Multi-mode cooperative control method for transformer area energy storage equipment

By collecting substation operation data in real time, dynamically identifying mode requirements, establishing a multi-objective optimization function and adopting a fuzzy decision-making algorithm, and generating multi-mode collaborative control instructions, the problem of a single control method for substation energy storage equipment is solved, and more efficient multi-scenario adaptability and power quality optimization are achieved.

CN120728689AActive Publication Date: 2025-09-30江苏优亿诺智能科技有限公司 +1
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Patent Information

Application Number
CN202511213582.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-28
Publication Date
2025-09-30
Estimated Expiration
2045-08-28

AI Technical Summary

Technical Problem

The existing substation energy storage equipment has a single control method and cannot effectively balance the conflicts when multiple scenarios coexist, especially when peak shaving and voltage support exist at the same time.

Method used

By collecting substation operation data in real time, dynamically identifying current mode requirements, establishing a multi-objective optimization function, generating weight coefficients, and using a fuzzy decision algorithm to select a combination of dominant and auxiliary control modes, multi-mode collaborative control instructions are generated to allocate the charging and discharging power of energy storage equipment and the reactive output of the converter.

Benefits of technology

It realizes the multi-mode adaptability of energy storage equipment control, optimizes the network loss, voltage deviation, electricity cost and life loss of the substation, and improves the quality of power supply.

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Abstract

The invention discloses a multi-mode cooperative control method for transformer area energy storage equipment, and the method comprises the steps: collecting the operation data of a transformer area in real time, so as to recognize the operation mode of the current transformer area in real time. Thirdly, a multi-objective optimization function is created, the minimum transformer area network loss, the minimum voltage deviation, the minimum power consumption cost and the optimal energy storage life loss serve as optimization objectives of the multi-objective optimization function, and then a weight coefficient of the multi-objective optimization function is generated; and then, dynamically selecting a combination of a dominant control mode and an auxiliary control mode by using a fuzzy decision algorithm based on the obtained weight coefficient, generating a multi-mode cooperative control instruction based on the combination of the dominant control mode and the auxiliary control mode, and distributing the charging and discharging power of the energy storage equipment and the reactive output quantity of the converter. Therefore, the control of the energy storage equipment is more adaptive.
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