Central Plant Optimization With Demand Charge Constraints
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Solution Overview
Problem
Central plants face challenges in optimizing the distribution of thermal energy loads across multiple subplants to minimize energy costs, particularly due to complexities in integrating thermal energy storage with real-time pricing and demand charges.
Innovation Solution
An optimization system that uses a processing circuit to receive load prediction data and utility rate data, generating an objective function to minimize monetary costs through linear programming, incorporating demand charges by adding a demand charge term and constraints to optimize the distribution of energy loads across subplants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If thermal energy storage is integrated with real-time pricing and demand charges to minimize energy costs, then energy cost reduction is improved, but system complexity increases
Solution Approach 1:
The optimization system is divided into distinct functional modules: a demand charge module that calculates demand charges based on peak power consumption, a thermal energy storage module that models storage capacity and discharge rates, and an objective function generator that integrates multiple cost components. This segmentation allows each module to handle specific aspects of the optimization problem independently, reducing overall system complexity while achieving comprehensive cost minimization.
Solution Approach 2:
The system performs preliminary calculations of demand charges and thermal energy storage requirements before optimizing the operational schedule. By pre-calculating these parameters and incorporating them into the objective function, the system simplifies the subsequent optimization process and enables more efficient determination of the optimal operational schedule.
2Adaptability or versatility
If multiple subplants are used to serve building thermal energy loads, then operational flexibility is improved, but load distribution optimization difficulty increases
Solution Approach 1:
The optimization system dynamically adjusts the operational schedule of multiple subplants based on varying energy prices, demand charges, and thermal energy storage states. The objective function incorporates time-varying cost parameters and constraints that adapt to changing conditions, enabling flexible load distribution across subplants while systematically optimizing for minimum energy cost.
3Measurement precision
If demand charge is incorporated into the objective function, then cost optimization accuracy is improved, but computational complexity increases
Solution Approach 1:
The system transforms the demand charge parameter from a nonlinear peak-based calculation into a linear constraint formulation. By introducing auxiliary variables and constraints that represent peak power consumption periods, the demand charge is integrated into the objective function in a computationally tractable manner, maintaining optimization accuracy while reducing computational complexity.
Data Source
AI summary
An optimization system for a central plant includes a processing circuit 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 of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant 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. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.