Optimal Placement of Distributed Generators for Maximum Savings using PSO-SSA Optimization Algorithm

2021 ◽  
Author(s):  
Rafi Vempalle ◽  
Dhal P. K
Keyword(s):  
Optimization Algorithm ◽  
Optimal Placement ◽  
Distributed Generators

scholarly journals A Novel Optimal Distributed Generation Planning in Distribution Network using Cuckoo Optimization Algorithm

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Ali Aranizadeh ◽  
Iman Niazazari ◽  
Mirpouya Mirmozaffari
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution System ◽  
Optimal Placement ◽  
Voltage Profile ◽  
Placement Problem ◽  
Cuckoo Optimization Algorithm ◽  
Distributed Generators ◽  
Generation Planning ◽  
Network Losses

The optimal sizing and placement of distributed generators have recently drawn a considerable attention to itself. This paper proposes an evolutionary cuckoo optimization algorithm (COA) for optimal placement of distributed generation (DG) in a distribution system. The optimal DG placement problem is formulated as a cost function of network losses, voltage profile, and DG expenses. The proposed method is validated on a 13-bus distribution system. The results show that any variation in the parameter’s weight in the objective function leads to a significant change in the prediction of the DG’s location and capacity.  

scholarly journals Application of the Multiverse Optimization Method to Solve the Optimal Power Flow Problem in Direct Current Electrical Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 8703
Author(s):  
Andrés Alfonso Rosales-Muñoz ◽  
Luis Fernando Grisales-Noreña ◽  
Jhon Montano ◽  
Oscar Danilo Montoya ◽  
Alberto-Jesus Perea-Moreno
Keyword(s):  
Objective Function ◽  
Successive Approximation ◽  
Direct Current ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Flow Problem ◽  
Distributed Generators ◽  
Optimal Power ◽  
Power Flow Problem

This paper addresses the optimal power flow problem in direct current (DC) networks employing a master–slave solution methodology that combines an optimization algorithm based on the multiverse theory (master stage) and the numerical method of successive approximation (slave stage). The master stage proposes power levels to be injected by each distributed generator in the DC network, and the slave stage evaluates the impact of each power configuration (proposed by the master stage) on the objective function and the set of constraints that compose the problem. In this study, the objective function is the reduction of electrical power losses associated with energy transmission. In addition, the constraints are the global power balance, nodal voltage limits, current limits, and a maximum level of penetration of distributed generators. In order to validate the robustness and repeatability of the solution, this study used four other optimization methods that have been reported in the specialized literature to solve the problem addressed here: ant lion optimization, particle swarm optimization, continuous genetic algorithm, and black hole optimization algorithm. All of them employed the method based on successive approximation to solve the load flow problem (slave stage). The 21- and 69-node test systems were used for this purpose, enabling the distributed generators to inject 20%, 40%, and 60% of the power provided by the slack node in a scenario without distributed generation. The results revealed that the multiverse optimizer offers the best solution quality and repeatability in networks of different sizes with several penetration levels of distributed power generation.

scholarly journals Optimal placement of distributed generation using colliding bodies optimization

2018 ◽  
Vol 7 (3.3) ◽  
pp. 168
Author(s):  
Gera Kalidas Babu ◽  
P V. Ramana rao
Keyword(s):  
Distributed Generation ◽  
Optimization Algorithm ◽  
Distribution Networks ◽  
Optimal Placement ◽  
Solar Photovoltaic ◽  
Load Power ◽  
Colliding Bodies Optimization ◽  
Power Loss Reduction ◽  
Radial Distribution Networks ◽  
Distribution Generation

The present paper foremost objective is to resolve best practicable location of solar photovoltaic distribution generation (DG) of several cases using different distribution load power factors and to analyze power loss reduction. This objective achieved by a recent developed method, the so called colliding bodies’ optimization algorithm, to perceive optimum location. Performances of colliding bodies’ optimization algorithm have been evaluated and compared with other search algorithms. The execution to test viability and efficiency, the proposed collid-ing bodies’ optimization is simulated on standard IEEE 38 bus radial distribution networks. The acquired outcome from colliding bodies Optimization algorithm exhibits the possible location of distributed generation through different pre assumed load power factors compared to the other stochastic search bat and genetic algorithm.  

A Novel Binary Whale Optimization Algorithm-Based Optimal Placement of Phasor Measurement Units

2018 ◽  
pp. 115-138 ◽  
Author(s):  
Tapan Prakash ◽  
Vinay Pratap Singh ◽  
Soumya Ranjan Mohanty
Keyword(s):  
Optimization Algorithm ◽  
Phasor Measurement Units ◽  
Operating Conditions ◽  
Whale Optimization Algorithm ◽  
Area Measurement ◽  
Optimal Placement ◽  
Wide Area Measurement System ◽  
Phasor Measurement ◽  
Whale Optimization ◽  
Measurement Units

Wide-area measurement system (WAMS) is an important part of present power system structure as it provides real-time synchronized measurements of the system with the aid of phasor measurement units (PMUs). Due to economic considerations, PMUs should be installed at optimal locations. The optimal placement of PMUs (OPP) is a problem of optimally placing PMUs at strategic locations maintaining the full observability of the system. In this chapter, a novel binary whale optimization algorithm (BWOA) is applied to solve OPP problem. The maximization of measurement redundancy is considered in the objective function. The proposed algorithm is examined on five different test systems operating under normal operating conditions with or without inclusion of zero-injection buses (ZIBs) and compared with the reports available in literature. The results show the effectiveness of the proposed algorithm in solving OPPP.

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