CONTROL STRATEGIES OF DC MICROGRID TO ENABLE A MORE WIDE-SCALE ADOPTION

Microgrids are gaining popularity in part for their ability to support increased penetration of distributed renewable energy sources, aiming to meet energy demand and overcome global warming concerns. DC microgrid, though appears promising, introduces many challenges in the design of control systems in order to ensure a reliable, secure and economical operation. To enable a wider adoption of DC microgrid, this dissertation examines to combine the characteristics and advantages of model predictive control (MPC) and distributed droop control into a hierarchy and fully autonomous control of the DC microgrid. In addition, new maximum power point tracking technique (MPPT) for solar power and active power decoupling technique for the inverter are presented to improve the efficiency and reliability of the DC microgrid. With the purpose of eliminating the oscillation around the maximum power point (MPP), an improved MPPT technique was proposed by adding a steady state MPP determination algorithm after the adaptive perturb and observe method. This control method is proved independent with the environmental conditions and has much smaller oscillations around the MPP compared to existing ones. Therefore, it helps increase the energy harvest efficiency of the DC microgrid with less continuous DC power ripple. A novel hierarchy strategy consisting of two control loops is proposed to the DC microgrid in study, which is composed of two PV boost converters, two battery bi-directional converters and one multi-level packed-u-cell inverter with grid connected. The primary loop task is the control of each energy unit in the DC microgrid based on model predictive current control. Compared with traditional PI controllers, MPC speeds up the control loop since it predicts error before the switching signal is applied to the converter. It is also free of tuning through the minimization of a flexible user-defined cost function. Thus, the proposed primary loop enables the system to be expandable by adding additional energy generation units without affecting the existing ones. Moreover, the maximum power point tracking and battery energy management of each energy unit are included in this loop. The proposed MPC also achieves unity power factor, low grid current total harmonics distortion. The secondary loop based on the proposed autonomous droop control identifies the operation modes for each converter: current source converter (CSC) or voltage source converter (VSC). To reduce the dependence on the high bandwidth communication line, the DC bus voltage is utilized as the trigger signal to the change of operation modes. With the sacrifice of small variations of bus voltage, a fully autonomous control can be realized. The proposed distributed droop control of different unit converters also eliminates the potential conflicts when more than two converters compete for the VSC mode. Single-phase inverter systems in the DC microgrid have low frequency power ripple, which adversely affects the system reliability and performance. A power decoupling circuit based on the proposed dual buck converters are proposed to address the challenges. The topology is free of shoot-through and deadtime concern and the control is independent with that of the main power stage circuit, which makes the design simpler and more reliable. Moreover, the design of both PI and MPC controllers are discussed and compared. While, both methods present satisfied decoupling performances on the system, the proposed MPC is simpler to be implemented. In conclusion, the DC microgrid may be more widely adopted in the future with the proposed control strategies to address the current challenges that hinder its further development

Distributed Apportioning in a Power Network for providing Demand Response Services

Greater penetration of Distributed Energy Resources (DERs) in power networks requires coordination strategies that allow for self-adjustment of contributions in a network of DERs, owing to variability in generation and demand. In this article, a distributed scheme is proposed that enables a DER in a network to arrive at viable power reference commands that satisfies the DERs local constraints on its generation and loads it has to service, while, the aggregated behavior of multiple DERs in the network and their respective loads meet the ancillary services demanded by the grid. The Net-load Management system for a single unit is referred to as the Local Inverter System (LIS) in this article . A distinguishing feature of the proposed consensus based solution is the distributed finite time termination of the algorithm that allows each LIS unit in the network to determine power reference commands in the presence of communication delays in a distributed manner. The proposed scheme allows prioritization of Renewable Energy Sources (RES) in the network and also enables auto-adjustment of contributions from LIS units with lower priority resources (non-RES). The methods are validated using hardware-in-the-loop simulations with Raspberry PI devices as distributed control units, implementing the proposed distributed algorithm and responsible for determining and dispatching realtime power reference commands to simulated power electronics interface emulating LIS units for demand response.Comment: 7 pages, 11 Figures, IEEE International Conference on Smart Grid Communication

Overview of AC microgrid controls with inverter-interfaced generations

Distributed generation (DG) is one of the key components of the emerging microgrid concept that enables renewable energy integration in a distribution network. In DG unit operation, inverters play a vital role in interfacing energy sources with the grid utility. An effective interfacing can successfully be accomplished by operating inverters with effective control techniques. This paper reviews and categorises different control methods (voltage and primary) for improving microgrid power quality, stability and power sharing approaches. In addition, the specific characteristics of microgrids are summarised to distinguish from distribution network control. Moreover, various control approaches including inner-loop controls and primary controls are compared according to their relative advantages and disadvantages. Finally, future research trends for microgrid control are discussed pointing out the research opportunities. This review paper will be a good basis for researchers working in microgrids and for industry to implement the ongoing research improvement in real systems

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency