Stochastic day-ahead optimal scheduling of multimicrogrids: an alternating direction method of multipliers (ADMM) approach

Multimicrogrid system is a novel notion in modern power systems as a result of developing renewable-based generation units and accordingly microgrids in distribution networks. Their energy management might be challenging due to presence of independent units. Thus, in this paper, a decentralized method for energy management of multimicrogrid systems has been proposed. Decentralized methods can enhance the privacy of users and reduce the burden of calculations. Alternating direction method of multipliers (ADMM) is selected as a decentralized approach which has the capability of breaking problems with complicating constraints in order to facilitate the solving process. Using decentralized approach not only reduces the burden of calculations, but also increases the privacy of entities. Wind turbines as renewable based generators are assumed to participate in this system. To model the uncertainties of these units, chance-constrained programming is employed. Also, due to clean output of hydrogen storage systems and fuel cells, the inclination for using these systems has expanded. Simulations on the test case study demonstrate the applicability and performance of the proposed methodology. Considering the reliability level as 0.9 results in 12, 989$ in the test case. By considering the reliability level as 0.8, the operational cost becomes 11, 712$ which shows a reduction of 1277$ which is achieved by jeopardizing the system reliability by 10%.

PDF

___

[1] Toledo OM, Oliveira Filho D, Diniz AS. Distributed photovoltaic generation and energy storage systems: A review. Renewable and Sustainable Energy Reviews. 2010;14 (1):506-11.
[2] Wang Z, Chen B, Wang J, Kim J, Begovic MM. Robust optimization based optimal DG placement in microgrids. IEEE Transactions on Smart Grid. 2014;5 (5):2173-82.
[3] Gholami K, Dehnavi E. A modified particle swarm optimization algorithm for scheduling renewable generation in a micro-grid under load uncertainty. Applied Soft Computing. 2019 May 1;78:496-514.
[4] Rabiee A, Sadeghi M, Aghaei J. Modified imperialist competitive algorithm for environmental constrained energy management of microgrids. Journal of cleaner production. 2018; 202:273-92.
[5] Luo L, Abdulkareem SS, Rezvani A, Miveh MR, Samad S 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.
[6] Xu Y, Zhao T, Zhao S, Zhang J, Wang Y. Multi-objective chance-constrained optimal day-ahead scheduling considering BESS degradation. CSEE Journal of Power and Energy Systems. 2018 ;4 (3):316-25.
[7] Shams MH, Shahabi M, Khodayar ME. Stochastic day-ahead scheduling of multiple energy carrier microgrids with demand response. Energy. 2018; 155:326-38.
[8] Sharma MK, Dhiman N, Mishra VN, Rosales HG, Dhaka A et al. A Fuzzy Optimization Technique for MultiObjective Aspirational Level Fractional Transportation Problem. Symmetry. 2021;13 (8):1465.
[9] Karimi H, Jadid S. Optimal energy management for multi-microgrid considering demand response programs: A stochastic multi-objective framework. Energy. 2020; 195:116992.
[10] Aghdam FH, Kalantari NT, Ravadanegh SN. Reconfiguration-based hierarchical energy management in multimicrogrid systems considering power losses, reliability index, and voltage enhancement. Turkish Journal of Electrical Engineering & Computer Sciences. 2020;28 (5):2433-47.
[11] Dimeas AL, Hatziargyriou ND. Operation of a multiagent system for microgrid control. IEEE Transactions on Power systems. 2005;20 (3):1447-55.
[12] Wang Z, Yang K, Wang X. Privacy-preserving energy scheduling in microgrid systems. IEEE transactions on Smart Grid. 2013 ;4 (4):1810-20.
[13] Crisostomi E, Liu M, Raugi M, Shorten R. Plug-and-play distributed algorithms for optimized power generation in a microgrid. IEEE transactions on Smart grid. 2014 ;5 (4):2145-54.
[14] Harmouch FZ, Krami N, Hmina N. A multiagent based decentralized energy management system for power exchange minimization in microgrid cluster. Sustainable cities and society. 2018 ;40:416-27.
[15] Ratnam EL, Weller SR, Kellett CM. An optimization-based approach to scheduling residential battery storage with solar PV: Assessing customer benefit. Renewable Energy. 2015 ;75:123-34.
[16] Li Y, Yang Z, Li G, Zhao D, Tian W. Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties. IEEE Transactions on Industrial Electronics. 2018 ;66 (2):1565-75.
[17] Yamchi HB, Shahsavari H, Kalantari NT, Safari A, Farrokhifar M. A cost-efficient application of different battery energy storage technologies in microgrids considering load uncertainty. Journal of Energy Storage. 2019 ;22:17-26.
[18] Merizio IF, Chavarette FR, Moro TC, Outa R, Mishra VN. Machine Learning Applied in the Detection of Faults in Pipes by Acoustic Means. Journal of The Institution of Engineers (India): Series C. 2021;22:1-6.
[19] Outa R, Chavarette FR, Gonçalves AC, da Silva SL, Mishra VN, Panosso AR, Mishra LN. Reliability analysis using experimental statistical methods and AIS: application in continuous flow tubes of gaseous medium. Acta Scientiarum. Technology. 2021;43:e55825-.
[20] Sharma MK, Yadav H, Dhiman N, Mishra V. Fuzzy reliability evaluation of complex systems with Monte Carlo simulation. InAIP Conference Proceedings 2021 Sep 23 (Vol. 2364, No. 1, p. 020025). AIP Publishing LLC.
[21] Sharma MK, Dhiman N, Mishra LN, Mishra VN, Sahani SK. Mediative Fuzzy Extension Technique and Its Consistent Measurement in the Decision Making of Medical Application. Mathematical Problems in Engineering. 2021
[22] Zhixin F, Ruiqian W, Yuan Y. Optimal Scheduling for Home Energy Based on Chance Constrained Programming. In2018 China International Conference on Electricity Distribution (CICED) 2018 (pp. 2889-2894). IEEE.
[23] Aghdam FH, Kalantari NT, Mohammadi-Ivatloo B. A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements. Journal of Energy Storage. 2020 ;29:101416.
[24] Aghdam FH, Kalantari NT, Mohammadi-Ivatloo B. A stochastic optimal scheduling of multi-microgrid systems considering emissions: A chance constrained model. Journal of Cleaner Production. 2020;275:122965.
[25] Shi W, Xie X, Chu CC, Gadh R. Distributed optimal energy management in microgrids. IEEE Transactions on Smart Grid. 2014;6 (3):1137-46.
[26] Aghdam FH, Kalantari NT. Distributed energy resources and microgrid infrastructure. InMicrogrid Architectures, Control and Protection Methods 2020 (pp. 69-86). Springer, Cham.
[27] Ghaemi S, Aghdam FH, Safari A, Farrokhifar M. Stochastic economic analysis of FACTS devices on contingent transmission networks using hybrid biogeography-based optimization. Electrical Engineering. 2019 ;101 (3):829-43.
[28] Arellano-Garcia H, Wozny G. Chance constrained optimization of process systems under uncertainty: I. Strict monotonicity. Computers & Chemical Engineering. 2009 Oct 14;33(10):1568-83.
[29] Su W, Wang J. Energy management systems in microgrid operations. The Electricity Journal. 2012 ;25 (8):45-60. [30] Fisher ML. The Lagrangian relaxation method for solving integer programming problems. Management science. 1981;27 (1):1-8.
[31] Geoffrion AM. Generalized benders decomposition. Journal of optimization theory and applications. 1972;10 (4):237- 60.
[32] Vanderbeck F, Savelsbergh MW. A generic view of Dantzig–Wolfe decomposition in mixed integer programming. Operations Research Letters. 2006 ;34 (3):296-306.
[33] Wang SJ, Shahidehpour SM, Kirschen DS, Mokhtari S, Irisarri GD. Short-term generation scheduling with transmission and environmental constraints using an augmented Lagrangian relaxation. IEEE Transactions on Power Systems. 1995;10 (3):1294-301.
[34] Cohen G. Auxiliary problem principle and decomposition of optimization problems. Journal of optimization Theory and Applications. 1980 ;32 (3):277-305.
[35] Hong M, Luo ZQ. On the linear convergence of the alternating direction method of multipliers. Mathematical Programming. 2017;162 (1-2):165-99.
[36] Boyd S, Parikh N, Chu E. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Publishers Inc; 2011.
[37] Höschle H, Le Cadre H, Smeers Y, Papavasiliou A, Belmans R. An ADMM-based method for computing risk-averse equilibrium in capacity markets. IEEE Transactions on Power Systems. 2018 ;33 (5):4819-30.