多存储类型混合可再生能源系统的模糊控制器

Majed Althubaiti, Michael Bernard, P. Musílek
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引用次数: 20

摘要

本工作的目的是设计一种方案来控制由多种可再生能源(太阳能和风能)和多种储能系统组成的混合可再生能源系统的功率流。由于可再生能源的间歇性和随之而来的峰值功率在源和负荷之间的转移,储能的使用是必要的。此外,储能的使用可以增加整个系统的可靠性和稳定性。在本工作中,电池作为中短期储能系统的主要储能系统,而氢燃料电池作为长期储能系统。监控系统通过对电力间歇性、电力调峰和长期储能的管理,来处理电力供应和电力需求的各种变化。由于供电和需求都是不可完全预测的,并且具有时变的非线性行为,因此引入计算智能来解决这一问题,并提供合适的控制算法。这种控制基于模糊逻辑,将系统知识与控制相结合。建议的方法可以优化,使系统能够适应其工作环境,并在特定情况下提供最佳结果。为了获得更真实的模拟结果,设计的模型使用了来自澳大利亚昆士兰大学的真实太阳能数据,而真实的风能和负荷数据则由加拿大阿尔伯塔省的阿尔伯塔电力系统运营商收集。设计了系统模型并在MATLAB SimPowerSystems™中进行了仿真,验证了所提方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy logic controller for hybrid renewable energy system with multiple types of storage
The objective of this work is to design a scheme to control the power flow of a hybrid renewable energy system with multiple renewable energy sources (solar energy and wind energy) and multiple energy storage systems. The use of energy storage is necessary due to the intermittency of the renewable energy sources and the consequent peak power shift between the sources and the load. In addition, the use of the energy storage can increase the overall system reliability and stability. In this work, batteries are used as the primary energy storage system for short to medium storage term, while hydrogen fuel cell is used as the long-term energy storage. A supervisory control system is designed to handle various changes in power supply and power demand by managing power intermittency, power peak shaving, and long-term energy storage. Since both power supply and demand are not fully predictable and they have time variant nonlinear behavior, computational intelligence is introduced to solve this issue and provide suitable control algorithm. This type of control, based on fuzzy logic, combines knowledge of the system and control. The proposed methodology can be optimized such that the system is able to adapt to its working environment and deliver best results under given circumstances. For more realistic simulation results, the designed model utilizes real solar power data collected from the University of Queensland in Australia, while real wind power and load data was collected by the Alberta Electric System Operator in Alberta, Canada. The model of the system is designed and simulated in MATLAB SimPowerSystems™ to verify the effectiveness of the proposed scheme.
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