A privacy-preserving distributed computational approach for distributed locational marginal prices
An important issue in today's electricity markets is the management of flexibilities offered by new practices, such as smart home appliances or electric vehicles. By inducing changes in the behavior of residential electric utilities, demand response (DR) seeks to adjust the demand of power to the supply for increased grid stability and better integration of renewable energies. A key role in DR is played by emergent independent entities called load aggregators (LAs). We develop a new decentralized algorithm to solve a convex relaxation of the classical Alternative Current Optimal Power Flow (ACOPF) problem, which relies on local information only. Each computational step can be performed in an entirely privacy-preserving manner, and system-wide coordination is achieved via node-specific distribution locational marginal prices (DLMPs). We demonstrate the efficiency of our approach on a 15-bus radial distribution network.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementPrivacy PreservingSimilar Papers 제목 키워드 기반
Privacy-Preserving Distributed Clustering for Electrical Load Profiling
Electrical load profiling supports retailers and distribution network operators in having a better understanding of the consumption behavior of consumers. However, traditional clustering methods for load profiling are ce…
ClusteringPrivacy PreservingDistributed Sketching Methods for Privacy Preserving Regression
In this work, we study distributed sketching methods for large scale regression problems. We leverage multiple randomized sketches for reducing the problem dimensions as well as preserving privacy and improving straggler…
Computational EfficiencyPrivacy PreservingregressionPrivacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although thi…
Blind quantum machine learning with quantum bipartite correlator
Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintainin…
Privacy PreservingQuantum Machine LearningPrivacy-Preserving Distributed Optimization and Learning
Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed op…
Distributed OptimizationPrivacy Preserving