Distributed charging management of multi‐class electric vehicles with different charging priorities

In the transactive energy (TE) paradigm, the devices of participative consumers, or prosumers, may be aggregated and employed to drive operational objectives at the network level. Home heating, ventilation and air-conditioning (HVAC) systems in particular are well-suited to modulate their behaviours based on both home thermal flexibility and requests from the utility grid. This paper develops a robust, hierarchical power dispatch scheme in the context of a residential distribution network. The formulation couples a unique, multiphase linear distribution optimal power flow (OPF) at the upper level with model predictive control (MPC)-based HVAC fleet controllers at the lower level. The proposed approach is tested on nearly 2000 homes with a three-phase distribution network in an intraday market setting, where two major applications are explored and analysed.

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“…This is like the optimal scheduling of a fleet of electric vehicles (EV), where much groundwork has already been laid, e.g. [13][14][15][16].…”

Section: Relevant Literaturementioning

confidence: 99%

“…This is like the optimal scheduling of a fleet of electric vehicles (EV), where much groundwork has already been laid, e.g. [13–16]. While an EV fleet can export power back out to the grid, however, an HVAC fleet may only shift its power consumption in time.…”

Section: Introductionmentioning

confidence: 99%

Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control

Rooks

Shi

et al. 2021

IET Generation Trans & Dist

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“…In [10, 11], a charging management algorithm to cope with fluctuating available power is presented. It also considers various user groups through prioritization.…”

Section: Introductionmentioning

confidence: 99%

Overcoming non‐idealities in electric vehicle charging management

Rauma

Simolin

Rautiainen

et al. 2021

IET Electrical Syst in Trans

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The inconvenient nature of non-ideal charging characteristics is demonstrated from a power system point of view. A new adaptive charging algorithm that accounts for nonideal charging characteristics is introduced. The proposed algorithm increases the local network capacity utilization rate and reduces charging times. The first unique element of the charging algorithm is exploitation of the measured charging currents instead of ideal or predefined values. The second novelty is the introduction of a short-term memory called expected charging currents. This makes the algorithm capable of adapting to the unique charging characteristics of each vehicle individually without the necessity to obtain any information from the vehicle or the user. The proposed algorithm caters to various non-idealities, such as phase unbalances or the offset between the current set point and the real charging current but is still relatively simple and computationally light. The algorithm is compatible with charging standard IEC 61851 and is validated under different test cases with commercial electric vehicles.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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“…In the utility management, the charge scheduling of EVs at off‐peak hours of the load curve and discharging of EV batteries during peak hours of the distribution system makes the system more sustainable for all types of loads [15]. In [16], different charging priorities are followed for charging the multiclass EVs based on certain conditions. Higher priority is given to the EVs with a lack of power and least priority is given to the EVs which have sufficient power.…”

Section: Introductionmentioning

confidence: 99%

“…FIGURE16 Heuristic load profile with EV load.FIGURE 17Voltage profile at each bus of the 16 bus reconfigured BRDS with and without EVCS for heuristic load.…”

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confidence: 99%

Optimal planning and utilisation of existing infrastructure with electric vehicle charging stations

Moupuri

Selvajyothi

2020

IET Generation Trans & Dist

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In order to address the increased demand for utility with the adoption of electrification in the transport sector, infrastructure development plays an important role. This research helps in planning the optimum utilisation of the existing infrastructure. The planning of electric vehicle (EV) infrastructure is analysed by considering various loads such as (1) static (2) dynamic and (3) heuristic on balanced radial distribution system (BRDS). The introduction of electric vehicle charging station (EVCS) into BRDS increases the total losses. The optimised reconfiguration helps in reducing the losses and ensures increased reliability of the system. The major objective of this process is to minimise both the real power loss and the reliability index energy not supplied of the radial distribution system (RDS) with the placement of charging stations. The planning is accomplished with a single‐step process in which the optimum location of the EVCS along with reconfiguration of BRDS is done using the multiple objective particle swarm optimisation (MOPSO). The performance of the BRDS with the proposed method is elaborated under three scenarios with three internal cases. The proposed methodology is validated through simulation for a three feeder IEEE 16 bus BRDS considering various load scenarios using MATLAB.

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Distributed charging management of multi‐class electric vehicles with different charging priorities

Cited by 24 publications

References 23 publications

Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control

Robust hierarchical dispatch for residential distribution network management considering home thermal flexibility and model predictive control

Overcoming non‐idealities in electric vehicle charging management

Optimal planning and utilisation of existing infrastructure with electric vehicle charging stations

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