Search

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of "source-load-storage" energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost-efficient operation strategy is proposed for VPP clusters. Initially, the influence of real-time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster-wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end-user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground-source heat pumps to restrict power output, thus achieving economical and low-carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario. [ABSTRACT FROM AUTHOR]

Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

With the continuous expansion of grid-connected wind, photovoltaic, and other renewable energy sources, their volatility and uncertainty pose significant challenges to system peak regulation. To enhance the system's peak-load management and the integration of wind (WD) and photovoltaic (PV) power, this paper introduces a distributionally robust optimization scheduling strategy for a WD–PV thermal storage power system incorporating deep peak shaving. Firstly, a detailed peak shaving process model is developed for thermal power units, alongside a multi-energy coupling model for WD–PV thermal storage that accounts for carbon emissions. Secondly, to address the variability and uncertainty of WD–PV outputs, a data-driven, distributionally robust optimization scheduling model is formulated utilizing 1-norm and ∞-norm constrained scenario probability distribution fuzzy sets. Lastly, the model is solved iteratively through the column and constraint generation algorithm (C&CG). The outcomes demonstrate that the proposed strategy not only enhances the system's peak-load handling and WD–PV integration but also boosts its economic efficiency and reduces the carbon emissions of the system, achieving a balance between model economy and system robustness. [ABSTRACT FROM AUTHOR]

In order to solve the difficulty that complex power quality disturbances (PQDs) are difficult to recognize accurately and efficiently under the new power system background, this paper proposes a novel PQDs recognition method based on markov transition field (MTF) and improved densely connected network (DenseNet). Firstly, the one-dimensional PQDs signal is mapped into the two- dimensional image with clear texture features by using MTF encoding method. Then, a DenseNet-S lightweight network is designed and the convolutional attention module (CBAM) is introduced to improve its feature extraction ability, so as to enhance the performance of the network. Finally, the images are input into the improved model for training and learning, and PQDs recognition is realized through the optimal model. In order to verify the effectiveness of the proposed method, experimental tests are carried out based on IEEE 1159 standard simulation dataset and real-world field measured signals dataset, and compared with existing recognition methods. The results show that the proposed method can effectively improve the recognition accuracy and noise robustness of complex PQDs, and has more advantages in disturbances recognition efficiency. It can meet the recognition accuracy and efficiency requirements of massive and complex PQDs events in engineering applications. [ABSTRACT FROM AUTHOR]

Active power filter can compensate harmonic and reactive power, Three level neutral point clamp (ANPC) has the characteristics of low output harmonic and low device loss. The control of three-level active power filter needs to consider multiple objectives. Finite control set model predictive control (FCS-MPC) is a new method which can add constraints and multi-objective control. According to the limit of the switch state, it can track the reference current, which has the characteristics of fast dynamic response and good compensation effect. This method not only keeps the neutral point voltage balance, but also solves the problem of unstable switching frequency.

This study proposes a novel integrated energy system (IES) cluster optimization structure that uses multi-energy sharing, multi-Nash games, and asymmetric profit allocation according to the energy supply demand and energy development planning for Tibet. First, it integrates clean energy units such as concentrated solar power, power to hydrogen to power, and vacuum pressure swing adsorption to build a novel IES including electricity, heat, and oxygen. Second, multiple novel IESs are combined to form an IES cluster and the IES cluster is divided into three stages of optimization: the first stage is to achieve optimal multi-energy sharing under cluster optimization, the second stage is to conduct multi-Nash games to achieve optimal sharing cost, and the third stage is to conduct asymmetric profit allocation. Finally, the case study is conducted and the results show that the multi-Nash games and asymmetric profit allocation can effectively improve the renewable energy consumption of the IES cluster, reduce the operation cost of the cluster, and reduce the cost of multi-energy sharing compared to only considering the cluster energy supply price as the sharing price, thereby improving the economy of multi-energy sharing. [ABSTRACT FROM AUTHOR]

With the intensive commissioning of high voltage direct current, transient voltage problems have become increasingly prominent, which seriously threatens the safe and stable operation of the power system. On the basis of cascaded CatBoost (CasCatBoost) and sparrow search algorithm (SSA), a novel temporal‐adaptive data‐driven method for short‐term voltage stability (STVS) assessment is proposed in this paper. First, normalized mutual information feature selection is employed for important feature selection to reduce the computational burden. Taking the important features as inputs, the CasCatBoost model is constructed to mine STVS informatization. On the basis of CatBoost and cascaded structure, CasCatBoost can enhance the assessment accuracy without sacrificing the assessment earliness. Furthermore, the SSA is embedded into the offline phases of CasCatBoost to determine the optimal confidence threshold. Extensive numerical tests are conducted on the improved New England 39‐bus system, and the simulation results demonstrate that the proposed method outperforms other representative machine learning algorithms and exhibits excellent reliability, rapidity, and applicability. [ABSTRACT FROM AUTHOR]