A comprehensive analysis for multi-objective distributed generations and capacitor banks placement in radial distribution networks using hybrid neural network algorithm

• Published in: Knowledge-based systems Vol. 231; p. 107387
• Main Authors: Nguyen, Tri Phuoc, Nguyen, Thi Anh, Phan, Thang Van-Hong, Vo, Dieu Ngoc
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 14.11.2021; Elsevier Science Ltd
• Subjects: Algorithms; Capacitor banks; Capacitors; Cost benefit analysis; Cost control; Distributed generation; Economic analysis; Electric power loss; Energy dissipation; Feasibility studies; Multi-objective optimization; Multiple objective analysis; Neural network algorithm; Neural networks; Optimization; Performance evaluation; Performance indices; Power factor; Radial distribution; Radial distribution network; Simultaneous distributed generation and capacitor placement; Symbiosis; Symbiotic organism search; Voltage stability; Voltage-dependent load model; Voltage-dependent load model; Simultaneous distributed generation and capacitor placement; Multi-objective optimization; Neural network algorithm; Radial distribution network; Symbiotic organism search
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.107387

This paper proposes a new methodology based on the combination of symbiosis organism search (SOS) and neural network algorithm (NNA), named SOS-NNA, for the optimal planning and operation of distributed generations (DGs) and capacitor banks (CBs) in the radial distribution networks (RDNs) considering single- and multi-objective optimization with various equality and inequality constraints. The multi-objective framework is a weighted combination of five component objectives including active power loss, voltage deviation, voltage stability, load balancing, and supply reliability. In addition, practical voltage-dependent non-linear load models are also examined. Two benchmark instances 33 and 69-bus networks have been utilized to evaluate the effectiveness and feasibility of the proposed SOS-NNA via various case studies. The obtained outcomes for different operating cases and test scenarios reveal that the proper combination of optimal power factor DGs (OPF-DGs) and CBs can boost the network performance indexes to an ever-highest degree for all test networks. A cost–benefit analysis is further implemented to evaluate the economic feasibility of the obtained multi-objective solutions. As a result, the proposed SOS-NNA shows a marked improvement regarding the solution quality compared to the recently well-established optimization algorithms as well as outweighs the original NNA in the performance indexes of the solution quality, convergence speed, and statistical results. In addition, the proposed SOS-NNA has been employed for allocating different DG types in RDNs with the consideration of actual 24-h load profiles and the obtained outcomes contribute to the further improvement of yearly energy loss mitigation as well as cost savings. •A new hybrid algorithm SOS-NNA is proposed for distribution systems optimization.•A multi-objective framework relating to five technical objectives is presented.•The best planning and operation solution is given for distribution systems.•Voltage-dependent load model and hourly generation scheduling cases are examined.•The superiority of proposed hybrid algorithm over other algorithms is verified.