Central Plant Load Allocation Under Demand Charges And Thermal Storage
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Solution Overview
Problem
Central plants face challenges in optimizing energy distribution 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 load prediction, 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 thermal energy storage is integrated with multiple subplants and real-time pricing, then energy cost minimization is improved, but control technology complexity increases
Solution Approach 1:
The control technology is segmented into a hierarchical structure with a high-level optimization module that handles strategic decisions about thermal energy storage charging/discharging and subplant load distribution, and low-level controllers that handle individual equipment operation. This segmentation reduces the complexity of the overall control system by dividing it into manageable modules with distinct functions.
Solution Approach 2:
The high-level optimization module acts as an intermediary between the utility pricing signals and the multiple subplants. It receives utility rate data, processes this information through linear programming optimization, and generates optimized load distribution signals to the subplants. This intermediary layer simplifies the control architecture by centralizing the complex optimization logic.
2Loss of energy
If load distribution across multiple subplants is optimized in real-time, then energy cost is reduced, but computational complexity increases
Solution Approach 1:
The high-level optimization module performs preliminary optimization calculations using linear programming to determine the optimal load distribution strategy before implementing it at the subplants. By pre-calculating the optimized distribution based on forecasted utility rates and thermal energy storage capabilities, the system reduces real-time computational requirements and simplifies the control implementation.
3Loss of energy
If demand charges are incorporated into optimization, then monetary cost minimization is improved, but optimization complexity increases
Solution Approach 1:
The optimization module incorporates demand charges by dynamically adjusting the cost parameters in the linear programming objective function. When demand charges are high, the optimization automatically shifts load to periods with lower rates or uses thermal energy storage to meet demand, effectively changing the economic parameters to minimize total cost while managing optimization complexity through the structured mathematical framework.
Data Source
AI summary
A controller for equipment that operate to provide heating or cooling to a building or campus includes a processing circuit configured to obtain utility rate data indicating a price of resources consumed by the equipment to serve energy loads of the building or campus, obtain an objective function that expresses a total monetary cost of operating the equipment over an optimization period as a function of the utility rate data and an amount of the resources consumed by the equipment, determine a relationship between resource consumption and load production of the equipment, optimize the objective function over the optimization subject to a constraint based on the relationship between the resource consumption and the load production of the equipment to determine a distribution of the load production across the equipment, and operate the equipment to achieve the distribution.