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Register a new userShort-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling
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Bo Tranberg
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
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The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 hours of electricity consumption in a 24 hour interval can on average reduce the resulting CO2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day.
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In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential trading risks, and supporting efficient system planning and operation. Unlike existing methods that only consider LMPs temporal features, this paper tailors a spectral graph convolutional network (GCN) to greatly improve the accuracy of short-term LMP forecasting. A three-branch network structure is then designed to match the structure of LMPs compositions. Such kind of network can extract the spatial-temporal features of LMPs, and provide fast and high-quality predictions for all nodes simultaneously. The attention mechanism is also implemented to assign varying importance weights between different nodes and time slots. Case studies based on the IEEE-118 test system and real-world data from the PJM validate that the proposed model outperforms existing forecasting models in accuracy, and maintains a robust performance by avoiding extreme errors.
Electricity prices strongly depend on seasonality on different time scales, therefore any forecasting of electricity prices has to account for it. Neural networks proved successful in forecasting, but complicated architectures like LSTM are used to integrate the seasonal behavior. This paper shows that simple neural networks architectures like DNNs with an embedding layer for seasonality information deliver not only a competitive but superior forecast. The embedding based processing of calendar information additionally opens up new applications for neural networks in electricity trading like the generation of price forward curves. Besides the theoretical foundation, this paper also provides an empirical multi-year study on the German electricity market for both applications and derives economical insights from the embedding layer. The study shows that in short-term price-forecasting the mean absolute error of the proposed neural networks with embedding layer is only about half of the mean absolute forecast error of state-of-the-art LSTM approaches. The predominance of the proposed approach is also supported by a statistical analysis using Friedman and Holms tests.
The electricity market, which was initially designed for dispatchable power plants and inflexible demand, is being increasingly challenged by new trends, such as the high penetration of intermittent renewables and the transformation of the consumers energy space. To accommodate these new trends and improve the performance of the market, several modifications to current market designs have been proposed in the literature. Given the vast variety of these proposals, this paper provides a comprehensive investigation of the modifications proposed in the literature as well as a detailed assessment of their suitability for improving market performance under the continuously evolving electricity landscape. To this end, first, a set of criteria for an ideal market design is proposed, and the barriers present in current market designs hindering the fulfillment of these criteria are identified. Then, the different market solutions proposed in the literature, which could potentially mitigate these barriers, are extensively explored. Finally, a taxonomy of the proposed solutions is presented, highlighting the barriers addressed by each proposal and the associated implementation challenges. The outcomes of this analysis show that even though each barrier is addressed by at least one proposed solution, no single proposal is able to address all the barriers simultaneously. In this regard, a future-proof market design must combine different elements of proposed solutions to comprehensively mitigate market barriers and overcome the identified implementation challenges. Thus, by thoroughly reviewing this rich body of literature, this paper introduces key contributions enabling the advancement of the state-of-the-art towards increasingly efficient electricity market.
A machine learning algorithm is developed to forecast the CO2 emission intensities in electrical power grids in the Danish bidding zone DK2, distinguishing between average and marginal emissions. The analysis was done on data set comprised of a large number (473) of explanatory variables such as power production, demand, import, weather conditions etc. collected from selected neighboring zones. The number was reduced to less than 50 using both LASSO (a penalized linear regression analysis) and a forward feature selection algorithm. Three linear regression models that capture different aspects of the data (non-linearities and coupling of variables etc.) were created and combined into a final model using Softmax weighted average. Cross-validation is performed for debiasing and autoregressive moving average model (ARIMA) implemented to correct the residuals, making the final model the variant with exogenous inputs (ARIMAX). The forecasts with the corresponding uncertainties are given for two time horizons, below and above six hours. Marginal emissions came up independent of any conditions in the DK2 zone, suggesting that the marginal generators are located in the neighbouring zones. The developed methodology can be applied to any bidding zone in the European electricity network without requiring detailed knowledge about the zone.
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