A Fast Adequacy Analysis for Radial Distribution Networks Considering Reconfiguration and DGs

Arefi, Ali; Ledwich, Gerard; Nourbakhsh, Ghavameddin; Behi, Behnaz

doi:10.1109/tsg.2020.2977211

Cited by 25 publications

(13 citation statements)

References 24 publications

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“…The proper identification of such sets is essential for a reliability assessment. Many different methods have been proposed by literature, for example, most of which are computationally intensive [26,39,40].…”

Section: Minimal Cut-sets Via Incidence Matrixmentioning

confidence: 99%

Reliability assessment of island multi‐energy microgrids

Santos

Huo

Wade

et al. 2021

Energy Conversion and Econom

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Multi-energy microgrids could result in more flexibility and increase reliability by interconnecting networks. Electricity and gas networks exhibit very different dynamic behaviours in response to a fault or failure. Gas networks have built-in energy storages that can continue to provide a reliable supply if gas inputs to the system are compromised. This study presents a novel reliability assessment method applied to multi-energy microgrids; the method combines an incidence matrix analysis that identifies the connectivity between sources and load points with a sequential Monte Carlo simulation and generation adequacy evaluation. A case study is conducted by using an electricity-gas microgrid. The electricity network is a multi-sourced grid, whereas the gas network is supplied by a biogas plant. The linepack (gas stored along the pipelines) is modelled to account for the slower gas dynamics. The proposed method is evaluated on a real-world electricity distribution network in Austria. The results indicate the reliability benefits of forming a multi-energy microgrid. INTRODUCTIONEnergy networks are being transformed into flexible, intelligent, and interconnected systems. Most of these changes are due to the advances in technology [1, 2], changes in energy policy, and economic factors, which have enabled the deployment of distributed energy resources (DERs).To further decarbonize energy systems, hydrogen can be used as a zero-carbon alternative to natural gas in energy networks [3]. This can be accomplished by employing power-togas (PtG) technologies [4] on the consumer side, with excess renewable electricity used to produce green hydrogen. For PtG to provide a sustainable solution, the electricity system must adopt decarbonization by shutting down fossil-based generators and accelerating the deployment of wind, solar, and nuclear power at the transmission level and DERs, such as biomass, microgeneration, and energy storage systems, at the distribution level [5].Integration between electric and gas grids could play a crucial role [6] in accommodating the demand growth while meeting climate-change targets. This aspect is vital in distribution systems, which are increasingly becoming more active, and the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Minimal Cut-sets Via Incidence Matrixmentioning

confidence: 99%

Reliability assessment of island multi‐energy microgrids

Santos

Huo

Wade

et al. 2021

Energy Conversion and Econom

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“…To sum up, the traditional model of passive repair, which is reported by electricity customers, can no longer meet the demand of current customers for high service quality nor does it make use of the current smart grid monitoring capability of power supply enterprises. Therefore, it is necessary to develop an approach based on the big data perception and intelligent processing of the power distribution networks, (5) and to propose the initiative repair model based on the BP neural network, which derives the development of the power distribution network. Its flow chart of active rush repair is shown in Fig.…”

Section: Current Research On Initiative Repair Of Power Distribution Networkmentioning

confidence: 99%

Realization of Initiative Repair of Power Distribution Network Based on Backpropagation Neural Network Optimization

Guo¹,

Li²,

Qiao³

et al. 2021

Sensors and Materials

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Protection and maintenance systems are necessary for transmission systems to ensure an efficient and reliable power supply when faults occur. However, most fault detection and location methods rely on the electricity measurement provided by current and voltage transformers. In addition, the initiative repair of a power distribution network is a very considerable portion of power grid operation, and the initiative repair efficiency is crucial for power supply enterprises to provide high-quality services. To give full attention to the value of perceived data and improve the accuracy of fault diagnosis, the technical performance of high-speed power line carriers is extensively investigated, and intelligent sensing devices are installed in power distribution network lines, stations, branch boxes, table boxes, and other parts to realize the initiative repair. To improve the accuracy and timeliness of fault diagnosis for the power distribution network, in this paper, we propose an initiative repair and judgment mechanism based on backpropagation (BP) neural network optimization for the power distribution network. After acquiring information data such as power consumption, the mechanism first takes advantage of the filtered data information to train the BP neural network to predict fault information and other data. Finally, the initiative repair of the power distribution network obtains the forecast data of the BP neural network to judge the fault of the power distribution network. The proposed mechanism considerably shortens the time for the initiative repair and improves the traditional repair mode of the power distribution network, thereby improving the initiative repair efficiency of the power distribution network.

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“…However, the transfer capability of DN is ignored in reliability calculation, without repeated searching under the fault state, switching, and DN connectivity discrimination by traditional reliability calculation methods. An algorithm based on s binary matrix operation set is proposed in the reference (Arefi et al, 2020), the switching moment information is matched and associated with the load node. Thus, the direct method can be used to evaluate DN reliability efficiently, but the load transfer is also not considered in reliability calculation.…”

Section: Introductionmentioning

confidence: 99%

A united credible capacity evaluation method of distributed generation and energy storage based on active island operation

Chen

Sun²,

Zhou³

et al. 2023

Front. Energy Res.

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Cooperating with distributed energy storage, distributed generation is with the potential of supply load stably under both normal and failure periods of distribution network. Therefore, distributed generation has not only electricity value, but also capacity value. The capacity value can be characterized by credible capacity index. However, the uncertainty of the distributed generation output and the sequential characteristics of energy-storage operation must be considered during a united credible capacity evaluation. A united credible capacity evaluation method of distributed generation and energy storage based on active island operation is proposed. The proposed method carries out day-ahead economic dispatching under a normal state and island partition under a fault state, alternately, to realize accurate reliability calculation, which is the key link of credible capacity searching. The main work is as follows. First, a day-ahead economic dispatching model under normal state is established to obtain the sequential remaining electricity information of energy storage. Second, the models of maximum island partition and optimal island rectification are established based on electricity sufficiency and power balance information. By solving the maximum island partition and optimal island rectification models alternately, optimal island partition schemes under the fault state could be achieved. Then, the convergence criterion based on variance coefficients instead of artificial selection is designed in reliability calculation. Finally, the united credible capacity of distributed generation and energy storage is evaluated in the PG&E 69-bus system. It is found that credible capacity value increases by 23%, 53%, and 61%, respectively, under the energy storage allocation ratios of 20%, 30%, and 40%. It can be seen that the integration of energy storage makes a significant impact on distributed generation credible capacity value.

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A Fast Adequacy Analysis for Radial Distribution Networks Considering Reconfiguration and DGs

Cited by 25 publications

References 24 publications

Reliability assessment of island multi‐energy microgrids

Reliability assessment of island multi‐energy microgrids

Realization of Initiative Repair of Power Distribution Network Based on Backpropagation Neural Network Optimization

A united credible capacity evaluation method of distributed generation and energy storage based on active island operation

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