LOCAL DIGITAL CONTROL OF POWER ELECTRONIC CONVERTERS IN A DC MICROGRID BASED ON A-PRIORI DERIVATION OF SWITCHING SURFACES

In power electronic basedmicrogrids, the computational requirements needed to implement an optimized online control strategy can be prohibitive. The work presented in this dissertation proposes a generalized method of derivation of geometric manifolds in a dc microgrid that is based on the a-priori computation of the optimal reactions and trajectories for classes of events in a dc microgrid. The proposed states are the stored energies in all the energy storage elements of the dc microgrid and power flowing into them. It is anticipated that calculating a large enough set of dissimilar transient scenarios will also span many scenarios not specifically used to develop the surface. These geometric manifolds will then be used as reference surfaces in any type of controller, such as a sliding mode hysteretic controller. The presence of switched power converters in microgrids involve different control actions for different system events. The control of the switch states of the converters is essential for steady state and transient operations. A digital memory look-up based controller that uses a hysteretic sliding mode control strategy is an effective technique to generate the proper switch states for the converters. An example dcmicrogrid with three dc-dc boost converters and resistive loads is considered for this work. The geometric manifolds are successfully generated for transient events, such as step changes in the loads and the sources. The surfaces corresponding to a specific case of step change in the loads are then used as reference surfaces in an EEPROM for experimentally validating the control strategy. The required switch states corresponding to this specific transient scenario are programmed in the EEPROM as a memory table. This controls the switching of the dc-dc boost converters and drives the system states to the reference manifold. In this work, it is shown that this strategy effectively controls the system for a transient condition such as step changes in the loads for the example case

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

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

Cooperative Control And Advanced Management Of Distributed Generators In A Smart Grid

Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced iii to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimize the losses. This distributed optimization is a gradient based technique and it is shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators

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

TOWARDS OPTIMAL OPERATION AND CONTROL OF EMERGING ELECTRIC DISTRIBUTION NETWORKS

The growing integration of power-electronics converters enabled components causes low inertia in the evolving electric distribution networks, which also suffer from uncertainties due to renewable energy sources, electric demands, and anomalies caused by physical or cyber attacks, etc. These issues are addressed in this dissertation. First, a virtual synchronous generator (VSG) solution is provided for solar photovoltaics (PVs) to address the issues of low inertia and system uncertainties. Furthermore, for a campus AC microgrid, coordinated control of the PV-VSG and a combined heat and power (CHP) unit is proposed and validated. Second, for islanded AC microgrids composed of SGs and PVs, an improved three-layer predictive hierarchical power management framework is presented to provide economic operation and cyber-physical security while reducing uncertainties. This scheme providessuperior frequency regulation capability and maintains low system operating costs. Third, a decentralized strategy for coordinating adaptive controls of PVs and battery energy storage systems (BESSs) in islanded DC nanogrids is presented. Finally, for transient stability evaluation (TSE) of emerging electric distribution networks dominated by EV supercharging stations, a data-driven region of attraction (ROA) estimation approach is presented. The proposed data-driven method is more computationally efficient than traditional model-based methods, and it also allows for real-time ROA estimation for emerging electric distribution networks with complex dynamics

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

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Security of electric power systems : cascading outage analysis, interdiction model and resilience to natural disasters

textSecure electric power system operation is key to social warfare. However, recent years have seen numerous natural disasters and terrorist attacks that threat the grid security. This dissertation summarizes the efforts to develop a model to analyze cascading outages, an interdiction model to analyze worst-case attacks on power grids, and research on grid resilience to natural disasters. The developed cascading outage analysis model uses outage checkers to systematically simulate the system behavior after an initial disturbance, and calculate the potential cascading outage path and electric load shedding. The new interdiction model combines the previously developed medium-term attack-defense model with the short-term cascading outage analysis model to find worst-case terrorist attack. The dissertation also reviews the research on power grid resilience to natural disaster, and develops a framework to simulate the impacts of hurricanes.Electrical and Computer Engineerin

Demand Response in Smart Grids

The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer