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研究生: 吳家維
Chia-Wei Wu
論文名稱: 基於交替方向乘子法的分散式頻率負載控制
Decentralized Frequency-Based Load Control by Alternating Direction Method of Multipliers
指導教授: 林士駿
Shih-Chun Lin
口試委員: 張縱輝
Tsung-Hui Chang
鍾偉和
Wei-Ho Chung
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 40
中文關鍵詞: 頻率控制附載控制需求端電源管理交替方向乘子法
外文關鍵詞: frequency control, load control, demand side management, alternating direction method of multipliers
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  • 本論文探討智慧電網的分散式需求響應問題,以達到電力供需平衡及調節供電頻率。在現存的分散式負載控制方法中,每個使用者通常需要互相通訊,進行交換訊息,最近的研究結果表明基於供電頻率的相關估測能達成全分散式的負載控制。然而現存的方法中限制了最佳化的目標式需要為強凸並且連續二次可微的函數。基於更廣泛的應用,本篇論文提出一個基於交替方向乘子法(proximalJacobi Alternating Direction Multipliers Method, ADMM)的負載控制演算法。此全分散式演算法可應用於目標式為任意凸函數,此外,本論文更延伸此方法至異步設置(asynchronous setup)。異步演算法可以分散負載避免其過度的同步更新,故其特性相當適用於負載數量很大的情況。模擬結果證明了我們提出的演算法即使在有雜訊干擾的情況,同步及異步演算法皆可以有效的達到頻率穩定的效果。


    In this paper, we consider a distributed load control problem for achieving supply-demand balance and frequency regulation in a power system. While most of the distributed load control schemes require the loads to exchange in- formation through two-way communications, recent results have shown that it is possible to achieve fully distributed control by frequency-based imbalance estimation. However, the existing methods need the load disutility functions or the sum of them to be strongly convex. For a wider range of application scenarios, we propose a new load control algorithm based on the proximal Ja- cobi alternating direction method of multipliers (PJ-ADMM). The proposed algorithm works for arbitrary convex disutility functions. Moreover, we ex- tend the PJ-ADMM based load control algorithm to an asynchronous setup.
    Asynchronous updates can spare the loads from strict synchronization and are particularly useful when the number of loads is large. Simulation results are presented to show that the proposed algorithms regulate the frequency to the nominal value well, in both synchronous and asynchronous scenarios and with and without imbalance estimation errors.

    1 Background 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Models and Problem Formulation 6 2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Dynamic Model . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Imbalance Estimation . . . . . . . . . . . . . . . . . . 9 2.3 The Load Control Optimization Problem . . . . . . . . . . . . 10 3 Existing Methods 12 3.1 Dual Subgradient (DS) Method . . . . . . . . . . . . . . . . . 12 3.1.1 Input Reconstructor . . . . . . . . . . . . . . . . . . . 14 3.2 Decentralized Multi-block ADMM Method . . . . . . . . . . . 16 4 Proposed Methods 19 4.1 PJ-ADMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Synchronous PJ-ADMM-based Method . . . . . . . . . . . . . 22 4.3 Asynchronous PJ-ADMM-based Method . . . . . . . . . . . . 25 5 Simulation Results 27 5.1 Simulation Setting . . . . . . . . . . . . . . . . . . . . . . . . 27 5.2 Load control performance with perfect imbalance estimation . 30 5.3 Load control performance in the presence of imbalance esti- mation error . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 6 Conclusions and Future Directions 36 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 6.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . 37

    [1] C. Zhao, U. Topcu, and S. Low, "Frequency-based load control in power
    systems," in Amer. Control Conf. (ACC), Montreal, QC, Canada.
    [2] A.Molina-Garcia, F.Bou ard, and D.S.Kirschen, "Decentralized
    demand-side contribution to primary frequency control," IEEE Trans.
    on Power Syst., vol. 26, no. 1, pp. 411-419, February 2001.
    [3] D.Callaway and I.Hiskens, "Achieving controllability of electric loads,"
    Proc. IEEE, vol. 99, no. 1, pp. 184-199, January 2011.
    [4] B. Kirby, Spinning Reserve From Responsive Loads. Oak Ridge Na-
    tional Laboratory, 2003.
    [5] Z. Tan, P. Yang, and A. Nehorai, "An optimal and distributed demand
    response strategy with electric vehicles in the smart grid," IEEE Trans.
    on Smart Grid, vol. 5, no. 2, pp. 861-869, March 2014.
    [6] V. Balijepalli, V. Pradhan, S.A.Khaparde, and R.M.Shereef, "Review
    of demand response under smart grid paradigm," in Proc. IEEE Power
    Energy Syst. Innov. Smart Grid Technol. Conf.
    [7] P. Palensky and D. Dietrich, "Demand side management: Demand re-
    sponse, intelligent energy systems, and smart loads," IEEE Trans. on
    Industrial Informatics, vol. 7, no. 3, pp. 381-388, August 2011.
    [8] N. Gatsis and G. B. Giannakis, "Residential load control: Distributed
    scheduling and convergence with lost AMI messages," IEEE Trans.
    Smart Grid, vol. 2, no. 3, pp. 1-17, Feb. 2012.
    [9] Y.Guo, M.Pan, and P.P.Khargonekar, "Decentralized coordination of
    energy utilization for residential households in the smart grid," IEEE
    Trans. on Smart Grid, vol. 4, no. 3, pp. 1341-1350, September 2013.
    [10] P.Yang, P.Chavali, E.Gilboa, and A.Nehorai, "Parallel load schedule op-
    timization with renewable distributed generators in smart grids," IEEE
    Trans. on Smart Grid, vol. 4, no. 3, pp. 1431-1441, September 2013.
    [11] I.Atzeni, L.G.Ordonez, G.Scutari, D.P.Palomar, and J.R.Fonollosa,
    "Demand-side management via distributed energy generation and stor-
    age optimization," IEEE Trans. on Smart Grid, vol. 4, no. 2, pp. 866-
    876, June 2013.
    [12] C. Zhao, U. Topcu, and S. Low, "Optimal load control via frequency
    measurement and neighborhood area communication," IEEE Trans. on
    POWER SYSTEMS, vol. 28, no. 4, pp. 3576-3587, November 2013.
    [13] P. Dimitriou, T. Leber, N. Nagele, L. Temmel, and C. Tessarek, "Volt-
    age and frequency measuring plug: As part of smart grids metering sys-
    tem," in Smart Grid Communications (SmartGridComm), 2014 IEEE
    International Conference on. IEEE, 2014, pp. 350-355.
    [14] J. Brooks, W. Hager, and J. Zhu, "A Decentralized Multi-block ADMM
    for Demand-side Primary Frequency Control using Local Frequency
    Measurements," ArXiv e-prints, Sep. 2015.
    [15] W. Deng, M.-J. Lai, Z. Peng, and W. Yin, "Parallel Multi-Block ADMM
    with o(1/k) Convergence," ArXiv e-prints, Dec. 2013.
    [16] K. J. Astrom and R. M. Murray, Feedback Systems:An Introduction for
    Scientists and Engineers, 2009.
    [17] A. Rubtsov, "Approach to stochastic modeling of power systems," Sci-
    enti c Journal of Riga Technical University, vol. 27, no. 4, 2010.
    [18] S. Gillijns and B. D. Moor, "Unbiased minimum-variance input and
    state estimation for linear discrete-time systems," Automatica, vol. 43,
    no. 1, pp. 111-116, 2007.
    [19] D. Bertsekas and J. Tsitsiklis, Parallel and Distributed Computation.
    Upper Saddle River, NJ: Prentice Hall, 1989.
    [20] S. Boyd and L. Vandenberghe, Convex Optimization. U.K.: Cambridge
    University Press, 2004.
    [21] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Echstein, Distributed
    Optimization and Statistical Learning via Alternating Direction Method
    of Multipliers. Foundations and Trends in Machine Learning, 2011.
    [22] T. H. Chang, "A Proximal Dual Consensus ADMM Method for Multi-
    Agent Constrained Optimization," ArXiv e-prints, Sep. 2014.
    [23] C. Zhao, U. Topcu, and S. Low, "Swing dynamics as primal-dual algo-
    rithm for optimal load control," in Smart Grid Communications (Smart-
    GridComm), 2012 IEEE Third International Conference on. IEEE,
    2012, pp. 570-575.
    [24] S. Hosseini, A. Chapman, and M. Mesbahi, "Online distributed admm
    on networks : Social regret, network e ect, and condition measures,"
    arXiv preprint arXiv:1412.7116, 2014.

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