Central Plant Load Scheduling With Thermal Storage and Demand Charges
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Solution Overview
Problem
Central plants with multiple subplants face challenges in optimizing energy distribution and thermal energy storage to minimize energy costs, especially when considering real-time pricing and demand charges, due to the complexity of integrating these systems effectively.
Innovation Solution
A cascaded optimization process involving a high level optimization module that uses linear programming to determine optimal subplant load distribution across various subplants, incorporating demand charges and load change penalties, while a low level optimization module adjusts individual device operating statuses to minimize energy consumption.
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 optimization problem is segmented into two distinct levels: high-level optimization that determines thermal energy storage charging/discharging schedules and subplant load distribution, and low-level optimization that controls individual device operating statuses. This segmentation allows each level to focus on specific decisions, reducing overall system complexity while achieving cost minimization across multiple time periods
Solution Approach 2:
The high-level optimization performs preliminary action by pre-determining the thermal energy storage charge/discharge schedules and subplant load distribution for future time periods. This preliminary planning allows the system to shift production to low-cost periods in advance, capturing energy cost savings while simplifying real-time control decisions at the low-level optimization stage
2Adaptability or versatility
If conventional optimization methods are used for load scheduling, then implementation is straightforward, but they cannot effectively handle real-time pricing and demand charges
Solution Approach 1:
The optimization system is made dynamic by incorporating time-varying energy prices, demand charges, and real-time pricing signals into the objective function. The high-level optimization dynamically adjusts thermal energy storage schedules and subplant load distribution based on changing pricing conditions, while the low-level optimization dynamically responds to these schedules by adjusting individual device operations. This dynamic two-level structure enables adaptability to complex pricing conditions without requiring a single monolithic complex optimizer
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).