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Le Xie - One of the best experts on this subject based on the ideXlab platform.
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Impact of power system network topology errors on real-time Locational Marginal Price
Journal of Modern Power Systems and Clean Energy, 2017Co-Authors: Daehyun Choi, Le XieAbstract:This paper examines the impact of power transmission network topology change on Locational Marginal Price (LMP) in real-time power markets. We consider the case where the false status of circuit breakers (CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch. The main goal of this paper is to assess the economic impact of this misconfigured network topology on real-time LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion Price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding Prices for Marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.
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Sensitivity Analysis of Real-Time Locational Marginal Price to SCADA Sensor Data Corruption
IEEE Transactions on Power Systems, 2014Co-Authors: Daehyun Choi, Le XieAbstract:This paper examines the impact of supervisory control and data acquisition (SCADA) data corruption on real-time Locational Marginal Price (LMP) in electricity markets. We present an analytical framework to quantify LMP sensitivity with respect to changes in sensor data. This framework consists of a unified LMP sensitivity matrix subject to sensor data corruption. This sensitivity matrix reflects a coupling among the sensor data, an estimation of the power system states, and the real-time LMP. The proposed framework offers system operators an online tool to: 1) quantify the impact of corrupted data at any sensor on LMP variations at any bus; 2) identify buses with LMPs highly sensitive to data corruption; and 3) find sensors that impact LMP changes significantly and influentially. It also allows system operators to evaluate the impact of SCADA data accuracy on real-time LMP. The results of the proposed sensitivity based analysis are illustrated and verified with IEEE 14-bus and 118-bus systems with both Ex-ante and Ex-post real-time pricing models.
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impact analysis of Locational Marginal Price subject to power system topology errors
International Conference on Smart Grid Communications, 2013Co-Authors: Daehyun Choi, Le XieAbstract:This paper formulates and analyzes the impact of power transmission network topology error on real-time electricity market Prices. We consider the scenario in which the undetected false status of circuit breakers from topology error processing may lead to wrong modeling of real-time network topology, which, in turn, misleads the results of state estimation and real-time economic dispatch. In particular, we focus on the economic impact of this circuit breaker-induced network topology error on Locational Marginal Price (LMP). The primary goal of this paper is to derive a simple LMP sensitivity index that accounts for the relationship between the change in network topology and LMP. The proposed sensitivity index provides system operators a simple but effective screening of the impact of topology errors on real-time LMP. The validity of the derived sensitivity index is verified and illustrated with numerical examples in the IEEE-14 bus system.
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Quantifying the impact of unscheduled line outages on Locational Marginal Prices
North American Power Symposium 2010, 2010Co-Authors: Saeed Lotfifard, Le Xie, Mladen KezunovicAbstract:In this paper, we present a systematic approach for quantifying the impact of unscheduled line outages on real-time Locational Marginal Price (LMP). The probabilistic LMP is formulated with consideration of generation, load, and topology uncertainties. A computationally efficient 2n+1 point estimation method is adopted to calculate statistical moments of LMP due to unscheduled transmission line outages. The proposed approach is demonstrated in a modified PJM five-bus system. Result of such study is beneficial for power market participants in developing a more comprehensive bidding strategy.
Daehyun Choi - One of the best experts on this subject based on the ideXlab platform.
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Impact of power system network topology errors on real-time Locational Marginal Price
Journal of Modern Power Systems and Clean Energy, 2017Co-Authors: Daehyun Choi, Le XieAbstract:This paper examines the impact of power transmission network topology change on Locational Marginal Price (LMP) in real-time power markets. We consider the case where the false status of circuit breakers (CBs) that bypass topology error processing can generate an incorrect power system network topology, subsequently distorting the results of the state estimation and economic dispatch. The main goal of this paper is to assess the economic impact of this misconfigured network topology on real-time LMP in an entire power system with network congestion. To this end, we start with our prior result, a simple and analytical congestion Price equation, which can be applied to any single line congestion scenario. This equation can be extended to better understand the degree to which the LMP at any bus changes due to any line status error. Furthermore, it enables a rigorous analysis of the relationship between the change in LMP at any bus with respect to any line error and various physical/economical grid conditions such as the bidding Prices for Marginal generators and the locations of the congested/erroneous lines. Numerical examples on the impact analysis of this topology error are illustrated in IEEE 14-bus and 118-bus systems.
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Sensitivity Analysis of Real-Time Locational Marginal Price to SCADA Sensor Data Corruption
IEEE Transactions on Power Systems, 2014Co-Authors: Daehyun Choi, Le XieAbstract:This paper examines the impact of supervisory control and data acquisition (SCADA) data corruption on real-time Locational Marginal Price (LMP) in electricity markets. We present an analytical framework to quantify LMP sensitivity with respect to changes in sensor data. This framework consists of a unified LMP sensitivity matrix subject to sensor data corruption. This sensitivity matrix reflects a coupling among the sensor data, an estimation of the power system states, and the real-time LMP. The proposed framework offers system operators an online tool to: 1) quantify the impact of corrupted data at any sensor on LMP variations at any bus; 2) identify buses with LMPs highly sensitive to data corruption; and 3) find sensors that impact LMP changes significantly and influentially. It also allows system operators to evaluate the impact of SCADA data accuracy on real-time LMP. The results of the proposed sensitivity based analysis are illustrated and verified with IEEE 14-bus and 118-bus systems with both Ex-ante and Ex-post real-time pricing models.
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impact analysis of Locational Marginal Price subject to power system topology errors
International Conference on Smart Grid Communications, 2013Co-Authors: Daehyun Choi, Le XieAbstract:This paper formulates and analyzes the impact of power transmission network topology error on real-time electricity market Prices. We consider the scenario in which the undetected false status of circuit breakers from topology error processing may lead to wrong modeling of real-time network topology, which, in turn, misleads the results of state estimation and real-time economic dispatch. In particular, we focus on the economic impact of this circuit breaker-induced network topology error on Locational Marginal Price (LMP). The primary goal of this paper is to derive a simple LMP sensitivity index that accounts for the relationship between the change in network topology and LMP. The proposed sensitivity index provides system operators a simple but effective screening of the impact of topology errors on real-time LMP. The validity of the derived sensitivity index is verified and illustrated with numerical examples in the IEEE-14 bus system.
Lang Tong - One of the best experts on this subject based on the ideXlab platform.
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Impact of Data Quality on Real-Time Locational Marginal Price
IEEE Transactions on Power Systems, 2014Co-Authors: Liyan Jia, Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of characterizing impacts of data quality on real-time Locational Marginal Price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into Price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.
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forecasting real time Locational Marginal Price a state space approach
Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
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ACSSC - Forecasting real-time Locational Marginal Price: A state space approach
2013 Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
Jinsub Kim - One of the best experts on this subject based on the ideXlab platform.
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Impact of Data Quality on Real-Time Locational Marginal Price
IEEE Transactions on Power Systems, 2014Co-Authors: Liyan Jia, Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of characterizing impacts of data quality on real-time Locational Marginal Price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into Price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.
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forecasting real time Locational Marginal Price a state space approach
Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
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ACSSC - Forecasting real-time Locational Marginal Price: A state space approach
2013 Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
Robert J. Thomas - One of the best experts on this subject based on the ideXlab platform.
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Impact of Data Quality on Real-Time Locational Marginal Price
IEEE Transactions on Power Systems, 2014Co-Authors: Liyan Jia, Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of characterizing impacts of data quality on real-time Locational Marginal Price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into Price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.
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forecasting real time Locational Marginal Price a state space approach
Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
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ACSSC - Forecasting real-time Locational Marginal Price: A state space approach
2013 Asilomar Conference on Signals Systems and Computers, 2013Co-Authors: Jinsub Kim, Robert J. Thomas, Lang TongAbstract:The problem of forecasting the real-time Locational Marginal Price (LMP) by a system operator is considered. A new probabilistic forecasting framework is developed based on a time in-homogeneous Markov chain representation of the realtime LMP calculation. By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future Locational Marginal Prices with forecast horizons of 6–8 hours. Such a short-term forecast provides actionable information for market participants and system operators. A Monte Carlo technique is used to estimate the posterior transition probabilities of the Markov chain, and the real-time LMP forecast is computed by the product of the estimated transition matrices. The proposed forecasting algorithm is tested on the PJM 5-bus system. Simulations show marked improvements over benchmark techniques.
