Data-driven modeling for long-term electricity price forecasting

This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

Costs and benefits of renewable energy use in electricity generation in the EU are assessed during low oil prices 1998-2002 and high oil prices 2003-2009.The EU statistical data is used.The renewable energy use in the EU was about 21% of the total energy inputs in 2008 and it was growing by 5% annual average during 2003-2008 compared to nearly nil growth of the fossil fuel input.During high and fast increasing oil prices, the correlations between the changes of consumers' electricity prices and the growth of renewable energy use indicates that the large and growing use did not increase the prices but decreased the consumers' electricity prices in several EU countries.An explanation is that the renewable energy enabled input diversification in electricity generation, which has reduced the costs.Consumers' electricity prices are simulated in case these had followed the fossil fuel input costs and compared with the observed prices.It is found that high oil prices invoked substantial efficiency-increase and that the renewable energy input has been net beneficial to the EU citizens even when the periods of low and high fossil fuel prices are taken together.

Can electricity market prices control power-to-heat production for peak shaving of renewable power generation? The case of Sweden

With the continuous promotion of the unified electricity spot market in the southern region, the formation mechanism of spot market price and its forecast will become one of the core elements for the healthy development of the market. Effective spot market price prediction, on one hand, can respond to the spot power market supply and demand relationship; on the other hand, market players can develop reasonable trading strategies based on the results of the power market price prediction. The methods adopted in this paper include: Analyzing the principle and mechanism of spot market price formation. Identifying relevant factors for electricity price prediction in the spot market. Utilizing a clustering model and Spearman’s correlation to classify diverse information on electricity prices and extracting data that aligns with the demand for electricity price prediction. Leveraging complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to disassemble the electricity price curve, forming a multilevel electricity price sequence. Using an XGT model to match information across different levels of the electricity price sequence. Employing the ocean trapping algorithm-optimized Bidirectional Long Short-Term Memory (MPA-CNN-BiLSTM) to forecast spot market electricity prices. Through a comparative analysis of different models, this study validates the effectiveness of the proposed MPA-CNN-BiLSTM model. The model provides valuable insights for market players, aiding in the formulation of reasonable strategies based on the market's supply and demand dynamics. The findings underscore the importance of accurate spot market price prediction in navigating the complexities of the electricity market. This research contributes to the discourse on intelligent forecasting models in electricity markets, supporting the sustainable development of the unified spot market in the southern region.

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios. This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average (SARIMA) model and historical data. The electricity price spikes are first identified by using an outlier detection method. Then, the historical data are decomposed into base and spike components. Next, the base and spike component scenarios are generated by using the SARIMA- and historical data-based methods, respectively. Finally, the electricity price scenarios are obtained by combining the base and spike component scenarios. Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method. The optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method, the SARIMA method, and a historical data-based method, respectively. Case study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and, therefore, can help the virtual bidder earn more profit in the electricity market.

A new hybrid adaptive autoregressive moving average (ARMA) and functional link neural network (FLNN) trained by adaptive cubature Kalman filter (ACKF) is presented in this paper for forecasting day-ahead mixed short-term demand and electricity prices in smart grids. The hybrid forecasting framework is intended to capture the dynamic interaction between the electricity consumers and the forecasted prices resulting in the shift of demand curve in electricity market. The proposed model comprises a linear ARMA-FLNN obtained by using a nonlinear expansion of the weighted inputs. The nonlinear functional block helps introduce nonlinearity by expanding the input space to higher dimensional space through basis functions. To train the ARMA-FLNN, an ACKF is used to obtain faster convergence and higher forecasting accuracy. The proposed method is tested on several electricity markets, and the performance metrics such as the mean average percentage error (MAPE), and error variance are compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability for a real-time forecasting.

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making.

The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.

The electricity sector deregulation has led to the formation of short‐term power markets where the consumers can purchase electricity by bidding at the electricity market. The electricity market price is volatile and changes are due to change in electricity demand and the bid price at different span of time during the day. The availability of the electricity price forecast is essential for the electricity market participants to make informed decisions. In this paper, the modified LSTM approach, wavelet‐LSTM, and Hilbert‐LSTM are proposed to predict the electricity price for bidding in the short‐term electricity market. The objective is to improve the precision and adaptability of electricity price predictions by utilizing the temporal dependence identification capability of LSTM and the multiresolution analysis capability of the transforms. The proposed models combine these two effective methods in order to capture both the long‐term trends and short‐term variations present in electricity price time series data. In this approach, the 8‐year dataset is used for training the models, and based on this the day‐ahead price is calculated and compared with the testing data. The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN‐LSTM. The prediction results achieved with wavelet‐LSTM and Hilbert‐LSTM (1‐month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. The results achieved with the proposed methods are better than the existing forecasting models, and the RMSE for Hilbert‐LSTM and wavelet‐LSTM techniques is improved by 61% and 43%, respectively, compared to the LSTM method. Also, results are calculated for the complete 8 years all 12 months with Hilbert‐LSTM, and the results achieved are rank correlation 0.9645, MSE 0.3876, and RMSE 0.6225. The results achieved with the proposed models are improved in terms of performance parameters compared to the conventional approaches. The proposed models can be used in the day‐ahead electricity price forecasting to bid for electricity accurately in the day‐ahead electricity market.

With the ever-increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short-term risk management scenario such as the spot market and real-time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value-at-risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania-New Jersey-Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short-term electricity price volatility risk evaluation.

Potential impact of (CET) carbon emissions trading on China’s power sector: A perspective from different allowance allocation options

Building a new power system is an important measure taken by China to cope with climate change problems. Establishing a flexible and perfect electricity market and price mechanism is an important means to ensure the safe and stable operation of the power system. As one of the important electricity price mechanisms, the change in real-time electricity price (RTP) is a key factor in the operation of the electricity market, and each market participant can formulate a response strategy according to the RTP. However, the uncertain, stochastic, and fluctuant characteristics are definitely difficult problems for the RTP prediction. With the aim of solving this issue, this paper proposed a RTP prediction method based on a window-based XGBoost model. Through the input conversion of the proposed model, it can help to reduce the complexity and capture the autocorrelation effect of the RTP. The case study is conducted through the actual load data of a province in China and the superiority is proved by comparing with several state-of-art models. The result shows that the window-based XGBoost model applied in this paper can decrease the prediction error by 69.48%-95.67% and greatly enhance RTP's prediction performance.

Electricity price forecasting in the electricity market is one of the important purposes for improving the performance of market players and increasing their profits in a competitive electricity market. Since the system load is one of the important factors affecting electricity price changes, a two-factorial model based on fuzzy time series is presented in this paper for electricity price forecasting using the electricity prices of the previous days and the system load. In the proposed method, price and system load time series are fuzzified by fuzzy sets created based on the fuzzy C-means clustering algorithm. After determining proposed model coefficients by the Teaching–Learning-Based Optimization algorithm, this model is used for forecasting the next day electricity price. The promising performance of the proposed model is examined using Australia and Singapore electricity markets data.

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of[-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of[-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

This research proposes an empirical method to estimate the impact on the wholesale electricity market of an increase in the price of CO2 emission allowances. The current literature in this field is mainly focused on long-term simulation analyses, while this study carries out a short-term analysis with microdata from the electricity market. A higher price of CO2 implies an increase in the electricity generation costs of polluting units and therefore an increase in the price of the electricity market. When CO2 becomes more expensive, polluting electricity generators are shifted in the hourly electricity supply curve towards less competitive positions (in favour of less polluting/cheaper units). Displaced polluting units could even be taken out of the market, which would imply a reduction in CO2 emissions. These short-term movements can be reproduced with our microdata of the day-ahead electricity market –data Provided by the Spanish Market Operator (OMIE). According to our results, increases in the carbon price of 10, 20, or 30 € per ton, respectively, cause increases of 1.8%, 4.2% and 5.3% in the electricity price (year 2018), while the negative effect on emissions is relatively small. Our analysis concludes with the estimation of an ARIMA-SARIMA model that looks for the main determinants of the variations in the hourly energy prices and the carbon emissions. The estimations show that the marginal supply technology in the electricity market is important in explaining these variations.Graphical abstract