Central plant control system with time dependent deferred load
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Solution Overview
Problem
Existing HVAC systems face challenges in accurately predicting power consumption, especially when the status of devices in the energy plant changes, making it difficult to assign loads efficiently across different time periods.
Innovation Solution
A controller system that determines the operating state of an energy plant based on thermal energy load allocation data, predicting power consumption by considering primary, deferred, and off-states, and adjusts operating parameters to optimize energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the status of devices in the energy plant changes, then the system can adapt to different operational conditions, but it becomes difficult to measure flow through load devices and determine power consumption
Solution Approach 1:
The system performs preliminary actions by predicting power consumption for multiple candidate operating parameters before actual operation. The controller predicts thermodynamic states and power consumption for each candidate set of operating parameters in advance, allowing the system to prepare multiple scenarios and select the optimal one, thereby avoiding measurement difficulties during actual operation when device status may change.
Solution Approach 2:
The system uses feedback by comparing predicted power consumptions of different candidate sets of operating parameters. The controller predicts power consumption for each candidate set, compares these predictions, and selects the set with the lowest predicted power consumption. This feedback mechanism allows the system to adapt to changing device statuses by continuously evaluating and selecting optimal operating parameters.
2Loss of energy
If the controller predicts power consumption for multiple candidate sets of operating parameters, then it can select the optimal set to minimize power consumption, but the computational complexity and time required for prediction increases
Solution Approach 1:
The system applies partial action by evaluating multiple candidate sets of operating parameters rather than exhaustively evaluating all possible combinations. The controller generates a plurality of candidate sets and predicts power consumption for each, but does not need to evaluate every possible operating parameter combination. This selective evaluation of representative candidates balances computational feasibility with finding sufficiently optimal solutions.
Solution Approach 2:
The system uses parameter changes by varying operating parameters (such as operating capacity of HVAC devices) across different candidate sets. The controller predicts thermodynamic states and power consumption for each candidate set with different operating parameters, allowing it to find the optimal balance between computational complexity and energy minimization by systematically exploring parameter variations.
3Use of energy by stationary object
If the supply device is turned off during a time period to reduce power consumption, then energy costs decrease, but the ability to meet thermal energy load during that period is reduced
Solution Approach 1:
The system performs preliminary action by predicting and planning operating states across multiple time periods in advance. The controller determines an operating state for each time period based on predicted power consumption, allowing it to proactively schedule when the supply device should be off or in deferred state to minimize overall power consumption while ensuring thermal energy load requirements are met through advance planning.
Solution Approach 2:
The system applies dynamics by allowing the operating state of the supply device to change dynamically across different time periods. The controller can transition the supply device between enabled states (primary state), disabled states (deferred state), or off-states based on thermal energy load allocation data and predicted power consumption for each time period, optimizing energy usage while maintaining reliability.
Data Source
AI summary
In one aspect, a system for operations an energy plant obtains thermal energy load allocation data indicating time dependent thermal energy load of the energy plant. The system determines, for a time period, an operating state of the energy plant from a plurality of predefined operating states based on the thermal energy load allocation data. The system determines operating parameters of the energy plant according to the determined operating state. The system operates the energy plant according to the determined operating parameters.