Scenario generation and reduction based on a single variable (e.g., photovoltaic power or load forecasting) is a mainstream approach in current power system planning. However, such methods often overlook the temporal correlation between source and load, which can compromise the credibility of the generated scenarios and lead to suboptimal planning outcomes. To address this issue, this paper proposes an integrated model for microgrid energy storage planning and operation that explicitly considers the joint distribution of source–load scenarios. First, a comprehensive similarity metric is developed by combining dynamic time warping (DTW) distance, slope distance, and source–load correlation distance. An improved K-medoids clustering algorithm is then employed to cluster the joint source–load time series, generating a set of typical scenarios that effectively preserve the coupling characteristics between photovoltaic generation and load demand. Subsequently, a bi-level optimization model is formulated, with energy storage capacity as the primary decision variable. The upper-level planning problem aims to maximize the return on investment (ROI) under energy storage investment constraints, determining the optimal capacity configuration. The lower-level operational problem maximizes the daily net revenue by optimizing the charging and discharging strategies of the energy storage system. Through iterative interaction between the two levels, the model achieves optimal coordination between investment decisions and economic dispatch. Case studies on a campus microgrid demonstrate that the proposed joint scenario generation method effectively captures the temporal correlation between source and load, enhancing both the credibility of the scenarios and the economic rationality of the integrated planning and operation framework.

Traditional symmetric voltage multiplier-based structures offer low current stress and high scalability. However, the equalization current flowing into each energy storage cell must overcome four diode voltage drops per switching cycle, significantly degrading energy transfer efficiency. A compact integrated equalizer based on multi-stacked buck-boost converters for large-scale energy storage systems is proposed. By replacing diodes with inductors, the design achieves high-efficiency cell balancing even at low cell voltages. The integrated design leverages the boost circuit’s inherent current ripple for driving the balancing system, eliminating extra switches and minimizing size and cost. Additionally, it provides independent balancing channels for each cell, eliminating equalization current superposition. This reduces cell current stress while enabling large-scale system balancing. Experimental validation on an eight-cell setup demonstrated successful balancing with 87.5% system efficiency.