Optimization techniques are widely applied in the power system planning and operation to achieve more efficient and reliable power supply. With the introduction of new technologies, the complexity of today’s power system increased significantly. Intelligent optimization techniques, such as Particle Swarm Optimization (PSO), can efficiently deal with the new challenges compared to conventional optimization techniques. This thesis presents applications of discrete PSO in two specific environments. The first one is for day-ahead optimal scheduling of the reconfigurable gird with distributed energy resources. The second one is a two-step method for rapid reconfiguration of shipboard power system. Effective techniques, such as graph theory, optimal power flow and heuristic mutation, are employed to make the PSO algorithm more suitable to application environments and achieve better performance.

Combined Cooling Heating and Power (CCHP) systems have been recognized as a viable alternative to conventional electrical and thermal energy generation in buildings because of their high efficiency, low environmental impact, and power grid independence. Many researchers have presented models for comparing CCHP systems to conventional systems and for optimizing CCHP systems. However, many of the errors and uncertainties that affect these modeling efforts have not been adequately addressed in the literature. This dissertation will focus on the following key issues related to errors and uncertainty in CCHP system modeling: (a) detailed uncertainty analysis of a CCHP system model with novel characterization of weather patterns, fuel prices and component efficiencies; (b) sensitivity analysis of a method for estimating the hourly energy demands of a building using Department of Energy (DOE) reference building models in combination with monthly utility bills; (c) development of a practical technique for selecting the optimal Power Generation Unit (PGU) for a given building that is robust with respect to fuel cost and weather uncertainty; (d) development of a systematic method for integrated calibration and parameter estimation of thermal system models. The results from the detailed uncertainty analysis show that CCHP operational strategies can effectively be assessed using steady state models with typical year weather data. The results of the sensitivity analysis reveal that the DOE reference buildings can be adjusted using monthly utility bills to represent the hourly energy demands of actual buildings. The optimal PGU sizing study illustrates that the PGU can be selected for a given building in consideration of weather and fuel cost uncertainty. The results of the integrated parameter estimation study reveal that using the integrated approach can reduce the effect of measurement error on the accuracy of predictive thermal system models.

A operação eficiente do sistema é uma ferramenta fundamental para que sua vida útil se prolongue o máximo possível, garantindo o perfeito atendimento aos consumidores, além de manter os custos com energia elétrica e manutenção dentro de padrões aceitáveis. Para uma eficiente operação, é fundamental o conhecimento do sistema, pois, através deste, com ferramentas como modelos de simulação hidráulica, otimização e definição de regras, é possível fornecer ao operador condições de operacionalidade das unidades do sistema de forma racional, não dependendo exclusivamente de sua experiência pessoal, mantendo a confiabilidade do mesmo. Neste trabalho é desenvolvido um modelo computacional direcionado ao controle operacional ótimo de sistemas de macro distribuição de água potável, utilizando um simulador hidráulico, um algoritmo de otimização, considerando dois objetivos (custos de energia elétrica e benefícios hidráulicos) e um algoritmo de aprendizado para extração de regras operacionais para o sistema. Os estudos foram aplicados no sistema de macro distribuição da cidade de Goiânia. Os resultados demonstraram que podem ser produzidas estratégias operacionais satisfatórias para o sistema em substituição ao julgamento pessoal do operador. / The efficient operation of a system is a fundamental tool to postpone the system’s service life as much as possible, thus ensuring a good service to the consumer while keeping electrical energy and maintenance costs at acceptable levels. Efficient operation requires knowledge of the system, for this knowledge, supported by tools such as models for hydraulic simulation, optimization, and definition of rules, provides the operator with proper conditions for the rational operating of the system’s units without depending exclusively on personal experience while maintaining the system’s reliability. In this work is developed a computational model for the optimal operation control of macro water distribution systems using a hydraulic simulator, an optimization algorithm, and a learn algorithm to extract operational rules (strategies) for the system. These studies are to be based on the macro system of the city of Goiânia, in Brazil. The results show that solutions for satisfactory operation can be quickly produced as a substitute to the personal judgment of the operator.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

During the last decades, batch distillation is preferably used with an increasing demand over continuous one, to separate fine chemicals in chemical and petroleum industries, due to its advantages like, flexibility and high product purity. Consequently, packed distillation columns, with newly generated packing materials, are advantageous compared to plate columns because of their smaller holdups, resistivity to corrosive materials and their higher separation efficiencies. Also, in many industrial applications, mathematical models of distillation systems are frequently used in order to design effective control systems, to train operating personnel and to handle fault diagnostics. Thus, the main objective of this study is to develop a mathematical model for a multicomponent batch distillation column, which is used to separate mixtures at low operating pressures, packed with random packing materials. In multicomponent batch packed distillation, operation with optimum reflux ratio profile is important for efficiency to maximize the amount of the distillate with a specified concentration, for a given time. Therefore, it is also aimed to find the optimum reflux ratio profile for the multicomponent batch packed distillation column. A simulation algorithm is written with the aid of MATLAB and FORTRAN programming languages by taking into account pressure drop and variation of physical properties. The selected incremental bed height, &amp / #916 / z, to be used in the simulation program has an effect on the accuracy of the results. This is analyzed and the optimal incremental height is found to be 3.5 cm for a 1.5m bed height. The change in distillate compositions with a given constant reflux ratio is found to be similar with those of previous studies. The simulation code is also used to obtain responses in distillate compositions for different reflux ratios, condenser holdups and reboiler duties and compared with similar studies found from literature and found to be adequate. Finally, experiments are conducted to verify simulation algorithm by using a lab-scale packed distillation column for the separation of a polar mixture of ethanol and water. It is observed that, there is a good agreement between the experimental and simulation results. After the verification of dynamic model, optimum operation policy to maximize product amount is investigated numerically by using capacity factor approach. The column is operated with and without recycling of the holdups of the slop cut tanks, in order to examine the effect of recycling on capacity factor, CAP. It is observed that, recycling of the molar holdups of the slop cut tanks is resulted in a 28% increase in the separation efficiency.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

A operação eficiente do sistema é uma ferramenta fundamental para que sua vida útil se prolongue o máximo possível, garantindo o perfeito atendimento aos consumidores, além de manter os custos com energia elétrica e manutenção dentro de padrões aceitáveis. Para uma eficiente operação, é fundamental o conhecimento do sistema, pois, através deste, com ferramentas como modelos de simulação hidráulica, otimização e definição de regras, é possível fornecer ao operador condições de operacionalidade das unidades do sistema de forma racional, não dependendo exclusivamente de sua experiência pessoal, mantendo a confiabilidade do mesmo. Neste trabalho é desenvolvido um modelo computacional direcionado ao controle operacional ótimo de sistemas de macro distribuição de água potável, utilizando um simulador hidráulico, um algoritmo de otimização, considerando dois objetivos (custos de energia elétrica e benefícios hidráulicos) e um algoritmo de aprendizado para extração de regras operacionais para o sistema. Os estudos foram aplicados no sistema de macro distribuição da cidade de Goiânia. Os resultados demonstraram que podem ser produzidas estratégias operacionais satisfatórias para o sistema em substituição ao julgamento pessoal do operador. / The efficient operation of a system is a fundamental tool to postpone the system’s service life as much as possible, thus ensuring a good service to the consumer while keeping electrical energy and maintenance costs at acceptable levels. Efficient operation requires knowledge of the system, for this knowledge, supported by tools such as models for hydraulic simulation, optimization, and definition of rules, provides the operator with proper conditions for the rational operating of the system’s units without depending exclusively on personal experience while maintaining the system’s reliability. In this work is developed a computational model for the optimal operation control of macro water distribution systems using a hydraulic simulator, an optimization algorithm, and a learn algorithm to extract operational rules (strategies) for the system. These studies are to be based on the macro system of the city of Goiânia, in Brazil. The results show that solutions for satisfactory operation can be quickly produced as a substitute to the personal judgment of the operator.

Heat exchanger networks (HENs) are the backbone of heat integration due to their ability in energy and environmental managements. This thesis deals with two issues on HENs. The first concerns with designing of economically optimal Heat exchanger network (HEN) whereas the second focus on optimal operation of HEN in the presence of uncertainties and disturbances within the network. In the first issue, a pinch technology based optimal HEN design is firstly implemented on a 3–streams heat recovery case study to design a simple HEN and then, a more complex HEN is designed for a coal-fired power plant retrofitted with CO2 capture unit to achieve the objectives of minimising energy penalty on the power plant due to its integration with the CO2 capture plant. The benchmark in this case study is a stream data from (Khalilpour and Abbas, 2011). Improvement to their work includes: (1) the use of economic data to evaluate achievable trade-offs between energy, capital and utility cost for determination of minimum temperature difference; (2) redesigning of the HEN based on the new minimum temperature difference and (3) its comparison with the base case design. The results shows that the energy burden imposed on the power plant with CO2 capture is significantly reduced through HEN leading to utility cost saving maximisation. The cost of addition of HEN is recoverable within a short payback period of about 2.8 years. In the second issue, optimal HEN operation considering range of uncertainties and disturbances in flowrates and inlet stream temperatures while minimizing utility consumption at constant target temperatures based on self-optimizing control (SOC) strategy. The new SOC method developed in this thesis is a data-driven SOC method which uses process data collected overtime during plant operation to select control variables (CVs). This is in contrast to the existing SOC strategies in which the CV selection requires process model to be linearized for nonlinear processes which leads to unaccounted losses due to linearization errors. The new approach selects CVs in which the necessary condition of optimality (NCO) is directly approximated by the CV through a single regression step. This work was inspired by Ye et al., (2013) regression based globally optimal CV selection with no model linearization and Ye et al., (2012) two steps regression based data-driven CV selection but with poor optimal results due to regression errors in the two steps procedures. The advantage of this work is that it doesn’t require evaluation of derivatives hence CVs can be evaluated even with commercial simulators such as HYSYS and UNISIM from among others. The effectiveness of the proposed method is again applied to the 3-streams HEN case study and also the HEN for coal-fired power plant with CO2 capture unit. The case studies show that the proposed methodology provides better optimal operation under uncertainties when compared to the existing model-based SOC techniques.

The Medium Voltage DC (MVDC) architecture of shipboard power system (SPS) with higher power density and enhanced power control is seen as a future prospect in warships by US Navy. Optimal operation of SPS is essential to enable efficient power and energy usage in the tightly coupled and power limited systems. It helps in obtaining adequate and reliable power supply by rescheduling generator output and energy storage devices for different operating scenarios and can also ensure power supply to critical loads during fault conditions. The self-commutated Voltage Source Converters (VSCs) with high dynamic performance and independent control over the real and reactive powers are best suited in the MVDC architecture. Therefore, an optimal operation tool is developed for the multi-terminal VSC based MVDC SPS which minimizes the system operating costs by optimally coordinating generators and energy storage, and will also implement preventive and corrective actions for managing credible contingencies.

The UK domestic sector accounts for more than a quarter of total energy use. This energy use can be reduced through more efficient building operations. The energy efficiency can be improved through better control of heating in houses, which account for a large portion of total energy consumption. The energy consumption can be lowered by using renewable energy systems, which will also help the UK government to meet its targets towards reduction in carbon emissions and generation of clean energy. Building control has gained considerable interest from researchers and much improved ways of control strategies for heating and hot water systems have been investigated. This intensified research is because heating systems represent a significant share of our primary energy consumption to meet thermal comfort and indoor air quality criteria. Advances in computing control and research in advanced control theory have made it possible to implement advanced controllers in building control applications. Heating control system is a difficult problem because of the non-linearities in the system and the wide range of operating conditions under which the system must function. A model of a two zone building was developed in this research to assess the performance of different control strategies. Two conventional (On-Off and proportional integral controllers) and one advanced control strategies (model predictive controller) were applied to a solar heating system combined with a heat pump. The building was modelled by using a lumped approach and different methods were deployed to obtain a suitable model for an air source heat pump. The control objectives were to reduce electricity costs by optimizing the operation of the heat pump, integrating the available solar energy, shifting electricity consumption to the cheaper night-time tariff and providing better thermal comfort to the occupants. Different climatic conditions were simulated to test the mentioned controllers. Both on-off and PI controllers were able to maintain the tank and room temperatures to the desired set-point temperatures however they did not make use of night-time electricity. PI controller and Model Predictive Controller (MPC) based on thermal comfort are developed in this thesis. Predicted mean vote (PMV) was used for controlling purposes and it was modelled by using room air and radiant temperatures as the varying parameters while assuming other parameters as constants. The MPC dealt well with the disturbances and occupancy patterns. Heat energy was also stored into the fabric by using lower night-time electricity tariffs. This research also investigated the issue of model mismatch and its effect on the prediction results of MPC. MPC performed well when there was no mismatch in the MPC model and simulation model but it struggled when there was a mismatch. A genetic algorithm (GA) known as a non-dominated sorting genetic algorithm (NSGA II) was used to solve two different objective functions, and the mixed objective from the application domain led to slightly superior results. Overall results showed that the MPC performed best by providing better thermal comfort, consuming less electric energy and making better use of cheap night-time electricity by load shifting and storing heat energy in the heating tank. The energy cost was reduced after using the model predictive controller.