Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources

Grossi, Luigi; Nan, Fany

doi:10.1016/j.techfore.2019.01.006

Cited by 36 publications

(24 citation statements)

References 60 publications

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“…[43] explains that providers need only some of the data from users and hence they presented a forecasting method which do not disturb user's privacy by their proposed method named group method of data handling (GMDH) with the help of one of the famous algorithms-artificial nuero network. On other hand, a model named Self Exciting Threshold Auto Regressive models with eXogenous regressors (SETARX) was offered by [45] as robust approach for spot prize forecasting of electricity. Paper presented modeling of SETAR, its simulation and prize forecasting for Italy's electricity market.…”

Section: Forecasting and Scheduling Methodsmentioning

confidence: 99%

A comprehensive review on integrating renewable energy sources into smart grid : Challenges and Solutions

Sarda¹,

Pandya²

2019

IJET

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Smart grid is considered as future grid having numbers of advantages, not only for owners but customers also. One of the advantages of this future grid is, it is more reliable and environment-friendly grid as it helps to reduce greenhouse gases emission by integrating renewable energy sources with it. Renewable integration brings lots of benefits but before that numbers of challenges are needed to be solved and hence large-scale renewable penetration is considered as one of the biggest challenges to future smart grid. Researches are going on to deal with these hurdles and to make this integration obstacle-free. This paper presents a literature survey on these challenges and their possible solutions to give a brief view on current research scenario on this topic.

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Section: Forecasting and Scheduling Methodsmentioning

confidence: 99%

A comprehensive review on integrating renewable energy sources into smart grid : Challenges and Solutions

Sarda¹,

Pandya²

2019

IJET

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“…The trend-seasonal pattern of electricity spot prices, which is also known as the longterm seasonal component (LTSC), has always attracted the attention of energy analysts [1][2][3][4][5][6][7]. This is especially so when modeling the average daily prices in the medium-or the longterm.…”

Section: Introductionmentioning

confidence: 99%

“…Nowotarski and Weron [8] only recently introduced the seasonal component (SC) approach and the seasonal component autoregressive (SCAR) models that decompose the electricity spot price series into a trendseasonal and stochastic component, predict them independently, and then combine their day-ahead forecasts. The seasonal component approach works well for autoregressive (AR) [7,9] as well as non-linear autoregressive (NARX) neural network-type models [10], in the context of point and probabilistic predictions [11]. However, the studies that have been published to date may be criticized for only utilizing parsimonious structures with a relatively small number of explanatory variables or features.…”

Section: Introductionmentioning

confidence: 99%

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Jędrzejewski¹,

Marcjasz²,

Weron³

2021

Energies

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Recent studies suggest that decomposing a series of electricity spot prices into a trend-seasonal and a stochastic component, modeling them independently, and then combining their forecasts can yield more accurate predictions than an approach in which the same parsimonious regression or neural network-based model is calibrated to the prices themselves. Here, we show that significant accuracy gains can also be achieved in the case of parameter-rich models estimated via the least absolute shrinkage and selection operator (LASSO). Moreover, we provide insights as to the order of applying seasonal decomposition and variance stabilizing transformations before model calibration, and propose two well-performing forecast averaging schemes that are based on different approaches for modeling the long-term seasonal component.

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“…An aggravating circumstance for transmission system operators is that production from DER is planned "day-ahead", which often deviates from the scheduled plan on that day, and in this case PS regulation is necessary by activating the balancing energy. Some research was aimed at better production planning from DER [1][2][3], but in -Structural data exchange -Planned data exchange -Real-data time exchange…”

Section: Introductionmentioning

confidence: 99%

Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme

Skok

Mutapčić

Rubesa³

et al. 2020

Energies

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By integrating distributed energy resources (DER, mostly renewable energy sources) in the depth of the distribution network transmission system operators (TSOs), planning and control of transmission systems has been greatly hindered due to the lack of knowledge about the circumstances at the transmission and distribution network’s interface and the lack of coordination with the distribution system operator (DSO). By adopting the Commission Regulation (EU) 2017/1485 (System Operational Guideline—SOGL) establishing a guideline on electricity transmission system operation, harmonized rules on system operation for TSOs, DSOs and significant grid users (SGUs) are set out, inter alia, in order to provide a clear legal framework for the exchange of necessary data and information between the aforementioned subjects. In this paper, the methodology of DER representation at the interface of the transmission and distributed system is presented, with the indicated interactive data exchange between TSO and DSO, for running and analyzing the operation of the entire power system (PS) in real and extended real time. The proposed methodology was tested on a real model of the Croatian transmission PS and with representative DER in the depth of the distribution network.

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Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources

Cited by 36 publications

References 60 publications

A comprehensive review on integrating renewable energy sources into smart grid : Challenges and Solutions

A comprehensive review on integrating renewable energy sources into smart grid : Challenges and Solutions

Importance of the Long-Term Seasonal Component in Day-Ahead Electricity Price Forecasting Revisited: Parameter-Rich Models Estimated via the LASSO

Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme

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