TY - JOUR
T1 - Dynamic Power Distribution System Management with a Locally Connected Communication Network
AU - Zhang, Kaiqing
AU - Shi, Wei
AU - Zhu, Hao
AU - Dallanese, Emiliano
AU - Basar, Tamer
N1 - Funding Information:
Manuscript received October 15, 2017; revised February 25, 2018; accepted April 11, 2018. Date of publication May 16, 2018; date of current version July 27, 2018. The work of K. Zhang and H. Zhu was supported in part by the National Science Foundation under Award ECCS-1610732 and in part by the Power Systems Engineering Research Center (PSERC) Project S-70. The work of E. Dall’Anese was supported by the Laboratory Directed Research and Development (LDRD) Program at the National Renewable Energy Laboratory (NREL). NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy operated by the Alliance for Sustainable Energy, LLC. The guest editor coordinating the review of this paper and approving it for publication was Prof. Deepa Kundur. (Corresponding author: Kaiqing Zhang.) K. Zhang and T. Bas¸ar are with the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Champaign, IL 61820 USA (e-mail:, kzhang66@illinois.edu; basar1@illinois.edu).
Publisher Copyright:
© 2007-2012 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - Coordinated optimization and control of distribution-level assets enables a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitates distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, and line losses, to be close to a desired profile. By and large, the performance of the DSM algorithms has been challenged by two factors: 1) the possibly nonstrongly connected communication network over DERs that hinders the coordination; and 2) the dynamics of the real system caused by the DERs with heterogeneous capabilities, time-varying operating conditions, and real-time measurement mismatches. In this paper, we investigate the modeling and algorithm design and analysis with the consideration of these two factors. In particular, a game-theoretic characterization is first proposed to account for a locally connected communication network over DERs, along with the analysis of the existence and uniqueness of the Nash equilibrium therein. To achieve the equilibrium in a distributed fashion, a projected-gradient-based asynchronous DSM algorithm is then advocated. The algorithm performance, including the convergence speed and the tracking error, is analytically guaranteed under the dynamic setting. Extensive numerical tests on both synthetic and realistic cases corroborate the analytical results derived.
AB - Coordinated optimization and control of distribution-level assets enables a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitates distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, and line losses, to be close to a desired profile. By and large, the performance of the DSM algorithms has been challenged by two factors: 1) the possibly nonstrongly connected communication network over DERs that hinders the coordination; and 2) the dynamics of the real system caused by the DERs with heterogeneous capabilities, time-varying operating conditions, and real-time measurement mismatches. In this paper, we investigate the modeling and algorithm design and analysis with the consideration of these two factors. In particular, a game-theoretic characterization is first proposed to account for a locally connected communication network over DERs, along with the analysis of the existence and uniqueness of the Nash equilibrium therein. To achieve the equilibrium in a distributed fashion, a projected-gradient-based asynchronous DSM algorithm is then advocated. The algorithm performance, including the convergence speed and the tracking error, is analytically guaranteed under the dynamic setting. Extensive numerical tests on both synthetic and realistic cases corroborate the analytical results derived.
KW - Distribution system management
KW - asynchronous algorithm
KW - communication
KW - distributed algorithm
KW - equilibrium learning
KW - game theoretical control
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U2 - 10.1109/JSTSP.2018.2837338
DO - 10.1109/JSTSP.2018.2837338
M3 - Article
AN - SCOPUS:85047010864
SN - 1932-4553
VL - 12
SP - 673
EP - 687
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 4
M1 - 8360012
ER -