
A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties
Chunqiu XIA, Wei LI, Xiaomin CHANG, Ting YANG, Albert Y. ZOMAYA
Front. Energy ›› 2023, Vol. 17 ›› Issue (2) : 198-210.
A rank-based multiple-choice secretary algorithm for minimising microgrid operating cost under uncertainties
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.
energy management systems / demand response / scheduling under uncertainty / renewable energy sources / multiple-choice secretary algorithm
[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
CrossRef
Google scholar
|
[2] |
Gouveia C, Moreira J, Moreira C L.
CrossRef
Google scholar
|
[3] |
Li Y, Li K, Yang Z.
CrossRef
Google scholar
|
[4] |
Verbraeken J, Wolting M, Katzy J.
CrossRef
Google scholar
|
[5] |
Nwulu N I, Xia X. Optimal dispatch for a microgrid incorporating renewables and demand response. Renewable Energy, 2017, 101: 16–28
CrossRef
Google scholar
|
[6] |
Mazidi M, Zakariazadeh A, Jadid S.
CrossRef
Google scholar
|
[7] |
Shi W, Li N, Chu C C.
CrossRef
Google scholar
|
[8] |
Elsied M, Oukaour A, Youssef T.
CrossRef
Google scholar
|
[9] |
Yan J, Menghwar M, Asghar E.
CrossRef
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
CrossRef
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
CrossRef
Google scholar
|
[12] |
Astriani Y, Shafiullah G M, Shahnia F. Incentive determination of a demand response program for microgrids. Applied Energy, 2021, 292: 116624
CrossRef
Google scholar
|
[13] |
Ebrahimi M R, Amjady N. Adaptive robust optimization framework for day-ahead microgrid scheduling. International Journal of Electrical Power & Energy Systems, 2019, 107: 213–223
CrossRef
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
CrossRef
Google scholar
|
[15] |
Aluisio B, Dicorato M, Forte G.
CrossRef
Google scholar
|
[16] |
Khodaei A, Bahramirad S, Shahidehpour M. Microgrid planning under uncertainty. IEEE Transactions on Power Systems, 2015, 30(5): 2417–2425
CrossRef
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
CrossRef
Google scholar
|
[18] |
Luo L, Abdulkareem S S, Rezvani A.
CrossRef
Google scholar
|
[19] |
Alotaibi I, Abido M A, Khalid M.
CrossRef
Google scholar
|
[20] |
Agüera-Pérez A, Palomares-Salas J C, González de la Rosa J J.
CrossRef
Google scholar
|
[21] |
Olivares D E, Lara J D, Cañizares C A.
CrossRef
Google scholar
|
[22] |
Li W, Chang X, Cao J.
CrossRef
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
CrossRef
Google scholar
|
[24] |
ZhangCKuppannagariS RKannanR,
|
[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
CrossRef
Google scholar
|
[26] |
XuS CWangY XWangY Z,
|
[27] |
Ferguson T S. Who solved the secretary problem?. Statistical Science, 1989, 4(3): 282–289
CrossRef
Google scholar
|
[28] |
BajnokBSemovS. The “thirty-seven percent rule” and the secretary problem with relative ranks, 2015, arXiv preprint:1512.02996
|
[29] |
Xia C, Li W, Chang X.
CrossRef
Google scholar
|
[30] |
BabaioffMImmorlicaNKempeD,
|
[31] |
Denis V. Lindley. Dynamic programming and decision theory. Journal of the Royal Statistical Society. Series C, Applied Statistics, 1961, 10(1): 39–51
|
[32] |
AustralianEnergy 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
CrossRef
Google scholar
|
dt | The total load demands at the time slot t |
The electricity unit price from external electricity resources at the time slot t | |
Electricity purchase from external resources at the time slot t | |
The set of all the load tasks | |
The status of the task | |
The total electricity load demands at the time slot t | |
Total electricity generations from all the local DGs at the time slot t | |
Total efficient electricity generations from all the local DGs at the time slot t | |
The battery capacity | |
The battery storage at the time slot | |
The battery utilization rate at the time slot | |
The cost function representing the total operating cost of the microgrid |
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