Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Robust optimisation for deciding on real-time flexibility of storage-integrated photovoltaic units controlled by intelligent software agents

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters present the end-users of the power system with new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from underutilizing their assets. In this work, we examine the effectiveness of Robust Optimization (RO) in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive to be included in the control algorithm of a residential energy storage controller. In the use-case, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal, while the storage controller is doing dynamic electricity price arbitrage. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations, while, in summer, when the price fluctuations are higher, uncertainty can be ignored.

References

    1. 1)
      • 30. Lawder, M., Suthar, B., Northrop, P., et al: ‘Battery Energy Storage System (BESS) and Battery Management System (BMS) for grid-scale applications’, Proc. IEEE, 2014, 102, (6), pp. 10141030.
    2. 2)
      • 35. ‘Mastervolt MLI-Ultra 24/5000’. Available at http://www.mastervolt.com/products/li-ion/mli-ultra-24-5000/, accessed 27 October 2016.
    3. 3)
      • 34. Russel, S., Norvig, P.: ‘Artificial intelligence: a modern approach’ (Upper Saddle River, Pearson Education, Inc., New Jersey 07458, 2009, 3rd edn.).
    4. 4)
      • 27. Karfopoulos, E., Hatziargyriou, N.: ‘Distributed coordination of electric vehicles providing V2G services’, IEEE Trans. Power Syst., 2015, 31, (1), pp. 329338.
    5. 5)
      • 11. Taylor, J., Callaway, D., Poolla, K.: ‘Competitive energy storage in the presence of renewables’, IEEE Trans. Power Syst., 2013, 28, (2), pp. 985996.
    6. 6)
      • 13. Hubert, T., Grijalva, S.: ‘Modeling for residential electricity optimization in dynamic pricing environments’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 22242231.
    7. 7)
      • 16. Lujano-Rojas, J., Dufo-Lopez, R., Bernal-Augustin, J., et al: ‘Optimizing daily operation of battery energy storage systems under real-time pricing schemes’, IEEE Trans. Smart Grid, 2016, 8, (1), pp. 316330.
    8. 8)
      • 18. Alamaniotis, M., Bargiotas, D., Bourbakis, N., et al: ‘Genetic optimal regression of relevance vector machines for electricity pricing signal forecasting in smart grids’, IEEE Trans. Smart Grid, 2015, 6, (6), pp. 29973005.
    9. 9)
      • 1. ‘Renewable 2015: Global status report’. Available at http://www.ren21.net/wp-content/uploads/2015/07/REN12-GSR2015_Onlinebook_low1.pdf, accessed 27 January 2016.
    10. 10)
      • 9. Khodabakhsh, R., Sirouspour, S.: ‘Optimal control of energy storage in a microgrid by minimizing conditional value-at-risk’, IEEE Trans. Sustain. Energy, 2016, 7, (3), pp. 12641273.
    11. 11)
      • 32. Bertsimas, D., Thiele, A.: ‘Robust and data-driven optimization: modern decision making under uncertainty’, ch. Chapter 4, pp. 95122.
    12. 12)
      • 21. Bertsimas, D., Sim, M.: ‘Robust discrete optimization and network flows’, Math. Program., 2003, 98, (1), pp. 4971.
    13. 13)
      • 12. Bompard, E., Ciwei, G., Napoli, R., et al: ‘Dynamic price forecast in a competitive electricity market’, IET. Gener. Transm. Distrib., 2007, 1, (5), pp. 776783.
    14. 14)
      • 17. Chau, C., Zhang, G., Chen, M.: ‘Cost minimizing online algorithms for energy storage management with worst-case guarantee’, IEEE Trans. Smart Grid, 2016, 7, (6), pp. 26912702.
    15. 15)
      • 2. Negnevitsky, M., Nguyen, D.H., Piekutowski, M.: ‘Risk assessment for power system operation planning with high wind power penetration’, IEEE Trans. Power Syst., 2015, 30, (3), pp. 13591368.
    16. 16)
      • 23. Chen, Z., Wu, L., Fu, Y.: ‘Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization’, IEEE Trans. Smart Grid, 2012, 3, (4), pp. 18221831.
    17. 17)
      • 25. McArthur, S., Davidson, E., Catterson, V., et al: ‘Multi-agent systems for power engineering applications Part I: concepts, approaches, and technical challenges’, IEEE Trans. Power Syst., 2007, 22, (4), pp. 17431752.
    18. 18)
      • 14. Holland, S., Mansur, E.: ‘The short-run effects of time-varying prices in competitive electricity markets’, Energy J., 2006, 27, (4), pp. 127155.
    19. 19)
      • 5. Vithayasrichareon, P., MacGill, I.: ‘Valuing large-scale solar photovoltaics in future electricity generation portfolios and its implications for energy and climate policies’, IET Renew. Power Gener., 2016, 10, (1), pp. 7987.
    20. 20)
      • 8. Vazquez, S., Lukic, S., Galvan, E., et al: ‘Energy storage systems for transport and grid applications’, IEEE Trans. Ind. Electron., 2010, 57, (12), pp. 38813895.
    21. 21)
      • 37. Karanasios, E., Ampatzis, M., Nguyen, P., et al: ‘A model for the estimation of the cost of use of li-ion batteries in residential storage applications integrated with PV panels’. Power Engineering Conf. (UPEC), 49th Int. Universities, 2014, pp. 16.
    22. 22)
      • 4. Paterakis, N., Erdinc, O., Bakirtzis, A., et al: ‘Qualification and quantification of reserves in power systems under high wind generation penetration considering demand response’, IEEE Trans. Sustain. Energy, 2014, 6, (1), pp. 88103.
    23. 23)
      • 6. Mari, L., Nabona, V.: ‘Renewable energies in medium-term power planning’, IEEE Trans. Power Syst., 2015, 30, (1), pp. 8897.
    24. 24)
      • 31. Wu, W., Chen, J., Zhang, B., et al: ‘A robust wind power optimization method for look-ahead power dispatch’, IEEE Trans. Sustain. Energy, 2014, 5, (2), pp. 507515.
    25. 25)
      • 33. Bertsimas, D., Sim, M.: ‘The price of robustness’, Oper. Res., 2004, 51, pp. 3553.
    26. 26)
      • 19. Chen, X., Dong, Z., Meng, K., et al: ‘Electricity price forecasting with extreme learning machine and bootstrapping’, IEEE Trans. Power Syst., 2012, 27, (4), pp. 20552062.
    27. 27)
      • 39. He, G., Chen, Q., Kang, C., et al: ‘Optimal bidding strategy of battery storage in power markets considering performance-based regulation and battery cycle life’, IEEE Trans. Smart Grid, 2015, 7, (5), pp. 19.
    28. 28)
      • 10. Tan, X., Wu, Y., Tsang, D.: ‘Pareto optimal operation of distributed battery energy storage systems for energy arbitrage under dynamic pricing’, IEEE Trans. Parallel Distrib. Syst., 2016, 27, (7), pp. 21032115.
    29. 29)
      • 40. Ameren Focused Energy. Real-time pricing for residential customers.
    30. 30)
      • 29. De Paola, A., Angeli, D., Strbac, G.: ‘Distributed control of micro-storage devices with mean field games’, IEEE Trans. Smart Grid, 2016, 7, (2), pp. 11191127.
    31. 31)
      • 7. Hartwig, K., Kockar, I.: ‘Impact of strategic behavior and ownership of energy storage on provision of flexibility’, IEEE Trans. Sustain. Energy, 2016, 7, (2), pp. 744754.
    32. 32)
      • 15. Doroudchi, E., Kumar-Pal, S., Kyyrä, J.: ‘Optimizing energy cost via battery sizing in residential PV/battery systems’. IEEE Innovative Smart Grid Technologies – Asia (ISGT ASIA), 2015, pp. 16.
    33. 33)
      • 38. Duggal, I., Venkatesh, B.: ‘Short-term scheduling of thermal generators and battery storage with depth of discharge-based cost model’, IEEE Trans. Power Syst., 2015, 30, (4), pp. 21102118.
    34. 34)
      • 24. Bellifemine, F., Caire, G., Greenwood, D.: ‘Designing multi-agent systems with JADE’. The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (John Wiley and Sons, Ltd, England, 2007, 1st edn.).
    35. 35)
      • 36. Hoke, A., Brissette, A., Smith, K., et al: ‘Accounting for lithium-ion battery degradation in electric vehicle charging optimization’, IEEE J. Emerging Sel. Top. Power Electron., 2014, 2, (3), pp. 691700.
    36. 36)
      • 22. Conejo, A., Morales, J., Baringo, L.: ‘Real-time demand response model’, IEEE Trans. Smart Grid, 2010, 1, (3), pp. 236242.
    37. 37)
      • 28. Thatte, A., Xie, L., Viassolo, D., et al: ‘Risk measure based robust bidding strategy for arbitrage using a wind farm and energy storage’, IEEE Trans. Smart Grid, 2013, 4, (4), pp. 21912199.
    38. 38)
      • 26. Elmitwally, A., Elsaid, M., Elgamal, M., et al: ‘A fuzzy-multiagent service restoration scheme for distribution system with distributed generation’, IEEE Trans. Sustain. Energy, 2015, 6, (3), pp. 810821.
    39. 39)
      • 20. Chen, X., Dong, Z., Meng, K., et al: ‘Economic impact of electricity market price forecasting errors: a demand-side analysis’, IEEE Trans. Power Syst., 2010, 25, (1), pp. 254262.
    40. 40)
      • 3. Nguyen, N., Mitra, J.: ‘An analysis of the effects and dependency of wind power penetration on system frequency regulation’, IEEE Trans. Sustain. Energy, 2016, 7, (1), pp. 354363.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2016.0967
Loading

Related content

content/journals/10.1049/iet-rpg.2016.0967
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address