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Open Access

Pareto Front Analysis Method for Optimization of PV Inverter Based Volt/Var Control Considering Inverter Lifetime

Qingmian Chai1, Cuo Zhang2( ), Yan Xu3, Zhao Yang Dong3, Rui Zhang1
School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, NSW 2052, Australia
School of Electrical and Information Engineering, The University of Sydney, Sydney, NSW 2006, Australia
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
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Abstract

Photovoltaic (PV) inverter-based volt/var control (VVC) is highly promising to tackle the emerging voltage regulation challenges brought by increasing PV penetration. However, PV inverter operational reliability has arisen as a critical concern for practical VVC implementation. This paper proposes a new PV inverter based VVC optimization model and a Pareto front analysis method for maintaining a satisfactory inverter lifetime. First, reliability of the vulnerable DC-link capacitor inside a PV inverter is analyzed, and long-term VVC impact on inverter operational reliability is identified. Second, a multi-objective PV inverter based VVC optimization model is proposed for minimizing both inverter apparent power output and network power loss with a weighting factor. Third, a Pareto front analysis method is developed to visualize the impact of the weighting factor on VVC performance and inverter reliability, thus determining the effective weighting factor to reduce network power loss with expected inverter lifetime. Effectiveness of the proposed VVC optimization model and Pareto front analysis method are verified in a case study.

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CSEE Journal of Power and Energy Systems
Pages 111-121

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Cite this article:
Chai Q, Zhang C, Xu Y, et al. Pareto Front Analysis Method for Optimization of PV Inverter Based Volt/Var Control Considering Inverter Lifetime. CSEE Journal of Power and Energy Systems, 2023, 9(1): 111-121. https://doi.org/10.17775/CSEEJPES.2022.00840

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Received: 11 February 2022
Revised: 02 May 2022
Accepted: 27 June 2022
Published: 09 December 2022
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