Dynamic Reconfiguration of Multiobjective Distribution Networks Considering DG and EVs Based on a Novel LDBAS Algorithm

Abstract

Aiming to improve the operation economy and power quality of distribution systems subject to the fluctuating and stochastic power outputs of distributed generation (DG) units and electric vehicles (EVs), a multiobjective optimization model for network reconfiguration and its corresponding solution method are proposed. First, a dynamic reconfiguration model is constructed based on the power loss rate (PLR) as well as the active power loss, the load balancing index and the maximum node voltage deviation, which serve as the optimization indexes. Second, the Lé vy flight and chaos disturbed beetle antennae search (LDBAS) algorithm is presented based on the grey target decision-making technique, which can not only improve the computational efficiency but also find the most satisfactory solution for the proposed dynamic reconfiguration model. Considering the uncertainties of loads and DG outputs, the influence on the load curve of EV connecting to the distribution network at different penetration rates and under different charging/discharging modes is analysed. Additionally, the modified IEEE 33-bus and 118-bus test radial distribution networks are simulated to verify the effectiveness and superiority of the presented reconfiguration model and the improved LDBAS method, and the results illustrate that the proposed reconfiguration method can improve the operation economy and power quality of the distribution system and encourage the penetration of EVs. INDEX TERMS Distribution system, time-varying characteristics of DGs and EVs, dynamic reconfiguration, multiobjective optimization, grey target decision-making, LDBAS algorithm

Short-Term Cooperative Operational Scheme of Distribution System with High Hosting Capacity of Renewable-Energy-Based Distributed Generations

“…As shown in Figure 11b, if 𝐼 > 𝐼 , the maximum power limit of each DG will be set lower than the expected output. From Figure 11, the real component (∆𝐼 , ) of the current difference in the representative section n can be derived using Equation (6).…”

“…As shown in Figure 11b, if I n > I limit n , the maximum power limit of each DG will be set lower than the expected output. From Figure 11, the real component (∆I n,R ) of the current difference in the representative section n can be derived using Equation (6). Vector diagram for ∆𝐼 , , the difference between the current in the representative section and the maximum current limit: (a) ∆𝐼 , when 𝐼 is within the limit; (b) ∆𝐼 , when 𝐼 exceeds the limit.…”

“…Recent studies on dynamic network reconfiguration methods focused on hourly control and included the abovementioned measures as well as load control objectives. Existing research on network reconfigurations, including dynamic network reconfiguration, aimed primarily at loss minimization [4][5][6][7]. Silva et al proposed a method of dynamic network reconfiguration for loss minimization and analyzed the computational time according to the algorithm execution cycle [4].…”

“…Their method used a hybrid algorithm that combined an exchange market algorithm and the wild goats algorithm to improve computational speed and accuracy [5]. In addition to loss minimization, some studies focused on load balancing, voltage fluctuation minimization, and voltage profile improvement [6,7]. Furthermore, a dynamic network reconfiguration can perform more frequent switching operations than a static network reconfiguration.…”

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

“…DER injected into modern distribution networks has become popular since the last decade. The advantage of integrating DER into the distribution network is that it can increase electrical power supply, reduce network losses, increase voltage stability, increase network reliability, and reduce environmental pollution, which continues to be a hot issue today [3]- [5].…”