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Exact and Heuristic Approaches for the Stochastic N-k Interdiction in Power Grids

Exact and Heuristic Approaches for the Stochastic N-k Interd...

引用

international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Kaarthik Sundar Andrew Mastin Manuel Garcia Russell Bent Jean-Paul Watson Los Alamos National Laboratory Los Alamos NM Lawrence Livermore National Laboratory Livermore CA

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

the article introduces the stochastic N-k interdiction problem for power grid operations and planning that aims to identify a subset of k components (out of N components) that maximizes the expected damage measured in terms of load shed. Uncertainty is modeled through a fixed set of outage scenarios, where each scenario represents a subset of components removed from the grid. We formulate the stochastic N-k interdiction problem as a bi-level optimization problem and propose two algorithmic solutions. the first approach reformulates the bi-level stochastic optimization problem to a single-level, mixed-integer linear program (MILP) by dualizing the inner problem and solving the resulting problem directly using a MILP solver to global optimality. the second is a heuristic cutting-plane approach, which is exact under certain assumptions. We compare these approaches in terms of computation time and solution quality using the IEEE-Reliability Test System and present avenues for future research.

关键词： Uncertainty Power measurement Stochastic processes Probabilistic logic Power grids Planning Optimization

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Vaffel - a tool for forecasting transmission line failures based on Bayesian updating, renalysis data and medium range weather forecasts

Vaffel - a tool for forecasting transmission line failures b...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Øystein Rognes Solheim Ingeborg Ligaarden Boye Annfelt Høverstad thomas Trøtscher Markus Andersen Magnus Korpås Dept. of Data Science Statnett SF Oslo Norway Dept. of Electric Power Engineering NTNU Oslo Norway Dept. of Data Science Statnett SF Trondheim Norway Dept. of Computer Science NTNU Trondheim Norway Dept. of Electric Power Engineering NTNU Trondheim Norway

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

this paper presents a method for forecasting the probability of weather-related transmission line failures. the method combines failure statistics, a Bayesian updating scheme and reanalysis weather data to produce fragility curves that model the relationship between weather exposure for a component and the probability of failure. the fragility curves are combined with medium range weather forecasts extracted at each span of the individual transmission lines to obtain a forecast of the hourly probability of failure for each line. A tool that implements the method has been developed and is currently being tested operationally within the Norwegian TSO Statnett. We show the data requirements and demonstrate the use of a dashboard that expose the forecasts to an end-user.

关键词： Power transmission lines Weather forecasting Probabilistic logic Data models Power system reliability Bayes methods Reliability

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Mitigating the Impact of Uncertain Wildfire Risk on Power Grids through Topology Control

Mitigating the Impact of Uncertain Wildfire Risk on Power Gr...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Yuqi Zhou Kaarthik Sundar Hao Zhu Deepjyoti Deka Department of Electrical Engineering University of Texas Austin TX Los Alamos National Laboratory Los Alamos NM

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

Wildfires pose a significant threat to the safe and reliable operations of the electric grid. To mitigate wildfire risk, system operators resort to public safety power shutoffs, or PSPS, that shed load for a subset of customers. As wildfire risk forecasts are stochastic, such decision-making may often be sub-optimal. this paper proposes a two-stage topology control problem that jointly minimizes generation and load-shedding costs in the face of uncertain fire risk. Compared to existing work, we include pre-and post-event topology control actions and consider scenarios where the wildfire risk is known with low and high confidence. the effectiveness of the proposed approach is demonstrated using a benchmark test system, artificially geo-located in Southern California, and using stochastic wildfire risk data that exists in the literature. Our work provides a crucial study of the comparative benefits of pre-event versus post-event control and the effects of wildfire risk accuracy on each control strategy.

关键词： Wildfires Costs Decision making Probabilistic logic Control systems Public security Power grids

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Development of Data Compression Techniques for IoT Data Transmission in Isolated Aquaculture Using LoRa through LoRaWAN Gateway Antares Telkom

Development of Data Compression Techniques for IoT Data Tran...

引用

international conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Helmi Ahmad Khaderani Aldo Agusdian Iskandar School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798331527686

ISBN: (纸本)9798331527693

Aquaculture in remote areas challenges significant monitoring due to limited internet access. this research develops and implements an IoT communication system using LoRa RFM95W module, ESP32 microcontroller, and Telkom's LoRaWAN Gateway Antares for efficient and reliable sensor data transmission. LoRa operates in the 920–923 MHz band which offers long-range communication with low power consumption and is ideal for remote IoT applications. Class A LoRaWANare selected for power efficiency and an optimal Spreading Factor (SF) is chosen to balance coverage, data rate and power consumption. Data compression techniques are applied to minimize data loss, reduce data size, optimize bandwidth usage and ensure real-time data integration. Experiments with various compression algorithms and distances showed that the unishox2 algorithm achieved the best compression ratio, capable of transmitting data sizes of up to 347 bytes with minimal signal loss on SF9.

关键词： Power demand Microcontrollers LoRaWAN Data compression logic gates Propagation losses Telecommunications Internet of things Compression algorithms Aquaculture

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Deep Probability Segmentation: Are Segmentation Models Probability Estimators?

Deep Probability Segmentation: Are Segmentation Models Proba...

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international conference on Application of Information and Communication Technologies (AICT)

作者： Simone Fassio Simone Monaco Daniele Apiletti Politecnico di Torino Torino Italy Deparment of Control and Computing Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Deep learning has revolutionized various fields by enabling highly accurate predictions and estimates. One important application is probabilistic prediction, where models estimate the probability of events rather than deterministic outcomes. this approach is particularly relevant and, therefore, still unexplored for segmentation tasks where each pixel in an image needs to be classified. Conventional models often overlook the probabilistic nature of labels, but accurate uncertainty estimation is crucial for improving the reliability and applicability of *** this study, we applied Calibrated Probability Estimation (CaPE) to segmentation tasks to evaluate its impact on model calibration. Our results indicate that while CaPE improves calibration, its effect is less pronounced compared to classification tasks, suggesting that segmentation models can inherently provide better probability estimates. We also investigated the influence of dataset size and bin optimization on the effectiveness of calibration. Our results emphasize the expressive power of segmentation models as probability estimators and incorporate probabilistic reasoning, which is crucial for applications requiring precise uncertainty quantification.

关键词： Image segmentation Accuracy Uncertainty Estimation Weather forecasting Predictive models Probabilistic logic Robustness Calibration Overfitting

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Endogenizing probabilistic resource adequacy risks in deterministic capacity expansion models

Endogenizing probabilistic resource adequacy risks in determ...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Gord Stephen Daniel Kirschen Department of Electrical and Computer Engineering University of Washington Seattle Washington USA National Renewable Energy Laboratory Golden Colorado USA

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

In this work, we demonstrate how power system capacity expansion models can understate the stochastic effects of thermal outages when considering resource availabilities on an hourly expected value basis, yielding system designs with multiple orders of magnitude more shortfall risk than stated adequacy targets. We develop a novel approximation approach to efficiently endogenize awareness of this risk in a deterministic, linear capacity expansion framework. We compare this approach to exogenous tuning of an energy reserve margin, the leading alternative method to compensate for unmodeled probabilistic shortfall risk. Empirical results from a test system show that the new endogenous method cost-effectively meets all regional reliability targets with a single optimization solve, and produces a near-identical system design as the incumbent method without the need for repeated re-optimizations to find an appropriate reserve level. the endogenous method may also use iterative re-optimizations to further improve solution quality, although these incremental benefits were modest in the system studied.

关键词： thermal expansion Stochastic processes Probabilistic logic Reliability engineering Power system reliability Iterative methods System analysis and design

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the Effects of Renewable Power and Forecast Error Correlation on Balancing Reserve Needs

The Effects of Renewable Power and Forecast Error Correlatio...

引用

international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Sunny Chen Johanna L. Mathieu Peter Seiler Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

Renewables introduce randomness into the operation of power systems through forecast error. this randomness is commonly assumed to increase reserve needs. We explore how, through renewable overbuilding and curtailment, we can reduce the burden on non-renewable resources to provide reserves, since overbuilt and curtailed renewables can provide steadier power than if they run at their maximum power point. In this work, we investigate the interplay between overcapacity of renewables, spatial correlation in forecast error, and consequent reserve requirements using the IEEE 9-bus system as our test case. To obtain reserve capacities, we formulate a dispatch-redispatch paradigm where the system is first dispatched on forecasted power, then redispatched to mitigate forecast error. We conduct Monte Carlo simulation to obtain the quantiles of setpoint deviations under redispatch. Our results show that without significant network constraints, non-renewable reserves approximately vary with the aggregate renewable variability. However, with significant load and network constraints, reserve needs increase significantly and no longer vary with aggregate renewable variability.

关键词： Procurement Renewable energy sources Correlation Monte Carlo methods Aggregates Predictive models Probabilistic logic

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Stock Price Forecasting through Sequential Models Enhanced with Dual-stage Attention and Denoising

Stock Price Forecasting through Sequential Models Enhanced w...

引用

Advanced Computing and Analytics (ACOMPA), international conference on

作者： Giang-Nam Luu Long-Phuoc Tôn Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City VietNam

ISBN: (数字)9798331542467

ISBN: (纸本)9798331542474

Predicting stock prices is challenging due to the market’s inherent volatility and complexity. this paper explores how sequential models like Recurrent Neural Networks, Long Short-Term Memory, and Gated Recurrent Units can be used for stock price forecasting. To enhance prediction accuracy, Wavelet transform techniques are applied to clean the data from noise. Additionally, a dual-stage attention mechanism is introduced. this mechanism helps in capturing long-term dependencies and focusing on the most relevant data for prediction. In the initial stage, the input attention mechanism determines the most significant features at each time step by referencing the previous encoder hidden state. In the subsequent stage, the temporal attention mechanism identifies the most pertinent hidden states from across all time steps. this two-stage attention approach enhances both the accuracy of predictions and the interpretability of the model. the models are tested on various stock indices, demonstrating that combining data denoising with attention mechanisms significantly enhances forecasting accuracy.

关键词： Analytical models Attention mechanisms Accuracy Recurrent neural networks Computational modeling Noise reduction Predictive models logic gates Forecasting Long short term memory

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Assessing Complementarity for Wind and Solar Energy in New Zealand

Assessing Complementarity for Wind and Solar Energy in New Z...

引用

international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Chantelle M Tomon Nyuk-Min Vong Ramesh Rayudu Power System Development Transpower New Zealand Wellington New Zealand School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

Generation complementarity refers to the negative correlation between different types of generation, which effectively smoothens out intermittencies that have become prevalent with the increasing uptake of non-dispatchable generation such as wind turbine and solar PV generation. this paper presents the findings of temporal, spatial, and spatio-temporal complementarity studies conducted for New Zealand. the study utilizes 5 years’ worth of hourly observational data from local meteorological services and real power outputs of selected operating wind farms. the typical data pre-processing steps were applied after which Spearman’s Rank Correlation, Cross-correlation, and Canonical Correlation techniques were applied to the modelled power data and derived datasets to determine complementarity. Previous correlation studies published in New Zealand have not presented findings on wind-solar complementarity until recently. With the predicted uptake of these intermittent energy sources in multiple regions, this paper can provide a baseline for generation complementarity in New Zealand.

关键词： Wind energy generation Correlation coefficient Correlation Wind energy Solar energy Wind farms Probabilistic logic

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Enhancing Grid Security through Bilevel Optimization of Aggregated Distributed Energy Resources for Flexible Operation in Distribution Networks

Enhancing Grid Security through Bilevel Optimization of Aggr...

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international conference on Probabilistic Methods Applied to Power Systems (PMAPS)

作者： Ifedayo Oladeji Ramon Zamora Avy Sheina Tek-Tjing Lie

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

this paper proposes a bilevel optimization strategy addressing the impact of aggregated distributed energy resources (DER) on distribution networks to ensure grid security. the first optimization level is the network reconfiguration model, which addresses a gap where existing models prioritize economic objectives over security constraints. the model incorporates probabilistic scenarios and proposes a probabilistic voltage risk index and hosting capacity to minimize total distribution network power loss. the second optimization level is the flexibility estimation process, which applies a decision tree classification algorithm to determine the minimum flexibility (MW) for the desired voltage deviation reduction. Where existing models are only suitable for offline estimation, the proposed approach is suitable for online flexibility estimation. Tested on the IEEE 33 network, the proposed technique yields a 16.67% improvement in worst-node voltage deviation under a 100% load increase. the results highlight the effectiveness of the proposed bilevel technique in optimizing network configuration and estimating flexibility support, ultimately enhancing the collaboration between transmission and distribution network operators.

关键词： Estimation Voltage Distribution networks Probabilistic logic Distributed power generation Power systems Security

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