Research on Dynamic Fault Recovery Strategy of Distribution Network Based on Multi-objective Optimization Considering Fault Hazard Degree

The increasing penetration of distributed generation (DG) brings about great fluctuation and uncertainty in distribution networks. In order to improve the ability of distribution networks to cope with disturbances caused by uncertainties and to evaluate the maximum accommodation capacity of DG, a multi-objective programming method for evaluation of the accommodation capacity of distribution networks for DG is proposed, considering the flexibility of distribution networks in this paper. Firstly, a multi-objective optimization model for determining the maximum accommodation of DG by considering the flexibility of distribution networks is constructed, aiming at maximizing the daily energy consumption, minimizing the voltage amplitude deviation, and maximizing the line capacity margin. Secondly, the comprehensive learning particle swarm optimization (CLPSO) algorithm is used to solve the multi-objective optimization model. Then, the mixed strategy Nash equilibrium is introduced to obtain the frontier solution with the optimal joint equilibrium value in the Pareto solution set. Finally, the effectiveness of the proposed method is demonstrated with an actual distribution network in China. The simulation results show that the proposed planning method can effectively find the Pareto optimal solution set by considering multiple objectives, and can obtain the optimal equilibrium solution for DG accommodation capacity and distribution network flexibility.

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Evolutionary multi-objective optimization in water distribution network design

Engineering Optimization ◽

10.1080/03052150512331303436 ◽

2005 ◽

Vol 37 (2) ◽

pp. 167-183 ◽

Cited By ~ 112

Author(s):

R. Farmani ◽

D. A. Savic ◽

G. A. Walters

Keyword(s):

Network Design ◽

Water Distribution ◽

Distribution Network ◽

Water Distribution Network ◽

Multi Objective Optimization ◽

Multi Objective ◽

Distribution Network Design ◽

Water Distribution Network Design

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Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

Jurnal Teknik Elektro ◽

10.15294/jte.v12i2.26353 ◽

2020 ◽

Vol 12 (2) ◽

pp. 57-71

Author(s):

Ramadoni Syahputra ◽

Indah Soesanti

Keyword(s):

Network Optimization ◽

Load Balance ◽

Distribution Network ◽

Reducing Power ◽

Distribution Networks ◽

Operating Conditions ◽

Network Reconfiguration ◽

Multi Objective Optimization ◽

Power Losses ◽

Multi Objective

This study proposes a multi-objective optimization for power distribution network reconfiguration by integrating distributed generators using an artificial immune system (AIS) method. The most effective and inexpensive technique in reducing power losses in distribution networks is optimizing the network reconfiguration. On the other hand, small to medium scale renewable energy power plant applications are growing rapidly. These power plants are operated on-grid to a distribution network, known as distributed generation (DG). The presence of DG in this distribution network poses new challenges in distribution network operations. In this study, the distribution network optimization was carried out using the AIS method. In optimization, the goal to be achieved is not only one objective but should be multiple objectives. Multi-objective optimization aims to reduce power losses, improve the voltage profile, and maintain a maintained network load balance. The AIS method has the advantage of fast convergence and avoids local minima. To test the superiority of the AIS method, the distribution network optimization with and without DG integration was carried out for the 33-bus and 71-bus models of the IEEE standard distribution networks. The results show that the AIS method can produce better system operating conditions than before the optimization. The parameters for the success of the optimization are minimal active power losses, suitable voltage profiles, and maintained load balance. This optimization has successfully increased the efficiency of the distribution network by an average of 0.61%.

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