Multidimensional Scenario Selection for Power Systems With Stochastic Failures

Mohammadi, Farshad; Sahraei-Ardakani, Mostafa

doi:10.1109/tpwrs.2020.2990877

Cited by 23 publications

(11 citation statements)

References 29 publications

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“…The resulting scenario set is created in a way that the first scenario represents the best possible case (no outage), and the last scenario represents the worst possible case in terms of outages, and the rest in between. Interested individuals can refer to [43] where MDSS is explained with details. Simulation Environment: While there are a number of appropriate software environment options, we chose to use Java in combination with IBM CPLEX as our simulation environment.…”

Section: Scenario Selection Methodmentioning

confidence: 99%

“…Simulation Environment: While there are a number of appropriate software environment options, we chose to use Java in combination with IBM CPLEX as our simulation environment. Java is fast and offers flexible memory management [43], [44]. The machine we used to run the methods utilizes Intel® Core™ i7-7700 CPU @3.60GHz as a central and only processor combined with 16.0 GB of RAM, which is configured as dual-channel bandwidth @ 2.40 GHz.…”

Section: Scenario Selection Methodmentioning

confidence: 99%

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Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Mohammadi

2020

IEEE Access

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Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

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Section: Scenario Selection Methodmentioning

confidence: 99%

Section: Scenario Selection Methodmentioning

confidence: 99%

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Mohammadi

2020

IEEE Access

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“…These four methods have their advantages and shortcomings, as well as their scope of application. There are more in-depth researches in the theory of performance appraisal management, and some large enterprises have made outstanding explorations in the practice of performance appraisal management [9]. The main problem is that the corporate culture in different regions is different, the assessment methods used, the assessment system established and the assessment indexes also differ, and a unified performance assessment management system has not been established.…”

Section: Current Status Of Researchmentioning

confidence: 99%

Optimization Study of Multidimensional Big Data Matrix Model in Enterprise Performance Evaluation System

2021

Wireless Communications and Mobile Computing

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This paper uses a multidimensional big data matrix model to optimize the analysis and conduct a systematic construction of the enterprise performance evaluation system. The adoption of new research methods and perspectives to promote the study of the use of performance information is of great significance to achieve the effectiveness, science, and sustainability of corporate performance management. To solve the problem of objectivity and scientificity of performance information use, this part attempts to analyze performance information use from the perspective of the multidimensional big data matrix, focusing on the techniques and methods in the process of promoting performance information use from the multidimensional big data matrix and tries to construct a system model of enterprise performance information use from two dimensions: the use of performance information sources and the use of performance information results. Based on multiple theoretical hypotheses, a theoretical and empirical basis is provided for the division of demand dimensions of enterprise performance evaluation system. Through social capital theory, three dimensions of network social capital, cognitive social capital, and structural social capital are hypothesized, and the logistic regression method is applied for empirical study. The results show that these three dimensions have significant effects on the knowledge demand of enterprise performance evaluation systems. It is verified that the multidimensional big data matrix can enhance the quality of performance information sources and improve the objectivity of performance information. In the performance information source use dimension, the analysis verified that the collection and preprocessing technology of big data can realize the automation, real-time, and diversification of information collection and preprocessing, and enhance the objectivity of performance information. Big data helps to improve the quality and effectiveness of performance information results use. In the dimension of using performance information results, the distributed computing and analysis processing technology of big data can assist the decision support system, and the use of information can be shifted from micromanagement to decision support, to realize the scientific use of performance information and improve the quality of enterprise management decisions.

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“…However, as the objective of this paper is to compare methods regarding their results and expectations, the formulation and algorithms are not discussed in detail in this paper. Interested readers are referred to [20][21][22] for further discussions of the mathematical modelling and algorithms.…”

Section: Figurementioning

confidence: 99%

“…Hence, the scenario set should be selected in a way that the uncertain future is well represented within a small number of scenarios. Recently, the multidimensional scenario creation method is developed, which is shown to have superior performance compared to many existing techniques for solving preventive unit commitment during severe weather [20].…”

Section: Introductionmentioning

confidence: 99%

Deterministic proxies for stochastic unit commitment during hurricanes

Mohammadi

Jafarishiadeh

Xue

et al. 2020

IET Generation Trans & Dist

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Severe weather threatens the reliability of the power supply by damaging the network. In the case of hurricanes, tens of elements may fail, which would lead to power outages. Under such circumstances, preventive unit commitment methods can model the probabilistic failure forecasts and minimise the power outages. Preventive stochastic unit commitment is an effective method to consider failure forecasts to reduce the power outage. Although stochastic unit commitment produces high‐quality solutions, it is computationally burdensome. Thus, this paper evaluates proxy deterministic methods with lighter computational compared with stochastic unit commitment on both the solution time and quality. Adjusted spinning reserve requirements, engineering judgment‐based rules, and robust preventive operation are among the evaluated methods. Numerical results are obtained for the synthetic grid on the footprint of Texas with 2000 buses. The results suggest that while some proxy methods, such as standard spinning‐reserve and adjusted spinning‐reserve with 6% to 30% of the spinning capacity, may not be as effective as the stochastic method, others, such as robust optimisation, deliver the majority of the stochastic benefits with substantially less (85%) computational time. Monte Carlo simulations are used to evaluate the quality of solutions in reducing the expected unserved load and over‐generation.

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Multidimensional Scenario Selection for Power Systems With Stochastic Failures

Cited by 23 publications

References 29 publications

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Optimization Study of Multidimensional Big Data Matrix Model in Enterprise Performance Evaluation System

Deterministic proxies for stochastic unit commitment during hurricanes

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