Operation Strategy of Multi-Energy Storage System for Ancillary Services

Kim, Wook-Won; Shin, Je-Seok; Kim, Jin-O

doi:10.1109/tpwrs.2017.2665669

Cited by 66 publications

(37 citation statements)

References 26 publications

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“…In order to make the same time resolution of energy consumption, the weather forecasting information is estimated at 30-min resolution using linear interpolation. In particular, to establish a more direct association with energy consumption, we calculate the discomfort index (DI) 51 and wind chill (WC) 52 with equations (8) and (9), respectively. Then, we use these calculated values as input variables where T and H are the temperature and the humidity, respectively, and WS is the wind speed.…”

Section: Weather Informationmentioning

confidence: 99%

“…5,6 The aim of STLF is to ensure the reliability of the electric power system equipment and prepare for losses caused by power failures and overloading by controlling electricity reserve margins. [7][8][9] STLF is typically used to predict the electric load on an hourly basis up to 1 week in advance for daily operation and cost minimization. 10,11 For instance, accurate STLF can provide economic benefits by storing energy at night when electric costs are relatively low and emitting electricity during the day when electric costs are high.…”

Section: Introductionmentioning

confidence: 99%

“…10,11 For instance, accurate STLF can provide economic benefits by storing energy at night when electric costs are relatively low and emitting electricity during the day when electric costs are high. 9 STLF includes daily peak electric load, total daily electric load, hourly electric load, and very short-term load forecasting (VSTLF; e.g., 15-min and 30-min interval load forecasting). 12 STLF is not an easy task because energy consumption patterns are complex, and uncertain external factors can cause a shift in the demand curve.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Moon

Park

Rho

et al. 2019

International Journal of Distributed Sensor Networks

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Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

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Section: Weather Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Moon

Park

Rho

et al. 2019

International Journal of Distributed Sensor Networks

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“…Each EDS lines can carry a maximum amount of electricity current because of their thermal capacity. 39 Accordingly, the line current is restricted in (23).…”

Section: Line Current Constraintmentioning

confidence: 99%

“…16,17 Meanwhile, the use of the ESSs in the service restoration process has been proposed in several studies. [18][19][20][21][22][23][24][25][26][27][28][29] In Chen et al, 18 a set of methods are proposed to assess the reliability of EDSs with the penetrations of mobile ESSs and intermittent distributed sources. Bahramirad et al 19 incorporated ESSs to increase reliability level of microgrids considering outage of generation units and intermittency of renewable generation units.…”

Section: Introductionmentioning

confidence: 99%

Reliability improvement in smart grid through incorporating energy storage systems in service restoration

Abniki

Taghvaei²,

Mohammadi‐Hosseininejad

2018

Int Trans Electr Energ Syst

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Summary Smart grid has been developing nowadays as an initiative to operate modern electric distribution systems (EDSs) in a more reliable and efficient manner. With the decreasing price of the energy storage systems (ESSs), ESSs are highly recommended to be exploited in the operation of EDSs. In this manuscript, a comprehensive framework is introduced for ESS incorporation in service restoration process which proposes 2 roles for ESSs in the service restoration. As the first role, ESSs are exploited in the service restoration to re‐energize customers in interrupted zones, as their backup units. As the second role, ESSs are used in the service restoration to increase the number of interrupted customers which transferred to the backup feeders through acting as storage units. The proposed objective function of service restoration consists of customer interruption cost, energy not sold cost, cost of electricity production in combined heat and power units, and cost of ESS incorporation in service restoration. Moreover, the stochastic characteristics of ESS contribution in service restoration are considered in the proposed problem formulation. Further, the impacts of the uncertainties of the service restoration on the proposed approach for ESS incorporation are investigated. The effectiveness of the proposed methodology is deliberated using a standard reliability test system (RBTS‐4). The obtained results show that the incorporating ESSs as storage units will lead to substantially improve the reliability level of EDS through increasing the adequacy of the EDS during service restoration process.

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Virtual generator‐based damping controller for microgrids with low inertia resources based on energy storage systems

Roudi

Ebrahimi

Ghanbari

et al. 2021

Int Trans Electr Energ Syst

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Summary In this paper, an adaptive concept of virtual generator (VG)‐based controller is presented for dynamic stability of stochastically‐modeled microgrids consisting of low inertia resources. For this purpose, considering a set of energy storage systems (ESSs) dispatched in the network, a controlled islanding scheme (CIS) is provided to dynamically control the microgrid frequency. In this context, based on the concept of center of inertia (COI), the proposed VG scheme is developed through mathematical formulation. Inter‐area torques evaluated by microgrid control areas in the COI frame are used as input signals of the developed VG‐based controller. Also, for the ESSs, an equivalent dispatch model is provided and developed through the microgrid dynamic model for improving frequency stability during inter‐area oscillations. The proposed VG‐based controller is an online and non‐model‐based scheme which controls several microgrid systems together as an integrated network connected to the upstream network. In order to evaluate the ability of the proposed scheme, real‐time simulations are carried out on two different test systems consisting of several microgrids. The system dynamic performances are evaluated in time‐domain simulations. Numerical results demonstrate the effectiveness of the proposed scheme in increasing system inertia constant resulting in a proper dynamic performance with a high damping ratio in facing severe inter‐area oscillations.

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Operation Strategy of Multi-Energy Storage System for Ancillary Services

Cited by 66 publications

References 26 publications

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

A comparative analysis of artificial neural network architectures for building energy consumption forecasting

Reliability improvement in smart grid through incorporating energy storage systems in service restoration

Virtual generator‐based damping controller for microgrids with low inertia resources based on energy storage systems

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