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Dynamic Control and Optimization of Distributed Energy Resources in a Microgrid
TL;DR: In this paper, an iterative distributed algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment, and the authors report results for simulations using real data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.
Abstract: As we transition towards a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DER) including photovoltaic (PV) arrays, controllable loads, energy storage and 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. Realizing such a decentralized and dynamic infrastructure will require the ability to solve large scale problems in real-time with hundreds of thousands of DERs simultaneously online. Because of the scale of the optimization problem, we use an iterative distributed algorithm previously developed in our group to operate each DER independently and autonomously within this environment. The algorithm is deployed within a framework that allows the microgrid to dynamically adapt to changes in the operating environment. Specifically, we consider a commercial site equipped with on-site PV generation, partially curtailable load, EV charge stations and a battery electric storage (BES) unit. The site operates as a small microgrid that can participate in the wholesale market on the power grid. We report results for simulations using real data that demonstrate the ability of the optimization framework to respond dynamically in real-time to external conditions while maintaining the functional requirements of all DERs.
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TL;DR: In this article, an energy management and control system for laboratory scale microgrid based on hybrid energy resources such as wind, solar, and battery is proposed, which operates in autonomous mode and has an open architecture platform for testing multiple different control configurations.
Abstract: This paper proposes an energy management and control system for laboratory scale microgrid based on hybrid energy resources such as wind, solar, and battery. Power converters and control algorithms have been used along with dedicated energy resources for the efficient operation of the microgrid. The control algorithms are developed to provide power compatibility and energy management between different resources in the microgrid. It provides stable operation of the control in all microgrid subsystems under various power generation and load conditions. The proposed microgrid, based on hybrid energy resources, operates in autonomous mode and has an open architecture platform for testing multiple different control configurations. A real-time control system has been used to operate and validate the hybrid resources in the microgrid experimentally. The proposed laboratory scale microgrid can be used as a benchmark for future research in smart grid applications.
333 citations
TL;DR: The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads.
Abstract: Battery storage is usually employed in photovoltaic (PV) system to mitigate the power fluctuations due to the characteristics of PV panels and solar irradiance. Control schemes for PV-battery systems must be able to stabilize the bus voltages as well as to control the power flows flexibly. This paper proposes a comprehensive control and power management system (CAPMS) for PV-battery-based hybrid microgrids with both ac and dc buses, for both grid-connected and islanded modes. The proposed CAPMS is successful in regulating the dc and ac bus voltages and frequency stably, controlling the voltage and power of each unit flexibly, and balancing the power flows in the systems automatically under different operating circumstances, regardless of disturbances from switching operating modes, fluctuations of irradiance and temperature, and change of loads. Both simulation and experimental case studies are carried out to verify the performance of the proposed method.
259 citations
TL;DR: In this article, a new multi-agent based distributed energy management system architecture is proposed in which the distributed generation system is composed of several distributed energy resources and a group of loads, and non-cooperative game theory is used for the multiagent coordination in the system.
168 citations
TL;DR: In this paper, two most emerging technologies belonging to smart cities i.e. xEVs and RESs based smart microgrid has been covered, and the detailed study of xEV charging infrastructure, enhancement in international standards for proper xEV deployment, and state of art in the xEV application such as the vehicle to grid (V2G) and V2H.
87 citations
TL;DR: This paper presents a control of smart photovoltaic (PV)-distribution static compensator (DSTATCOM) grid tied system using an adaptive reweighted zero attracting control algorithm with perturb and observe maximum power point tracking technique for a three phase system to improve power quality and support the three phase ac grid.
Abstract: This paper presents a control of smart photovoltaic (PV)-distribution static compensator (DSTATCOM) grid tied system using an adaptive reweighted zero attracting control algorithm with perturb and observe maximum power point tracking technique for a three phase system to improve power quality and support the three phase ac grid by supplying power both to the grid and the connected loads. The proposed PV grid tied system is capable of working round the clock. During sunshine conditions, the proposed system performs dual functions of improving power quality by working as DSTATCOM and also transfers power to the load and the grid obtained from PV array. However, during night or cloudy conditions, the proposed system works as DSTATCOM improving power quality and the power is transferred from the grid to the load. The system is termed as a smart as it is able to perform both modes automatically sensing the PV power and is capable of multidirectional power flow. The experimental validation is carried out on a developed prototype in the laboratory under varying modes.
82 citations
References
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Book•
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TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Abstract: Convex optimization problems arise frequently in many different fields. A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.
33,341 citations
Book•
23 May 2011TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Abstract: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.
17,433 citations
TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
8,064 citations
Book•
01 Dec 2001
TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.
Abstract: 1. Introduction to Predictive Control. 2. A Standard Formulation of Predictive Control. 3. Solving Predictive Control Problems. 4. Step Response and Transfer Function Formulations. 5. Tuning. 6. Stability. 7. Robust Predictive Control. 8. Perspectives. 9. Case Studies. 10. The Model Predictive Control Toolbox. References Appendices A. Some Commercial MPC Products B. MATLAB Program basicmpc C. The MPC Toolbox D. Solutions to Problems
5,468 citations
TL;DR: In this article, the authors proposed a coordinated charging strategy to minimize the power losses and to maximize the main grid load factor of the plug-in hybrid electric vehicles (PHEVs).
Abstract: Alternative vehicles, such as plug-in hybrid electric vehicles, are becoming more popular The batteries of these plug-in hybrid electric vehicles are to be charged at home from a standard outlet or on a corporate car park These extra electrical loads have an impact on the distribution grid which is analyzed in terms of power losses and voltage deviations Without coordination of the charging, the vehicles are charged instantaneously when they are plugged in or after a fixed start delay This uncoordinated power consumption on a local scale can lead to grid problems Therefore, coordinated charging is proposed to minimize the power losses and to maximize the main grid load factor The optimal charging profile of the plug-in hybrid electric vehicles is computed by minimizing the power losses As the exact forecasting of household loads is not possible, stochastic programming is introduced Two main techniques are analyzed: quadratic and dynamic programming
2,601 citations