Towards real-time microgrid power management using computational intelligence methods
Towards real-time microgrid power management using computational intelligence methods
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DOI：
10.1109/pes.2010.5588053
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
C. Colson;M. H. Nehrir;S. A. Pourmousavi
中科院分区：
文献类型：
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作者：
C. Colson;M. H. Nehrir;S. A. Pourmousavi
微电网是一项新兴技术，有望为电力系统利益相关者（从发电机到消费者）实现许多同时目标。微电网框架提供了一种以分散方式利用各种能源的手段，同时通过在消费者附近发电来减轻公用电网的负担。作为实现电力系统多样性和灵活性的关键组成部分，微电网包括分布式发电机和负荷中心，具有与宏电网孤岛运行或互连的能力。为了使微电网可行，需要新的和创新的技术来管理微电网的多目标，多约束的决策环境。在这篇文章中，两个例子的计算智能方法，粒子群优化（PSO）和蚁群优化（ACO），应用于微电网的电源管理问题。提出了一个多目标优化的数学框架，并讨论了智能方法优于传统优化计算技术的优点。最后，一个三台发电机的微电网与基于ACO的电源管理算法进行了演示，并显示结果。
Microgrids are an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. The microgrid framework offers a means to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the utility grid by generating power close to the consumer. As a critical component to enabling power system diversity and flexibility, microgrids encompass distributed generators and load centers with the capability of operating islanded from or interconnected to the macrogrid. To make microgrids viable, new and innovative techniques are required for managing microgrid operations given its multi-objective, multi-constraint decision environment. In this article, two example computational intelligence methods, particle swarm optimization (PSO) and ant colony optimization (ACO), for application to the microgrid power management problem are introduced. A mathematical framework for multi-objective optimization is presented, as well as a discussion of the advantages of intelligent methods over traditional computational techniques for optimization. Finally, a three-generator microgrid with an ACO-based power management algorithm is demonstrated and results are shown.