A review of hosting capacity quantification methods for photovoltaics in low-voltage distribution grids

The 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].”

In the rule-based variation of the deterministic method, three different hosting capacity values for the distribution network are obtained and the actual grid HC is given as a range between these values.

“In the end, three different hosting capacity values for the distribution network are obtained, and the actual grid HC is given as a range between these values [11,63].”

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].”

Although 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.”

The 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].”

For a huge number of small installations with several uncertainties that require large computations, the deterministic method becomes insufficient for HC quantification.

“However, for a huge number of small installations with several uncertainties that require large computations, the deterministic method becomes insufficient for HC quantification [11,73].”

The 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].”

A primary uncertainty is the stochastic nature of the PV output, which is heavily dependent on irradiation influenced by changing weather conditions.

“A primary uncertainty is the stochastic nature of the PV output that is heavily dependent on irradiation, which is influenced by changing weather conditions [11].”

Aleatory uncertainties are unknown variables that occur due to variability associated with PV generation and consumers' load consumption, while epistemic uncertainties are unknown variables related to lack of information such as the size or location of the PV on the distribution grid.

“Aleatory uncertainties are unknown variables that occur due to variability associated with PV generation and consumers' load consumption while epistemic uncertainties are unknown variables related to lack of information, such as the size of the PV or location of the PV on the distribution grid [11,91].”

The increase in scenarios translates to a need for more measurement data, leading to a tedious computational process.

“This will translate to a need for more measurement data leading to a tedious computational process [11,91].”

Other unknown variables in stochastic HC include load consumption, the number of PV installations, and the location and size of PV installation.

“Similarly, other unknown variables include load consumption, the number of PV installations, and the location and size of PV installation [11].”

Several uncertainty modeling approaches exist in the literature, including the probabilistic method, robust optimization, information gap decision theory, interval-based analysis, and hybrid probabilistic and possibilistic methods.

“Several uncertainty modeling approaches exist in the literature including the probabilistic method, robust optimization, information gap decision theory, interval-based analysis, and hybrid probabilistic and possibilistic methods [11,93].”

The streamlined ICA method applies a set of equations to assess the impact of distributed PV at each node of the distribution network without using a modeling tool or software.

“On the other hand, the streamlined ICA method applies a set of equations to assess the impact of distributed PV at each node of the distribution network without using a modeling tool or software.”

There are two types of streamlined methods in the literature: the first developed by EPRI, and the second is the streamlined ICA method.

“There are two types of streamlined methods in the literature, the first was developed by Electric Power Research Institute (EPRI) [10,130,131], while the other is the streamlined ICA (Integrated Capacity Analysis) method [11].”