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As concerns for the environment and energy independence leads a transition towards a power grid that depends increasingly on energy from renewable resources like solar and wind, the integration and intelligent control of distributed energy resources (DER) including photovoltaic (PV) arrays, controllable loads, energy storage (ES) and the batteries in plug-in electric vehicles (EVs) will be critical to realizing a power grid that can handle both the variability and unpredictability of renewable energy sources as well as increasing system complexity. In addition to providing added system reliability, DERs acting in coordination can be leveraged to address supply-demand imbalances through Demand Response (DR) and/or price signals on the electric power grid by enabling continuous bidirectional load balancing. Intelligent control and integration has the capability to reduce or shift demand peaks and improve grid efficiency by offsetting the need for spinning reserves and peaking power plants. In this dissertation, the use of dynamic and distributed algorithms that can handle the higher penetration of renewable resources in a more open and transparent energy market is explored. Specifically, we look at solving the power scheduling problem using Model Predictive Control (MPC) to ensure a dynamic response and the Alternating Direction Method of Multipliers (ADMM) to distribute the optimization problem when apropos. MPC allows the DERs to be adaptive and robust while ADMM encourages each DER to cooperate to achieve system-level goals while still operating and functioning independently. This enables policies and incentives to co-develop and work with optimization and control technologies to ensure a smooth transition to an infrastructure that can run on renewable energy resources and a more distributed grid. Climate change and ecological concerns coupled with economic concerns over fossil fuel prices are addressed not only through the integration of cleaner energy sources, but also by providing a way to encourage participation throughout the grid. Since the forthcoming grid will require integrating renewable energy at all different levels, we look specifically at an example of generation in a grid-connected PV system with storage as well as a distributed microgrid with onsite PV generation and demand response capabilities. We present simulation results that demonstrate the ability of the algorithms to respond dynamically to external price signals and provide benefits to the grid while respecting and maintaining the functional requirements of the local resources. Each example uses real data taken from measurements at generation and demand sites. While we focus on two of the more popular solutions currently being explored, the platforms using the algorithms are application agnostic in the sense that they can include a range of DERs with varying objectives. Since the algorithms used are both flexible and scalable, devices can be easily integrated as more come online. The platforms can also be implemented in remote areas and developing countries. In addition to demonstrating that such platforms can be implemented dynamically in real-time, the algorithms can also be used in models and simulations as a design tool to inexpensively develop future systems with more generation from renewable resources which can still operate efficiently and reliably.
Go in-depth with this comprehensive discussion of distributed energy management Distributed Energy Management of Electrical Power Systems provides the most complete analysis of fully distributed control approaches and their applications for electric power systems available today. Authored by four respected leaders in the field, the book covers the technical aspects of control, operation management, and optimization of electric power systems. In each chapter, the book covers the foundations and fundamentals of the topic under discussion. It then moves on to more advanced applications. Topics reviewed in the book include: System-level coordinated control Optimization of active and reactive power in power grids The coordinated control of distributed generation, elastic load and energy storage systems Distributed Energy Management incorporates discussions of emerging and future technologies and their potential effects on electrical power systems. The increased impact of renewable energy sources is also covered. Perfect for industry practitioners and graduate students in the field of power systems, Distributed Energy Management remains the leading reference for anyone with an interest in its fascinating subject matter.
The book deals with integrated distributed energy resources in existing power systems optimally to mitigate power quality issues in power systems. The book is designed for research using modern optimization techniques and a thorough analysis of renewable energy. The book provides an in-depth study of recent trends of research scope around the globe and also includes modern heuristic approaches, hands-on data, and case studies of all important dimensions of distributed energy resources. It addresses key issues such as the integration of DERs and electric vehicles, optimization algorithms, management of DERs with electric vehicles, energy pool management mechanisms, protection, and reliability in the restructured power system. This book will be useful for students, research scholars, practitioners, and academicians.
This brief focuses on stochastic energy optimization for distributed energy resources in smart grids. Along with a review of drivers and recent developments towards distributed energy resources, this brief presents research challenges of integrating millions of distributed energy resources into the grid. The brief then proposes a novel three-level hierarchical architecture for effectively integrating distributed energy resources into smart grids. Under the proposed hierarchical architecture, distributed energy resource management algorithms at the three levels (i.e., smart home, smart neighborhood, and smart microgrid) are developed in this brief based on stochastic optimization that can handle the involved uncertainties in the system.
Distributed Energy Resources in Microgrids: Integration, Challenges and Optimization unifies classically unconnected aspects of microgrids by considering them alongside economic analysis and stability testing. In addition, the book presents well-founded mathematical analyses on how to technically and economically optimize microgrids via distributed energy resource integration. Researchers and engineers in the power and energy sector will find this information useful for combined scientific and economical approaches to microgrid integration. Specific sections cover microgrid performance, including key technical elements, such as control design, stability analysis, power quality, reliability and resiliency in microgrid operation. - Addresses the challenges related to the integration of renewable energy resources - Includes examples of control algorithms adopted during integration - Presents detailed methods of optimization to enhance successful integration
Operation of Distributed Energy Resources in Smart Distribution Networks defines the barriers and challenges of smart distribution networks, ultimately proposing optimal solutions for addressing them. The book considers their use as an important part of future electrical power systems and their ability to improve the local flexibility and reliability of electrical systems. It carefully defines the concept as a radial network with a cluster of distributed energy generations, various types of loads, and energy storage systems. In addition, the book details how the huge penetration of distributed energy resources and the intermittent nature of renewable generations may cause system problems. Readers will find this to be an important resource that analyzes and introduces the features and problems of smart distribution networks from different aspects. - Integrates different types of elements, including electrical vehicles, demand response programs, and various renewable energy sources in distribution networks - Proposes optimal operational models for the short-term performance and scheduling of a distribution network - Discusses the uncertainties of renewable resources and intermittent load in the decision-making process for distribution networks
This book provides the insight of various topology and control algorithms used for power control in distributed energy power conversion systems such as solar, wind, and other power sources. It covers traditional and advanced control algorithms of power filtering including modelling and simulations, and hybrid power generation systems. The adaptive control, model predictive control, fuzzy-based controllers, Artificial Intelligence-based control algorithm, and optimization techniques application for estimating the error regulator gains are discussed. Features of this book include the following: Covers the schemes for power quality enhancement, and voltage and frequency control. Provides complete mathematical modelling and simulation results of the various configurations of the renewable energy-based distribution systems. Includes design, control, and experimental results. Discusses mathematical modelling of classical and adaptive control techniques. Explores recent application of control algorithm and power conversion. This book is aimed at researchers, professionals, and graduate students in power electronics, distributed power generation systems, control engineering, Artificial Intelligent-based control algorithms, optimization techniques, and renewable energy systems.
This volume consists of selected essays by participants of the workshop Control at Large Scales: Energy Markets and Responsive Grids held at the Institute for Mathematics and its Applications, Minneapolis, Minnesota, U.S.A. from May 9-13, 2016. The workshop brought together a diverse group of experts to discuss current and future challenges in energy markets and controls, along with potential solutions. The volume includes chapters on significant challenges in the design of markets and incentives, integration of renewable energy and energy storage, risk management and resilience, and distributed and multi-scale optimization and control. Contributors include leading experts from academia and industry in power systems and markets as well as control science and engineering. This volume will be of use to experts and newcomers interested in all aspects of the challenges facing the creation of a more sustainable electricity infrastructure, in areas such as distributed and stochastic optimization and control, stability theory, economics, policy, and financial mathematics, as well as in all aspects of power system operation.
This text is an introduction to the use of control in distributed power generation. It shows the reader how reliable control can be achieved so as to realize the potential of small networks of diverse energy sources, either singly or in coordination, for meeting concerns of energy cost, energy security and environmental protection. The book demonstrates how such microgrids, interconnecting groups of generating units and loads within a local area, can be an effective means of balancing electrical supply and demand. It takes advantage of the ability to connect and disconnect microgrids from the main body of the power grid to give flexibility in response to special events, planned or unplanned. In order to capture the main opportunities for expanding the power grid and to present the plethora of associated open problems in control theory Control and Optimization of Distributed Generation Systems is organized to treat three key themes, namely: system architecture and integration; modelling and analysis; and communications and control. Each chapter makes use of examples and simulations and appropriate problems to help the reader study. Tools helpful to the reader in accessing the mathematical analysis presented within the main body of the book are given in an appendix. Control and Optimization of Distributed Generation Systems will enable readers new to the field of distributed power generation and networked control, whether experienced academic migrating from another field or graduate student beginning a research career, to familiarize themselves with the important points of the control and regulation of microgrids. It will also be useful for practising power engineers wishing to keep abreast of changes in power grids necessitated by the diversification of generating methods.
Traditionally, distribution power utilities use centralized or local architecture for voltage control and optimization in distribution power systems. However, with increasing penetration of renewable inverter-based resources in distribution systems, voltage control is becoming increasingly challenging. Since converter interfaced distributed energy resources (DERs) also act as voltage control agents in addition to already deployed legacy devices like voltage regulators and line capacitors, voltage optimization is a large-scale optimization problem. Centralized optimization algorithms, though they provide optimal solutions, suffer from scalability issues since computation of setpoints is performed in a centralized entity. Centralized approaches are also prone to single point of failures since these are dependent on secure network-wide communication for data-delivery. Local schemes, however, depend only on local measurements and local communication but provide non-optimal solutions. Distributed approaches combine the advantages of both centralized and local strategies. Distributed optimization (DO) facilitates parallel computation and distributed decision-making while providing near-optimal solutions. Moreover, DO involves communication only among neighbor agents thereby reducing communication overhead resulting in improved robustness to communication failures. This work presents distributed algorithms for optimal voltage control and optimization for multiphase unbalanced distribution feeders.First, a taxonomy of existing distributed algorithms used for optimal control in distribution power grids is developed based on communication infrastructure, database required, and method of implementation. The taxonomy provides two major classes of distributed algorithms- static optimization approaches and dynamic optimization approaches. Second, these two classes have been distinguished by providing two use cases. Static optimization approaches involve optimization problems solved by agents with local optimization subproblems, while dynamic approaches involve agents interacting with the grid as feedback control. Third, a distributed online voltage control algorithm is developed for unbalanced multiphase feeders considering the reactive power capability of DERs. Fourth, another distributed online voltage control algorithm considering both active and reactive power capability of DERs is developed. Both algorithms are validated and are shown to provide superior results for IEEE benchmark unbalanced feeders including the IEEE 13 bus and IEEE 123 bus feeder under communication delays, modeling errors, and asynchronous communication among distributed control agents.