A rule-based energy management scheme for uninterrupted electric vehicles charging at constant price using photovoltaic-grid system

Bhatti, Abdul Rauf; Salam, Zainal

doi:10.1016/j.renene.2018.02.126

Cited by 97 publications

(56 citation statements)

References 64 publications

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“…Rule‐based control approaches can be allocated by the following Deterministic rule‐based algorithm, Fuzzy rule‐based algorithm. Supervisory control in Figure categorized a rule and an optimization‐based strategy …”

Section: Inner Control Loops For the Distributed Power Generation Systemmentioning

confidence: 99%

Control of distributed generation systems for microgrid applications: A technological review

Arfeen

Khairuddin

Larik

et al. 2019

Int Trans Electr Energ Syst

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Summary Microgrid implementation requires effective and efficient strategies for controlling the grid parameters. Various problems are encountered with the deployment of distributed generation in terms of reverse power, an imbalance between power generation and nonlinear load. This paper is focused on the existing controllers in terms of their merits and limitations. Furthermore, the state of the art of the local power distribution system especially on renewable energy resources along with energy storage methods is explored. Reliability and stability of power flow between sources and consumers via voltage source inverter are also considered. The emerging microgrid concept in islanding and grid‐connected mode using different controller along with soft computing algorithm are presented. This paper gives the reader fast insight into the context of the controller and its application.

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Section: Inner Control Loops For the Distributed Power Generation Systemmentioning

confidence: 99%

Control of distributed generation systems for microgrid applications: A technological review

Arfeen

Khairuddin

Larik

et al. 2019

Int Trans Electr Energ Syst

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“…The output current of PV primarily depends on temperature ( T ) and solar irradiance intensity ( G ). The output current of PV, ie, I pv , is given in

I_{pv} = ([], ((), I_{italicSC_italicSTC} + k_{i} ((), T - T_{STC})) \frac{G}{G_{italicSTC}}) - I_{0} ([], e^{(\frac{V_{pv} + I_{pv} R_{S}}{V_{T}})} - 1) - ((), \frac{V_{italicpv} + I_{italicpv} R_{S}}{R_{P}})

where I 0 is the saturation current of the diode, I SC_STC represents short circuit current at STC, k i is coefficient of I SC_STC , V T = akT / q is the thermal voltage, q is the electron charge (1.602 × 10 −19 C), a is the ideality factor (1 < a < 2), and k is Boltzmann constant (1.381 × 10 −23 J/K). The R S and R P are series and shunt resistances, respectively .…”

Section: Modeling Of the Pv‐esu Grid Charging Systemmentioning

confidence: 99%

“…η bat shows charging as well as the discharging efficiency of the battery; it is assumed as 100%. The V bus is the dc common bus voltage; for this research, it is considered as 500 V; P B ( t ) is charging and discharging power; it depends upon required as well as the available power of ESU …”

Section: Modeling Of the Pv‐esu Grid Charging Systemmentioning

confidence: 99%

“…Likewise, the power available with ESU (ie, Avl_ESU_Pwr ) is defined as a maximum power that can be supplied continuously by ESU for the time step ∆t before the state of charge approaches to its lower limit (called SOCL ). This can be written as

italicAvl_italicESU_italicPwr ((), t) = \frac{((), italicSOC ((), t) - italicSOCL) \times C_{bat} \times N_{bat}}{normalΔ t}

…”

Section: Modeling Of the Pv‐esu Grid Charging Systemmentioning

confidence: 99%

“…In a recent work, the concept of EV charging with constant price is introduced. It is an interesting idea because most previous authors considered the price for charging that varies based on the time‐of‐use electricity .…”

Section: Introductionmentioning

confidence: 99%

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Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

et al. 2018

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Summary In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (Npv) and ESU batteries (Nbat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed Npv and Nbat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station.

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Optimal electrical vehicle charging park in residential‐commercial microgrid based on probability of vehicles routing and cost minimization approach

Rajamand

2021

Int J Energy Res

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Electrical vehicles (EVs) have a main role in the new power systems. Charging/discharging, energy supporting, voltage/power variations, and cost are some main issues relating to the EV-based networks. The property of energy storage in EVs using charging/discharging pattern can assist the microgrid (MG) in power balance and supporting the load demand specially in on-peak times. Location and size optimization of charging parks are also effective parameters in improvement of these systems. Optimal location and capacity of EV charging park are the main focus of this article, which is done based on the probability of routing of vehicles and arrival time to the park in different parts of MG identified as residential and commercial parts. Selecting the optimal locations and sizes of charging park can improve the performance of MG in terms of voltage/power profile, energy cost, and energy trading with the main grid, which is discussed in this article. Simulation results show that optimal EV charging parks help the MG to support more load demands in on-peak times and reserve energy in off-peak times to balance the energy flow in the network. Using the proposed strategy results in 31.96% reduction in cost function and causes 10.54% reduction in total energy cost of the MG in a year.

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A rule-based energy management scheme for uninterrupted electric vehicles charging at constant price using photovoltaic-grid system

Cited by 97 publications

References 64 publications

Control of distributed generation systems for microgrid applications: A technological review

Control of distributed generation systems for microgrid applications: A technological review

Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

Optimal electrical vehicle charging park in residential‐commercial microgrid based on probability of vehicles routing and cost minimization approach

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