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Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage systems. More importantly, the power generation prediction is of great significance to the demand response management (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the Whale Optimization Algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable resources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Due to the inherent uncertainty and the mismatch between renewable energy output and load demand, renewable energy-based system optimization is increasingly important in China. Therefore, electricity demand response (EDR) is critical to the stability and efficiency of an integrated renewable energy system (IRES). The customers' demand response under the time-of-use (TOU) mechanism is the key to the entire process. However, how to optimize integrated renewable energy system based on accurately identifying the electricity demand response participation of customers is one of the main issues at this stage. To solve the problem, this paper presents a novel approach for integrated renewable energy system optimization considering electricity demand response management and multistage energy storage systems from the perspective of Fujian Province, China. To enable large-scale renewable energy integration, the original output is processed using the Hampel identifier (HI)-Savitzky-Golay filter (SGF) technology. A machine learning (ML)-based identification model is developed for EDR to determine whether the customers are participating or not. Driven by the time-of-use mechanism, customers can make dynamic adjustments based on the Logistics function. After that, an integrated renewable energy system model optimization strategy is proposed based on satisfying customers' electricity demand response. Throughout the optimization process, the multistage energy storage system plays a vital role in the residual fluctuation absorption for renewable energy filtering, the dynamic adjustment process of the demand response, and the integrated renewable energy system optimization. Through extensive case studies and discussions, it is demonstrated that random forest (RF) and support vector machine (SVM) reach 80 % identification accuracy with a 20 % testing ratio. According to the optimization approach proposed in this paper, the minimum operation cost can be calculated to be 9.336*106 CNY. The methodology presented in this paper establishes a special relationship between electricity demand response identification and dynamic adjustments, and provides an optimization model for Fujian Province, China, which is a reliable reference for future work. [ABSTRACT FROM AUTHOR]

Faced with the growing renewable energy requirements, there is increased interest in cross-region of large-scale renewable energy market, which provides an alternative path for building sustainable power systems. Critically, the development of a renewable energy-dominated electricity market is an important way to achieve global climate goals and energy conversion. Despite improved achievement of electricity demand response (EDR) market, only limited development in the coordination capacity and development scale of EDR are dominated by renewable energy. Moreover, the normal operation and work efficiency of the system is greatly affected due to data transmission errors and other human factors. Thus, it is of great importance for the State Grid to accurately identify the data sources and realize the interactive development of cross-region power systems. This paper presents a new cross-region (province) electricity demand response (CR-EDR) model in China for large-scale renewable energy participating in EDR market. This model is applied to three provinces in China based on renewable energy, and fully consider how wind power is integrated with EDR in terms of operation, grid connection and optimization. Presently, China is in the initial stage of development, and the identification of data anomalies is inevitable for the CR-EDR. To solve this problem, a novel machine learning (ML)-based approach is proposed for effective identification of EDR. By calculating wind power output and customers' EDR results, reliable feature sequences can be obtained. Finally, all the feature sequences are uploaded to cross-region system operators (CR-SO) for classification and identification by ML. In the case studies and discussions, we integrated all feature sequences to CR-SO for identification and present a novel process approach to implement EDR, the random forest (RF) enables 100% accuracy for training set and 99.7685% for testing set with small training samples. Compared with RF, support vector machine only achieves 79.1667% for testing set with small training samples. With accurate RF identification results, the stable operation capability and management level of the system can be effectively improved. The proposed methodology creates a new provincial perspective of China and the establishment of CR-EDR, which provides a theoretical and methodological guidance for all countries and regions to develop CR-EDR. • A new cross-region (province) electricity demand response management is proposed. • Large-scale renewable energy participating in demand response is fully considered. • Machine learning-based approach is adopted for data integration and identification. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]