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Optimal planning and operation of power grid with electric vehicles considering cost reduction

  • Optimization
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Abstract

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness.

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Abbreviations

RES:

Renewable energy sources

WTs:

Wind turbines

DGs:

Distributed generators

PEVs:

Electric vehicles

PV:

Photovoltaic

PEVs:

Plug-in electric vehicles

G2V:

Grid-to-vehicle

FCSs:

Fuel cell systems

DSOs:

Distribution operators

V2G:

Vehicle-to-grid

CS:

Charging station

DOD:

Depth of discharge

MG:

Microgrid

ENS:

Energy not supplied

UT:

Unscented transformation

A i :

Amplitude of the frequency

DoDi & DoDf :

The original and final depth of discharge

a,b :

Wöhler curve’s parameter

\({E}_{v}^{{\text{ini}}}\) & \({E}_{v}^{{\text{fin}}}\) :

The initial and concluding energy levels of fleet v

d :

The size or scale of the problem.

\({E}_{v}^{{\text{min}}}\) & \({E}_{v}^{{\text{max}}}\) :

The maximum and minimum energy bounds

E bat :

The stored energy of battery (kWh)

N DG :

The total number of DGs

N br /N bus :

The number of branches/buses of the network

N Cus :

The overall count of consumers being supplied

Nv :

Total number of electric vehicles

Np :

The population sizes

Ndis :

The total count of battery discharge cycles

m :

The count of uncertain variables

Nc :

Cycle life

rand:

Operator for generating random values

T :

Scheduling period

ε :

Charging and discharging efficiencies

CostGrid :

The cost of supplying energy by the upstream system

C Bat :

The cost of interrupting load at bus i ($/kW)

CostPEV :

The aggregated costs related to PEVs

C Grid :

The 1-h time resolution energy price provided by the grid, V2G technology

CostENS :

The cost of energy not served of the customers

C PEV :

The 1-h time resolution energy price provided by the grid, V2G technology

CostDG :

The cost operation of distributed generators (DGs)

C ENS :

The 1-h time resolution cost associated with V2G technology

Ct DG,k :

The cost of battery investment in US dollars ($)

\({E}_{D,v}^{t}\) :

The energy consumed by fleet v for driving at hour t

\({P}_{v}^{t}\) :

The rate of charging or discharging power for fleet v at time t

\({E}_{v}^{t}\) :

The available energy for fleet v at hour t

\({P}_{i}^{t}\) &\({Q}_{i}^{t}\) :

The powers injected to bus i at time t

\({P}_{DG,k}^{t}\) :

The output of DG k at time t

\({S}_{ij}^{t}\) &\({S}_{ij}^{{\text{max}}}\) :

The apparent power and maximum active power from bus i to bus j at time t, respectively

\({P}_{{\text{Grid}}}^{t}{ \& P}_{{\text{Grid}}}^{{\text{max}}}\) :

The hourly power exchange and the peak electricity transaction with the external grid

\({U}_{v}^{t}\) :

The functioning condition of fleet v's link with the grid at time t

\({{P}_{d,v}^{t} \& P}_{c,v}^{t}\) :

Discharging and charging capacity of fleet

\({V}_{i}^{t}\) :

The voltage magnitude at bus i and at time t

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Acknowledgements

This work was supported by the 2024 Key Research and Scientific Research Capability Improvement Project in Qiannan Normal University for Nationalities (2024zdzk06), National Natural Science Foundation of China (No. 61862051), the Science and Technology Foundation of Guizhou Province (No. ZK[2022]549), the Natural Science Foundation of Education of Guizhou province (No. [2019]203, No. KY[2019]067), and the Funds of Qiannan Normal University for Nationalities (No. qnsy2019rc09) and the Al-Mustaqbal University.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Ankang University, Ankang, China

    Tao Hai

  2. School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou, 558000, China

    Tao Hai

  3. Cyber Security Department, College of Sciences, Al-Mustaqbal University, Babylon, 51001, Iraq

    Muammer Aksoy

  4. Computer Information Systems Department, Ahmed Bin Mohammed Military College, P.O. Box 22988, Doha, Qatar

    Muammer Aksoy

  5. Solar Energy and Power Electronic Co, Ltd, Istanbul, Turkey

    Mehrdad Khaki

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  1. Tao Hai
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Hai, T., Aksoy, M. & Khaki, M. Optimal planning and operation of power grid with electric vehicles considering cost reduction. Soft Comput (2024). https://doi.org/10.1007/s00500-023-09597-5

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