Hourly Spot Price Research Articles

Can weather variables and electricity demand predict carbon emissions allowances prices? Evidence from the first three phases of the EU ETS

This study examines the predictive impact of weather conditions and electricity demand on hourly spot prices of emissions allowances during the first three phases of the European Union Emissions Trading System (EU ETS) (2005–2019). We propose an original methodology for constructing European-scale electricity demand and weather indices and characterize the relationship between those indices and emissions allowances prices by means of an advanced predictive modeling technique (Extreme Gradient Boosting). Empirical findings assert that electricity demand and the weather variables under study were of importance for estimating EUA prices during the first three phases of the EU ETS, with air temperature and electricity demand being most relevant to emissions allowances prices. Conversely, total precipitation and relative humidity proved to be the least relevant variables to the outcome. The results also indicate that the relationship between emissions allowances prices and their weather-related predictors was not linear in the studied period. The paper contributes to the growing body of literature on the structural determinants of carbon prices in the EU ETS and enhances our understanding of the impact of climate and weather variability – in the provision of renewable energy production – on the most prominent market-based measure to reduce CO2 emissions in Europe.

Combining varying training data-based artificial intelligence models for energy price forecasting

ABSTRACT In this work, the price-forecasting accuracy of an ensemble of seasonal auto-regressive integrated moving average (SARIMA), multiple linear regression (MLR), feed-forward neural network (FFNN), and radial basis function (RBF) network models has been assessed in a single series modeling framework. The methodology involved training the individual models using past data windows of varying sizes and making the forecast for the next five days on a daily rolling basis. The hourly spot price data of the Iberian Electricity Market (MIBEL) is selected as the test case system. All the models have been tested on high and low-volatile data sets to assess their forecasting abilities. The forecast performance has also been assessed by participating in a real-time forecasting competition and comparing it with the earlier models proposed in the literature. The results show that the ensemble model is effective in producing better forecasts. But its forecast accuracy is greatly affected by the size of the training window and the combination of various models. The ensemble of the FFNN and RBF network models performs best when conditions are volatile. Moreover, during volatile periods it is better to use a small window size for training as compared to a large window size.

Forecasting Hourly Spot Prices in Indian Electricity Market

In this paper, we present a study on forecasting hourly electricity spot prices for India. Forecasting electricity prices is challenging for its special characteristics. Based on hourly data covering several years, ARMAX models are estimated for forecasting for individual regions. Along with ARMA, intra-day, inter-day and hourly variations and seasonalities on weekdays, holidays and festive days are incorporated. Combinations of static and dynamic forecasts are also examined. The results indicate that prices for Sundays and holidays differ significantly from weekdays; also, a clear region-specific difference between prices in winter and summer months is observed. Price behaviour in the Southern region is different from other regions. The fitted models perform well for forecasting horizons of hourly prices of up to five days. The fitted ARMAX models have an easily implementable simple structure. Such forecasts provide valuable insights for policymakers and market participants, as they incorporate several features of the Indian electricity market, including various dimensions of seasonality of demand. JEL Classifications: C53, Q47, C51

Performance estimation of photovoltaic energy production

This article deals with the production of energy through photovoltaic (PV) panels. The efficiency and quantity of energy produced by a PV panel depend on both deterministic factors, mainly related to the technical characteristics of the panels, and stochastic factors, essentially the amount of incident solar radiation and some climatic variables that modify the efficiency of solar panels such as temperature and wind speed. The main objective of this work is to estimate the energy production of a PV system with fixed technical characteristics through the modeling of the stochastic factors listed above. Besides, we estimate the economic profitability of the plant, net of taxation or subsidiary payment policies, considered taking into account the hourly spot price curve of electricity and its correlation with solar radiation, via vector autoregressive models. Our investigation ends with a Monte Carlo simulation of the models introduced. We also propose the pricing of some quanto options that allow hedging both the price risk and the volumetric risk.

Reforming the colombian electricity market for an efficient integration of renewables: A proposal

The Colombian short-term electricity market is characterised by a single settlement and by the clearing of a single national hourly spot price for the entire grid. This price is computed ex post, based on the real-time operation of the system. In the day ahead, there is only an operational dispatch, which does not set any binding economic commitment. A deviation from such dispatch (due, for instance, to an outage), if it is informed in advance, has no economic consequence for market agents. As recognised by Colombian regulatory institutions, this design is not suitable to efficiently integrate large shares of variable renewable resources.This paper presents a regulatory proposal for introducing, in Colombia, a multi-settlement system, consisting of a binding day-ahead market, followed by intraday sessions and a balancing market. The main discussion focuses on how to solve the complexities arising from the introduction of a multi-settlement system in a context where sessions are cleared based on uniform pricing. The paper also analyses the interactions of the proposed design with other aspects of the Colombian power sector regulation (such as the impact of this reform on long-term contracts or on the reliability charge mechanism).

A Hybrid Regression Model for Day-Ahead Energy Price Forecasting

Accurate forecast of the hourly spot price of electricity plays a vital role in energy trading decisions. However, due to the complex nature of the power system, coupled with the involvement of multi-variable, the spot prices are volatile and often difficult to forecast. Traditional statistical models have limitations in improving forecasting accuracies and reliably quantifying the spot electricity price under uncertain market conditions. This paper presents a hybrid model that combines the results from multiple linear regression (MLR) model with an auto-regressive integrated moving average (ARIMA) and Holt-Winters models for better forecasts. The proposed method is tested for the Iberian electricity market data set by forecasting the hourly day-ahead spot price with dataset duration of 7, 14, 30, 90, and 180 days. The results indicate that the hybrid model outperforms the benchmark models and offers promising results under most of the testing scenarios.

A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets

Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA) with other forecasting models to improve residual errors in predicting the hourly spot prices. In Stage-1, the day-ahead price is forecasted using ARIMA and then the resulting residuals are fed to another forecasting method in Stage-2. This approach was successfully tested using datasets from the Iberian electricity market with duration periods ranging from one-week to ninety days for variables such as price, load and temperature. A comprehensive set of 17 variables were included in the proposed model to predict the day-ahead electricity price. The Mean Absolute Percentage Error (MAPE) results indicate that ARIMA-GLM combination performs better for longer duration periods, while ARIMA-SVM combination performs better for shorter duration periods.

Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

Regime-Switching Model on Hourly Electricity Spot Price Dynamics

A robust time-varying regime-switching model for price dynamics of hourly spot price of electricity on the electricity market is developed. We propose a two-state Markov Regime Switching (MRS) model that gives weight to the existence of different variance for each regime. Our model is tractable as it integrates the main features exhibited in the hourly spot price dynamics on the electricity market. The parameters of our hourly spot price of electricity market model are estimated using the Expectation Maximization algorithm. Based on this model, an efficient and tractable pricing technique can be developed to price the dynamics of the hourly spot price of electricity.

Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

A spot-forward model for electricity prices with regime shifts

We propose a novel regime-switching approach for electricity prices in which simulated and forecasted prices are consistent with currently observed forward prices. Additionally, the model is able to reproduce spikes and negative prices. We distinguish between a base regime as well as upper and lower spike regimes. We derive hourly price forward curves for EEX Phelix, and together with historical hourly spot prices, historical hourly price forward curves are the basis for model calibration. The model can be used for simulation and forecasting of electricity spot prices over short- and medium-term horizons. Tests imply that it shows a better performance than classical time series approaches.

The Performance of U.S. Wind and Solar Generating Plants

Using data on hourly outputs and spot prices for a sample 25 wind and nine solar generating plants covering all seven U.S. ISOs for 2011 and up to 12 adjacent months, this study examines capacity factors, average output values, and several aspects of intermittency. Most performance measures studied vary substantially within and between ISOs, and some vary substantially over time. Implications for research, market design, and policies to support renewable generation are briefly discussed.

A simplified approach for optimizing hydropower generation scheduling

This paper presents a simplified model for optimizing hydropower scheduling for a small producer with a high degree of flexibility. The approach involves selecting the hours with the highest prices. The power plants run at full capacity all hours of the next days when hourly spot prices are greater than the upper p percentile of the hourly spot prices for the last two years (17 520 hours), and do not run otherwise. The simulations for an eight-year period, 2002-9, show a considerable increase in income. The producer can achieve 19% more income compared with a more naive strategy of generating at peak hours on weekdays. Our simulations also show that an optimal choice of p is a value lower than the load factor of the plants, due to the general increase in prices in the studied period.

Demand Response From Household Customers: Experiences From a Pilot Study in Norway

This paper presents experiences from a pilot study focusing on daily demand response from households, utilizing smart metering, remote load control, pricing based on the hourly spot price combined with a time of day network tariff, and a token provided to the customers indicating peak hours. The observed demand response was 1 kWh/h for customers with standard electrical water heaters. By aggregating this response, the potential for demand response from 50% of Norwegian households can be estimated at 1000 MWh/h (4.2% of registered peak load demand in Norway). A cost-effective realization of this potential should have high focus when considering smart metering technology. From a market perspective, a potential load reduction of this size should be bid into the day ahead market. Demand response to price (the day after) will not affect the price, but might create imbalances and the need for activating balancing resources, creating additional costs.

Anti-correlation and multifractal features of Spain electricity spot market

We use multifractal detrended fluctuation analysis (MF-DFA) to numerically investigate correlation, persistence, multifractal properties and scaling behavior of the hourly spot prices for the Spain electricity exchange-Compania O Peradora del Mercado de Electricidad (OMEL). Through multifractal analysis, fluctuations behavior, the scaling exponents and generalized Hurst exponents are studied. Moreover, contribution of fat-tailed probability distributions and nonlinear temporal correlations to multifractality is studied.

Relations Among Prices at Adjacent Nodes in an Electric Transmission Network

In a standard transportation network, if two nodes are adjacent (directly connected), with flow on the arc between them, then the difference in equilibrium prices at the nodes is explained by the arc’s marginal cost of use, marginal losses, and congestion charge (if the arc is used to its capacity). This paper develops, for electric transmission networks, a relationship between the hourly spot price at a node, and the prices at all nodes adjacent to it: a weighted average of the separate relationships that would exist between the node’s price and each of its adjacent nodes if the network were a standard one.