Smart grid is about integration of distributed energy resources (DERs) into the energy systems, especially electricity grid. DERs include renewable energy resources such as wind and solar, energy storages such as electrical and thermal energy storage, demand response programs, smart homes, and electric vehicles with their charging stations. DERs have significant advantages such as reduction of operation costs, emission, and peak as well as the increase of reliability, resiliency, stability, and voltage profile in smart grids. They also prevent establishment of fossil fuel power plants and expansion of transmission lines by locating in electricity distribution grid and transmission lines. The advantages approve the financial, technical, and environmental effects of the DERs in smart grids. An operation/planning approach such as EHs/IEHs is required to utilization of DERs in the Smart Grid. EH is a super node in electricity power system which connects different energy networks such as gas, electricity, heating, or cooling. The EH can be developed by DERs for operation and planning purposes. The EHs can be located in different parts of the energy networks to form IEHs. Despite the significant advantages of utilization of DERs in EHs of Smart Grids, they should be utilized by information and communication technologies (ICTs), which results in Cyber-Physical Power Systems (CPPSs) vulnerable to different cyberattacks. The vulnerability of DERs in EHs of Smart Grid leads to jeopardizing the reliability, stability, and resiliency of power systems since integrity, confidentiality, or availability cyberattacks might bypass the detection systems to take control of DERs for malicious purposes such as congestion, cascading failure, blackout, undervoltage/overvoltage, or costs. In this research, some cyberattacks are modeled on DERs in EHs and IEHs of Smart Grid, and the vulnerabilities of DERs to the cyberattacks in the developed EHs/IEs are approved: First, an integrity cyberattack is modeled and applied to the DR program (time/incentive-based) in the developed EH in electricity distribution grid in order to control the performance of the EH and its negative effects on the grid. The attacker aims to manipulate the system by both raising peak demand and lowering customers' energy bills simultaneously. This strategy is designed to deceive customers into participating in falsified Demand Response (DR) programs, ultimately leading to an increase in the overall peak demands of the system which jeopardizes the reliability of the system. Second, an integrity FDI cyberattack is modeled and applied on the developed IEHs in transmission lines in order to control the performance of the IEH and its negative effects on the transmission lines. This cyberattack is modeled to manipulate the transmission lines energy demands in order to threaten reliability and stability of the system by bypassing detection systems. Finally, the attacker targets the developed EHs integrated by DERs by maximizing the costs associated with operation, emission, and energy not supplied costs. The attacker objective is to adversely affect the financial, technical, and environmental advantages of integration of DERs to the system. Hence, powerful remedial actions are required to alleviate the adverse effects of DERs, manipulated by attackers, in the developed EHs. Therefore, a remedial action is designed by min-max formulation in order to mitigate the adverse effects of DERs on financial, technical, and environmental terms. The remedial action reduces the imposed costs by changing the status of EH devices. The results highlight the role of DERs in reducing costs and emphasize the need for their proactive security measures in cyber-physical power systems.

Electric power distribution systems are gradually adopting new advancements in communication, control, measurement, and metering technologies to help realize the evolving concept of Smart Grids. Future distribution systems will facilitate increased and active participation of customers in Demand Side Management activities, with customer load profiles being primarily governed by real-time information such as energy price, emission, and incentive signals from utilities. In such an environment, new mathematical modeling approaches would allow Local Distribution Companies (LDCs) and customers the optimal operation of distribution systems and customer's loads, considering various relevant objectives and constraints. This thesis presents a mathematical model for optimal and real-time operation of distribution systems. Thus, a three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which incorporates comprehensive and realistic models of relevant distribution system components. A novel optimization objective, which minimizes the energy purchased from the external grid while limiting the number of switching operations of control equipment, is considered. A heuristic method is proposed to solve the DOPF model, which is based on a quadratic penalty approach to reduce the computational burden so as to make the solution process suitable for real-time applications. A Genetic Algorithm based solution method is also implemented to compare and benchmark the performance of the proposed heuristic solution method. The results of applying the DOPF model and the solution methods to two distribution systems, i.e., the IEEE 13-node test feeder and a Hydro One distribution feeder, are discussed. The results demonstrate that the proposed three-phase DOPF model and the heuristic solution method may yield some benefits to the LDCs in real-time optimal operation of distribution systems in the context of Smart Grids. This work also presents a mathematical model for optimal and real-time control of customer electricity usage, which can be readily integrated by industrial customers into their Energy Hub Management Systems (EHMSs). An Optimal Industrial Load Management (OILM) model is proposed, which minimizes energy costs and/or demand charges, considering comprehensive models of industrial processes, process interdependencies, storage units, process operating constraints, production requirements, and other relevant constraints. The OILM is integrated with the DOPF model to incorporate operating constraints required by the LDC system operator, thus combining voltage optimization with load control for additional benefits. The OILM model is applied to two industrial customers, i.e., a flour mill and a water pumping facility, and the results demonstrate the benefits to the industrial customers and LDCs that can be obtained by deploying the proposed OILM and three-phase DOPF models in EHMSs, in conjunction with Smart Grid technologies.

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.

The increasing price of fuel and energy, combined with environmental laws and regulations, have led many different energy producers to integrate renewable, clean energy sources with non-renewable ones, forming the idea of energy hubs. Energy hubs are systems of technologies where different energy forms are conditioned and transformed. These energy hubs offer many advantages compared to traditional single-energy sources, including increased reliability and security of meeting energy demand, maximizing use of energy and materials resulting in increasing the overall system efficiency. In this thesis, we consider an energy hub consisting of natural gas (NG) turbines for the main source of energy— electricity and heat— combined with two renewable energy sources—wind turbines and PV solar cells. The hub designed capacity is meant to simulate and replace the coal-fired Nanticoke Generating Station with NG-fired power plant. The generating station is integrated with renewable energy sources, including wind and solar. The hub will also include water electrolysers for hydrogen production. The hydrogen serves as an energy storage vector that can be used in transportation applications, or the hydrogen can be mixed into the NG feed stream to the gas turbines to improve their emission profile. Alkaline electrolysers’ technology is fully mature to be applied in large industrial applications. Hydrogen, as an energy carrier, is becoming more and more important in industrial and transportation sectors, so a significant part of the thesis will focus on hydrogen production and cost. In order to achieve the goal of replacing the Nanticoke Coal-fired Power Plant by introducing the energy hub concept, the study investigates the modeling of the combined system of the different technologies used in terms of the total energy produced, cost per kWh, and emissions. This modeling is done using GAMS® in order to make use of the optimization routines in the software. The system is modeled so that a minimum cost of energy is achieved taking into account technical and thermodynamic constrains. Excess energy produced during off-peak demand by wind turbines and PV solar cells is used to feed the electrolyser to produce H2 and O2. Through this method, a significant reduction in energy cost and greenhouse gas (GHG) emissions are achieved, in addition to an increased overall 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.

The increasing price of fuel and energy, combined with environmental laws and regulations, have led many different energy producers to integrate renewable, clean energy sources with non-renewable ones, forming the idea of energy hubs. Energy hubs are systems of technologies where different energy forms are conditioned and transformed. These energy hubs offer many advantages compared to traditional single-energy sources, including increased reliability and security of meeting energy demand, maximizing use of energy and materials resulting in increasing the overall system efficiency. In this thesis, we consider an energy hub consisting of natural gas (NG) turbines for the main source of energy— electricity and heat— combined with two renewable energy sources—wind turbines and PV solar cells. The hub designed capacity is meant to simulate and replace the coal-fired Nanticoke Generating Station with NG-fired power plant. The generating station is integrated with renewable energy sources, including wind and solar. The hub will also include water electrolysers for hydrogen production. The hydrogen serves as an energy storage vector that can be used in transportation applications, or the hydrogen can be mixed into the NG feed stream to the gas turbines to improve their emission profile. Alkaline electrolysers’ technology is fully mature to be applied in large industrial applications. Hydrogen, as an energy carrier, is becoming more and more important in industrial and transportation sectors, so a significant part of the thesis will focus on hydrogen production and cost. In order to achieve the goal of replacing the Nanticoke Coal-fired Power Plant by introducing the energy hub concept, the study investigates the modeling of the combined system of the different technologies used in terms of the total energy produced, cost per kWh, and emissions. This modeling is done using GAMS® in order to make use of the optimization routines in the software. The system is modeled so that a minimum cost of energy is achieved taking into account technical and thermodynamic constrains. Excess energy produced during off-peak demand by wind turbines and PV solar cells is used to feed the electrolyser to produce H2 and O2. Through this method, a significant reduction in energy cost and greenhouse gas (GHG) emissions are achieved, in addition to an increased overall efficiency.