Deep learning-based forecasting of aggregated CSP production

This paper introduces deep learning-based forecasting models for the continuous prediction of the aggregated production generated by CSP plants in Spain. These models use as inputs the expected top of atmosphere irradiance values and available weather conditions forecasts for the locations where the main CSP power plants are installed. The performances of the forecast models are analysed and compared by means of the most extended metrics in the literature for a whole year of CSP energy production

The renewable energy targets of the Maghreb countries: Impact on electricity supply and conventional power markets

Morocco, Algeria and Tunisia, the three countries of the North African Maghreb region, are showing increased efforts to integrate renewable electricity into their power markets. Like many other countries, they have pronounced renewable energy targets, defining future shares of “green” electricity in their national generation mixes. The individual national targets are relatively varied, reflecting the different availability of renewable resources in each country, but also the different political ambitions for renewable electricity in the Maghreb states. Open questions remain regarding the targets’ economic impact on the power markets. Our article addresses this issue by applying a linear electricity market optimization model to the North African countries. Assuming a competitive, regional electricity market in the Maghreb, the model minimizes dispatch and investment costs and simulates the impact of the renewable energy targets on the conventional generation system until 2025. Special emphasis is put on investment decisions and overall system costs.North Africa; Renewable energy sources; Electricity markets

Short-term solar irradiation forecasting based on dynamic harmonic regression

Solar power generation is a crucial research area for countries that have high dependency on fossil energy sources and is gaining prominence with the current shift to renewable sources of energy. In order to integrate the electricity generated by solar energy into the grid, solar irradiation must be reasonably well forecasted, where deviations of the forecasted value from the actual measured value involve significant costs. The present paper proposes a univariate Dynamic Harmonic Regression model set up in a State Space framework for short-term (1 to 24 hours) solar irradiation forecasting. Time series hourly aggregated as the Global Horizontal Irradiation and the Direct Normal Irradiation will be used to illustrate the proposed approach. This method provides a fast automatic identification and estimation procedure based on the frequency domain. Furthermore, the recursive algorithms applied offer adaptive predictions. The good forecasting performance is illustrated with solar irradiance measurements collected from ground-based weather stations located in Spain. The results show that the Dynamic Harmonic Regression achieves the lowest relative Root Mean Squared Error; about 30% and 47% for the Global and Direct irradiation components, respectively, for a forecast horizon of 24 hours ahead

A structural analysis of the merit-order effect in the Spanish day-ahead power market

Renewable generation has increased exceptionally its weight in power markets, and its relevance is due to increase with the introduction of recent climate policies in Europe. The merit-order effect ranks first on the direct impacts of renewables on electricity markets. However, in order to analyse its impact, it is important to control for the different forces driving electricity prices. As a result, the analysis through a structural model of demand and supply of electricity is interesting to capture price drivers and therefore measure correctly the merit-order effect. The objective of this paper is tointroduce this framework on the Spanish day-ahead market, using weekly data for the period 2013-2019. The empirical analysis is carried out using structural vector autoregressive models (SVAR) and autoregressive distributed lag models (ARDL) to each equation, with the addition of GARCH models to control for the possible autoregressive volatility behaviour of the residuals. In line with previous literature, we obtain that demand of electricity is elastic to economic growth, price-inelastic and shows a significant level of substitution between electricity and natural gas. The supply function is also price-inelastic, after controlling for capacity factors, inputs prices and external balance, that are shown to be significant. The estimated values of the merit-order effect is aligned with previous literature. We obtain that a 10% increase in the average quantity generated by the special regime technologies (wind, solar and CHP) is associated with a 5 % reduction in electricity prices, around 2.35Euros/MWh of the average price for the analysed period.The first author acknowledges the funding received by the Ministry of Economics of Spain (ECO2016-00105-001, MDM 2014-0431), the Community of Madrid (MadEco-CM S2015/HUM-3444 and the Agencia Estatal de Investigación (2019/00419/001)

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

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

Impacts of intermittent renewable generation on electricity system costs [WP-IEB]

A successful deployment of power generation coming from variable renewable sources (VRES-E), such as wind and solar photovoltaic, strongly depends on the economic cost of system integration. This paper, in seeking to look beyond the impact of RES-E generation on the evolution of the total economic costs associated with the operation of the electricity system, aims to estimate the sensitivity of balancing market requirements and costs to the variable and non-fully predictable nature of intermittent renewable generation. The estimations reported in this paper for the Spanish electricity system stress the importance of both attributes as well as power system flexibility when accounting for the cost of balancing services