两种MPC算法在实际微电网系统中降低需求费用的比较

Yun Xue, M. Todd, S. Ula, M. Barth, A. Martinez-Morales
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引用次数: 13

摘要

本文介绍了两种模型预测控制(MPC)算法在实际微电网系统中结合太阳能发电和电池储能的微电网能源管理中的评估。第一种控制算法是恒阈值MPC (CT-MPC),该算法适用于相对稳定的太阳能发电系统和众所周知的建筑负荷分布。CT-MPC可以在整个峰期费率期内将峰期需求维持在一定值以下。第二种控制算法是需求阈值调节MPC算法(ADT-MPC)。ADT-MPC可以更好地处理不可预测的太阳能发电和/或不断变化的建筑负荷。该算法的峰值阈值在峰值速率周期内调整为最优值。正如预期的那样,CT-MPC算法在与准确的预测模型相结合时表现良好,而ADT-MPC算法在预测更不可预测时表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system
This paper describes an evaluation between two model predictive control (MPC) algorithms for microgrid energy management combined with solar production and battery energy storage for demand charge reduction in a real-world microgrid system. The first control algorithm is a constant threshold MPC (CT-MPC) that works well on a system with relatively stable solar generation and a well-known building load profile. CT-MPC can maintain the on-peak demand under a certain value during the entire on-peak rate period. The second control algorithm is an adjusting demand threshold MPC (ADT-MPC). ADT-MPC can better deal with unpredictable solar generation and/or changing building loads. The on-peak threshold under this algorithm is adjusted to the optimal value during the on-peak rate period. As expected, The CT-MPC algorithm performs well when coupled with accurate forecast models while the ADT-MPC algorithm excels when forecasting is more unpredictable.
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