分布式需求侧应急服务供应，同时通过本地频率测量和负载间通信最小化用户负效用
Distributed demand-side contingency-service provisioning while minimizing consumer disutility through local frequency measurements and inter-load communication
摘要：
考虑了智能灵活负荷向电网提供应急备用的问题，提出了一种分布式梯度投影（DGP）算法，在提供应急服务的同时最小化负荷的负效用。每个负载使用本地获得的电网频率测量和负载间通信来协调它们的动作，并且每个负载的隐私被保留：仅交换梯度信息-而不是负效用或消耗信息。我们提供了一个证明所提出的DGP算法的收敛性，我们通过模拟的“对偶算法”以前提出的文献中，解决了双重最优化问题的性能进行比较。DGP算法解决了原始问题。它的主要优点是对偶算法是，它适用于凸-但不一定是严格凸-消费者负效用函数，如消费者行为模型，是不敏感的消费小的变化，而对偶算法不是。仿真结果表明，DGP算法有助于阻止电网频率偏差，以应对突发事件，并在两者可以比较的情况下表现得更好或类似于对偶算法。
英文摘要：
We consider the problem of smart and flexible loads providing contingency reserves to the electric grid and provide a Distributed Gradient Projection (DGP) algorithm to minimize loads' disutility while providing contingency services. Each load uses locally obtained grid-frequency measurements and inter-load communication to coordinate their actions, and the privacy of each load is preserved: only gradient information is exchanged---not disutility or consumption information. We provide a proof of convergence of the proposed DGP algorithm, and we compare its performance through simulations to that of a "dual algorithm" previously proposed in the literature that solved the dual optimization problem. The DGP algorithm solves the primal problem. Its main advantage over the dual algorithm is that it is applicable to convex---but not necessarily strictly convex---consumer disutility functions, such as a model of consumer behavior that is insensitive to small changes in consumption, while the dual algorithm is not. Simulations show the DGP algorithm aids in arresting grid-frequency deviations in response to contingency events and performs better or similarly to the dual algorithm in cases where the two can be compared.
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。
