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Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid

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Abstract

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

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Abbreviations

\(P_{\text{loss}} / {\text{VD}}\) :

The total power losses/voltage deviation

VSI:

Voltage stability index

\(\delta_{ij}\) :

The voltage angle difference between i and bus j

\(\theta_{ji} /V_{Gi}\) :

The phase angle of term \(F_{ji} \,\)/voltage magnitude for generator at bus i

\(N_{\text{PV}} ,\,N_{\text{PQ}}\) :

The number of PV and PQ buses, respectively

\(G_{k}\) :

Conductance of kth branch connected between bus i and j

\(V_{i} ,V_{j} /V_{{L,N_{\text{PQ}} }}\) :

Voltage magnitude of bus i and j/voltage magnitude for load bus i

\(\left| {Y_{ij} } \right| /S_{i}\) :

The elements of bus admittance matrix/apparent power flow of branch i

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

The active/reactive load consumption at bus i

\(P_{Gi} /Q_{Gi}\) :

The active/reactive power generation at bus i

\(P_{{L,N_{\text{PQ}} }} ,\;Q_{{L,N_{\text{PQ}} }}\) :

The active and reactive power at each load bus

\(V_{i}^{\hbox{max} } ,V_{i}^{\hbox{min} }\) :

The maximum and minimum bus voltage magnitude at bus i

\(Q_{Gi}^{\hbox{min} } \,,\,Q_{Gi}^{\hbox{max} }\) :

The minimum and maximum value of power generation at bus i

\(T_{k}^{\hbox{max} } /T_{k}^{\hbox{min} }\) :

The maximum/minimum tap ratio of kth tap-changing transformer

\(Q_{Ci}^{\hbox{min} } \,,\,Q_{Ci}^{\hbox{max} }\) :

The minimum and maximum VAR injection limits of shunt capacitor banks

\(S_{i}^{\hbox{max} }\) :

The maximum apparent power flow limit of branch i

NB/NTL:

The number of buses in the test system/number of transmission lines

NLB/NG:

The number of load buses/the number of generators buses

NT/NC:

The number of the transformer taps/number of shunt capacitor banks

\(\lambda_{V} ,\,\,\lambda_{Q} ,\,\,\lambda_{l}\) :

The penalty factors

\(X_{i}^{\lim }\) :

The limit value of the dependent variables \(V_{i}^{\lim } ,\,\,Q_{i}^{\lim } ,\,\,{\text{and}}\,\,S_{i}^{\lim }\)

\(X_{i}^{\hbox{max} } /X_{i}^{\hbox{min} }\) :

The maximum/minimum limit of state variables

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Acknowledgements

We gratefully acknowledge the support of the Algerian electricity company SONELGAZ. This work was supported in part by the Exceptional National Program of Algeria PNE 2019/2020 and the Key Program of Fundamental Research of Electrical Engineering Department at Jaen University, Spain 2020.

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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Jaén, 23700 EPS Linares, Jaén, Spain

    Souhil Mouassa & Francisco Jurado

  2. Department of Electrical Engineering, University of Bouira, Bouira, Algeria

    Souhil Mouassa & Tarek Bouktir

  3. Department of Electrical Engineering, University of Farhat Abbas, Sétif 1, Sétif, Algeria

    Souhil Mouassa

  4. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC

    Muhammad Asif Zahoor Raja

  5. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Muhammad Asif Zahoor Raja

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  1. Souhil Mouassa
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  4. Muhammad Asif Zahoor Raja
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Correspondence to Souhil Mouassa.

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Appendix

Appendix

See Fig. 17.

Fig. 17
figure 17

Algerian electricity grid DZA 114 bus

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Mouassa, S., Jurado, F., Bouktir, T. et al. Novel design of artificial ecosystem optimizer for large-scale optimal reactive power dispatch problem with application to Algerian electricity grid. Neural Comput & Applic 33, 7467–7490 (2021). https://doi.org/10.1007/s00521-020-05496-0

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