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A novel artificial electric field strategy for economic load dispatch problem with renewable penetration

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Abstract

This article presents an innovative method to address the economic load dispatch (ELD) problem in power systems incorporating renewable energy sources within thermal units. Employing the 2-m point estimation technique for determining renewable energy output power, the proposed approach effectively addresses challenges associated with renewable-based ELD by utilizing the artificial electric field method. Inspired by the electrostatic force principle among charged particles, this approach guides particles toward optimal solutions within the search space. Validation on power systems featuring 3, 5, 6, 15, and 40 units demonstrates superior performance compared to established algorithms, confirmed by the Wilcoxon signed-rank test. The research contributes to the advancement of sustainable and efficient power systems.

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Abbreviations

\(\textrm{a}_{\textrm{i}}\),\(\textrm{b}_{\textrm{i}}, \textrm{c}_{\textrm{i}}\),\(\textrm{e}_{\textrm{i}}\), \(\textrm{f}_{\textrm{i}}\) :

Cost variable of the \(\textrm{i}_{\textrm{th}}\) thermal unit

\(\textrm{N}\) :

Count of connected generators

\(\textrm{Ti}\) :

Generated power of \(\textrm{i\;th}\) thermal generator in MW

\(\textrm{W}_{\textrm{p}}, \textrm{S}_{\textrm{p}}\) :

Generated power of wind-solar in MW

\(\textrm{C}_{\textrm{w}}\) :

Cost variable of wind in $/h

\(\textrm{N}_{\textrm{W}}, \textrm{N}_{\textrm{s}}\) :

Count of wind and solar unit

Bidl:

Bid price of \(\textrm{l\;th}\) solar unit

TL:

Transmission loss

TD:

Power requirement

\(\textrm{B}_{\textrm{ij}}, \textrm{B}_{0\textrm{i}}, \textrm{B}_{00}\) :

Loss matrix

\(\textrm{T}_{\textrm{imin}}, \textrm{T}_{\textrm{imax}}\) :

Boundary limit Minimum-maximum power \(\textrm{i\;th}\) unit

\(\textrm{S}_{\textrm{hp}}, \textrm{S}_{\textrm{cp}}\) :

Weibull variables

\(\textrm{v, v}_{\textrm{r}}\) :

Instant and rated haste of wind unit

\(\textrm{v}_{\textrm{in}}, \textrm{v}_{\textrm{out}}\) :

Cut in–cut out the haste of wind unit

\(\textrm{W}_{\textrm{p}}, \textrm{W}_{\textrm{pt}}\) :

Instant and rated power of wind unit

\(\upomega , \uppsi\) :

Beta variables

\(\Gamma\) :

Gamma objectives

\(\textrm{S}_{\mathrm{rad(t)}}\) :

Cellular solar radiation at time t

\(\textrm{S}_{\textrm{rad,stc}}\) :

Solar radiation in normal circumstances

\(\textrm{S}_{\textrm{P,stc}}\) :

Solar power in normal circumstances

\(\upgamma\) :

Temperature variable in %/°\(\hbox {C}\)

Tcell:

The degree of heat in a solar cell

\(\textrm{T}_{\textrm{cell,stc}}\) :

The solar cell’s temperature under the usual test conditions

NOT:

The cell’s typical operating temperature

\(\textrm{N}_{\textrm{sc}}, \textrm{N}_{\textrm{pc}}\) :

Number of solar cells in series and parallel

\(\upmu , \upsigma\) :

The average and standard deviation

\(\textrm{I}_{\textrm{k}}\) :

Input constant

\(\textrm{S}_{\textrm{e}}\) :

Total electricity production, including solar and wind

\(\textrm{z}_{\textrm{l}}\) :

Uncertainty in the input variable

\(\mathrm{Qi(t)}, \mathrm{Qj(t)}\) :

Charges of \(\textrm{i\;th}\) and \(\textrm{j\;th}\) fleck

K(t):

Coulomb’s variable

E:

Modestly positive constant

\(\textrm{R}_{\textrm{ij(t)}}\) :

The distance in Euclid between two particles

\(\upalpha\), K0 :

Parameter and starting point

\(\textrm{F}_{\textrm{i}}, \textrm{M}_{\textrm{i}}\) :

Force and mass of the \(\textrm{i\;th}\) particle

\(\textrm{V}_{\textrm{i}}, \textrm{X}_{\textrm{i}}\) :

Particle’s location and speed

References

  1. Dhillon J, Parti S, Kothari D (1993) Stochastic economic emission load dispatch. Electr Power Syst Res 26(3):179–186

    Article  Google Scholar 

  2. Bhattacharjee K, Shah K, Soni J (2022) Solving economic dispatch using artificial eco system-based optimization. Electr Power Compon Syst 49(11–12):1034–1051

    Google Scholar 

  3. Soni J, Bhattacharjee K (2022) Sooty tern optimization algorithm for solving the multi-objective dynamic economic emission dispatch problem. Int J Swarm Intell Res (IJSIR) 13(1):1–15

    Article  Google Scholar 

  4. Patel N, Bhattacharjee K (2020) A comparative study of economic load dispatch using sine cosine algorithm. Sci Iran 27(3):1467–1480

    Google Scholar 

  5. Bhattacharjee K, Bhattacharya A, nee Dey S.H (2014) Oppositional real coded chemical reaction based optimization to solve short-term hydrothermal scheduling problems. Int J Electr Power Energy Syst 63:145–157

    Article  Google Scholar 

  6. Kempton W, Letendre S (1997) Electric vehicle as a new source of power for electric vehicles. Transp Res 2:157–175

    Google Scholar 

  7. Soni J, Bhattacharjee K (2024) A multi-objective economic emission dispatch problem in microgrid with high penetration of renewable energy sources using equilibrium optimizer. Electr Eng 342:103780

    Google Scholar 

  8. Verma D, Soni J, Kalathia D, Bhattacharjee K (2022) Sine cosine algorithm for solving economic load dispatch problem with penetration of renewables. Int J Swarm Intell Res (IJSIR) 13(1):1–21

    Google Scholar 

  9. Galus MD, Andersson G (2008) Demand management of grid connected plug-in hybrid electric vehicles (phev). In: 2008 IEEE energy 2030 conference. IEEE, pp 1–8

  10. Soni JM, Pandya MH (2018) Power quality enhancement for PV rooftop and Bess in islanded mode. In: 2018 4th international conference on electrical energy systems (ICEES). IEEE, pp 242–247

  11. Soni J, Bhattacharjee K (2024) Equilibrium optimizer for multi-objective dynamic economic emission dispatch integration with plug-in electric vehicles and renewable sources. Multiscale Multidiscip Model Exp Des 1–17

  12. Bhattacharjee K, Bhattacharya A, Shah K, Patel N (2022) Backtracking search optimization applied to solve short-term electrical real power generation of hydrothermal plant. Eng Optim 54(9):1525–1543

    Article  Google Scholar 

  13. Ma H, Yang Z, You P, Fei M (2017) Multi-objective biogeography-based optimization for dynamic economic emission load dispatch considering plug-in electric vehicles charging. Energy 135:101–111

    Article  Google Scholar 

  14. Qu B, Qiao B, Zhu Y, Jiao Y, Xiao J, Wang X (2017) Using multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVS. In: International conference on swarm intelligence. Springer, pp 31–39

  15. Zou D, Li S, Xuan K, Ouyang H (2022) A NSGA-II variant for the dynamic economic emission dispatch considering plug-in electric vehicles. Comput Ind Eng 173:108717

    Article  Google Scholar 

  16. Bhattacharjee K, Patel N (2020) An experimental study regarding economic load dispatch using search group optimization. Sci Iran 27(6):3175–3189

    Google Scholar 

  17. Bhattacharjee K, Bhattacharya A, nee Dey SH (2015) Backtracking search optimization based economic environmental power dispatch problems. Int J Electr Power Energy Syst 73:830–842

    Article  Google Scholar 

  18. Chen F, Zhou J, Wang C, Li C, Lu P (2017) A modified gravitational search algorithm based on a non-dominated sorting genetic approach for hydro-thermal-wind economic emission dispatching. Energy 121:276–291

    Article  Google Scholar 

  19. Liu G, Zhu YL, Jiang W (2018) Wind-thermal dynamic economic emission dispatch with a hybrid multi-objective algorithm based on wind speed statistical analysis. IET Gener Transm Distrib 12(17):3972–3984

    Article  Google Scholar 

  20. Basu M (2019) Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system. Renew Energy Focus 28:11–35

    Article  Google Scholar 

  21. Zhu Z, Wang J, Baloch MH (2016) Dynamic economic emission dispatch using modified NSGA-II. Int Trans Electr Energy Syst 26(12):2684–2698

    Article  Google Scholar 

  22. Kheshti M, Ding L, Ma S, Zhao B (2018) Double weighted particle swarm optimization to non-convex wind penetrated emission/economic dispatch and multiple fuel option systems. Renew Energy 125:1021–1037

    Article  Google Scholar 

  23. Zhao J, Wen F, Dong ZY, Xue Y, Wong KP (2012) Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Trans Ind Inform 8(4):889–899

    Article  Google Scholar 

  24. Jin J, Zhou D, Zhou P, Miao Z (2014) Environmental/economic power dispatch with wind power. Renew Energy 71:234–242

    Article  Google Scholar 

  25. Faramarzi A, Heidarinejad M, Stephens B, Mirjalili S (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190

    Article  Google Scholar 

  26. Varzaneh ZA, Hossein S, Mood SE, Javidi MM (2022) A new hybrid feature selection based on improved equilibrium optimization. Chemom Intell Lab Syst 228:104618

    Article  Google Scholar 

  27. Basu M (2019) Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system. Renew Energy Focus 28:11–35

    Article  Google Scholar 

  28. Chen M-R, Zeng G-Q, Lu K-D (2019) Constrained multi-objective population extremal optimization based economic-emission dispatch incorporating renewable energy resources. Renew Energy 143:277–294

    Article  Google Scholar 

  29. Ding Y, Cano ZP, Yu A, Lu J, Chen Z (2019) Automotive Li-ion batteries: current status and future perspectives. Electrochem Energy Rev 2(1):1–28

    Article  Google Scholar 

  30. Gupta S, Abderazek H, Yıldız BS, Yildiz AR, Mirjalili S, Sait SM (2021) Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems. Expert Syst Appl 183:115351

    Article  Google Scholar 

  31. Zhang Y, Le J, Liao X, Zheng F, Liu K, An X (2018) Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO. Renew Energy 128:91–107

    Article  Google Scholar 

  32. Shao C, Wang X, Wang X, Du C, Dang C, Liu S (2014) Cooperative dispatch of wind generation and electric vehicles with battery storage capacity constraints in SCUC. IEEE Trans Smart Grid 5(5):2219–2226

    Article  Google Scholar 

  33. Soni J, Bhattacharjee K (2024) Integrating renewable energy sources and electric vehicles in dynamic economic emission dispatch: an oppositional-based equilibrium optimizer approach. Eng Optim 1–35

  34. Shao C, Wang X, Wang X, Du C, Dang C, Liu S (2014) Cooperative dispatch of wind generation and electric vehicles with battery storage capacity constraints in SCUC. IEEE Trans Smart Grid 5(5):2219–2226

    Article  Google Scholar 

  35. Qu B, Qiao B, Zhu Y, Liang J, Wang L (2017) Dynamic power dispatch considering electric vehicles and wind power using decomposition based multi-objective evolutionary algorithm. Energies 10(12):1991

    Article  Google Scholar 

  36. Qiao B, Liu J (2020) Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm. Renew Energy 154:316–336

    Article  Google Scholar 

  37. Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  38. Soni J, Bhattacharjee K (2023) Equilibrium optimiser for the economic load dispatch problem with multiple fuel option and renewable sources. Int J Ambient Energy 44(1):2386–2397

    Article  Google Scholar 

  39. Yadav A, Kumar N et al (2020) Artificial electric field algorithm for engineering optimization problems. Expert Syst Appl 149:113308

    Article  Google Scholar 

  40. Yadav A et al (2019) AEFA: artificial electric field algorithm for global optimization. Swarm Evol Comput 48:93–108

    Article  Google Scholar 

  41. Soni J, Bhattacharjee K (2024) Multi-objective dynamic economic emission dispatch integration with renewable energy sources and plug-in electrical vehicle using equilibrium optimizer. Environ Dev Sustain 26(4):8555–8586

    Article  Google Scholar 

  42. Basu M (2016) Multi-objective optimal reactive power dispatch using multi-objective differential evolution. Int J Electr Power Energy Syst 82:213–224

    Article  Google Scholar 

  43. Basu M (2014) Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II. Energy 78:649–664

    Article  Google Scholar 

  44. Ghasemi M, Akbari E, Zand M, Hadipour M, Ghavidel S, Li L (2019) An efficient modified HPSO-TVAC-based dynamic economic dispatch of generating units. Electr Power Compon Syst 47(19–20):1826–1840

    Article  Google Scholar 

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Authors and Affiliations

Authors

Contributions

Diwakar Verma: Methodology, software, writing original Draft, data curation, conceptualization, investigation, validation, resources, project administration, format analysis, visualization, supervision. Jatin Soni: Writing original Draft, data curation, conceptualization, investigation, validation, format analysis, visualization, supervision. Kuntal Bhattacharjee: Investigation, validation, format analysis, visualization, supervision.

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Correspondence to Jatin Soni.

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Verma, D., Soni, J. & Bhattacharjee, K. A novel artificial electric field strategy for economic load dispatch problem with renewable penetration. Evol. Intel. (2024). https://doi.org/10.1007/s12065-024-00946-3

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