach 1:
The system dynamically adjusts the pre-cooling strategy by changing operational parameters such as temperature setpoints, HVAC runtime, and cooling intensity based on forecasted peak demand, weather conditions, and building occupancy patterns. This optimization ensures that pre-cooling is applied only when and where it will most effectively reduce peak period energy costs without unnecessarily increasing off-peak consumption
Solution Approach 2:
The system applies pre-cooling to only the portions of the building that will be occupied during peak periods, rather than uniformly pre-cooling the entire building. This partial action approach reduces off-peak energy consumption while still achieving the goal of reducing peak period energy costs through targeted thermal storage in occupied zones
Data Source
AI summary
Systems and methods for limiting power consumption by a heating, ventilation, and air conditioning (HVAC) subsystem of a building are shown and described. A feedback controller is used to generate a manipulated variable based on an energy use setpoint and a measured energy use. The manipulated variable may be used for adjusting the operation of an HVAC device.