access icon free Multi-agent transactive energy management system considering high levels of renewable energy source and electric vehicles

The future smart grids (SGs) consist of considerable amount of renewable energy sources (RESs), electrical vehicles (EVs), and energy storage systems (ESSs). The uncertainties associated with EVs and uncontrollable nature of RESs have magnified voltage stability challenges and the importance of an effective energy management system (EMS) in SGs as a practical solution. This study presents a multi-agent transactive energy management system (TEMS) to control demand and supply in the presence of high levels of RESs and EVs, and maximises profit of each participant in addition to satisfying voltage regulation constraints. For this purpose, a real-time pricing is considered based on Cournot oligopoly competition model for demand and merit order effect for production to compensate RESs’ fluctuations in real time by an indirect control method. Simulations are conducted in the modified IEEE 37-bus test system with 1141 customers, 670 EVs, two solar plants, four wind turbines, and one ESS. The results show that the proposed multi-agent TEMS can indirectly control EVs, elastic loads, and ESSs to balance the RESs oscillation, minimise customers cost, and regulate voltage in a real-time manner.

Inspec keywords: wind power plants; smart power grids; voltage control; solar power stations; wind turbines; energy management systems; electric vehicles; energy storage; oligopoly; pricing; power system stability; multi-agent systems

Other keywords: energy storage systems; voltage regulation constraints; smart grids; multiagent transactive energy management system; renewable energy source; Cournot oligopoly competition model; RESs oscillation; wind turbines; customer cost minimization; electric vehicles; EVs; capacitor bank; ESSs; profit maximization; modified IEEE 37-bus test system; indirect control method; merit order effect; TEMS; real-time pricing; solar plants; voltage stability

Subjects: Control of electric power systems; Power system management, operation and economics; Transportation; Solar power stations and photovoltaic power systems; Voltage control; Wind power plants; Power system control

References

    1. 1)
      • 22. Sensfuss, F., Ragwitz, M., Genoese, M.: ‘The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany’, Energy Policy, 2008, 36, (8), pp. 30863094.
    2. 2)
      • 10. Su, W., Wang, J., Roh, J.: ‘Stochastic energy scheduling in microgrids with intermittent renewable energy resources’, IEEE Trans. Smart Grid, 2014, 5, (4), pp. 18761883.
    3. 3)
      • 16. Gridwise Transactive Energy Framework (Version 1.0)’ (Pacific Northwest National Laboratory (PNNL), Richland, WA, USA, 2015).
    4. 4)
      • 30. Neubauer, J., Brooker, A., Wood, E.: ‘Sensitivity of battery electric vehicle economics to drive patterns, vehicle range, and charge strategies’, J. Power Sources, 2012, 209, (1), pp. 269277.
    5. 5)
      • 26. Moradzadeh, B., Tomsovic, K.: ‘Two-stage residential energy management considering network operational constraints’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 23392346.
    6. 6)
      • 14. Divshali, P.H., Choi, B.J.: ‘Electrical market management considering power system constraints in smart distribution grids’, Energies, 2016, 9, (6), pp. 117.
    7. 7)
      • 32. Yao, E., Samadi, P., Wong, V.W., et al: ‘Residential demand side management under high penetration of rooftop photovoltaic units’, IEEE Trans. Smart Grid, 2016, 7, (3), pp. 15971608.
    8. 8)
      • 1. Richardson, D.B.: ‘Electric vehicles and the electric grid: a review of modeling approaches, impacts, and renewable energy integration’, Renew. Sustain. Energy Rev., 2013, 19, (1), pp. 247254.
    9. 9)
      • 4. Luh, P.B., Yu, Y., Zhang, B., et al: ‘Grid integration of intermittent wind generation: A markovian approach’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 732741.
    10. 10)
      • 18. Kirschen, D.S., Strbac, G.: ‘Fundamentals of power system economic’ (John Wiley & Sons, New York, 2004).
    11. 11)
      • 28. Richardson, I., Thomson, M., Infield, D., et al: ‘Domestic electricity use: a high-resolution energy demand model’, Energy Build., 2010, 42, (10), pp. 18781887.
    12. 12)
      • 29. 'Domestic Electricity Demand Model – Simulation Example’, Available at https://dspace.lboro.ac.uk/dspace-jspui/handle/2134/5786CREST_Domestic_electricity_demand_model_1.0e(1).xlsm, accessed June 13, 2016.
    13. 13)
      • 11. Xiao, H., Huimei, Y., Chen, W., et al: ‘A survey of influence of electrics vehicle charging on power grid’. Industrial Electronics and Applications (ICIEA), June 2014.
    14. 14)
      • 5. Murillo-Sanchez, C.E., Zimmerman, R.D., Lindsay Anderson, C., et al: ‘Secure planning and operations of systems with stochastic sources, energy storage, and active demand’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 22202229.
    15. 15)
      • 20. Hasanpor Divshali, P., Choi, B.J.: ‘Indirect demand side management program under realtime pricing in smart grids using oligopoly market model’. GREEN 2016, 2016.
    16. 16)
      • 15. Kok, K., Widergren, S.: ‘A society of devices: integrating intelligent distributed resources with transactive energy’, IEEE Power Energy Mag., 2016, 14, (3), pp. 3445.
    17. 17)
      • 6. Jiang, B., Fei, Y.: ‘Smart home in smart microgrid: a cost-effective energy ecosystem with intelligent hierarchical agents’, IEEE Trans. Smart Grid, 2015, 6, (1), pp. 313.
    18. 18)
      • 23. Chang, G., Chu, S., Wang, H.: ‘An improved backward/forward sweep load flow algorithm for radial distribution systems’, IEEE Trans. Power Syst., 2007, 22, (2), pp. 882884.
    19. 19)
      • 17. Chang, T.-H., Alizadeh, M., Scaglione, A.: ‘Real-time power balancing via decentralized coordinated home energy scheduling’, IEEE Trans. Smart Grid, 2013, 4, (3), pp. 14901504.
    20. 20)
      • 12. Kuran, M.S., Viana, A.C., Iannone, L., et al: ‘A smart parking lot management system for scheduling the recharging of electric vehicles’, IEEE Trans. Smart Grid, 2015, 6, (6), pp. 29422953.
    21. 21)
      • 2. EN 50160: ‘Voltage Disturbances Standard’, 2011.
    22. 22)
      • 3. Macedo, L.H., Franco, J.F., Rider, M.J., et al: ‘Optimal operation of distribution networks considering energy storage devices’, IEEE Trans. Smart Grid, 2015, 6, (6), pp. 28252836.
    23. 23)
      • 21. Shafie-khah, M., Heydarian-Forushani, E., Osorio, G.J., et al: ‘Optimal behavior of electric vehicle parking lots as demand response aggregation agents’, IEEE Trans. Smart Grid, 2016, 7, (6), pp. 26542665.
    24. 24)
      • 27. 'The Operation Data of the Australian Energy Market Operator (AEMO)’, Available at http://www.nemweb.com.au/REPORTS/ARCHIVE/Dispatch_SCADA/, accessed June, 2016.
    25. 25)
      • 24. Della Vedova, M.L., Facchinetti, T.: ‘Real-time scheduling for peak load reduction in a large set of Hvac loads’. The Third Int. Conf. on Smart Grids, Green Communications and IT Energy-Aware Technologies (ENERGY), Iaria, 2013.
    26. 26)
      • 13. Dallinger, D., Wietschel, M.: ‘Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles’, Renew. Sustain. Energy Rev., 2012, 16, (5), pp. 33703382.
    27. 27)
      • 7. Yang, Z., Wu, R., Yang, J., et al: ‘Economical operation of microgrid with various devices via distributed optimization’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 857867.
    28. 28)
      • 9. Braithwait, S.: ‘Behavior modification’, IEEE Power Energy Mag., 2010, 8, (3), pp. 3645.
    29. 29)
      • 19. La, Q.D., Chan, Y.W.E., Soong, B.-H.: ‘Power management of intelligent buildings facilitated by smart grid: A market approach’, IEEE Trans. Smart Grid, 2016, 7, (3), pp. 13891400.
    30. 30)
      • 31. ‘Smartgridcity Pricing Plan Comparison Chart’, http://smartgridcity.xcelenergy.com/media/pdf/SGC-pricing-plan-chart.pdf, accessed Jun 13, 2016.
    31. 31)
      • 25. Hasanpor Divshali, P., Choi, B.J.: ‘Efficient indirect real-time EV charging method based on imperfect competition market’. 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia, November 2016, pp. 453459.
    32. 32)
      • 8. Kumar Nunna, H., Doolla, S.: ‘Energy management in microgrids using demand response and distributed storage – a multiagent approach’, IEEE Trans. Power Deliv., 2013, 28, (2), pp. 939947.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2016.1916
Loading

Related content

content/journals/10.1049/iet-gtd.2016.1916
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading