Reza Habibifar | Sharif University of Technology (original) (raw)
Papers by Reza Habibifar
Sustainability
The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V)... more The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer modes, make scheduling for an intelligent parking lot (IPL) more complex; thus, they have not been considered simultaneously during IPL planning in other studies. To fill this gap, this paper presents a complete optimal schedule for an IPL in which all the above-mentioned items are considered simultaneously. Additionally, using a complete objective function—including charging/discharging rates and prices, together with penalties, discounts, and reward sets—increases the profits of IPL and EV owners. In addition, during peak times, the demand for energy from the distribution system is decreased. The performance of the proposed schedule is validated by comparing three different scenarios during numerical simulations. The results confirm that the proposed algorithm can improve the IPL’s benefits up to USD 1000 and USD 2500 compared to the cases tha...
Energies
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertai... more Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncer...
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020
Supplying sustainable energy is of a critical prominence nowadays. A main outcome of the gallopin... more Supplying sustainable energy is of a critical prominence nowadays. A main outcome of the galloping development in energy generation technologies is the ability to integrate multi-carrier energy systems that facilitates meeting the fast growth of energy demand. Energy hub is one of the main infrastructures making the incorporation of multi-carrier systems smoother. In this paper, the optimal scheduling problem of an energy hub structure equipped with combined heat and power, boiler, power-to-gas storage, thermal energy storage and electrical energy storage in order to supply power, heat and gas demands is presented. Power to gas is regarded as a new technology that creates a connection between power and gas networks by converting power to the natural gas through two processes of electrolysis and mechanization, respectively. The proposed framework is formulated as a mixed-integer linear programming (MILP) model. The simulation results demonstrate the effectiveness of the integration of multi-carrier energy storage systems in energy hub on the operation cost reduction.
Nowadays, the energy storage systems (ESSs) are widely used in different level of power systems f... more Nowadays, the energy storage systems (ESSs) are widely used in different level of power systems for dealing with the challenges caused by the integration of renewable energy sources (RESs). Among the ESSs, batteries are more suitable for being used in microgrids (MGs) at distribution level due to their special features. Therefore, this paper presents a novel approach for planning of distributed battery energy storage systems (BESSs) to enhance MGs operation, economically. The model is formulated as a mixed integer nonlinear programming problem (MINLP) and considers various types of BESSs. In order to verify the effectiveness of the proposed approach, it is applied to the IEEE 33-bus network test system and the results are reported. The outcomes show effectiveness of proposed model.
2018 Smart Grid Conference (SGC), 2018
Electric Power Systems Research, 2020
Abstract The growing trend of electric vehicles (EVs) in recent years has led to the emergence of... more Abstract The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggregators in electricity markets. Generally, the main goal of an EV aggregator is to buy electricity from the wholesale market in a cost-effective manner while satisfying the charging requirements of EV owners. Accordingly, this paper presents a decision support tool for EV aggregators which enables them to determine the optimal bidding strategy to effectively participate in the day-ahead and real-time energy, and frequency regulation markets. Indeed, the aggregator mainly obtains profit by selling energy during the high-price hours (via vehicle-to-grid (V2G) capability) and providing primary frequency regulation service to the system operator. The proposed approach is based on a two-stage stochastic programming method, where risk aversion is modeled through the conditional value-at-risk (CVaR). The underlying uncertainties including the real-time energy prices, real-time regulation service deployments, and the uncertainties associated with the EV owners (i.e., arrival time, departure time, and initial battery state-of-charge (SOC)) are modeled as stochastic processes that are represented by different sets of scenarios. The cardinality of the combined scenario set is reduced using backward probability distance algorithm to make the resulting mixed-integer linear programming (MILP) problem tractable in large-scale and real-world cases. Extensive numerical analysis with one thousand EVs and real-world market data are conducted to validate the efficiency of the proposed approach in terms of solution optimality, robustness, and computation efficiency.
2021 IEEE Texas Power and Energy Conference (TPEC), 2021
Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of p... more Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of power systems. In order to improve the flexibility of modern power systems, the surplus electricity generated by renewable sources can be deployed into several carriers, such as natural gas and heating energy via power-to-gas (PtG) and power-to-heat (PtH) technologies. This paper proposes an optimal daily energy management model of residential energy hubs integrated with power-to-X technologies. The proposed energy hub is incorporated with PtG, PtH, combined heat and power (CHP) facilities, and thermal storage to meet the required electrical, gas, and heating demands. In order to capture the electricity price fluctuations, a robust optimization model is utilized which enables controlling the robustness level in the proposed scheduling model. The model is implemented in different residential energy hub test cases and the numerical results demonstrate the effectiveness of the proposed model in terms of the operating cost minimization and reliable operation.
IEEE Access
This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system... more This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system (MRES) including distributed energy resources (DERs). The main objective of the proposed scheduling approach is the minimization of the total costs of an MRES consisting of both electricity and gas energy carriers. The proposed model considers both electrical and natural gas distribution networks, DER technologies including renewable energy resources, energy storage systems (ESSs), and combined heat and power. The uncertainties pertinent to the demand and generated power of renewable resources are modeled using the chance-constrained approach. The proposed model is applied on the IEEE 33-bus distribution system and 14-node gas network, and the results demonstrate the efficacy of the proposed approach in the matters of diminishing the total operation costs and enhancing the reliability of the system.
Sustainability
Microgrids have emerged as a practical solution to improve the power system resilience against un... more Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are ad...
Sustainability
Microgrids have emerged as a practical solution to improve the power system resilience against un... more Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are ad...
Sustainable Cities and Society
IEEE, Iranian Conference on Electrical Engineering (ICEE), 2018
Nowadays, the energy storage systems (ESSs) are widely used in different levels of power systems ... more Nowadays, the energy storage systems (ESSs) are widely used in different levels of power systems for dealing with the challenges caused by the integration of renewable energy sources (RESs). Among the ESSs, batteries are more suitable for being used in microgrids (MGs) at distribution level due to their special features. Therefore, this paper presents a novel approach for the planning of distributed battery energy storage systems (BESSs) to enhance MGs operation, economically. The model is formulated as a mixed-integer nonlinear programming problem (MINLP) and considers various types of BESSs. In order to verify the effectiveness of the proposed approach, it is applied to the IEEE 33-bus network test system and the results are reported. The outcomes show the effectiveness of the proposed model.
IEEE Smart Grid Conference, 2018
Due to the benefits of renewable energy sources (RESs), the prevalence of Microgrids (MGs) has in... more Due to the benefits of renewable energy sources (RESs), the prevalence of Microgrids (MGs) has increased in the distribution systems. The intermittent and unpredictable nature of RESs causes some challenges for the distribution system and MGs. One of the most effective solutions for the mitigation of these challenges is by using battery energy storage systems (BESSs). Despite the many benefits of BESSs, due to the high investment cost of BESSs, a planning approach should be conducted to obtain the optimal location, size, and type of BESSs in the network. For this purpose, this paper proposes a novel planning framework for BESSs to improve the MG operation economically. In this paper, the linearized AC power flow was utilized to mitigate optimization error and computational efforts of DC and AC power flow. Finally, in order to evaluate the applicability of the proposed BESS planning methodology, it is implemented on the modified IEEE 33-bus distribution network test system and its anticipated applicability is well verified.
IEEE PES Innovative Smart Grid Technologies , 2020
Supplying sustainable energy is of critical prominence nowadays. The main outcome of the gallopin... more Supplying sustainable energy is of critical prominence nowadays. The main outcome of the galloping development in energy generation technologies is the ability to integrate multi-carrier energy systems that facilitates meeting the fast growth of energy demand. Energy hub is one of the main infrastructures making the incorporation of multi-carrier systems smoother. In this paper, the optimal scheduling problem of an energy hub structure equipped with combined heat and power, boiler, power-to-gas storage, thermal energy storage, and electrical energy storage in order to supply power, heat, and gas demands is presented. Power to gas is regarded as a new technology that creates a connection between power and gas networks by converting power to natural gas through two processes of electrolysis and mechanization, respectively. The proposed framework is formulated as a mixed-integer linear programming (MILP) model. The simulation results demonstrate the effectiveness of the integration of multi-carrier energy storage systems in the energy hub on operation cost reduction.
Elsevier, Electric Power Systems Research, 2020
The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggre... more The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggregators in electricity markets. Generally, the main goal of an EV aggregator is to buy electricity from the wholesale market in a cost-effective manner while satisfying the charging requirements of EV owners. Accordingly, this paper presents a decision support tool for EV aggregators which enables them to determine the optimal bidding strategy to effectively participate in the day-ahead and real-time energy, and frequency regulation markets. Indeed, the aggregator mainly obtains profit by selling energy during the high-price hours (via vehicle-to-grid (V2G) capability) and providing primary frequency regulation service to the system operator. The proposed approach is based on a two-stage stochastic programming method, where risk aversion is modeled through the conditional value-at-risk (CVaR). The underlying uncertainties including the real-time energy prices, real-time regulation service deployments, and the uncertainties associated with the EV owners (i.e., arrival time, departure time, and initial battery state-of-charge (SOC)) are modeled as stochastic processes that are represented by different sets of scenarios. The cardinality of the combined scenario set is reduced using the backward probability distance algorithm to make the resulting mixed-integer linear programming (MILP) problem tractable in large-scale and real-world cases. Extensive numerical analysis with one thousand EVs and real-world market data are conducted to validate the efficiency of the proposed approach in terms of solution optimality, robustness, and computation efficiency.
Sustainability
The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V)... more The random decisions of electric vehicle (EV) drivers, together with the vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) energy transfer modes, make scheduling for an intelligent parking lot (IPL) more complex; thus, they have not been considered simultaneously during IPL planning in other studies. To fill this gap, this paper presents a complete optimal schedule for an IPL in which all the above-mentioned items are considered simultaneously. Additionally, using a complete objective function—including charging/discharging rates and prices, together with penalties, discounts, and reward sets—increases the profits of IPL and EV owners. In addition, during peak times, the demand for energy from the distribution system is decreased. The performance of the proposed schedule is validated by comparing three different scenarios during numerical simulations. The results confirm that the proposed algorithm can improve the IPL’s benefits up to USD 1000 and USD 2500 compared to the cases tha...
Energies
Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertai... more Battery energy systems are playing significant roles in smart homes, e.g., absorbing the uncertainty of solar energy from root-top photovoltaic, supplying energy during a power outage, and responding to dynamic electricity prices. For the safe and economic operation of batteries, an optimal battery-management system (BMS) is required. One of the most important features of a BMS is state-of-charge (SoC) estimation. This article presents a robust central-difference Kalman filter (CDKF) method for the SoC estimation of on-site lithium-ion batteries in smart homes. The state-space equations of the battery are derived based on the equivalent circuit model. The battery model includes two RC subnetworks to represent the fast and slow transient responses of the terminal voltage. Moreover, the model includes the nonlinear relationship between the open-circuit voltage (OCV) and SoC. The proposed robust CDKF method can accurately estimate the SoC in the presence of the time-varying model uncer...
2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020
Supplying sustainable energy is of a critical prominence nowadays. A main outcome of the gallopin... more Supplying sustainable energy is of a critical prominence nowadays. A main outcome of the galloping development in energy generation technologies is the ability to integrate multi-carrier energy systems that facilitates meeting the fast growth of energy demand. Energy hub is one of the main infrastructures making the incorporation of multi-carrier systems smoother. In this paper, the optimal scheduling problem of an energy hub structure equipped with combined heat and power, boiler, power-to-gas storage, thermal energy storage and electrical energy storage in order to supply power, heat and gas demands is presented. Power to gas is regarded as a new technology that creates a connection between power and gas networks by converting power to the natural gas through two processes of electrolysis and mechanization, respectively. The proposed framework is formulated as a mixed-integer linear programming (MILP) model. The simulation results demonstrate the effectiveness of the integration of multi-carrier energy storage systems in energy hub on the operation cost reduction.
Nowadays, the energy storage systems (ESSs) are widely used in different level of power systems f... more Nowadays, the energy storage systems (ESSs) are widely used in different level of power systems for dealing with the challenges caused by the integration of renewable energy sources (RESs). Among the ESSs, batteries are more suitable for being used in microgrids (MGs) at distribution level due to their special features. Therefore, this paper presents a novel approach for planning of distributed battery energy storage systems (BESSs) to enhance MGs operation, economically. The model is formulated as a mixed integer nonlinear programming problem (MINLP) and considers various types of BESSs. In order to verify the effectiveness of the proposed approach, it is applied to the IEEE 33-bus network test system and the results are reported. The outcomes show effectiveness of proposed model.
2018 Smart Grid Conference (SGC), 2018
Electric Power Systems Research, 2020
Abstract The growing trend of electric vehicles (EVs) in recent years has led to the emergence of... more Abstract The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggregators in electricity markets. Generally, the main goal of an EV aggregator is to buy electricity from the wholesale market in a cost-effective manner while satisfying the charging requirements of EV owners. Accordingly, this paper presents a decision support tool for EV aggregators which enables them to determine the optimal bidding strategy to effectively participate in the day-ahead and real-time energy, and frequency regulation markets. Indeed, the aggregator mainly obtains profit by selling energy during the high-price hours (via vehicle-to-grid (V2G) capability) and providing primary frequency regulation service to the system operator. The proposed approach is based on a two-stage stochastic programming method, where risk aversion is modeled through the conditional value-at-risk (CVaR). The underlying uncertainties including the real-time energy prices, real-time regulation service deployments, and the uncertainties associated with the EV owners (i.e., arrival time, departure time, and initial battery state-of-charge (SOC)) are modeled as stochastic processes that are represented by different sets of scenarios. The cardinality of the combined scenario set is reduced using backward probability distance algorithm to make the resulting mixed-integer linear programming (MILP) problem tractable in large-scale and real-world cases. Extensive numerical analysis with one thousand EVs and real-world market data are conducted to validate the efficiency of the proposed approach in terms of solution optimality, robustness, and computation efficiency.
2021 IEEE Texas Power and Energy Conference (TPEC), 2021
Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of p... more Growing interest in the intermittent renewable energy sources may jeopardize the flexibility of power systems. In order to improve the flexibility of modern power systems, the surplus electricity generated by renewable sources can be deployed into several carriers, such as natural gas and heating energy via power-to-gas (PtG) and power-to-heat (PtH) technologies. This paper proposes an optimal daily energy management model of residential energy hubs integrated with power-to-X technologies. The proposed energy hub is incorporated with PtG, PtH, combined heat and power (CHP) facilities, and thermal storage to meet the required electrical, gas, and heating demands. In order to capture the electricity price fluctuations, a robust optimization model is utilized which enables controlling the robustness level in the proposed scheduling model. The model is implemented in different residential energy hub test cases and the numerical results demonstrate the effectiveness of the proposed model in terms of the operating cost minimization and reliable operation.
IEEE Access
This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system... more This paper presents a day-ahead scheduling approach for a multi-carrier residential energy system (MRES) including distributed energy resources (DERs). The main objective of the proposed scheduling approach is the minimization of the total costs of an MRES consisting of both electricity and gas energy carriers. The proposed model considers both electrical and natural gas distribution networks, DER technologies including renewable energy resources, energy storage systems (ESSs), and combined heat and power. The uncertainties pertinent to the demand and generated power of renewable resources are modeled using the chance-constrained approach. The proposed model is applied on the IEEE 33-bus distribution system and 14-node gas network, and the results demonstrate the efficacy of the proposed approach in the matters of diminishing the total operation costs and enhancing the reliability of the system.
Sustainability
Microgrids have emerged as a practical solution to improve the power system resilience against un... more Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are ad...
Sustainability
Microgrids have emerged as a practical solution to improve the power system resilience against un... more Microgrids have emerged as a practical solution to improve the power system resilience against unpredicted failures and power outages. Microgrids offer substantial benefits for customers through the local supply of domestic demands as well as reducing curtailment during possible disruptions. Furthermore, the interdependency of natural gas and power networks is a key factor in energy systems’ resilience during critical hours. This paper suggests a probabilistic optimization of networked multi-carrier microgrids (NMCMG), addressing the uncertainties associated with thermal and electrical demands, renewable power generation, and the electricity market. The approach aims to minimize the NMCMG costs associated with the operation, maintenance, CO2e emission, startup and shutdown cost of units, incentive and penalty payments, as well as load curtailment during unpredicted failures. Moreover, two types of demand response programs (DRPs), including time-based and incentive-based DRPs, are ad...
Sustainable Cities and Society
IEEE, Iranian Conference on Electrical Engineering (ICEE), 2018
Nowadays, the energy storage systems (ESSs) are widely used in different levels of power systems ... more Nowadays, the energy storage systems (ESSs) are widely used in different levels of power systems for dealing with the challenges caused by the integration of renewable energy sources (RESs). Among the ESSs, batteries are more suitable for being used in microgrids (MGs) at distribution level due to their special features. Therefore, this paper presents a novel approach for the planning of distributed battery energy storage systems (BESSs) to enhance MGs operation, economically. The model is formulated as a mixed-integer nonlinear programming problem (MINLP) and considers various types of BESSs. In order to verify the effectiveness of the proposed approach, it is applied to the IEEE 33-bus network test system and the results are reported. The outcomes show the effectiveness of the proposed model.
IEEE Smart Grid Conference, 2018
Due to the benefits of renewable energy sources (RESs), the prevalence of Microgrids (MGs) has in... more Due to the benefits of renewable energy sources (RESs), the prevalence of Microgrids (MGs) has increased in the distribution systems. The intermittent and unpredictable nature of RESs causes some challenges for the distribution system and MGs. One of the most effective solutions for the mitigation of these challenges is by using battery energy storage systems (BESSs). Despite the many benefits of BESSs, due to the high investment cost of BESSs, a planning approach should be conducted to obtain the optimal location, size, and type of BESSs in the network. For this purpose, this paper proposes a novel planning framework for BESSs to improve the MG operation economically. In this paper, the linearized AC power flow was utilized to mitigate optimization error and computational efforts of DC and AC power flow. Finally, in order to evaluate the applicability of the proposed BESS planning methodology, it is implemented on the modified IEEE 33-bus distribution network test system and its anticipated applicability is well verified.
IEEE PES Innovative Smart Grid Technologies , 2020
Supplying sustainable energy is of critical prominence nowadays. The main outcome of the gallopin... more Supplying sustainable energy is of critical prominence nowadays. The main outcome of the galloping development in energy generation technologies is the ability to integrate multi-carrier energy systems that facilitates meeting the fast growth of energy demand. Energy hub is one of the main infrastructures making the incorporation of multi-carrier systems smoother. In this paper, the optimal scheduling problem of an energy hub structure equipped with combined heat and power, boiler, power-to-gas storage, thermal energy storage, and electrical energy storage in order to supply power, heat, and gas demands is presented. Power to gas is regarded as a new technology that creates a connection between power and gas networks by converting power to natural gas through two processes of electrolysis and mechanization, respectively. The proposed framework is formulated as a mixed-integer linear programming (MILP) model. The simulation results demonstrate the effectiveness of the integration of multi-carrier energy storage systems in the energy hub on operation cost reduction.
Elsevier, Electric Power Systems Research, 2020
The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggre... more The growing trend of electric vehicles (EVs) in recent years has led to the emergence of EV aggregators in electricity markets. Generally, the main goal of an EV aggregator is to buy electricity from the wholesale market in a cost-effective manner while satisfying the charging requirements of EV owners. Accordingly, this paper presents a decision support tool for EV aggregators which enables them to determine the optimal bidding strategy to effectively participate in the day-ahead and real-time energy, and frequency regulation markets. Indeed, the aggregator mainly obtains profit by selling energy during the high-price hours (via vehicle-to-grid (V2G) capability) and providing primary frequency regulation service to the system operator. The proposed approach is based on a two-stage stochastic programming method, where risk aversion is modeled through the conditional value-at-risk (CVaR). The underlying uncertainties including the real-time energy prices, real-time regulation service deployments, and the uncertainties associated with the EV owners (i.e., arrival time, departure time, and initial battery state-of-charge (SOC)) are modeled as stochastic processes that are represented by different sets of scenarios. The cardinality of the combined scenario set is reduced using the backward probability distance algorithm to make the resulting mixed-integer linear programming (MILP) problem tractable in large-scale and real-world cases. Extensive numerical analysis with one thousand EVs and real-world market data are conducted to validate the efficiency of the proposed approach in terms of solution optimality, robustness, and computation efficiency.