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2021 ◽  
Author(s):  
Per Aaslid ◽  
Magnus Korpås ◽  
Michael M Belsnes ◽  
Olav Bjarte Fosso

The operation of electric energy storages (EES) in power systems where variable renewable energy sources (VRES) and EES must contribute to securing the supply can be considered as an arbitrage against scarcity. The value of using stored energy instantly must be balanced against its potential future value and future risk of scarcity. This paper proposes a multi-stage stochastic programming model for the operation of microgrids with VRES, EES and thermal generation that is divided into a short- and a long-term model. The short-term model utilizes information from forecasts updated every six hours, while the long-term model considers the value of stored energy beyond the forecast horizon. The model is solved using stochastic dual dynamic programming and Markov chains, and the results show that the significance of accounting for short- and long-term uncertainty increases for systems with a high degree of variable renewable generation and EES and decreasing dispatchable generation capacity.<br>


2021 ◽  
Author(s):  
Per Aaslid ◽  
Magnus Korpås ◽  
Michael M Belsnes ◽  
Olav Bjarte Fosso

The operation of electric energy storages (EES) in power systems where variable renewable energy sources (VRES) and EES must contribute to securing the supply can be considered as an arbitrage against scarcity. The value of using stored energy instantly must be balanced against its potential future value and future risk of scarcity. This paper proposes a multi-stage stochastic programming model for the operation of microgrids with VRES, EES and thermal generation that is divided into a short- and a long-term model. The short-term model utilizes information from forecasts updated every six hours, while the long-term model considers the value of stored energy beyond the forecast horizon. The model is solved using stochastic dual dynamic programming and Markov chains, and the results show that the significance of accounting for short- and long-term uncertainty increases for systems with a high degree of variable renewable generation and EES and decreasing dispatchable generation capacity.<br>


Life Sciences ◽  
2021 ◽  
Vol 285 ◽  
pp. 119962
Author(s):  
Lindsay T. Michalovicz ◽  
Kimberly A. Kelly ◽  
Diane B. Miller ◽  
Kimberly Sullivan ◽  
James P. O'Callaghan

2021 ◽  
Vol 17 (4) ◽  
pp. 639-659
Author(s):  
Hongbing Yu

Abstract This paper provides an integrative and updated view of modeling in semiotics. It postulates that the essence of modeling is supersession. In any act or instance of modeling, the model supersedes and is brought to the front for salience, accessibility, and operability, whereas at the same time the modeled recedes and exists in the background, inaccessible and inoperable. The paper goes on to differentiate between two major types of modeling, the underlying “existential modeling,” functioning as the fundamental scaffold and the genuine foundation of all other types of modeling as we know them, and the overlaying “semiotic modeling,” designating the process of creation and use of “forms of meaning,” a process that underlies both cognition and communication. By focusing on semiotic modeling, the paper features an unconventional view that casts a new light on the relation between a model and a sign and thus the relation between semiotic modeling and semiosis. Endorsing an embodied approach to meaning-making as semiotic modeling, the paper finds it important to stress the appropriateness and necessity of understanding the term “model” as a verb rather than as a noun, in that modeling is never static and should be properly regarded in terms of embodied action.


2021 ◽  
Vol 27 (3) ◽  
pp. 3911-3918
Author(s):  
Nikolay Atanasov ◽  

Purpose: The aim of the study is to build a long-term model and conduct a Monte Carlo simulation of the public health expenditure (PHE) of Bulgaria with the gross domestic product (GDP) as an independent variable. Material/Methods: Statistical models are used for modeling the long-term dependence between the macroeconomic dynamic rows, testing of hypotheses of stationarity (Augmented Dickey-Fuller tests), for serial autocorrelation and others. Results: There is a well-defined, statistically significant long-term relationship between public health expenditure and gross domestic product. The long-term model of health expenditure has an estimate of the cointegration constant of 1.023 (p-value < 0.05). Monte Carlo simulations are presented with 1 000, 2 000 and 3 000 experiments, generated based on the normal distribution of the input variable. Conclusions: In the period after the year 1990, a well-defined long-term relationship between public health expenditure and GDP exists. The Monte Carlo simulation can be regarded as a reliable instrument for studying the most likely fluctuations in health expenditure caused by the GDP.


2021 ◽  
Author(s):  
Ahmed Y. Hassebo

This chapter examines and explores the potential of how the capabilities of the emerging 5G cellular technologies can be integrated with a given mission-critical xIoT application (e., g., smart grid) to enable a truly converged xIoT-ICT infrastructure that would further enhance and enable the adequate support of the strict performance requirement of such an xIoT application. Since the smart grid believed to be one of the most necessitated IoT services. in this work, it has been nominated as a descriptive xIoT case. As the smart grid comprises an extensive collection of applications extended from mission-critical services which have rigorous necessities in terms of end-to-end (E2E) latency and reliability (e.g., real-time system protection and control utilizing PMU measurements) to those that require support of massive number of connected machine-to-machine (M2M) devices with relaxed latency and reliability requirements (e.g., smart meters). Based on time-to-market strategy, we identify and propose two different 5G-based business and architectural models that enable a truly converged power grid-ICT infrastructure, namely, near-term model and long-term model.


2021 ◽  
Author(s):  
Nahid Atashi ◽  
Dariush Rahimi ◽  
Victoria A. Sinclair ◽  
Martha A. Zaidan ◽  
Anton Rusanen ◽  
...  

2021 ◽  
pp. 80-88
Author(s):  
Fedor Kudryavtsev ◽  
Elena Tokareva

The results of a crime localization study in Russian cities are presented. A long-term model of urban environment influence on criminal intent is proposed. Social and spatial causes of rising criminal level and later urban reconstruction methods are shown on the example of Pruitt-Igoe (St. Louis) and Bijlmer (Amsterdam) districts. A model for the reconstruction of the Novo-Lenino district in Irkutsk is proposed. Measures have been developed considering the historical dynamics of the region and its unique features.


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