An Efficient Hybrid Particle Swarm and Gradient Descent Method for the Estimation of the Hosting Capacity of Photovoltaics by Distribution Networks
Esau Zulu (),
Ryoichi Hara and
Hiroyuki Kita
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Esau Zulu: Faculty of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan
Ryoichi Hara: Faculty of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan
Hiroyuki Kita: Faculty of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan
Energies, 2023, vol. 16, issue 13, 1-17
Abstract:
With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches.
Keywords: distribution networks; hosting capacity; gradient descent; particle swarm optimization; photovoltaics; PV integration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:13:p:5207-:d:1188297
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