面向配电网的基于激励的在线优化框架
An Incentive-Based Online Optimization Framework for Distribution Grids
AI总结：
本文提出了一种面向配电网的基于激励的在线优化框架，通过解决时间变化的社会福利最大化问题，开发了在线分布式算法以识别和跟踪解决方案，同时确保电压在规定范围内。
摘要：
本文针对包含分布式能源资源（DERs）的配电网，提出了一个时间变化的社会福利最大化问题，并开发了在线分布式算法以识别和跟踪其解决方案。在考虑的设置中，网络运营商和DER所有者同时追求给定的操作和经济目标，同时确保电压在规定范围内。所提出的算法提供了一种在线实现方式，以在操作条件和优化目标变化的情况下跟踪解决方案。该策略包括网络运营商在整个馈线中收集电压测量数据，以实时构建激励信号，供DER所有者使用；然后DERs调整生成/消耗的功率，以避免电压约束的违反，同时最大化给定的目标。所提出的方案的稳定性通过分析和数值验证得到证实。
英文摘要：
This paper formulates a time-varying social-welfare maximization problem for distribution grids with distributed energy resources (DERs) and develops online distributed algorithms to identify (and track) its solutions. In the considered setting, network operator and DER-owners pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. The proposed algorithm affords an online implementation to enable tracking of the solutions in the presence of time-varying operational conditions and changing optimization objectives. It involves a strategy where the network operator collects voltage measurements throughout the feeder to build incentive signals for the DER-owners in real time; DERs then adjust the generated/consumed powers in order to avoid the violation of the voltage constraints while maximizing given objectives. The stability of the proposed schemes is analytically established and numerically corroborated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
