Review of Power Sharing, Voltage Restoration and Stabilization Techniques in Hierarchical Controlled DC Microgrids

Abstract

In order to overcome the problem of power generation in distributed energy, microgrid(MG) emerges as an alternative scheme. Compared with the ac microgrids, the dc microgrids have the advantages of high system efficiency, good power quality, low cost, and simple control. However, due to the complexity of the distributed generation system, the conventional droop control shows the drawbacks of low current sharing accuracy. Therefore, the improved primary control methods to enhance current sharing accuracy are systematically reviewed, such as particle swarm optimization programming, probabilistic algorithm and voltage correction factor scheme. However, it is difficult to achieve stable and coordinated operation of the dc microgrids by relying on the primary control. Hence, the various secondary control approaches, such as dynamic current sharing scheme, muti-agent system (MAS) control and virtual voltage control methods have been summarized for voltage regulation. Furthermore, the energy management system (EMS), modular-based energy router (MBER) and other coordinated control methods are reviewed to achieve power management. Besides, various control methods to compensate the effect of communication delay are summarized. Moreover, linear matrix inequality (LMI), Lyapunov-Krasovskii functional stability and Takagi-Sugeno model prediction scheme can be adopted to eliminate the influence of communication delay. In addition, due to the constant power loads (CPL) exhibit negative impedance characteristics, which may result in the output oscillation of filter. Thus, various control approaches have been reviewed to match the impedance, such as the nonlinear disturbance observer (NDO) feedforward compensation method, linear programming algorithm, hybrid potential theory and linear system analysis of polyhedral uncertainty. The merits and drawbacks of those control strategies are compared in this paper. Finally, the future research trends of hierarchical control and stability in dc microgrids and dc microgrid clusters are also presented. INDEX TERMS DC microgrid, nonlinear droop control, multi-agent system, consensus control, communication delay, constant power load, hierarchical control.

Distributed Economic Dispatch for Islanded DC Microgrids Based on Finite-Time Consensus Protocol

“…Wherein, the decision-making objectives of the islanded DC MGs are to preserve its operational stability and generations-demands power balance in a low-cost way, namely, economic dispatch problem (EDP) [5]. Solving the EDP is of paramount importance for MGs as it defines the optimum operating point of the MG to reduce its total generation cost (TGC) and simultaneously meet both the equality (generation-demand balance) and inequality (production limits) constraints [6]. Many optimization techniques have been conducted to solve the EDP such as analytical algorithms (e.g., dynamic programming [7] and lambda iteration method [8]) and heuristic algorithms (e.g., genetic algorithm [9] and particle swarm algorithm [10]), which are employed in a centralized way.…”

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

“…In addition, it is assumed that the DG works at duty cycle mode as in [21], where the duty cycle mode is proved to be better than a load-following mode, and one-hour is proposed as a good trade-off between fuel consumption and start-up frequency. The DG power p DG is limited by the maximal DG supply power P DG_MAX , as given in Equation (9). Due to the slow dynamic behavior of the DG start-up stage, the DG cannot support the microgrid until it satisfies the conditions expressed by Equation (10), which can also prevent the converter from being broken by the peak power of the DG start-up stage.…”

“…3.2. Case 5, 6,7,8,9,10 Case 5, case 6, case 7, case 8, case 9, and case 10 happen when ∆p is negative or p L is less than its critical load. The start-time of SC recharging is decided by soc SC .…”

“…However, decentralized control has an intrinsic advantage in flexibility. An example of decentralized control is using the multi-agent system [8,9] consisting of many intelligent agents, which are composed of different architectures with different goals and communicate with each other to reach global goals [10]. As a classic control method, centralized control can make a power supply system comply with the design in detail [11,12].…”

Distributed Control Strategies for Microgrids: An Overview

“…The review presented in [25] focuses on a wider range of topics related to DC-MGs, such as protection systems, plug and play issues and MG topologies (including multimicrogrid DC clusters). Another review, the work in [26], presents an overview of control strategies for DC-MGs and DC multi-microgrid clusters. This paper reviews communication issues in the controllers used for voltage restoration and power management.…”