通过模型预测控制与马尔科夫奖励过程来动态优化不平衡的分销网络管理
César Álvarez-Arroyo1, Salvatore Vergine2, Guglielmo D'Amico3
1Department of Electrical Engineering, Universidad de Sevilla, 41092 Sevilla, Spain.
Heliyon
|February 2, 2024
概括
本研究介绍了一种使用基于模型的预测控制 (MPC) 的两级控制系统,以最大限度地减少主动配电系统的功率损失. 该MPC方法优化了可再生能源和储存的能源管理,优于短期方法.
科学领域:
- 电气工程 电气工程
- 控制系统 控制系统
- 整合可再生能源的整合
背景情况:
- 活跃的配电系统需要高效的管理,以尽量减少能源损失.
- 整合风能和太阳能等可再生能源在电力系统稳定性和效率方面提出了挑战.
- 电压控制设备在维持电源质量和减少损失方面发挥着至关重要的作用.
研究的目的:
- 开发和评估一个双层控制系统,以最大限度地减少活跃配电系统中总活跃功率损失.
- 为了比较基于模型的预测控制 (MPC) 框架与对损失最小化进行短期分析的有效性.
- 调查不同可再生能源发电和电池储能容量的对系统性能的影响.
主要方法:
- 实施一个两级控制系统,在第一级采用基于模型的预测控制 (MPC).
- 使用非均和均的马尔科夫奖励模型,分别准确预测风能和光伏功率.
- 采用电压控制资产管理的优化算法,包括电压调节变压器,以最大限度地减少系统损失.
主要成果:
- 与短期分析相比,MPC框架在最大限度地减少总主动功率损失方面表现出卓越的表现.
- 在MPC中的长视野优化导致了活力功率损失的显著减少,尽管变量增加了.
- 短期分析导致变量减少,但损失最小化结果的质量有所妥协.
结论:
- 拟议的双层控制系统,特别是MPC框架,有效地将主动配电系统中的主动功率损失降至最低.
- 控制地平线的选择显著影响了变量数量和实现的损失减少之间的权衡.
- 该研究强调了预测控制策略的好处,以优化复杂电网的运行,并整合可再生能源.
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