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Bibliografías temáticas / Intra-day demand

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Artículos de revistas

Literatura académica sobre el tema "Intra-day demand"

Autor: Grafiati

Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Intra-day demand"

1

Zhai, Jingjing, Xiaobei Wu, Zihao Li, Shaojie Zhu, Bo Yang, and Haoming Liu. "Day-Ahead and Intra-Day Collaborative Optimized Operation among Multiple Energy Stations." Energies 14, no. 4 (2021): 936. http://dx.doi.org/10.3390/en14040936.

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An integrated energy system (IES) shows great potential in reducing the terminal energy supply cost and improving energy efficiency, but the operation scheduling of an IES, especially integrated with inter-connected multiple energy stations, is rather complex since it is affected by various factors. Toward a comprehensive operation scheduling of multiple energy stations, in this paper, a day-ahead and intra-day collaborative operation model is proposed. The targeted IES consists of electricity, gas, and thermal systems. First, the energy flow and equipment composition of the IES are analyzed, and a detailed operation model of combined equipment and networks is established. Then, with the objective of minimizing the total expected operation cost, a robust optimization of day-ahead and intra-day scheduling for energy stations is constructed subject to equipment operation constraints, network constraints, and so on. The day-ahead operation provides start-up and shut-down scheduling of units, and in the operating day, the intra-day rolling operation optimizes the power output of equipment and demand response with newly evolved forecasting information. The photovoltaic (PV) uncertainty and electric load demand response are also incorporated into the optimization model. Eventually, with the piecewise linearization method, the formulated optimization model is converted to a mixed-integer linear programming model, which can be solved using off-the-shelf solvers. A case study on an IES with five energy stations verifies the effectiveness of the proposed day-ahead and intra-day collaborative robust operation strategy.

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2

Ralston Fonseca, Francisco, Paulina Jaramillo, Mario Bergés, and Edson Severnini. "Seasonal effects of climate change on intra-day electricity demand patterns." Climatic Change 154, no. 3-4 (2019): 435–51. http://dx.doi.org/10.1007/s10584-019-02413-w.

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3

Shahryari, E., H. Shayeghi, B. Mohammadi-ivatloo, and M. Moradzadeh. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets." Energy 155 (July 2018): 205–14. http://dx.doi.org/10.1016/j.energy.2018.04.170.

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4

Sun, Ziru, Minyu Chen, Qian Ai, Long Zhao, Xiaoming Liu, and Donglei Sun. "An Optimization Strategy for Intra-day Demand Response Based on Security Constraints." Journal of Physics: Conference Series 1754, no. 1 (2021): 012211. http://dx.doi.org/10.1088/1742-6596/1754/1/012211.

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5

Coroamă, Iulia, Gianfranco Chicco, Mihai Gavrilaş, and Angela Russo. "Distribution system optimisation with intra-day network reconfiguration and demand reduction procurement." Electric Power Systems Research 98 (May 2013): 29–38. http://dx.doi.org/10.1016/j.epsr.2013.01.004.

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6

Abramova, Ekaterina, and Derek Bunn. "Forecasting the Intra-Day Spread Densities of Electricity Prices." Energies 13, no. 3 (2020): 687. http://dx.doi.org/10.3390/en13030687.

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Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions.

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7

Li, Zhengjie, and Zhisheng Zhang. "Day-Ahead and Intra-Day Optimal Scheduling of Integrated Energy System Considering Uncertainty of Source & Load Power Forecasting." Energies 14, no. 9 (2021): 2539. http://dx.doi.org/10.3390/en14092539.

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At present, due to the errors of wind power, solar power and various types of load forecasting, the optimal scheduling results of the integrated energy system (IES) will be inaccurate, which will affect the economic and reliable operation of the integrated energy system. In order to solve this problem, a day-ahead and intra-day optimal scheduling model of integrated energy system considering forecasting uncertainty is proposed in this paper, which takes the minimum operation cost of the system as the target, and different processing strategies are adopted for the model. In the day-ahead time scale, according to day-ahead load forecasting, an integrated demand response (IDR) strategy is formulated to adjust the load curve, and an optimal scheduling scheme is obtained. In the intra-day time scale, the predicted value of wind power, solar power and load power are represented by fuzzy parameters to participate in the optimal scheduling of the system, and the output of units is adjusted based on the day-ahead scheduling scheme according to the day-ahead forecasting results. The simulation of specific examples shows that the integrated demand response can effectively adjust the load demand and improve the economy and reliability of the system operation. At the same time, the operation cost of the system is related to the reliability of the accurate prediction of wind power, solar power and load power. Through this model, the optimal scheduling scheme can be determined under an acceptable prediction accuracy and confidence level.

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8

Ramírez-Mendiola, José Luis, Philipp Grünewald, and Nick Eyre. "Linking intra-day variations in residential electricity demand loads to consumers’ activities: What's missing?" Energy and Buildings 161 (February 2018): 63–71. http://dx.doi.org/10.1016/j.enbuild.2017.12.012.

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9

Téllez-Gutiérrez, Sandra, and Oscar Duarte-Velasco. "A Model for Quantifying Expected Effects of Demand-Side Management Strategies." TecnoLógicas 25, no. 54 (2022): e2357. http://dx.doi.org/10.22430/22565337.2357.

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This paper presents a quantitative dynamic model that can assess the response of a set of users to different Demand-Side Management strategies that are available. The main objective is to conceptualize, implement, and validate said model. As a result of a literature review, the model includes classical demand response techniques and proposes new customer actions and other novel aspects, such as energy culture and energy education. Based on the conceptualization of the model, this paper presents the structure that interrelates customer actions, demand proposals, cost-benefit analysis, and customer response. It also details the main aspects of the mathematical model, which was implemented in the Modelica modeling language. This paper includes simulations of intra-day and inter-day load shifting strategies using real data from the electricity sector in Colombia and different tariff factors. Finally, the results obtained show changes in daily consumption profiles, energy cost, system power peak, and load duration curve. Three conclusions are drawn: (i) Energy culture and pedagogy are essential to accelerate customer response time. (ii) The amount of the bill paid by customers decreases more quickly in the intra-day strategy than in its inter-day counterpart; in both cases, the cost reduction percentage is similar. (iii) Tariff increases accelerate customer response, and this relationship varies according to the Demand-Side Management strategies that are available

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10

Golia, Silvia, Luigi Grossi, and Matteo Pelagatti. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices." Forecasting 5, no. 1 (2022): 81–101. http://dx.doi.org/10.3390/forecast5010003.

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In this paper we assess how intra-day electricity prices can improve the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) with and without interactions among predictors, and non-parametric models taken from the machine learning literature. In particular, we implement Random Forests and support vector machines, which should automatically capture the relevant interactions among predictors. Given the large number of predictors, ARX models are also estimated using LASSO regularization, which improves predictions when regressors are many and selects the important variables. In addition to zonal intra-day prices, among the predictors we include also the official demand forecasts and wind generation expectations. Our results show that the prediction performance of the simple ARX model is mostly superior to those of machine learning models. The analysis of the relevance of exogenous variables, using variable importance measures, reveals that intra-day market information successfully contributes to the forecasting performance, although the impact differs among the estimated models.

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