Building Energy Cost Optimization via Dynamic Setpoint Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for minimizing energy costs in building systems are inadequate in handling variable pricing scenarios, particularly critical-peak pricing (CPP) and real-time pricing (RTP), and struggle to account for multiple demand charge regions and system disturbances without requiring model retraining.
Innovation Solution
A method that uses an optimization procedure to determine optimal power usage in building systems, incorporating energy models, system state information, and time-varying pricing information to minimize total energy cost, while adhering to temperature and demand charge constraints, using a cascaded model predictive control system with inner and outer controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional energy cost minimization methods are used, then simple pricing scenarios can be handled, but they cannot handle rapidly changing RTP pricing scenarios or CPP pricing scenarios with multiple demand charge regions
Solution Approach 1:
The patent implements a dynamic optimization approach where the control system continuously adjusts power usage based on real-time pricing signals. The system uses receding horizon optimization to adapt to rapidly changing RTP pricing scenarios and handles multiple demand charge regions in CPP scenarios, transforming a static control approach into a dynamic one that responds to varying price conditions
Solution Approach 2:
The patent segments the pricing periods into distinct regions (on-peak, partial-peak, off-peak, and critical-peak periods) and applies different optimization strategies to each segment. This segmentation allows the system to handle complex pricing scenarios by breaking them into manageable time-based segments with specific control objectives for each
2Loss of energy
If pre-cooling methods are used to minimize demand charges, then power use during peak periods can be reduced, but zone temperatures may fall below comfortable levels or demand charges are not significantly reduced
Solution Approach 1:
The patent applies preliminary action through pre-cooling strategies that lower zone temperatures before anticipated peak pricing periods. The optimization algorithm determines optimal pre-cooling schedules that store thermal energy in the building's thermal mass, reducing peak power demand while ensuring temperatures remain within comfort bounds through constraint-based optimization
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor zone temperatures and adjust control actions to maintain comfort constraints. The optimization algorithm uses real-time temperature measurements to modify power usage decisions, ensuring that pre-cooling actions do not push temperatures below comfortable levels while still achieving demand charge reduction
3Loss of energy
If equipment is turned off to respond to variable pricing scenarios, then energy cost can be reduced, but building temperature constraints cannot be maintained
Solution Approach 1:
The patent uses dynamic optimization to continuously adjust equipment operation based on pricing signals and temperature constraints. Rather than simple on/off control, the system dynamically modulates equipment operation to minimize energy cost while ensuring temperature constraints are satisfied at all times through real-time optimization
Solution Approach 2:
The system changes operational parameters (such as equipment setpoints, modulation levels, and operating schedules) in response to pricing scenarios. By adjusting these parameters dynamically, the system can reduce energy cost during high-price periods while maintaining temperature constraints through optimized parameter selection rather than simple equipment shutdown
4Use of energy by moving object
If large expensive non-standard equipment is used for energy storage, then future cooling loads can be met, but system cost and complexity increase significantly
Solution Approach 1:
The patent exploits the building's existing thermal mass as a natural energy storage medium. Instead of installing dedicated thermal energy storage equipment, the system uses the building's inherent thermal capacity (walls, floors, furniture) to store cooling energy during low-price periods and release it during high-price periods, making the building itself serve the energy storage function
Solution Approach 2:
The system makes the building's thermal mass serve multiple functions: it acts as both the building structure and the energy storage medium. This multi-functionality eliminates the need for separate storage equipment, reducing system complexity and cost while still providing the energy storage capability needed to respond to variable pricing scenarios
Data Source
AI summary
A controller is configured to use an energy cost function to predict a total cost of energy purchased from an energy provider as a function of one or more setpoints provided by the controller. The energy cost function includes a demand charge term defining a cost per unit of power corresponding to a maximum power usage of the building system. The controller is configured to linearize the demand charge term by imposing demand charge constraints and to mask each of the demand charge constraints that applies to an inactive pricing period. The controller is configured to determine optimal values of the one or more setpoints by performing an optimization procedure that minimizes the total cost of energy subject to the demand charge constraints and to provide the optimal values of the one or more setpoints to equipment of the building system that operate to affect the maximum power usage.