Multi-target active dispatching solving method and system

Abstract

An embodiment of the invention provides a multi-target active dispatching solving method and system. The multi-target state variable constraint is processed by a processing mode based on constraint priority non-dominated sorting, so that the problem of penalty coefficient selection difficulty in the conventional processing mode is reduced; and according to the algorithm, in the multi-target optimization process, multiple non-domination solutions can be obtained according to the domination relation and congestion distance determined by the non-dominated sorting, namely a Pareto optimal solution set can be obtained. Information interaction and resource sharing between multiple groups based on the constraint priority non-dominated sorting are provided to obtain the more accurate and more uniform Pareto optimal solution set; and in addition, one best compromising solution is determined from the Pareto optimal solution set by adopting a fuzzy subordination method in the fuzzy mathematics to be used as the solution scheme of the multi-target optimization power flow problem.

Description

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AI Technical Summary

Benefits of technology

This technology provides an improved way to select weights during multiobjectives (MOPs) optimizing energy flows through networks with various object states variables such as cost or performance metrics. By utilising this approach, it simplifies selection procedures while still providing precise results compared to traditional approaches like greedy algorithms. Additionally, there are also techniques called Fuzzy Logic Interval Analysis(FLCA), which help identify better ways to trade off certain costs associated with each objective function. Overall these technical improvements improve efficiency and effectiveness across different types of network systems.

Problems solved by technology

This patents describes two technical aspects: firstly, considering complicated factors like security concerns or environmental regulations, secondly, studying multiobjective optimal electricity flows through efficient scheduling systems. These challenges require finding a way to efficiently operate these types of electrical grids effectively without sacrificing their economies due to high costs associated with fossil fuel consumption.

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