A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks

Sadeghian, Hamidreza; Wang, Zhifang

doi:10.1016/j.renene.2019.09.117

Cited by 41 publications

(28 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The calculation of the HC of a distribution network can be thought of in two ways, customer-based and utility based [17]. When considered from the customer-based perspective, the problem of the HC calculation becomes a probabilistic one.…”

Section: Hosting Capacity Assessment Methodsmentioning

confidence: 99%

“…There are various methods for generating random scenarios. The most common is the Monte Carlo simulation [17,18,20,26,41,[47][48][49][50][51][52][53][54][55][56][57][58][59][60]. Other methods include random DER deployment [61], sparse grid technique [55] and Quasi Monte Carlo [54].…”

Section: Stochastic Methodsmentioning

confidence: 99%

“…In some of the studies [18,41,52,53,56], the number of DER units in the distribution network is set by the authors and is increased from the minimum value to 100% (DER is connected to each potential integration location) while Monte Carlo simulations are performed at each increment. The Monte Carlo simulation is used for different purposes such as, to generate the DER location [17,18,20,22,41,47,50,52,53,56,57,60], size [17,18,20,47,53,56,57], connection phase in the three-phase distribution networks [50,57,60], type [22] and DER generation and load profiles scenarios [56].…”

Section: Stochastic Methodsmentioning

confidence: 99%

“…In the optimization-based method, DER integration is defined as an optimization problem where the objective is to maximize the active power injection of DER into the distribution network while making sure that the distribution network operational limits are satisfied. Sometimes [17,62,63], a multi-objective function is set up to find the HC that results in maximizing the DER integration while minimizing the distribution losses or costs. Other times, a single objective function is defined to maximize the HC [23,[64][65][66][67].…”

Section: Optimization-based Methodsmentioning

confidence: 99%

“…An impact assessment framework, based on Monte Carlo technique, has been proposed in [17] to evaluate the effects of two types of solar PV systems (customer-based installation and utility-based installation) on a given distribution network. Here, reverse power flow and voltage violation have been considered as the limiting factors to minimize energy loss, voltage deviation and voltage fluctuation in a realistic distribution network.…”

Section: Voltage Deviationmentioning

confidence: 99%

See 4 more Smart Citations

A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation

2020

View full text Add to dashboard Cite

Integration of distributed energy resources (DERs) has numerous advantages as well as some disadvantages. To safely integrate DERs into a given distribution network and to maximize their benefits, it is important to thoroughly analyze the impact of DERs on that particular network. The maximum amount of DERs that a given distribution network can accommodate without causing technical problems or without requiring infrastructure modifications is defined as the hosting capacity (HC). In this work, a review of the recent literature regarding the HC is presented. The major limiting factors of HC are found to be voltage deviation, phase unbalance, thermal overload, power losses, power quality, installation location and protection devices’ miscoordination. The studies are found to employ one of four different methods for HC calculation: (i) deterministic, (ii) stochastic, (iii) optimization-based and (iv) streamlined. Commercially available tools for HC calculation are also presented. The review concludes that the choice of tools and methods for HC calculation depends on the data available and the type of study that is to be performed.

show abstract

Section: Hosting Capacity Assessment Methodsmentioning

confidence: 99%