Browsing by Author "Rahman, Saifur"

Now showing 1 - 20 of 114
  • Thumbnail Image
    Adaptive optimal control of AC/DC systems
    Rostamkolai, Niusha (Virginia Polytechnic Institute and State University, 1986)
    The dissertation presents a new control strategy for two terminal HVDC systems embedded in an AC network. The control is based upon real-time measurements performed on the AC/DC system. Use is made of a technique for high speed accurate measurement of positive sequence voltages and currents, first developed in the field of computer relaying. The real-time measurements provides a term in the control law to compensate for inaccuracies following departure from the operating point. The control criterion is to damp out the electromechanical oscillations following a disturbance. The main contribution of the dissertation is to describe a new optimal controller formulation which contains a measurement based component. Optimal controllers are commonly constructed using linearized equations of the system around the operating point. In DC systems this approach is of a very limited value because of a highly nonlinear nature of the system. With the controller developed in this dissertation, it becomes possible to describe the system as a nonlinear dynamic system. The approximation resulting from the usual linearization of the system equations is thus avoided, and leads to a better controller design. The control technique is illustrated with a small AC/DC system. However, the equations formulated are sufficiently general, so that the technique can be applied to a larger system. Simulation results are included to represent the effectiveness of the developed controller.
  • Thumbnail Image
    Adaptive power system control
    Manansala, Edgardo Celestino (Virginia Polytechnic Institute and State University, 1989)
    This work presents a centralized control scheme applied to a power system. The scheme has adaptive characteristics which allow the controller to keep track of the changing power system operating point and to control nonlinear functions of state variables. Feedback to the controller is obtained from phasor measurements at chosen power system buses, generator field voltage measurements, and state estimators. Control effort is aimed at minimizing the oscillations and influencing the power system state trajectory through the control of linear and nonlinear functions of state variables during a power system disturbance. The main contributions of this dissertation are the simultaneous introduction and utilization of measurement based terms in the state and output equations in the derivation and implementation of the control law, the study of limits on controller performance as the state residual vector becomes very large, and the simulation of the performance of local state estimators to prove the need for faster phasor measurement systems. The test system is a hypothetical 39-Bus AC power system consisting of typical components which have been sufficiently modelled for the simulation of power system performance in a dynamic stability study.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Analysis and simulation of dynamics of spacecraft power systems
    Lee, Jae Ryong (Virginia Polytechnic Institute and State University, 1988)
    Comprehensive analyses, including dc, small-signal and large-signal analyses, of the dynamics of various spacecraft power systems are performed. Systems' dynamics are analyzed for various operating modes, such as the shunt, battery-charge and battery-discharge modes, as well as the transition mode. Computer models using the EASY5 program are developed for the Direct Energy Transfer (DET) system, solar array switching system and partial shunt system to facilitate design, analysis and performance verification. Large-signal analyses are performed to identify stability conditions and to predict large-signal dynamic behavior for each mode of operation. The equivalent source and load characteristics of a solar array power system with a constant-power load, shunt regulator, battery charger and discharger, are identified to predict large-signal dynamic behavior. Employing the equivalent source and load, the state trajectories of shunt failure, battery discharger failure and solar array/battery lockup are predicted and verified through time-domain simulations. Small-signal analyses of the DET system are performed for the three modes of operation. The system loop gain is defined. Design guidelines for the feedback control loop of the shunt regulator, battery charger and discharger are developed to shape the system loop gain for the optimum bus dynamic performance and stability of the system. Designed subsystems are simulated both in frequency-domain and time-domain to verify the design concept. Various spacecraft power systems, such as solar array switching systems, a partial shunt system, a peak power tracking system and the COBE (Cosmic Background Explorer) power system are analyzed and simulated. Design guidelines of the power conditioning equipment for each system are provided.
  • Thumbnail Image
    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.
  • Thumbnail Image
    An Analysis of the Financial Incentives Impact on the Utility Demand-Side Management Programs
    Prastawa, Andhika (Virginia Tech, 1998-07-10)
    Many utilities implement the financial incentive plans in promoting their Demand-Side Management (DSM) programs. The plans are intended to reduce the customer investment cost for a high efficiency equipment option, so that to make the investment more attractive. Despite its potential to increase customer participation, the financial incentives could cause a considerable increase in program cost to the utility. An analysis of financial incentive impact on the utility DSM program is conducted in this thesis. The analysis uses the combination of the customer participation modeling and the cost-benefit analysis of a DSM program. A modeling of customer participation by a discrete choice model is presented. The model uses the logistic probability functions. The benefit and cost of DSM programs are explored to develop the analysis methodology. Two typical energy conservation options of DSM programs are taken for case studies to demonstrate the analysis. The analysis is also conducted to see the effect of financial incentives on the performance of DSM programs in a fluctuating marginal energy cost. The result of this research shows that the financial incentive could induce the customer participation, thus provide an increase of benefit and costs. However, this research also reveals that, in certain circumstances, the financial incentive may result in a decrease of net benefit due to significant increase of cost. These imply that utilities must carefully evaluate the financial incentive plan in their DSM programs, before the programs are implemented.
  • Thumbnail Image
    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.
  • Thumbnail Image
    Analysis of time varying load for minimum loss distribution reconfiguration
    Khan, Asif H. (Virginia Tech, 1992-04-05)
    A reconfiguration algorithm for electrical distribution system to reduce system losses is presented. The algorithm determines the switching patterns as a function of time. Either seasonal or daily time studies may be performed. Both manual and automatic switches are used to reconfigure the system for seasonal studies, whereas only automatic switches are considered for daily studies. An algorithm for load estimation is developed. The load estimation algorithm provides load information for each time point to be analyzed. The load estimation algorithm can incorporate any or all of the following: spot loads, circuit measurements, and customer time-varying diversified load characteristics. Voltage dependency of loads is considered at the circuit level. It is shown that switching at the system peak can reduce losses but may cause a marginal increase in system peak. Voltage and current constraints are incorporated in the reconfiguration algorithm. Data base tables and data structures used in the algorithm are described. Example problems are provided to illustrate results.
  • Thumbnail Image
    Analysis, monitoring and control of voltage stability in electric power systems
    Begovic, Miroslav M. (Virginia Polytechnic Institute and State University, 1989)
    The work presented in this text concentrates on three aspects of voltage stability studies: analysis and determination of suitable proximity indicators, design of an effective real-time monitoring system, and determination of appropriate emergency control techniques. A simulation model of voltage collapse was built as analytical tool on 39-bus, 10-generator power system model. Voltage collapse was modeled as a saddle-node bifurcation of the system dynamic model reached by increasing the system loading. Suitable indicators for real-time monitoring were found to be the minimum singular value of power flow Jacobian matrix and generated reactive powers. A study of possibilities for reducing the number of measurements of voltage phasors needed for voltage stability monitoring was also made. The idea of load bus coherency with respect to voltage dynamics was introduced. An algorithm was presented which determines the coherent clusters of load buses in a power system based on an arbitrary criterion function, and the analysis completed with two proposed coherency criteria. Very good agreement was obtained by simulation between the results based on accurate and approximate measurements of the state vector. An algorithm was presented for identification of critical sets of loads in a voltage unstable power system, defined as a subset of loads whose changes have the most pronounced effect on the changes of minimum singular value of load flow Jacobian or generated reactive powers. Effects of load shedding of critical loads were investigated by simulation and favorable results obtained. An investigation was also done by sensitivity analysis of proximity indicators of the effects that locations and amounts of static var compensation have on the stability margin of the system. Static compensation was found to be of limited help when voltage instabilities due to heavy system loading occur in power systems. The feasibility of implementation of the analyses and algorithms presented in this text relies on development of a feasible integrated monitoring and control hardware. The phasor measurement system which was designed at Virginia Polytechnic institute and State University represents an excellent candidate for implementation of real-time monitoring and control procedures.
  • Thumbnail Image
    Analytical methods for electromechanical forces and torque computation in brushless permanent magnet machines
    Gangla, Vineeta (Virginia Tech, 1991-02-05)
    The calculation of electromechanical forces that are present in a machine due to the magnetic field set up by current-carrying conductors and coils, especially when in the presence of permeable iron, is one of the most important and difficult problems in the vast field of the theory and design of electrical machines. It is a problem, moreover, which is usually dealt with by empirical methods based upon test results or by the use of numerical techniques such as Finite Element Analysis (FEA). In this thesis, analytical formulas are developed to evaluate the electromechanical forces and torques involved in brushless surface-mounted permanent magnet machines directly from, design parameters. In the first model, a slotless stator design is assumed while in the second model, the conductors are considered as being embedded in the stator iron. Both the models thus developed are then tested by means of a numerical method (FEA) and their utility in performing parametric studies is demonstrated in the case of the first model.
  • Thumbnail Image
    Application of Distributed Ledger Technology in Distribution Networks
    Zhou, Yue; Manea, Andrei Nicolas; Hua, Weiqi; Wu, Jianzhong; Zhou, Wei; Yu, James; Rahman, Saifur (IEEE, 2022-06-24)
    In the transition to a society with net-zero carbon emissions, high penetration of distributed renewable power generation and large-scale electrification of transportation and heat are driving the conventional distribution network operators (DNOs) to evolve into distribution system operators (DSOs) that manage distribution networks in a more active and flexible way. As a radical decentralized data management technology, distributed ledger technology (DLT) has the potential to support a trustworthy digital infrastructure facilitating the DNO-DSO transition. Based on a comprehensive review of worldwide research and practice, as well as the engagement of relevant industrial experts, the application of DLT in distribution networks is identified and analyzed in this article. The DLT features and DSO needs are first summarized, and the mapping relationship between them is identified. Detailed DSO functions are identified and classified into five categories (i.e., 'planning,' 'operation,' 'market,' 'asset,' and 'connection') with the potential of applying DLT to various DSO functions assessed. Finally, the development of seven key DSO functions with high DLT potential is analyzed and discussed from the technical, legal, and social perspectives, including peer-to-peer energy trading, flexibility market facilitation, electric vehicle charging, network pricing, distributed generation register, data access, and investment planning.
  • Thumbnail Image
    Applications of superconducting magnetic energy storage systems in power systems
    Kumar, Prem (Virginia Tech, 1989-08-05)
    A Superconducting Magnetic Energy Storage (SMES) system is a very efficient storage device capable of storing large amounts of energy. The primary applications it has been considered till now are load-leveling and system stabilization.This thesis explores new applications/benefits of SMES in power systems. Three areas have been identified. • Using SMES in conjunction with PV systems.SMES because of their excellent dynamic response and PV being an intermittent source complement one another.A scheme for this hybrid system is developed and simulation done accordingly. • Using SMES in an Asynchronous link between Power Systems. SMES when used in a series configuration between two or more systems combines the benefits of asynchronous connection, interconnection and energy storage. A model of such a scheme has been developed and the control of such a scheme is demonstrated using the EMTP. The economic benefits of this scheme over pure power interchange, SMES operation alone and a battery/dc link is shown. Improvement of transmission through the use of SMES. SMES when used for diurnal load leveling provides additional benefits like reduced transmission losses, reduced peak loading and more effective utilization of transmission facility, the impact of size and location on these benefits were studied, and if used as an asynchronous link provides power flow control.
  • Thumbnail Image
    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.
  • Thumbnail Image
    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.
  • An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy Management
    Goh, Hui Hwang; Huang, Yifeng; Lim, Chee Shen; Zhang, Dongdong; Liu, Hui; Dai, Wei; Kurniawan, Tonni Agustiono; Rahman, Saifur (IEEE, 2022-06-01)
    Reinforcement learning based energy management strategy has been an active research subject in the past few years. Different from the baseline reward function (BRF), the work proposes and investigates a multi-stage reward mechanism (MSRM) that scores the agent's step and final performance during training and returns it to the agent in real time as a reward. MSRM will also improve the agent's training through expert intervention which aims to prevent the agent from being trapped in sub-optimal strategies. The energy management performance considered by MSRM-based algorithm includes the energy balance, economic cost, and reliability. The reward function is assessed in conjunction with two deep reinforcement learning algorithms: double deep Q-learning network (DDQN) and policy gradient (PG). Upon benchmarking with BRF, the numerical simulation shows that MSRM tends to improve the convergence characteristic, reduce the explained variance, and reduce the tendency of the agent being trapped in suboptimal strategies. In addition, the methods have been assessed with MPC-based energy management strategies in terms of relative cost, self-balancing rate, and computational time. The assessment concludes that, in the given context, PG-MSRM has the best overall performance.
  • Thumbnail Image
    Blockchain-based Peer-to-peer Electricity Trading Framework Through Machine Learning-based Anomaly Detection Technique
    Jing, Zejia (Virginia Tech, 2022-08-31)
    With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies.
  • Thumbnail Image
    Building occupancy analytics based on deep learning through the use of environmental sensor data
    Zhang, Zheyu (Virginia Tech, 2023-05-24)
    Balancing indoor comfort and energy consumption is crucial to building energy efficiency. Occupancy information is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy.