• Energy Research

In recent years, the power system transient stability assessment (TSA) based on a data-driven method has been widely studied. However, the topology and modes of operation of power systems may change frequently due to the complex time-varying characteristics of power systems. which makes it difficult for prediction models trained on stationary distributed data to meet the requirements of online applications. When a new working situation scenario causes the prediction model accuracy not to meet the requirements, the model needs to be updated in real-time. With limited storage space, model capacity, and infinite new scenarios to be updated for learning, the model updates must be sustainable and scalable. Therefore, to address this problem, this paper introduces the continual learning Sliced Cramér Preservation (SCP) algorithm to perform update operations on the model. A deep residual shrinkage network (DRSN) is selected as a classifier to construct the TSA model of SCP-DRSN at the same time. With the SCP, the model can be extended and updated just by using the new scenarios data. The updated prediction model not only complements the prediction capability for new scenarios but also retains the prediction ability under old scenarios, which can avoid frequent updates of the model. The test results on a modified New England 10-machine 39-bus system and an IEEE 118-bus system show that the proposed method in this paper can effectively update and extend the prediction model under the condition of using only new scenarios data. The coverage of the updated model for new scenarios is improving.

With the development and application of large-scale renewable energy sources, the electric power grid is becoming huge and complicated; one of the most concerning problems is how to ensure coordination between a large number of varied controllers. Differential games theory is used to solve the problem of collaborative control. However, it is difficult to solve the differential game problem with constraints by using conventional algorithm. Furthermore, simulation models established by existing research are almost linear, which is not conducive to practical engineering application. To solve the above problem, we propose a co-evolutionary algorithm based on the improved weighted fruit fly optimization algorithm (IWFOA) to solve a multi-area frequency collaborative control model with non-linear constraints. Simulation results show that the control strategy can achieve system control targets, and fully utilize the various characteristics of each generator to balance the interests of different areas. Compared with a co-evolutionary genetic algorithm and a collaborative multi-objective particle swarm optimization algorithm, the co-evolutionary algorithm based on the IWFOA has a better suppression effect on the frequency deviation and tie-line power deviation caused by the disturbance and has a shorter adjustment time.

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.

Selection of the kernel function by the support vector regression (SVR), for the purposes of load forecasting, is affected by the power load characteristics. The non-ideal SVR with a kernel function has low forecasting accuracy and poor generalization ability. A novel load forecasting method combining SVR and stacking is proposed in this paper. Base models are constructed based on SVRs with different kernel functions, then multiple base models are merged to obtain a base model layer via stacking algorithm. Finally, an SVR is connected as the meta-model layer. The stacking fusion model is composed of base model layer and meta-model layer. This model is trained with k-fold cross validation to enhance its generalization ability. An improved artificial fish swarm algorithm is employed to optimize the parameters to improve the forecasting accuracy of the stacking fusion model; speed variables are introduced to replace step lengths and improve the convergence speed and search ability. The forecasting accuracy and generalization ability of the proposed method are verified by comparative analysis.

Voltage stability has always been one of the most important concerns. As the increasing integration of large-scale renewable energy sources in power systems, the correlation between load demands and renewable energy systems becomes more and more complex and important for probabilistic voltage stability. There are two significant issues for probabilistic voltage stability assessment: (i) how to choose the reasonable power increment direction which determines the reliability of voltage stability assessment when considering the actual operating characteristics of the power system; and (ii) how to obtain the samples characterized with the specified distribution and the desired correlation. We propose methodologies to define the reasonable power increment direction with theoretical proof. Moreover, power method transformation combined with Latin hypercube sampling and twice-permutation technique is proposed for probabilistic voltage stability assessment. Case studies with two modified IEEE test systems show that the proposed method is accurate and efficient.

To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.

Real-time online simulation based on a real-time workshop (RTW) plays a vital role in the study and application of power electronics. However, restricted by the performance of equipment and hardware, the simulators so far available in the market mainly support simulation steps over 50 μs, while large step simulation may result in the action delay of pulse-width modulating (PWM), numerical oscillation and high-level non-characteristic harmonic distortion. In view of these problems, this paper puts forward a modeling method based on integral prediction and interpolation compensation. First of all, prediction is performed one step in advance by the implicit trapezoidal method to find out the accurate time when the triangle carrier wave intersects with the modulation wave. At the same time, a mathematic model is built for the insulated gate bipolar transistor (IGBT) to output equivalent voltage waveform according to the principle of area equivalent. Next, in MATLAB/Simulink, offline simulation is performed with the three-phase AC-DC-AC converter as the subject. By comparing the control accuracy, the content of harmonic wave and the simulation time, the simulation effects of the 50 μs fixed-step interpolation prediction model are the same as that for a 5 μs fixed-step standard model. Finally, the effectiveness and high efficiency of this algorithm are verified on a real-time simulator, marking the application of offline models on real-time simulators.

To address the issue where the grid integration of renewable energy field stations may exacerbate the power fluctuation in tie-line agreements and jeopardize safe grid operation, we propose a hybrid energy storage system (HESS) capacity allocation optimization method based on variational mode decomposition (VMD) and a multi-strategy improved salp swarm algorithm (ISSA). From typical wind load power and contact line agreement power, the HESS power is obtained. VMD decomposes this power into high- and low-frequency power, respectively, for the super capacitor and the Li-ion battery. Considering charging and discharging power and state of charge (SOC) constraints, an optimization model minimizing the system equivalent annual value cost is established. ISSA optimizes the best decomposition layer K and penalty coefficients α in VMD. The optimal cut-off point and corresponding energy storage allocation scheme are analyzed. A simulation and analysis on MATLAB show that the proposed ISSA-VMD HESS capacity allocation scheme saves 7.53% in costs compared to an empirical mode decomposition (EMD) scheme, proving the method’s effectiveness and superiority.