Fully Distributed Controller for Economic Load Sharing of DC Microgrid Clusters

This paper addresses a fully distributed hierarchical coordination controller, which minimizes the global total generation cost (TGC) for a cluster of DC microgrids (MGs) through handling the optimal power-sharing between MGs. It consists of local and global tertiary controllers, in which a two-layer communication network is modeled to share information with a reduced complexity as one or some distributed generators (DGs) of each MG are pinned from the local network to broadcast the information between MGs at the global network. Under this control structure, the global tertiary controllers, based on dynamic consensus technique, generate the global optimum incremental cost (IC) reference to attain the entire cluster's economic operation. Moreover, using pinning-based leader-follower consensus algorithm, the local tertiary controllers assign MG average voltage reference at which the ICs of all DGs are matched at the global reference to ensure optimal operation of the cluster with considering DGs generation boundaries. Finally, the nominal voltage set points of each DG can be determined by the secondary controller to guarantee the generation-demand power balance. The feasibility of the proposed controller is demonstrated by simulation and experiment under various test scenarios.

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

This work accentuates on using unified interphase power controller (UIPC) to control power flow between clusters of multiple microgrids in a hybrid microgrid. The clusters of microgrids are interconnected through the UIPC, which is able to provide bidirectional power flow between the clusters. A new optimal fractional order-based control strategy is described for UIPC control. Simulation results are provided for power exchange control performance between clusters of AC and DC microgrids as well as with the main power grid.

Power management scheme of DC micro-grid integrated with photovoltaic - Battery - Micro hydro power plant

This paper proposes a bidirectional virtual inertial control (BVIC) strategy for an islanded hybrid AC/DC microgrid (MG) including two renewable-energy power-generation systems and an energy-storage system (ESS). The studied hybrid AC/DC MG consists of a wind farm, a PV farm, a bidirectional DC-DC converter, and a bidirectional interlinking converter (BIC). The vanadium redox flow battery (VRFB)-based ESS is connected to the DC MG through a bidirectional DC-DC converter, while the DC MG is connected to the AC MG through the BIC. On the basis of the virtual synchronous generator, this paper uses the BVIC strategy based on BIC to achieve power distribution between the DC and AC MGs, suppress the deviations of both DC-link voltage and frequency of the AC side, and compare the differences for the studied system with and without the BVIC. The steady-state frequency-domain analysis of the studied system under different operating conditions is performed. Both dynamic and transient time-domain simulations of the studied system under various disturbance conditions are also carried out.

The concept of microgrid (MG) in the field of power engineering rapidly adapted for the rapid improvement in power electronics technology. Due to the benefit of such a distribution system with direct current (DC) minimum losses and simple to integrate with an electrical energy storage system DC MG have attracted more attention these days. DC MG is a hot research topic consists of various sources due to the expansion of distributed generation. The critical issue is to dispense regulated voltage support and accurate power-sharing capability in such a multisource DC MGs. Since the control scheme plays an essential role in establishing premier power quality and high efficiency of MG, a thorough review of trending control methodology in DC MGs is required. This paper explains the primary control methodology within the hierarchical control architecture for DC MGs.

Reliability and flexibility can be increased by connecting multiple nearby microgrids (MG) to form a cluster. This report provides a thorough and comparative analysis of previous research on the control mechanisms used by DC MG clusters. Various techniques are examined for the two important control features of networked DC MGs: power flow control between MGs, and DC-link voltage control. There is also dispute on the DC MG clusters’ architectural configuration. This study discusses all the benefits and drawbacks of the various control mechanisms used in previous studies. Additionally, three different consensus protocol types—linear, finite, and fixed—with varying time limitations are covered in this study. To support this research, continuous development of microgrids is being carried out.

With the large-scale application of microgrids (MGs), interconnecting nearby MGs to form an MG cluster (MGC) enables a higher utilization of renewable sources. This article presents a pinning-based hierarchical and distributed cooperative control strategy for AC MGC, which includes distributed generation (DG)-layer, MG-layer, and MGC-layer controls. The DG-layer control regulates the local voltage/current of each DG unit. The MG-layer control is performed for each individual MG, managing DG units in a cooperative manner through several sparse communication networks. By representing each MG as an MG agent, the MGC-layer control coordinates MGs based on a higher level peer-to-peer communication network among MG agents. The interaction between MG-layer and MGC-layer is established by pinning some DG units of each MG to communicate with the MG agent. Compared with the existing literature, the contributions of this article are: 1) simultaneously realizing multiple control objectives in the MGC system level including frequency/voltage regulation and active/reactive power sharing; 2) presenting a systematic approach to construct a unified small-signal dynamic model of the MGC system; and 3) performing a detailed small-signal stability analysis to evaluate the system dynamic performance. Time-domain simulation and experiments are carried out to validate the effectiveness of the proposed methods.

Coordinated power control with virtual inertia for fuel cell-based DC microgrids cluster

Interconnected microgrids (MGs) system is regarded as one of the future power system configurations, where individual MGs can support each other through interchanging energy. Specifically, MGs with surplus power can trade with those with deficit power. This paper presents a two-level hierarchical structure to manage the energy trading among MGs and also with the main grid. In lower level, individual MGs would operate as self-controlled entities; in upper level, an independent and nonprofit third party would coordinate the energy transaction among different MGs. To guarantee the fairness and autonomy, a local energy exchange market is established, in the presence of the main grid. Apart from the surplus or deficit energy, adjustable power is also incorporated in the individual MGs’ bidding strategies. The local trading prices are determined via the market clearing. We analyze the economic benefits of such an energy trading mechanism and also the value of adjustable power originally hidden in MGs. Numerical results demonstrate the effectiveness of the proposed framework for the energy trading among multiple MGs system.

Nowadays, cyber-attacks are immoral ways to disturb power grids and make them devitalized or unstable. To avoid such issues, detection and estimation of cyber-attacks are essential in modern power utilities. This paper investigates a new nonlinear Kalman filter, called Spherical Simplex Radial Cubature Kalman Filter (SSRCKF), as a reliable and accurate estimator to detect cyber-attacks in nonlinear Direct Current (DC) microgrids (MGs) with Constant Power Loads (CPLs). The developed nonlinear Kalman filter offers a high resilient State Estimation (SE) performance in presence of noise as well as desired accuracy, and low computational burden. The SSRCKF is modified for the attack detection issue by augmenting the occurring attacks to the original nonlinear dynamics. The developed SSRCKF approach is deployed to estimate the DC MG states in the presence of Denial-of-Service (DoS) and data integrity attacks. Simulation results show that the presented filter estimates the DC MGs states of the system immediately after the attack.

Due to improved efficiency, reduced conversion stages, integration of distributed generators (DGs), and increased use of DC loads, DC microgrids (MGs) are earning extensive approval in modern power grids. Islanding detection is a critical issue for both AC and DC MGs. If the detection technique fails, it may lead to instability in the system, affecting the load-side devices and employees. Therefore, this article aims for the development of a novel passive islanding detection scheme (IDS) based on the cumulative sum of superimposed impedance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> ) for DC MGs. In the proposed IDS, voltage and current samples are collected at DGs terminals to analyze a DC MG response for an islanding condition. Superimposed impedance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SI</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> are calculated, and the variations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> are measured to detect islanding phenomena. The proposed passive IDS is simulated using MATLAB/Simulink environment, and the experimental validation is accomplished on a DC MG hardware setup. The obtained results depict that the proposed technique effectively discriminates islanding events from other nonislanding scenarios and acts effectively during islanding scenarios for radial and ring-type systems irrespective of the type and number of DGs present in the DC MGs. The effectiveness of the proposed scheme is also tested for both constant resistance and constant power loads.

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

Robust nonlinear control of regenerative fuel cell, supercapacitor, battery and wind based direct current microgrid

Discrete-time control for interconnected microgrids (MGs) is critical in promoting the energy flow balance among distributed generations (DGs) and loads due to the converters’ discrete system characteristic. We propose a two-layer discrete-time iterative cooperative (TDIC) framework for multiple dc MGs, in which a two-layer iterative voltage estimator is designed. Based on this, the TDIC strategy allows all slave-DGs’ current outputs and estimated voltage to track that of their respective master-DGs, which are then guided to achieve consensus current output ratio and reference voltage synchronization. As long as the sampling period of the lower-cyber layer is less than that of the upper-cyber layer, all DGs’ weighted average voltages can be regulated to the reference voltage, meanwhile the accurate current sharing can be realized within each MG and among multiple MGs. Compared with most continuous communication approaches, the designed control inputs, supported by intermittent communication across sparse two-layer cyber networks, are merely updated at the end of each round of discrete-time iteration, which can significantly reduce the communication pressure as well as ensure a faster convergence speed.