Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm


Providing of energy is one of the most important issues for each country. Also, environmental issues due to fossil fuel depletion are other serious concern of them. In this regard, moving toward energy sustainability is a constructive solution for each country. This paper studies the short-term planning of generating units in renewable energy-based distribution networks equipped with plug-in electric vehicles (PEVs). PEVs can cause problems for distributed energy sources in the electrical grid, as well as power units inside the grid. So, to overcome this problem, an efficient stochastic programming technique is designed to allow the control entity to control the charging behavior of PEVs for managing power units. In this paper, to obtain the least total cost, a new method is suggested to decrease the reliability expenses. In other words, the vehicle-2-grid (V2G) is applied to decrease the operating. On the other hand, a novel stochastic flow using the unscented transform is suggested to improve the model of the severe uncertainty due to the wind power, photovoltaic (PV) and charging/discharging power of PEVs. In this research work, a novel and efficient optimization algorithm called ‘θ-modified krill herd (θ-MKH)” is used as an applicable technique to optimize the microgrid (MG) operation. This algorithm is useful and has many advantages like the runaway from the local optima with fast converging in comparison with other methods. Also, the satisfactory efficiency of the suggested randomized manner is validated on an MG connected to the main grid.

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Plug-in electric vehicles






θ-modified krill herd




Renewable energy sources


Distributed generation


Distributed energy resources


Fuel cells






Electric vehicles


Virtual power plants


Mixed-integer linear programming


Demand response programs


Unified modeling language


Model predictive control


MG central control






Energy not supplied

C DG,k :

The price of energy, supplied by DG units at each hour

C Grid :

The price, relating to transacting energy with the utility grid at each hour


The cost that should be tolerated as a result of load curtailment at node i ($/kW)

N DG :

The total number of DGs, existing in the network

N Cus :

Total number of customers with satisfied load demand

La(i) :

The average load demand at node i

Cost DG :

The cost of energy generation by DG units.

\(P_{(DG,k)}^{t}\) :

Power generation of DG unit k at time interval t

\(P_{v}^{t}\) :

The power charged/discharged by the PEV fleet v at each time interval t

DoD i & DoD f :

The initial value of DOD and final value of DOD during a discharge cycle respectively

\(V_{r,i}^{K}\) :

The velocity of the ith

\(V_{ind\;i}^{k}\) :

Induction motion

θi :

Phase vector

M nk :

Mean value of the krill population

Np :

The size of population


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This project was funded by King Abdulaziz University, Jeddah, Saudi Arabia and King Abdullah City for Atomic and Renewable Energy, Riyadh, Saudi Arabia under grant no. (KCR-KFL-09-20). Therefore, the authors gratefully acknowledge their technical and financial support.

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Correspondence to Alireza Rezvani.

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Data of the two fleets of PEVs.

Fleet # Capacity (kWh)
Min Max
1 263 1973
2 219 1644
Fleet # Charging/discharging rate (kW)
Min Max
1 7.3 496
2 7.3 292

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Aldosary, A., Rawa, M., Ali, Z.M. et al. Energy management strategy based on short-term resource scheduling of a renewable energy-based microgrid in the presence of electric vehicles using θ-modified krill herd algorithm. Neural Comput & Applic (2021).

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  • Stochastic management
  • Renewable energy
  • Microgrid (MG)
  • Krill herd algorithm
  • Electric vehicle