Automated Quantification of PV Hosting Capacity In Distribution Networks Under User-Defined Control and Optimisation Procedures

Martin, William R.; Stauffer, Yves; Ballif, Christophe; Hütter, Andreas; Alet, Pierre-Jean

doi:10.1109/isgteurope.2018.8571427

Cited by 8 publications

(16 citation statements)

References 6 publications

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“…The HC is more pronounced in the LV systems with more midday loading as per the findings of this study. Moreover, the HC values of a three-phase four-wire unbalanced LV network in Perth, Australia, and radial test feeder in Switzerland was found to be as 63.81 kW (31.9%) and 43 kVA in [39] and [46], respectively. The researchers in [46] investigated the application of the user-defined control and application of the Smart Inverter control for the increase in HC and concluded that the appropriate adjustment of inverter control parameters results in larger HC improvement as compared to fixed parameters.…”

Section: Transformer Ratingmentioning

confidence: 97%

“…Moreover, the HC values of a three-phase four-wire unbalanced LV network in Perth, Australia, and radial test feeder in Switzerland was found to be as 63.81 kW (31.9%) and 43 kVA in [39] and [46], respectively. The researchers in [46] investigated the application of the user-defined control and application of the Smart Inverter control for the increase in HC and concluded that the appropriate adjustment of inverter control parameters results in larger HC improvement as compared to fixed parameters. A rural small farm that experiences large variations in local demand from zero to its rated value is analyzed for HC value with reference to the transformer rating in [13].…”

Section: Transformer Ratingmentioning

confidence: 97%

“…HC can alternatively be defined with reference to the feeding transformer's capacity. Here HC is defined as the ratio of total PV production to the transformer rated capacity in [6,8,13,16,34,39,46,[70][71][72][73]. HC analysis of a radial LV network in Brazil ended up in a maximum penetration level of 38.2% of transformer capacity in [8].…”

Section: Transformer Ratingmentioning

confidence: 99%

“…The Power Quality standards and the feeder's constraints in terms of thermal limits are the major deciding factors for the determination of maximum permissible PV penetration in LV networks [46]. Different Power Quality limiting factors result in different HC values and thus it is not a single value.…”

Section: Limiting Factorsmentioning

confidence: 99%

“…The dynamic loading of components alleviates the voltage stability issues resulting from cloud movements especially in case of small geographical areas that are otherwise not handled by static load modeling [41]. Moreover, some HC increasing methods employ ancillary devices for voltage regulation such as inverter PF control [3,44], Smart Inverters [2,45,46], Static Compensators [47,48] and Secondary VAr Controllers (SVCs) [10].…”

Section: Introductionmentioning

confidence: 99%

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Review on the PV Hosting Capacity in Distribution Networks

2020

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The increasing penetration of Photovoltaic (PV) generation results in challenges regarding network operation, management and planning. Correspondingly, Distribution Network Operators (DNOs) are in the need of totally new understanding. The establishment of comprehensive standards for maximum PV integration into the network, without adversely impacting the normal operating conditions, is also needed. This review article provides an extensive review of the Hosting Capacity (HC) definitions based on different references and estimated HC with actual figures in different geographical areas and network conditions. Moreover, a comprehensive review of limiting factors and improvement methods for HC is presented along with voltage rise limits of different countries under PV integration. Peak load is the major reference used for HC definition and the prime limiting constraint for PV HC is the voltage violations. However, the varying definitions in different references lead to the conclusion that, neither the reference values nor the limiting factors are unique values and HC can alter depending on the reference, network conditions, topology, location, and PV deployment scenario.

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Section: Transformer Ratingmentioning

confidence: 97%

Section: Transformer Ratingmentioning

confidence: 97%

Section: Transformer Ratingmentioning

confidence: 99%

Section: Limiting Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review on the PV Hosting Capacity in Distribution Networks

2020

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State of the Art - Drones/Surface Platforms and Green Energy

Popa,

Popescu,

Mocanu

2023

IOP Conf. Ser.: Earth Environ. Sci.

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This paper presents a summary of what surface drones mean and the possibility of using these platforms in pollution problems. Also, in this paper, the authors have proposed to predict (in the form of a review) the possibility of producing the electricity needed for these platforms with the help of photovoltaic panels and the storage of energy in different types of batteries. This paper is a summary of what drones/surface platforms entail, the production of electricity using photovoltaic panels and an overview of battery types.

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Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

Chang

Chinh²,

Sinatra

2022

IEEE Access

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This paper presents an Equilibrium Optimizer (EO)-based method that simultaneously determines locations and sizes of multiple photovoltaic (PV) units for maximizing a distribution system's PV hosting capacity (PVHC). The proposed method avoids various search-space restrictions seen in recent literature. The constraints in this paper are separated into two types based on the necessity of power flow analysis. The proposed method is implemented through co-simulation between MATLAB and OpenDSS. PV output and load demand variation is considered through a 24-hour profile representing a season. Smart inverter is considered through the PV inverter volt-var control (VVC) scheme. The proposed method is tested on the IEEE 123-bus benchmark system. Four test cases are considered to determine the impact of reverse power flow (RPF) limit and the impact of VVC toward the optimal PV planning. The results show that EO produces superior results compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Coyote Optimization Algorithm (COA).

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Automated Quantification of PV Hosting Capacity In Distribution Networks Under User-Defined Control and Optimisation Procedures

Cited by 8 publications

References 6 publications

Review on the PV Hosting Capacity in Distribution Networks

Review on the PV Hosting Capacity in Distribution Networks

State of the Art - Drones/Surface Platforms and Green Energy

Equilibrium Optimizer-Based Approach of PV Generation Planning in a Distribution System for Maximizing Hosting Capacity

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