Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

Wu, Jiaqi; Yuan, Jingyi; Weng, Yang

doi:10.1109/tsg.2022.3196943

Cited by 18 publications

(5 citation statements)

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“…In [225], ML techniques have been used for fault prediction diagnosis (type and location) on reconfigured IEEE-33 bus ADNs developed in Typhoon HIL's real-time environment. Moreover, three DL algorithms were applied to increase the accuracy during calculating the HC through DSs (IEEE 34, 123-bus feeders) using CYME in [222].…”

Section: B Tools For Data-driven Hc Methodsmentioning

confidence: 99%

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

Mousa,

Mahmoud,

Lehtonen

2024

IEEE Access

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Recently, several types of distributed energy resources (DERs) have been developed to reduce the environmental impact and support the global demand for electrical energy. However, the continuous penetration of the DERs into modern power systems (MPSs) may cause several adverse impacts in terms of operation performance indices (PIs) and power quality issues, especially in low-voltage distribution systems (LVDSs). To cope with these serious impacts and achieve optimal control, the hosting capacity (HC) of DERs must be accurately estimated and optimally optimized. However, it requires an extensive communication infrastructure, which is hardly offered without clear financial benefits. In this regard, this article investigates the historical developments of HC definitions as well as the recent developments in terms of operational performance indices, and estimation methods for active distribution networks (ADNs) with the highpenetration level existence of the DERs, energy storage systems (ESSs), electric vehicle (EV) charging systems, sector coupling, hydrogen technologies, and multi-carrier energy systems (MESs) to deal with electrical, thermal, and cooling demands. In this regard, this review article is intended to exhibit an appropriate reference for comprehensive research trends in HC estimation and optimization based on ADNs. Additionally, it involves and covers most current research HC topics in detail compared to other published review articles. Moreover, the authors deliberate the recent approaches for evaluating and improving the HC, especially concerning data-driven methods, with the aid of various software for simulating real systems. Moreover, modern research trends and main factors of MPS operations are deliberated with current energy market developments. Also, prominent challenges, current status, and future aspects are discussed.

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Section: B Tools For Data-driven Hc Methodsmentioning

confidence: 99%

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

Mousa,

Mahmoud,

Lehtonen

2024

IEEE Access

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“…A high entropy implies a more exploratory policy with less exploitation, while a low entropy implies a more deterministic policy with less exploration. The Bellman equation for entropy regularized action value function Q π (s, a) for the SAC algorithm is given in (8).…”

Section: Soft Actor-critic Algorithmmentioning

confidence: 99%

“…In contrast to traditional HC assessment methods that calculate a static HC value, deep learning methods for HC assessments are capable of quantifying the real-time HC of LV networks and have garnered significant attention among researchers in recent years. In [8], long short-term memory (LSTM) neural networks were utilized to evaluate the real-time HC of distribution networks by identifying a mapping rule between power flow data and HC data. However, despite being a very powerful strategy for real-time HC quantification, this method requires an electrical model of the distribution network, which may not be readily available for most LV distribution systems.…”

Section: Introductionmentioning

confidence: 99%

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Suchithra,

Robinson,

Rajabi

2024

Energies

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Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

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“…The ensemble model showcased improved accuracy and robustness in predicting the maximum DER capacity. Building upon this, [17] introduced a deep learning-based model utilizing a long short-term memory (LSTM) neural network for HC estimation in distribution systems. The LSTM model successfully captured temporal dependencies and demonstrated an accurate estimation of the maximum DER capacity.…”

Section: Introductionmentioning

confidence: 99%

Machine learning based hosting capacity determination methodology for low voltage distribution networks

Qammar,

Arshad,

Miller

et al. 2023

IET Generation Trans & Dist

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For the past few years, the addition of renewable energy sources has been on the rise, but the unregulated addition of these sources can cause severe harm to the grid. Therefore, it is necessary to have a predefined limit for a grid, beyond which no further addition of renewables should be allowed without reinforcement. That limit is called the hosting capacity (HC), which is addressed in the literature by search‐based methods with heavy computational burdens. This manuscript first presents a framework to find the HC for a grid. The same framework is then applied to 503 different simulated but realistic LV distribution feeders in Finland to find their HCs and the most effective network parameters in defining a network specific HC. Next, different machine learning models, that is, Decision Tree (DT), Random Forest (RF), Linear Regression (LR), K nearest neighbours (KNN), Logistic Regression, and Support Vector Machine (SVM) are implemented on the generated data. For the classification case, the accuracy values for logistic regression, KNN, and SVM were 0.89, 0.84, and 0.81, respectively. The findings demonstrate that the developed machine learning based technique will enable distribution network operators in finding the HC without applying any deterministic or probabilistic approaches.

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Spatial-Temporal Deep Learning for Hosting Capacity Analysis in Distribution Grids

Cited by 18 publications

References 50 publications

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

A Comprehensive Review on Recent Developments of Hosting Capacity Estimation and Optimization for Active Distribution Networks

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Machine learning based hosting capacity determination methodology for low voltage distribution networks

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