T/JES 004-2025 Active Group standards

T/JES 004-2025 Assessment of load regulation potential for demand-side management of new power system requirements

T/JES 004-2025 Assessment of load regulation potential for demand-side management of new power system requirements

Publish Date: 2025-03-05 Implement Date: 2025-03-05 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us
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Basic Information

Standard Code: T/JES 004-2025
Standard Type: Group standards
Standard Status: Active
is_force_gb: no
CCS Name: -
ICS Name: Comprehensive Electrical Engineering
Publish Date: 2025-03-05
Implement Date: 2025-03-05

Scope

Main technical content: The standard specifies the basic technical requirements for assessing the potential of demand-side regulation in new power systems. Based on historical data related to different load types, a prediction model for load adjustment potential is established. Models can be developed separately for different load types, different load levels, and different electricity consumption plans. A comprehensive evaluation model for regional demand-side load adjustment potential can also be established according to business needs. The selection is based on comprehensive consideration of factors such as power user types, user load characteristics, demand-side regulation plans, dates, and weather conditions. The evaluation algorithm can include machine learning, deep learning, long-short term memory networks, fuzzy logic, and data mining, etc. One or more algorithms can be selected to form a combined evaluation strategy. The assessment of load regulation potential is divided into three stages: constructing indicators for regulation potential factors, calculating indicator weights, and evaluating potential. For constructing indicators for influencing factors, a multi-dimensional indicator system needs to be established from the perspectives of user types, economic demand, society, and the system. The indicator factors can include historical data of loads, user behavior patterns, the adjustability of loads, economic incentive measures, social acceptance, and system operation constraints, etc. For calculating indicator weights, this step can use positive and negative indicators to quantify the importance of each influencing factor. The regulation potential of loads can be evaluated using methods such as deep learning, long-short term memory networks, and fuzzy logic.

Development Information

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