A Multi-Stage Optimization Approach for Active Distribution Network Scheduling Considering Coordinated Electrical Vehicle Charging Strategy

Zhu, Xiaojun; Han, Haiteng; Gao, Shan; Shi, Qingxin; Cui, Hantao; Zu, Guoqiang

doi:10.1109/access.2018.2868606

Cited by 58 publications

(26 citation statements)

References 32 publications

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“…DSO-level storage-based resources are also naturally have potential to be providers of flexibility for distribution network needs through different active network management (ANM) schemes. For example, reference [59] proposed to coordinate load shedding and EV charging by ANM in future distribution networks. The work in [59] took into account the satisfaction of EV customers along with interruptible load compensation in its optimization problem.…”

Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

“…For example, reference [59] proposed to coordinate load shedding and EV charging by ANM in future distribution networks. The work in [59] took into account the satisfaction of EV customers along with interruptible load compensation in its optimization problem. The paper concludes that the proposed flexible resources increased the average qualified voltage ratio from 82.7% to 100% while the average ratio of primary line load decreased from 41.57% to 38.32%.…”

Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

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Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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The serious problem of climate change has led the energy sector to modify its generation resources from fuel-based power plants to environmentally friendly renewable resources. However, these green resources are highly intermittent due to weather dependency and they produce increased risks of stability issues in power systems. The deployment of different flexible resources can help the system to become more resilient and secure against uncertainties caused by renewables. Flexible resources can be located at different levels in power systems like, for example, at the transmission-level (TSO), distribution-level (DSO) and customer-level. Each of these levels may have different structures of flexibility trading as well. This paper conducts a comprehensive review from the recent research related to flexible resources at various system levels in smart grids and assesses the trading structures of these resources. Finally, it analyzes the application of a newly emerged ICT technology, blockchain, in the context of flexibility trading.

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Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

Section: Flexible Resources At Dso Levelmentioning

confidence: 99%

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

et al. 2019

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“…With the increased growth of EVs in urban areas, the extensive EV charging network would be in place to meet the anticipated EV demand shortly. Nonetheless, the number of available charging facilities could be inadequate, so an efficient smart EV charging management would be required for managing and allocating the limited EV charging facilities [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Smart electric vehicle charging management for smart cities

Vaidya

Mouftah

2020

IET Smart Cities

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In recent years, attraction to alternative urban mobility paradigms such as electric vehicles (EVs) is increasing since EVs can significantly minimise fossil fuel dependency and reduce carbon emission in urban areas. Nonetheless, there are several barriers toward widespread adoption of EVs. Moreover, as EV penetration increases in urban areas, uncoordinated charging may cause power outage. Deployment of EV charging network can allow EVs to communicate with the service provider to coordinate charging activities. Taking into account, increased growth of EVs, number of charging facilities will be inadequate in urban areas, so efficient EV charging management is required for managing and allocating scarce charging station (CS) resources. In this study, the authors have designed and implemented a smart EV charging management system utilizing charging strategy that includes effective reservation management and efficient slot allocation of CSs. Considering composite cost that includes waiting time, estimated charging time, estimated charging cost, user discontent factor and CS congestion impact in such a method, their scheduling scheme shall furnish a set of optimal solutions. Viewing user discontent factor and average waiting time, they have evaluated performance of proposed strategy. The proposed charging strategy is effective than the existing one in terms of average waiting time. Nomenclature ϵ i req requested energy of EV i χ i ini initial SoC value of EV i χ i tar targeted SoC value of EV i Δ χ i difference SoC value between targeted and initial for EV i t i ar expected arrival time of EV i at the CS t i dep expected departure time of EV i from the CS t i wa waiting (queuing) time of EV i at the CS t i rs reservation start time t i rf reservation finish time of EV i τ i r reservation duration for EV i δ time-slot interval 2

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“…That is, under certain constraints, the optimal function is obtained through optimizing the DG output, power consumption plan of prosumers, etc. A multi-stage optimization approach for active distribution network scheduling is proposed in [3], which takes into account the coordinated charging strategy of electric vehicles. A two-stage robust optimal dispatch model is built in [4], which is employed to address the uncertainties of photovoltaic generation in distribution network.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

Wang

Peng

2020

IEEE Access

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With large-scale penetrations of distributed generation (DG) and flexible loads, there will be multiple entities in traditional distribution network, such as distribution network operator (DNO), DG owner, and prosumers. Aiming at the collaborative optimization problem of distribution network among multiple entities, a distributed optimal scheduling approach of distribution network considering demand response and edge computing is proposed in this paper. Firstly, the virtual region decomposition method is proposed to divide the original distribution network into multiple regions according to different entities, and the bi-level optimization framework based on edge computing is constructed. Secondly, the optimal models of DNO, DG owner, and prosumers are established respectively, and the distributed optimal scheduling approach of distribution network with collaboration of control center and edge nodes is proposed. Then, the KKT conditions are adopted to realize the transformation of optimal models of DG owner and prosumers. Finally, the proposed distributed optimization scheduling approach is verified based on the modified IEEE33-node system. The results show that the proposed distributed optimal scheduling method can achieve better collaborative optimization among different entities in distribution network compared with the centralized optimization method. INDEX TERMS Demand response, edge computing, distributed optimization, KKT transformation, optimal dispatching.

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A Multi-Stage Optimization Approach for Active Distribution Network Scheduling Considering Coordinated Electrical Vehicle Charging Strategy

Cited by 58 publications

References 32 publications

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

Towards Flexibility Trading at TSO-DSO-Customer Levels: A Review

Smart electric vehicle charging management for smart cities

Distributed Optimal Dispatching of Multi-Entity Distribution Network With Demand Response and Edge Computing

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