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User Comfort Aware Energy Management System for Smart Homes
The residential sector is responsible for the consumption of almost 40% energy along-with carbon dioxide emissions. Approximately 50% of overall energy consumed in residential sector is directly related to cooling, heating, ventilation and lighting. Thus, it is crucial to optimally control heating, cooling and ventilation systems for the reduction of energy consumption in a residential sector. Consequently, increasing energy demand and pressure of carbon emission reduction in a residential sector encourage the people to purchase green energy or adopt energy management techniques. In this regard, Demand Response (DR) is an important topic due to the integration of Information and Communication Technology (ICT) into power grid to meet the growing energy demand. To increase the effectiveness of DR programs, Demand Side Management (DSM) systems have been widely used to enable end users to optimally manage and control their load. The design and implementation of various DSM strategies have been widely studied in the literature since last decade. Although, the performance of these techniques have been scarcely analysed and the results show remarkable impacts regarding residential energy management. However, there are still many factors i.e., user involvements for comfort management, thermal and environmental constraints, and real-time dynamic pricing which need to be incorporated, holistically. In this dissertation, we have proposed various residential energy management solutions while introducing human participation with thermal, environmental and utility constrains (limits on generation, transmission and distribution systems). Since, the centralized solutions are more attractive, although, they have more communication overhead and computational complexity. So, we majorly focus on the development of energy management algorithms based upon heuristic optimization techniques focussing on residential side. Furthermore, we also develop a co-optimization program to consider utility constraints; energy generation, transmission, distribution, storage and locational marginal pricing. This work gives a holistic view of the overall energy and water systems including generation and consumption facilities. Keeping in view the aforementioned challenges and needs related to energy management in a smart home, we first model different types of homes and smart appliances and then design mathematical optimization models by taking into consideration social welfare constrains (i.e., cost and comfort). Homes and appliances are categorized based on customer lifestyle and energy consumption requirements. Then heuristic based Wind Driven Optimization (WDO) and Knapsack-WDO algorithms have been used for cost, user discomfort and peak reductions. The similar problem has also been solved using Genetic Algorithm (GA) and ”fmincon” solver. However, the thermal, environmental and user presence constraints are considered in these algorithms. Furthermore, the WDO, K-WDO, GA and fmincon algorithms use day-ahead pricing schemes which are not very difficult to utilize as compared to real time price signal. Because DR is a function of aggregate energy consumption and is dynamic in nature. So to develop an optimization program considering real time price signal, Optimal Stopping Rule (OSR) theory has been used. We then address the “Cyber Physical System” aspect of residential load management using multi-agent technology. We present a system framework which controls the interactions among various agents through Agent Communication Language (ACL). Moreover, it has also been demonstrated that how the intelligent framework is used to optimize the energy consumption and electricity cost while improving the user comfort. The next chapter discusses the role of electricity prices in the context of residential energy management. Residential buildings use hourly electricity prices and optimization algorithms to control the loads. Whereas, these prices are set by utility on aggregated energy consumption basis. To analyse the impact of aggregated DR signal on users, we first develop a model to manage the residential load. We then present a novel mathematical model to distribute electricity prices on the basis of net consumption. The last contribution of the dissertation explores the energy generation and water production facilities in the context of energy water-nexus domain. As the en- ergy and water are two separate and uncoupled infrastructure systems, however in reality, these are interlinked systems, serving in their respective domains. So to provide energy and potable water to residential as well as industrial sectors, a co-optimization is required. In this regard, a co-optimization program has been developed for the supply side of energy and water networks. This program uses power, water, and co-generation facilities in relation to generation, transmission, and distribution constraints. Then MATLAB and General Algebraic Modelling System (GAMS) tools have been used to solve the optimization problem. Further- more, to meet power and water demands during on-peak hours, a storage facility is also introduced. At the end, extensive simulations have been conducted to analyse the performance of the proposed algorithms. All simulation scenarios are considered in terms of energy generation, electricity cost and Peak to Average Ratio (PAR) reductions subject to variable load, utility and consumer constraints. The results show the enhancements in energy consumption and cost reductions and also highlight the possible trade-offs in different scenarios.