AGC Signal Modeling for Energy Storage Operations

Donadee, Jonathan; Wang, Jianhui

doi:10.1109/tpwrs.2014.2301592

Cited by 51 publications

(16 citation statements)

References 2 publications

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“…This way of modelling the PFC energy content is only accurate when the reserve capacity is constant during the whole period, because the energy content of a certain hour is correlated with the content of previous hours. This relationship is used by [20] to predict the future energy content based on the values of the last two hours. By using scenarios it is possible to take the behaviour of the frequency into account without having the same reserve capacity in all hours.…”

Section: Introductionmentioning

confidence: 99%

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Thingvad

Ziras

Marinelli

2019

Journal of Energy Storage

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Section: Introductionmentioning

confidence: 99%

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Thingvad

Ziras

Marinelli

2019

Journal of Energy Storage

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“…We refer to these random variables as state variables because we model them as following Markov random processes with distributions as in (9). (9) Hourly electric energy prices and some AGC signals are known to have a statistical dependence on recently observed values [11], [12]. A Markov model is simple way to model this dependence.…”

Section: Exogenous State Dynamicsmentioning

confidence: 99%

“…Conditionally expected values, given the most recently observed state, are denoted as for some random variable . The state transition equation, (8) is simply replaced by the deterministic state transition equation in (12). This method was used in [4] and [5] for the V2G aggregator problem.…”

Section: Possible Solution Approachesmentioning

confidence: 99%

Stochastic Optimization of Grid to Vehicle Frequency Regulation Capacity Bids

Donadee

Ilić

2014

IEEE Trans. Smart Grid

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136

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This paper investigates the application of stochastic dynamic programming to the optimization of charging and frequency regulation capacity bids of an electric vehicle (EV) in a smart electric grid environment. We formulate a Markov decision problem to minimize an EV's expected cost over a fixed charging horizon. We account for both Markov random prices and a Markov random regulation signal. We also propose an enhancement to the classical discrete stochastic dynamic programming method. This enhancement allows optimization over a continuous space of decision variables via linear programming at each state. Simple stochastic process models are built from real data and used to simulate the implementation of the proposed method. The proposed method is shown to outperform deterministic model predictive control in terms of average EV charging cost.

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“…(4): system related state X p (state 1), thermal unit related states X T (states 2-5), and gas unit related states X G (states [6][7][8][9][10][11].…”

Section: State Space Representation Of Thermal and Natural Gas Generamentioning

confidence: 99%

“…This was accomplished by means of an independent AGC control strategy for the battery storage in conjunction with a proportional ACE signal distribution to all the AGC participants, which proved to further improve the system frequency response. Donadee and Wang [9] proposed a look-ahead predictive model of estimating AGC signal that needs to be fed in future periods to the energy limited storage devices such that their state of charge (SoC) can be effectively controlled and utilized for maximum economic gain. While the above cited studies have contributed towards improving the dispatch efficiencies of such storage devices in AGC, there have been studies that have looked into developing integrated systems and novel AGC structures [10] towards achieving smart grid objectives.…”

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confidence: 99%

Slow dynamics model of compressed air energy storage and battery storage technologies for automatic generation control

Krishnan

Das

2015

Energy Syst

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Increasing variable generation penetration and the consequent increase in short-term variability makes energy storage technologies look attractive, especially in the ancillary market for providing frequency regulation services. This paper presents slow dynamics model for compressed air energy storage and battery storage technologies that can be used in automatic generation control studies to assess the system frequency response and quantify the benefits from storage technologies in providing regulation service. The paper also represents the slow dynamics model of the power system integrated with storage technologies in a complete state space form. The storage technologies have been integrated to the IEEE 24 bus system with single area, and a comparative study of various solution strategies including transmission enhancement and combustion turbine have been performed in terms of generation cycling and frequency response performance metrics.

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AGC Signal Modeling for Energy Storage Operations

Cited by 51 publications

References 2 publications

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Economic value of electric vehicle reserve provision in the Nordic countries under driving requirements and charger losses

Stochastic Optimization of Grid to Vehicle Frequency Regulation Capacity Bids

Slow dynamics model of compressed air energy storage and battery storage technologies for automatic generation control

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