Decentralized DC Microgrid Monitoring and Optimization via Primary Control Perturbations

Abstract

We treat the emerging power systems with direct current (DC) MicroGrids, characterized with high penetration of power electronic converters. We rely on the power electronics to propose a decentralized solution for autonomous learning of and adaptation to the operating conditions of the DC Mirogrids; the goal is to eliminate the need to rely on an external communication system for such purpose. The solution works within the primary droop control loops and uses only local bus voltage measurements. Each controller is able to estimate (i) the generation capacities of power sources, (ii) the load demands, and (iii) the conductances of the distribution lines. To define a well-conditioned estimation problem, we employ decentralized strategy where the primary droop controllers temporarily switch between operating points in a coordinated manner, following amplitude-modulated training sequences. We study the use of the estimator in a decentralized solution of the Optimal Economic Dispatch problem. The evaluations confirm the usefulness of the proposed solution for autonomous MicroGrid operation.The proposed solution is illustrated in Fig. 1. We consider a generic DC MG model with multiple buses, described in Sections III and IV. We assume that the MG does not have access to reliable external communication resources. The physical state of DC MGs is characterized by the steady state bus voltages. We introduce a parameter vector θ that collects all system variables whose values are determined by exogenous influences; this includes the generation capacities of the DERs, the load demands and the distribution network topology, i.e., the conductance matrix, see Section IV-A. Using the power balance equation, we represent the bus voltages thorough a non-linear and implicit model, parametrized by θ, see Section III-B. Evidently, θ varies with time; to respond to its variations on different time scales, the DC MG is governed by a hierarchical control system, comprising primary and upper control layer. The primary control is decentralized: several controllers regulate the bus voltages, using only local feedbacks without exchanging any information with peer controllers. They are very fast and capable of responding to high frequency variations in θ. Popular primary controller in DC MGs is the Voltage Source Converter (VSC) with voltage droop control, which is reminiscent to the widespread frequency droop control in AC systems, but defined over the DC voltage; it is therefore standard practice to refer to it simply as droop controller [2], [3]. The upper control layer, on the other hand, responds to less frequent changes in θ that affect the global behavior of the system; examples include changes of the load/generation profile, faults, attacks, etc. Its main role is to adapt the system to the new conditions by computing updated optimal control references for the primary controllers; all upper layer control applications require full/partial knowledge of θ to determine the control references that adequately reflect the new conditions [15]...

Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review

“…According to [28][29][30], the economic dispatch of DC-based dispatchable DG units was achieved based on linearized models of the MG, while an optimal control problem was formulated to enhance common bus voltage regulation by tuning controller parameters on one hand [27,[31][32][33] and by achieving optimal storage cycle on the other [34,35]. The DC droop settings were selected based on the economic criteria in [36] and the environmentally oriented objectives in [37].…”

“…The operation of tertiary control usually involves collecting data and measurements and then using them in an optimization process to yield the optimum setting for the MG-controllable components. This complete strategy, also referred to as EMS, aims to achieve the desired objectives such as optimal droop coefficients for an environmentally friendly DG unit operation [84,85]; secondary PI controller parameter selection to preserve MG stability [60,71]; efficient charge and discharge of BESS [86][87][88]; contingency reserve management [89][90][91][92]; and economic dispatch of DG units [28,93].…”

“…Control problems combined with dispatch and scheduling problems form the bases for optimal EMS problems, which are responsible for implementing the hierarchal control strategy of DCIMG. Therefore, solving an EMS problem acts as a buffer to serve a variety of technical [33,35,54,59,85,91,92,117,134,148,154,[163][164][165][166], economic [28][29][30][75][76][77][78][79][80][81]84,85,91,[124][125][126][127][128][129][130][131][132][133]135,144,145,[148][149][150][151]167,168], and environmental [84,…”

“…This is attributed to the fact that most DCIMG without direct access to the grid must have some form of dispatchable diesel or gas DG units. Fuel cost minimization could be the sole purpose of the EDP in AC microgrids [95,122,136,143,173], or this purpose could be extended to cover DC microgrids EDP as well [28][29][30]. Additionally, it could be combined with environmental objectives as in [84,85,89,121,146,147] or with technical objectives as in [106,148,153].…”

“…A global variable found in every dispatch and scheduling problem is the power output of dispatchable DG units. Depending on the purpose of the dispatch problem, the active [28][29][30][75][76][77][78][79][80][81]86,90,93,117,128,129,[132][133][134][135][136]138,140,141,[143][144][145][146][149][150][151][152]158,161,[163][164][165]171] and reactive [89,109,110,125,130,137,142,147,155,160] power output of dispatchable units has to be adjusted between the min-max range. Alternatively, in the DG allocation problem, the...…”