Operation and Control of DC Microgrid

Power harnessing technology from the renewable energy resources has been developed over the past two decades. This technology enabled us to integrate renewable energy-based power generation to the conventional electric power grid. This study aims to improve the dynamic response and the load regulation using improved control strategies of the dc converters used to interface utility and renewable energy-based power generation. The power sharing between multiple dc microgrids/ac-dc microgrids is also investigated

Overview of Technical Challenges, Available Technologies and Ongoing Developments of AC/DC Microgrids

Gradual depletion of fossil fuel resources, poor energy efficiency of conventional power plants, and environmental pollution have led to a new grid architecture known as smart microgrid. The smart microgrid concept provides a promising solution that enables high penetration of distributed generation from renewable energy sources without requiring to redesign the distribution system, which results in stable operation during faults and disturbances. However, distributed generators/loads and interaction between all nodes within a microgrid will substantially increase the complexity of the power system operation, control, and communications. Many innovative techniques and technologies have been proposed to address the complexity and challenges of microgrids including power quality, power flow balancing, real‐time power management, voltage and frequency control, load sharing during islanding, protection, stability, reliability, efficiency, and economical operation. All key issues of the microgrids, different solutions, and available methods and technologies to address such issues are reviewed in this chapter. Pros and cons of each method are discussed. Furthermore, an extensive comprehensive review for researchers and scholars working on microgrid applications is provided in this chapter to help them identify the areas that need improvements and innovative solutions for increasing the efficiency of modern power distribution grid

Cooperative Strategies for Management of Power Quality Problems in Voltage-Source Converter-based Microgrids

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by emerging wireless communications technology, machine learning techniques, and power electronics. While some possible applications of the cooperative control theory to microgrids have been described in the research literature, a comprehensive survey of this approach with respect to its limitations and wide-ranging potential applications has not yet been provided. In this regard, an important area of research into microgrids is developing intelligent cooperative operating strategies within and between microgrids which implement and allocate tasks at the local level, and do not rely on centralized command and control structures. Multi-agent techniques are one focus of this research, but have not been applied to the full range of power quality problems in microgrids. The ability for microgrid control systems to manage harmonics, unbalance, flicker, and black start capability are some examples of applications yet to be fully exploited. During islanded operation, the normal buffer against disturbances and power imbalances provided by the main grid coupling is removed, this together with the reduced inertia of the microgrid (MG), makes power quality (PQ) management a critical control function. This research will investigate new cooperative control techniques for solving power quality problems in voltage source converter (VSC)-based AC microgrids. A set of specific power quality problems have been selected for the application focus, based on a survey of relevant published literature, international standards, and electricity utility regulations. The control problems which will be addressed are voltage regulation, unbalance load sharing, and flicker mitigation. The thesis introduces novel approaches based on multi-agent consensus problems and differential games. It was decided to exclude the management of harmonics, which is a more challenging issue, and is the focus of future research. Rather than using model-based engineering design for optimization of controller parameters, the thesis describes a novel technique for controller synthesis using off-policy reinforcement learning. The thesis also addresses the topic of communication and control system co-design. In this regard, stability of secondary voltage control considering communication time-delays will be addressed, while a performance-oriented approach to rate allocation using a novel solution method is described based on convex optimization

Virtual inertia for suppressing voltage oscillations and stability mechanisms in DC microgrids

Renewable energy sources (RES) are gradually penetrating power systems through power electronic converters (PECs), which greatly change the structure and operation characteristics of traditional power systems. The maturation of PECs has also laid a technical foundation for the development of DC microgrids (DC-MGs). The advantages of DC-MGs over AC systems make them an important access target for RES. Due to the multi-timescale characteristics and fast response of power electronics, the dynamic coupling of PEC control systems and the transient interaction between the PEC and the passive network are inevitable, which threatens the stable operation of DC-MGs. Therefore, this dissertation focuses on the study of stabilization control methods, the low-frequency oscillation (LFO) mechanism analysis of DC-MGs and the state-of-charge (SoC) imbalance problem of multi-parallel energy storage systems (ESS). Firstly, a virtual inertia and damping control (VIDC) strategy is proposed to enable bidirectional DC converters (BiCs) to damp voltage oscillations by using the energy stored in ESS to emulate inertia without modifications to system hardware. Both the inertia part and the damping part are modeled in the VIDC controller by analogy with DC machines. Simulation results verify that the proposed VIDC can improve the dynamic characteristics and stability in islanded DC-MG. Then, inertia droop control (IDC) strategies are proposed for BiC of ESS based on the comparison between conventional droop control and VIDC. A feedback analytical method is presented to comprehend stability mechanisms from multi-viewpoints and observe the interaction between variables intuitively. A hardware in the loop (HIL) experiment verifies that IDC can simplify the control structure of VIDC in the promise of ensuring similar control performances. Subsequently, a multi-timescale impedance model is established to clarify the control principle of VIDC and the LFO mechanisms of VIDC-controlled DC-MG. Control loops of different timescales are visualized as independent loop virtual impedances (LVIs) to form an impedance circuit. The instability factors are revealed and a dynamic stability enhancement method is proposed to compensate for the negative damping caused by VIDC and CPL. Experimental results have validated the LFO mechanism analysis and stability enhancement method. Finally, an inertia-emulation-based cooperative control strategy for multi-parallel ESS is proposed to address the SoC imbalance and voltage deviation problem in steady-state operation and the voltage stability problem. The contradiction between SoC balancing speed and maintaining system stability is solved by a redefined SoC-based droop resistance function. HIL experiments prove that the proposed control performs better dynamics and static characteristics without modifying the hardware and can balance the SoC in both charge and discharge modes

MULTIDIMENSIONAL OPTIMAL DROOP CONTROL FOR WIND RESOURCES IN DC MICROGRIDS

Two important and upcoming technologies, microgrids and electricity generation from wind resources, are increasingly being combined. Various control strategies can be implemented, and droop control provides a simple option without requiring communication between microgrid components. Eliminating the single source of potential failure around the communication system is especially important in remote, islanded microgrids, which are considered in this work. However, traditional droop control does not allow the microgrid to utilize much of the power available from the wind. This dissertation presents a novel droop control strategy, which implements a droop surface in higher dimension than the traditional strategy. The droop control relationship then depends on two variables: the dc microgrid bus voltage, and the wind speed at the current time. An approach for optimizing this droop control surface in order to meet a given objective, for example utilizing all of the power available from a wind resource, is proposed and demonstrated. Various cases are used to test the proposed optimal high dimension droop control method, and demonstrate its function. First, the use of linear multidimensional droop control without optimization is demonstrated through simulation. Next, an optimal high dimension droop control surface is implemented with a simple dc microgrid containing two sources and one load. Various cases for changing load and wind speed are investigated using simulation and hardware-in-the-loop techniques. Optimal multidimensional droop control is demonstrated with a wind resource in a full dc microgrid example, containing an energy storage device as well as multiple sources and loads. Finally, the optimal high dimension droop control method is applied with a solar resource, and using a load model developed for a military patrol base application. The operation of the proposed control is again investigated using simulation and hardware-in-the-loop techniques

Optimal energy management and control of microgrids in modern electrical power systems

Microgrids (MGs) are becoming more popular in modern electric power systems owing to their reliability, efficiency, and simplicity. The proportional-integral (PI) based droop control mechanism has been widely used in the MG control domain as the setpoint generator for the primary controller which has several drawbacks. In order to mitigate these issues, and to enhance the transient and steady-state operations in islanded MGs, advanced control and intelligent optimization methodologies are presented in this dissertation. First, to improve the existing PI-based droop relationship in DCMGs, a multi-objective optimization (MOO) based optimal droop coefficient computation method is proposed. Considering the system voltage regulation, system total loss minimization, and enhanced current sharing among the distributed generators (DGs), the Pareto optimal front is obtained using the Elitist non dominated sorting genetic algorithm (NSGA II). Then, a fuzzy membership function approach is introduced to extract the best compromise solution from the Pareto optimal front. The drawbacks of PI-based droop control cannot be entirely mitigated by tuning the droop gains. Hence, a droop free, approximate optimal feedback control strategy is proposed to optimally control DGs in islanded DCMGs. Further, to gain the fully optimal behavior, and to mitigate constant power load (CPL) instabilities, a decentralized optimal feedback control strategy is also introduced for the active loads (ALs) in the MG. In both algorithms, the approximate dynamic programming (ADP) method is employed to solve the constrained input infinite horizon optimal control problem by successive approximation of the value function via a linear in the parameter (LIP) neural network (NN). The NN weights are updated online by a concurrent reinforcement learning (RL) based tuning algorithm, and the convergence of the unknown weights to a neighborhood of the optimal weights is guaranteed without the persistence of excitation (PE). Finally, a local optimal control strategy is presented to path optimization of islanded ACMGs to enhance the transient operations while mitigating the voltage and frequency deviations caused by the traditional droop control. Optimal state and control transient trajectories in the d-q reference frame are obtained by Pontryagin's minimum principle which drives each DG from a given initial condition to their steady-state manifold. Both simulation and experimental results are presented to validate the concepts

Advanced Controls Of Cyber Physical Energy Systems

Cyber system is a fairly important component of the energy systems. The network imperfections can significantly reduce the control performance if not be properly treated together with the physical system during the control designs. In the proposed research, the advanced controls of cyber-physical energy systems are explored in depth. The focus of our research is on two typical energy systems including the large-scale smart grid (e.g. wide-area power system) and the smart microgrid (e.g. shipboard power system and inverter-interfaced AC/DC microgrid). In order to proactively reduce the computation and communication burden of the wide-area power systems (WAPSs), an event/self-triggered control method is developed. Besides, a reinforcement learning method is designed to counteract the unavoidable network imperfections of WAPSs such as communication delay and packet dropout with unknown system dynamics. For smart microgrids, various advanced control techniques, e.g., output constrained control, consensus-based control, neuro network and game theory etc., have been successfully applied to improve their physical performance. The proposed control algorithms have been tested through extensive simulations including the real-time simulation, the power-hardware-in-the-loop simulation and on the hardware testbed. Based on the existing work, further research of microgrids will be conducted to develop the improved control algorithms with cyber uncertainties

Impact of Load Modeling on Power System Voltage Stability

Under normal operating conditions, the power system is operated such that acceptable steady voltages are maintained throughout the system buses. However, during disturbances, system voltage deviates from the rated values. A stable system restores its voltage to a stable equilibrium value. However, in an unstable system, the voltage cannot be restored to acceptable steady value, and the system voltage falls progressively. This will force the system into a cascading outage, leading to voltage collapse. It was reported that several blackouts throughout the world were caused due to voltage collapse, which caused huge financial losses and badly impacted social life. As the load models make a significant impact on the voltage stability phenomenon, power system loads are to be modeled such that they closely represent the real system loads. Dynamic load models were used as better load models than static load models for voltage stability study. However, a single dynamic load model cannot represent the dynamics of different types of loads connected to the system. So, aggregated load models such as complex loads were developed which incorporates the models of major types of loads connected in the system. In this thesis, impacts of load models in the static and dynamic voltage stability are analyzed. For static analysis, maximum loadability of power system using different load models are determined using Newton-Raphson power flow method and continuous power flow method. For dynamic analysis, short-term voltage stability is analyzed implementing a various combination of load models. It was observed that short-term voltage stability of the power system largely depends on the types of load and load combinations connected to the system. In static loads, constant power loads have a greater impact on voltage instability than constant impedance type of loads. Dynamic loads such as motor loads support the voltage instability mechanism. It was observed that the voltage collapse occurs for certain combination of static and dynamic loads. However, the voltage collapse was prevented when the combination of loads connected to the bus was changed. Thus, this can be one of the solutions to reduce voltage instability of the power system.Electrical Engineerin

Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201