Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management

Hu, Junjie; Liu, Xuetao; Shahidehpour, Mohammad; Xia, Shiwei

doi:10.1109/tste.2021.3064375

Cited by 151 publications

(50 citation statements)

References 34 publications

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“…There are also methods that take advantage of the different types of energy used by consumers, and the flexibility of the operation is improved. Thus, by making use of electricity, natural gas, cooling, and heat, the congestion in the network is resolved [57].…”

Section: Combined Methodsmentioning

confidence: 99%

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

2021

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Distribution networks were conceived to distribute the energy received from transmission and subtransmission to supply passive loads. This approach, however, is not valid anymore due to the presence of distributed generation, which is mainly based on renewable energies, and the increased number of plug-in electric vehicles that are connected at this voltage level for domestic use. In this paper the ongoing transition that distribution networks face is addressed. Whereas distributed renewable energy sources increase nodal voltages, electric vehicles result in demand surges higher than the load predictions considered when planning these networks, leading to congestion in distribution lines and transformers. Additionally, centralized control techniques are analyzed to reduce the impact of distributed generation and electric vehicles and increase their effective integration. A classification of the different methodologies applied to the problems of voltage control and congestion management is presented.

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Section: Combined Methodsmentioning

confidence: 99%

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

2021

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“…The overall efficiency in an energy hub can be enhanced through close couplings and conversion, such as power-to-gas (P2G), combined heat and power (CHP), gas furnace, ground source heat pump (GSHP), and electric boiler [10]. With the increasing prevalence of intermittent renewable energy sources (RES), energy hubs have received a growing attention that seeks to offset the intermittency of RES through conversion of distinct, multienergy systems [11,12].…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Co-Optimization for Utility-Social Systems With Social-Aware P2P Trading

Zhao

et al. 2023

IEEE Trans. Comput. Soc. Syst.

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Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different sub-utility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multi-energy and water sectors via close energy conversion, and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than RO and SO for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

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“…With the continuous implementation of the national energy development strategy in recent years, vigorously developing renewable energy has become an essential trend in building a new generation of low-carbon and clean power systems [1,2]. Taking China Southern Power Grid as an example, its renewable energy production accounts for more than 40% of the entire society's electricity consumption, and the overall consumption rate is the world's leading, far exceeding China's average, the European Union and the United States' renewable energy power generation ratio [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…In terms of prediction, existing studies mainly focused on forecasting renewable energy [9] and load [10]. In [9], a new closed-loop clustering method is proposed, which organically connects clustering with prediction through a new feedback mechanism and minimizes prediction error through FIGURE 1 The framework of empirical PSOM judgment continuous iteration, effectively improving the accuracy of load prediction. In [10], the authors proposed a multi-task learning load forecasting method using long and short-term memory neural networks as the shared layer, which simulated the coupling characteristics of multiple loads through the shared layer to improve the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Multi‐level refined power system operation mode analysis: A data‐driven approach

Bai

Meng

et al. 2022

IET Generation Trans & Dist

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The high renewable penetration will cause the power system operation mode (PSOM) to change frequently. At present, the selection of PSOM mainly depends on the experience of relevant staff. However, selecting PSOM based on experience is difficult to comprehensively and elaborately analyze the possible PSOMs within a dispatching cycle, which inevitably invites troubles to the power system planning and dispatching. In this paper, a data‐driven analysis method is proposed, which integrates the techniques of preprocessing, dimensionality reduction, clustering, and data visualization of high‐dimensional data sets of power system operation. Simultaneously, qualitative and quantitative methods are used to analyze, evaluate the PSOM and their development laws. Finally, to verify the effectiveness of the proposed multi‐level refinement extraction method of PSOM, the normal power system operation mode (NPSOM), transition power system operation mode (TPSOM), extreme power system operation mode (EPSOM) are obtained and analyzed according to the real operation data of the urban power system. The abundant analysis results show that the proposed fine‐grained extraction method of PSOM is compelling, facilitating the power grid enterprise operation and planning.

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Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management

Cited by 151 publications

References 34 publications

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

Two-Stage Co-Optimization for Utility-Social Systems With Social-Aware P2P Trading

Multi‐level refined power system operation mode analysis: A data‐driven approach

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