Design and Implementation of Multi-Source and Multi-Consumer Energy ‎Sharing System in Collaborative Smart Microgrid Installation

Document Type : Research paper


1 National Engineering School of Sfax (ENIS), Laboratory of signals, systems, artificial intelligence and networks ‎‎(SM@RTS), Digital Research Center of Sfax (CRNS), University of Sfax, Sfax, Tunisia

2 National School of Electronics and Telecommunications of Sfax, Laboratory of signals, systems, artificial intelligence ‎and networks (SM@RTS), Digital Research Center of Sfax (CRNS), University of Sfax, Sfax, Tunisia


Many published studies debated electrical energy management. They mainly investigate the multi-source installation to develop energy efficiency during its different phases: production, distribution, and consumption. Although it is rarely discussed, energy sharing is a critical part of the energy management system. In this contribution, a demand-side management algorithm is developed, that incorporates energy consumption scheduler capacity. It provides optimal energy sharing, counting on suitable energy cost parameters and adequate multi-source installation. Using this proposal, the electrical bill decreases thanks to the optimal daily attribution of schedules to households formed by a multi-consumer microgrid. This application guarantees a maximal reduction of electrical cost for the set of energy partners as one prosumer used to consume and produce power. In addition, it maintains energy efficiency as it aids in avoiding breakdowns, and depressing the peak-to-average ratio. It admits that the utility company is, as usual, always reachable non-renewable source. At the same time, renewable energy was engendered by photovoltaic panels concomitant with wind turbines stations. The application is based on the JNET protocol stack. The proposed energy sharing algorithm is implemented by using Arduino board and JN5148 nodes as a star Wireless Sensors Network topology. It is installed as a prototype in the Digital Research Center of Sfax in Tunisia.  This proposed incentive-based algorithm managed to reduce the smart microgrid annual cost by almost 55% without harming the public utility. It can even ensure a more significant diminution by selling the surplus of renewable power at the end of each day.


  1. Wang et al., “Layered stochastic approach for residential demand response based on real-time pricing and incentive mechanism”,IET Gener., Transm. Distrib., vol. 14,  pp. 423- 31, 2020.
  2. Parizy, H. Bahrami, S. Choi, “A low complexity and secure demand response technique for peak load reduction”, IEEE Trans. Smart Grid, vol. 10, pp. 3259- 68, 2019.
  3. Song, G. Lee, Y. Yoon, “Optimal operation of critical peak pricing for an energy retailer considering balancing costs”, Energies, vol. 12, 2019.
  4. Nikzad, A. Samimi, “Integration of optimal time-of-use pricing in stochastic programming for energy and reserve management in smart micro-grids”, Iranian J. Sci. Tech. Trans. Electr. Eng., vol. 44, pp. 1449-66, 2020.
  5. Shabshab et al., “Demand smoothing in military microgrids through coordinated direct load control”, IEEE Trans. Smart Grid, vol. 11, pp. 1917-27, 2020.
  6. Dejamkhooy et al., “Fuel consumption reduction and energy management in stand-alone hybrid microgrid under load uncertainty and demand response by linear programming”, J. Oper. Autom. Power Eng., vol. 8, pp. 273-81, 2020.
  7. Shahryari et al., “Optimal energy management of microgrid in day-ahead and intra-day markets using a copula-based uncertainty modeling method”, J. Oper. Autom. Power Eng., vol. 8, pp. 86-96, 2020.
  8. Masoudi, H. Abdi, “Multi-objective stochastic programming in microgrids considering environmental emissions”, J. Oper. Autom. Power Eng., vol. 8, pp. 141-51, 2020.
  9. Sedighizadeh et al., “Stochastic multi-objective economic environmental energy and reserve scheduling of microgrids considering battery energy storage system”, Electr. Power Energy Syst., vol. 106, pp. 1-16, 2019.
  10. Bornapour, R Hooshmand, M. Parastegari, “An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in Micro Grids” Renew. Energy, vol. 120, pp. 1049-66, 2019.
  11. Firouzmakan et al., “A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs”, Renew. Sustain. Energy Rev., vol. 108, pp. 355-68, 2019.
  12. Ben Belgacem, B. Gassara, A. Fakhfakh, “Shared energy algorithm and parameters influence on multi-sources and multi-consumers smart microgrid”, Proc. 19th Int. Conf. Sci. Tech. Autom. Control Comput. Eng., Tunisia, 2019.
  13. Ben Belgacem, B. Gassara, A. Fakhfakh, “New approach and layers designs of sharing energy system for interconnected microgrid”, Proc. 19th Int. Conf. Sci. Tech. Autom. Control Comput. Eng., Tunisia, 2018.
  14. Ben Belgacem, B. Gassara, A. Fakhfakh, “Design of an energy sharing system for a smart mircogrid application: New concept and preliminary study”, Int. J. Advanc. Res. Sci. Eng. Tech., vol. 8, pp. 16428-41, 2021.
  15. Mohsenian et al., “Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid”, IEEE Trans. Smart Grid, vol. 1, pp. 320-31, 2010.
  16. “GuidEnR Photovoltaic: photovoltaic information,” “ [Online]. Available: http://www.photovoltaique.guidenr. fr/III_effet_inclinaison_module_photovoltaique.php
  17. Gerges et al., “Wind energy in lebanon: annual review, efficiency and profitability”, 6th Int. Conf. Electromech. Power Syst., 2007.
Volume 10, Issue 3
December 2022
Pages 189-199
  • Receive Date: 09 May 2021
  • Revise Date: 15 June 2021
  • Accept Date: 16 August 2021
  • First Publish Date: 06 September 2021