This paper proposes a Lyapunov optimization-based online distributed (LOOD) algorithmic framework for active distribution networks (ADNs) with numerous photovoltaic inverters and inverter air conditionings (IACs). In the proposed scheme, ADNs can track an active power setpoint reference at the substation in response to transmission-level requests while concurrently minimizing the social utility loss and ensuring the security of voltages. Conventional distributed optimization methods are rarely feasible to track the optimal solutions in fast variable environments using a fine-grained sampling interval where the underlying optimization problem evolves with the iterations of the algorithms. In contrast, based on the framework of online convex optimization (OCO), the developed approach uses a distributed algebraic update to compute the next round decisions relying on the current feedback of measurements. Notably, the time-coupling constraints of IACs are decoupled for online implementation with Lyapunov optimization technique. An incentive scheme is tailored to coordinate the customer-owned assets in lieu of the direct control from network operators. Optimality and convergency are characterized analytically. Finally, we corroborate the proposed method on a modified version of 33-node test feeder. Benchmark tests show that the proposed method is computationally and economically efficient, and outperforming existing algorithms.

中文翻译：

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具有时间耦合约束的网络化分布式能源在线优化

本文针对具有众多光伏逆变器和逆变器空调（IAC）的有源配电网（ADN），提出了一种基于Lyapunov优化的在线分布式（LOOD）算法框架。在提出的方案中，ADN可以响应传输级别的请求在变电站跟踪有功功率设定值参考，同时又将社会公用事业损失降到最低，并确保电压的安全性。常规的分布式优化方法很难使用细粒度的采样间隔来跟踪快速可变环境中的最佳解决方案，在这种情况下，基础优化问题随着算法的迭代而发展。相比之下，基于在线凸优化（OCO）框架，开发的方法使用分布式代数更新，根据当前的测量反馈来计算下一轮决策。值得注意的是，IAC的时间耦合约束是通过Lyapunov优化技术在线执行时解耦的。制定了激励计划，以协调客户拥有的资产，代替网络运营商的直接控制。最优性和收敛性通过分析来表征。最后，我们在33节点测试馈送器的修改版本上证实了所提出的方法。基准测试表明，该方法在计算和经济上均有效，并且优于现有算法。制定了激励计划，以协调客户拥有的资产，代替网络运营商的直接控制。最优性和收敛性通过分析来表征。最后，我们在33节点测试馈送器的修改版本上证实了所提出的方法。基准测试表明，该方法在计算和经济上均有效，并且优于现有算法。制定了激励计划，以协调客户拥有的资产，代替网络运营商的直接控制。最优性和收敛性通过分析来表征。最后，我们在33节点测试馈送器的修改版本上证实了所提出的方法。基准测试表明，该方法在计算和经济上均有效，并且优于现有算法。