Hamiltonian control design for DC microgrids with stochastic sources and loads with applications

Wilson, David G.; Neely, Jason C.; A., Cook, Marvin; Glover, Steven F.; Young, Joseph G.; Robinett, Rush D.

doi:10.1109/speedam.2014.6872094

Cited by 32 publications

(22 citation statements)

References 11 publications

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“…One important feature for a dc microgrid with the Hamiltonian-based controller developed in [25] and used in [13] and [26] is that the bus storage serves as an indication of the microgrid's power balance. That is, if the bus storage is supplying power to the bus load, then the power from the boost converters is insufficient.…”

Section: Decentralized Mode-adaptive Guidance Lawmentioning

confidence: 99%

“…Inspection of (26) shows that as long as (24), and (26) it follows that all three control methods will be able to handle bus load fluctuations within the specified constraints, and return the bus voltage to the reference bus voltage after each load change to obtain zero steady state error. If the DMA control scheme and droop control are initialized in an OXD state, they will settle back to an OXD steady state for bus load fluctuations.…”

Section: A Steady Statementioning

confidence: 99%

“…The stochastic nature of many renewable power sources leads to an analysis of a microgrids response to source fluctuations. The results from the steady state analysis, (23), (24), and (26), highlight the shortcomings of droop control as compared to OXD and DMA for maintaining a constant bus voltage when there are source inconsistencies that can occur with the integration of renewables. When a source drops out, N j=1 α j < 1.…”

Section: B Dynamic Responsementioning

confidence: 99%

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Decentralized Mode-Adaptive Guidance and Control for DC Microgrid

Cook

Parker

Robinett

et al. 2017

IEEE Trans. Power Delivery

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This paper presents a decentralized, mode-adaptive (DMA) guidance law for a Hamiltonian-based controller of an N-source, dc microgrid. Droop control is commonly used for decentralized control of microgrids. Unfortunately, droop control lacks the ability to autonomously adapt to the addition or removal of a source without the augmentation of an outer loop controller. Centralized control methods provide solutions to these limitations, as well as providing the ability to globally optimize the system. To their detriment, centralized control methods are not scalable, and require system-wide information to be funneled through a central controller yielding a single point of failure. The DMA power apportionment scheme presented here aims to reduce the gap between droop control and centralized control by providing a method that can operate autonomously in the event of source and bus load fluctuations as well as reduce communication requirements of centralized control, and thus increase resiliency and scalability. After development of the DMA, its performance is compared to the centrally-controlled optimal exergy destruction (OXD) power apportionment strategy, as well as a Hamiltonianbased droop control strategy. Lastly, it is shown that for a sufficiently large number of converters supplying a microgrid, the presented decentralized, mode-adaptive strategy provides an efficient and practical alternative to both droop control and centralized control schemes.Index Terms-Dc power systems, decentralized control, distributed control, microgrids, power system control 0885-8977 (c)

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Section: Decentralized Mode-adaptive Guidance Lawmentioning

confidence: 99%

Section: A Steady Statementioning

confidence: 99%

Section: B Dynamic Responsementioning

confidence: 99%

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Decentralized Mode-Adaptive Guidance and Control for DC Microgrid

Cook

Parker

Robinett

et al. 2017

IEEE Trans. Power Delivery

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“…Microgrid applications such as military forward operating bases and electric ships have a zone based architecture (Wilson et al, 2014). Each zone of a microgrid can be a self-contained power distribution microgrid of sources, loads and storage.…”

Section: Simulation Examplesmentioning

confidence: 99%

Distributed control and energy storage requirements of networked Dc microgrids

Weaver

Robinett

Parker

et al. 2015

Control Engineering Practice

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a b s t r a c tMicrogrids are a key technology to help improve the reliability of electric power systems and increase the integration of renewable energy sources. Interconnection and networking of smaller microgrids into larger systems have potential for even further improvements. This paper presents a novel approach to a distributed droop control and energy storage in networked dc microgrids. Distributed control is necessary to prevent single points of failure along with flexibility and adaptability to changing energy resources. The results show that systems with random sources and fast update rates, a networked microgrid structure can minimize required energy storage requirements.

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“…The influence of control parameters on FOM outcomes is measured using a cross-correlation calculation given the voluminous dataset generated. In [5], the authors investigate the trade-off between communication and performance in a system that includes generation and ESS to mitigate disturbances. Therein, a DC microgrid testbed that included a wind generator, diesel generator, variable resistive load, and ESS was regulated using a hierarchical control scheme.…”

Section: Introductionmentioning

confidence: 99%

Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid

Chae

Neely

et al. 2017

Energies

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Abstract:The energy research community is continuously pursuing improvements in power system resiliency and reliability. Microgrids offer a unique opportunity for enhanced reliability and resiliency by utilizing localized generation and energy storage when grid power is unavailable or too expensive. Energy management is a critical aspect of these systems to ensure proper balancing of sources and ensuring power supply to critical loads with minimum cost, especially in an islanded microgrid. This paper presents a hierarchical real-time optimization with mathematical formulations to achieve optimal operation for an islanded microgrid. The optimization is implemented using simple numerically tractable model predictive control strategies and enables appropriate decisions in response to constantly changing conditions. The optimization method is extended for experimentation within the real-time simulation. Simulation results show that the proposed resource management algorithm shows near-optimal performance while effectively dealing with uncertainties in forecasting.

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Hamiltonian control design for DC microgrids with stochastic sources and loads with applications

Cited by 32 publications

References 11 publications

Decentralized Mode-Adaptive Guidance and Control for DC Microgrid

Decentralized Mode-Adaptive Guidance and Control for DC Microgrid

Distributed control and energy storage requirements of networked Dc microgrids

Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid

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