Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Analysis of a Queueing Model for Energy Storage Systems with Self-discharge

Published: 11 November 2020 Publication History
  • Get Citation Alerts
  • Abstract

    This article presents an analysis of a recently proposed queueing system model for energy storage with discharge. Even without a load, energy storage systems experience a reduction of the stored energy through self-discharge. In some storage technologies, the rate of self-discharge can exceed 50% of the stored energy per day. We consider a queueing model, referred to as leakage queue, where, in addition to an arrival and a service process, there is a leakage process that reduces the buffer content by a factor ɣ ( 0 < ɣ < 1) in each time slot. When the average drift is positive, we discover that the leakage queue operates in one of two regimes, each with distinct characteristics. In one of the regimes, the stored energy always stabilizes at a point that lies below the storage capacity, and the stored energy closely follows a Gaussian distribution. In the other regime, the storage system behaves similar to a conventional finite capacity system. For both regimes, we derive expressions for the probabilities of underflow and overflow. In particular, we develop a new martingale argument to estimate the probability of underflow in the second regime. The methods are validated in a numerical example where the energy supply resembles a wind energy source.

    References

    [1]
    C. J. Ancker Jr. and A. V. Gafarian. 1963. Some queuing problems with balking and reneging, I. Oper. Res. 11, 1 (Jan./Feb. 1963), 88--100.
    [2]
    O. Ardakanian, S. Keshav, and C. Rosenberg. 2012. On the use of teletraffic theory in power distribution systems. In Proceedings of the 3rd International Conference on Energy-efficient Computing and Networking (ACM e-Energy’12). 1--10.
    [3]
    S. Athuraliya, S. H. Low, V. Li, and Q. Yin. 2001. REM: Active queue management. IEEE Netw. 15, 3 (2001), 48--53.
    [4]
    A. Azzalini. 2013. The Skew-normal and Related Families. Cambridge University Press.
    [5]
    F. Baccelli. 1986. Exponential martingales and Wald’s formulas for two-queue networks. J. Appl. Probab. 23, 3 (1986), 812--819.
    [6]
    F. Baccelli, P. Boyer, and G. Hebuterne. 1984. Single-server queues with impatient customers. Adv. Appl. Probab. 16, 4 (1984), 887--905.
    [7]
    A. . Bahaj, L. Myers, and P. A. B. James. 2007. Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings. Energy Build. 39, 2 (2007), 154--165.
    [8]
    N. Barjesteh. 2013. Duality Relations in Finite Queueing Models. Master’s Thesis. University of Waterloo, Canada. Retrieved from http://hdl.handle.net/10012/7715.
    [9]
    O. Boxma and B. Zwart. 2018. Fluid flow models in performance analysis. Comput. Commun. 131 (2018), 22--25.
    [10]
    R. Chedid, H. Akiki, and S. Rahman. 1998. A decision support technique for the design of hybrid solar-wind power systems. IEEE Trans. Energy Convers. 13, 1 (1998), 76--83.
    [11]
    M. Chen and G. A. Rincon-Mora. 2006. Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans. Energy Convers. 21, 2 (2006), 504--511.
    [12]
    F. Ciucu, F. Poloczek, and A. Rizk. 2019. Queue and loss distributions in finite-buffer queues. Proc. ACM Meas. Anal. Comput. Syst. 3, 2 (June 2019), 31:1–31:29.
    [13]
    J. W. Cohen. 1969. The Single Server Queue. North-Holland.
    [14]
    R. L. Cruz and H. N. Liu. 1993. Single server queues with loss: A formulation. In Proceedings of the Conference on Information Sciences and Systems (CISS’93). John Hopkins University.
    [15]
    A. Dekka, R. Ghaffari, B. Venkatesh, and B. Wu. 2015. A survey on energy storage technologies in power systems. In Proceedings of the IEEE Electrical Power and Energy Conference (EPEC’15). 105--111.
    [16]
    K. C. Divya and J. Østergaard. 2009. Battery energy storage technology for power systems—An overview. Electr. Pow. Syst. Res. 79, 4 (2009), 511--520.
    [17]
    N. Duffield. 1994. Exponential bounds for queues with Markovian arrivals. Queue. Syst. 17, 3–4 (1994), 413--430.
    [18]
    R. Durrett. 2010. Probability: Theory and Examples (4th Edition). Cambridge University Press.
    [19]
    S. Floyd and V. Jacobson. 1993. Random early detection gateways for congestion avoidance. IEEE/ACM Trans. Netw. 1, 4 (1993), 397--413.
    [20]
    D. Fooladivanda, G. Mancini, S. Garg, and C. Rosenberg. 2014. State of charge evolution equations for flywheels. CoRR abs/1411.1680 (Nov. 2014).
    [21]
    D. Fooladivanda, C. Rosenberg, and S. Garg. 2016. Energy storage and regulation: An analysis. IEEE Trans. Smart Grid 7, 4 (2016), 1813--1823.
    [22]
    L. Gelazanskas and K. Gamage. 2014. Demand side management in smart grid: A review and proposals for future direction. Sustain. Cities Society 11 (2014), 22--30.
    [23]
    E. Gelenbe, P. Glynn, and K. Sigman. 1991. Queues with negative arrivals. J. Appl. Probab. 28, 1 (1991), 245--250.
    [24]
    Y. Ghiassi-Farrokhfal, S. Keshav, and C. Rosenberg. 2015. Toward a realistic performance analysis of storage systems in smart grids. IEEE Trans. Smart Grid1 (2015), 402--410.
    [25]
    Y. Ghiassi-Farrokhfal, C. Rosenberg, S. Keshav, and M. B. Adjaho. 2016. Joint optimal design and operation of hybrid energy storage systems. IEEE J. Select. Areas Commun. 34, 3 (Mar. 2016), 639--650.
    [26]
    P. G. Harrison and E. Pitel. 1996. The M/G/1 queue with negative customers. Adv. Appl. Probab. 28, 2 (June 1996), 540--566.
    [27]
    H. Ibrahim, A. Ilinca, and J. Perron. 2008. Energy storage systems–characteristics and comparisons. Renew. Sustain. Energy Rev. 12, 5 (2008), 1221--1250.
    [28]
    D.-M. Chiu, R. Jain. 1989. Analysis of the increase and decrease algorithms for congestion avoidance in computer networks. Comput. Netw.orks and ISDN Syst. 17, 1 (1989), 1--14.
    [29]
    G. L. Jones, P. G. Harrison, U. Harder, and T. Field. 2011. Fluid queue models of battery life. In Proceedings of the IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS’11). 278--285.
    [30]
    F. Kazhamiaka, C. Rosenberg, S. Keshav, and K.-H. Pettinger. 2016. Li-ion storage models for energy system optimization: The accuracy-tractability tradeoff. In Proceedings of the 7th International Conference on Future Energy Systems (ACM e-Energy’16). 17:1–17:12.
    [31]
    F. P. Kelly. 1991. Effective bandwidths at multi-class queues. Queue. Syst.ems 9, 1--2 (1991), 5--15.
    [32]
    J. F. C. Kingman. 1964. A martingale inequality in the theory of queues. Math. Proc. Cambr. Philos. Society 60, 2 (1964), 359--361.
    [33]
    H. Kobayashi and A. Konheim. 1977. Queueing models for computer communications system analysis. IEEE Trans. Commun. 25, 1 (1977), 2--29.
    [34]
    I. Koutsopoulos, V. Hatzi, and L. Tassiulas. 2011. Optimal energy storage control policies for the smart power grid. In Proceedings of the International Conference on Communications, Control, and Computing Technologies for Smart Grids (IEEE SmartGridComm’11). 475--480.
    [35]
    J. Y. Le Boudec and P. Thiran. 2001. Network Calculus (Lecture Notes in Computer Science, Vol. 2050). Springer Verlag.
    [36]
    J.-Y. Le Boudec and D.-C. Tomozei. 2012. A demand-response calculus with perfect batteries. In Proceedings of the 16th International GI/ITG Conference (MMB & DFT), Workshop on Network Calculus (WoNeCa). Springer Berlin, 273--287.
    [37]
    N. Li, L. Chen, and S. H. Low. 2011. Optimal demand response based on utility maximization in power networks. In Proceedings of the IEEE Power and Energy Society General Meeting. 1--8.
    [38]
    Z. Liu, I. Liu, S. Low, and A. Wierman. 2014. Pricing data center demand response. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. 111--123.
    [39]
    A.-H. Mohsenian-Rad, V. W. S. Wong, J. Jatskevich, R. Schober, and A. Leon-Garcia. 2010. Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans. Smart Grid 1, 3 (2010), 320--331.
    [40]
    National Renewable Energy Laboratory. 1992. National Solar Radiation Data Base, 1961–1990: Typical Meteorological Year 2. Retrieved from http://rredc.nrel.gov/solar/old_data/nsrdb/1961-1990/tmy2/.
    [41]
    National Renewable Energy Laboratory. 2017. System Advisor Model Version 2017.9.5 (SAM 2017.9.5). Retrieved from https://sam.nrel.gov/downloads.
    [42]
    M. A. Pedrasa, T. D. Spooner, and I. F. MacGill. 2010. Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans. Smart Grid 1, 2 (2010), 134--143.
    [43]
    R. M. Phatarfod. 1963. Application of methods in sequential analysis to dam theory. Ann. Math. Statist. 34, 4 (1963), 1588--1592.
    [44]
    A. Rizk, F. Poloczek, and F. Ciucu. 2015. Computable bounds in fork-join queueing systems. In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. 335--346.
    [45]
    S. M. Ross. 1974. Bounds on the delay distribution in GI/G/1 queues. J. Appl. Probab. 11, 2 (June 1974), 417--421.
    [46]
    Z. M. Salameh, M. A. Casacca, and W. A. Lynch. 1992. A mathematical model for lead-acid batteries. IEEE Trans. Energy Convers. 7, 1 (1992), 93--98.
    [47]
    P. Samadi, A.-H. Mohsenian-Rad, R. Schober, V. W. S. Wong, and J. Jatskevich. 2010. Optimal real-time pricing algorithm based on utility maximization for smart grid. In Proceedings of the 1st IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm’10). 415--420.
    [48]
    J. V. Seguro and T. W. Lambert. 2000. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J. Wind Eng. Industr. Aerody. 85, 1 (2000), 75--84.
    [49]
    S. Singla, Y. Ghiassi-Farrokhfal, and S. Keshav. 2014. Using storage to minimize carbon footprint of diesel generators for unreliable grids. IEEE Trans. Sustain. Energy 5, 4 (2014), 1270--1277.
    [50]
    H. I. Su and A. E. Gamal. 2013. Modeling and analysis of the role of energy storage for renewable integration: Power balancing. IEEE Trans. Power Syst. 28, 4 (2013), 4109--4117.
    [51]
    S. Sun, M. Dong, and B. Liang. 2014. Real-time power balancing in electric grids with distributed storage. IEEE J. Select. Topics Sig. Proc. 8, 6 (2014), 1167--1181.
    [52]
    S. Sun, B. Liang, M. Dong, and J. A. Taylor. 2016. Phase balancing using energy storage in power grids under uncertainty. IEEE Trans. Power Syst. 31, 5 (2016), 3891--3903.
    [53]
    Tesla Inc. 2019. Tesla Powerwall. Retrieved from https://www.tesla.com/powerwall.
    [54]
    C. Thrampoulidis, S. Bose, and B. Hassibi. 2016. Optimal placement of distributed energy storage in power networks. IEEE Trans. Automat. Contr. 61, 2 (2016), 416--429.
    [55]
    D. Wang, C. Ren, A. Sivasubramaniam, B. Urgaonkar, and H. Fathy. 2012. Energy storage in datacenters: What, where, and how much? In Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. 187--198.
    [56]
    K. Wang, F. Ciucu, C. Lin, and S. H. Low. 2012. A stochastic power network calculus for integrating renewable energy sources into the power grid. IEEE J. Select. Areas Commun. 30, 6 (2012), 1037--1048.
    [57]
    A. R. Ward. 2012. Asymptotic analysis of queueing systems with reneging: A survey of results for FIFO, single class models. Surv. Oper. Res. Manag. Sci. 17, 1 (2012), 1--14.
    [58]
    World Energy Council. 2016. Energy Efficiency Indicators. Retrieved from https://wec-indicators.enerdata.net/household-electricity-use.html.
    [59]
    K. Wu, Y. Jiang, and D. Marinakis. 2012. A stochastic calculus for network systems with renewable energy sources. In Proceedings of the IEEE INFOCOM Workshops. 109--114.
    [60]
    P. Yang and A. Nehorai. 2014. Joint optimization of hybrid energy storage and generation capacity with renewable energy. IEEE Trans. Smart Grid 5, 4 (2014), 1566--1574.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Transactions on Modeling and Performance Evaluation of Computing Systems
    ACM Transactions on Modeling and Performance Evaluation of Computing Systems  Volume 5, Issue 3
    September 2020
    130 pages
    ISSN:2376-3639
    EISSN:2376-3647
    DOI:10.1145/3403640
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 November 2020
    Accepted: 01 September 2020
    Revised: 01 July 2020
    Received: 01 November 2019
    Published in TOMPECS Volume 5, Issue 3

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Queueing theory
    2. energy storage
    3. exponential martingale

    Qualifiers

    • Research-article
    • Research
    • Refereed

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 174
      Total Downloads
    • Downloads (Last 12 months)54
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 11 Aug 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    Full Access

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media