VTechWorks Repository :: Browsing by Author "Pipattanasomporn, Manisa"

Browsing by Author "Pipattanasomporn, Manisa"

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An Agent-based Platform for Demand Response Implementation in Smart Buildings
Khamphanchai, Warodom (Virginia Tech, 2016-04-28)
The efficiency, security and resiliency are very important factors for the operation of a distribution power system. Taking into account customer demand and energy resource constraints, electric utilities not only need to provide reliable services but also need to operate a power grid as efficiently as possible. The objective of this dissertation is to design, develop and deploy the Multi-Agent Systems (MAS) - together with control algorithms - that enable demand response (DR) implementation at the customer level, focusing on both residential and commercial customers. For residential applications, the main objective is to propose an approach for a smart distribution transformer management. The DR objective at a distribution transformer is to ensure that the instantaneous power demand at a distribution transformer is kept below a certain demand limit while impacts of demand restrike are minimized. The DR objectives at residential homes are to secure critical loads, mitigate occupant comfort violation, and minimize appliance run-time after a DR event. For commercial applications, the goal is to propose a MAS architecture and platform that help facilitate the implementation of a Critical Peak Pricing (CPP) program. Main objectives of the proposed DR algorithm are to minimize power demand and energy consumption during a period that a CPP event is called out, to minimize occupant comfort violation, to minimize impacts of demand restrike after a CPP event, as well as to control the device operation to avoid restrikes. Overall, this study provides an insight into the design and implementation of MAS, together with associated control algorithms for DR implementation in smart buildings. The proposed approaches can serve as alternative solutions to the current practices of electric utilities to engage end-use customers to participate in DR programs where occupancy level, tenant comfort condition and preference, as well as controllable devices and sensors are taken into account in both simulated and real-world environments. Research findings show that the proposed DR algorithms can perform effectively and efficiently during a DR event in residential homes and during the CPP event in commercial buildings.
Aggregator-Assisted Residential Participation in Demand Response Program
Hasan, Mehedi (Virginia Tech, 2012-05-09)
The demand for electricity of a particular location can vary significantly based on season, ambient temperature, time of the day etc. High demand can result in very high wholesale price of electricity. The reason for this is very short operating duration of peaking power plants which require large capital investments to establish. Those power plants remain idle for most of the time of a year except for some peak demand periods during hot summer days. This process is inherently inefficient but it is necessary to meet the uninterrupted power supply criterion. With the advantage of new technologies, demand response can be a preferable alternative, where peak reduction can be obtained during the short durations of peak demand by controlling loads. Some controllable loads are with thermal inertia and some loads are deferrable for a short duration without making any significant impact on users' lifestyle and comfort. Demand response can help to attain supply - demand balance without completely depending on expensive peaking power plants. In this research work, an incentive-based model is considered to determine the potential of peak demand reduction due to the participation of residential customers in a demand response program. Electric water heating and air-conditioning are two largest residential loads. In this work, hot water preheating and air-conditioning pre-cooling techniques are investigated with the help of developed mathematical models to find out demand response potentials of those loads. The developed water heater model is validated by comparing results of two test-case simulations with the expected outcomes. Additional energy loss possibility associated with water preheating is also investigated using the developed energy loss model. The preheating temperature set-point is mathematically determined to obtain maximum demand reduction by keeping thermal loss to a minimal level. Case studies are performed for 15 summer days to investigate the demand response potential of water preheating. Similarly, demand response potential associated with pre-cooling operation of air-conditioning is also investigated with the help of the developed mathematical model. The required temperature set-point modification is determined mathematically and validated with the help of known outdoor temperature profiles. Case studies are performed for 15 summer days to demonstrate effectiveness of this procedure. On the other hand, total load and demand response potential of a single house is usually too small to participate in an incentive-based demand response program. Thus, the scope of combining several houses together under a single platform is also investigated in this work. Monte Carlo procedure-based simulations are performed to get an insight about the best and the worst case demand response outcomes of a cluster of houses. In case of electrical water heater control, aggregate demand response potential of 25 houses is determined. Similarly, in case of air-conditioning control (pre-cooling), approximate values of maximum, minimum and mean demand reduction amounts are determined for a cluster of 25 houses. Expected increase in indoor temperature of a house is calculated. Afterwards, the air-conditioning demand scheduling algorithm is developed to keep aggregate air-conditioning power demand to a minimal level during a demand response event. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Algorithms and Simulation Framework for Residential Demand Response
Adhikari, Rajendra (Virginia Tech, 2019-02-11)
An electric power system is a complex network consisting of a large number of power generators and consumers interconnected by transmission and distribution lines. One remarkable thing about the electric grid is that there has to be a continuous balance between the amount of electricity generated and consumed at all times. Maintaining this balance is critical for the stable operation of the grid and this task is achieved in the long term, short term and real-time by operating a three-tier wholesale electricity market consisting of the capacity market, the energy market and the ancillary services market respectively. For a demand resource to participate in the energy and the capacity markets, it needs to be able to reduce the power consumption on-demand, whereas to participate in the ancillary services market, the power consumption of the demand resource needs to be varied continuously following the regulation signal sent by the grid operator. This act of changing the demand to help maintain energy balance is called demand response (DR). The dissertation presents novel algorithms and tools to enable residential buildings to participate as demand resources on such markets to provide DR. Residential sector consumes 37% of the total U.S. electricity consumption and a recent consumer survey showed that 88% of consumers are either eager or supportive of advanced technologies for energy efficiency, including demand response. This indicates that residential sector is a very good target for DR. Two broad solutions for residential DR are presented. The first is a set of efficient algorithms that intelligently controls the customers' heating, ventilating and air conditioning (HVAC) devices for providing DR services to the grid. The second solution is an extensible residential demand response simulation framework that can help evaluate and experiment with different residential demand response algorithms. One of the algorithms presented in this dissertation is to reduce the aggregated demand of a set of HVACs during a DR event while respecting the customers' comfort requirements. The algorithm is shown to be efficient, simple to implement and is proven to be optimal. The second algorithm helps provide the regulation DR while honoring customer comfort requirements. The algorithm is efficient, simple to implement and is shown to perform well in a range of real-world situations. A case study is presented estimating the monetary benefit that can be obtained by implementing the algorithm in a cluster of 100 typical homes and shows promising result. Finally, the dissertation presents the design of a python-based object-oriented residential DR simulation framework which is easy to extend as needed. The framework supports simulation of thermal dynamics of a residential building and supports house hold appliances such as HVAC, water heater, clothes washer/dryer and dish washer. A case study showing the application of the simulation framework for various DR implementation is presented, which shows that the simulation framework performs well and can be a useful tool for future research in residential DR.
Analysis of Blockchain-based Smart Contracts for Peer-to-Peer Solar Electricity Transactive Markets
Lin, Jason (Virginia Tech, 2019-02-08)
The emergence of blockchain technology and increasing penetration of distributed energy resources (DERs) have created a new opportunity for peer-to-peer (P2P) energy trading. However, challenges arise in such transactive markets to ensure individual rationality, incentive compatibility, budget balance, and economic efficiency during the trading process. This thesis creates an hour-ahead P2P energy trading network based on the Hyperledger Fabric blockchain and explores a comparative analysis of different auction mechanisms that form the basis of smart contracts. Considered auction mechanisms are discriminatory and uniform k-Double Auction with different k values. This thesis also investigates effects of four consumer and prosumer bidding strategies: random, preference factor, price-only game-theoretic approach, and supply-demand game-theoretic approach. A custom simulation framework that models the behavior of the transactive market is developed. Case studies of a 100-home microgrid at various photovoltaic (PV) penetration levels are presented using typical residential load and PV generation profiles in the metropolitan Washington, D.C. area. Results indicate that regardless of PV penetration levels and employed bidding strategies, discriminatory k-DA can outperform uniform k-DA. Despite so, discriminatory k-DA is more sensitive to market conditions than uniform k-DA. Additionally, results show that the price-only game-theoretic bidding strategy leads to near-ideal economic efficiencies regardless of auction mechanisms and PV penetration levels.
Analysis of the Impact of Solar Thermal Water Heaters on the Electrical Distribution Load
Jesudhason Maria Therasammal, Terry Bruno (Virginia Tech, 2011-09-23)
In this research, the impact of solar thermal water heaters on the electric water heating load curve in a residential distribution circuit is analyzed with realistic hot water draw profiles. For this purpose, the electric and solar thermal water heater models are developed in MATLAB and validated with results from GridLAB-D and TRNSYS respectively. The solar thermal water heater model is developed for two types of collectors namely the flat plate and evacuated glass tube collector. Simulations are performed with the climate data from two cities - Madison, WI and Tampa, FL - which belong to two very different climate zones in the United States. Minute-by-minute electric energy consumptions in all three configurations of water heaters are modeled for a single water heater as well as a residential distribution circuit with 100 water heaters for daily as well as monthly time frames. The research findings include: The electric energy saving potential of a solar thermal water heater powered by auxiliary electric element is in the range of 40-80% as compared to an all-electric water heater depending on the site conditions such as ambient temperature, sunshine and wind speed. The simulation results indicate that the energy saving potential of a solar thermal water heater is in the range of 40-70% during winter and 60-80% during summer. Solar thermal water heaters aid in reducing the peak demand for electric water heating in a distribution feeder during sunshine hours when ambient temperatures are higher. The simulation results indicate that the peak reduction potential of solar thermal water heaters in a residential distribution feeder is in the range of 25-40% during winter and 40-60% during summer. The evacuated glass tube collectors save an additional 7-10% electric energy compared to the flat plate collectors with one glass pane during winter and around 10-15% during summer. The additional savings result from the capability of glass tube collectors to absorb ground reflected radiation and diffuse as well as direct beam radiation for a wider range of incidence angles. Also, the evacuated glass tube structure helps in reducing wind convective losses. From the simulations performed for Madison, WI and Tampa, FL, it is observed that Tampa, FL experiences more energy savings in winter than Madison, WI, while the energy savings are almost the same in summer. This is due to the fact that Tampa, FL has warmer winters with higher ambient temperatures and longer sunshine hours during the day compared to Madison, WI while the summer temperatures and sunshine hours are almost the same for the two cities. As expected, the simulation results prove the fact that lowering the hot water temperature set point will result in the reduction of electricity consumption. For a temperature reduction from 120 deg. F to 110 deg. F, electric water heaters save about 25-35% electric energy whereas solar thermal water heaters save about 30-40% auxiliary electric energy for the same temperature reduction. For the flat plate collectors, glass panes play an important role in auxiliary electric energy consumption. Flat plate collectors with two glass panes save about 10-15% auxiliary electric energy compared to those with no glass panes and about 3-5% energy saving compared to collectors with one glass pane. This is because there are reduced wind convective losses with glass panes. However, there are also transmittance losses from glass panes and there are upper limits on how many glass panes can be used. Results and findings from this research provide valuable insight into the benefits of solar thermal water heaters in a residential distribution feeder, which include the energy savings and peak demand reduction.
An Approach to Demand Response for Alleviating Power System Stress Conditions due to Electric Vehicle Penetration
Shao, Shengnan (Virginia Tech, 2011-10-17)
Along with the growth of electricity demand and the penetration of intermittent renewable energy sources, electric power distribution networks will face more and more stress conditions, especially as electric vehicles (EVs) take a greater share in the personal automobile market. This may cause potential transformer overloads, feeder congestions, and undue circuit failures. Demand response (DR) is gaining attention as it can potentially relieve system stress conditions through load management. DR can possibly defer or avoid construction of large-scale power generation and transmission infrastructures by improving the electric utility load factor. This dissertation proposes to develop a planning tool for electric utilities that can provide an insight into the implementation of demand response at the end-user level. The proposed planning tool comprises control algorithms and a simulation platform that are designed to intelligently manage end-use loads to make the EV penetration transparent to an electric power distribution network. The proposed planning tool computes the demand response amount necessary at the circuit/substation level to alleviate the stress condition due to the penetration of EVs. Then, the demand response amount is allocated to the end-user as a basis for appliance scheduling and control. To accomplish the dissertation objective, electrical loads of both residential and commercial customers, as well as EV fleets, are modeled, validated, and aggregated with their control algorithms proposed at the appliance level. A multi-layer demand response model is developed that takes into account both concerns from utilities for load reduction and concerns from consumers for convenience and privacy. An analytic hierarchy process (AHP)-based approach is put forward taking into consideration opinions from all stakeholders in order to determine the priority and importance of various consumer groups. The proposed demand response strategy takes into consideration dynamic priorities of the load based on the consumers' real-time needs. Consumer comfort indices are introduced to measure the impact of demand response on consumers' life style. The proposed indices can provide electric utilities a better estimation of the customer acceptance of a DR program, and the capability of a distribution circuit to accommodate EV penetration. Research findings from this work indicate that the proposed demand response strategy can fulfill the task of peak demand reduction with different EV penetration levels while maintaining consumer comfort levels. The study shows that the higher number of EVs in the distribution circuit will result in the higher DR impacts on consumers' comfort. This indicates that when EV numbers exceed a certain threshold in an area, other measures besides demand response will have to be taken into account to tackle the peak demand growth. The proposed planning tool is expected to provide an insight into the implementation of demand response at the end-user level. It can be used to estimate demand response potentials and the benefit of implementing demand response at different DR penetration levels within a distribution circuit. The planning tool can be used by a utility to design proper incentives and encourage consumers to participate in DR programs. At the same time, the simulation results will give a better understanding of the DR impact on scheduling of electric appliances.
An Approach to Mitigate Electric Vehicle Penetration Challenges through Demand Response, Solar Photovoltaics and Energy Storage Applications in Commercial Buildings
Sehar, Fakeha (Virginia Tech, 2017-07-18)
Electric Vehicles (EVs) are active loads as they increase the demand for electricity and introduce several challenges to electrical distribution feeders during charging. Demand Response (DR) or performing load control in commercial buildings along with the deployment of solar photovoltaic (PV) and ice storage systems at the building level can improve the efficiency of electricity grids and mitigate expensive peak demand/energy charges for buildings. This research aims to provide such a solution to make EV penetration transparent to the grid. Firstly, this research contributes to the development of an integrated control of major loads, i.e., Heating Ventilation and Air Conditioning (HVAC), lighting and plug loads while maintaining occupant environmental preferences in small- and medium-sized commercial buildings which are an untapped DR resource. Secondly, this research contributes to improvement in functionalities of EnergyPlus by incorporating a 1-minute resolution data set at the individual plug load level. The research evaluates total building power consumption performance taking into account interactions among lighting, plug load, HVAC and control systems in a realistic manner. Third, this research presents a model to study integrated control of PV and ice storage on improving building operation in demand responsive buildings. The research presents the impact of deploying various combinations of PV and ice storage to generate additional benefits, including clean energy generation from PV and valley filling from ice storage, in commercial buildings. Fourth, this research presents a coordinated load control strategy, among participating commercial buildings in a distribution feeder to optimally control buildings' major loads without sacrificing occupant comfort and ice storage discharge, along with strategically deployed PV to absorb EV penetration. Demand responsive commercial building load profiles and field recorded EV charging profiles have been added to a real world distribution circuit to analyze the effects of EV penetration, together with real-world PV output profiles. Instead of focusing on individual building's economic benefits, the developed approach considers both technical and economic benefits of the whole distribution feeder, including maintaining distribution-level load factor within acceptable ranges and reducing feeder losses.
Competitive Microgrid Electricity Market Design
Krovvidi, Sai S. (Virginia Tech, 2010-05-05)
The electric power grid forms the foundation for several other critical infrastructures of national importance such as public health, transportation and telecommunication systems, to thrive. The current power grid runs on the century-old technology and faces serious challenges of the 21st century - Ever-increasing demand and the need to provide a sustainable way to meet the growing demand, increased requirement of resilience against man-made and natural disasters, ability to defend against cyber attacks, increasing demand for reliable power, requirement to integrate with alternate energy generation and storage technologies. Several countries, including the United States, have realized the immediate need to modernize the grid and to pursue the goal of a smart grid. Majority of recent grid modernization efforts are directed towards the distribution systems to be able to meet these new challenges. One of the key enablers of a fully functional Smart Grid are microgrids — subsystems of the grid, utilizing small generation capacities at the distribution system level to increase the overall reliability and power quality of the local grid. It is one of the key directions recommended by national electric delivery technologies roadmap in United States as well as policy makers for electricity delivery in many countries. Microgrids have witnessed serious research activity in the past few years, especially in areas such as multi-agent system (MAS) architectures for microgrid control and auction algorithms for microgrid electricity transaction. However, most of the prior research on electricity transaction in microgrids fails to recognize and represent the true nature of the microgrid electricity market. In this research, a comprehensive microgrid electricity market has been designed, taking into account several unique characteristics of this new market place. This thesis establishes an economic rationale to the vision of wide-scale deployment of microgrids serving residential communities in near future and develops a comprehensive understanding of microgrid electricity market. A novel concept of Community Microgrids is introduced and the market and business models for electricity transaction are proposed and validated based on economic forecasts of key drivers of distributed generation. The most important contribution of this research deals with establishing a need for a trustworthy model framework for microgrid market and introducing the concept of reputation score to market participants. A framework of day-ahead energy market (DAEM) for electricity transaction, incorporating an approach of using the reputation score to incentivize the sellers in the market to be trustworthy, has been designed and implemented in MATLAB with a graphical user interface (GUI). Current implementation demonstrates a market place with two sellers and nine buyers and is easily scalable to support multiple market participants. The proposed microgrid electricity market may spur the deployment of residential microgrids, incorporating distributed generation, thereby making significant contribution to increase the overall reliability and power quality of the local grid.
A Coordinated Voltage Management Method Utilizing Battery Energy Storage Systems and Smart PV Inverters in Distribution Networks with High PV and Wind Penetrations
Alrashidi, Musaed Owehan (Virginia Tech, 2021-08-16)
Electrical distribution networks face many operational challenges as various renewable distributed generation (DG), such as solar photovoltaic (PV) systems and wind, become part of their structure. Unlike conventional distribution systems, where the only unpredictable aspect is the load level, the intermittent nature of DG poses additional uncertainty levels for distribution system operators (DSO). The voltage quality problem considers the most restrictive issue that hinders high DG integration into distribution grids. Voltage deviates from the nominal grid voltage limits due to the excess power from the DG. DSOs are accustomed to improving the voltage profile by optimal adjustments of the on-load tap changers, voltage regulator taps and capacitor banks. Nevertheless, due to the frequent variability of the output energy from DG, these devices may fail in doing the needful. Battery energy storage systems (BESS) and smart PV inverter functionalities are regarded as promising solutions to promote the seamless integration of renewable resources into distribution networks. BESS are utilized to store the surplus energy during the high penetration of renewable DG that causes high voltage levels and discharge the stored energy when the distribution grid is heavily loaded, which leads to the low voltage levels. Smart PV inverters regulate the network voltage by controlling the reactive power injection or absorption at the inverter end. This dissertation proposes a management strategy that coordinates BESS and smart PV inverter reactive power capability to improve voltage quality in the distribution systems with high PV and wind penetrations. The proposed management method is based on a bi-level optimization algorithm consisting of upper and lower optimization levels. The proposed method determines the optimal location, capacity, numbers and BESS charging and discharging rates to support the distribution system voltage and to ensure optimal deployment of BESS. Case studies are conducted to evaluate the proposed voltage control method. The large size PV system and wind turbine impacts are studied and simulated on the modified IEEE-34 bus test feeder. In addition, the proposed method is applied to the modified IEEE low voltage test feeder to investigate the effectiveness of installing residential rooftop PV systems on the distribution system's voltage. Experimental results show promising outcomes of the proposed method in controlling the distribution networks' voltage. In addition, a day-ahead forecast of PV power output is developed in this dissertation to assist the DSOs to accurately predict the future amounts of PV energy available and reinforcing the decision-making process of batteries operation. Hybrid forecasting models are proposed based on machine learning algorithms, which utilize support vector regression and backpropagation neural network, optimized with three metaheuristic optimization algorithms, namely Social Spider Optimization (SSO), Particle Swarm Optimization (PSO) and Cuckoo Search Optimization (CSO). These algorithms are used to improve the predictive efficacy of the selected algorithms, where the optimal selection of their hyperparameters and architectures plays a significant role in yielding precise forecasting outcomes.
CU-BEMS, smart building electricity consumption and indoor environmental sensor datasets
Pipattanasomporn, Manisa; Chitalia, Gopal; Songsiri, Jitkomut; Aswakul, Chaodit; Pora, Wanchalerm; Suwankawin, Surapong; Audomvongseree, Kulyos; Hoonchareon, Naebboon (2020-07-20)
This paper describes the release of the detailed building operation data, including electricity consumption and indoor environmental measurements, of the seven-story 11,700-m(2) office building located in Bangkok, Thailand. The electricity consumption data (kW) are that of individual air conditioning units, lighting, and plug loads in each of the 33 zones of the building. The indoor environmental sensor data comprise temperature (degrees C), relative humidity (%), and ambient light (lux) measurements of the same zones. The entire datasets are available at one-minute intervals for the period of 18 months from July 1, 2018, to December 31, 2019. Such datasets can be used to support a wide range of applications, such as zone-level, floor-level, and building-level load forecasting, indoor thermal model development, validation of building simulation models, development of demand response algorithms by load type, anomaly detection methods, and reinforcement learning algorithms for control of multiple AC units.
A Data-driven Approach for Coordinating Air Conditioning Units in Buildings during Demand Response Events
Zhang, Xiangyu (Virginia Tech, 2019-02-06)
Among many smart grid technologies, demand response (DR) is gaining increasing popularity. Many utility companies provide a variety of programs to encourage DR participation. Under these circumstances, various building energy management (BEM) systems have emerged to facilitate the building control during a DR event. Nonetheless, due to the cost and return on investment, these solutions mainly target homes and large commercial buildings, leaving aside small- and medium-sized commercial buildings (SMCB). SMCB, however, accounts for 90% of commercial buildings in the US, and offer great potential of load reduction during peak hours. With the advent of Internet-of-Things (IoT) devices and technologies, low cost smart building solutions have become possible for the SMCB; nonetheless, related intelligent algorithms are not widely available. This dissertation work investigates automated building control algorithms, tailored for the SMCB, to realize automatic device control during DR events. To be specific, a control framework for Air-Conditioning (AC) units' coordination is proposed. The goal of such framework is to reduce the aggregated AC power consumption while maintaining the thermal comfort inside a building during DR events. To achieve this goal, three major components of the framework were studied: building thermal property modeling, AC power consumption modeling and control algorithms design. Firstly, to consider occupants' thermal comfort, a reverse thermal model was designed to predict the indoor temperature of thermal zones under different AC control signals. The model was trained with supervised learning using coarse-grained temperature data recorded by smart thermostats; thus, it requires no lengthy configuration as a forward model does. The cost efficiency and plug-and-play feature of the model make it appropriate for SMCB. Secondly, a power disaggregation algorithm is proposed to model the power-outdoor temperature relationship of multiple AC units, using data from a single power meter and thermostats. Finally, algorithms based on mixed integer linear programming (MILP) and reinforcement learning (RL) were devised to coordinate multiple AC units in a building during a DR event. Integrated with the thermal model and AC power consumption model, these algorithms minimize occupants' thermal discomfort while restricting the aggregated AC power consumption below the DR limit. The efficiency of these control algorithms was tested, which demonstrate that they can generate AC control schedule in short notice (5 minutes) ahead of a DR event. Verification and validation of the proposed framework was conducted in both simulation and actual building environments. In addition, though the framework is designed for SMCBs, it can also be applied to large homes with multiple AC units to coordinate. This work is expected to give an insight into the BEM sector, helping the popularization of implementing DR in buildings. The research findings from this dissertation work shows the validity of the proposed algorithms, which can be used in BEM systems and cloud-based smart thermostats to exploit the untapped DR resource in SMCB.
A Deep Learning-based Dynamic Demand Response Framework
Haque, Ashraful (Virginia Tech, 2021-09-02)
The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated.
Design and Development of an Internet-Of-Things (IoT) Gateway for Smart Building Applications
Nugur, Aditya (Virginia Tech, 2017-11-02)
With growing concerns on global energy demand and climate change, it is important to focus on efficient utilization of electricity in commercial buildings, which contribute significantly to the overall electricity consumption. Accordingly, there has been a number of Building Energy Management (BEM) software/hardware solutions to monitor energy consumption and other measurements of individual building loads. BEM software serves as a platform to implement smart control strategies and stores historical data. Although BEM software provides such lucrative benefits to building operators, in terms of energy savings and personalized control, these benefits are not harnessed by most small to mid-sized buildings due to a high cost of deployment and maintenance. A cloud-based BEM system can offer a low-cost solution to promote ease of use and support a maintenance-free installation. In a typical building, a conventional router has a public address and assigns private addresses to all devices connected to it. This led to a network topology, where the router is the only device in the Internet space with all other devices forming an isolated local area network behind the router. Due to this scenario, a cloud-based BEM software needs to pass through the router to access devices in a local area network. To address this issue, some devices, during operation, make an outbound connection to traverse through the router and provide an interface to itself on the Internet. Hence, based on their capability to traverse through the router, devices in a local area network can be distinguished as cloud and non-cloud devices. Cloud-based BEM software with sufficient authorization can access cloud devices. In order to access devices adhering to non-cloud protocols, cloud-based BEM software requires a device in the local area network which can perform traversal through the router on behalf of all non-cloud devices. Such a device acts as an IoT gateway, to securely interconnect devices in a local area network with cloud-based BEM software. This thesis focuses towards architecting, designing and prototyping an Internet-of-Things (IoT) gateway which can perform traversal on behalf of non-cloud devices. This IoT gateway enables cloud-based BEM software to have a comprehensive access to supported non-cloud devices. The IoT gateway has been designed to support BACnet, Modbus and HTTP RESTful, which are the three widely adopted communication protocols in the building automation and control domain. The developed software executes these three communication protocols concurrently to address requests from cloud-based BEM system. The performance of the designed architecture is independent of the number of devices supported by the IoT gateway software.
Design and Implementation of a Secure Web Platform for a Building Energy Management Open Source Software
Rathinavel, Kruthika (Virginia Tech, 2015-08-04)
Commercial buildings consume more than 40% of the total energy consumption in the United States. Almost 90% of these buildings are small- and medium-sized buildings that do not have a Building Energy Management (BEM) system. The reasons behind this are – lack of awareness, unavailability of inexpensive packaged solutions, and disincentive to invest in a BEM system if the tenant is not the owner. Several open source tools and technologies have emerged recently that can be used for building automation and energy management. However, none of these systems is turnkey and deployment ready. They also lack consistent and intuitive navigation, security, and performance required for a BEM system. The overall project - of which this thesis research is a part - addresses the design and implementation of an open source secure web based user platform to monitor, schedule, control, and perform functions needed for a BEM system serving small and medium-size buildings. The focus of this work are: principles of intuitive graphical user interface design, abstracting device functions into a comprehensive data model, identifying threats and vulnerabilities, and implementing a security framework for the web platform. Monitor and control solutions for devices such as load controllers and sensors are abstracted and their decentralized control strategies are proposed and implemented using an open source robust scalable user platform accessible locally and remotely. The user platform is open-source, scalable, provides role-based access, dynamic, and modular in design. The comprehensive data model includes a user management model, device model, session model, and a scheduling model. The data model is designed to be flexible, robust and can be extended for any new device type. Security risks are analyzed using a threat model to identify security goals. The proposed security framework includes user authentication, device approval, role-based access, secure information exchange protocols, and web platform security. Performance of the user interface platform is evaluated for responsiveness in different screen sizes, page response times, throughput, and the performance of client side entities.
Design and Implementation of a Web-based Home Energy Management System for Demand Response Applications
Rahman, Md Moshiur (Virginia Tech, 2013-08-06)
The objective of this work is to design and implement an architectural framework for a web-based demand management system that allows an electric utility to reduce system peak load by automatically managing end-use appliances based on homeowners' preferences. The proposed framework comprises the following components: human user interface, home energy management (HEM) algorithms, web services for demand response communications, selected ZigBee and smart energy profile features for appliance interface, and security aspects for a web-based HEM system. The proposed web-based HEM system allows homeowners to be more aware about their electricity consumption by allowing visualization of their real-time and historical electricity consumption data. The HEM system enables customers to monitor and control their household appliances from anywhere with an Internet connection. It offers a user-friendly and attractive display panel for a homeowner to easily set his/her preferences and comfort settings. An algorithm to autonomously control appliance operation is incorporated in the proposed web-based HEM system, which makes it possible for residential customers to participate in demand response programs. In this work, the algorithm is demonstrated to manage power-intensive appliances in a single home, keeping the total household load within a certain limit while satisfying preset comfort settings and user preferences. Furthermore, an extended version of the algorithm is demonstrated to manage power-intensive appliances for multiple homes within a neighborhood. As one of the demand response (DR)-enabling technologies, the web services-based DR communication has been developed to enable households without smart meters or advanced metering infrastructure (AMI) to participate in a DR event via the HEM system. This implies that an electric utility can send a DR signal via a web services-enabled HEM system, and appropriate appliances can be controlled within each home based on homeowner preferences. The interoperability with other systems, such as utility systems, third-party Home Area Network (HAN) systems, etc., is also taken into account in the design of the proposed web services-based HEM system. That is, it is designed to allow interaction with authorized third-party systems by means of web services, which are collectively an interface for machine-to-machine interaction. This work also designs and implements device organization and interface for end-use appliances utilizing ZigBee Device Profile and Smart Energy Profile. Development of the Home Area Network (HAN) of appliances and the HAN Coordinator has been performed using a ZigBee network. Analyses of security risks for a web-based HEM system and their mitigation strategies have been discussed as well.
Development of a Software Platform with Distributed Learning Algorithms for Building Energy Efficiency and Demand Response Applications
Saha, Avijit (Virginia Tech, 2017-01-24)
In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings.
Effects of Large-Scale Penetration of Electric Vehicles on the Distribution Network and Mitigation by Demand Side Management
Oriaifo, Stacey I. (Virginia Tech, 2014-07-25)
For the purpose of this study, data for low voltage distribution transformer loading in small communities in Maryland was collected from a local electric utility company. Specifically, analysis was done on three distribution transformers on their system. Each of these transformers serves at least one electric vehicle (EV) owner. Of the three transformers analyzed, Transformer 2 serves eight residential homes and has the highest risk of experiencing an overload if all customers purchase at least one EV. Transformer 2 has a nameplate rating of 25kVA (22.5kW assuming a 0.9 power factor). With one EV owner, Transformer 2 has a peak load of 46.82kW during the study period between August 4 and August 17, 2013. When seven additional EVs of different types were added in a simulated scenario, the peak load for Transformer 2 increased from 46.82kW to 89.76kW, which is outside the transformer thermal limit. With the implementation of TOU pricing, the peak load was reduced to 56.71kW from 89.76kW. By implementing a combination of TOU pricing and appliance cycling through demand side management (DSM), the peak load was further reduced to 52.27kW.
Electrical Load Disaggregation and Demand Response in Commercial Buildings
Rahman, Imran (Virginia Tech, 2020-01-28)
Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach.
An Expert-based Approach for Demand Curtailment Allocation Subject to Communications and Cyber Security Limitations
Bian, Desong (Virginia Tech, 2017-02-03)
A smart grid is different from a traditional power system in that it allows incorporation of intelligent features and functions, e.g., meter reading, adaptive demand response, integration of distributed energy sources, substation automation, etc. All these intelligent features and functions are achieved by choosing appropriate communication technologies and network structures for the smart grid appropriately. The objective of this dissertation is to develop an AHP (analytic hierarchy process) - based strategy for demand curtailment allocation that is subject to communications and cyber security limitations. Specifically, it: (1) proposes an electrical demand curtailment allocation strategy to keep the balance between supply and demand in case of the sudden supply shortage; (2) simulates the operation of the proposed demand curtailment allocation strategy considering the impact from communication network limitations and simultaneous operations of multiple smart grid applications sharing the same communication network; and (3) analyzes the performance of the proposed demand curtailment allocation strategy when selected cyber security technologies are implemented. These are explained in more details below. An AHP-based approach to electrical demand curtailment allocation management is proposed, which determines load reduction amounts at various segments of the network to maintain the balance between generation and demand. Appropriate communication technologies and the network topology are used to implement these load reduction amounts down to the end-user. In this proposed strategy, demand curtailment allocation is quantified taking into account the demand response potential and the load curtailment priority of each distribution substation. The proposed strategy helps allocate demand curtailment (MW) among distribution substations or feeders in an electric utility service area based on requirements of the central load dispatch center. To determine how rapidly the proposed demand curtailment strategy can be implemented, the capability of the communication network supporting the demand curtailment implementation needs to be evaluated. To evaluate the capability of different communication technologies, selected communication technologies are compared in terms of their latency, throughput, reliability, power consumption and implementation costs. Since a number of smart grid applications share the same communication network, the performance of this communication network is also evaluated considering simultaneous operation of popular smart grid applications. Lastly, limitations of using several cyber security technologies based on different encryption methods - 3EDS (Triple Data Encryption Standard), AES (Advanced Encryption Standard), Blowfish, etc. - in deploying the proposed demand curtailment allocation strategy are analyzed.
An Expert-based Approach for Grid Peak Demand Curtailment using HVAC Thermostat Setpoint Interventions in Commercial Buildings
Ramdaspalli, Sneha Raj (Virginia Tech, 2021-07-01)
This dissertation explores the idea of inducing grid peak demand curtailment by turning commercial buildings into interactive assets for building owners during the demand control period. The work presented here is useful for both ab initio design of new sites and for existing or retrofitted sites. An analytical hierarchy process (AHP)-based framework is developed to curtail the thermal load effectively across a group of commercial buildings. It gives an insight into the amount of peak demand reduction possible for each building, subject to indoor thermal comfort constraints as per ASHRAE standards. Furthermore, the detailed operation of buildings in communion with the electric grid is illustrated through case studies. This analysis forms an outline for the assessment of transactive energy opportunities for commercial buildings in distribution system operations and lays the foundation for a seamless building-to-grid integration framework. The contribution of this dissertation is fourfold – (a) an efficient method of developing high-fidelity physics-based building energy models for understanding the realistic operation of commercial buildings, (b) identification of minimal dataset to achieve a target accuracy for the building energy models (c) quantification of building peak demand reduction potential and corresponding energy savings across a stipulated range of thermostat setpoint temperatures and (d) AHP-based demand curtailment scheme. By careful modeling, it is shown that commercial building models developed using this methodology are both accurate and robust. As a result, the proposed approach can be extended to other commercial buildings of diverse characteristics, independent of the location. The methodology presented here takes a holistic approach towards building energy modeling by accounting for several building parameters and interactions between them. In addition, parametric analysis is done to identify a useful minimal dataset required to achieve a specified accuracy for the building energy models. This thesis describes the concept of commercial buildings as interactive assets in a transactive grid environment and the idea behind its working.

Browsing by Author "Pipattanasomporn, Manisa"

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