Short-term forecasting of day-ahead electricity market price

Menniti, Daniele; Scordino, N.; Sorrentino, N.; Violi, Antonio

doi:10.1109/eem.2010.5558771

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The preliminary step to the model solution is the generation of the required input data. Market price values have been derived by using a Monte Carlo simulation technique, based on a mean reverting process [30]. The choice of this approach has been motivated by the need of taking into account both seasonal effects and tendency of spot market prices to fluctuate around a drift over time.…”

Section: Computational Resultsmentioning

confidence: 99%

A Probabilistically Constrained Approach for the Energy Procurement Problem

et al. 2017

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Abstract:The definition of the electric energy procurement plan represents a fundamental problem that any consumer has to deal with. Bilateral contracts, electricity market and self-production are the main supply sources that should be properly combined to satisfy the energy demand over a given time horizon at the minimum cost. The problem is made more complex by the presence of uncertainty, mainly related to the energy requirements and electricity market prices. Ignoring the uncertain nature of these elements can lead to the definition of procurement plans which are infeasible or overly expensive in a real setting. In this paper, we deal with the procurement problem under uncertainty by adopting the paradigm of joint chance constraints to define reliable plans that are feasible with a high probability level. Moreover, the proposed model includes in the objective function a risk measure to control undesirable effects caused by the random variations of the electricity market prices. The proposed model is applied to a real test case. The results show the benefit deriving from the stochastic optimization approach and the effect of considering different levels of risk aversion.

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Section: Computational Resultsmentioning

confidence: 99%

A Probabilistically Constrained Approach for the Energy Procurement Problem

et al. 2017

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“…The authors in [15] propose an ANN architecture with the premise that the electricity price profile for a given year can be assigned a structure that can then be related to the structure of the reference year, in such a way that a transformation can be found from the reference year's structure to the forecasting year's structure. The authors in [16] propose a technique to forecast day-ahead electricity prices based on the mean reverting process using a properly fitted model. Contreras et al [17] implemented price forecasting using time series procedures, dynamic regression, and transfer function methodologies.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [16] propose a technique to forecast day-ahead electricity prices based on the mean reverting process using a properly fitted model. Contreras et al [17] implemented price forecasting using time series procedures, dynamic regression, and transfer function methodologies.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic day-ahead system marginal price forecasting with ANN for the Turkish electricity market

Özgüner¹,

Tör²,

Güven³

2017

Turk J Elec Eng & Comp Sci

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This study presents a system day-ahead hourly market clearing price forecasting tool for the day-ahead (DA) market and a system DA hourly marginal price forecasting tool for the real-time market of the Turkish electric market (TEM). These forecasting tools are developed based on artificial neural networks (ANNs). A series of historical price data of the TEM are utilized to model and optimize the ANN structure and to develop the ANN-based price forecasting tool.The methodology used to select the optimum ANN architecture provides the minimum daily mean absolute percentage error for both day-ahead market prices in the TEM. Performances of the proposed ANN model and the multiple linear regression model in forecasting the day-ahead hourly market clearing price are compared. The proposed ANN model is modified using volatility analysis and the Bienayme-Chebyshev inequality in order to forecast system marginal prices probabilistically within a lower and an upper boundary.

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