Optimal Sizing and Deployment of Renewable Energy Generators in Practical Transmission Network Using Grid-Oriented Multiobjective Harmony Search Algorithm for Loss Reduction and Voltage Profile Improvements

This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Pra...

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Published inInternational transactions on electrical energy systems Vol. 2023; pp. 1 - 19
Main Authors Kumar, Pramod, Swarnkar, Nagendra Kumar, Mahela, Om Prakash, Khan, Baseem, Anand, Divya, Singh, Aman, Mazon, Juan Luis Vidal, Alharithi, Fahd S.
Format Journal Article
LanguageEnglish
Published Hoboken Hindawi 2023
Hindawi Limited
Wiley
Subjects
Algorithms
Alternative energy
Cost control
Deviation
Electric potential
Electric power loss
Generators
Genetic algorithms
Heuristic
Loss reduction
Multiple objective analysis
Optimization
Optimization techniques
Parameter sensitivity
Payback periods
Performance evaluation
Performance indices
Reactive power
Renewable energy
Renewable resources
Search algorithms
Voltage
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Summary:This paper presents grid-oriented multiobjective harmony search algorithm (GOMOHSA) to incorporate the multiple grid parameters for minimization of the active power loss, reactive power loss, and total voltage deviations (TVD) in a part of practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Limited (RVPN) in southern parts of Rajasthan state of India. This is achieved by optimal deployment of optimally sized renewable energy (RE) generators using GOMOHSA. Performance indexes such as active power loss minimization index (APMLI), the reactive power loss minimization index (RPMLI), and the total voltage deviation improvement index (TVDII) are introduced to evaluate the health of the test network with different load scenarios. Performance of proposed GOMOHSA has been tested for five different operating scenarios of loads and RE generation. It is established that the proposed GOMOHSA finds the optimal deployment of optimally sized RE generators, and the investment cost of deployment of these RE generators can be recovered within a time period that is less than 5 years. Performance of GOMOHSA is superior compared to a conventional genetic algorithm (GA) in terms of performance indexes, RE generator capacity, payback period, and parameter sensitivity. Study is performed using MATLAB software for loading scenario of base year 2021 and projected year 2031.
ISSN:2050-7038
2050-7038
DOI:10.1155/2023/6315918