Optimal scheduling and operation of the ESS for prosumer market environment in grid-connected industrial complex

Choi, Sungyun; Min, Sang-Won

doi:10.1109/ias.2017.8101727

Cited by 22 publications

(34 citation statements)

References 14 publications

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“…Effects of real-time controlling on BSS state of charge (SOC) are investigated by applying the proposed probabilistic scenario-based method and deterministic ones like the method of. 25…”

Section: A New Probabilistic Scenario-based Cost Function Formentioning

confidence: 99%

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Faraji

Hashemi‐Dezaki

Ketabi

2020

Energy Science & Engineering

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“…Effects of real-time controlling on BSS state of charge (SOC) are investigated by applying the proposed probabilistic scenario-based method and deterministic ones like the method of. 25…”

Section: A New Probabilistic Scenario-based Cost Function Formentioning

confidence: 99%

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Faraji

Hashemi‐Dezaki

Ketabi

2020

Energy Science & Engineering

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“…At the same time, with the institutional liberalization of energy markets, other parties are allowed to participate in the local energy mix as they provide "clean" energy to the grid, thereby opening the way for small scale self-producersconsumers (presumes) to become active members of the energy system providing/selling their surplus energy to the grid (Marta 2018). In this context, the "smart energy-autonomous cities of tomorrow" could consist of multiple presumes, which generally combine plug-in electric vehicles (EVs) in vehicle-to-grid/grid-to-vehicle shapes, where in combination with applications of microwave technologies and ICT services can cause local and potentially large-scale positive impacts on the network (Choi and Min 2018).…”

Section: Smart Energy-autonomous Citiesmentioning

confidence: 99%

Smart Cities and the Challenge of Cities’ Energy Autonomy

Meleti

Delitheou

2020

Handbook of Smart Cities

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“…Price based demand response programs included real-time pricing and day-ahead pricing schemes were also incentivized for prosumers. In this perspective, an energy storage system coordinated for real-time and day-ahead scheduling of industrial complex was examined for bidirectional energy flow [11]. The Machine Learning (ML) models were developed to practically analyze the test-bed results for demand response algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

et al. 2020

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The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.

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Optimal scheduling and operation of the ESS for prosumer market environment in grid-connected industrial complex

Cited by 22 publications

References 14 publications

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Optimal probabilistic scenario‐based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Smart Cities and the Challenge of Cities’ Energy Autonomy

Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts

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