District Cooling Demand Response Setpoints for Thermal Comfort
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Solution Overview
Problem
Current demand response systems for District Cooling Plants (DCPs) face challenges in achieving target demand energy reduction during peak power situations, as they struggle to optimize decision parameters such as temperature set points and lighting intensity across multiple buildings while minimizing thermal discomfort and preventing snapback effects, especially when hundreds of buildings participate.
Innovation Solution
A processor-implemented method and system that determines optimal decision parameters by iteratively calculating energy reduction vectors, temperature set points, and lighting intensity factors for each building and the DCP, using input parameters like time intervals, target energy reduction values, and supply chilled water temperatures, while minimizing thermal discomfort and peak power usage, and satisfying the condition of achieving the target energy reduction without causing a snapback effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If common methods such as controlling temperature set points and dimming lighting units are used for building level target demand energy reduction during DR event, then energy reduction is achieved, but thermal comfort and ambient conditions for occupants are impacted
Solution Approach 1:
The system performs pre-cooling of buildings before the DR event starts by lowering temperature set points in advance. This stores thermal energy in the building structures (walls, floors, furniture) so that during the DR event, the buildings can maintain comfortable temperatures without active cooling, thereby reducing energy consumption while preserving occupant thermal comfort.
2Loss of energy
If DCP and buildings are considered together to achieve high overall target demand energy reduction, then energy reduction capability is improved, but system complexity and difficulty of determining optimal decision parameters increases
Solution Approach 1:
The system segments the complex DCP-buildings system into independent controllable units. Each building is treated as a separate entity with its own energy reduction potential, and the DCP is divided into individual chiller units. This segmentation allows the optimization algorithm to handle each unit separately while still achieving coordinated system-wide energy reduction, thereby managing complexity.
Solution Approach 2:
The system changes key operating parameters including DCP supply water temperature, individual building temperature set points, and lighting intensity factors. By dynamically adjusting these parameters based on real-time conditions and iterative optimization, the system achieves coordinated energy reduction across the entire DCP-buildings system while managing complexity through parameter-based control rather than complex structural changes.
3Measurement precision
If iterative optimization is performed to determine optimal decision parameters for DCP and buildings, then energy reduction precision is improved, but computational time and processing requirements increase
Solution Approach 1:
The system performs iterative optimization to determine optimal decision parameters for DCP and buildings, but applies practical limits to the iteration process. The optimization continues until convergence criteria are met or a maximum number of iterations is reached, balancing precision with computational time constraints. This partial action approach ensures sufficient precision for operational decision-making without excessive computational overhead.
4Loss of energy
If target demand energy reduction is maximized during DR event, then energy conservation is improved, but snapback effect triggers peak power demand immediately after DR event
Solution Approach 1:
The system performs pre-cooling before the DR event, storing thermal energy in building structures. This reduces the need for intensive cooling immediately after the DR event ends, thereby mitigating the snapback effect and preventing secondary peak power demands when normal operations resume.
Data Source
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AI summary
The present disclosure provides system and method for determining optimal decision parameters for a demand response (DR) event involving a District Cooling Plant (DCP). Most of conventional DR event techniques address control of building-level energy consumption loads alone while in presence of District Cooling (DC) has not received much attention when a plurality of buildings are served by a District Cooling Plant (DCP). The disclosed system and method determine set points of optimal decision parameters of the plurality of buildings and the DCP, by conditioning and un-conditioning on the DCP parameters such that a thermal discomfort of occupants residing in the plurality of buildings is minimum and achieves a maximum target energy demand reduction during the DR event. The disclosed system and method work for hundreds of buildings and able to determine the optimal decision parameters for each building and the DCP efficiently.