scholarly journals A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning

2021 ◽  
pp. 155-181
Author(s):  
Hendro Wicaksono ◽  
Tina Boroukhian ◽  
Atit Bashyal
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Renewable Energy ◽  
Demand Response ◽  
Short Term Memory ◽  
Renewable Energy Sources ◽  
Heterogeneous Systems ◽  
Energy Sources ◽  
Demand Response Program ◽  
Mapping Techniques

AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.

scholarly journals Evaluating the Effect of Demand Response Programs (DRPs) on Robust Optimal Sizing of Islanded Microgrids

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5750
Author(s):  
Mahdi Karami Karami Darabi ◽  
Hamed Ganjeh Ganjeh Ganjehlou ◽  
Amirreza Jafari ◽  
Morteza Nazari-Heris ◽  
Gevork B. B. Gharehpetian ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Energy Sources ◽  
Small Scale ◽  
Project Cost ◽  
Power Sources ◽  
Demand Response Program ◽  
Battery State Of Charge

A microgrid is a small-scale energy system with its own generation and storage facilities and energy management system, which includes shiftable and traditional loads. The purpose of this research is to determine the size of the microgrid through (i) investigating the effect of a shiftable demand response program (DRP) on sizing of an islanded microgrid and (ii) studying the uncertainty of power output of renewable energy sources by applying the robust optimization (RO) method. Since the RO method solves the problem for lower power outputs of renewable energy sources (RES) than the predicted values, the results obtained are pessimistic and will increase the project cost. To deal with the increment of project cost, the application of a load shifting DRP is proposed to reduce the cost. In addition, DRPs are suitable means to reduce the effects of uncertain power sources. Therefore, it is shown that a shiftable DRP is effective in reducing the overall project cost and the dependency on energy storage systems by defining different scenarios and simulating them with General Algebraic Modeling System (GAMS) software. Moreover, it is indicated that the shiftable DRP and battery state of charge have correlations with solar irradiance and wind speed, respectively.

scholarly journals An Efficient Energy Management in Smart Grid Considering Demand Response Program and Renewable Energy Sources

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ateeq Ur Rehman ◽  
Ghulam Hafeez ◽  
Fahad R. Albogamy ◽  
Zahid Wadud ◽  
Faheem Ali ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Smart Grid ◽  
Energy Management ◽  
Demand Response ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Efficient Energy ◽  
Demand Response Program

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

2019 ◽  
Vol 9 (9) ◽  
pp. 1844 ◽  
Author(s):  
Jesús Ferrero Bermejo ◽  
Juan F. Gómez Fernández ◽  
Fernando Olivencia Polo ◽  
Adolfo Crespo Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Energy Production ◽  
Renewable Energy Sources ◽  
Operating Conditions ◽  
Energy Sources ◽  
Solar Pv ◽  
Ann Models ◽  
Artificial Neural

The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

Sign in / Sign up

Export Citation Format

Share Document