Next Article in Journal
Multilevel and Multiregional Analysis of the Electricity Metabolism of Mexico across Sectors
Previous Article in Journal
Integration of EV in the Grid Management: The Grid Behavior in Case of Simultaneous EV Charging-Discharging with the PV Solar Energy Injection
Previous Article in Special Issue
A Novel Combined Control Strategy for a Two-Stage Parallel Full-Wave ZCS Quasi Resonant Boost Converter for PV-Based Battery Charging Systems with Maximum Power Point Tracking
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Solar PV Stochastic Hosting Capacity Assessment Considering Epistemic (E) Probability Distribution Function (PDF)

Electric Power Engineering, Division of Energy Science, Department of Engineering Sciences and Mathematics (TVM), Luleå University of Technology, 93187 Skellefteå, Sweden
Electricity 2022, 3(4), 586-599; https://doi.org/10.3390/electricity3040029
Submission received: 6 September 2022 / Revised: 16 November 2022 / Accepted: 28 November 2022 / Published: 5 December 2022
(This article belongs to the Special Issue Recent Advances in Grid Connected Photovoltaic Systems)

Abstract

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks’ hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO’s planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network.
Keywords: hosting capacity; Monte Carlo methods; solar power; stochastic assessment; uncertainty hosting capacity; Monte Carlo methods; solar power; stochastic assessment; uncertainty

Share and Cite

MDPI and ACS Style

Mulenga, E. Solar PV Stochastic Hosting Capacity Assessment Considering Epistemic (E) Probability Distribution Function (PDF). Electricity 2022, 3, 586-599. https://doi.org/10.3390/electricity3040029

AMA Style

Mulenga E. Solar PV Stochastic Hosting Capacity Assessment Considering Epistemic (E) Probability Distribution Function (PDF). Electricity. 2022; 3(4):586-599. https://doi.org/10.3390/electricity3040029

Chicago/Turabian Style

Mulenga, Enock. 2022. "Solar PV Stochastic Hosting Capacity Assessment Considering Epistemic (E) Probability Distribution Function (PDF)" Electricity 3, no. 4: 586-599. https://doi.org/10.3390/electricity3040029

APA Style

Mulenga, E. (2022). Solar PV Stochastic Hosting Capacity Assessment Considering Epistemic (E) Probability Distribution Function (PDF). Electricity, 3(4), 586-599. https://doi.org/10.3390/electricity3040029

Article Metrics

Back to TopTop