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Trudie Wang

Bio: Trudie Wang is an academic researcher from Stanford University. The author has contributed to research in topics: Stand-alone power system & Grid-connected photovoltaic power system. The author has an hindex of 3, co-authored 3 publications receiving 296 citations.

Papers
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Journal ArticleDOI
TL;DR: Simulation results for simulations using real-data 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 toward a power grid that is increasingly based on renewable resources like solar and wind, the intelligent control of distributed energy resources (DERs) 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 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.

163 citations

Posted Content
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.

104 citations

Journal ArticleDOI
TL;DR: Simulation results are presented that demonstrate the ability of this optimization framework to respond dynamically in real time to external price signals and provide increased system benefits including smoother power output while respecting and maintaining the functional requirements of the storage units and power converters.
Abstract: In this paper, we develop optimization and control methods for a grid-tied photovoltaic (PV) storage system. The storage component consists of two separate units, a large slower moving unit for energy shifting and arbitrage and a small rapid charging unit for smoothing. We use a Model Predictive Control (MPC) framework to allow the units to automatically and dynamically adapt to changes in PV output while responding to external system operator requests or price signals. At each time step, the system is modeled using convex objectives and constraints and solved to obtain a control schedule for the storage units across the MPC horizon. For each subsequent time step, the first step of the schedule is executed before repeating the optimization process to account for changes in the operating environment and predictions due to availability of additional information. We present simulation results that demonstrate the ability of this optimization framework to respond dynamically in real time to external price signals and provide increased system benefits including smoother power output while respecting and maintaining the functional requirements of the storage units and power converters.

88 citations


Cited by
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Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: In this paper, a comprehensive literature review on problems associated when the intermittent PV is connected to grid and the methods of smoothing the output power fluctuation from PV is presented, also briefly discusses control strategy built for battery energy storage pertaining to this issue.
Abstract: Renewable Energy Sources (RESs) particularly photovoltaic (PV) and wind are becoming important sources for power generation. Frequently varying output of PV and wind caused by clouds movement, weather condition and wind speed make them an intermittent and unreliable sources when connected to grid. Connecting intermittent sources to grid introduces challenges in various technical aspects such as power quality, protection, generation dispatch control and reliability. In this context, leveling intermittent source׳s output is necessary inorder to maintain grid׳s stability. This paper is aimed at bringing out the latest comprehensive literature review on problems associated when the intermittent PV is connected to grid and the methods of smoothing the output power fluctuation from PV. This paper also briefly discusses control strategy built for battery energy storage pertaining to this issue.

304 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: A brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage serve as a foundation for a discussion of EMS optimization methods and design.
Abstract: Today, the stability of the electric power grid is maintained through real time balancing of generation and demand. Grid scale energy storage systems are increasingly being deployed to provide grid operators the flexibility needed to maintain this balance. Energy storage also imparts resiliency and robustness to the grid infrastructure. Over the last few years, there has been a significant increase in the deployment of large scale energy storage systems. This growth has been driven by improvements in the cost and performance of energy storage technologies and the need to accommodate distributed generation, as well as incentives and government mandates. Energy management systems (EMSs) and optimization methods are required to effectively and safely utilize energy storage as a flexible grid asset that can provide multiple grid services. The EMS needs to be able to accommodate a variety of use cases and regulatory environments. In this paper, we provide a brief history of grid-scale energy storage, an overview of EMS architectures, and a summary of the leading applications for storage. These serve as a foundation for a discussion of EMS optimization methods and design.

234 citations

Journal ArticleDOI
TL;DR: In this paper, a convex programming (CP) problem is formulated to rapidly and efficiently optimize both the control decision and parameters of the home battery energy storage system (BESS), considering different time horizons of optimization, home BESS prices, types and control modes of PEVs.

181 citations