Multi-Objective Optimal Dispatching of Microgrid With Large-Scale Electric Vehicles

Jiang, Haoyu; Ning, Shiyuan; Ge, Quanbo

doi:10.1109/access.2019.2945597

Cited by 43 publications

(19 citation statements)

References 9 publications

(21 reference statements)

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“…Apart from the different challenges brought by the virtuous DGs which alters the system operation and topology, modern system's dynamic operating conditions are always changing. Some conditions are network reconfiguration [10]- [12], optimal dispatch and consumption [13], optimal dispatch and reserve scheduling [14], dispatch for islanded microgrid [15], optimal reactive power dispatch [16], demand response [17], optimal dispatch of large-scale electric vehicles [18], maintenance and contingency.…”

Section: A Literature Reviewmentioning

confidence: 99%

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An Adaptive Overcurrent Coordination Scheme Withstanding Active Network Operations

Enrı́quez

Shih

2022

IEEE Access

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This paper aims to present a centralized adaptive protection scheme (APS) for the directional overcurrent relays (DOCRs) considering system dynamics such as load demand variations, topology changes and DGs to mitigate the above-mentioned effects. The scheme is suitable for different types of networks whether its radial or meshed. Afterall, it can determine the actual topology with the micro-PMUs and breaker status, automatically generate relay names accordingly with the topology bus locations, establish protection coordination pairs, coordinate DOCRs for parallel lines, identify transformers to offer backup operation for differential protection, perform load flow and fault studies, execute contingency and sensitivity analysis, and lastly, coordinate the DOCRs using differential evolution algorithm so that they always have the proper settings to operate in appropriate time providing selectivity. The performance of the proposed centralized adaptive overcurrent protection scheme is tested on the highly meshed IEEE 14 bus system. The proposed scheme has shown that it can adequately consider impacts of system dynamics and DG.

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Section: A Literature Reviewmentioning

confidence: 99%

“…However, the routine procedure is to consider boarder or worst conditions that all electrical elements are active such as generators, lines and transformers. Which is not appropriate since topology changes due to contingency or planned dispatch scenarios as previously mentioned [10]- [18].…”

Section: A Literature Reviewmentioning

confidence: 99%

An Adaptive Overcurrent Coordination Scheme Withstanding Active Network Operations

Enrı́quez

Shih

2022

IEEE Access

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“…From the perspective of a microgrid, a model is proposed that solves the economic dispatch problem of parked EV storages based on direct control [22]. A model is established to minimise the operating cost of the microgrid and the cost of environmental protection considering the peak-valley price [23]. For EVs, most studies optimise the economy of EVs based on price and the peak/base hour's contract [24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A dispatching strategy for electric vehicle aggregator combined price and incentive demand response

Hou

Wang

Xie

et al. 2021

IET Energy Syst Integration

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Electric vehicles (EVs) have excellent demand response potential, but a single type of demand response is difficult to fully utilise the dispatching potential of EVs. To improve the utilisation of the demand response potential of EVs, this study proposes two kinds of dispatching strategies for electric vehicle aggregator (EVA) combined price-based and incentive-based demand response: long-term contract strategy and short-term contract strategy. The difference of the two strategies depends on the length of time for EV users to sign incentive agreements with EVA. The long-term contract strategy assumes that some EV users sign a long-term incentive agreement with the EVA. The short-term contract strategy takes an EV charging process as the dispatching cycle. The objective of maximising revenues and minimising load fluctuation of EVA is optimised in two strategies. An improved multi-objective particle swarm optimisation algorithm that combines region selection strategy and grouping strategy is used to solve the problem. The simulation results show that the two dispatching strategies can improve the comprehensive benefits of EVA as well as EV users. The two dispatching strategies can effectively improve the utilisation of dispatching potential of EVs. K E Y W O R D Sdemand response, electric vehicle (EV), electric vehicle aggregator (EVA), long-term contract, short-term contractThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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“…en, the economic operation of the distribution network can be realized without violating the safe operation constraints of the distribution network [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

Jiang

Xiong

2021

Advances in Civil Engineering

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The rapid increase of the number of electric vehicles (EVs) has posed great challenges to the safe operation of the distribution network. Therefore, this paper proposes an ordered charging scheduling method for EV in the cloud-edge collaborative environment. Firstly, the uncertainty of user load demands, charging station requirements, and renewable outputs are taken into consideration. Correspondingly, the residential distribution points, EV charging stations, and renewable plants are regarded as the edge nodes. Then, the load demands and renewable outputs are predicted by a model combined with the convolutional neural network and deep belief network (CNN-DBN). Secondly, the power supply plans for charging stations are determined at the cloud side aiming at minimizing the operating cost of the distribution network via collecting the forecasting results. Finally, the charging station formulates the personalized charging scheduling strategies according to EV’s charging plans and the charging demands in order to follow the supply plan. The simulation results show that the load peak-to-valley difference and standard deviation of the proposed algorithm are reduced by 30.13% and 16.94%, respectively, compared with the disorderly charging and discharging behavior, which has verified the effectiveness and feasibility of the proposed method.

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Multi-Objective Optimal Dispatching of Microgrid With Large-Scale Electric Vehicles

Cited by 43 publications

References 9 publications

An Adaptive Overcurrent Coordination Scheme Withstanding Active Network Operations

An Adaptive Overcurrent Coordination Scheme Withstanding Active Network Operations

A dispatching strategy for electric vehicle aggregator combined price and incentive demand response

An Orderly EV Charging Scheduling Method Based on Deep Learning in Cloud‐Edge Collaborative Environment

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