Secondary Control in Microgrids for Dynamic Power Sharing and Voltage/Frequency Adjustment

This paper discusses the operation of the central controller for a microgrid. The central controller is responsible to maintain the voltage and frequency of the microgrid within the acceptable ranges while the distributed energy resources supply the required power demand of the loads based on the droop control. The desired ratio among the output active power of the distributed energy resources is determined by the distribution network tertiary controller. To achieve a dynamic variation for the output active power ratios among the distributed energy resources, the central controller has to adjust the references of the rated active power and the droop control coefficients. As this may lead to unacceptable voltage and frequency in the microgrid, the central controller has to adjust the references of the voltage and frequency consequently. The proposed central controller strategy for the microgrid is validated through PSCAD/EMTDC simulations

Design and Implementation of a True Decentralized Autonomous Control Architecture for Microgrids

Microgrids can serve as an integral part of the future power distribution systems. Most microgrids are currently managed by centralized controllers. There are two major concerns associated with the centralized controllers. One is that the single controller can become performance and reliability bottleneck for the entire system and its failure can bring the entire system down. The second concern is the communication delays that can degrade the system performance. As a solution, a true decentralized control architecture for microgrids is developed and presented. Distributing the control functions to local agents decreases the possibility of network congestion, and leads to the mitigation of long distance transmission of critical commands. Decentralization will also enhance the reliability of the system since the single point of failure is eliminated. In the proposed architecture, primary and secondary microgrid controls layers are combined into one physical layer. Tertiary control is performed by the controller located at the grid point of connection. Each decentralized controller is responsible of multicasting its status and local measurements, creating a general awareness of the microgrid status among all decentralized controllers. The proof-of concept implementation provides a practical evidence of the successful mitigation of the drawback of control command transmission over the network. A Failure Management Unit comprises failure detection mechanisms and a recovery algorithm is proposed and applied to a microgrid case study. Coordination between controllers during the recovery period requires low-bandwidth communications, which has no significant overhead on the communication infrastructure. The proof-of-concept of the true decentralization of microgrid control architecture is implemented using Hardware-in-the-Loop platform. The test results show a robust detection and recovery outcome during a system failure. System test results show the robustness of the proposed architecture for microgrid energy management and control scenarios

Different Power Sharing Techniques for Converter-Interfaced DERs in an Autonomous Microgrid

A three-level hierarchical control system is considered for microgrids. The microgrid central controller receives the desired ratio for the output power of the available distributed generation units (DG) from the network tertiary controller. It then passes this information to the primary controller of each DG in the form of setpoints. In addition, the central controller receives some information from the DGs or the network and considers them to adjust the setpoints for the DGs. In this paper, the effect of the data transfer delays in the communication system of future microgrids is investigated on the dynamic operation of the distributed energy resources

Coordinated Control of Energy Storage in Networked Microgrids under Unpredicted Load Demands

In this paper a nonlinear control design for power balancing in networked microgrids using energy storage devices is presented. Each microgrid is considered to be interfaced to the distribution feeder though a solid-state transformer (SST). The internal duty cycle based controllers of each SST ensures stable regulation of power commands during normal operation. But problem arises when a sudden change in load or generation occurs in any microgrid in a completely unpredicted way in between the time instants at which the SSTs receive their power setpoints. In such a case, the energy storage unit in that microgrid must produce or absorb the deficit power. The challenge lies in designing a suitable regulator for this purpose owing to the nonlinearity of the battery model and its coupling with the nonlinear SST dynamics. We design an input-output linearization based controller, and show that it guarantees closed-loop stability via a cascade connection with the SST model. The design is also extended to the case when multiple SSTs must coordinate their individual storage controllers to assist a given SST whose storage capacity is insufficient to serve the unpredicted load. The design is verified using the IEEE 34-bus distribution system with nine SST-driven microgrids.Comment: 8 pages, 10 figure

Dynamic modeling, stability analysis and control of interconnected microgrids:A review

This paper reviews concepts of interconnected microgrids (IMGs) as well as compare and classify their modeling, stability analysis, and control methods. To develop benefits of isolated microgrids (MGs) such as reliability improvement and their renewable energy integration, they should be interconnected, share power, support the voltage/frequency of overloaded MGs, etc. Despite maximizing their benefits and decreasing weaknesses of isolated MGs, IMGs require maintaining stability in different operation modes and employing appropriate control methods. Moreover, a basic requirement for stability analysis and controller design is system modeling. Since many articles have addressed these topics on IMGs from different views, a comparison is necessary. Therefore, IMG dynamic modeling methods are classified and their main features and challenges are discussed. Then, stability analysis and control methods of IMGs are reviewed and compared. The provided review is supported by conceptual diagrams, classification tables, off-line and real-time simulations using MATLAB and OPAL-RT simulator for comparison. Furthermore, a data set is provided to study fundamentals as well as research gaps, which are addressed for future works

Centralized and Decentralized control of Microgrids

ABSTRACT Microgrid can be seen as an important controllable sub-system in future power systems. As a part of distribution network, the microgrid can operate in grid-connected or islanded mode to supply its local loads, and it consists of different renewable and non-renewable distribution generations that are connected to the system through power electronics (PE) interfaces. However, the control of microgrids is one of the important issues to focus on in order to overcome the challenges raised by high penetration of of renewable energy sources (RES). Depending on the responsibilities assumed by the different control levels, the microgrid can be controlled in centralized or decentralized modes. In centralized approach, the microgrid central controller (MGCC) is mainly responsible for the maximization of the microgrid value and optinization of its operation, and the MGCC determines the amount of power that the microgrid should import or export from the upstream distribution system by optimizing the local production or consumption capabilities. However, the MGCC should always consider the market prices of electricity, grid security concerns and ancillary services requested by the DSO when taking decisions. In this case an optimized operating scenario is realized by controlling the microsources and controllable loads within the microgrid, where non-critical, flexible loads can be shed, when profitable. Furthermore, the actual active and reactive power of the components are monitored. When a full decentralized control is implemented, the Management Center (MC) takes responsibilities and it competes or collaborates to optimize the production, satisfy the demand and provide the maximum possible export to the grid but all is done by considering the real time market prices. This thesis discusses the concepts of centralized and decentralized control of MG, where the main chapters introduce different control methods and PE interfaces that are involved in the microgrid control, while the final work presents simulation models that demonstrate how microgrids are controlled through inverters and the results. Using MATLAB/Simulink environment, PQ and V/f control modes of inverter are simulated and the results are discussed to point out their significant effect on balancing the voltage magnitude, maintaining the frequency and power sharing

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted