Congestion management rules and trading strategies in the Spanish electricity market

Furió, Dolores; Lucia, Julio J.

doi:10.1016/j.eneco.2008.07.004

Cited by 27 publications

(13 citation statements)

References 6 publications

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“…Out-sample data set: all the hours of the weeks with numbers 5,10,15,20,25,30,35,40,45,50 in 2012, and weeks number 2, 7, 12, 17, 22, 27, 32, 37, 42, 47 in 2013; a total of 3360 cases (h).…”

Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

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This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models) for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

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Section: Data Characteristicsmentioning

confidence: 99%

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

et al. 2016

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“…The fact that market participants may consider the outcome of the sequence of markets, and not each market in isolation, was addressed by [24] for the Californian market. Also for the Spanish case, [25] pointed out that some generation plants could have been submitting sale orders in the day-ahead market at high prices that would not be matched-and so that they would finally be required to produce electricity to solve congestion. This approach would have been more profitable according to the regulations in force during the period in question (from July 2004 to February 2005).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

Impact of Wind Electricity Forecasts on Bidding Strategies

Ballester¹,

Furió

2017

Sustainability

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Abstract:The change in the generation mix from conventional electricity sources to renewables has important implications for bidding behaviour and may have an impact on prices. The main goal of this work is to discover the role played by expected wind production, together with other relevant factors, in explaining the day-ahead market price through a data panel model. The Spanish market, given the huge increase in wind generation observed in the last decade, has been chosen for this study as a paradigmatic example. The results obtained suggest that wind power forecasts are a new key determinant for supply market participants when bidding in the day-ahead market. We also provide a conservative quantification of the effect of such trading strategies on marginal prices at an hourly level for a specific year in the sample. The consequence has been an increase in marginal price to levels higher than what could be expected in a context with notable wind penetration. Therefore, the findings of this work are of interest to practitioners and regulators and support the existence of a wind risk premium embedded in electricity prices to compensate for the uncertainty of wind production.

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“…This paper focuses on the congestion management mechanism used in Spain [14]- [16]. This mechanism is carried out subsequent to the outcome of the day-ahead market.…”

Section: Congestion Management Mechanism In Spainmentioning

confidence: 99%

Analysis of the Spanish congestion management mechanism

Delgadillo

Reneses

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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In electricity markets, different mechanisms are used to solve congestion on the system. In the Spanish electricity market, the day-ahead is cleared without taking into account the technical constraints, and subsequently a counter-trading mechanism is used to solve the congestion that appear in the system. Even assuming that generation companies cannot modify the electricity price, they may behave strategically. They may modify their bids to avoid being dispatched in the day-ahead market, but being dispatched in the counter-trading mechanism. Therefore, the counter-trading mechanism allows generation companies to behave strategically in the electricity market. Meanwhile, that behavior does not exist in a zonal price system. This paper presents a simple case to analyze the inefficiencies of the congestion management mechanism used in Spain, comparing it with a zonal price system.

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Congestion management rules and trading strategies in the Spanish electricity market

Cited by 27 publications

References 6 publications

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

Impact of Wind Electricity Forecasts on Bidding Strategies

Analysis of the Spanish congestion management mechanism

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