Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

Arshad, Ammar; Püvi, Verner; Lehtonen, Matti

doi:10.3390/en11061467

Cited by 30 publications

(46 citation statements)

References 16 publications

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“…A Monte Carlo-based algorithm was proposed for the determination of HC, considering its dependence on various operational scenarios. The load types depending on heating modes were randomly selected by running the MC simulations 1000 times [14] to ensure the accuracy of HC results. The changing loading profiles of each region were sampled randomly according to the percentages of three types of loads: storage heating, district heating, and direct electric heating, as given in Table 2.…”

Section: Assessment Methodology For Pv Hc Determinationmentioning

confidence: 99%

“…The unbalance condition of naturally unbalanced systems can be increased further by the connection of single-phase PV installations. The lognormal distribution function of load unbalance data employed in this study was based on a single household in Helsinki, Finland, as used also in a study conducted by [14] that was further used for the determination of voltage unbalance magnitude and the angle.…”

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confidence: 99%

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Comparison of Different References When Assessing PV HC in Distribution Networks

2021

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The burgeoning photovoltaics’ (PVs) penetration in the low voltage distribution networks can cause operational bottlenecks if the PV integration exceeds the threshold known as hosting capacity (HC). There has been no common consensus on defining HC, and its numerical value varies depending on the reference used. Therefore, this article compared the HC values of three types of networks in rural, suburban, and urban regions for different HC reference definitions. The comparison was made under balanced and unbalanced PV deployment scenarios and also for two different network loading conditions. A Monte Carlo (MC) simulation approach was utilized to consider the intermittency of PV power and varying loading conditions. The stochastic analysis of the networks was implemented by carrying out a large number of simulation scenarios, which led towards the determination of the maximum amount of PV generation in each network case.

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Section: Assessment Methodology For Pv Hc Determinationmentioning

confidence: 99%

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confidence: 99%

Comparison of Different References When Assessing PV HC in Distribution Networks

2021

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“…The synthetic networks are randomly generated sets of nodes. The scale of network area, number of nodes and estimated average loads are based on typical Finnish networks, developed in [27]. Load nodes are placed on a plane in a random manner.…”

Section: Problem Formulationmentioning

confidence: 99%

Slime Mold Inspired Distribution Network Initial Solution

et al. 2020

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Electricity distribution network optimisation has attracted attention in recent years due to the widespread penetration of distributed generation. A considerable portion of network optimisation algorithms rely on an initial solution that is supposed to bypass the time-consuming steps of optimisation routines. The aim of this paper is to present a nature inspired algorithm for initial network generation. Based on slime mold behaviour, the algorithm can generate a large-scale network in a reasonable computation time. A mathematical formulation and parameter exploration of the slime mold algorithm are presented. Slime mold networks resemble a relaxed minimum spanning tree with better balance between the investment and loss costs of a distribution network. Results indicate lower total costs for suburban and urban networks.

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“…In addition, based on the state sampling non-sequential Monte Carlo simulation and the DC load flow-based load curtailment model, Bakkiyaraj R.A. et al simulated the random fault of the power system, and put forward a power system reliability evaluation method [19]. Also, by using the Monte Carlo method to simulate unbalanced voltage and then analyzing the effects of unbalanced voltage on PV hosting capacity, the hosting capacity of PV in different regions was shown in [20]. Moreover, the Monte Carlo method was used to simulate the uncertainty of wind power generation and users' electric vehicle charging behaviors in [21].…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a joint operation model is proposed to decrease the high uncertainty of a wind power system by centralizing electric vehicle charging stations. In [18][19][20][21], the Monte Carlo method was successfully used to model the uncertainty of power systems, but this method has some limitations, such as for example, high complexity and low computation efficiency. In order to avoid the limitations of the Monte Carlo method, factors (excluding the uncertainty of renewable energy resource) that can affect energy storage system installation capacity are comprehensively considered when sizing hybrid energy storage systems.…”

Section: Introductionmentioning

confidence: 99%

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

Liu

et al. 2018

Applied Sciences

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Abstract:With the rapid development of industry, more fossil energy is consumed to generate electricity, which increases carbon emissions and aggravates the burden of environmental protection. To reduce carbon emissions, traditional centralized power generation networks are transforming into distributed renewable generation systems. However, the deployment of distributed generation systems can affect power system economy and stability. In this paper, under different time scales, system economy, stability, carbon emissions, and renewable energy fluctuation are comprehensively considered to optimize battery and super-capacitor installation capacity for an off-grid power system. After that, based on the genetic algorithm, this paper shows the optimal system operation strategy under the condition of the theoretical best energy storage capacity. Finally, the theoretical best capacity is tested under different renewable energy volatility rates. The simulation results show that by properly sizing the storage system's capacity, although the average daily costs of the system can increase by 10%, the system's carbon emissions also reduce by 42%. Additionally, the system peak valley gap reduces by 23.3%, and the renewable energy output's fluctuation range and system loss of load probability are successfully limited in an allowable range. Lastly, it has less influence on the theoretical best energy storage capacity if the renewable energy volatility rate can be limited to within 10%.

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Monte Carlo-Based Comprehensive Assessment of PV Hosting Capacity and Energy Storage Impact in Realistic Finnish Low-Voltage Networks

Cited by 30 publications

References 16 publications

Comparison of Different References When Assessing PV HC in Distribution Networks

Comparison of Different References When Assessing PV HC in Distribution Networks

Slime Mold Inspired Distribution Network Initial Solution

Sizing Hybrid Energy Storage Systems for Distributed Power Systems under Multi-Time Scales

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