Uncertainty Modeling for Participation of Electric Vehicles in Collaborative Energy Consumption

Hashemipour, Naser; Aghaei, Jamshid; Granado, Pedro Crespo del; Kavousi‐Fard, Abdollah; Niknam, Taher; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1109/tvt.2022.3184514

Cited by 9 publications

(8 citation statements)

References 39 publications

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“…Traditional methods can be categorized into four main types, taking into account node information and network structure. (1) The first approach focuses on the local network structure, such as degree centrality [3], which evaluates node importance based on their degree. Nodes with higher degrees are regarded as more critical due to they enhance the connectivity of the network.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence-based methods can be categorized into two main types. (1) The first approach applies machine learning-based methods to identify critical nodes, such as LS-SVM [10] and decision trees [11]. (2) The second approach employs deep learning-based methods, such as RCNN [12] and InfGCN [13].…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of smart grids, the power grid has a critical impact on accelerating social development, such as promoting the popularity of electric vehicles and facilitating the realization of internet of things [1][2]. Consequently, it is of great significance in enhancing the security and reliability of the power grid.…”

Section: Introductionmentioning

confidence: 99%

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Identifying critical nodes in the power grid via deep learning methods

Zhang,

Liu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Critical nodes play a pivotal role in ensuring the security and reliability of power grids, given that attacking them can lead to widespread power outages. Consequently, identifying critical nodes within the power grid holds immense significance. However, prior research either focuses on a single node metric or subjectively assigns weights to multiply metrics, resulting in inaccurate ranking lists. To overcome these limitations, this paper introduces the CGA algorithm to identify critical nodes in the power grid using deep learning methods. Specifically, CGA employs the contraction algorithm to establish feature matrices and utilizes the Susceptible-Infectious-Recovered (SIR) model to generate labels for nodes. Subsequently, CGA leverages the Convolutional Neural Network (CNN) to encode node information, effectively reducing computational complexity, then applies the Graph Neural Network (GNN) with an attention mechanism to learn node hidden representation. In addition, this paper employs two evaluation metrics to assess the effectiveness and distinguishability of CGA, including Kendall's tau correlation coefficient and monotonicity index. Through conducting extensive experiments on the three datasets, simulation results demonstrate that CGA performs better than the six baseline algorithms, in which the effectiveness of identifying critical nodes is improved and the distinguishability of the ranking list is enhanced.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying critical nodes in the power grid via deep learning methods

Zhang,

Liu,

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Few studies assessed the impacts of CET on LVDN if the grid constraints were not included in the model. These studies assessed the impacts of CET on different components and operation limits of LVDNs, such as peak demand, losses, voltage deviations, congestions of distribution network components, and voltage unbalance [6], [8]- [14]. The results of these studies showed that under low DER penetration levels and low local energy trading, no violations of grid constraints occur.…”

Section: Introductionmentioning

confidence: 99%

Mitigating the Impacts of Community Energy Trading on Distribution Networks by Considering Contracted Power Network Charges

Nour,

Chaves-Ávila,

Troncia

et al. 2024

IEEE Access

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Driven by environmental and economic motives, different distributed energy resources (DERs) are being installed at a fast pace. The high penetration levels of DERs could result in technical issues at low voltage distribution networks (LVDNs) and lead to complex power system management. Therefore, many approaches were proposed in the literature to manage DERs to maximize their economic benefits while respecting the LVDNs' limits. One of the new approaches for managing DERs is community energy trading (CET). CET allows end users to exchange energy with each other besides energy exchange with retailers. Recent studies showed that CET could result in violations of the LVDNs limits if the grid constraints are not considered in the optimization model. These violations mainly happen due to the synchronized charging of electric vehicles and battery energy storage (i.e., flexible devices) connected to the LVDNs, which could require an infrastructure upgrade at LVDN. This paper proposes including contracted power cost in the CET objective function for energy cost minimization besides the energy cost to mitigate the impacts on LVDN. The proposed approach does not require the consideration of grid constraints in the CET model or interaction with the distribution system operator. The results showed that the proposed approach reduced the peak demand of the energy community (EC) by 34.3% without affecting its economic performance. Moreover, the proposed approach prevents violations of unbalanced LVDN limits in line loading, voltage unbalance, and voltage magnitude that occur in the CET scenario that does not consider contracted power cost. INDEX TERMSLocal electricity market, peer to peer energy trading, energy community trading, energy community, transactive energy, grid tariff, network tariff, demand charges, distributed energy resources.

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“…However, this study did not consider the connection of ESS and EVs simultaneously at the LVDN. Ref [35] assessed the impact of CET on LVDN voltage considering the presence of PV, WG, ESS, and EVs. The study found that the voltage was within acceptable limits.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Community Energy Trading on Low Voltage Distribution Networks

et al. 2023

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The wide spread of distributed energy resources (DERs) enabled the transformation of the passive consumer to an active prosumer. One of the promising approaches for optimal management of DERs and maximizing benefits for the community and prosumers is community energy trading (CET). CET gives the prosumers the flexibility and freedom to trade electricity within the neighborhood and maximize their economic benefits besides maximizing local consumption of renewable energy sources generation. Despite the economic benefits of CET for individuals and the whole community, it could cause impacts on the low voltage distribution network (LVDN). Therefore, there is a need for a comprehensive evaluation of the potential impacts of CET on LVDN. This study compared CET with the home energy management system (HEMS) regarding community operation costs and interaction with the retailer. Furthermore, this paper focused on assessing the impacts of CET between prosumers on the phase unbalance of LVDN. Moreover, the impacts on transformer loading, lines loading, and voltage deviations are assessed. Compared to the corresponding HEMS scenarios, the results demonstrate that CET reduces the community electricity cost by up to 31%. CET resulted in better self-consumption by reducing the exports to the retailer by 93% and better self-sufficiency by covering up to 54% of energy demand by community DERs. However, CET resulted in increasing the community peak demand, accordingly, higher impacts on the LVDN. The transformer is lightly loaded in all scenarios. CET resulted in limit violations in some lines, whereas most are lightly loaded. The voltage magnitude and voltage unbalance exceeded the acceptable limits at some nodes of the LVDN.

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Uncertainty Modeling for Participation of Electric Vehicles in Collaborative Energy Consumption

Cited by 9 publications

References 39 publications

Identifying critical nodes in the power grid via deep learning methods

Identifying critical nodes in the power grid via deep learning methods

Mitigating the Impacts of Community Energy Trading on Distribution Networks by Considering Contracted Power Network Charges

Impacts of Community Energy Trading on Low Voltage Distribution Networks

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