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<p>The advent of small-scale, distributed generators of energy has resulted in the problem of integrating them in the conventional electric power system, which is characterized by large-scale, centralized energy generators. MicroGrids have emerged as a promising solution to the integration problem and have duly received increasing research attention. Microgrids are semi-autonomous collections of controllable microsources and loads, which present themselves to the utility grid as single, controlled entities. In order to achieve the semi-autonomous and controlled nature of microgrids, especially,overcoming the challenge of balancing demand and power generation, an intelligent energy management scheme is required.</p> <p>Developing an energy management scheme is an interesting and challenging task, which provides the potential to exploit ideas from a plethora of fields like Artificial Intelligence and Machine Learning, Constrained Optimization, etc. However, testing energy management strategies on a microgrid would pose a multitude of problems,the most important of them being the unreliability and inconvenience of testing an energy management strategy, which is not optimal, on a functional microgrid. Errors in a test strategy might cause power outages and damage installed devices. Hence it is necessary to test energy management strategies on simulated microgrids.</p> <p>This thesis presents a Software Testbed of MicroGrids, specifically designed to suit the purposes of development of energy management strategies. The testbed consists of two components: Simulation Framework and Analysis Tool. The modular simulation framework enables simulation of a microgrid with microsources and loads,whose configurations can be specified by the user. The analysis tool enables visual analysis of data generated using simulations, which would enable the improvement of not only the management strategy and prediction techniques, but also the computer models used in the simulation framework. A demonstration of the software testbed's simulation and analysis capabilities is presented and possible directions for future research are suggested.</p> / Master of Science (MSc)

<p>An increasing concern over environmental impacts of fossil fuels and sustainability of energy resources is leading to significant changes in the electric power systems. Decentralized power generation, in particular, is emerging as one of the most effective and promising tools in addressing these concerns.</p> <p>Microgrids are small-scale electricity grids with elements of load, generation and storage. Microgrids have emerged as an essential building block of a future smart grid, and an enabling technology for distributed power generation and control. This thesis presents an optimization-based approach for the design and control of energy management systems (EMS) for electric microgrids. A linear programming formulation of power/energy management is proposed to minimize energy cost for a microgrid with energy storage and renewable energy generation, by taking advantage of time-of-use (TOU) pricing. The thesis also addresses the issue of sizing of the battery storage and solar power generation capacity by formulating and solving a mixed integer linear programming (MILP) problem. The aim of the optimization is to minimize the combined capital and electricity usage cost subject to applicable physical constraints. Several case scenarios are analyzed for grid-connected microgrids in residential, commercial and industrial settings, as well as a case of an islanded microgrid intended for a remote community.</p> <p>Finally, the thesis investigates circuit level control of a microgrid with EMS. A finite state machine based control logic is proposed that enables outage ride through and smooth transition between islanded and grid connected operation. Simulation results are provided to demonstrate the effectiveness of the proposed controller under various possible scenarios.</p> / Master of Applied Science (MASc)

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. / Ph. D.

In the context of wireless communications, low latency autonomous systems continue to grow in importance. Some applications of autonomous systems where low latency communication is essential are (i) vehicular network's safety performance depends on how recently the vehicles are updated on their neighboring vehicle's locations, (ii) updates from IoT devices need to be aggregated appropriately at the monitoring station before the information gets stale to extract temporal and spatial information from it, and (iii) sensors and controllers in a smart grid need to track the most recent state of the system to tune system parameters dynamically, etc. Each of the above-mentioned applications differs based on the connectivity between the source and the destination. First, vehicular networks involve a broadcast network where each of the vehicles broadcasts its packets to all the other vehicles. Secondly, in the case of UAV-assisted IoT networks, packets generated at multiple IoT devices are transmitted to a final destination via relays. Finally for the smart grid and generally for distributed systems, each source can have varying and unique destinations. Therefore in terms of connectivity, they can be categorized into one-to-all, all-to-one, and variable relationship between the number of sources and destinations. Additionally, some of the other major differences between the applications are the impact of mobility, the importance of a reduced AoI, centralized vs distributed manner of measuring AoI, etc. Thus the wide variety of application requirements makes it challenging to develop scheduling schemes that universally address minimizing the AoI. All these applications involve generating time-stamped status updates at a source which are then transmitted to their destination over a wireless medium. The timely reception of these updates at the destination decides the operating state of the system. This is because the fresher the information at the destination, the better its awareness of the system state for making better control decisions. This freshness of information is not the same as maximizing the throughput or minimizing the delay. While ideally throughput can be maximized by sending data as fast as possible, this may saturate the receiver resulting in queuing, contention, and other delays. On the other hand, these delays can be minimized by sending updates slowly, but this may cause high inter-arrival times. Therefore, a new metric called the Age of Information (AoI) has been proposed to measure the freshness of information that can account for many facets that influence data availability. In simple terms, AoI is measured at the destination as the time elapsed since the generation time of the most recently received update. Therefore AoI is able to incorporate both the delay and the inter-packet arrival time. This makes it a much better metric to measure end-to-end latency, and hence characterize the performance of such time-sensitive systems. These basic characteristics of AoI are explained in detail in Chapter 1. Overall, the main contribution of this dissertation is developing scheduling and resource allocation schemes targeted at improving the AoI of various autonomous systems having different types of connectivity, namely vehicular networks, UAV-assisted IoT networks, and smart grids, and then characterizing and quantifying the benefits of a reduced AoI from the application perspective. In the first contribution, we look into minimizing AoI for the case of broadcast networks having one-to-all connectivity between the source and destination devices by considering the case of vehicular networks. While vehicular networks have been studied in terms of AoI minimization, the impact of mobility and the benefit of a reduced AoI from the application perspective has not been investigated. The mobility of the vehicles is realistically modeled using the Simulation of Urban Mobility (SUMO) software to account for overtaking, lane changes, etc. We propose a safety metric that indicates the collision risk of a vehicle and do a simulation-based study on the ns3 simulator to study its relation to AoI. We see that the broadcast rate in a Dedicated Short Range Network (DSRC) that minimizes the system AoI also has the least collision risk, therefore signifying that reducing AoI improves the on-road safety of the vehicles. However, we also show that this relationship is not universally true and the mobility of the vehicles becomes a crucial aspect. Therefore, we propose a new metric called the Trackability-aware AoI (TAoI) which ensures that vehicles with unpredictable mobility broadcast at a faster rate while vehicles that are predicable are broadcasting at a reduced rate. The results obtained show that minimizing TAoI provides much better on-road safety as compared to plain AoI minimizing, which points to the importance of mobility in such applications. In the second contribution, we focus on networks with all-to-one connectivity where packets from multiple sources are transmitted to a single destination by taking an example of IoT networks. Here multiple IoT devices measure a physical phenomenon and transmit these measurements to a central base station (BS). However, under certain scenarios, the BS and IoT devices are unable to communicate directly and this necessitates the use of UAVs as relays. This creates a two-hop scenario that has not been studied for AoI minimization in UAV networks. In the first hop, the packets have to be sampled from the IoT devices to the UAV and then updated from the UAVs to the BS in the second hop. Such networks are called UAV-assisted IoT networks. We show that under ideal conditions with a generate-at-will traffic generation model and lossless wireless channels, the Maximal Age Difference (MAD) scheduler is the optimal AoI minimizing scheduler. When the ideal conditions are not applicable and more practical conditions are considered, a reinforcement learning (RL) based scheduler is desirable that can account for packet generation patterns and channel qualities. Therefore we propose to use a Deep-Q-Network (DQN)-based scheduler and it outperforms MAD and all other schedulers under general conditions. However, the DQN-based scheduler suffers from scalability issues in large networks. Therefore, another type of RL algorithm called Proximal Policy Optimization (PPO) is proposed to be used for larger networks. Additionally, the PPO-based scheduler can account for changes in the network conditions which the DQN-based scheduler was not able to do. This ensures the trained model can be deployed in environments that might be different than the trained environment. In the final contribution, AoI is studied in networks with varying connectivity between the source and destination devices. A typical example of such a distributed network is the smart grid where multiple devices exchange state information to ensure the grid operates in a stable state. To investigate AoI minimization and its impact on the smart grid, a co-simulation platform is designed where the 5G network is modeled in Python and the smart grid is modeled in PSCAD/MATLAB. In the first part of the study, the suitability of 5G in supporting smart grid operations is investigated. Based on the encouraging results that 5G can support a smart grid, we focus on the schedulers at the 5G RAN to minimize the AoI. It is seen that the AoI-based schedulers provide much better stability compared to traditional 5G schedulers like the proportional fairness and round-robin. However, the MAD scheduler which has been shown to be optimal for a variety of scenarios is no longer optimal as it cannot account for the connectivity among the devices. Additionally, distributed networks with heterogeneous sources will, in addition to the varying connectivity, have different sized packets requiring a different number of resource blocks (RB) to transmit, packet generation patterns, channel conditions, etc. This motivates an RL-based approach. Hence we propose a DQN-based scheduler that can take these factors into account and results show that the DQN-based scheduler outperforms all other schedulers in all considered conditions. / Doctor of Philosophy / Age of information (AoI) is an exciting new metric as it is able to characterize the freshness of information, where freshness means how representative the information is of the current system state. Therefore it is being actively investigated for a variety of autonomous systems that rely on having the most up-to-date information on the current state. Some examples are vehicular networks, UAV networks, and smart grids. Vehicular networks need the real-time location of their neighbor vehicles to make maneuver decisions, UAVs have to collect the most recent information from IoT devices for monitoring purposes, and devices in a smart grid need to ensure that they have the most recent information on the desired system state. From a communication point of view, each of these scenarios presents a different type of connectivity between the source and the destination. First, the vehicular network is a broadcast network where each vehicle broadcasts its packets to every other vehicle. Secondly, in the UAV network, multiple devices transmit their packets to a single destination via a relay. Finally, with the smart grid and the generally distributed networks, every source can have different and unique destinations. In these applications, AoI becomes a natural choice to measure the system performance as the fresher the information at the destination, the better its awareness of the system state which allows it to take better control decisions to reach the desired objective. Therefore in this dissertation, we use mathematical analysis and simulation-based approaches to investigate different scheduling and resource allocation policies to improve the AoI for the above-mentioned scenarios. We also show that the reduced AoI improves the system performance, i.e., better on-road safety for vehicular networks and better stability for smart grid applications. The results obtained in this dissertation show that when designing communication and networking protocols for time-sensitive applications requiring low latency, they have to be optimized to improve AoI. This is in contrast to most modern-day communication protocols that are targeted at improving the throughput or minimizing the delay.

The changing nature of the energy grid in recent years has prompted key stakeholders to think of ways to address incoming challenges. Transactive energy is an approach that promises to dynamically align active grid elements coming up in the previously inactive consumers' side to achieve a reliable and smarter grid. This work models the distribution grid structure as a combination of microgrids. A blockchain-in-the loop simulation framework is modelled and simulated for a residential microgrid using power system simulators and transactive agents. Blockchain smart contracts are used to coordinate peer-to-peer energy transactions in the microgrid. The model is used to test three market coordination schemes: a simple auction-less scheme, an auction-less scheme with a normalized sorting metric and an hour ahead single auction scheme with penalties for unfulfilled bids. Case studies are presented of a microgrid with 30 homes, at different levels of solar and energy storage penetration within the microgrid, all equipped with responsive and unresponsive appliances and transactive agents for the HVAC systems. The auction-less scheme with a normalized sorting metric is observed to provide a fairer advantage to smaller solar installations in comparison to the simple auction-less method. It is then concluded that the auction-less schemes are most beneficial to users, as they would not need sophisticated forecasting technology to reduce penalties from bid quantity inaccuracies, as long as the energy mix within the microgrid is diverse enough. / Master of Science / The legacy energy industry involved the bulk transfer of energy from huge generation plants through long transmission lines to the end consumers. However, with the onset of improved renewable energy and information technologies, energy is now being generated closer to the consumer side with appliances capable of actively participating in the energy system now widely available. Transactive energy with blockchain has been proposed in order to dynamically coordinate these systems to work towards a more reliable and smarter grid using economic value in a transparent and secure way. This work models a transactive power grid as a combination of microgrids using a blockchain network to coordinate hourly peer-to-peer energy transactions. The blockchain-in-the-loop simulation model is used to compare three different market mechanisms in a residential microgrid of 30 homes with varying levels of solar panels, batteries and transactive thermostats installed. Two auction-less schemes - one with a normalized sorting metric - and an hour ahead single auction mechanism are analyzed. While the auction-less scheme with the normalized metric is seen to be fairer than the simple auction-less scheme, it is concluded that the auction-less schemes are most beneficial to residents. This is because sophisticated forecasting technology would not be needed like in the hour ahead auction scheme, provided that the microgrid has participants with diverse energy consumption and production profiles throughout the day.

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles). / Doctor of Philosophy / Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.

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. / PHD / For power system operation, the demand and supply should be equal at all time. During peak hours, the demand becomes very high. One way to keep the balance is to provide more generation capacity, and thus more expensive and less efficient generators are brought online, which causes higher production cost and more pollution. Instead, an alternative is to encourage the load reduction via demand response (DR): customers reduce load upon receiving a signal sent by the utility company, usually in exchange for some monetary payback. For buildings to participate in DR, an affordable automation system and related control algorithms are needed. This dissertation proposed a cost-effective, self-learning and data-driven framework to facilitate small- and medium-sized commercial buildings or large homes in air-conditioner (AC) units control during DR events. The devised framework requires little human configuration; it learns the building behavior by analyzing the operation data. Two algorithms are proposed to coordinate multiple AC units in a building with two goals: firstly, reducing the total AC power consumption below certain limit, as agreed between the building owners and their utility company. Secondly, minimizing occupants’ thermal discomfort caused by limiting AC operation. The effectiveness of the framework is investigated in this dissertation based on data collected from a real building.