A Chaos Disturbed Beetle Antennae Search Algorithm for a Multiobjective Distribution Network Reconfiguration Considering the Variation of Load and DG

Abstract

As the distributed generation (DG) in a power supply and the user load demand constantly change in an actual distribution network, multiobjective optimal network reconfiguration considering variations in load and DG has become a major concern, which is important and required to make system operations safe and economical. The aim is to minimize the sum of the active power loss, the sum of the load balancing index and the sum of the maximum node voltage deviation index simultaneously during the reconfiguration period. Here, this article proposes a new Chaos Disturbed Beetle Antennae Search (CDBAS) algorithm to reduce the computational time and solve the multiobjective optimal problem of network reconfiguration. To adopt the Chaos Disturbed Beetle Antennae Search algorithm for solving this multiobjective problem, grey target decision-making technology is used to rank the beetles. Additionally, to the enhance the system static voltage stability and voltage quality, a grey target decision-making model is established to achieve a layer relationship between each index and the switching operation index. The plausibility and effectiveness of the presented methodology is verified on the modified IEEE 33, 69 and 118-Bus Test Radial Distribution Network. Finally, compared with other research methods in the literature, the CDBAS algorithm outperforms other algorithms and produces a quality decision solution. INDEX TERMS Distribution system, load and distributed generation variation, multiobjective optimization, grey target decision-making, CDBAS algorithm. HAIYUN WANG received the B.E. degree in electric power systems and automation from Xin

Study on the Capacity of an Active Phase Controller for Autonomous Grid Connection

“…In addition, the values of θ 1 and θ 2 obtained from Equations (15) and (16) are the same as those obtained using Equations (19) and 20:…”

“…In addition, the distribution network may be arbitrarily connected or separated to ensure optimum power supply to each line [15][16][17][18][19]. However, uncontrolled circulating current and inrush current are generated in the line when the voltage and phase between the lines are different from each other at the connection point (CP).…”

A Social Beetle Swarm Algorithm Based on Grey Target Decision-Making for a Multiobjective Distribution Network Reconfiguration Considering Partition of Time Intervals

“…Users usually have relatively fixed demand proportion curves during a typical day in the same region and season, the loads can be described by demand proportion curves per day [36], which is shown in Fig.6. In general, the daily loads involve three categories: residential, industrial and commercial, the concrete parameters are given in Tables 2 and 3, they are shown in Fig.7.…”

“…where S∈{s|1,2,3} is the set of load types; s =1 is residential, s = is industrial, and s = 3 is commercial; PNi is the rated power of load node 'i'; and θsi represents the ratio of load type 's' in the total load of node 'i', δs represents the demand curves of load type 's' [36].…”

“…The scheme before reconfiguration set as one that does not consider the uncertainty of wind power and load, that was to disconnect five tie switches. The original network was to disconnect 33,34,35,36, and 37 switches, without considering network reconfiguration. The total network loss was 3467.45 kWh, the voltage deviation was 0.9560 p.u, and the load balance was 7.2797 p.u.…”