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Optimal operation method coping with uncertainty in multi-area small power systems

1 Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara-cho, Nakagami, Okinawa 903-0213, Japan
2 Hawaii Natural Energy Institute, University of Hawaii, Manoa, Honolulu, Hawaii 96822, USA
3 Institute of Materials and Systems for Sustainalbility (IMaSS), Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

Topical Section: Smart Grids and Networks

Japan contains a vast number of isolated islands. Majority of these islands are poweredby diesel generators (DGs), which are operationally not economical. Therefore, the introduction of renewableenergy systems (RESs) into these area is very much vital. However, the variability of RESs asa result of weather condition as well as load demand , battery energy storage system (BESS) is broughtinto play. Demand response (DR) programs have also been so attractive in the energy management systemsfor the past decades. Among them, the real-time pricing (RTP) has been one of the most effectivedemand response program being utilized. This program encourages the customer to increase or reducethe load consumption by varying the electricity price. Also, due to the increase in power transactionmarket, Japan electric power exchange (JEPX) has established spot (day-ahead), intraday hour-ahead,and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these marketscan make it possible to deal with uncertainty related to generated power fluctuations. Therefore,this paper presents the optimal operation method coping with the uncertainties of RESs in multi-areasmall power systems. The proposed method enables flexibility to correspond to the forecasting error byproviding two kinds of power markets among multi-area small power systems and trading the shortageand surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, theproposed method was able to reduce operational cost for multi-area small power systems. The processof creating operational plan for RTP, power trading at the markets and the unit commitment of DGs arealso presented in this paper. Simulation results corroborate the merit of the proposed program.
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References

1. Amenedo JLR, Arnalte S, Burgos JC, et al. (2002) Automatic generation control of a wind farm with variable speed wind turbines. IEEE T Energy Conver 17: 279–284.    

2. Senjyu T, Hayashi D, Sakamoto Y, et al. (2005) Generating power leveling of renewable energy for small power system in isolated island. IEE Japan 125: 1209–1215.

3. Senjyu T, Tokudome M, Yona A, et al. (2009) A Frequency Control Approach by Decentralized Controllable Loads in Small Power Systems. IEE Japan 129: 1074–1080.

4. Senjyu T, Kikunaga T, Tokudome M, et al. (2009) Coordinate Control ofWind Turbine and Battery in Wind Turbine Generator System. IEE Japan 129: 653–660.

5. Kurohane K, Senjyu T, Yona A, et al. (2010) A High Quality Power Supply System with DC Smart Grid. 2010 IEEE PES Transmission and Distribution Conference and Exposition.

6. Ogimi K, Kamiyama S, Palmer M, et al. (2013) Optimal OperationPlanning ofWind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error. Int J Emerg Electric Power Syst 14: 207–218.

7. Bracale A, Carpinelli G, Fazio AD, et al. (2015) Advanced, Cost-Based Indices for Forecasting the Generation of Photovoltaic Power. Int J Emerg Electric Power Syst 16: 195–206.

8. Shimoji T, Tahara H, Matayoshi H, et al. (2015) Comparison and Validation of Operational Cost in Smart Houses with the Introduction of a Heat Pump or a Gas Engine. Int J Emerg Electric Power Syst 16: 59–74.

9. MengyanW, HigaW, Yona A, et al. (2012) Optimal Operation of Power Systems with Power Players. 2012 International Conference on Renewable Energy Research and Applications (ICRERA).

10. Higa S, Mengyan W, Yona A, et al. (2013) Optimal Operation Method Considering Uncertainly of Renewable Energy and Load Demand in Micro-grids. The 5th International Conference on Advanced Power System Automation and Protection.

11. Higa S, Howlader AM, Shiroma Y, et al. (2014) Optimal Operation Method Considering Replanning and Uncertainly in Power Systems. The International Conference on Electrical Engineering 2014.

12. Zheng Y, Dong ZY, Luo FJ, et al. (2014) Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations. IEEE T Power Syst 29: 212–220.    

13. Zhang L, Li Y (2013) Optimal Energy Management of Wind-Battery Hybrid Power System With Two-Scale Dynamic Programming. IEEE T Sust Energy 4: 765–773.    

14. Ahn SJ, Nam SR, Choi JH, et al. (2013) Power Scheduling of Distributed Generators Economic and Stable Operation of a Microtrid. IEEE T Smart Grid 4: 398–405.    

15. Zhao B, Zhang X, Chen J, et al. (2013) Operation Optimization of Standalone Microgrids Considering Lifetime Characteristics of Battery Energy StorageSystem. IEEE T Sust Energy 4: 934–943.    

16. He D, Tan Z, Harley RG, et al. (2012) Chance Constrained Unit Commitment With Wind Generation and Superconducting Magnetic Energy Storages. 2012 IEEE Power and Energy Society General Meeting.

17. Reddy SS, Abhyankar AR, Bijwe PR, et al. (2014) Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets. Int J Emerg Electric Power Syst 15: 77–91.

18. Nguyen MY, Nguyen DM (2015) A Generalized Formulation of Demand Response under Market Environments. Int J Emerg Electric Power Syst 16: 217–224.

19. Aalami HA, Moghaddam MP, Yousefi GR, et al. (2010) Demand response modeling considering Interruptible/Curtailable loads and capacity market programs. Appl Energy 87: 243–250.    

20. Andebili MR, Shen H (2017) Energy Management of End Users Modeling their Reaction from a GENCOs Point of View. 2017 International Conference on Computing, Networking and Communications (ICNC).

21. Andebili MR (2016) Modeling nonlinear incentive-based and price-based demand response programs and implementing on real power markets. Electr Pow Syst Res 132: 115–124.    

22. Andebili MR, Shen H (2017) Price-Controlled Energy Management of Smart Homes for Maximizing Profit of a GENCO. IEEE T Syst Man Cy A.

23. Andebili MR (2015) Risk-Cost Based Generation Scheduling Smartly Mixed with Reliability and Market-Driven Demand Response Measures. Int T Electr Energy Syst 25: 994–1007.    

24. AndebiliMR(2016) Nonlinear demand response programs for residential customers with nonlinear behavioral models. Energ Buildings 119: 352–362.

25. Andebili MR (2013) Investigating effects of responsive loads models on unit commitment collaborated with demand-side resources. IET Gener Transm D 7: 420–430.    

26. Japan Electric Power Exchange, JEPX, 2017. Available from: http://www.jepx.org/index.html.

27. Nguyen MY, Yoon YT (2014) Optimal Scheduling and Operation of Battery/Wind Generation System in Response to Real-time Market Prices. IEE T Electr Electr Eng 9: 129–135.    

28. Havel P (2014) Utilization of Real-Time Balancing Market for Transmission System Control Under Uncertainty. IEEE T Power Syst 29: 450–457.    

29. Kiviluoma J, O'Malley M, Tuohy A, et al. (2011) Impact ofWind Power on the Unit Commitment, Operating Reserves, and Market Design. 2011 IEEE Power and Energy Society General Meeting.

30. Vlachos AG, Biskas PN, et al. (2011) Balancing Supply and Demand Under Mixed Pricing Rules in Multi-Area Electricity Markets. IEEE T Power Syst 26: 1444–1453.

31. Ma J, Silva V, Belhomme R, et al. (2013) Evaluating and Planning Flexibility in Sustainable Power Systems. 2013 IEEE Power and Energy Society General Meeting (PES).

Copyright Info: © 2017, Shota Tobaru, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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