1. Introduction
The Paris Agreement calls for achieving net-zero emissions by the second half of this century and achieving the goal of holding the global average temperature increase to well below 2 °C and preferably 1.5 °C. To achieve carbon neutrality, several countries have set policies and targets and are taking a variety of measures, for example, focusing on energy transformation of power systems, making full use of renewable energy sources at the source, adopting carbon capture and storage (CCS), or planting trees to make full use of the negative emission capacity of bioenergy [
1].
As a renewable energy source, wind energy plays an important role in mitigating environmental change by avoiding the energy consumption and carbon emissions caused by traditional fossil energy combustion. The installed capacity of wind power is increasing year by year, but wind power has strong volatility and randomness, and how to effectively utilize wind energy becomes an urgent issue [
2,
3,
4].
In response to the volatility and stochastic nature of wind power, developments in machine learning and artificial intelligence have encouraged researchers to explore data-driven models to achieve accurate wind power forecasts [
5].
Predictive models commonly applied to renewable energy can be classified into four categories: statistical models, machine learning models, artificial intelligence models, and hybrid models. Statistical models include regression models such as AR integrated moving average (ARIMA). Traditional forecasting methods mainly use statistical methods to establish the relationship between historical values and wind energy, such as time series and regression analysis methods. However, traditional statistical methods cannot accurately characterize the strongly fluctuating variability of wind power, limiting the accuracy of forecasting. Machine learning includes least squares support vector machines (LSSVM), support vector machine regression (SVR), etc. Artificial intelligence models include some neural network models such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory recurrent neural network (LSTM), etc. LSTM has complex storage units that remember previous information and can be applied to the computation of the current output, i.e., the nodes between hidden layers become connected [
6]. Hybrid model is a combination of two or more of the above models, which can give full play to the advantages of different models, reduce model training speed, and improve prediction accuracy [
7,
8,
9,
10].
According to dispatchers, schedulers, and energy planners, the most important aspect of low-carbon economic dispatch for the power system requires wind generation forecasts for energy trading and unit commitment plans a day in advance, followed by hourly forecasts (in megawatts). This includes error lines and uncertainty intervals, as well as forecasts for maintenance schedules several days in advance. To meet these needs, many forecasting studies have been conducted, focusing on wind speed and power forecasting over different time horizons, uncertainty forecasting, and slope event forecasting. As input data for low-carbon dispatch, wind energy plays an important role in guiding the low-carbon economic dispatch. The highly chaotic, intermittent, and random nature of wind energy poses a great challenge to wind power forecasting [
11].
The whole forecasting process can be divided into data pre-processing, model construction, and evaluation. In data pre-processing, wind power forecasting is performed using ensemble empirical mode decomposition (EEMD). By decomposing highly irregular values into IMFs with some regularity, it helps to reduce the difficulty of wind power forecasting. EEMD is an empirical decomposition method that decomposes a time series into many sub-series according to different frequencies. Common decomposition methods include wavelet, empirical modal decomposition, seasonal adjust methods, variational modal decomposition, and intrinsic time-scale decomposition methods. EEMD is often used as a pre-processing technique for mixed forecasting models. Different forecasting models are used to predict the sub-sequences generated by the decomposition.
A number of new methods have been proposed to address the shortcomings present in EMD. For example, EEMD; Median EMD (MEMD); Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEDAN); Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEDAN), etc. EEMD is based on the principle of adding Gaussian white noise to the original signal several times, then performing EMD decomposition and averaging the EMD decomposition results. MEMD is another algorithm that solves the problem of modal confusion by using a variable window median filter to process the IMF, eliminating the effect of impulse noise while reducing modal mixing. CEEMDAN solved the problem of different number of modes for different realizations of signal plus noise. ICEEMDAN improves on the former by improving on some spurious modes that may occur in the early stages of the decomposition, enabling a less noisy and more physically meaningful component to be obtained [
12,
13].
For the selection of algorithms in model construction, in recent years, based on the large amount of data generated by the operation of power systems and the development of artificial intelligence algorithms, traditional wind power prediction algorithms have gradually evolved to intelligent processing algorithms represented by artificial neural networks [
14]. LSSVM can be used for classification and prediction [
15,
16,
17]. SVR greatly increases the speed of operation by adding the concept of vectors to LSSVM [
18].
In the field of deep learning, RNN and LSTM have unique advantages in processing time series. To deal with the complex noise in the target value, noise reduction or modal decomposition is often carried out before sending it into the model. LSTM solves the gradient disappearance of RNN during remote transmission.
Each of the above single wind power prediction models has advantages and disadvantages. Furthermore, to improve the forecasting performance, hybrid models combine different methods and take advantage of the strengths of each method. Hybrid models based on decomposition, which take advantage of time series decomposition methods, have been frequently reported.
Where SVM is widely used to cope with non-linear time series, the use of EMD-based decomposition methods can improve the wind power prediction capability of SVM. It has been shown that EMD can improve the performance of SVM models in 10-min, 15-min, 30-min and daily wind power forecasting. The case study also shows that the hybrid EEMD-SVM model outperforms the hybrid EMD-SVM model and the SVM model for monthly wind power forecasting. The combination of CEEMDAN and SVM is suitable for hourly wind power forecasting. EMD (and its variants) can also be used for the pre-processing process in the hybrid forecasting model. The wind power time series are first noise-reduced by EMD, then the SVM model is used to determine the individual model weights and finally the components are fed into the appropriate model: ARIMA, Error Encoding Network (ENN), and Multilayer Perceptron (MLP). The forecasting results show that the hybrid forecasting models of the EMD pre-processing series outperform the individual models in terms of wind power forecasting. In conclusion, the EMD decomposition and its improved algorithms have been widely adopted for improving wind power forecasting accuracy. In addition, EMD-based decomposition methods using hybrid models are usually better than individual forecasting models. In addition, improved EMD algorithms can improve model prediction performance in wind power forecasting as they can reduce the mode mixing problem that exists in EMD methods [
19].
It is widely accepted that wind power time series are highly volatile and non-stationary. Modeling the raw time series with a single method is challenging. Decomposition-based methods take advantage of decomposition methods to decompose the original time series into different sub-series, which can be modeled more effectively than the original time series. In this paper, a combined model prediction method based on EEMD decomposition is proposed for wind power time series. Due to the nature of high-frequency jitter, high-frequency IMFs will get better prediction results using a LSTM, while low-frequency IMFs use a SVR to improve the model prediction speed.
The use of carbon capture power plants is an effective way to achieve low carbon economic dispatch of power systems [
20]. The conventional operation methods of carbon capture power plants include split-flow and storage type. In the case of split-flow operation, the processes of CO
2 absorption and capture are coupled with each other, which often reduces the carbon capture level of carbon capture plants when the demand for carbon capture in the system conflicts with the demand for load; in the case of liquid storage operation, although the process of CO
2 absorption and capture can be decoupled, it cannot actively emit CO
2, which leads to poor economics when the price of carbon trading is low. Therefore, this method is rarely used [
21]. In order to overcome the shortcomings of these two conventional approaches, a study has proposed an integrated and flexible operation of carbon capture power plants consisting of a split-flow type and a liquid storage type. This approach can not only improve the flexibility of dispatch by actively emitting CO
2 but also decouple the CO
2 absorption and capture process, so that the carbon capture power plant can have the energy time-shifting characteristics and achieve efficient peaking; meanwhile, it can provide rotating backup by adjusting the carbon capture energy consumption and effectively reduce the number of start-ups and shutdowns of high-carbon thermal power by sharing the backup pressure of high-carbon thermal power [
22].
In summary, the current research is mainly based on different perspectives such as power-side, load-side, and source-load sides. The related results are of great significance to the low-carbon economic operation of power systems, but there are still issues that need to be studied in depth:
There are more studies on split-flow carbon capture power plants but fewer studies on integrated carbon capture power plants. Integrated carbon capture plants applied to the source side have better results and should be studied further.
Most of the studies deal with wind power prediction by directly applying existing prediction values, but these values often have poor prediction results. There is a need to study prediction models with higher prediction accuracy.
Few studies have jointly applied precise wind power predictions with integrated carbon capture plants. The low-carbon characteristics and scheduling advantages of the above two tools have not been fully explored, and there is a lack of research on the operational mechanism of the two working together to achieve low carbon.
To this end, this paper proposes an economic dispatching method for power systems that considers the accuracy of wind power forecasting as well as integrated carbon capture plants. The main studies are as follows:
First, the EEMD-LSTM-SVR model is used to forecast the wind power in the Belgian grid so that the forecast values are as close as possible to the real values. This allows us to get closer to the real dispatch costs, unit start-up and shutdown plans, and unit output. This provides the grid dispatchers with a better dispatch strategy and avoids the loss of system safety in the pursuit of low dispatch costs.
Then, the low-carbon economic dispatching model of power system with integrated flexible operation of carbon capture power plant is built by integrating the split-flow type and liquid storage type carbon capture power plant on the traditional thermal power plant.
Finally, the advantages of the dispatching method proposed in this paper are verified by simulation. The results show that the wind power prediction is more accurate and the dispatching results are closer to the real value based on the original one.
2. Operational Mechanisms Considering Wind Power Uncertainty and Low Carbon Characteristics of Carbon Capture Power Plants
In this paper, wind power is predicted by accurate artificial intelligence algorithm at the source side, carbon capture consumption is cut by the solution storage of carbon capture power plant, and the two complement each other to deeply explore the lower carbon potential. Thermal power, carbon capture energy, wind power, and load are all considered as dispatchable resources and are classified into three categories according to their different carbon emission characteristics: Category 1 is high carbon units, such as conventional thermal power; Category 2 is low carbon units, such as carbon capture power plants; and Category 3 is zero carbon units, such as wind power.
The integrated flexible operation method of carbon capture power plant consists of two parts: shunt type and liquid storage type.
Figure 1 shows the schematic diagram of the integrated flexible operation method of the carbon capture power plant.
The split-flow operation of carbon capture plants includes both flue gas splitting (as shown in the blue module in
Figure 1) and liquid-rich splitting. By controlling the flue gas bypass, the flue gas split operation method adjusts the proportion of flue gas directly discharged to the atmosphere, thus achieving flexible adjustment of carbon capture energy consumption and net output power. The key equipment of the liquid storage operation method (as shown in the green module in
Figure 1) is the solution storage, so that the rich liquid absorbed from the absorption tower and the rich liquid entering the regeneration tower at this time are no longer equal, i.e., the CO
2 absorption process, which determines the amount of carbon capture, and the solution regeneration process, which determines the energy consumption of carbon capture, are decoupled to a certain extent.
The combined consideration of shunt operation and storage operation allows for both shifting the impact of carbon capture energy consumption to load during peak-load times and proactive CO
2 emission when the system needs it. This integrated and flexible low-carbon operation can expand the net output range of carbon capture power plants, as shown in
Figure 2.
At peak-load times, split-flow carbon capture plants need to increase their output and accordingly produce more CO2. If all of this CO2 is captured, the carbon capture energy consumption will also be increased largely, which is not conducive to increasing the output of the carbon capture plant. If the carbon capture plant is operated in a flexible way, the CO2 at peak load can be stored in solution storage. This will help to reduce the CO2 emissions directly into the atmosphere to ensure a low carbon system. It also helps to reduce the energy consumption of carbon capture at peak load, ensuring the system’s economy.
In the low-load period, the split-flow carbon capture power plant reduces the unit output and expands the net output range mainly through the energy consumption generated during CO
2 capture (
Figure 2 in mode II). At the same time, the energy consumption generated by carbon capture increases the wind power consumption capacity. However, if we also consider the liquid storage type carbon capture operation, which releases CO
2 from the solution storage, we can further increase the carbon capture energy consumption. The system will have a lower output limit and a larger output range (
Figure 2 in mode III). At the same time, it further promotes wind power consumption. The whole process can be seen as replacing expensive high-carbon units at peak-load times by increasing wind power output at low-load times and utilizing economical low-carbon units.
In summary, the storage and liquid carbon capture method shift CO2 from the peak load to the low-load for processing. In turn, the energy consumption of the carbon capture process is delayed in the time dimension, and the expensive high-carbon thermal power plants are replaced by low-carbon carbon capture power plants and wind power plants, making the system more low-carbon and economical.