The Experts below are selected from a list of 4269 Experts worldwide ranked by ideXlab platform
Abdullah Abusorrah - One of the best experts on this subject based on the ideXlab platform.
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Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks
IEEE Transactions on Power Systems, 2016Co-Authors: Xiaping Zhang, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper studies the role of hourly Economic Demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly Economic Demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity Demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.
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Demand response exchange in the stochastic day ahead scheduling with variable renewable generation
IEEE Transactions on Sustainable Energy, 2015Co-Authors: Hongyu Wu, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper proposes a pool-based Demand response exchange (DRX) model in which Economic Demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-Demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling.
Ahmed Alabdulwahab - One of the best experts on this subject based on the ideXlab platform.
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Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks
IEEE Transactions on Power Systems, 2016Co-Authors: Xiaping Zhang, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper studies the role of hourly Economic Demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly Economic Demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity Demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.
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Demand response exchange in the stochastic day ahead scheduling with variable renewable generation
IEEE Transactions on Sustainable Energy, 2015Co-Authors: Hongyu Wu, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper proposes a pool-based Demand response exchange (DRX) model in which Economic Demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-Demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling.
Mohammad Shahidehpour - One of the best experts on this subject based on the ideXlab platform.
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Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks
IEEE Transactions on Power Systems, 2016Co-Authors: Xiaping Zhang, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper studies the role of hourly Economic Demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly Economic Demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity Demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.
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Demand response exchange in the stochastic day ahead scheduling with variable renewable generation
IEEE Transactions on Sustainable Energy, 2015Co-Authors: Hongyu Wu, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper proposes a pool-based Demand response exchange (DRX) model in which Economic Demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-Demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling.
James A. Yunker - One of the best experts on this subject based on the ideXlab platform.
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Stochastic CVP Analysis with Economic Demand and Cost Functions
Review of Quantitative Finance and Accounting, 2001Co-Authors: James A. YunkerAbstract:The analysis focuses on key concepts associated with the extensive CVP under uncertainty literature which has developed since the seminal contribution by Jaedicke and Robichek (1964). For the most part the previous literature has not incorporated Economic functions relating production quantity to price and/or average cost. This model developed herein incorporates a linear Demand function and a quadratic average cost function. Explicit solutions are found for five “special quantities”: (1) the lowest quantity which sets breakeven probability equal to the minimum acceptable level, (2) the quantity which maximizes breakeven probability, (3) the quantity which maximizes a Cobb-Douglas utility function defined on expected profits and breakeven probability, (4) the quantity which maximizes expected profits, and (5) the highest quantity which sets breakeven probability equal to the minimum acceptable level. Comparative statics effects are determined of the various model parameters on the five special quantities. A “CVP possibilities graph” is developed showing attainable combinations of expected profits and breakeven probability. Possible applications of the model are discussed.
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Stochastic CVP Analysis with Economic Demand and Cost Functions
Review of Quantitative Finance and Accounting, 2001Co-Authors: James A. YunkerAbstract:The analysis focuses on key concepts associated with the extensive CVP under uncertainty literature which has developed since the seminal contribution by Jaedicke and Robichek (1964). For the most part the previous literature has not incorporated Economic functions relating production quantity to price and/or average cost. This model developed herein incorporates a linear Demand function and a quadratic average cost function. Explicit solutions are found for five "special quantities": (1) the lowest quantity which sets break even probability equal to the minimum acceptable level, (2) the quantity which maximizes break even probability, (3) the quantity which maximizes a Cobb-Douglas utility function defined on expected profits and break even probability, (4) the quantity which maximizes expected profits, and (5) the highest quantity which sets break even probability equal to the minimum acceptable level. Comparative statics effects are determined of the various model parameters on the five special quantities. A "CVP possibilities graph" is developed showing attainable combinations of expected profits and break even probability. Possible applications of the model are discussed. Copyright 2001 by Kluwer Academic Publishers
Hongyu Wu - One of the best experts on this subject based on the ideXlab platform.
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Demand response exchange in the stochastic day ahead scheduling with variable renewable generation
IEEE Transactions on Sustainable Energy, 2015Co-Authors: Hongyu Wu, Mohammad Shahidehpour, Ahmed Alabdulwahab, Abdullah AbusorrahAbstract:This paper proposes a pool-based Demand response exchange (DRX) model in which Economic Demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-Demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling.
