Demand response under real-time pricing for domestic households with renewable DGs and storage

Qiang Yang; Xinli Fang

Demand response under real-time pricing for domestic households with renewable DGs and storage

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Author(s): Qiang Yang ¹ and Xinli Fang ^{1, 2, 3}
- Affiliations: 1: College of Electrical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China ;
  2: Hangzhou Huachen Electric Power Control Co. Ltd, Hangzhou 311122, People's Republic of China ;
  3: Powerchina Huadong Engineering Corporation Limited, Hangzhou 311122, People's Republic of China
Source: Volume 11, Issue 8, 01 June 2017, p. 1910 – 1918
DOI: 10.1049/iet-gtd.2016.1066 , Print ISSN 1751-8687, Online ISSN 1751-8695

Received 12/07/2016, Accepted 08/02/2017, Revised 27/01/2017, Published 13/03/2017

With the increasing penetration of small-scale distributed generators (DGs) and storage units into the domestic areas and the emergence of real-time pricing (RTP) of electricity, residents have more opportunities to obtain a cost-effective energy management through the participation of demand side management. Unlike existing online solutions, this paper presents an alternative one-day-ahead energy dispatch solution to achieve the economic benefits of meeting the demand with minimised electricity purchase cost by optimising the DG and storage utilisation efficiency in the presence of RTP. The performance of the proposed solution is assessed through a comparative study by carrying out simulation experiments for a set of operational scenarios. The numerical result demonstrates that the proposed energy dispatch solution can well meet the demand requirement with significantly reduced electricity purchase cost. With the recognition that the prediction of RTP and DG output can often be inaccurate, the robustness of the solution is further verified under prediction errors.

References

1. 1)
  - 14. Marra, F., Yang, G., Traeholt, C., et al: ‘A decentralized storage strategy for residential feeders with photovoltaics’, IEEE Trans. Smart Grid, 2014, 5, pp. 974–981.
2. 2)
  - 1. Liu, T., Ai, Q.: ‘Interactive energy management of networked microgrids-based active distribution system considering large-scale integration of renewable energy resources’, Appl. Energy, 2016, 163, pp. 408–422.
3. 3)
  - 26. The California Energy Almanac. http://energyalmanac.ca.gov/renewables/solar/pv.html.
4. 4)
  - 9. Beaudin, M., Zareipour, H., Bejestani, A.K., et al: ‘Residential energy management using a two-horizon algorithm’, IEEE Trans. Smart Grid, 2014, 3, pp. 1712–1723.
5. 5)
  - 10. Delfino, F., Minciardi, R., Pampararo, F., et al: ‘A multilevel approach for the optimal control of distributed energy resources and storage’, IEEE Trans. Smart Grid, 2014, 5, pp. 2155–2162.
6. 6)
  - 29. Bayod-Rújulaa, Á.A., Haro-Larrodéa, M.E., Martínez-Graciab, A.: ‘Sizing criteria of hybrid photovoltaic-wind system with battery storage and self-consumption considering interaction with the grid’, Sol. Energy, 2013, 98, pp. 582–591.
7. 7)
  - 17. Kim, T.T., Poor, H.V.: ‘Scheduling power consumption with price uncertainty’, IEEE Trans. Smart Grid, 2011, 2, pp. 519–527.
8. 8)
  - 16. Chen, Z., Wu, L., Fu, U.: ‘Real-time price-based demand response management for residential appliances via stochastic optimization and robust optimization’, IEEE Trans. Smart Grid, 2012, 3, pp. 1822–1831.
9. 9)
  - 24. Wang, Q., Yang, Q., Yan, W.: ‘Optimal dispatch in residential community with DGs and storage under real-time pricing’. Proc. IEEE Int. Conf. Information and Automation, Yunnan, August 2015.
10. 10)
  - 3. Finn, P., Fitzpatrick, C.: ‘Demand side management of industrial electricity consumption: promoting the use of renewable energy through real-time pricing’, Appl. Energy, 2014, 113, pp. 11–21.
11. 11)
  - 8. Munkhammar, J., Widén, J., Rydén, J.: ‘On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production’, Appl. Energy, 2015, 142, pp. 135–143.
12. 12)
  - 6. Adika, C.O., Wang, L.F.: ‘Autonomous appliance scheduling for household energy management’, IEEE Trans. Smart Grid, 2014, 5, pp. 673–682.
13. 13)
  - 11. Flores, R.J., Shaffer, B.P., Brouwer, J.: ‘Dynamic distributed generation dispatch strategy for lowering the cost of building energy’, Appl. Energy, 2014, 123, pp. 196–208.
14. 14)
  - 2. Bhandari, B., Lee, K., Lee, C.S., et al: ‘A novel off-grid hybrid power system comprised of solar photovoltaic, wind, and hydro energy sources’, Appl. Energy, 2014, 133, pp. 236–242.
15. 15)
  - 4. Barzin, R., Chen, J.J.J., Young, B.R., et al: ‘Application of weather forecast in conjunction with price-based method for PCM solar passive buildings – an experimental study’, Appl. Energy, 2016, 163, pp. 9–18.
16. 16)
  - 30. Real-time hourly prices, ComEd. https://rrtp.comed.com/live-prices/.
17. 17)
  - 5. EI-Baz, W., Tzscheutschler, P.: ‘Short-term smart learning electrical load prediction algorithm for home energy management systems’, Appl. Energy, 2015, 147, pp. 10–19.
18. 18)
  - 28. Fang, X., Yang, Q., Wang, J., et al: ‘Coordinated dispatch in multiple cooperative autonomous islanded microgrids’, Appl. Energy, 2016, 162, pp. 40–48.
19. 19)
  - 13. Igualada, L., Corchero, C., Zambrano, M.C., et al: ‘Optimal energy management for a residential microgrid including a vehicle-to-grid system’, IEEE Trans. Smart Grid, 2013, 5, pp. 2163–2172.
20. 20)
  - 22. Erdinc, O., Paterakis, N.G., Pappi, I.N., et al: ‘A new perspective for sizing of distributed generation and energy storage for smart households under demand response’, Appl. Energy, 2015, 143, pp. 26–37.
21. 21)
  - 25. Electricity price and demand, AEMO. http://www.aemo.com.au/Electricity/Data/Price-and-Demand.
22. 22)
  - 21. Erdinc, O.: ‘Economic impacts of small-scale own generating and storage units and electric vehicles under different demand response strategies for smart households’, Appl. Energy, 2014, 126, pp. 142–150.
23. 23)
  - 27. The Wind Power. http://www.thewindpower.net/.
24. 24)
  - 18. Hong, Y.Y., Lin, J.K., Wu, C.P., et al: ‘Multiobjective air-conditioning control considering fuzzy parameters using immune clonal selection programming’, IEEE Trans. Smart Grid, 2012, 3, pp. 1603–1610.
25. 25)
  - 23. Vrettos, E., Lai, K., Oldewurtel, F., et al: ‘Predictive control of buildings for demand response with dynamic day-ahead and real-time prices’. European Control Conf. (ECC), Zürich, Switzerland, July 17–19, 2013.
26. 26)
  - 19. Guo, Y., Pan, M., Fang, Y.: ‘Optimal power management of residential customers in the smart grid’, IEEE Trans. Parallel Distrib. Syst., 2012, 23, pp. 1593–1606.
27. 27)
  - 7. Giorgio, A.D., Pimpinella, L.: ‘An event driven smart home controller enabling consumer economic saving and automated demand side management’, Appl. Energy, 2012, 96, pp. 92–103.
28. 28)
  - 20. Zhou, S., Wu, Z., Li, J., et al: ‘Real-time energy control approach for smart home energy management system’, Electr. Power Compon. Syst., 2014, 42, (3–4), pp. 315–326.
29. 29)
  - 15. Pengwei, D., Ning, L.: ‘Appliance commitment for household load scheduling’, IEEE Trans. Smart Grid, 2011, 2, pp. 411–419.
30. 30)
  - 12. Karami, H., Sanjari, M.J., Hosseinian, S.H., et al: ‘An optimal dispatch algorithm for managing residential distributed energy resources’, IEEE Trans. Smart Grid, 2014, 5, pp. 2360–2367.

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Demand response under real-time pricing for domestic households with renewable DGs and storage

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