Optimal Operation of Automated Distribution Networks Based-MRFO Algorithm

Abstract

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This paper addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques. INDEX TERMSDistributed generators, Power losses minimization, switched capacitors, distribution reconfiguration, manta ray foraging algorithm. Nomenclature: Nbn Whole number of the branches in the system OFn Objective function CoV Control variables OT Tie branches which are opened NT Number of branches which could be opened to retain the radial structure of distribution system. Qsc Reactive output power from switching of the capacitors NC Number of the current switched capacitors. Pog Dispatchable output power of DG Ndg Number of located DGs Qsc min Minimum of reactive power resulting from switched capacitors Qsc max Maximum of reactive power resulting from switched capacitors Pog min Minimum of active power resulting from DG Pog max Maximum of active power resulting from DG V Voltage magnitude Ibn Flow of the current in the branches Ibn max Upper limit of the current flow in the branches Pd Active load demand Qd Reactive load demand PRG & PRQ Penetration level which is acceptable from the DGs and CBs Mb Buses number of the system QGsub Substation reactive power

Improving Distribution Networks’ Consistency by Optimal Distribution System Reconfiguration and Distributed Generations

“…Planners and operators of distribution system aim at providing quantitative as well as qualitative power service and reducing the wasted energy in the electrical networks. This in turn not only enhances distribution systems (DSs) performance but also directly enhances the performance of transmission and generation systems as well as providing luxury and consumers' satisfaction [1]. Automating the DSs with DGs installation is considered one of the best followed solutions which can be implemented by optimal allocation and control of distribution system reconfigurations (DSRs), capacitor banks (CBs), distributed generators (DGs) and automatic voltage regulators (AVRs) in separate or combined manner [2,3].…”

“…One such endeavor is simultaneous control of DSRs and allocation of DGs. As such, recent studies to the implementation of an effective integration strategy has been presented; for example; manta ray foraging optimization [1]; harmony search algorithm (HSA) with an objective of minimizing real power loss and improving voltage profile [29]; combined GA and branch exchange [30]; artificial bee colony optimizer based on maximization of system loadability [31]; improved spotted hyena algorithm [32], improved elitist-jaya algorithm (IEJAYA) [5], FWA [33], firefly (FF) algorithm [34], sinecosine algorithm [35], Harris Hawks Optimizer (HHO) [36], invasive weed optimizer [37], salp swarm algorithm [38] and an improved beetle swarm optimization algorithm [39]. In [40], multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) have been applied effectively for DGs allocation in distribution systems.…”

Effective Automation of Distribution Systems With Joint Integration of DGs/ SVCs Considering Reconfiguration Capability by Jellyfish Search Algorithm

“…As a result, the suggested JFSA is used to optimize the distribution of DGs and CBs when considering the maximum loading condition and the original configuration. The CBs are thought of in distinct sizes that are produced in 300 kVAr steps, while the highest rated capacity of any DG is 3 MW [49]. For the first system, the obtained DGs and CBs allocations based on the proposed JFSA are tabulated in Table 1 in comparison to manta ray foraging algorithm (MRFA) [47], [50], EGA [51], TSA [52], Improved TSA [52], WCA [35] and BFOA [59].…”

An Effective Bi-Stage Method for Renewable Energy Sources Integration into Unbalanced Distribution Systems Considering Uncertainty

“…In [21], the allocation of renewable energy resources considering network reconfiguration was carried out optimally by using the equilibrium optimization algorithm. In [22], an optimal approach for of automated operation of distribution systems was carried out by using a manta ray optimizer. In [23], a coordinated approach between various enhancement devices for power system operation was employed involving the existence of renewable energies, fixed capacitor banks, and voltage regulators.…”