A method for operating at least one distributed energy ressource comprising a refrigeration system
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Solution Overview
Problem
Existing methods for managing refrigeration systems in smart grids are inadequate as they primarily focus on reducing energy losses due to ripples, failing to provide accurate and flexible power consumption information to aggregators, which limits their ability to efficiently adjust power distribution.
Innovation Solution
A method that estimates and communicates power consumption parameters such as maximum, minimum, and change in power consumption of refrigeration systems to smart grids, allowing for improved flexibility and adjustment of power distribution without compromising primary operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If existing methods focus on reducing energy losses due to ripples, then energy loss is reduced, but power consumption information accuracy and flexibility deteriorate
Solution Approach 1:
The refrigeration system is divided into multiple cooling entities (e.g., freezers, refrigerators), and power consumption is estimated separately for each entity based on its individual state (temperature, load, compressor operation). This segmentation enables precise tracking of power consumption at the component level, improving overall measurement accuracy while allowing targeted energy optimization strategies.
Solution Approach 2:
The system continuously monitors the state of cooling entities (temperature, humidity, door openings, load changes) and uses this feedback to dynamically adjust power consumption estimates. This real-time feedback mechanism ensures accurate power information is provided to the aggregator, enabling flexible demand response while maintaining energy efficiency.
2Productivity
If power consumption information is simplified for communication, then communication efficiency is improved, but flexibility in power distribution adjustment deteriorates
Solution Approach 1:
The system communicates multiple power consumption parameters (maximum power, minimum power, average power, rate of change) instead of a single simplified value. These parameter changes provide the aggregator with comprehensive information about the refrigeration system's capabilities, enabling flexible power distribution adjustments while maintaining efficient communication through standardized data formats.
Data Source
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AI summary
The invention relates to a method of operating at least one distributed energy resource comprising a refrigeration system (1) with a number of cooling entities, wherein a power consumption information is communicated to a smart-grid setup (SG). According to the invention the method comprises the steps of: - requesting (S0) a power consumption information from the refrigeration system; - transmitting (S1) the power consumption information from the refrigeration system (1), wherein a total amount of power consumption (Pm in, Pmax) of the refrigeration system (1) is provided; wherein: - a cooling capacity (dQ/dt_i) of at least one cooling entity is determined wherein an entity operation condition (CE) of the cooling entity (E1, E2) is taken into account (D1); - a power consumption (W_i) of at least one cooling entity (E1, E2) is determined from the cooling capacity (dQ/dt_i) wherein a performance estimation (COP) of a refrigeration cycle for the cooling entity (E1, E2) is taken into account (D2); - providing (D3) the total amount of power consumption (Pmin, Pmax) as a sum of power consumptions (W_i) of at least the one cooling entity of the number of cooling entities (E1, E2), in particular as a sum of relevant power consumptions of the number of cooling entities (E1, E2), - receiving (S2) at the refrigeration system (1) a power reference (Wref) from the smart-grid setup (SG). The method presented enables power control of a centralized refrigeration system in a sm art-grid setup where an aggregator provides the power reference. In addition, the method also enables the refrigeration system to improve determining flexibility margins beyond absolute max./min values of nominal and zero.