A day-ahead energy management and reserve declaration strategy for parking lot based on electric vehicle accessing guidance

Liu, Zhijian; Li, Pengcheng; Liu, Jie; He, Xu; Han, Jiangbei; Zi, Chao; Liang, Ning

doi:10.1016/j.ijepes.2021.107704

Cited by 13 publications

(6 citation statements)

References 32 publications

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“…The coordination of EVPL and EHs considering responsive leads in reducing operating cost with high penetration of uncertain parameters is investigated in [23]. The EVPL can function as a reserve in the reserve market, and ancillary service [24], given that the operator of EVPL can charge and discharge the EVs in different circumstances [25]. Thermal and electrical storage and the impact of DR based on outdoor air temperature is investigated in [26] the presence of HVAC and CHP units.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

et al. 2023

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Section: Literature Reviewmentioning

confidence: 99%

“…Thus, the electric power generates cooling energy with respect to the maximum/ minimum input power capacity of electric chiller P ec and P ec in Eq. (24).…”

Section: Cchp Formulationmentioning

confidence: 99%

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

et al. 2023

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“…Liu et al, 37 have explained the conception of electricity fluctuation feasible region to achieve the reliable evaluation of reserve declaration capacity. Tan et al, 38 have presented double‐layer robust optimization dispatch model to diminish the functioning cost of multi‐microgrids and enhance the convergence performance of the solution algorithms utilized to its optimized electric power dispatch when deeming the uncertainties corresponding to previous loads, renewable energy sources, and electric vehicle usage.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Simultaneous allocation of EV charging stations and renewable energy sources: An Elite RERNN‐m2MPA approach

Vijayalakshmi

Arumugam²,

Christy

et al. 2022

Intl J of Energy Research

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This article proposes a hybrid optimization technique for optimal location and sizing of electric vehicle fast charging stations (EVFCSs) and renewable energy sources (RESs). The proposed hybrid optimization technique is the consolidation of recalling-enhanced recurrent neural network (RERNN) and Marine Predators Algorithm (MPA), hence it is called RERNN-m2MPA technique.Here, an enhanced MPA (m2MPA) is proposed. The major objective of this article is to energy loss reduction, voltage deviation of the power system network and minimization of the land cost with maximum weightage to serve maximum EV with minimum installation cost. The lessening of voltage Abbreviations: F l , load force; F r , rolling resistance force; F acc , acceleration force; S, area; m ev , m ess , mass of EV and ESS; ψ, solar irradiance random variable; μ, σ, mean and SD of solar irradiance; F f , fill factor; i z , output of solar module temperature current; i MPP , V module current at the maximum power point; i SC , short circuit current; t, solar module temperature; t a , ambient temperature; i SC , short circuit current of the module; S I , scale index; T, temperature of electrolyte; T f , electrolyte freezing temperature; i b , battery current; i dis , average current of the discharge battery module; D D , Deviation distance; i B , branch current; n B , total branch present in the network; V Bus T ð Þ, bus voltage at time period T; ABC, phases; W g , weightage of the location of EVCS; L c , total cost of all the EVCS; EV De Ve , required energy of EV at Vth vehicle; C Bat Ve , battery capacity of EV at Vth vehicle; SOC Arrival Ve , SOC Depart Ve , arrival SOC and departure SOC of Vth vehicle; E Ra Ve , electric range of Vth vehicle; D ET , each trip distance; D Rate , discharging rate in G2V mode; l EV Bus , load of EVCS; EV Arrival Ve

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“…Liu et al 42 presented the conception of electricity fluctuation feasible region to attain reliable evaluation of reserve declaration capacity. Tan et al 43 illustrated the need to decrease the operating cost of multiple microgrids as well as upgrade the convergence presentation of the solution approaches employed to its optimized electric power dispatch while assuming the uncertainties associated with existing loads, renewable energy sources (RES), and electric vehicle utilization through dual layer robust optimization dispatch mode.…”

Section: Recent Research Work: a Brief Reviewmentioning

confidence: 99%

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

Alphonse

Raj

Arumugam

2022

Intl J of Energy Research

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Summary This paper proposes a simultaneously allocating electric vehicle charging station (EVCS) and photovoltaic (PV) energy resources in smart grid considering uncertainties using a hybrid technique. The proposed hybrid technique is the combination of capuchin search algorithm (CapSA) and wild horse optimizer (WHO), and hence it is named as ECapSA technique. The dynamic behaviour of capuchin monkeys is enhanced by the wild horse's social life behaviour. That's why, it is called as enhanced CapSA (ECapSA) technique. In the proposed work, the following four main objectives are considered and formulated by the ECapSA technique: (a) minimization of energy loss, (b) minimization of 24‐h voltage deviation, (c) maximization of weightage to offer electric vehicle charging service for maximal users, and (d) minimization of land cost. The locations of electric vehicle charging station and distributed generation (DG) affect the power loss along voltage at the distribution network. Besides, the locations on the road network determine the land cost together with weightage. The proposed formulation solution is carried out in two layers: upper and lower layers. The electric vehicle charging station allocation is modelled in upper layer and distributed generation of the optimization in lower layer. Here, 24 h slots are considered for load variation and photovoltaic generation variation at the charging station (CS). The energy loss reduction is an objective of problem formulations instead of power loss reduction. The ECapSA technique is implemented in the MATLAB platform, its performance is analyzed with existing techniques.

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A day-ahead energy management and reserve declaration strategy for parking lot based on electric vehicle accessing guidance

Cited by 13 publications

References 32 publications

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

Simultaneous allocation of EV charging stations and renewable energy sources: An Elite RERNN‐m2MPA approach

Simultaneously allocating electric vehicle charging stations ( EVCS ) and photovoltaic ( PV ) energy resources in smart grid considering uncertainties: A hybrid technique

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