Peak-Load Management in Commercial Systems With Electric Vehicles

Mahmud, Khizir; Hossain, M. J.; Ravishankar, Jayashri

doi:10.1109/jsyst.2018.2850887

Cited by 72 publications

(36 citation statements)

References 29 publications

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“…Here, the indices p and c are denoted for parents and children respectively, To calculate the available power by a parent and the requested power by a child, it is essential to identify their load demand. Let us assume that the power demand ℒ of a parent is a function of its active power consumptions over a certain period and expressed as [32]:…”

Section: + -mentioning

confidence: 99%

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Mahmud

Nizami

Ravishankar

et al. 2020

IEEE Trans. on Ind. Applicat.

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This paper proposes a new technique to manage the domestic peak load demand through peer-to-peer (P-P) energy transaction among multiple homes. In this process, the houses willing to sell energy are identified as the Parent, and the houses that require energy are identified as a Child. The parents having energy resources such as photovoltaics (PV), battery storage and electric vehicles (EVs) will utilize their resources to meet their peak power demand and sell the extra energy to a child. A mixed integer linear programming optimization is used to find the parent-child matching based on their energy availability, power demand and distances. After selecting the parent-child match, the power demand of a child is forecasted using two different techniques, i.e. autoregressive moving average and artificial neural networks, to identify to child's need in a day ahead of the actual operation. The proposed algorithm calculates the available energy of a parent to sell in real-time and the required energy of a child in a day-ahead, while ensuring to minimize the peak load demand. The proposed method, as confirmed by the presented analysis using data of a real Australian power distribution network, is able to significantly minimize the peak load demand, which in-turn is expected to minimize the electricity costs. The method also facilitates two agreed prosumers to transact energy between themselves without the involvement of a third party.

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Section: + -mentioning

confidence: 99%

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Mahmud

Nizami

Ravishankar

et al. 2020

IEEE Trans. on Ind. Applicat.

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“…The study in [15] is a continuation of [14], but instead of a household, algorithms are applied in a commercial building with six EVs, a battery energy storage and a photovoltaic generator. The study does not use real EV charging data.…”

Section: Related Research Workmentioning

confidence: 99%

Quantification of peak shaving capacity in electric vehicle charging – findings from case studies in Helsinki Region

et al. 2020

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“…The domestic load demand [4, 7, 13, 31] can be expressed as a function of power and time as follows:

{scriptS}_{P}^{t} = f ()(, P, t_{ℓ}^{j})

The time (

t_{ℓ}^{j}

) in the load demand function is the summation of both off‐peak

()(, t_{B}^{j})

and peak load periods

()(, t_{scriptP}^{j})

as follows:

t_{ℓ}^{j} = ()(, t_{p}^{j} \cup t_{b}^{j})

t_{scriptP}^{j} = {scriptO}_{scriptP}^{f} * \sum_{j = 0}^{n} t_{scriptP}^{j}

t_{B}^{j} = {scriptO}_{B}^{f} * \sum_{j = 0}^{n} t_{B}^{j}

where

t_{scriptP}^{0}

and

t_{scriptP}^{n}

are the initial and final time of the peak load occurrence periods, respectively. Similarly,

t_{B}^{0}

and

t_{B}^{n}

are the initial and final times of the off‐peak load (base load) periods, respectively.…”

Section: Ems Algorithms and Problem Formulationmentioning

confidence: 99%

“…Numerous studies in the field of microgrid and domestic energy management have focused on the control techniques and components (storages, EVs, PV units). Mahmud et al [7] propose a decision-tree-based algorithm to reduce the domestic peak load using EVs, PV units, and battery storage. The EVs are used in [8] to flatten the fluctuating load profile of a building.…”

Section: Introductionmentioning

confidence: 99%

Rebound behaviour of uncoordinated EMS and their impact minimisation

2020

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In this paper, the impacts of uncoordinated energy management systems (EMS), with a rebound effect, on a renewable energy-dependent microgrid are discussed and feasible solutions are presented. Two different approaches, i.e. loadbased and price-based EMS are modelled which consider PV units, battery energy storage systems (BESS), and electric vehicles (EVs). Taking account of each component's boundary conditions, the load-based approach intelligently charges the EV and BESS from the grid/PV during off-peak hours, and provides a combined discharge response during peak load hours. In the price-based approach, the charging-discharging of BESSs and EVs from/to grid and PV depends on the time-of-use tariff signal. The primary objective of both models is to minimise the customers' peak electricity consumption and the saturation issues of distribution transformers. It is observed that the simultaneous response of the EMS due to the identical behaviour of load or price curves, and the rebound effect after mode switching transition create large power demand spikes. To mitigate its negative consequence, an improved locking and randomisation technique is designed and implemented. Additionally, the impact of the PV power fluctuations on the load-support systems due to fast-moving clouds and their consequences to the behaviour of the EMS response are investigated.

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Peak-Load Management in Commercial Systems With Electric Vehicles

Cited by 72 publications

References 29 publications

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Multiple Home-to-Home Energy Transactions for Peak Load Shaving

Quantification of peak shaving capacity in electric vehicle charging – findings from case studies in Helsinki Region

Rebound behaviour of uncoordinated EMS and their impact minimisation

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