Central Plant Load Optimization With Thermal Storage Scheduling
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Solution Overview
Problem
Central plants face challenges in optimizing the distribution of building thermal energy loads across multiple subplants to minimize energy costs, particularly when integrating thermal energy storage with real-time pricing and demand charges, due to complex control technology requirements.
Innovation Solution
An optimization system that uses a high-level optimization module to generate an objective function based on utility rate data and resource consumption, optimizing the distribution of energy loads across subplants through linear programming, and a cascaded optimization approach that splits the problem into high-level and low-level optimizations to determine optimal operating statuses for individual devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional control technology is used to distribute loads across multiple subplants, then equipment operation is simple, but energy cost optimization is insufficient
Solution Approach 1:
The control system is segmented into two distinct levels: high-level optimization that determines optimal load distribution across subplants over a time horizon, and low-level control that executes real-time adjustments. This segmentation allows complex optimization algorithms to be applied strategically while keeping real-time control mechanisms simpler and more responsive.
Solution Approach 2:
The high-level optimization module performs preliminary calculations to determine optimal load distribution strategies before real-time operation begins. By pre-computing optimal schedules considering forecasted energy prices and loads, the system avoids the need for complex real-time optimization, thereby reducing the complexity of the low-level control while still achieving significant energy cost savings.
2Loss of energy
If thermal energy storage is integrated with multiple subplants to shift production to low cost times, then energy costs decrease, but system integration complexity increases
Solution Approach 1:
The system segments the thermal energy storage integration into distinct charging and discharging phases managed by the high-level optimization. The optimizer determines when to charge storage (during low-cost periods) and when to discharge (during high-cost periods), separating these complex decisions from real-time control and reducing overall system integration complexity.
Solution Approach 2:
The system dynamically adjusts the operation of thermal energy storage based on varying energy prices and building load requirements. The high-level optimization continuously updates charging and discharging schedules in response to changing conditions, allowing the system to adapt to dynamic pricing signals without requiring complex real-time control mechanisms.
3Loss of energy
If real-time pricing and demand charges are considered in optimization, then energy cost minimization improves, but computational complexity increases
Solution Approach 1:
The high-level optimization module performs preliminary computations that incorporate forecasted real-time pricing and demand charge structures into the optimization objective function. By pre-integrating these cost signals into the optimization model, the system avoids the computational burden of processing real-time pricing data during execution, thereby reducing computational complexity while still achieving accurate cost minimization.
Solution Approach 2:
The high-level optimization acts as an intermediary layer between complex energy pricing signals and the simpler low-level control system. It translates variable real-time pricing and demand charges into stable load distribution schedules that the low-level control can execute without requiring complex computational processing of pricing signals.
4Loss of energy
If load distribution is optimized across multiple subplants, then monetary cost decreases, but control system complexity increases
Solution Approach 1:
The control system is divided into high-level optimization that handles complex load distribution decisions across multiple subplants, and low-level control that manages individual equipment. This segmentation concentrates the complexity in the high-level module while keeping the low-level control simple and maintainable.
Solution Approach 2:
The high-level optimization module serves as an intermediary that translates complex optimization objectives into simplified control setpoints for the low-level system. It absorbs the complexity of multi-subplant coordination while presenting simple, actionable commands to the execution layer, thereby reducing overall control system complexity.
Data Source
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AI summary
An optimization system for a central plant (10) includes a processing circuit (106) configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment (60) of the central plant (10) to serve the building energy loads. The optimization system includes a high level optimization module (130) configured to generate an objective function that expresses a total monetary cost of operating the central plant (10) over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment (60). The high level optimization module (130) is configured to optimize the objective function over the optimization period subject to load equality constraints and capacity constraints on the central plant equipment (60) to determine an optimal distribution of the building energy loads over multiple groups of the central plant equipment (60).