Storage energies (SEs) have the flexibility of bi-directional power regulation, so they have been integrated into the power system via power electronic converters to reduce the power fluctuations introduced by converter-based renewables (CBRs), such as solar and wind. However, converter-based SEs may interact with CBRs through the power network, which increases the complexity and difficulty of the phase-lock-loop (PLL)-induced small-signal stability analysis in multi-CBR systems, especially in weak grids. In this context, it remains unclear how the placement of SEs influences the PLL-induced small-signal stability, particularly when the SEs absorb active power from grids. To fill this gap, this paper analyzes the impact of SEs on the PLL-induced small-signal stability in multi-converter systems from the perspective of grid strength and proposes a method for optimally placing SEs to enhance the small-signal stability. First, the analytic results reveal that when the SEs absorb active power from the power grid, they can enhance grid strength and thus the PLL-induced small-signal stability; moreover, the degree of system stability improvement depends on the location of SE placement. On this foundation, a grid-strength-based method is proposed for the PLL-induced small-signal stability improvement via SE placements. The proposed method is validated based on three test systems. This paper provides an effective way of better understanding the interaction among SEs and CBRs through power network, and coordinating the placements of SEs in future converter-dominated power systems.

In order to explore the correlation between meteorological factors and power load changes, as well as the role of these factors in load forecasting, a hybrid load forecasting modeling framework based on quantile Granger causality test and bidirectional long short-term memory (QGCT-BiLSTM) is proposed. The Augmented Dickey–Fuller test (ADF) is used to test the smoothness of the influencing factor series and the load series, and the variables that passed the smoothness test are subjected to QGCT for identification of the characteristic variables with significant causal associations. Furthermore, the BiLSTM model is then constructed using the selected factors to generate load forecasts. Using real data from Fujian, China, we demonstrate that QGCT-based feature screening reduces forecasting errors by an average of 34.96%, where the RMSE, MAE and MAPE are 29.19%, 30.06% and 45.63%, respectively, thereby validating the necessity of causal factor selection. Additionally, single-factor perturbation analysis at seasonal scales quantifies load sensitivity to environmental changes, while geographic–climatic coupling mechanisms explain observed load variation patterns. The results confirm that QGCT-BiLSTM effectively isolates critical meteorological drivers and significantly enhances prediction accuracy compared to conventional approaches, achieving 20.3% lower RMSE and 16.8% lower MAE than LSTM.