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An enhanced equilibrium optimizer for strategic planning of PV-BES units in radial distribution systems considering time-varying demand

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Abstract

The integration of the distributed generations with the distribution systems provides several advantages to the utility and customers, such as enhancing the stability and voltage profile, reducing the system losses, and reducing the fossil-fuel dependability. However, the integration represents a great challenge, especially with the associated uncertainty in load and generation powers. In this paper, the enhanced equilibrium optimizer (EEO), a modified version from the equilibrium optimizer (EO) algorithm, is derived, proposed, and implemented to allocate PV-BES units optimally. The EEO algorithm optimizes the PV-BES size, location, and power factor to reduce the total system power and energy losses during 24-h and a year operation periods. Detailed modeling of photovoltaic modules and battery energy system combination (PV-BES) units are provided and analyzed. The current study includes optimizing PV-BES combinations from one to five units operating either with unity or optimal power factors. Moreover, the proposed EEO algorithm's obtained results are favorably compared to SCA and EO algorithms in terms of the mean, standard deviation, and standard error of different optimization problems. The standard 69-bus radial distribution system is taken as a test system. The obtained results show the proposed EEO algorithm's superiority and effectiveness compared to other algorithms in terms of convergence speed and optimal values. Moreover, EEO algorithms can continuously optimize different PV and BES units during the 24-h and an entire year, taking the probabilistic nature of load and distributed generations.

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References

  1. Milovanović M, Radosavljević J, Perović B (2020) A backward/forward sweep power flow method for harmonic polluted radial distribution systems with distributed generation units. Int Trans Electr Energy Syst 30(5):1–17

    Article  Google Scholar 

  2. Eid A (2020) Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations. Alex Eng J 59(6):4771–4786

    Article  Google Scholar 

  3. Bai K, Yildizbasi A (2020) Optimal siting and sizing of battery energy storage system for distribution loss reduction based on meta-heuristics. J Control Autom Electr Syst 31(6):1469–1480

    Article  Google Scholar 

  4. Eid A, Kamel S, Korashy A, Khurshaid T (2020) An enhanced artificial ecosystem-based optimization for optimal allocation of multiple distributed generations. IEEE Access 8:178493–178513

    Article  Google Scholar 

  5. Kumar S, Mandal KK, Chakraborty N (2020) A novel opposition-based tuned-chaotic differential evolution technique for techno-economic analysis by optimal placement of distributed generation. Eng Optim 52(2):303–324

    Article  MathSciNet  Google Scholar 

  6. Wong LA, Ramachandaramurthy VK, Taylor P, Ekanayake JB, Walker SL, Padmanaban S (2019) Review on the optimal placement, sizing and control of an energy storage system in the distribution network. J Energy Storage 21:489–504

    Article  Google Scholar 

  7. Eid A, Abdel-Akher M (2019) Power loss reduction using adaptive PSO in unbalanced distribution networks. In: 2019 21st int. middle east power syst. conf. MEPCON 2019—proc., pp 675–680

  8. Elattar EE, Elsayed SK (2019) Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement. Energy 178:598–609

    Article  Google Scholar 

  9. Nikkhah S, Rabiee A (2018) Optimal wind power generation investment, considering voltage stability of power systems. Renew Energy 115:308–325

    Article  Google Scholar 

  10. Youssef H, Kamel S, Aly MM (2018) Optimal allocation and size of appropriate compensation devices for voltage stability enhancement of power systems. In: Proc. 2018 int. conf. innov. trends comput. eng. ITCE 2018, vol 2018, pp 305–310

  11. Adewuyi OB, Shigenobu R, Ooya K, Senjyu T, Howlader AM (2019) Static voltage stability improvement with battery energy storage considering optimal control of active and reactive power injection. Electr Power Syst Res 172:303–312

    Article  Google Scholar 

  12. Moravej Z, Ardejani PE, Imani A (2018) Optimum placement and sizing of DG units based on improving voltage stability using multi-objective evolutionary algorithm. J Renew Sustain Energy 10(5):055304

    Article  Google Scholar 

  13. Lotfi H, Ghazi R, Bagher Naghibi-Sistani M (2020) Multi-objective dynamic distribution feeder reconfiguration along with capacitor allocation using a new hybrid evolutionary algorithm, vol 11, no 3. Springer, Berlin, Heidelberg

  14. Pegado R, Ñaupari Z, Molina Y, Castillo C (2019) Radial distribution network reconfiguration for power losses reduction based on improved selective BPSO. Electr Power Syst Res 169:206–213

    Article  Google Scholar 

  15. Shukla J, Das B, Pant V (2018) Stability constrained optimal distribution system reconfiguration considering uncertainties in correlated loads and distributed generations. Int J Electr Power Energy Syst 99:121–133

    Article  Google Scholar 

  16. Azizivahed A, Narimani H, Fathi M, Naderi E, Safarpour HR, Narimani MR (2018) Multi-objective dynamic distribution feeder reconfiguration in automated distribution systems. Energy 147:896–914

    Article  Google Scholar 

  17. Arabi Nowdeh S et al (2019) Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Appl Soft Comput J 77:761–779

    Article  Google Scholar 

  18. Ghorbani N, Kasaeian A, Toopshekan A, Bahrami L, Maghami A (2018) Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability. Energy 154:581–591

    Article  Google Scholar 

  19. Maleki A, Khajeh MG, Ameri M (2016) Optimal sizing of a grid independent hybrid renewable energy system incorporating resource uncertainty, and load uncertainty. Int J Electr Power Energy Syst 83:514–524

    Article  Google Scholar 

  20. Bakhshi Yamchi H, Shahsavari H, Taghizadegan Kalantari N, Safari A, Farrokhifar M (2019) A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty. J Energy Storage 22:17–26

    Article  Google Scholar 

  21. Home-Ortiz JM, Pourakbari-Kasmaei M, Lehtonen M, SanchesMantovani JR (2019) Optimal location-allocation of storage devices and renewable-based DG in distribution systems. Electr Power Syst Res 172:11–21

    Article  Google Scholar 

  22. Eid A, Abdelaziz AY, Dardeer M (2021) Energy loss reduction of distribution systems equipped with multiple distributed generations considering uncertainty using manta-ray foraging optimization. Int J Renew Energy Dev 10(4):779–788

    Article  Google Scholar 

  23. Eid A, El-kishky H (2021) Multi-objective Archimedes optimization algorithm for optimal allocation of renewable energy sources in distribution networks. Lect Notes Netw Syst 211:65–75

    Article  Google Scholar 

  24. Hassan MH, Kamel S, Abualigah L, Eid A (2021) Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Syst Appl 182:115205

    Article  Google Scholar 

  25. Eid A, Kamel S, Abualigah L (2021) Marine predators algorithm for optimal allocation of active and reactive power resources in distribution networks. Neural Comput Appl 33:14327–14355

    Article  Google Scholar 

  26. Hung DQ, Mithulananthan N, Bansal RC (2014) Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability. Appl Energy 113:1162–1170

    Article  Google Scholar 

  27. Hung DQ, Member SS, Mithulananthan N, Member SS, Lee KY (2014) Determining PV penetration for distribution systems with time-varying load models. IEEE Trans Power Syst 29(6):3048–3057

    Article  Google Scholar 

  28. Hung DQ, Mithulananthan N, Bansal RC (2013) Analytical strategies for renewable distributed generation integration considering energy loss minimization. Appl Energy 105:75–85

    Article  Google Scholar 

  29. Avril S, Arnaud G, Florentin A, Vinard M (2010) Multi-objective optimization of batteries and hydrogen storage technologies for remote photovoltaic systems. Energy 35(12):5300–5308

    Article  Google Scholar 

  30. Chen SX, Gooi HB, Wang MQ (2012) Sizing of energy storage for microgrids. IEEE Trans Smart Grid 3(1):142–151

    Article  Google Scholar 

  31. Kerdphol T, Fuji K, Mitani Y, Watanabe M, Qudaih Y (2016) Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids. Int J Electr Power Energy Syst 81:32–39

    Article  Google Scholar 

  32. Aziz AS, Tajuddin MFN, Adzman MR, Ramli MAM, Mekhilef S (2019) Energy management and optimization of a PV/diesel/battery hybrid energy system using a combined dispatch strategy. Sustainability 11(3):683

    Article  Google Scholar 

  33. Chedid R, Sawwas A (2019) Optimal placement and sizing of photovoltaics and battery storage in distribution networks. Energy Storage 1(4):1–12

    Article  Google Scholar 

  34. Wong LA, Ramachandaramurthy VK, Walker SL, Ekanayake JB (2020) Optimal placement and sizing of battery energy storage system considering the duck curve phenomenon. IEEE Access 8:197236

    Article  Google Scholar 

  35. Jannesar MR, Sedighi A, Savaghebi M, Anvari-Moghadam A, Guerrero JM (2020) Optimal multi-objective integration of photovoltaic, wind turbine, and battery energy storage in distribution networks. J Energy Manag Technol 4(4):76–83

    Google Scholar 

  36. Saboori H, Hemmati R (2017) Maximizing DISCO profit in active distribution networks by optimal planning of energy storage systems and distributed generators. Renew Sustain Energy Rev 71:365–372

    Article  Google Scholar 

  37. Wong LA, Ramachandaramurthy VK, Walker SL, Taylor P, Sanjari MJ (2019) Optimal placement and sizing of battery energy storage system for losses reduction using whale optimization algorithm. J Energy Storage 26:100892

    Article  Google Scholar 

  38. Selim A, Kamel S, Jurado F, Lopes JAP, Matos M (2021) Optimal setting of PV and battery energy storage in radial distribution systems using multi-objective criteria with fuzzy logic decision-making. IET Gener Transm Distrib 15(1):135–148

    Article  Google Scholar 

  39. Adewuyi OB, Shigenobu R, Senjyu T, Lotfy ME, Howlader AM (2019) Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study. Electr Power Syst Res 168:269–282

    Article  Google Scholar 

  40. Yuan Z, Wang W, Wang H, Yildizbasi A (2020) A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction. J Energy Storage 29:101368

    Article  Google Scholar 

  41. Alzahrani A, Alharthi H, Khalid M (2019) Minimization of power losses through optimal battery placement in a distributed network with high penetration of photovoltaics. Energies 13(140):1

    Google Scholar 

  42. Monteiro RVA et al (2018) A medium-term analysis of the reduction in technical losses on distribution systems with variable demand using artificial neural networks: an electrical energy storage approach. Energy 164:1216–1228

    Article  Google Scholar 

  43. Zebarjadi M, Askarzadeh A (2016) Optimization of a reliable grid-connected PV-based power plant with/without energy storage system by a heuristic approach. Sol Energy 125:12–21

    Article  Google Scholar 

  44. Mohamed AAA, Ali S, Alkhalaf S, Senjyu T, Hemeida AM (2019) Optimal allocation of hybrid renewable energy system by multi-objectivewater cycle algorithm. Sustainability 11(23):1–20

    Article  Google Scholar 

  45. Singh B, Singh S (2019) GA-based optimization for integration of DGs, STATCOM and PHEVs in distribution systems. Energy Rep 5:84–103

    Article  Google Scholar 

  46. Selim A, Kamel S, Jurado F (2020) Efficient optimization technique for multiple DG allocation in distribution networks. Appl Soft Comput J 86:105938

    Article  Google Scholar 

  47. Eid A (2021) Performance improvement of active distribution systems using adaptive and exponential PSO algorithms. Int Rev Electr Eng 16(2):147–157

    Google Scholar 

  48. Kharrich M et al (2021) Optimal design of an isolated hybrid microgrid for enhanced deployment of renewable energy sources in Saudi Arabia. Sustainability 13(9):1–26

    Article  Google Scholar 

  49. Kharrich M et al (2021) Economic and ecological design of hybrid renewable energy systems based on a developed IWO/BSA algorithm. Electronics 10(6):1–31

    Article  Google Scholar 

  50. Eid A, Abdel-Akher M (2017) Voltage control of unbalanced three-phase networks using reactive power capability of distributed single-phase PV generators. Int Trans Electr Energy Syst 27(11):e2394

    Article  Google Scholar 

  51. Abdel-Akher M, Eid A, Ali A (2017) Effective demand side scheme for PHEVs operation considering voltage stability of power distribution systems. Int J Emerg Electr Power Syst 18(2):1–16

    Google Scholar 

  52. Dehghani M, Montazeri Z, Malik OP (2020) Optimal sizing and placement of capacitor banks and distributed generation in distribution systems using spring search algorithm. Int J Emerg Electr Power Syst 21(1):1–9

    Google Scholar 

  53. Radosavljevic J, Arsic N, Milovanovic M, Ktena A (2020) Optimal placement and sizing of renewable distributed generation using hybrid metaheuristic algorithm. J Mod Power Syst Clean Energy 8(3):499–510

    Article  Google Scholar 

  54. Bayat A, Bagheri A, Noroozian R (2016) Optimal siting and sizing of distributed generation accompanied by reconfiguration of distribution networks for maximum loss reduction by using a new UVDA-based heuristic method. Int J Electr Power Energy Syst 77:360–371

    Article  Google Scholar 

  55. Haider W, Hassan SJU, Mehdi A, Hussain A, Adjayeng GOM, Kim C-H (2021) Voltage profile enhancement and loss minimization using optimal placement and sizing of distributed generation in reconfigured network. Machines 9(1):20

    Article  Google Scholar 

  56. Mahboubi-Moghaddam E, Narimani MR, Khooban MH, Azizivahed A, JavidSharifi M (2016) Multi-Objective Distribution feeder reconfiguration to improve transient stability, and minimize power loss and operation cost using an enhanced evolutionary algorithm at the presence of distributed generations. Int J Electr Power Energy Syst 76:35–43

    Article  Google Scholar 

  57. Sawle Y, Gupta SC, Bohre AK (2018) Socio-techno-economic design of hybrid renewable energy system using optimization techniques. Renew Energy 119:459–472

    Article  Google Scholar 

  58. Heydari A, Askarzadeh A (2016) Optimization of a biomass-based photovoltaic power plant for an off-grid application subject to loss of power supply probability concept. Appl Energy 165:601–611

    Article  Google Scholar 

  59. Ramli MAM, Bouchekara HREH, Alghamdi AS (2018) Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm. Renew Energy 121:400–411

    Article  Google Scholar 

  60. Nadjemi O, Nacer T, Hamidat A, Salhi H (2017) “Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms. Renew Sustain Energy Rev 70:1352–1365

    Article  Google Scholar 

  61. Ahmad S, Ullah I, Jamil F, Kim DH (2020) Toward the optimal operation of hybrid renewable energy resources in microgrids. Energies 13(20):1–19

    Article  Google Scholar 

  62. Morales-Castañeda B, Zaldívar D, Cuevas E, Fausto F, Rodríguez A (2020) A better balance in metaheuristic algorithms: Does it exist? Swarm Evol Comput 54:100671

    Article  Google Scholar 

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

    Article  Google Scholar 

  64. Mohamed AW, Hadi AA, Mohamed AK, Awad NH (2020) Evaluating the performance of adaptive gainingsharing knowledge based algorithm on CEC 2020 benchmark problems. In: 2020 IEEE congr. evol. comput. CEC 2020—confer. Proc.

  65. Simon D, Member S (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713

    Article  Google Scholar 

  66. Kennedy J, Eberhart R (2011) Particle swarm optimization-based feature selection for cognitive state detection. In: Proc. annu. int. conf. IEEE eng. med. biol. soc. EMBS, pp 6556–6559

  67. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  68. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  69. Mirjalili S, Lewis A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  70. Mirjalili S (2016) SCA: a Sine Cosine algorithm for solving optimization problems. Knowl-Based Syst 96:120–133

    Article  Google Scholar 

  71. Teng JH, Luan SW, Lee DJ, Huang YQ (2013) Optimal charging/discharging scheduling of battery storage systems for distribution systems interconnected with sizeable PV generation systems. IEEE Trans Power Syst 28(2):1425–1433

    Article  Google Scholar 

  72. Aman MM, Jasmon GB, Bakar AHA, Mokhlis H (2014) Optimum network reconfiguration based on maximization of system loadability using continuation power flow theorem. Int J Electr Power Energy Syst 54:123–133

    Article  Google Scholar 

  73. Kaur S, Kumbhar G, Sharma J (2014) A MINLP technique for optimal placement of multiple DG units in distribution systems. Int J Electr Power Energy Syst 63:609–617

    Article  Google Scholar 

  74. Hung DQ, Mithulananthan N (2013) Multiple distributed generator placement in primary distribution networks for loss reduction. IEEE Trans Ind Electron 60(4):1700–1708

    Article  Google Scholar 

  75. Kefayat M, Ara AL, Niaki SAN, Lashkar Ara A, Nabavi Niaki SA (2015) A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers Manag 92:149–161

    Article  Google Scholar 

  76. Martín García JA, Gil Mena AJ (2013) Optimal distributed generation location and size using a modified teaching-learning based optimization algorithm. Int J Electr Power Energy Syst 50(1):65–75

    Article  Google Scholar 

  77. Sanjay R, Jayabarathi T, Raghunathan T, Ramesh V, Mithulananthan N (2017) Optimal allocation of distributed generation using hybrid grey Wolf optimizer. IEEE Access 5(c):14807–14818

    Article  Google Scholar 

  78. Mahmoud K, Yorino N, Ahmed A (2016) Optimal distributed generation allocation in distribution systems for loss minimization. IEEE Trans Power Syst 31(2):960–969

    Article  Google Scholar 

  79. Kansal S, Kumar V, Tyagi B (2016) Hybrid approach for optimal placement of multiple DGs of multiple types in distribution networks. Int J Electr Power Energy Syst 75:226–235

    Article  Google Scholar 

  80. Abu-Mouti FS, El-Hawary ME (2011) Optimal distributed generation allocation and sizing in distribution systems via artificial bee colony algorithm. IEEE Trans Power Deliv 26(4):2090–2101

    Article  Google Scholar 

  81. Tolba MA, Rezk H, Al-Dhaifallah M, Eisa AA (2020) Heuristic optimization techniques for connecting renewable distributed generators on distribution grids, vol 32, no 17. Springer, London

  82. Cao X, Wang J, Zeng B (2019) Networked microgrids planning through chance constrained stochastic conic programming. IEEE Trans Smart Grid 10(6):6619–6628

    Article  Google Scholar 

  83. Cao X, Cao T, Gao F, Guan X (2021) Risk-averse storage planning for improving RES hosting capacity under uncertain siting choices. IEEE Trans Sustain Energy 12(4):1984–1995

    Article  Google Scholar 

  84. Meng D, Zhang J (2021) Robust optimization-based iterative learning control for nonlinear systems with nonrepetitive uncertainties. IEEE/CAA J Autom Sin 8(5):1001–1014

    Article  MathSciNet  Google Scholar 

  85. Cao X, Wang J, Zeng B (2018) A chance constrained information-gap decision model for multi-period microgrid planning. IEEE Trans Power Syst 33(3):2684–2695

    Article  Google Scholar 

  86. Bi J, Yuan H, Duanmu S, Zhou MengChu, Abusorrah A (2020) Energy-optimized partial computation offloading in mobile-edge computing with genetic simulated-annealing-based particle swarm optimization. IEEE Internet Things J 8(5):3774–3785

    Article  Google Scholar 

  87. Xu C, He X (2021) A fully distributed approach to optimal energy scheduling of users and generators considering a novel combined neurodynamic algorithm in smart grid. IEEE/CAA J Automat Sin 8(7):1325–1335

    Article  MathSciNet  Google Scholar 

  88. Cao Y, Zhang H, Li W, Zhou M, Zhang Y (2018) Chaovalitwongse WA (2018) Comprehensive learning particle swarm optimization algorithm with local search for multimodal functions. IEEE Trans Evolut Comput 23(4):718–731

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Ahmad Eid & Salah Kamel

  2. Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah, 56452, Saudi Arabia

    Ahmad Eid

  3. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

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  1. Ahmad Eid
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  2. Salah Kamel
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Contributions

Ahmad Eid: Conceptualization, Methodology, Software. Salah Kamel: Data curation, Writing- Original draft preparation. Essam Houssein: Visualization, Investigation. Salah Kamel: Supervision. Essam Houssein: Software, Validation. Ahmad Eid: Writing- Reviewing and Editing.

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Eid, A., Kamel, S. & Houssein, E.H. An enhanced equilibrium optimizer for strategic planning of PV-BES units in radial distribution systems considering time-varying demand. Neural Comput & Applic 34, 17145–17173 (2022). https://doi.org/10.1007/s00521-022-07364-5

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