Skip to main content
SpringerLink
Log in

A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties

  • Research Article
  • Published:
Frontiers in Energy Aims and scope Submit manuscript

Abstract

The increasing use of distributed energy resources changes the way to manage the electricity system. Unlike the traditional centralized powered utility, many homes and businesses with local electricity generators have established their own microgrids, which increases the use of renewable energy while introducing a new challenge to the management of the microgrid system from the mismatch and unknown of renewable energy generations, load demands, and dynamic electricity prices. To address this challenge, a rank-based multiple-choice secretary algorithm (RMSA) was proposed for microgrid management, to reduce the microgrid operating cost. Rather than relying on the complete information of future dynamic variables or accurate predictive approaches, a lightweight solution was used to make real-time decisions under uncertainties. The RMSA enables a microgrid to reduce the operating cost by determining the best electricity purchase timing for each task under dynamic pricing. Extensive experiments were conducted on real-world data sets to prove the efficacy of our solution in complex and divergent real-world scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

d t :

The total load demands at the time slot t

η t :

The electricity unit price from external electricity resources at the time slot t

δ t :

Electricity purchase from external resources at the time slot t

Θ:

The set of all the load tasks

τ i :

τi = {ri,ei,pi,di}. τi denotes the task i, with its release time ri, execution time ei, power consumption pi, and absolute deadline di.

θ(τ i)(t):

The status of the task i at the time t, θ(τi)(t) = 0 means task i is idle at the time t while θ(τi)(t) = 1 means task i is executing at the time t.

l t :

The total electricity load demands at the time slot t

α t :

Total electricity generations from all the local DGs at the time slot t

\(\alpha _t^{\rm{e}}\) :

Total efficient electricity generations from all the local DGs at the time slot t

β c :

The battery capacity

β t :

The battery storage at the time slot t. The range is from the minimum 0 to the maximum βmax.

b t :

The battery utilization rate at the time slot t. When its value is positive (bt ⩾ 0), the battery is charging at the moment; otherwise (bt < 0), the battery is discharging at the moment. C(δ) The cost function representing the total operating cost of the microgrid

References

  1. Harsh P, Das D. Energy management in microgrid using incentive-based demand response and reconfigured network considering uncertainties in renewable energy sources. Sustainable Energy Technologies and Assessments, 2021, 46: 101225

    Article  Google Scholar 

  2. Gouveia C, Moreira J, Moreira C L, et al. Coordinating storage and demand response for microgrid emergency operation. IEEE Transactions on Smart Grid, 2013, 4(4): 1898–1908

    Article  Google Scholar 

  3. Li Y, Li K, Yang Z, et al. Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach. Journal of Cleaner Production, 2022, 330: 129840

    Article  Google Scholar 

  4. Verbraeken J, Wolting M, Katzy J, et al. A survey on distributed machine learning. ACM Computing Surveys, 2021, 53(2): 1–33

    Article  Google Scholar 

  5. Nwulu N I, Xia X. Optimal dispatch for a microgrid incorporating renewables and demand response. Renewable Energy, 2017, 101: 16–28

    Article  Google Scholar 

  6. Mazidi M, Zakariazadeh A, Jadid S, et al. Integrated scheduling of renewable generation and demand response programs in a microgrid. Energy Conversion and Management, 2014, 86: 1118–1127

    Article  Google Scholar 

  7. Shi W, Li N, Chu C C, et al. Real-time energy management in microgrids. IEEE Transactions on Smart Grid, 2017, 8(1): 228–238

    Article  Google Scholar 

  8. Elsied M, Oukaour A, Youssef T, et al. An advanced real time energy management system for microgrids. Energy, 2016, 114: 742–752

    Article  Google Scholar 

  9. Yan J, Menghwar M, Asghar E, et al. Real-time energy management for a smart-community microgrid with battery swapping and renewables. Applied Energy, 2019, 238: 180–194

    Article  Google Scholar 

  10. Carpinelli G, Mottola F, Proto D. Optimal scheduling of a microgrid with demand response resources. IET Generation, Transmission & Distribution, 2014, 8(12): 1891–1899

    Article  Google Scholar 

  11. Jung S, Yoon Y T. Optimal operating schedule for energy storage system: Focusing on efficient energy management for microgrid. Processes (Basel, Switzerland), 2019, 7(2): 80

    Google Scholar 

  12. Astriani Y, Shafiullah G M, Shahnia F. Incentive determination of a demand response program for microgrids. Applied Energy, 2021, 292: 116624

    Article  Google Scholar 

  13. Ebrahimi M R, Amjady N. Adaptive robust optimization framework for day-ahead microgrid scheduling. International Journal of Electrical Power &amp; Energy Systems, 2019, 107: 213–223

    Article  Google Scholar 

  14. Kumar K P, Saravanan B. Day ahead scheduling of generation and storage in a microgrid considering demand side management. Journal of Energy Storage, 2019, 21: 78–86

    Article  Google Scholar 

  15. Aluisio B, Dicorato M, Forte G, et al. An optimization procedure for microgrid day-ahead operation in the presence of CHP facilities. Sustainable Energy, Grids and Networks, 2017, 11: 34–45

    Article  Google Scholar 

  16. Khodaei A, Bahramirad S, Shahidehpour M. Microgrid planning under uncertainty. IEEE Transactions on Power Systems, 2015, 30(5): 2417–2425

    Article  Google Scholar 

  17. Motevasel M, Seifi A R. Expert energy management of a micro-grid considering wind energy uncertainty. Energy Conversion and Management, 2014, 83: 58–72

    Article  Google Scholar 

  18. Luo L, Abdulkareem S S, Rezvani A, et al. Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty. Journal of Energy Storage, 2020, 28: 101306

    Article  Google Scholar 

  19. Alotaibi I, Abido M A, Khalid M, et al. A comprehensive review of recent advances in smart grids: A sustainable future with renewable energy resources. Energies, 2020, 13(23): 6269

    Article  Google Scholar 

  20. Aguera-Pérez A, Palomares-Salas J C, Gonzalez de la Rosa J J, et al. Weather forecasts for microgrid energy management: Review, discussion and recommendations. Applied Energy, 2018, 228: 265–278

    Article  Google Scholar 

  21. Olivares D E, Lara J D, Canizares C A, et al. Stochastic-predictive energy management system for isolated microgrids. IEEE Transactions on Smart Grid, 2015, 6(6): 2681–2693

    Article  Google Scholar 

  22. Li W, Chang X, Cao J, et al. A sustainable and user-behavior-aware cyber-physical system for home energy management. ACM Transactions on Cyber-Physical Systems, 2019, 3(4): 1–24

    Article  Google Scholar 

  23. Li H P, Wan Z Q, He H B. Real-time residential demand response. IEEE Transactions on Smart Grid, 2020, 11(5): 4144–4154

    Article  Google Scholar 

  24. Zhang C, Kuppannagari S R, Kannan R, et al. Building HVAC scheduling using reinforcement learning via neural network-based model approximation. In: Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2019, 287–296

  25. Lu R, Hong S H, Yu M. Demand response for home energy management using reinforcement learning and artificial neural network. IEEE Transactions on Smart Grid, 2019, 10(6): 6629–6639

    Article  Google Scholar 

  26. Xu S C, Wang Y X, Wang Y Z, et al. One for many: Transfer learning for building HVAC control. In: Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Virtual Event, Japan, 2020

  27. Ferguson T S. Who solved the secretary problem? Statistical Science, 1989, 4(3): 282–289

    MathSciNet  MATH  Google Scholar 

  28. Bajnok B, Semov S. The “thirty-seven percent rule” and the secretary problem with relative ranks, 2015, arXiv preprint:1512.02996

  29. Xia C, Li W, Chang X, et al. Lightweight online scheduling for home energy management systems under uncertainty. IEEE Transactions on Sustainable Computing, 2022, 7(4): 887–898

    Article  Google Scholar 

  30. Babaioff M, Immorlica N, Kempe D, et al. A knapsack secretary problem with applications. In: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques. Springer, 2007, 16–28

  31. Denis V. Lindley. Dynamic programming and decision theory. Journal of the Royal Statistical Society. Series C, Applied Statistics, 1961, 10(1): 39–51

    MathSciNet  Google Scholar 

  32. Australian Energy Market Operator. AEMO data model archieve. 2021, available at the website of nemweb

  33. Zico Kolter J, Johnson M J. Redd: A public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, 2011, 25: 59–62

  34. Kelly J, Knottenbelt W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data, 2015, 2(1): 1–14

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Distributed and High Performance Computing, School of Information Technologies, The University of Sydney, Camperdown, NSW, 2006, Australia

    Chunqiu Xia, Wei Li, Xiaomin Chang & Albert Y. Zomaya

  2. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, 300072, China

    Ting Yang

Authors
  1. Chunqiu Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaomin Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ting Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Albert Y. Zomaya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunqiu Xia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, C., Li, W., Chang, X. et al. A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties. Front. Energy 17, 198–210 (2023). https://doi.org/10.1007/s11708-023-0874-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11708-023-0874-8

Keywords

Search

Navigation