虚拟发电厂的分布式调度策略使用区块链背景下的粒子群优化神经网络
1College of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Computational intelligence and neuroscience
|September 16, 2024
概括
一个新的能源区块链模型通过安全地集成分布式能源 (DER) 来增强虚拟发电厂 (VPP). 这种优化的系统提高了可靠性,降低了成本,在稳定的电源管理中实现了94.98%的准确性.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 能源系统 能源系统
背景情况:
- 虚拟发电厂 (VPPs) 汇总分布式能源资源 (DERs) 用于电网服务.
- 目前的VPP模型面临着数据安全,信任和高交互成本的挑战.
- 区块链技术为安全和透明的能源管理提供了潜在的解决方案.
研究的目的:
- 为VPPs提出一个能源区块链网络模型.
- 解决当前VPP模型中关于分布式调度和负载分配的局限性.
- 在VPP操作中增强数据安全,存储安全和主体间信任.
主要方法:
- 能源区块链网络模型的开发.
- 集成粒子集群优化 (PSO) 来优化神经网络.
- 对拟议模型的性能进行模拟和分析.
主要成果:
- 拟议的模型实现了最小的错误率和94.98%的准确性.
- 该系统展示了改善的实时需求侧信息捕获,以实现稳定的VPP调度.
- 观察到增强的数据和存储安全性,以及减少的交互成本.
结论:
- 能源区块链模型有效地克服了VPP中的信任和成本问题.
- 优化的神经网络可以实现更准确,更稳定的VPP管理.
- 这种方法通过DER集成促进了可靠,高质量和安全的电力服务.
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