Sensitivity of Hosting Capacity to Data Resolution and Uncertainty Modeling

Abad, Mohammad Seydali Seyf; Ma, Jin; Zhang, Diwei; Ahmadyar, Ahmad Shabir; Marzooghi, Hesamoddin

doi:10.1109/aupec.2018.8757980

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…However, it has to be tackled from a probabilistic perspective, considering relevant parameter uncertainties. This conclusion has been recently verified by numerous studies, as it was concluded that DHC studies, which ignore the uncertainty of electrical parameters, resulting in conservative HC levels that usually lead to a noticeable underestimation of the HC levels [28][29][30]. To perform a PHC analysis, the MCS is utilized to generate an appropriate number of probabilities for the studied uncertain parameters.…”

Section: Phc Resultsmentioning

confidence: 83%

“…However, a dynamic framework that employs numerous uncertain parameters such as variable DG-produced power caused by climate fluctuations, the uncertainty of DG integration location and unit ratings, daily load profile variations, and uncertainties in network modeling in the case of the absence of confirmed real-time measurements is required to express HC better. In this regard, it was found that that deterministic HC (DHC) assessment methodologies only show a conservative (worst-case) figure for a network's capability to host more DG units [28]. For that reason, recent studies have started to use the probabilistic hosting capacity (PHC), unlike DHC studies, which ignore the uncertainty of the electrical parameters [29,30].…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm

et al. 2019

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The high penetration of distributed generation (DG) units with their power-electronic interfaces may lead to various power quality problems, such as excessive harmonic distortions and increased non-sinusoidal power losses. In this paper, the probabilistic hosting capacity (PHC) due to the high penetration of photovoltaic units in a non-sinusoidal power distribution system is investigated. A C-type harmonic filter is proposed, to maximize the harmonic-constrained PHC. An optimization problem is formulated by using a Monte Carlo simulation, taking into account various uncertain parameters, such as the intermittent output power of the DGs, background voltage harmonics, load alteration, and the filter parameters’ variations. In addition, different operational constraints have been considered, such as the bus voltage, line thermal capacity, power factor, and individual and total harmonic distortion limits. A swarm-based, meta-heuristic optimization algorithm known as the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) has been examined for the optimal design of the proposed filter. Besides, other optimization algorithms were examined for validation of the solution. The PHC results obtained are compared with the conventional deterministic HC (DHC) results, and it is found that the PHC levels are higher than those obtained by conservative HC procedures, practical rules of thumb, and the DHC approaches.

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Section: Phc Resultsmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm

et al. 2019

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“…This is because the constant generation method is very simplistic and can only be used to estimate the HC value. Furthermore, it is very easy to underestimate the HC when calculating it based on the worst-case scenario [14,41]. The reason for this is that minimum load consumption and maximum DER generation is unlikely to occur at the same time for most of the cases [42].…”

Section: Constant Generation Methodsmentioning

confidence: 99%

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

2020

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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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“…On the one hand, long time scale historical data, such as one year or a few years [16], is needed to capture various combinations of production and demand. On the other hand, a relatively short time interval [34] (e.g., 1-min, 10-min, and 15-min) is critical for capturing the variabilities of these uncertainties. However, the adoption of time series analysis introduces a significant number of power flow calculations into assessments.…”

Section: Time-varying Renewables and Demandmentioning

confidence: 99%

Probabilistic Assessment of Hybrid Wind-PV Hosting Capacity in Distribution Systems

et al. 2020

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In recent years, hybrid wind-photovoltaic (PV) systems are flourishing due to their advantages in the utilization of renewable energy. However, the accurate assessment of the maximum integration of hybrid renewable generation is problematic because of the complex uncertainties of source and demand. To address this issue, we develop a stochastic framework for the quantification of hybrid energy hosting capacity. In the proposed framework, historical data sets are adopted to represent the stochastic nature of production and demand. Moreover, extreme combinations of production and demand are introduced to avoid multiple load flow calculations. The proposed framework is conducted in the IEEE 33-bus system to evaluate both single and hybrid energy hosting capacity. The results demonstrate that the stochastic framework can provide accurate evaluations of hosting capacity while significantly reducing the computational burden. This study provides a comprehensive understanding of hybrid wind-PV hosting capacity and verifies the excellent performance of the hybrid energy system in facilitating integration and energy utilization.

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Sensitivity of Hosting Capacity to Data Resolution and Uncertainty Modeling

Cited by 12 publications

References 15 publications

Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm

Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm

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

Probabilistic Assessment of Hybrid Wind-PV Hosting Capacity in Distribution Systems

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