Relevant bibliographies by topics / Intra-day demand

Academic literature on the topic 'Intra-day demand'

Author: Grafiati
Published: 11 March 2023

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Journal articles on the topic "Intra-day demand"

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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 (February 10, 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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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 (March 25, 2019): 435–51. http://dx.doi.org/10.1007/s10584-019-02413-w.

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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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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 (February 1, 2021): 012211. http://dx.doi.org/10.1088/1742-6596/1754/1/012211.

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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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Abramova, Ekaterina, and Derek Bunn. "Forecasting the Intra-Day Spread Densities of Electricity Prices." Energies 13, no. 3 (February 5, 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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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 (April 28, 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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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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Téllez-Gutiérrez, Sandra, and Oscar Duarte-Velasco. "A Model for Quantifying Expected Effects of Demand-Side Management Strategies." TecnoLógicas 25, no. 54 (June 22, 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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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 (December 30, 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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Dissertations / Theses on the topic "Intra-day demand"

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Filippo, Favero. "Empirical investigation of natural gas consumption and price elasticity: evidence from billing and distribution data in Italy." Doctoral thesis, 2022. http://hdl.handle.net/11562/1062815.

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The thesis is formed of three essays linked by a common topic regarding the analysis of natural gas consumption at the local level in Italy. The first chapter is an empirical investigation analyzing the monthly billing data of 51,177 end-users in Veneto (Italy) in the period 2016-2018. Several panel-data models are developed to study determinants of individual natural gas consumption and, especially, price elasticity. The second chapter investigates changes in natural gas consumption patterns caused by the COVID-19 pandemic. We employed the Gini index in the study of intra-day energy seasonality, analyzing an original data-set of hourly natural gas demand in northern Italy. The third chapter considers spatial heterogeneity as a new source of information in energy consumption data at the micro level. The analysis integrates a billing data-set of 8,924 natural gas consumers with spatial coordinates. Then, we applied a two-stage technique to study price elasticity and consumption determinants, solving the endogeneity problem through the spatial information of end-users.
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Book chapters on the topic "Intra-day demand"

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Shayeghi, H., E. Shahryari, Hamed Pashaei-Didani, and Sayyad Nojavan. "Modeling an Improved Demand Response Program in Day-Ahead and Intra-day Markets." In Demand Response Application in Smart Grids, 93–111. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-31399-9_4.

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Llarens, Daniel, Laura Souilla, Santiago Masiriz, and Gastón Lestard. "Variable Renewable Energy: How the Energy Markets Rules Could Improve Electrical System Reliability." In Advances in Green Electronics Technologies [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.107062.

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In the last 10 years, significant changes have been observed in the operation of electrical systems resulting from the increasing incorporation of Variable Renewable Energy (NCRE—Solar PV, WIND) characterized by strong volatility in its energy production, due to climatic effects, which affect the reliability in the operation of the electrical system. These technologies also show a significant reduction in their capital costs, which are currently competitive compared to conventional alternatives for energy production, with the advantage of contributing to reducing the production of greenhouse gases. Therefore, increasing reliability operational problems are expected in the future, which must be resolved to supply the demand safely and at minimum cost. LATAM’s countries are making slow progress in updating their regulatory frameworks for the electricity sector to include changes that improve the integration of NCRE generation without reducing the quality of service. This document describes possible regulatory changes that could be implemented to promote a system safe operation including (a) intra-hours marginal costs, (b) day-ahead/intraday energy markets, (c) incentives to better forecast the NCRE generation production profile, (d) participation of NCRE generation in the capacity market, and (e) including BESS as ancillary service for frequency/ramp power control.
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Conference papers on the topic "Intra-day demand"

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Wang, Zheng, Adrian A. Clarke, James R. Moyne, and Dawn M. Tilbury. "Utilizing Intra-Day Prediction Modification Strategies to Improve Peak Power Shaving Using Energy Storage Systems for Smart Buildings." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-5933.

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Peak power shaving is a technique that can be used to reduce monthly electricity bills. As control of Energy Storage Systems (ESS) is based on predicted power demand, power demand forecasting is a necessary component of entire building power optimization. Various forecasting methods have been developed. However, the importance of intra-day prediction error is overlooked by present models. In this paper, a variety of dynamic intra-day model modification strategies utilizing intra-day prediction error are proposed to improve power demand prediction and peak shaving performance. These modification strategies could be applied to any models which do the prediction at the beginning of the day. A Self-Organizing Map (SOM) & Support Vector Regression (SVR) Adaptive Hybrid Model proposed in previous literature is chosen as baseline in this paper. The method of bisection is adopted to calculate the optimal threshold to control the ESS. Simulation results demonstrate effectiveness of intra-day prediction modification strategies.
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