Real-time microgrid optimization algorithm

Microgrid, a local energy provider, to self-provide and resilient system. Microgrids can be tailored per user, regardless standalone or grid tied. Many microcontrollers (PLCs) are used to generate data for feedback and control. The data produced can vary from reading load profile of users, detect...

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Main Author: Khoo, Ding Yuan
Other Authors: Xu Yan
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/149065
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1490652023-07-07T18:05:15Z Real-time microgrid optimization algorithm Khoo, Ding Yuan Xu Yan School of Electrical and Electronic Engineering Rolls-Royce-NTU Corp Lab Md.Samar Ahmad xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Microgrid, a local energy provider, to self-provide and resilient system. Microgrids can be tailored per user, regardless standalone or grid tied. Many microcontrollers (PLCs) are used to generate data for feedback and control. The data produced can vary from reading load profile of users, detecting extra load consumption and even used to distribute the energy generated smartly. Except, the way the microcontroller worked is through lots of the algorithms, developed through the years. One of the algorithms implemented is machine learning. Neural Network, a type of machine learning, would be a common usage for engineering related solution. Artificial Neural Network (ANN) can do classification or regression. Classification for classify types of load profile, mainly peak and non-peak load consumption. Regression, to predict trend. Regression can be split to 2 types, linear and non-linear. This paper’s aim is to generate a general ANN model that can be used as a load prediction algorithm. Mainly the algorithm must be simplified, to be utilised by PLC. The results of the model will be benchmark against a MATLAB tool-made model to prove the accuracy of the model. The project will be hand coded with the relevant mathematical equations, using MATLAB as a base, without the tools. Input and Target for the training model will be drawn from EMA’s SES 2020 statistic. The purpose of the paper is a prequel to the Real-Time Microgrid Optimization Algorithm. The results produced by this paper achieve part of the major project. The forecast algorithm will produce a predicted value that will be fed into the optimization algorithm Bachelor of Engineering (Electrical and Electronic Engineering) 2021-05-25T05:33:21Z 2021-05-25T05:33:21Z 2021 Final Year Project (FYP) Khoo, D. Y. (2021). Real-time microgrid optimization algorithm. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149065 https://hdl.handle.net/10356/149065 en B1195 - 201 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
spellingShingle Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution
Khoo, Ding Yuan
Real-time microgrid optimization algorithm
description Microgrid, a local energy provider, to self-provide and resilient system. Microgrids can be tailored per user, regardless standalone or grid tied. Many microcontrollers (PLCs) are used to generate data for feedback and control. The data produced can vary from reading load profile of users, detecting extra load consumption and even used to distribute the energy generated smartly. Except, the way the microcontroller worked is through lots of the algorithms, developed through the years. One of the algorithms implemented is machine learning. Neural Network, a type of machine learning, would be a common usage for engineering related solution. Artificial Neural Network (ANN) can do classification or regression. Classification for classify types of load profile, mainly peak and non-peak load consumption. Regression, to predict trend. Regression can be split to 2 types, linear and non-linear. This paper’s aim is to generate a general ANN model that can be used as a load prediction algorithm. Mainly the algorithm must be simplified, to be utilised by PLC. The results of the model will be benchmark against a MATLAB tool-made model to prove the accuracy of the model. The project will be hand coded with the relevant mathematical equations, using MATLAB as a base, without the tools. Input and Target for the training model will be drawn from EMA’s SES 2020 statistic. The purpose of the paper is a prequel to the Real-Time Microgrid Optimization Algorithm. The results produced by this paper achieve part of the major project. The forecast algorithm will produce a predicted value that will be fed into the optimization algorithm
author2 Xu Yan
author_facet Xu Yan
Khoo, Ding Yuan
format Final Year Project
author Khoo, Ding Yuan
author_sort Khoo, Ding Yuan
title Real-time microgrid optimization algorithm
title_short Real-time microgrid optimization algorithm
title_full Real-time microgrid optimization algorithm
title_fullStr Real-time microgrid optimization algorithm
title_full_unstemmed Real-time microgrid optimization algorithm
title_sort real-time microgrid optimization algorithm
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/149065
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