Building energy management and demand response

Recently, the energy efficiency of a building has been receiving much attention from the researchers. Many papers discuss the optimizations around the Heating, Ventilation, and Air-Conditioning (HVAC) system, such as sensors and model predictive control (MPC) based thermostats. However, most of them...

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Main Author: Zhang, Hanwen
Other Authors: So Ping Lam
Format: Thesis-Master by Research
Language:English
Published: Nanyang Technological University 2020
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Online Access:https://hdl.handle.net/10356/138684
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spelling sg-ntu-dr.10356-1386842023-07-04T17:15:59Z Building energy management and demand response Zhang, Hanwen So Ping Lam School of Electrical and Electronic Engineering EPLSo@ntu.edu.sg Engineering::Electrical and electronic engineering Recently, the energy efficiency of a building has been receiving much attention from the researchers. Many papers discuss the optimizations around the Heating, Ventilation, and Air-Conditioning (HVAC) system, such as sensors and model predictive control (MPC) based thermostats. However, most of them do not consider the energy usage on the drive devices where the fans and pumps are powered by the induction machines which consume about 50% to 75% of total energy consumption in the overall HVAC system. Various drive control strategies could render different energy savings by considering the losses in an induction machine under the different scenarios of a HVAC system. Hence, one of the research works in this thesis investigates the energy efficiency of an induction machine controlled by a MPC based approach for the central fan application in a HVAC system. The integrated HVAC and model predictive flux control (MPFC) drive model is constructed in Matlab/Simulink where a HVAC is formulated by a RC-network model. The simulations are carried out to study the drive performance under various scenarios in a HVAC system where the induction machine’s torque ripple, speed ripple, torque loss, and power consumption are investigated. Besides improving the energy efficiency of system components in a HVAC system, optimizing the Energy Management System (EMS) and Demand Response (DR) of a building is another essential way to reduce the building’s electricity bill. Thus, the other research work is presented in this thesis to shed light on how the EMS with multi-storage systems can be optimally and coordinately controlled in a demand responsive building by providing a price-based integrated automation model for managing various building loads, renewable energy, and multi-storage systems. This work focuses on the optimizing an EMS framework for the Demand Side Management (DSM) instead of the utility side optimization discussed in the literature. It also extends the previous works done by other researchers and addresses their presented issues for deploying the ice storage system. The proposed method is validated by the simulation environment conducted on EnergyPlus™ and CPLEX®. In the proposed EMS framework, the deterministic approach is adopted for the battery and renewable energy optimization that operate in a coordinated manner with the DR programs in a building. The smart price-priority strategy is designed in EnergyPLus™ using EnergyPlus runtime language (Erl) to make a smart decision for coordinated control of the thermal energy storage (TES) device, battery, HVAC-based DR, and the Demand Responses (DRs) of lighting and miscellaneous loads. The simulation results show that the proposed smart price-priority strategy for coordinated control of ice storage with other DR programs can make a proper trade-off between the peak-load reduction and energy consumption in the building with storage systems. In addition, a hybrid cooling and-ventilation control algorithm is developed for a HVAC-based DR event, which shows a similar performance of the conventional global temperature adjustment of all zones in the building for a HVAC-based DR, in terms of the net operating cost and energy consumption. Master of Engineering 2020-05-11T11:31:02Z 2020-05-11T11:31:02Z 2019 Thesis-Master by Research Zhang, H. (2019). Building energy management and demand response. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/138684 10.32657/10356/138684 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). 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
spellingShingle Engineering::Electrical and electronic engineering
Zhang, Hanwen
Building energy management and demand response
description Recently, the energy efficiency of a building has been receiving much attention from the researchers. Many papers discuss the optimizations around the Heating, Ventilation, and Air-Conditioning (HVAC) system, such as sensors and model predictive control (MPC) based thermostats. However, most of them do not consider the energy usage on the drive devices where the fans and pumps are powered by the induction machines which consume about 50% to 75% of total energy consumption in the overall HVAC system. Various drive control strategies could render different energy savings by considering the losses in an induction machine under the different scenarios of a HVAC system. Hence, one of the research works in this thesis investigates the energy efficiency of an induction machine controlled by a MPC based approach for the central fan application in a HVAC system. The integrated HVAC and model predictive flux control (MPFC) drive model is constructed in Matlab/Simulink where a HVAC is formulated by a RC-network model. The simulations are carried out to study the drive performance under various scenarios in a HVAC system where the induction machine’s torque ripple, speed ripple, torque loss, and power consumption are investigated. Besides improving the energy efficiency of system components in a HVAC system, optimizing the Energy Management System (EMS) and Demand Response (DR) of a building is another essential way to reduce the building’s electricity bill. Thus, the other research work is presented in this thesis to shed light on how the EMS with multi-storage systems can be optimally and coordinately controlled in a demand responsive building by providing a price-based integrated automation model for managing various building loads, renewable energy, and multi-storage systems. This work focuses on the optimizing an EMS framework for the Demand Side Management (DSM) instead of the utility side optimization discussed in the literature. It also extends the previous works done by other researchers and addresses their presented issues for deploying the ice storage system. The proposed method is validated by the simulation environment conducted on EnergyPlus™ and CPLEX®. In the proposed EMS framework, the deterministic approach is adopted for the battery and renewable energy optimization that operate in a coordinated manner with the DR programs in a building. The smart price-priority strategy is designed in EnergyPLus™ using EnergyPlus runtime language (Erl) to make a smart decision for coordinated control of the thermal energy storage (TES) device, battery, HVAC-based DR, and the Demand Responses (DRs) of lighting and miscellaneous loads. The simulation results show that the proposed smart price-priority strategy for coordinated control of ice storage with other DR programs can make a proper trade-off between the peak-load reduction and energy consumption in the building with storage systems. In addition, a hybrid cooling and-ventilation control algorithm is developed for a HVAC-based DR event, which shows a similar performance of the conventional global temperature adjustment of all zones in the building for a HVAC-based DR, in terms of the net operating cost and energy consumption.
author2 So Ping Lam
author_facet So Ping Lam
Zhang, Hanwen
format Thesis-Master by Research
author Zhang, Hanwen
author_sort Zhang, Hanwen
title Building energy management and demand response
title_short Building energy management and demand response
title_full Building energy management and demand response
title_fullStr Building energy management and demand response
title_full_unstemmed Building energy management and demand response
title_sort building energy management and demand response
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/138684
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