园区综合能源系统功率预测参数设计目前主要以新能源或者负荷的功率预测误差最小为目标，旨在获得最接近源荷实际运行功率的预测结果，但未考虑预测结果对运行目标的差异化影响。预测误差最小化目标与运行目标的不一致性导致预测参数应用后的实际运行经济效益偏低。针对上述问题，本文提出一种运行目标导向的园区综合能源系统源荷功率预测参数联合优化方法。首先，以日前-日内两阶段运行模型为基础，构建融合日前-日内总经济运行指标的预测参数联合优化目标函数。而后，通过日前运行模型最优性条件的构建，将日前-日内运行策略嵌入预测参数联合优化环节，建立内嵌两阶段运行策略的源荷功率预测参数联合优化模型，并设计热启动方案，加速预测参数联合优化问题求解。最后，依托美国安克雷奇市的商业和居民用能数据，构建仿真模型对所提方法进行了验证。验证结果表明：所提方法可以有效降低城市园区综合能源系统运行成本，减少弃风弃光量，提高储能运行收益。

Current optimization of power forecasting parameters in park integrated energy systems focuses on minimizing the forecasting error of either the source or the load power, without considering the comprehensive impact of forecasting results on operation objectives. This oversight leads to lower comprehensive economic benefits when the forecasting parameters are applied in practice. To address this issue, this paper proposes a joint optimization method for power forecasting parameters in park integrated energy systems, oriented towards operational target. Firstly, based on a two-stage day-ahead and intraday operational model, a joint optimization objective function for forecasting parameters is constructed, integrating total economic operational indices from both day-ahead and intraday stages. Subsequently, based on linear regression equation, this paper establishes a joint optimization model for source-load power forecasting parameters with an embedded two-stage operational strategy. Simultaneously, a warm-start scheme for the forecasting model is designed to accelerate the solving speed of the joint optimization problem of forecasting parameters. Finally, leveraging commercial and residential energy consumption data from Anchorage, USA, a simulation model is constructed to validate the proposed method. The validation results indicate that the proposed method can effectively reduce the operating costs of urban park integrated energy systems, decrease the curtailment of wind and solar energy, and increase the operational revenue of energy storage.