scholarly journals A Multi-Objective Hybrid Genetic Algorithm for Sizing and Siting of Renewable Distributed Generation

2021 ◽  
Vol 11 (16) ◽  
pp. 7442
Author(s):  
Paulo S. Zanin ◽  
Lina Paola Garcés Negrete ◽  
Gelson A. A. Brigatto ◽  
Jesús M. López-Lezama
Keyword(s):  
Genetic Algorithm ◽  
Distributed Generation ◽  
Distribution System ◽  
Hybrid Genetic Algorithm ◽  
Test System ◽  
Environmental Benefits ◽  
New Paradigm ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Pareto Sets

Renewable generation has been addressed in several aspects but it still represents a new paradigm for the expansion of the electricity supply. This paper aims to propose a new model for the sizing and siting problem of distributed generation (DG), based on renewable sources and considering three main aspects: technical, from the distribution utility viewpoint; economical, from the DG owner’s viewpoint, and environmental, from a sustainability perspective. A multi-objective Genetic Algorithm and the Maximin metric are implemented to obtain optimal Pareto sets; also, three decision criteria, considering the concept of preference, are applied to select a final solution from Pareto sets. Case-studies are carried out in medium voltage systems: the 69-bus distribution test system, known from literature, and a 918-bus Brazilian distribution system. Diversity of alternatives in the obtained Pareto sets testify algorithm effectiveness in searching for solutions to the distributed generation sizing and siting problem, in order to ensure power loss reductions, investment return, and environmental benefits. The proposed methodology contributes to the discussions and perspectives among electricity utilities, DG owners, society, and regulators regarding planning and decision making tools.

scholarly journals A Weighted Sum of Multi-Objective Function based Reliability Analysis with the Integration of Distributed Generation

2020 ◽  
Vol 9 (4) ◽  
pp. 340-343
Keyword(s):  
Power System ◽  
Distributed Generation ◽  
Distribution System ◽  
Test System ◽  
Reliability Indices ◽  
Multi Objective ◽  
Distributed Generators ◽  
System A ◽  
Negative Impacts ◽  
Improvement Index

New trends in power system include the placement of the distributed generators (DGs) to overcome the drawbacks of the conventional power system, it can be connected near to the load points. Hence, the placement of DG is an important factor to be considered for the analysis due to its positive as well as negative impacts. An improved analytical approach for enhancing the reliability of the power system has been developed in this paper. By integrating DG of selected penetration level at all nodes of the test system, a set of reliability indices are evaluated based on interruption, improvement indices and blended as the multi-objective functions. Combinations of LVDI and PLRI with reliability improvement index are calculated by selecting the blended indices. Hence, enhanced system reliability is achieved. The analysis is carried out under the MATLAB platform on the standard RTBS bus distribution system

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

Sign in / Sign up

Export Citation Format

Share Document