The current state of Ontario???s electricity system and natural environment has prompted the provincial government to call for the province to adopt a ???culture of conservation.??? Answering this call will involve the promotion of a variety of solutions. Included in that will be the use of information and communication technology, which encompasses technologies such as home energy management system (HEMS). It is believed that the feedback and home automation features of the HEMS will enable its users to alter their electricity consumption behaviours, via net reductions and/or load shifting. This study has assessed the ability of HEMS to encourage reduction in total and on-peak electricity consumption while in a time-of-use pricing environment. Additional focus was on which consumers had the greatest success using the HEMS to adopt electricity conservation behaviours. Two hundred and sixteen participants of a Milton, Ontario HEMS pilot study were chosen to take part in this case study. These participants were divided into two equal groups: a sample group, those who received a HEMS, and a control group, those who did not receive a HEMS. Participants from both groups were asked to complete two surveys and allow their electricity consumption data to be analyzed. The initial survey was to establish some baseline information about the participants. The second survey was designed to determine if changes had occurred in the household since the initial baseline survey. Through the analysis of the survey and households electricity consumption data, conclusions were drawn on how participants used the HEMS. The study had a 2.9% relative reduction in total electricity consumption and a 13.2% relative reduction in on-peak electricity consumption. However, additional analysis of the results revealed promising findings with regard to the HEMS ability to catalyze conservation and demand management among recent time-of-use pricing adopters.

As the number of autonomous decision-making entities in the electricity grid increases, it is necessary to develop (1) new decision-making capabilities embedded within the grid's control and management, and (2) new grid architecture models ensuring that both individual and system objectives are met. This work develops (1) new decision-making mechanisms enabling residential energy users and electricity providers to interact through the use of dynamic price signals, and (2) policy recommendations to facilitate the emergence of shared architecture models describing the future state of the electricity grid. In the first part, two optimization models that capture the emerging flexible consumption, storage, and generation capabilities of residential end-users are formulated. An economic dispatch model that explicitly accounts for end-users' internal dynamics is proposed. A non-iterative pricing algorithm using convex and inverse linear programming is developed to induce autonomous residential end-users to behave cooperatively and minimize the provider's generation costs. In the second part, several factors that make the development of grid architecture models necessary from a public policy standpoint are identified and discussed. The grid architecture problem is rigorously framed as both a market failure legitimizing government intervention, and a meta-problem requiring the development of non-conventional methods of solution. A policy approach drawing on the theoretical concepts of broker, boundary object and boundary organization is proposed.

The current state of Ontario’s electricity system and natural environment has prompted the provincial government to call for the province to adopt a ‘culture of conservation.’ Answering this call will involve the promotion of a variety of solutions. Included in that will be the use of information and communication technology, which encompasses technologies such as home energy management system (HEMS). It is believed that the feedback and home automation features of the HEMS will enable its users to alter their electricity consumption behaviours, via net reductions and/or load shifting. This study has assessed the ability of HEMS to encourage reduction in total and on-peak electricity consumption while in a time-of-use pricing environment. Additional focus was on which consumers had the greatest success using the HEMS to adopt electricity conservation behaviours. Two hundred and sixteen participants of a Milton, Ontario HEMS pilot study were chosen to take part in this case study. These participants were divided into two equal groups: a sample group, those who received a HEMS, and a control group, those who did not receive a HEMS. Participants from both groups were asked to complete two surveys and allow their electricity consumption data to be analyzed. The initial survey was to establish some baseline information about the participants. The second survey was designed to determine if changes had occurred in the household since the initial baseline survey. Through the analysis of the survey and households electricity consumption data, conclusions were drawn on how participants used the HEMS. The study had a 2.9% relative reduction in total electricity consumption and a 13.2% relative reduction in on-peak electricity consumption. However, additional analysis of the results revealed promising findings with regard to the HEMS ability to catalyze conservation and demand management among recent time-of-use pricing adopters.

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. / Master of Science

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. / Ph. D.

The global energy demand in residential sector is increasing steadily every year due to advancement in technologies. The present electricity grid is designed to support peak demand rather than Peak to Average (PAR) demand. Utilities are investigating the residential Demand Response (DR) to lower the (PAR) ratio and eliminate the need of building new power infrastructure. This requires Home Energy Management System (HEMS) at grid edge to manage and control the energy demand. In this thesis, we presented an MDPSO based DR enabled HEMS model for optimal allocation of energy resources in a smart dwelling. The algorithm is designed to lower peak energy demand as well as encourage the active participation of customers by offering a reward to comply with DR request. We categorized appliances as elastic non-deferrable loads and inelastic deferrable loads based on their DR potential and operating characteristics. The scheduling of elastic and inelastic class of appliances is performed separately using canonical and binary version of PSO given how we expressed out load categories. We performed use case simulation to validate the performance of MDPSO for combination of different tariffs: Time of Use (TOU), TOU and Critical peak rebate signal (CPR), TOU and upper demand limit. Simulation results show that algorithm can reduce the electricity cost in range of 28% to 7% under increasing comfort conditions in response to TOU prices and Peak demand reduction of about 24% under TOU pricing and medium comfort conditions for single household. Under CPR DR requests, with respect to TOU pricing, there is effectively no change in the peak under the minimum comfort scenario. Furthermore, algorithm is able to suppress the peak upto 25% under combination of TOU and hard constraint on maximum power withdrawn from grid with no change in the electricity cost. Scheduling of multiple houses under TOU pricing results in peak reduction of 7 % as compared to baseline state. Under combination of TOU and CPR the aggregate peak energy demand of multiple households during DR activation time intervals is reduced by 32 %. The algorithm can suppress the peak demand by 27% under TOU and hard constraint on maximum power withdrawn from grid by multiple houses.

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Cette thèse propose un concept d'optimisation de la consommation d'énergie dans les maisons intelligentes basées sur la gestion de la demande qui repose sur l'utilisation de système d e gestion de l'énergie à la maison (HEMS) qui est en mesure de contrôler les appareils ménagers. L'avantage de ce concept est l'optimisation de la consommation d'énergie sans réduire les utilisateurs vivant confort. Un mécanisme adaptatif pour une croissance intelligente système de gestion de l'énergie de la maison qui a composé des algorithmes qui régissent l'utilisation des différents types de charges par ordre de priorité pré-sélectionné dans la maison intelligente est proposé. En outre, une méthode pourl'optimisation de la puissance générée à partir d'un hybride de systèmes d'énergie renouvelables (HRES) afin d'obtenir la demande de charge. particules technique d'optimisation essaim (PSO) est utilisé comme l'optimisation algorithme de recherche en raison de ses avantages par rapport à d'autres techniques pour réduire le coût moyen actualisé de l'énergie (LCE) avec une plage acceptable de la production en tenant compte des pertes entre la production et la demande. Le problème est défini et la fonction objective est introduite en tenant compte des valeurs de remise en forme de sensibilité dans le processus d’essaim de particules. La structure de l'algorithme a été construite en utilisant un logiciel MATLAB et Arduino 1.0.5 du logiciel.Ce travail atteint le but de réduire la charge de l'électricité et la coupure du rapport pic-moyenne (PAR). / This thesis proposes a concept of power consumption optimization in smart homes based on demand side management that reposes on using Home Energy Management System (HEMS) that is able to control home appliances. The advantage of the concept is optimizing power consumption without reducing the users living comfort. An adaptive mechanism for smart home energy management system which composed of algorithms that govern the use of different types of loads in order of pre-selected priority in smart home is proposed. In addition a method for the optimization of the power generated from a Hybrid Renewable Energy Systems (HRES) in order to achieve the load demand. Particle Swarm Optimization Technique (PSO) is used as optimization searching algorithm due to its advantages over other techniques for reducing the Levelized Cost of Energy (LCE) with an acceptable range of the production taking into consideration the losses between production and demand sides. The problem is defined and the objective function is introduced taking into consideration fitness values sensitivity in particle swarm process. The algorithm structure was built using MATLAB software and Arduino 1.0.5 Software. This work achieves the purpose of reducing electricity expense and clipping the Peak-toAverage Ratio (PAR). The experimental setup for the smart meter implementing HEMS is built relying on the Arduino Mega 2560 board as a main controller and a web application of URL http://www.smarthome-em.com to interface with the proposed smart meter using the Arduino WIFI Shield.

The increasing levels of renewable generation and the electrification of transport and heating as parts of the movement towards low-carbon energy systems to cope with climate change will place significant challenges on the electricity system to facilitate the way towards future low carbon energy systems in a cost effective way and ensure secure power delivery. New solutions and higher levels of flexibility are required than currently exist in order to reduce the integration costs of low carbon generation and demand technologies. Price-based demand response in residential sector is considered as one of these potential solutions. However, a certain level of automation is required to reduce both the uncertainty in the consumer response and the complexity for consumers to react to the price signal. This thesis presents a comprehensive and general residential optimization-based Automated Demand Response (ADR). The modelling of home appliances has been extensively developed to include all the classifications proposed in the literature, namely, deferrable and thermal in addition to new groups of critical and fully curtailable loads. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer’s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user’s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer’s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different case studies show the effectiveness of the proposed controller to minimize the household’s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks. This thesis also proposes a two-stage distribution-planning framework to assess the benefits of the proposed ADR models in response to a location-specific time of use Distribution Use of Systems Charge (DUoSC) on the required investments to connect future low-carbon technologies. The network investments and the satisfaction of consumers in terms of energy curtailment are both quantified. The first stage aims to generate location-specific time of use price signals for all users in the network, which represents their contributions in future network investments due to congestion and security constraints. The second stage relates to a group of ADR controllers at residential premises that aims to minimise the daily energy payment whilst maximising consumer comfort in response to the corresponding price signal produced from the first stage.

This thesis's main objective is to conduct a comparison study between measured values and simulated results of a demonstrator, of the intelligent home energy management (iHEM) project. The comparison helps to validate the simulation. TRNSYS software is used for the design. In this study, only the thermal energy side of the project is considered. In which system-level (both domestic hot water (DHW), space heating (SH)) and component level (solar collector, gas boiler) are considered as the parameters to compare. An attempt is made to optimize both system-level and component-level simulation outputs with measured values by adopting measured boundary conditions as simulation inputs.During the comparison, the DHW loop simulation design is modified. The measured data were given as input files for simulation, replacing the estimated values used before. This is done to optimize the simulation output with measured data. In the space heating loop (SH), the simulated building model’s parameters were changed to optimize the SH demand. After the system-level validation and optimization, the component level comparison is carried out. For this, the simulation output of solar thermal collectors and gas boiler are compared with measured values. The solar collector loop in the simulation is modified to optimize the simulated results. The seasonal and yearly efficiencies of the collector have been calculated. Solar supply fraction and gas boiler supply fraction is also determined. For the comparison, graphs are plotted for three different weeks, representing the spring, summer, and winter months of 2018.The final optimized simulation output of DHW demand is 7% less than the measured value. Even after optimizing the Space heating loop (SH), the simulated building demand is 17% more heat than the demonstrator building. The simulation's solar collector output is optimized close to the measured values. The simulated gas boiler produces 19% more than the demonstrator system to meet excess SH demand in the simulation (including losses). The overall yearly collector efficiency calculated for measured and simulated values are 58% and 50%, respectively. The estimated solar collector supply fraction and gas boiler supply fraction is 26%, 76% for measured, and 23%, 81% for simulation, respectively.