Advertisement

Robust Optimal Multi-agent-Based Distributed Control Scheme for Distributed Energy Storage System

  • Desh Deepak SharmaEmail author
  • Jeremy Lin
Chapter

Abstract

The multi-agent system is emerging as an effecting tool for the realization of the smart power distribution system. The smart power distribution system comprises the different scattered entities such as grid supply, renewable generations, customers, etc. In this distributed system, with the use of information and communication technology (ICT) and control systems, multi-agent system can implement different control and management schemes. The renewable generations such as solar photovoltaic (PV), and wind, as well as electrical load are associated with uncertainties. In this chapter, different battery agents are designed to work for scattered distributed battery energy storage system (BESS). These battery agents decide the power exchange for charging and discharging of BESS in order to balance the power mismatch and cater uncertainties in the smart power distribution system. The LQR-based distributed robust optimal control schemes are designed for battery agents to achieve the objective of balancing the power mismatch in the presence of uncertainties. The proposed control schemes show that the effects of uncertainties in power distribution system, in terms of power and energy sharing, are distributed and catered by all energy storage devices as per their energy storing capacities.

Keywords

Battery energy storage system Multi-agent system Robust optimal control Solar PV system Uncertainty 

References

  1. 1.
    Sumathi, S., Kumar, L. A., & Surekha, P. (2015). Solar PV and wind energy conversion systems an introduction to theory, modeling with MATLAB/SIMULINK, and the role of soft computing techniques. Cham: Springer.Google Scholar
  2. 2.
    Song, S., Ko, B., Suh, J., Han, C., & Jang, G. (2017). Operation algorithm of PV/BESS application considering demand response uncertainty in an independent microgrid system. Journal of International Council on Electrical Engineering, 7(1), 242–248.CrossRefGoogle Scholar
  3. 3.
    Prusty, B., Ali, S., & Sahoo, D. (2012). Modeling and control of grid connected hybrid photo voltaic/battery distributed generation system. International Journal of Engineering Research and Technology, 24(1), 125–132.Google Scholar
  4. 4.
    Rai, V., Reeves, C., & Margolis, R. (2016). Overcoming barriers and uncertainties in the adoption of residential solar PV. Renewable Energy, 89, 498–505.CrossRefGoogle Scholar
  5. 5.
    Attarha, A., Amjady, N., & Dehghan, S. (2018). Affinely adjustable robust bidding strategy for a solar plant paired with a battery storage. IEEE Transactions on Smart Grid, PP(99), 1.  https://doi.org/10.1109/TSG.2018.2806403.
  6. 6.
    Ela, E., Diakov, V., Ibanez, E., & Heaney, M. (2013). Impacts of variability and uncertainty in solar photovoltaic generation at multiple timescales. National Renewable Energy Laboratory, 1, 41.Google Scholar
  7. 7.
    de la Fuente, D. V., Rodriguez, C. L. T., Garcera, G., Figueres, E., & Gonzalez, R. O. (2013). Photovoltaic power system with battery backup with grid-connection and islanded operation capabilities. IEEE Transactions on Industrial Electronics, 60(4), 1571–1581.CrossRefGoogle Scholar
  8. 8.
    Kim, S.-T., Bae, S., Kang, Y. C., & Park, J.-W. (2015). Energy management based on the photovoltaic hpcs with an energy storage device. IEEE Transactions on Industrial Electronics, 62(7), 4608–4617.CrossRefGoogle Scholar
  9. 9.
    Atwa, Y. M., El-Saadany, E. F., Salama, M. M. A., & Seethapathy, R. (2010). Optimal renewable resources mix for distribution system energy loss minimization. IEEE Transactions on Power Systems, 25(1), 360–370.CrossRefGoogle Scholar
  10. 10.
    Zeng, J., Liu, J. F., Wu, J., & Ngan, H. W. (2011). A multi-agent solution to energy management in hybrid renewable energy generation system. Renewable Energy, 36(5), 1352–1363.CrossRefGoogle Scholar
  11. 11.
    Conti, S., & Raiti, S. (2007). Probabilistic load flow using Monte Carlo techniques for distribution networks with photovoltaic generators. Solar Energy, 81, 1473–1481.CrossRefGoogle Scholar
  12. 12.
    Paparoditis, E., & Sapatinas, T. (2013). Short-term load forecasting: the similar shape functional time-series predictor. IEEE Transactions on Power Systems, 28(4), 3818–3825.CrossRefGoogle Scholar
  13. 13.
    Xydas, E., Qadrdan, M., Marmaras, C., Cipcigan, L., Jenkins, N., & Ameli, H. (2017). Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators. Applied Energy, 192, 382–394.CrossRefGoogle Scholar
  14. 14.
    Sun, X., Luh, P. B., Michel, L. D., Corbo, S., Cheung, K. W., Guan, W., & Chung, K. (2013). An efficient approach for short-term substation load forecasting. IEEE Power & Energy Society General Meeting.  https://doi.org/10.1109/PESMG.2013.6673009.
  15. 15.
    Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2014). Integration of PV and BES units in commercial distribution systems considering energy loss and voltage stability. Applied Energy, 113, 1162–1170.CrossRefGoogle Scholar
  16. 16.
    Murata, A., Ohtake, H., & Oozeki, T. (2018). Modeling of uncertainty of solar irradiance forecasts on numerical weather predictions with the estimation of multiple confidence intervals. Renewable Energy, 117, 193–201.CrossRefGoogle Scholar
  17. 17.
    Wolfs, P., Emami, K., Lin, Y., & Palmer, E. (2018). Load forecasting for diurnal management of community battery systems. Journal of Modern Power System and Clean Energy, 6(2), 215–222.CrossRefGoogle Scholar
  18. 18.
    Rahbari-Asr, N., Zhang, Y., & Mo-Yuen, C. (2015). Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids. IET Generation, Transmission and Distribution, 10(5), 1268–1277.CrossRefGoogle Scholar
  19. 19.
    Zhao, T., & Ding, Z. (2018). Distributed agent consensus-based optimal resource management for microgrids. IEEE Transactions on Sustainable Energy, 9(1), 443–452.MathSciNetCrossRefGoogle Scholar
  20. 20.
    Fortenbacher, P., Mathieu, J. L., & Andersson, G. (2017). Modeling and optimal operation of distributed battery storage in low voltage grids. IEEE Transactions on Power Systems, 32(6), 4340–4350.CrossRefGoogle Scholar
  21. 21.
    Xu, Y. (2015). Optimal distributed charging rate control of plug-in electric vehicles for demand management. IEEE Transactions on Power Systems, 30(3), 1536–1545.CrossRefGoogle Scholar
  22. 22.
    Xu, Y., Yang, Z., Gu, W., Li, M., & Deng, Z. (2017). Robust real-time distributed optimal control based energy management in a smart grid. IEEE Transactions on Smart Grid, 8(4), 1568–1579.CrossRefGoogle Scholar
  23. 23.
    Morstyn, T., Hredzak, B., & Agelidis, V. G. (2018). Network topology independent multi-agent dynamic optimal power flow for microgrids with distributed energy storage systems. IEEE Transactions on Smart Grid, 9(4), 3419–3429.CrossRefGoogle Scholar
  24. 24.
    Dimeas, A. L., & Hatziargyrious, N. D. (2005). Operation of multi agent system for micro grid control. IEEE Transaction Power System, 20(3), 1447–1445.CrossRefGoogle Scholar
  25. 25.
    Dimeas, A. L., & Hatziargyriou, N. D. (2007, November). Agent based control of virtual power plants. Interference conference on intelligent systems applications to power systems. ISAP 2007, Toki Messe, Niigata.Google Scholar
  26. 26.
    McArthur, S. D. J., Davidson, E. M., Catterson, M. V., Dimeas, A. L., Ponci, F., Hatziargyriou, N. D., & Funabashi, T. (2007). Multi agent systems for power engineering applications –part I: Concepts, approaches and technical challenges. IEEE Transaction on Power Systems, 22(4), 1743–1752.CrossRefGoogle Scholar
  27. 27.
    McArthur, S. D. J., Davidson, E. M., Catterson, V. M., Dimeas, A. L., Ponci, F., Hatziargyriou, N. D., & Funabashi, T. (2007). Multi agent systems for power engineering applications –part II: Technologies, standards and tools for building multi agent systems. IEEE Transaction on Power Systems, 22(4), 1753–1759.CrossRefGoogle Scholar
  28. 28.
    Colson, C. M., Nehrir, M. H., & Gunderson, R. W. (2011, August–September). Multi agent microgrid power management. 18th IFAC World Congress, Milano.CrossRefGoogle Scholar
  29. 29.
    Fakham, H., Doniec, A., Colas, F., & Guillaud, X. (2010). A multi-agent system for a distributed power management of micro turbine generators connected to grid. IFAC Proceedings. 43(1), 175–180.CrossRefGoogle Scholar
  30. 30.
    Davidson, E. M., McArthur, S. D. J., McDonald, J. R., Cumming, T., & Watt, I. (2006). Applying multi-agent system technology in practice: Automated management and analysis of SCADA and digital fault recorder data. IEEE Transaction on Power Systems, 21(2), 559–566.CrossRefGoogle Scholar
  31. 31.
    Huang, K., Srivastava, S. K., Cartes, D. A., & Sun, L. (2009). Market based multi-agent system for reconfiguration of shipboard power systems. Electric Power Systems, 79, 550–556.CrossRefGoogle Scholar
  32. 32.
    Praca, I., Ramos, C., Vale, Z., & Corderio, M. (2003). MASCEM: A multi agent system that simulates competitive electricity markets. IEEE Intelligent System, 18(6), 54–60.CrossRefGoogle Scholar
  33. 33.
    Karitov, V. S. (2004). Real-world market representation with agents. IEEE Power Energy Magazine, 2(4), 39–46.CrossRefGoogle Scholar
  34. 34.
    Nagata, T., & Sasaki, H. (2002). A multi-agent approach to power system restoration. IEEE Transaction Power System, 17(2), 457–462.CrossRefGoogle Scholar
  35. 35.
    McArthur, S. D. J., Strachan, S. M., & Jahn, G. (2004). The design of a multi-agent transformer condition monitoring system. IEEE Transaction Power System, 19(4), 1845–1852.CrossRefGoogle Scholar
  36. 36.
    Mangina, E. E., McArthur, S. D. J., McDonald, J. R., & Moyes, A. (2001). A multi agent system for monitoring industrial gas turbine start-up sequences. IEEE Transaction Power System, 16(3), 396–401.CrossRefGoogle Scholar
  37. 37.
    Buse, D. P., Sun, P., Wu, Q. H., & Fitch, J. (2003, March–April). Agent – Based substation automation. IEEE Power Energy Magazine, 1(2), 50–55.CrossRefGoogle Scholar
  38. 38.
    Logenthiran, T., Srinivasan, D., & Khambadkone, A. M. (2011). Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electrical Power System Research, 81, 138–148.CrossRefGoogle Scholar
  39. 39.
    Hossack, J., Menal, A. J., McArthur, S. D. J., & McDonald, J. R. (2003). A multi-agent architecture for protection engineering diagnostic assistance. IEEE Transaction on Power System, 18(2), 639–647.CrossRefGoogle Scholar
  40. 40.
    Fax, J. A., & Murray, R. M. (2004). Information flow and cooperative control of vehicle formations. IEEE Transactions Automatic Control, 49, 1464–1476.MathSciNetCrossRefGoogle Scholar
  41. 41.
    Reza, O.-S., Fax, J. A., & Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE, 95(1), 215–233.CrossRefGoogle Scholar
  42. 42.
    Ren, W., & Beard, R. W. (2008). Distributed consensus in multi-vehicle cooperative control: Theory and applications. London: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentM.J.P. Rohilkhand UniversityBareillyIndia
  2. 2.Transmission AnalyticsAustinUSA

Personalised recommendations