Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

Article Sidebar

PDF HTML
Published: Mar 1, 2023
DOI: https://doi.org/10.29070/44p1p225
Keywords:
microgrid, hybrid energy sources, swarm intelligence, renewable energy sources, wind power, demand side management, demand response, smart grid, demand dispatch, wind power production

Main Article Content

Authors

Mrs. Sarika Satpute

PhD Scholar, Kalinga University, Raipur (CG)

Dr. Sunil Kumar

PhD Guide, Department of Electrical Engineering, Kalinga University, Raipur (CG)

Abstract

To combat the widening gap between demand and supply, it is crucial to include renewableenergy sources as a Distributed Generation newline (DG). Despite their usefulness in reducing the gap, REsources place a heavy pressure on generation scheduling, particularly wind power. Utilities are compelledto use wind curtailment due to fluctuations in wind energy output, which reduces the incentive to switch torenewable sources of power. Demand Side Management (DSM) is one approach to this problem, since itadjusts demand in real time to match generation while the grid is operating. Conventional DemandResponse (DR) systems were created to minimise consumption during peak hours by arranging userloads in exchange for incentives. The Smart Grid (SG) concept provides a high-tech means ofcommunication for keeping tabs on and managing the power grid. This motivated the creation of asophisticated DR technique known as Demand Dispatch (DD), in which consumption is timesynchronizedwith electricity production. In order to deal with the fluctuations in wind power production,this thesis proposes a DD method. The entities involved in DD and the way in which data is sharedbetween them are laid out in an operational framework. In order to find and evaluate all of the studiesthat have been published on a certain topic, researchers often undertake systematic literature reviews.

Downloads

Download data is not yet available.

Article Details

Issue

Vol. 20 No. 1 (2023): Journal of Advances in Science and Technology (JAST)

Section

Articles

References

  1. Shailendra Kr. Tiwari* , Member, IEEE, Bhim Singh* , Fellow, IEEE, Puneet K. Goel “Design and Control of Micro-Grid fed by Renewable Energy Generating Sources” IEEE Transactions on Industrial Application, vol 0093-994, 2017
  2. Faisal R. Badal , Purnima Das, Subrata K. Sarker1 and Sajal K. Das” A survey on control issues in renewable energy integration and microgrid” Springer open access, 4, Article number: 8 (2019)
  3. M. Das and V. Agarwal, “Novel High-Performance Stand-Alone Solar PV System With High-Gain High-Efficiency DC–DC Converter Power Stages,” IEEE Transactions on Industry Applications, vol. 51, no. 6, pp. 4718-4728, Nov.-Dec. 2015.
  4. Ataji, Y. Miura, T. Ise and H. Tanaka, “Direct Voltage Control With Slip Angle Estimation to Extend the Range of Supported Asymmetric Loads for Stand-Alone DFIG,” IEEE Transactions on Power Electronics, vol. 31, no. 2, pp. 1015-1025, Feb. 2016.
  5. N.A. Orlando, M. Liserre, R.A. Mastromauro and A. Dell’Aquila, “A survey of control issues in PMSG-based small wind turbine system,” IEEE Trans. Industrial Informatics, vol.9, no.3, pp 1211-1221, July 2013
  6. Z. Qi, “Coordinated Control for Independent Wind-Solar Hybrid Power System,” 2012 Asia-Pacific Power and Energy Engineering Conference, Shanghai, 2012, pp. 1-4.
  7. M. Rezkallah, S. Sharma, A. Chandra and B. Singh, “Implementation and control of small-scale hybrid standalone power generation system employing wind and solar energy,” 2016 IEEE Industry Applications Society Annual Meeting, Portland, OR, 2016, pp. 1-7.
  8. Hamadi, S. Rahmani, K. Addoweesh and K. Al-Haddad, “A modeling and control of DFIG wind and PV solar energy source generation feeding four wire isolated load,” IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, 2013, pp. 7778-7783.
  9. S. K. Tiwari, B. Singh and P. K. Goel, "Design and control of autonomous wind-solar energy system with DFIG feeding 3-phase 4- wire network," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-6.
  10. S. K. Tiwari, B. Singh and P. K. Goel, “Design and control of microgrid fed by renewable energy generating sources,” 2016 IEEE 6th Inter. Conference on Power Systems (ICPS), New Delhi, 2016, pp. 1-6.
  11. Zhan, S. Member, M. Ghamkhari, and D. Xu, “Extending Demand Response to Tenants in Cloud Data Centers via Non-intrusive Workload Flexibility Pricing,” vol. 3053, no. c, pp. 1–12, 2016.
  12. Dodrill, Keith. "Demand Dispatch—Intelligent Demand for a More Efficient Grid." 2011.
  13. Brooks, E. Lu, D. Reicher, C. Spirakis, and B. Weihl, “Demand dispatch,” IEEE Power Energy Mag., vol. 8, no. 3, pp. 20–29, 2010.
  14. Abbey and G. Joos, “Supercapacitor energy storage for wind energy applications,” IEEE Trans. Ind. Appl., vol. 43, no. 3, pp. 769–776, 2007.
  15. H. Fathabadi, “Utilization of electric vehicles and renewable energy sources used as distributed generators for improving characteristics of electric power distribution systems,” Energy, vol. 90, pp. 1100–1110, 2015.
  16. X. Huang, Q., Jia, Q.-S., and Guan, “Robust Scheduling of EV Charging Load with Uncertain Wind Power Integration,” IEEE Trans. Smart Grid, vol. PP, no. 99, pp. 1–12, 2016.
  17. M. F. M. Arani and Y. Mohamed, “Cooperative Control of Wind Power Generator and Electric Vehicles for Microgrid Primary Frequency Regulation,” IEEE Trans. Smart Grid, vol. 3053, no. c, pp. 1–1, 2017.
  18. W. Leterme, F. Ruelens, B. Claessens, and R. Belmans, “A flexible stochastic optimization method for wind power balancing with PHEVs,” IEEE Trans. Smart Grid, vol. 5, no. 3, pp. 1238–1245, 2014.
  19. Y. Ota, H. Taniguchi, T. Nakajima, K. M. Liyanage, J. Baba, and A. Yokoyama, “Autonomous distributed V2G (vehicle-to-grid) satisfying scheduled charging,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 559–564, 2012.
  20. Ericsson, Göran N. "Cyber security and power system communication—essential parts of a smart grid infrastructure." , IEEE Transactions on Power Delivery vol. 25, no. 3, pp.1501-1507,2010.
  21. Du, W. et al. A Comparative Study of Two Widely Used Grid‐Forming Droop Controls on Microgrid Small‐Signal Stability. IEEE J. Emerg. Sel. Top. Power Electron. 2020, 8, 963–975. https://doi.org/10.1109/JESTPE.2019.2942491.
  22. Tan, K.H.; Li, M.Y.; Weng, X.Y. Droop Controlled Microgrid with DSTATCOM for Reactive Power Compensation and Power Quality Improvement. IEEE Access 2022, 10, 121602–14.
  23. Benadli, R.; Khiari, B.; Memni, M.; Bjaoui, M.; Sellami, A. An Improved Super‐Twisting Sliding Mode Control for Standalone Hybrid Wind/Photovoltaic/Fuel Cell System Based on Energy Management of Battery/Hydrogen. J. Sol. Energy Eng. 2022, 144, 031003.
  24. Trivedi, R.; Khadem, S. Implementation of artificial intelligence techniques in microgrid control environment: Current progress and future scopes. Energy AI 2022, 2, 100147.
  25. Zaki, D.A.; Hasanien, H.M.; El‐Amary, N.H.; Abdelaziz, A.Y. Tuning PI controllers for optimal migrogrid operation based on Artificial Neural Network. In Proceedings of the IEEE Conference on Power Electronics and Renewable Energy (CPERE), As‐ wan, Egypt, 23–25 October 2019; pp. 371–378.
  26. Sindi, H.F.; Alghamdi, S.; Rawa, M.; Omar, A.I.; Elmetwaly, A.H. Robust control of adaptive power quality compensator in Multi‐Microgrids for power quality enhancement using puzzle optimization algorithm. Ain Shams Eng. J. 2022, 17, 102047.
  27. Saxena, N.K.; Gao, W.D.; Kumar, A.; Mekhilef, S.; Gupta, V. Frequency regulation for microgrid using genetic algorithm and particle swarm optimization tuned STATCOM. Int. J. Circuit Theory Appl. 2022, 50, 3231–3250.
  28. Guha, D.; Roy, P.K.; Banerjee, S. Performance evolution of different controllers for frequency regulation of a hybrid energy power system employing chaotic crow search algorithm. ISA Trans. 2022, 120, 128–146.
  29. Rostom, D.Z.; Hasanien, H.M.; El Amary, N.H.; Abdelaziz, A.Y. Adaptive PI Control Strategy for Microgrid Performance En‐ hancement. Int. J. Energy Conv. 2019, 7. https://doi.org/10.15866/irecon.v7i3.17327.
  30. Nagaraja, Y.; Devaraju, T.; Sankar, A.M.; Narasimhulu, V. PV and Wind Energy Conversion Exploration based on Grid Inte‐ grated Hybrid Generation Using the Cuttlefish Algorithm. Eng. Technol. Appl. Sci. Res. 2022, 12, 9670–9675
  31. Muqeet, H.A.; Javed, H.; Akhter, M.N.; Shahzad, M.; Munir, H.M.; Nadeem, M.U.; Bukhari, S.S.H.; Huba, M. Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges. Sensors 2022, 22, 2345.
  32. Zaki, H. M. Hasanien, N. H. El-Amary and A. Y. Abdelaziz, "Performance Enhancement of an Inverter-Based Decentralized energy generation System Using Evaporation Rate Based Water Cycle Algorithm," 2019 IEEE Conference on Power Electronics and Renewable Energy (CPERE), Aswan, Egypt, 2019, pp. 270-277, doi: 10.1109/CPERE45374.2019.8980152.
  33. Xiaofei Liu, Pei Zhang, Xiaoyang Deng, Dawei Sun, Hierarchical overvoltage predictive control scheme for a DFIG-based wind farm, Electric Power Systems Research, Volume 217, 109172, ISSN 0378-7796,
  34. Chakraborty, A.; Maity, T. Integrated control algorithm for fast and accurate detection of the voltage sag with low voltage ride‐ through (LVRT) enhancement for doubly‐fed induction generator (DFIG) based wind turbines. Control. Eng. Pract. 131, 105393.
  35. Azeem, O.; Ali, M.; Abbas, G.; Uzair, M.; Qahmash, A.; Algarni, A.; Hussain, M.R. A Comprehensive Review on Integration Challenges, Optimization Techniques and Control Strategies of Hybrid AC/DC Microgrid. Appl. Sci. 2021, 11, 6242.
  36. Badarla, A. & Junnarkar, A.A. CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. J Ambient Intell Human Comput 12, 7777–7791 (2021). https://doi.org/10.1007/s12652-020-02502-0
  37. Badarla, A. (2018). Application of Internet of Things for Smart Precision Farming: Solutions and Challenges. International Journal of Advanced Science and Technology, Vol. Dec. 2018, PP. 37-45.
  38. Badarla, A. (2019). Experimental Analysis of Recent Clustering Algorithms for Wireless Sensor Network: Application of IoT based Smart Precision Farming. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, No. 9. 10.5373/JARDCS/V11I9/20193162.
  39. Badarla, A. (2020). Detecting HTTP Vulnerabilities in IoT-based Precision Farming Connected with Cloud Environment using Artificial Intelligence. International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 214 - 226.
  40. Mikhail, A., Kamil, I. A. (2017). Increasing SCADA System Availability by Fault Tolerance Techniques. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463911
  41. Mikhail, A., Kareem, H. (2017). Fault Tolerance to Balance for Messaging Layers in Communication Society. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463871
  42. Alhayani, B., Abbas, S.T., Mohammed, H.J Intelligent Secured Two-Way Image Transmission Using Corvus Corone Module over WSN. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08484-2.
  43. Badarla, A. A. Junnarkar Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm. Wireless Pers Commun 121, 3125–3149 (2021). https://doi.org/10.1007/s11277-021-08866-6
  44. Uke, N., Pise, P. et.al. (2021). Healthcare 4.0 Enabled Lightweight Security Provisions for Medical Data Processing. Turkish Journal of Computer and Mathematics (2021), Vol. 12, No. 11. https://doi.org/10.17762/turcomat.v12i11.5858.
  45. Alhayani, B., Kwekha-Rashid, A.S., et al. 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system. Appl Nanosci (2022). https://doi.org/10.1007/s13204-021-02152-4.
  46. Rashid, A.S., Junnarkar, A.A. et al. Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems. Appl Nanosci (2022). https://doi.org/10.1007/s13204-021-02164-0.
  47. A.Junnarkar, Dr & Tiwari, Mohit & Tiwari, Tripti & Upadhyaya, Dr. (2022). LCIPA: Lightweight Clustering protocol for Industry 4.0 enabled Precision Agriculture. Microprocessors and Microsystems. 94. 10.1016/j.micpro.2022.104633.
  48. A.A.Junnarkar Emergence of Healthcare 4.0 and Blockchain into Secure Cloud-based Electronic Health Records Systems: Solutions, Challenges, and Future Roadmap. Wireless Pers Commun 126, 2425–2446 (2022). https://doi.org/10.1007/s11277-022-09535-y
  49. Uke, N., Pise, P. et al. Automatic robot Manoeuvres detection using computer vision and deep learning techniques: a perspective of internet of robotics things (IoRT). Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-14253-5.