Stochastic Unit Commitment Performance Considering Monte Carlo Wind Power Scenarios

Rachunok, Benjamin; Staid, Andrea; Watson, Jean‐Paul; Woodruff, David L.; Yang, Dongyun

doi:10.1109/pmaps.2018.8440563

Cited by 18 publications

(10 citation statements)

References 15 publications

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“…In recent times, the stochastic UC problem has been an interesting area for researchers due to the high penetration of renewable generation into the grid [41][42][43][44][45][46][47]. Renewable generations have the uncertainty of power output; that is why the introduction of stochastic UC programming is becoming very necessary [48,49]. Most of the recent papers have considered two or multistage stochastic UC [50][51][52][53][54][55].…”

Section: Stochastic Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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Energy storage systems (ESSs) are essential to ensure continuity of energy supply and maintain the reliability of modern power systems. Intermittency and uncertainty of renewable generations due to fluctuating weather conditions as well as uncertain behavior of load demand make ESSs an integral part of power system flexibility management. Typically, the load demand profile can be categorized into peak and off-peak periods, and adding power from renewable generations makes the load-generation dynamics more complicated. Therefore, the thermal generation (TG) units need to be turned on and off more frequently to meet the system load demand. In view of this, several research efforts have been directed towards analyzing the benefits of ESSs in solving optimal unit commitment (UC) problems, minimizing operating costs, and maximizing profits while ensuring supply reliability. In this paper, some recent research works and relevant UC models incorporating ESSs towards solving the abovementioned power system operational issues are reviewed and summarized to give prospective researchers a clear concept and tip-off on finding efficient solutions for future power system flexibility management. Conclusively, an example problem is simulated for the visualization of the formulation of UC problems with ESSs and solutions.

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Section: Stochastic Uc Problemmentioning

confidence: 99%

Energy Storage System Analysis Review for Optimal Unit Commitment

et al. 2019

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“…A comprehensive study on real-world data in [18] shows the trade-off between computational complexity and the quality of simulated scenarios when using Monte-Carlo techniques. Another method uses a Monte-Carlo approach [5], to study a planning tool that takes various renewable resources from different locations into account -further, the authors consider temporal effects in simulations for load scenarios.…”

Section: Related Workmentioning

confidence: 99%

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic

Schreiber

Jessulat

Sick

2019

Lecture Notes in Computer Science

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For the integration of renewable energy sources, power grid operators need realistic information about the effects of energy production and consumption to assess grid stability. Recently, research in scenario planning benefits from utilizing generative adversarial networks (GANs) as generative models for operational scenario planning. In these scenarios, operators examine temporal as well as spatial influences of different energy sources on the grid. The analysis of how renewable energy resources affect the grid enables the operators to evaluate the stability and to identify potential weak points such as a limiting transformer. However, due to their novelty, there are limited studies on how well GANs model the underlying power distribution. This analysis is essential because, e.g., especially extreme situations with low or high power generation are required to evaluate grid stability. We conduct a comparative study of the Wasserstein distance, binary-cross-entropy loss, and a Gaussian copula as the baseline applied on two wind and two solar datasets with limited data compared to previous studies. Both GANs achieve good results considering the limited amount of data, but the Wasserstein GAN is superior in modeling temporal and spatial relations, and the power distribution. Besides evaluating the generated power distribution over all farms, it is essential to assess terrain specific distributions for wind scenarios. These terrain specific power distributions affect the grid by their differences in their generating power magnitude. Therefore, in a second study, we show that even when simultaneously learning distributions from wind parks with terrain specific patterns, GANs are capable of modeling these individualities also when faced with limited data. These results motivate a further usage of GANs as generative models in scenario planning as well as other areas of renewable energy.

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“…In stochastic unit commitment, the main problem is uncertainty. In the order to solve the unit commitment problem, several studies in the literature discussed scenario-based stochastic programming with different methods such as progressive hedging [32,33], dual decomposition [34], benders decomposition [35], spatial decomposition [36], cutting plane [37], dynamic formulation [38] and heuristic methods [39][40][41][42]. Moreover, data mining is used to obtain the useful data from large data sets gathered from various sites [43].…”

Section: Introductionmentioning

confidence: 99%

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

et al. 2021

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The thirst of the Earth for energy is lurching towards catastrophe in an era of increasing water shortage where most of the power plants are hydroelectric. The hydro-based power systems are facing challenges in determining day-ahead generation schedules of cascaded hydropower plants. The objective of the current study is to find a speedy and practical method for predicting and classifying the future schedules of hydropower plants in order to increase the overall efficiency of energy by utilizing the water of cascaded hydropower plants. This study is significant for water resource planners in the planning and management of reservoirs for generating energy. The proposed method consists of data mining techniques and approaches. The energy production relationship is first determined for upstream and downstream hydropower plants by using multiple linear regression. Then, a cluster analysis is used to find typical generation curves with the help of historical data. The decision tree algorithm C4.5, Iterative Dichotomiser 3-IV, improved C4.5 and Chi-Squared Automatic Interaction Detection are adopted to quickly predict generation schedules, and detailed comparison among different algorithms are made. The decision tree algorithms are solved using SIPINA software. Results show that the C4.5 algorithm is more feasible for rapidly generating the schedules of cascaded hydropower plants. This decision tree algorithm is helpful for the researchers to make fast decisions in order to enhance the energy production of cascaded hydropower plants. The major elements of this paper are challenges and solution of head sensitive hydropower plants, using the decision-making algorithms for producing the generation schedules, and comparing the generation from the proposed method with actual energy production.

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Stochastic Unit Commitment Performance Considering Monte Carlo Wind Power Scenarios

Cited by 18 publications

References 15 publications

Energy Storage System Analysis Review for Optimal Unit Commitment

Energy Storage System Analysis Review for Optimal Unit Commitment

Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic

Generation of Hydro Energy by Using Data Mining Algorithm for Cascaded Hydropower Plant

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