A virtual power plant dynamic support capacity quantification and boundary calculation method based on inverse optimization

By constructing a dynamic optimal power flow model with second-order cone relaxation and a sequential update algorithm, the equivalent parameters of the virtual power plant are inverted, solving the problem of quantifying the dynamic support capacity of the virtual power plant and realizing safe and reliable power grid dispatch.

CN122292397BActive Publication Date: 2026-07-24NANJING TECH UNIV +2
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Patent Information

Application Number
CN202610737228.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-05-27
Publication Date
2026-07-24
Estimated Expiration
2046-05-27

AI Technical Summary

Technical Problem

Existing technologies cannot effectively quantify the dynamic support capabilities of virtual power plants, especially when the underlying resource status is variable and the parameters are not transparent. This leads to the risk of overestimating the capabilities of virtual power plants and exceeding energy dissipation limits during grid dispatch.

Method used

By employing an inverse optimization approach, a state-dependent flexibility envelope is constructed by building a dynamic optimal power flow model with second-order cone relaxation and a sequential update algorithm, combined with observation data to invert the equivalent parameters of the virtual power plant, thereby quantifying its dynamic support capability.

Benefits of technology

Precise quantification of the dynamic support capacity of virtual power plants avoids the risk of exceeding limits due to energy depletion or overcharging, and improves the safety and reliability of dispatching.

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Abstract

The application discloses a kind of virtual power plant dynamic support capacity quantification and boundary measurement method based on inverse optimization, it is related to power system operation and control field, first, the physical model of virtual power plant multi-time period dynamic optimal power flow based on second-order cone relaxation is constructed;Second, with the minimum of virtual power plant grid connection point limited measurement data residual as the goal, a composite inverse optimization model containing feasible region compactness penalty is constructed;Then, using the asymmetric moving average sequential updating algorithm with attenuation, the equivalent power boundary and virtual energy capacity of the system are accurately inversed from multi-scenario operation data;Finally, the target response time length is explicitly introduced, and the state-dependent dynamic flexibility envelope considering instantaneous energy state constraints is constructed.The application overcomes the limitation of bottom-layer parameter black box, and quantifies the dynamic support potential of virtual power plant under multi-time scale in detail, providing a defense over-limit decision basis for safe dispatch.
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