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Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm

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Abstract

The establishment of an electric vehicle charging station (EVCS) infrastructure plays a vital role in fostering the sustainable expansion of the electric vehicle sector. The unplanned placement of EVCS raises various technical and economic issues in the distribution network, and it can lead to increased energy losses in the distribution system. Installing EVCSs and DGs at strategically chosen positions is essential to minimize the impact of EV load on the distribution network. To address these issues, this work has proposed a whale optimization technique (WOT) for optimizing the placement and sizing of EVCS and DG. This novel approach of formulating the multi-objective problem of allocating electric vehicle (EV) charging stations, along with DGs and capacitors simultaneously improves the voltage stability, minimizes active and reactive power losses in the radial distribution system, and requires execution time. A forward–backward power flow method has been carried out for load flow calculations. The efficacy of the proposed approach has been applied on the standard IEEE-33 and 69-bus test systems and the obtained results have been compared with the other latest optimization techniques. The simulation results validate the performance and effectiveness of the Whale Optimization Technique, and it has been analyzed that the optimized sizes and allocation of EVCSs, DGs, and capacitors lead to an extensive reduction in power losses of 81.33% and 80% in the case of active power and 78.46% and 80.26% in the case of reactive power for the IEEE-33 and 69-bus test systems, respectively.

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Abbreviations

RDS:

Radial distribution system

DG:

Distributed generators

EVCS:

Electric vehicle charging Station

WOT:

Whale optimization technique

TLBO:

Teacher’s learner’s based optim8ization

SCs:

Shunt capacitors

BBO:

Biogeography-based optimization

PHEV:

Plug-in hybrid electric vehicle

IMDE:

Intersect mutation differential evolution

ABC:

Artificial bee colony

BSS:

Battery swapping stations

GA:

Genetic algorithm

VSI:

Voltage stability index

GWO:

Gray wolf optimization

AVDI:

Average voltage deviation index

BIBC:

Bus injection to branch current

BCBV:

Branch current to the bus voltage

KVL and KCL:

Kirchoff’s voltage law and Kirchhoff’s current law

BFOA-PSO:

Foraging optimization algorithm & particle swarm optimization

BFOA:

Bacterial foraging optimization algorithm

IDBES:

Improved-decomposition-based evolutionary algorithm

AOSAOA:

Atomic orbital search and arithmetic optimization algorithm

PGS:

Plant growth simulation

\(\omega_{1} ,\omega_{2}\) :

Weighting factors

Sec.:

Time in seconds

\(P_{{{\text{Loss}}}}\) :

Power losses in the system

CS:

Charging station

\(x_{{{\text{ij}}}} ,y_{{{\text{ij}}}}\) :

Coefficients

\(r_{{{\text{ij}}}}\) :

Resistance between i th bus and \(j\;th\) bus

\(I_{i}\) :

Current at i th bus

\(U_{\min } ,U_{\max }\) :

Minimum and maximum voltages

\(n\) :

Total number of buses

\(\Gamma\) :

Set of all buses

\(\overline{I}_{L} (i)\) :

Load current of each bus

\(\overline{I} ({\text{ij}})\) :

Current in each branch

\(Z_{{{\text{ij}}}}\) :

Impedance of branch ij

\(P_{L} (i)\) :

Active power demand

\(Q_{L} (i)\) :

Reactive power demand

\(I_{\min } ,I_{\max }\) :

Minimum and maximum currents

\(P_{k}^{L} ,Q_{k}^{L}\) :

Total real and reactive power fed to the receiving end

\(P_{j}^{D} ,Q_{j}^{D}\) :

Active and reactive load demands at \(j\;th\) bus

\(P_{i} ,Q_{i}\) :

Active and reactive power losses

\(N_{{{\text{DG}}}} ,N_{{{\text{bus}}}}\) :

Number of DGs and buses

\(N_{L}\) :

Total number of load buses

\(M,N\) :

Whale’s position point

\(P_{{{\text{slack}}}} ,Q_{{{\text{slack}}}}\) :

Active and reactive power for slack bus

\(\overline{d}\) :

Distance between the prey and whale

\(\overline{\rho }\) :

Coefficient vector

\(\alpha\) :

Coefficient

\(\overline{{M^{*} }} (\tau )\) :

Vector with new position

\(S_{i}\) :

Power in complex form

\(\phi\) :

Numbers uniformly distributed within the range of {0,1}

\(\mathop {M_{\rho } }\limits^{ \to }\) :

The position vector of a whale that is selected randomly

\(M\) :

Positioning vector

\(P_{i}^{{{\text{DG}}}} ,Q_{i}^{{{\text{DG}}}}\) :

DG’s active and reactive power at i th bus

\(P_{i}^{D} ,Q_{i}^{D}\) :

Active & reactive power demand at i th bus

\(Q_{c}^{L}\) :

The total reactive power demand of the network

\(\sum\limits_{n = 1}^{{{\text{NC}}}} {Q_{{{\text{cj}}}} }\) :

The total reactive power injection by the shunt capacitors

\(Q_{{{\text{cn}}}}\) :

KVAR injection by shunt capacitors

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  1. Maulana Azad National Institute of Technology, Bhopal, India

    Shweta Mehroliya & Anoop Arya

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  1. Shweta Mehroliya
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SM: Manuscript Text writing and Table formation. Development of the Figures, implementation of algorithms and obtaining the Results, discussion, review, and formatting.

AA: Paper submission and correspondence, finalization of title of the manuscript, abstract, conclusion and order of the references.

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Correspondence to Anoop Arya.

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Mehroliya, S., Arya, A. Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm. Electr Eng 106, 1373–1389 (2024). https://doi.org/10.1007/s00202-023-02198-3

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