Central Plant Load Allocation Using Hierarchical Energy Optimization
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Solution Overview
Problem
Central plants face challenges in optimally allocating energy loads across subplants due to real-time pricing and resource management complexities, leading to inefficiencies in energy production and consumption.
Innovation Solution
A high-level optimizer is configured to manage energy assets by defining storage elements, adding decision variables for resource storage and discharge, and incorporating constraints on operational domains, distribution costs, and efficiency losses to optimize resource allocation across sources, subplants, and sinks, ensuring resource balance and minimizing economic costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple control strategies are implemented in a central plant, then system control capability is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple control strategies (rule-based control, model predictive control, and real-time optimization) into a unified hierarchical control architecture. The optimizer receives data from all control layers and provides coordinated setpoints, merging previously separate control functions into an integrated system that improves overall control capability while managing complexity through structured organization.
Solution Approach 2:
The real-time optimizer serves multiple functions simultaneously: it performs economic optimization, coordinates different control strategies, adapts to changing plant conditions, and provides setpoints to various control layers. This multi-functional approach allows a single system to address multiple control needs without proportionally increasing complexity.
2Productivity
If real-time data processing is implemented for dynamic optimization, then operational efficiency is improved, but computational requirements increase
Solution Approach 1:
The system pre-processes sensor data and maintains updated plant models before real-time optimization is needed. By preparing data structures, validation rules, and model parameters in advance, the system reduces the computational burden during real-time execution, allowing efficient dynamic optimization without excessive computational requirements.
Solution Approach 2:
The optimizer processes data at different levels of detail depending on the situation. For routine operations, it uses simplified models and processed data summaries. For exceptional conditions or when high precision is needed, it performs more comprehensive calculations. This selective processing approach maintains operational efficiency while managing computational requirements.
Data Source
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AI summary
A central plant includes a high level optimizer configured to determine an optimal allocation of energy loads across central plant equipment. The high level optimizer identifies sources configured to supply input resources, subplants configured to convert the input resources to output resources, and sinks configured to consume the output resources. The high level optimizer generates a cost function and a resource balance constraint. The resource balance constraint requires balance between a total amount of each resource supplied by the sources and the subplants and a total amount of each resource consumed by the subplants and the sinks. The high level optimizer determines the optimal allocation of the energy loads across the central plant equipment by optimizing the cost function subject to the resource balance constraint. The high level optimizer is configured to control the central plant equipment to achieve the optimal allocation of the energy loads.