Distributed neurodynamic algorithm for multi-objective problem optimization and its applications to isolated micro-grid energy management

•A novel distributed framework is proposed to solve the multi-objective problem.•The whole process is independent for each agent only needs its own information.•A problem in micro-grid is proposed to prove the effectiveness of this framework. In this paper, a novel distributed framework is proposed...

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Bibliographic Details
Published in:	Sustainable cities and society Vol. 70; p. 102866
Main Authors:	He, Xing, Liang, Xiaowei, Wang, Hui
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.07.2021
Subjects:	A distributed framework A normalized function Linear weighting method Multi-objective optimization A normalized function A distributed framework Multi-objective optimization Linear weighting method
ISSN:	2210-6707, 2210-6715
Online Access:	Get full text
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Summary:	•A novel distributed framework is proposed to solve the multi-objective problem.•The whole process is independent for each agent only needs its own information.•A problem in micro-grid is proposed to prove the effectiveness of this framework. In this paper, a novel distributed framework is proposed to solve the multi-objective problem which composed of several conflicting objective functions. In order to overcome the impact of inconsistent units of measurement for each objective function, a normalized function is applied to eliminate it. It will split a multi-objective optimization problem into three subproblems: the problem of maximization, the problem of minimization, the problem after normalization. Linear weighting method is adopted to assign weights to each normalized objective function and convert them into a single objective problem. Specifically, each objective function which regarded as an agent will calculate its maximum and minimum values respectively and finally the Pareto optimal solution is obtained through a distributed neuro-dynamic algorithm. The whole process is distributed and independent for each agent only needs its own rather than the global information. Furthermore, the convergence of the proposed algorithm can be proved through Lyapunov function. Eventually, a numerical example and a multi-objective optimization problem in micro grid are used to prove the credibility of the framework.
ISSN:	2210-6707 2210-6715
DOI:	10.1016/j.scs.2021.102866