Model Predictive Control of a Microgrid With Plug-In Vehicles: Error Modeling and the Role of Prediction Horizon

Peters, Diane L.; Mechtenberg, Abigail R.; Whitefoot, John; Papalambros, Panos Y.

doi:10.1115/dscc2011-6030

Cited by 14 publications

(5 citation statements)

References 14 publications

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“…The constraint in (23) in conjunction with a term in the cost reduces the difference between the microgrid minimum and maximum powers at the point of coupling to the grid. The inequality in (24) is also added to reduce the peak usage over some baseline denoted by p base g . It should be noted that the grid power profile related objectives and constraints are introduced to allow for smoothing of the demand profile.…”

Section: Preliminariesmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Khodabakhsh

Sirouspour

2016

IEEE Trans. Sustain. Energy

109

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This paper presents two methods for online rolling horizon optimal control of an energy storage unit in a gridconnected microgrid, subject to uncertainty in demand and electricity pricing. The proposed methods are based on the concept of rolling horizon control, where battery charge/discharge activities are determined by repeatedly solving a linear optimization problem over a moving control window. The predicted values of the microgrid net electricity demand and electricity prices over the control horizon are assumed to be uncertain. The first formulation of the control is based on the scenario-based stochastic conditional value at risk (CVaR) optimization, where the cost function includes electricity usage cost, battery operation costs, and grid signal smoothing objectives. Multivariate Gaussian distribution is used to model the variations of electricity prices and net demand power around their predicted nominal values. The second formulation of the control reduces the computations by taking a worst-case CVaR stochastic optimization approach. In this case, the uncertainty in demand is still stochastic but the problem constraints are made robust with respect to price variations in a range. Simulation results under different scenarios are presented to demonstrate the effectiveness of the proposed methods.

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Section: Preliminariesmentioning

confidence: 99%

“…There are examples of EMS with MPC in the literature, e.g., see [19]- [24]. Most online optimization-based MPC techniques rely on solving a real-time optimization problem using a nominal model of the system.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Khodabakhsh

Sirouspour

2016

IEEE Trans. Sustain. Energy

109

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“…del Real et al [37] propos hybrid modelling and predictive control for photovoltaic system and fuel cells which are integrated together as two generator subsystems. In [38], a hub model is considered for microgrid, based on which a predictive model is designed and optimised control is demonstrated. In [39], a networked controlled model is presented for multi-inverter systems.…”

Section: Concise Review Of Microgrid Architectures and Modelsmentioning

confidence: 99%

Review of microgrid architectures – a system of systems perspective

Mahmoud

Rahman

Sunni

2015

IET Renewable Power Generation

102

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To better cope with the challenges posed by the world today, the transition of systems engineering principles to complex, large scale, integrated and evolutionary system of systems (SoSs) has occurred. On the other hand, distributed generation has made its mark in the power distribution system, as a consequence of economic and environmental considerations. With the exponential advancement of technology, unconventional sources of generation, storage and microturbines have been enhanced. The microgrid has paved its way into distributed generation and looks promising for future prospects. A review of microgrid architectures and models is presented in this study. Various control schemes devised for microgrids are also reviewed. The concept of SoSs is introduced and its applications are discussed. A framework is proposed for microgrids from an SoS perspective and control paradigms based on SoS are explained in terms of microgrid control.

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“…It shows that an optimal sizing study that considers only the energy capacity of the battery, e.g., [17]- [19], [22], [23], can potentially undersize the battery.…”

Section: Implications For Component Sizingmentioning

confidence: 99%

Coupling Between Component Sizing and Regulation Capability in Microgrids

Ersal

Ahn

Peters

et al. 2013

IEEE Trans. Smart Grid

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Abstract-Increasing energy security and reliability concerns are intensifying the interest in microgrids. In this setting, design optimization is vital to achieve a reliable infrastructure without overbuilding. This paper considers the impact of frequency and voltage regulation on the optimal design of a conceptual, autonomous military microgrid. This microgrid comprises a solar panel and vehicles as power sources, with each vehicle incorporating a battery and generator. The power output and terminal voltage of these inverter-based sources must be regulated. The paper investigates the effects of battery DC voltage variations on a decentralized regulation scheme, and the resulting influence on optimal component sizing. To this end, controllers are first designed based on the typical assumption that the voltage on the DC side of each inverter is constant. The battery internal resistance is then considered and its impact on regulation performance is investigated. The results show that the battery internal resistance can affect the performance of both frequency and voltage regulation, and consequently must be taken into account in component sizing decisions. Thus, the paper identifies an important coupling between regulation and component sizing problems through battery characteristics, and highlights the need for a combined sizing and regulation framework for microgrid design.

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Model Predictive Control of a Microgrid With Plug-In Vehicles: Error Modeling and the Role of Prediction Horizon

Cited by 14 publications

References 14 publications

Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Optimal Control of Energy Storage in a Microgrid by Minimizing Conditional Value-at-Risk

Review of microgrid architectures – a system of systems perspective

Coupling Between Component Sizing and Regulation Capability in Microgrids

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