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An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles

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Abstract

According to statistical reports, thermal power plants have long played a critical role in supplying electricity using fossil fuels. However, due to the high investment and operation costs of these power plants and their destructive effects on the environment, renewable energy sources (RESs) in power networks have been considered an effective alternative to traditional power plants. Optimal scheduling of microgrid in smart grids has a significant impact on reducing energy consumption, environmental pollutants and end-user's energy prices. In this paper, the influence of plug-in hybrid electric vehicles (PHEVs) charging on microgrids' optimal operation is evaluated. To assess the behaviour of PHEVs, three various charging patterns consisting of uncontrolled, controlled and smart charging methods are considered. Uncertainties due to forecast error in the PHEVs, loads, prices and renewable source output power are also considered in microgrid modelling energy management. To deal with the optimisation scheduling of microgrid considering uncertainty, a modified harmony search (MHS) algorithm is used. The suggested scheme is validated using simulations and without the PHEV charging effects compared with the conventional methods.

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Abbreviations

t start :

Start time of PHEV charging

B Gi :

Bids of the DGs

B sj :

Bids of storage devices

S G :

Start-up or shutdown costs for ith DG

S sj :

Start-up or shutdown costs for jth storage

P Grid :

Active power

P Grid :

Utility bid

X :

Control vector

n :

State variables

N g :

Total number of generation units

N s :

Total number of storage units

P g :

Power vector including active powers

U g :

State vector

P Gi(t):

Real power outputs of ith generator

P sj(t):

Real power outputs jth storage

P Lk :

Value of the load level k

N k :

Load levels

Δt :

Available energy in the battery at each time slot

W ess,t :

Intertemporal feature

t :

Current number of iterations

P S, charge :

Charging power

P S, discharge :

Discharging power

max:

Upper bounds

min:

Lower bounds

η charge :

Efficiencies in the charging modes

η discharge :

Efficiencies in the discharging modes

X r :

Random position vector

X w :

Worst frog position between (0, 1)

X b :

Best frog position between (0, 1

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Acknowledgements

This work was supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum & Minerals (KFUPM) under Project DF191006.

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Authors and Affiliations

  1. Systems Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia

    Mujahed AL-Dhaifallah

  2. College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, 11991, Saudi Arabia

    Ziad M. Ali

  3. Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt

    Ziad M. Ali

  4. Electrical Engineering Department, College of Engineering, Al Jouf University, Sakaka, 42421, Saudi Arabia

    Mohana Alanazi

  5. Department of Electrical Engineering, Islamic Azad University, Saveh, Iran

    Sajjad Dadfar

  6. Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran

    Mohammad Hosein Fazaeli

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  1. Mujahed AL-Dhaifallah
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Correspondence to Mohammad Hosein Fazaeli.

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AL-Dhaifallah, M., Ali, Z.M., Alanazi, M. et al. An efficient short-term energy management system for a microgrid with renewable power generation and electric vehicles. Neural Comput & Applic 33, 16095–16111 (2021). https://doi.org/10.1007/s00521-021-06247-5

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  • DOI: https://doi.org/10.1007/s00521-021-06247-5

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