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An interactive class topper optimization with energy management scheme for an interconnected microgrid

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Abstract

In recent years, energy management (EM) has become a vital tool for the planning of various energy sources in the microgrid. The EM scheme helps in the effective planning of various energy sources so that a proper balance between generation and load demand can be maintained. In this aspect, different management schemes have been developed to effectively plan various sources. In this work an interactive class topper optimization (I-CTO) based energy management scheme for an interconnected microgrid considering renewable energy sources, battery storage systems, demand side management, etc. is presented. The proposed scheme aims to optimally plan different energy sources in the microgrid so that the generation and emission cost may be minimized. To achieve this objective of having an optimal distribution of generation among different energy sourest, an objective function is formulated. This developed objective function is minimized using the proposed I-CTO scheme while maintaining some of the operational constraints encountered in real-time scenario. The reduced objective corresponds to the optimal distribution of generation among different energy sources which would help to fulfill the load demand with less generation and emission cost. To test the effectiveness of the proposed schemes and show their supremacy over some existing methods, a comparative study is presented using a numerical test example and CEC-2020 benchmark function.

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References

  1. IRENA I (2019) Renewable power generation costs in 2018. report. International Renewable Energy Agency, Abu Dhabi

  2. Kumar KP, Saravanan B (2019) Day ahead scheduling of generation and storage in a microgrid considering demand side management. J Energy Storage 21:78–86

    Google Scholar 

  3. Parra D, Swierczynski M, Stroe DI, Norman SA, Abdon A, Worlitschek J, O’Doherty T, Rodrigues L, Gillott M, Zhang X et al (2017) An interdisciplinary review of energy storage for communities: challenges and perspectives. Renew Sustain Energy Rev 79:730–749

    Google Scholar 

  4. Faisal M, Hannan MA, Ker PJ, Hussain A, Mansor MB, Blaabjerg F (2018) Review of energy storage system technologies in microgrid applications: issues and challenges. IEEE Access 6:35143–35164

    Google Scholar 

  5. Olabi A, Wilberforce T, Sayed ET, Abo-Khalil AG, Maghrabie HM, Elsaid K, Abdelkareem MA (2022) Battery energy storage systems and SWOT (strengths, weakness, opportunities, and threats) analysis of batteries in power transmission. Energy 254:123987

    Google Scholar 

  6. Yang Y, Bremner S, Menictas C, Kay M (2022) Modelling and optimal energy management for battery energy storage systems in renewable energy systems: a review. Renew Sustain Energy Rev 167:112671

    Google Scholar 

  7. Tawalbeh M, Murtaza SZ, Al-Othman A, Alami AH, Singh K, Olabi AG (2022) Ammonia: a versatile candidate for the use in energy storage systems. Renew Energy 194:955–977

    Google Scholar 

  8. Samineni S, Johnson BK, Hess HL, Law JD (2006) Modeling and analysis of a flywheel energy storage system for voltage sag correction. IEEE Trans Ind Appl 42(1):42–52

    Google Scholar 

  9. Yan X, Nie S, Chen B, Yin F, Ji H, Ma Z (2023) Strategies to improve the energy efficiency of hydraulic power unit with flywheel energy storage system. J Energy Storage 59:106515

    Google Scholar 

  10. Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252

    Google Scholar 

  11. Zakariazadeh A, Jadid S, Siano P (2014) Smart microgrid energy and reserve scheduling with demand response using stochastic optimization. Int J Electr Power Energy Syst 63:523–533

    Google Scholar 

  12. Montuori L, Alcázar-Ortega M, Álvarez-Bel C, Domijan A (2014) Integration of renewable energy in microgrids coordinated with demand response resources: economic evaluation of a biomass gasification plant by homer simulator. Appl Energy 132:15–22

    Google Scholar 

  13. Mazidi M, Monsef H, Siano P (2016) Robust day-ahead scheduling of smart distribution networks considering demand response programs. Appl Energy 178:929–942

    Google Scholar 

  14. Jordehi AR (2019) Optimisation of demand response in electric power systems, a review. Renew Sustain Energy Rev 103:308–319

    Google Scholar 

  15. Zhang X, Hug G, Kolter JZ, Harjunkoski I (2016) Model predictive control of industrial loads and energy storage for demand response. In: 2016 IEEE power and energy society general meeting (PESGM). IEEE, pp 1–5

  16. Park L, Jang Y, Bae H, Lee J, Park CY, Cho S (2017) Automated energy scheduling algorithms for residential demand response systems. Energies 10(9):1326

    Google Scholar 

  17. Deng R, Yang Z, Chow MY, Chen J (2015) A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans Ind Inf 11(3):570–582

    Google Scholar 

  18. Li C, Yu X, Yu W, Chen G, Wang J (2016) Efficient computation for sparse load shifting in demand side management. IEEE Trans Smart Grid 8(1):250–261

    Google Scholar 

  19. Yaghmaee MH, Leon-Garcia A, Moghaddassian M (2017) On the performance of distributed and cloud-based demand response in smart grid. IEEE Trans Smart Grid 9(5):5403–5417

    Google Scholar 

  20. Jahani MTG, Nazarian P, Safari A, Haghifam M (2019) Multi-objective optimization model for optimal reconfiguration of distribution networks with demand response services. Sustain Cities Soc 47:101514

    Google Scholar 

  21. Elkamel M, Ahmadian A, Diabat A, Zheng QP (2021) Stochastic optimization for price-based unit commitment in renewable energy-based personal rapid transit systems in sustainable smart cities. Sustain Cities Soc 65:102618

    Google Scholar 

  22. Mansouri SA, Ahmarinejad A, Sheidaei F, Javadi MS, Jordehi AR, Nezhad AE, Catalao JP (2022) A multi-stage joint planning and operation model for energy hubs considering integrated demand response programs. Int J Electr Power Energy Syst 140:108103

    Google Scholar 

  23. Cai T, Dong M, Liu H, Nojavan S (2022) Integration of hydrogen storage system and wind generation in power systems under demand response program: a novel p-robust stochastic programming. Int J Hydrogen Energy 47(1):443–458

    Google Scholar 

  24. Marzband M, Alavi H, Ghazimirsaeid SS, Uppal H, Fernando T (2017) Optimal energy management system based on stochastic approach for a home microgrid with integrated responsive load demand and energy storage. Sustain Cities Soc 28:256–264

    Google Scholar 

  25. Malliotakis E, Founti M (2017) Energy management and primary energy optimization of a thermally interconnected semi-autonomous commercial district via optimized \(\mu \)-chp dispatch strategy. Sustain Cities Soc 32:160–170

    Google Scholar 

  26. Lokeshgupta B, Sivasubramani S (2019) Multi-objective home energy management with battery energy storage systems. Sustain Cities Soc 47:101458

    Google Scholar 

  27. Haghshenas M, Falaghi H (2016) Environmental/economic operation management of a renewable microgrid with wind/PV/FC/MT and battery energy storage based on MSFLA. J Electr Syst 12(1):85–101

    Google Scholar 

  28. Lazar E, Ignat A, Petreus D, Etz R (2018) Energy management for an islanded microgrid based on harmony search algorithm. In: 2018 41st international spring seminar on electronics technology (ISSE). IEEE, pp 1–6

  29. Cau G, Cocco D, Petrollese M, Kær SK, Milan C (2014) Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system. Energy Convers Manag 87:820–831

    Google Scholar 

  30. Mohamed FA, Koivo HN (2010) System modelling and online optimal management of microgrid using mesh adaptive direct search. Int J Electr Power Energy Syst 32(5):398–407

    Google Scholar 

  31. Niknam T, Golestaneh F, Malekpour A (2012) Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational search algorithm. Energy 43(1):427–437

    Google Scholar 

  32. Motevasel M, Seifi AR (2014) Expert energy management of a micro-grid considering wind energy uncertainty. Energy Convers Manag 83:58–72

    Google Scholar 

  33. Thirunavukkarasu GS, Seyedmahmoudian M, Jamei E, Horan B, Mekhilef S, Stojcevski A (2022) Role of optimization techniques in microgrid energy management systems-a review. Energ Strat Rev 43:100899

    Google Scholar 

  34. Srivastava A, Das DK (2022) Criminal search optimization algorithm: a population-based meta-heuristic optimization technique to solve real-world optimization problems. Arab J Sci Eng 47(3):3551–3571

    Google Scholar 

  35. Nguyen DT, Le LB (2014) Optimal bidding strategy for microgrids considering renewable energy and building thermal dynamics. IEEE Trans Smart Grid 5(4):1608–1620

    Google Scholar 

  36. Srivastava A, Das DK (2022) A bottlenose dolphin optimizer: an application to solve dynamic emission economic dispatch problem in the microgrid. Knowl-Based Syst 243:108455

    Google Scholar 

  37. Abbaspour M, Satkin M, Mohammadi-Ivatloo B, Lotfi FH, Noorollahi Y (2013) Optimal operation scheduling of wind power integrated with compressed air energy storage (CAEs). Renew Energy 51:53–59

    Google Scholar 

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

    Google Scholar 

  39. Dhiman G (2020) Moshepo: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems. Appl Intell 50(1):119–137

    Google Scholar 

  40. Xin-gang Z, Ze-qi Z, Yi-min X, Jin M (2020) Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization. Energy 195:117014

    Google Scholar 

  41. Alham M, Elshahed M, Ibrahim DK, El Zahab EEDA (2016) A dynamic economic emission dispatch considering wind power uncertainty incorporating energy storage system and demand side management. Renew Energy 96:800–811

    Google Scholar 

  42. Mehdizadeh A, Taghizadegan N (2017) Robust optimisation approach for bidding strategy of renewable generation-based microgrid under demand side management. IET Renew Power Gener 11(11):1446–1455

    Google Scholar 

  43. Das P, Das DK, Dey S (2018) A new class topper optimization algorithm with an application to data clustering. IEEE Trans Emerging Top Comput

  44. Sharma D, Gaur P, Mittal A (2014) Comparative analysis of hybrid gapso optimization technique with GA and PSO methods for cost optimization of an off-grid hybrid energy system. Energy Technol Policy 1(1):106–114

    Google Scholar 

  45. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks. IEEE, vol 4, pp 1942–1948

  46. Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34

    Google Scholar 

  47. Khatsu S, Srivastava A, Das DK (2020) Solving combined economic emission dispatch for microgrid using time varying phasor particle swarm optimization. In: 2020 6th international conference on advanced computing and communication systems (ICACCS). IEEE, pp 411–415

  48. Dey B, Roy SK, Bhattacharyya B (2019) Solving multi-objective economic emission dispatch of a renewable integrated microgrid using latest bio-inspired algorithms. Int J Eng Sci Technol 22(1):55–66

    Google Scholar 

  49. Basu M (2020) Optimal generation scheduling of hydrothermal system with demand side management considering uncertainty and outage of renewable energy sources. Renew Energy 146:530–542

    Google Scholar 

  50. Basu M (2019) Dynamic economic dispatch with demand-side management incorporating renewable energy sources and pumped hydroelectric energy storage. Electr Eng 101(3):877–893

  51. Srivastava A, Das DK (2020) A new Kho-Kho optimization algorithm: an application to solve combined emission economic dispatch and combined heat and power economic dispatch problem. Eng Appl Artif Intell 94:103763

    Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Chumukeidma, Dimapur, India

    Abhishek Srivastava & Dushmanta Kumar Das

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  1. Abhishek Srivastava
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  2. Dushmanta Kumar Das
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Correspondence to Dushmanta Kumar Das.

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Srivastava, A., Das, D.K. An interactive class topper optimization with energy management scheme for an interconnected microgrid. Electr Eng 106, 2069–2086 (2024). https://doi.org/10.1007/s00202-023-02048-2

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