Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids

Shaaban, Mostafa F.; Mohamed, S. Raja; Ismail, Muhammad; Qaraqe, Khalid; Serpedin, Erchin

doi:10.1109/tsg.2019.2891900

Cited by 112 publications

(44 citation statements)

References 20 publications

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“…By far, the original sizing optimisation problem has been transformed into a non‐linear convex programming problem. Then convex optimisation solving software can be used to efficiently solve the problem, comparing other heuristic methods used in [13, 21].…”

Section: Stochastic Planning Model Of Charging Stationmentioning

confidence: 99%

Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Yan

2020

IET Generation, Transmission & Distribution

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Section: Stochastic Planning Model Of Charging Stationmentioning

confidence: 99%

Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Yan

2020

IET Generation, Transmission & Distribution

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“…In [13], the authors proposed a planning method for CSs based on queuing theory using a 24-node distribution grid, taking into consideration the spatio-temporal distribution of EVs. In [14], the authors solved the joint EVs and DGs allocation problem using Genetic algorithm such that deployment and operation costs as well as green house gas emissions are minimized. The proposed allocation algorithm was tested on a 38-bus radial distribution system.…”

Section: Related Workmentioning

confidence: 99%

“…• The existing works [11]- [14] do not present a framework for dynamic allocation of CSs that accounts for gradual increase in number of EVs over the years.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Joint Allocation of EV Charging Stations and DGs in Spatio-Temporal Expanding Grids

et al. 2020

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The number of electric vehicles (EVs) and the size of smart grid are witnessing rapid expansion in both spatial and temporal dimensions. This requires an efficient dynamic spatio-temporal allocation strategy of charging stations (CSs). Such an allocation strategy should provide acceptable charging services at different deployment stages while meeting financial and technical constraints. As new CSs get allocated, distributed generation (DG) units need to be also dynamically allocated in both space and time to compensate for the increment in the loads due to the EV charging requests. Unfortunately, existing power grid models are not suitable to reflect such spatio-temporal evolution, and hence, new models need to be developed. In this paper, we propose a spatio-temporal expanding power grid model based on stochastic geometry. Using this flexible model, we perform a dynamic joint allocation of EV CSs and DG units based on a constrained Markov decision process. The proposed dynamic allocation strategy accounts for charging coordination mechanism within each CS, which in turn allows for maximal usage of deployed chargers. We validate the proposed stochastic geometry-based power grid model against IEEE 123-bus test system. Then, we present a case study for a 5-year CSs deployment plan. INDEX TERMS Charging stations allocation, dynamic program, electric vehicles, expanding power grid, and stochastic geometry modeling.

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“…In reference [11], an efficient planning algorithm for MGs in remote isolated communities has been presented. Different from the existing research that assumes a specific MG topology, the authors presented a planning algorithm that jointly specifies the optimal grid topology, namely AC, DC, or hybrid AC/DC, along with the optimal locations and sizes of distributed energy resources, energy storage systems, and AC-DC converters; and in reference [12], Mostafa F. Shaaban and et al proposed an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocated and sized a set of DGs along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood

Fathi

Ghiasi

2019

Smart Cities

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: Distributed Generations (DGs) have a productive capacity of tens of kilowatts to several megawatts, which are used to produce electrical energy at close proximity to consumers, which of the types of DGs can be named solar cells and Photovoltaics (PVs), fuel cells, micro turbines, wind power plants, and etc. If such kinds of power plants are connected to the network in optimal places, they will have several positive effects on the system, such as reducing network losses, improving the voltage profile, and increasing network reliability. The lack of optimal placement of DGs in the network will increase the costs of energy production and losses in transmission lines. Therefore, it is necessary to optimize the location of such DGs in the network so that the number of DGs, installation locations, and their capacity are determined to which the maximum reduction in network losses occurs. Besides, by applying an appropriate objective function, the evolutionary algorithm can find the optimal location of renewable units with respect to the constraints of the issue. In this paper, the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm are used to address the placement of wind and photovoltaic generators simultaneously in two states: With and without considering the effects of greenhouse gas emission. In this regard, first, an analytical method for optimal DG (wind and PV) placement is presented, then, the proposed approach is applied over a real study case, and the simulation carried out using the MATLAB program; hence, the placement problem was solved using GA and PSO and implemented in the IEEE 33-bus radial distribution system. The obtained results were compared and analyzed. The results of the simulation show the improvement of the voltage profile and the reduction of losses in the network.

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Joint Planning of Smart EV Charging Stations and DGs in Eco-Friendly Remote Hybrid Microgrids

Cited by 112 publications

References 20 publications

Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Stochastic planning of electric vehicle charging station integrated with photovoltaic and battery systems

Dynamic Joint Allocation of EV Charging Stations and DGs in Spatio-Temporal Expanding Grids

Optimal DG Placement to Find Optimal Voltage Profile Considering Minimum DG Investment Cost in Smart Neighborhood

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