A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation
Open accessAbstract
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.
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There are five major methods for HC quantification in distribution networks: deterministic, time series, stochastic, streamlined-stochastic, and optimization-based methods.
“There are five major methods for HC quantification in the distribution networks found in the literature.They include; deterministic, time series, stochastic, streamlined-stochastic, and optimization-based methods [11,14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3, “Hosting Capacity Assessment Methods,” explicitly says there are four major methods—not five—and lists deterministic, stochastic, optimization-based, and streamlined methods. Section 3.1.2 discusses time series as a method within that framework, but the paper does not list it as a fifth major category or call the streamlined method “streamlined-stochastic.” (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe deterministic method generally adopts the constant PV generation approach, with the PV output assumed to be maximum and not varying throughout the calculation.
“The deterministic method generally adopts the constant PV generation approach, with the PV output as the independent variable assumed to be maximum and does not vary throughout the calculation [11,14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.1.1 describes a constant-generation method and gives a PV example that assumes maximum generation, supporting part of the claim. But it treats maximum generation as an assumption in a particular study or worst-case scenario, not a general rule. Section 3.1.2 also describes a time-series method that uses varying generation profiles, so the claim overgeneralizes deterministic methods. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe deterministic method does not consider the uncertainty of PV production, load consumption, and the size and location of the PV; these parameters are assumed known and assigned fixed input values before the HC calculation begins.
“The deterministic method does not consider the uncertainty of the PV production, load consumption of consumers, and the size and location of the PV.Instead, these parameters are assumed to be known and assigned fixed input values before the HC calculation begins [11,14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.1 (Deterministic Method; page unavailable) says deterministic calculations use known generation and load values, with production, consumption, and location defined before the HC calculation. It does not say PV size is fixed beforehand; Section 3.1.1 describes a deterministic procedure that incrementally increases DER size to find hosting capacity. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThe deterministic method is very simple and useful for quick estimation and overview of the HC of electrical distribution networks.
“The deterministic method is very simple and useful for quick estimation and overview of the HC of electrical distribution networks [14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
The Conclusions call the deterministic method the most basic technique, and Table 6 describes its constant-source variant as simple, requiring little calculation time, and producing approximate results. Section 3.1 also includes the time-series method under deterministic methods; Table 6 rates that variant as moderately complex and moderately time-consuming. The claim therefore overgeneralizes the quick, simple estimate to all deterministic methods. (LLM, conf=0.85)
Experimental — accuracy not yet measuredAlthough the five major HC methods are unique in implementation, they all use power flow calculations to find the values of the performance indices and follow the same general approach.
“Although these methods are unique in terms of actual implementation, they all use power flow calculations to find the values of the performance indices in the network and they all follow the same general approach shown in Figure 1.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3, “Hosting Capacity Assessment Methods,” says the methods differ in implementation but all use power-flow calculations and follow the same general steps. It identifies four major methods, not the five stated in the claim; it also describes power flow as finding voltages and currents rather than explicitly saying it finds all performance-index values. (LLM, conf=0.90)
Experimental — accuracy not yet measured
The worst-case scenario often adopted in the deterministic method can easily underestimate the HC because the minimum load demand and maximum solar PV output are overestimated and unlikely to happen simultaneously.
“Additionally, the worst-case scenario often adopted in the deterministic method can easily underestimate the HC because the minimum load demand and maximum solar PV output are overestimated and unlikely to happen simultaneously [14,74].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.1.1 (Constant Generation Method; page unavailable) says worst-case calculations can underestimate hosting capacity because minimum load and maximum DER generation are unlikely to occur simultaneously. It does not say that the minimum load or maximum solar PV output values themselves are overestimated; its statement concerns DER generation generally, rather than solar PV specifically. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe time series HC calculation method is an upgrade of the deterministic method that replaces the fixed values with actual system measurements of load and PV generation for HC estimation.
“The time series HC calculation method is an upgrade of the deterministic method.This method replaces the fixed values in the deterministic method with actual system measurements of load and PV generation for HC estimation [14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.1.2 (page unavailable) calls the time series method an improvement on the constant generation method and says it replaces constant generation values with profiles for dynamic network components. It does not say the method necessarily uses actual system measurements of load and PV generation: the section also describes profiles generated from average or minimum consumption and generation values. The source calls the predecessor the constant generation method, rather than the deterministic method. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe need for high-resolution simulations in the time series method is time-consuming and poses a huge computational burden.
“Additionally, the need for high-resolution simulations in this method is time-consuming and poses a huge computational burden [11,14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.1.2 says time-series calculations use the smallest available time step and that large amounts of data can increase computational time. That supports the time-consuming aspect, but the claim of a “huge computational burden” overstates the review: Table 6 classifies time-series calculation time as moderate, not large. Page numbers were not available in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFor customer-based HC calculations, the stochastic method is mostly preferred.
“In this case, the stochastic method is mostly preferred for HC calculations.”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3 says customer-based hosting-capacity calculations are probabilistic and that stochastic methods can be used; its table also labels a customer-based scenario “Stochastic.” The paper does not say stochastic methods are mostly preferred for such calculations, so the claim overstates its support. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTo begin the PLF, random scenarios for the number, location, and/or size of PV are created as input in the distribution network using probability distribution functions (PDFs).
“To begin the PLF, random scenarios for the number, location, and/or size of PV are created as input in the distribution network using probability distribution functions (PDFs) [14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Section 3.2 (page unavailable) describes creating random scenarios for the number, location and/or size of distributed energy resources as an initial step in probabilistic power flow, and gives PV placement and sizing as examples. It does not specify that those scenarios are generated using probability distribution functions (PDFs). (LLM, conf=0.85)
Experimental — accuracy not yet measuredTraditional deterministic models are not reliable for modeling systems with a high level of unpredictability.
“Traditional deterministic models are not reliable for modeling systems with a high level of unpredictability.”
From Stochastic Approach for Increasing the PV Hosting Capacity of a Low-Voltage Distribution Network (2022) · cites this paper as [26]
Section 3.1.1 (Constant Generation Method; page unavailable) says that this method is simplistic, can only estimate hosting capacity, and may underestimate it under worst-case assumptions. Section 3.2 (Stochastic Method; page unavailable) explains that stochastic techniques account for unknown variables in DER integration. These passages support limitations of one deterministic approach in distribution-network analysis, but not the broader assertion that traditional deterministic models are unreliable for all highly unpredictable systems. (LLM, conf=0.85)
Experimental — accuracy not yet measuredStochastic approaches combining probabilistic definitions of unpredictable parameters and the Monte Carlo method on unknown parameters must be applied to problems with a high level of unpredictability.
“Zain et al. have shown that stochastic approaches combining the probabilistic definitions of unpredictable parameters and the Monte Carlo method on unknown parameters must be applied to these problems [26].”
From Stochastic Approach for Increasing the PV Hosting Capacity of a Low-Voltage Distribution Network (2022) · cites this paper as [26]
Section 3.2, “Stochastic Method” (page not provided), describes using probabilistic power flow to account for unknown variables in distribution-network hosting-capacity calculations and identifies Monte Carlo simulation as the most common way to generate random scenarios. It says using stochastic techniques is reasonable, not that combining them with Monte Carlo is mandatory for all highly unpredictable problems; it also lists other methods. (LLM, conf=0.87)
Experimental — accuracy not yet measuredFrom a customer-centric viewpoint, the HC calculation problem becomes probabilistic due to uncertainties since distribution network operators do not have control over the location, size, or number of PV installations.
“From a customer-centric viewpoint, the HC calculation problem becomes a probabilistic one due to uncertainties since the distribution network operators do not have control over the location, size, or number of PV installations [14,55].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
Utility-based HC calculation is often characterized as an optimization problem with the objective of maximizing PV or EV charging integration without endangering the technical operation of the distribution grid.
“However, the utility-based HC calculation is often characterized as an optimization problem with the objective of maximizing PV or EV charging integration without endangering the technical operation of the distribution grid [14].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
The streamlined HC may compromise accuracy for complex feeders with branch diversity.
“However, the streamlined HC may compromise accuracy for complex feeders with branch diversity [10,14,131].”
From Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review (2023) · cites this paper as [14]
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- Yuan, J.; Weng, Y.; Tan, C. Quantifying Hosting Capacity for Rooftop PV System in LV Distribution Grids. 2019. Available online: https://arxiv.org/abs/1909.00864 (accessed on 29 January 2020). · arXiv
- Authors
- Zain ul Abideen, M., Ellabban, O., & Al-Fagih, L.
- Affiliations
- Division of Sustainability, College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar
- Qatar Science and Technology Park (QA)
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2020-06-01
- Volume
- 13
- Issue
- 11
- Pages
- 2758
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W3029673302
- Cited by
- 161
- References
- 71
- Citations by year
- 2026: 17 · 2025: 39 · 2024: 22 · 2023: 41 · 2022: 26 · 2021: 12 · 2020: 4
- FWCI
- 7.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- Research Repository (Kingston University London) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Qatar Foundation
- Hamad Bin Khalifa University
- Khalifa University of Science, Technology and Research
- Qatar National Library
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Optimal Power Flow Distribution
- Physical Sciences › Engineering › Control and Systems Engineering › Thermal Analysis in Power Transmission
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- hosting capacity, distribution networks, distributed energy resources, voltage deviation, phase imbalance, thermal overload, power losses, power quality
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