Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Acquah, Moses Amoasi; Kodaira, Daisuke; Han, Sekyung

doi:10.20944/preprints201804.0056.v1

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…Since electric demand exist in real-life with uncertainties a DMS solution that ignores the uncertainty factor will be susceptible to errors. In such a case BESS schedule is not robust and peak demand might not be resolved in a cases where forecasted demand deviates from observed demand [19].…”

Section: Dynamic-interval Density Forecastmentioning

confidence: 99%

“…We fall on Piecewise Peak Approximation as proposed by [19] to perform dimensionality reduction of the data with high fidelity, to reduce the dimension of the DPD.…”

Section: Dimensionality Reduction Modulementioning

confidence: 99%

“…The work in [18] deals with real-time forecast and control of HVAC for cost minimization and users' thermal comfort but it is not focused on peak demand control using BESS. The research by [19] provide a real-time dynamic interval density forecast and stochastic optimisations to account for demand uncertainties. The solution is focused on the use of the algorithm in conjunction with stationary BESS for peak demand control.…”

Section: Introductionmentioning

confidence: 99%

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Online Demand Side Management with PEVs Using Stochastic Optimization

Acquah¹,

Yu²,

Han³

2018

Preprint

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This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach.

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Section: Dynamic-interval Density Forecastmentioning

confidence: 99%

“…We fall on Piecewise Peak Approximation as proposed by [19] to perform dimensionality reduction of the data with high fidelity, to reduce the dimension of the DPD.…”

Section: Dimensionality Reduction Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online Demand Side Management with PEVs Using Stochastic Optimization

Acquah¹,

Yu²,

Han³

2018

Preprint

Self Cite

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“…These ancillary services have evolved to use battery energy storage systems (BESS) in their operation due to their inherent advantages. Compared with other traditional storage systems, BESS are modular, tractable, and have a fast and high-power density; this makes them suitable for many applications [1,2].…”

Section: Introductionmentioning

confidence: 99%

Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function

Park

Choi

et al. 2022

Energies

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Frequency regulation (FR) using a battery energy storage system (BESS) has been expanding because of the growth of renewable energy. This study introduces the wear density function, which considers battery degradation factors such as the rate of current, temperature, and depth of discharge (DOD) to provide a precise lifespan prediction. Furthermore, an equivalent system model is developed to evaluate the FR performance of the BESS for various operating parameters. Finally, a quantitative tradeoff relationship between performance and battery lifecycle is derived from the analysis using operational data of the actual BESS for FR.

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“…This study combines the concept of learning activities with grammar teaching, which helps to reverse the lack of practical, situational, and pan theoretical research on the theory of English learning activities, builds a teaching operation mode based on the concept of English learning activities, and improves the theory of English learning activities [13]. It is entirely possible for teachers to analyze the learning data of each student [14]. When using DBN to mine teaching big data, there are problems of low efficiency and slow convergence speed [15].…”

Section: Introductionmentioning

confidence: 99%

Construction of Multimedia‐Assisted English Teaching Mode in Big Data Network Environment

Zhao

2021

Wireless Communications and Mobile Computing

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Oral English teaching is the weakest link in multimedia English teaching at this stage. English teachers are constantly exploring effective approaches to improve oral English Teaching in their own educational practice. The big data multimedia English teaching mode conforms to embark on the historical stage. Firstly, this paper constructs the big data architecture of English teaching model mining and divides the construction of the teaching model into three parts: data mining, teaching model evaluation, and improvement optimization. Data mining uses the advanced DBN network to send data into the DBN-DELM network, which significantly improves the accuracy of the multimedia assisted English construction model. The simulation results show that teaching mode construction based on big data can effectively improve students’ interest in English learning; attitude; and oral English level including pronunciation, pronunciation and intonation, dialogue and communication, and oral expression and improve students’ group cooperation and communication ability, autonomous learning ability, evaluation consciousness, and ability.

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Real-Time Demand Side Management Algorithm Using Stochastic Optimization

Cited by 8 publications

References 12 publications

Online Demand Side Management with PEVs Using Stochastic Optimization

Online Demand Side Management with PEVs Using Stochastic Optimization

Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function

Construction of Multimedia‐Assisted English Teaching Mode in Big Data Network Environment

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