http://iet.metastore.ingenta.com
1887

Two-stage load-scheduling model for the incentive-based demand response of industrial users considering load aggregators

Two-stage load-scheduling model for the incentive-based demand response of industrial users considering load aggregators

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Generation, Transmission & Distribution — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The demand response (DR) resources provided by modern industrial users tend to be diversified because of the popularisation of renewable energy. In this context, for the problem of satisfaction and uncertainty in the DR, a load-scheduling model considering load aggregators (LAs) is presented. In the multi-level DR scheduling system, the users and aggregator simultaneously participate in the decision; thus, the proposed methodology is a two-stage optimisation process. The first-stage optimisation considers the load interruption strategies of complex industrial processes, where a multi-objective optimisation model is established to coordinate the user benefits and satisfaction. This model is solved by non-dominated sorting genetic algorithm-II (NSGA-II) and the entropy weight double-base point method to obtain the optional interruptible load (IL) contracts for production processes. The second-stage optimisation maximises the economic returns of the LA considering the uncertainty of the resource's response, where chance-constraint programming is applied to solve the problem and select the appropriate IL contract. The effectiveness of the proposed methodology is examined according to the actual industrial production process. The multi-objective coordination effect of production load-scheduling is shown in an example. Finally, the effects of joint resource scheduling and different confidence levels on the profit of the aggregator are analysed.

References

    1. 1)
      • 1. Aghaei, J., Alizadeh, M.-I., Siano, P., et al: ‘Contribution of emergency demand response programs in power system reliability’, Energy, 2016, 103, pp. 688696.
    2. 2)
      • 2. Safdarian, A., Degefa, M.Z, Lehtonen, M., et al: ‘Distribution network reliability improvements in presence of demand response’, IET Gener. Transm. Distrib., 2014, 8, (12), pp. 20272035.
    3. 3)
      • 3. Nunna, K.H.S.V.S., Doolla, S.: ‘Responsive end-user-based demand side management in multimicrogrid environment’, IEEE Trans. Ind. Inf., 2014, 10, (2), pp. 12621272.
    4. 4)
      • 4. Lo, C.H., Ansari, N.: ‘The progressive smart grid system from both power and communications aspects’, IEEE Commun. Surv. Tutor., 2012, 14, (3), pp. 799821.
    5. 5)
      • 5. Chen, Z., Wu, L., Li, Z.: ‘Electric demand response management for distributed large-scale internet data centers’, IEEE Trans. Smart Grid, 2014, 5, (2), pp. 651661.
    6. 6)
      • 6. National bureau of statistics annual data’. Available at http://data.stats.gov.cn/english/easyquery.htm?cn=C01, accessed 23 February 2017.
    7. 7)
      • 7. Choobineh, M., Mohagheghi, S.: ‘Optimal energy management in an industrial plant using on-site generation and demand scheduling’, IEEE Trans. Ind. Appl., 2016, 52, (3), pp. 19451952.
    8. 8)
      • 8. Fahrioglu, M., Alvarado, F.L.: ‘Designing incentive compatible contracts for effective demand management’, IEEE Trans. Power Syst., 2000, 15, (4), pp. 12551260.
    9. 9)
      • 9. Kreuder, L., Gruber, A., Roon, S.V.: ‘Quantifying the costs of demand response for industrial businesses’. Proc. Int. Conf. IECON 2013 – 39th Annual Conf. IEEE Industrial Electronics Society, Vienna, Austria, November 2013, pp. 80468051.
    10. 10)
      • 10. Ding, Y.M., Hong, S.H., Li, X.H.: ‘A demand response energy management scheme for industrial facilities in smart grid’, IEEE Trans. Ind. Inf., 2014, 10, (4), pp. 22572269.
    11. 11)
      • 11. Mohagheghi, S., Raji, N.: ‘Intelligent demand response scheme for energy management of industrial systems’. Proc. Int. Conf. 2012 IEEE Industry Applications Society Annual Meeting, Las Vegas, NV, USA, October 2012, pp. 19.
    12. 12)
      • 12. Zhang, G., Jiang, C., Wang, X., et al: ‘Bidding strategy analysis of virtual power plant considering demand response and uncertainty of renewable energy’, IET Gener. Transm. Distrib., 2017, 11, (13), pp. 32683277.
    13. 13)
      • 13. Erdinc, O., Mendes, T.D.P., Catalao, J.P.S.: ‘Impact of electric vehicle V2G operation and demand response strategies for smart households’. Proc. Int. Conf. T&D Conf. Exposition, Chicago, IL, USA, April 2014, pp. 15.
    14. 14)
      • 14. Argiento, R., Faranda, R., Pievatolo, A., et al: ‘Distributed interruptible load shedding and micro-generator scheduling to benefit system operations’, IEEE Trans. Power Syst., 2012, 27, (2), pp. 840848.
    15. 15)
      • 15. Mathieu, J.L., Callaway, D.S., Kiliccote, S.: ‘Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing’. Proc. Int. Conf. Decision and Control and European Control Conf., Orlando, FL, USA, December 2011, pp. 43324339.
    16. 16)
      • 16. Mahmoudi, N., Shafie-khah, M., Saha, T.K., et al: ‘User-driven demand response model for facilitating roof-top PV and wind power integration’, IET Renew. Power Gener., 2017, 11, (9), pp. 12001210.
    17. 17)
      • 17. Eyer, J., Corey, G.: ‘Energy storage for the electricity grid: benefits and market potential assessment guide’ (Sandia National Laboratories, Albuquerque, NM, USA, 2010).
    18. 18)
      • 18. Boait, P., Ardestani, B.M., Snape, J.R.: ‘Accommodating renewable generation through an aggregator-focused method for inducing demand side response from electricity consumers’, IET Renew. Power Gener., 2013, 7, (6), pp. 689699.
    19. 19)
      • 19. Li, Z., Wang, S., Zheng, X., et al: ‘Dynamic demand response using user coupons considering multiple load aggregators to simultaneously achieve efficiency and fairness’, IEEE Trans. Smart Grid, 2016, pp. 11, doi:10.1109/TSG.2016.2627140.
    20. 20)
      • 20. Panwar, K., Konda, S.R., Verma, A., et al: ‘Demand response aggregator coordinated two-stage responsive load scheduling in distribution system considering user behaviour’, IET Gener. Transm. Distrib., 2017, 11, (4), pp. 10231032.
    21. 21)
      • 21. Lu, N.: ‘An evaluation of the HVAC load potential for providing load balancing service’, IEEE Trans. Smart Grid, 2012, 3, (3), pp. 12631270.
    22. 22)
      • 22. Gao, C., Li, Q., Li, Y.: ‘Bi-level optimal schedule and control strategy for air-conditioning load based on direct load control’, Proc. CSEE, 2014, 34, (10), pp. 15461555.
    23. 23)
      • 23. Shafie-khah, M., Siano, P.: ‘A stochastic home energy management system considering satisfaction cost and response fatigue’, IEEE Trans. Ind. Inf., 2018, 14, (2), pp. 629638.
    24. 24)
      • 24. Liu, B., Zhao, R., Wang, G.: ‘Uncertain programming and its application’ (Tsinghua University Press, Beijing, China, 2003), pp. 7991.
    25. 25)
      • 25. Wu, H., Shahidehpour, M., Li, Z., et al: ‘Chance-constrained day-ahead scheduling in stochastic power system operation’, IEEE Trans. Power Syst., 2014, 29, (4), pp. 15831591.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2018.0089
Loading

Related content

content/journals/10.1049/iet-gtd.2018.0089
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address