Central Plant Load Optimization Under Real-Time Utility Pricing
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Solution Overview
Problem
Existing systems face challenges in optimizing the distribution of building thermal energy loads across multiple subplants in a central plant, particularly in minimizing energy costs when considering electrical demand charges and real-time pricing.
Innovation Solution
An optimization system that includes a processing circuit and a high-level optimization module, which uses load prediction data and utility rate data to generate an objective function that minimizes the total monetary cost of operating the central plant. The system optimizes this function subject to load equality constraints and capacity constraints, determining an optimal distribution of energy loads across subplants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If thermal energy storage is integrated with multiple subplants to shift production to low cost times, then energy costs are decreased, but system complexity and difficulty of optimization increase
Solution Approach 1:
The patent segments the central plant into multiple subplants (e.g., chiller subplant, heat pump subplant, thermal energy storage subplant) and optimizes each independently while coordinating their interaction. This segmentation allows the complex system to be managed through modular optimization, where each subplant's control can be developed and tuned separately, reducing the overall complexity burden.
Solution Approach 2:
The system performs preliminary action by using thermal energy storage to pre-cool or pre-heat fluids during off-peak hours when energy costs are lower. This allows the central plant to shift production timing, storing thermal energy in advance of peak demand periods, thereby reducing energy costs while managing complexity through temporal separation of operations.
2Productivity
If load distribution across multiple subplants is optimized to minimize energy cost, then operational efficiency is improved, but control complexity increases
Solution Approach 1:
The patent implements dynamic load distribution that continuously adjusts the allocation of thermal loads across subplants based on real-time conditions such as energy prices, demand charges, and equipment efficiency. This dynamic approach optimizes operational efficiency by responding to changing conditions, while the use of automated control algorithms manages the complexity of real-time decision-making across multiple subplants.
Solution Approach 2:
The system optimizes operational efficiency by dynamically changing operating parameters such as subplant load ratios, temperature setpoints, and flow rates. These parameter changes allow the system to adapt to varying energy prices and demand conditions, improving efficiency while the automated control system manages the complexity of coordinating multiple parameter adjustments across subplants.
3Loss of energy
If real-time optimization considering demand charges and pricing is implemented, then energy cost is reduced, but computational complexity and data processing requirements increase
Solution Approach 1:
The system performs preliminary computational work by predicting energy costs and optimal load distributions in advance, using forecasted pricing data and demand charge structures. This allows the optimization algorithm to prepare optimal schedules before peak pricing periods occur, reducing actual energy costs while minimizing real-time computational complexity through pre-computation.
Solution Approach 2:
The optimization system is designed to automatically process pricing data, calculate optimal load distributions, and adjust subplant operations without requiring extensive external computational resources or manual intervention. This self-service capability reduces the burden on external computational systems while still achieving real-time cost optimization through automated internal processing.
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.