Double-layer optimization method and system based on energy concentrator

By constructing a two-layer optimization model with a master-slave game architecture, and combining KKT conditions and Huber fuzzy sets, the problem of coordinating emerging resources and the interests of multiple stakeholders in the energy hub is solved. This achieves efficient and robust optimization of the energy hub and comprehensive benefits of the multi-energy system, and improves the scientific nature and adaptability of scheduling decisions.

CN122264329APending Publication Date: 2026-06-23CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Patent Information

Application Number
CN202610108670.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-26
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing technologies have failed to fully tap the potential of emerging flexible resources, neglected the characteristics of multiple stakeholders and the game of interests, resulting in limited improvement in the operating efficiency and flexibility of energy hubs, and an imbalance in uncertainty handling methods, which makes it impossible to achieve the comprehensive benefits of multi-energy systems and the effectiveness of scheduling decisions.

Method used

A two-layer optimization model with a master-slave game architecture is constructed. Combining the Caro-Kuhn-Tucker KKT conditions and strong duality theory, the linearization optimization problem is solved. Huber fuzzy sets and CVaR risk measures are embedded. The min-max-min sub-Blule bar optimization model is solved by column and constraint generation algorithm to coordinate multi-variable heterogeneous flexible resources and the interests of multiple stakeholders.

Benefits of technology

It improves the operational economy and robustness of energy hubs, coordinates and optimizes resources from multiple stakeholders, enhances the ability to cope with uncertainties and the scientific and adaptable nature of scheduling decisions, and solves the limitations and inefficiencies of traditional methods.

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Abstract

The invention relates to a double-layer optimization method and system based on an energy hub, and the method comprises the steps: firstly constructing a double-layer optimization model of a master-slave game architecture, enabling an upper-layer model of the double-layer optimization model to take the minimization of the total operation cost as an optimization target, and enabling the upper-layer model to comprise a first-stage day-ahead scheduling decision and a second-stage real-time adjustment decision; reconstructing the double-layer optimization model into a single-layer mixed integer linear programming model through a KKT condition and a strong dual theory; empirical distribution is generated based on historical data, a Huber fuzzy set is constructed to describe RG uncertainty, and a min-max-min distribution robust optimization model under a first-stage day-ahead scheduling decision-worst distribution selection-second-stage real-time adjustment decision architecture is formed; finally, customized Camp is adopted; and carrying out iterative solution by using a CG algorithm. According to the method, the economical efficiency and robustness are balanced, the efficiency of multi-agent cooperation and source-load interaction is improved, and a stable and efficient scheduling scheme is provided for the energy concentrator.
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