A strategy to optimize the multi-energy system in microgrid based on neurodynamic algorithm

• Published in: Applied soft computing Vol. 75; pp. 588 - 595
• Main Authors: Fang, Xinxin, He, Xing, Huang, Junjian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2019
• Subjects: Microgrid; Multi-energy management; Neurodynamic algorithm; Nonconvex problem transformation; Social welfare maximization; Neurodynamic algorithm; Multi-energy management; Microgrid; Social welfare maximization; Nonconvex problem transformation
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2018.06.053

In this paper, we design multi-energy management strategy for power-supply participants, heat-supply participants and consumers in a microgrid. The objectives of the management strategy are to maximize the social welfare and to balance the energy supply and demands. With the transmission loss, the proposed social welfare model, subject to practical operation constraints, is formulated as a nonconvex optimization problem. Based on a sufficient condition, a equivalent problem transformed from the primary optimization problem is presented as a convex problem. A neurodynamic algorithm based Karush–Kuhn–Tucker(KKT) conditions and projection with fewer neurons is developed to regulate the resource output of each participant. The convergence of the applied algorithm is proved by Lyapunov function. Finally, numerical simulations are used to prove the effectiveness of the designed management strategy. •Using heat and power participants, a multi-energy management strategy is designed.•A neurodynamic algorithm with fewer neurons is developed to regulate output.•The convergence of the algorithm prove the effectiveness of management strategy.