光伏出力预测与多目标控制策略对并网光伏-储能电池系统（PV-BESS）微电网优化运行的影响
The Impact of PV Generation Forecast and Multi-Objective Control Policy on Optimal Operation of Grid Connected PV-BESS Microgrid
- Norwegian University of Science and Technology (NTNU)（挪威科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于 LSTM 的光伏预测模型与多目标调度框架，对比三种预测场景，发现 LSTM 预测可提升光伏自消费率、降低电网注入功率，同时指出电池吞吐量与老化的权衡，凸显准确光伏预测的重要性。
AI中文摘要：
光伏（PV）出力的波动性给并网微电网的可靠高效运行带来重大挑战。准确的光伏输出功率预测与高效的能量调度策略，不仅对优化光伏系统运行至关重要，还能提升系统整体性能与可靠性。本研究针对并网光伏-储能电池系统（PV-BESS），提出一种基于长短期记忆网络（LSTM）的光伏出力预测模型，并集成多目标调度框架。该方法通过量化光伏预测精度对关键运行指标的影响，实现详细的性能监测与评估，关键指标包括光伏自消费率、电网购能成本、电网注入功率及电池利用率。研究对比三种预测场景（完美预测、 persistence 模型、基于 LSTM 的预测），评估其对系统性能与运行可靠性的影响。结果显示，与 persistence 模型相比，基于 LSTM 的预测将均方根误差（RMSE）降低 6%，光伏自消费率从 78.1%提升至 84.5%，电网注入功率减少 82%。分析还指出存在权衡关系：性能提升伴随的更高电池吞吐量可能加速电池老化。这些发现表明准确的光伏预测对提升系统性能、保障可靠运行具有重要意义。未来工作将聚焦于概率预测以合理量化不确定性，纳入负荷预测，并开发能实现光伏侧电网支撑功能的智能控制策略。
英文摘要：
The variability of photovoltaic (PV) generation poses significant challenges to the reliable and efficient operation of grid-connected microgrids. Accurate PV output power forecasting and efficient energy scheduling strategies are essential not only for optimizing PV system operation but also for improving the overall performance and reliability of the system. This study proposes a long short-term memory (LSTM)-based PV power forecasting model integrated with a multi-objective scheduling framework for a grid-connected PV-battery energy storage system (BESS). The proposed approach enables detailed performance monitoring and assessment by quantifying how PV forecast accuracy influences key operational metrics, including PV self-consumption ratio, grid energy cost, grid injection, and battery utilization. Three forecasting scenarios (perfect forecast, persistence model, and LSTM-based forecast) are compared to evaluate their impact on system performance and operational reliability. Results show that the LSTM-based forecast reduces root mean squared error (RMSE) by 6% compared with the persistence model, increases the PV self-consumption ratio from 78.1% to 84.5%, and reduces grid injections by 82%. The analysis also highlights trade-offs, as higher battery throughput associated with improved performance may contribute to accelerated aging. These findings demonstrate the importance of accurate PV forecasting in improving system performance and ensuring reliable operation. Future work will focus on probabilistic forecasting to properly quantify uncertainties, incorporate load prediction, and develop smart control strategies that allow grid-support functionalities from the PV side.
