光伏出力具有强随机性和多尺度时序波动特征，在系统长期规划问题中，需要通过场景生成与压缩来平衡场景多样性与计算复杂度，现有方法主要依赖距离或均值误差准则，难以系统揭示不同压缩策略与场景特征间的内在关系。该文提出一种基于多差异指标与Spearman相关性分析的光伏出力场景压缩评价方法。首先通过K-means聚类方法对光伏出力场景进行压缩，得到不同的压缩场景集，再通过Markov模型拼接出一年的数据；然后在不同的优化场景集上进行光伏和储能的优化配置；最后，采用Spearman秩相关系数分析不同场景集的差异指标与优化结果之间的关系，提出基于Spearman权重的综合评价模型。研究结果表明，在不同的压缩重构场景下，部分场景配置结果波动幅度大于5%，场景的差异特征决定了优化结果的敏感性与稳定性。该文为新能源系统规划中场景压缩方法的选择与评估提供了定量分析依据。

Photovoltaic (PV) output exhibits strong randomness and multi-scale temporal volatility. In long-term system planning, scenario generation and reduction are required to balance scenario diversity and computational complexity. However, existing methods primarily depend on distance or mean error criteria, struggling to systematically uncover the intrinsic relationship between reduction strategies and scenario characteristics. This paper proposes an evaluation method for PV scenario reduction based on multiple discrepancy metrics and Spearman correlation. First, the K-means clustering is applied to compress PV output scenarios into diverse reduced sets, which are then extrapolated into annual time series via a Markov chain model. Subsequently, the optimal sizing of PV and energy storage systems is executed across these scenarios. Finally, the Spearman rank correlation coefficient is used to quantify the correlation between scenario discrepancy metrics and optimization results, thereby establishing a comprehensive evaluation model weighted by these coefficients. The results show that specific configuration outcomes fluctuate by over 5% across different reduced and reconstructed scenarios, proving that scenario discrepancy features dictate the sensitivity and stability of optimization results. This paper offers a quantitative basis for selecting and assessing scenario reduction techniques in renewable energy planning.