Utility Plant Control via Dynamic Interval Segmentation
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Solution Overview
Problem
Current energy management systems for utility plants struggle to efficiently balance varying energy demands in building complexes due to inflexible scheduling intervals and inadequate consideration of device response times and capacities, leading to suboptimal operation and increased costs.
Innovation Solution
A computer-implemented method that divides the utility plant scheduling interval into control intervals, adjusts device set points based on forecasted energy demands, and optimizes device operation to minimize energy costs by considering response times and capacities, using a nonlinear controller to dynamically allocate energy production among multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed scheduling intervals (15-60 minutes) are used for utility plant operation, then operational simplicity is maintained, but the system cannot respond to real-time energy demand variations
Solution Approach 1:
The patent divides the scheduling interval into multiple control intervals, allowing the system to maintain simplicity at the scheduling level while achieving fine-grained control at the execution level. Each control interval handles specific demand variations, enabling responsive operation without complicating the overall scheduling framework.
Solution Approach 2:
The system transitions from static fixed-interval scheduling to dynamic control by adjusting device set points in real-time based on actual energy demand. The controller dynamically modifies operation parameters within each control interval, enabling the plant to adapt to varying demand conditions while maintaining a structured scheduling approach.
2Device complexity
If a fixed sequence of switching devices is used to reach balance, then control logic is simple, but device response times and capacities are not optimized
Solution Approach 1:
The system pre-determines an optimal sequence for switching devices based on their response times and capacities before actual demand changes occur. This preliminary optimization allows the controller to quickly respond to demand variations by following a pre-calculated switching sequence, maintaining simple real-time control logic while achieving efficient energy balancing.
Solution Approach 2:
The patent optimizes device operation by adjusting set points and switching sequences based on device-specific parameters such as response times and capacities. The controller selects and adjusts parameters for each device dynamically, ensuring that devices with faster response times are utilized first, thereby optimizing energy balancing efficiency without requiring complex real-time control algorithms.
3Speed
If device set points are adjusted without considering response times, then control responsiveness is improved, but device capacities may be exceeded or wasted
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor device response times and capacity utilization. Based on this feedback, the controller adjusts set points for each device to ensure that capacity limits are not exceeded while maintaining rapid response to demand changes. The feedback loop enables the system to learn and optimize device performance characteristics over time.
Solution Approach 2:
The controller pre-calculates optimal set points for each device considering their response times and capacities before demand changes occur. This preliminary action ensures that when demand variations are detected, the controller can immediately apply pre-optimized set points that respect device capacity constraints while achieving rapid response, eliminating the need for complex real-time capacity checking.
Data Source
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AI summary
A computer implemented method optimizes a utility plant having multiple devices to convert input energy into output energy for a building. The method includes dividing a utility plant scheduling interval into several control intervals and for each control interval, obtaining a difference between a desired and a measured in-building condition controlled by output power from the utility plant, obtaining current values of multiple factors that influence operation of the utility plant, determining a new power demand of the building expected to decrease the difference, and finding set points for the multiple devices that satisfy the new power demand, take into account response times of the devices and their capacities, and optimize utility plant operation costs.