A Flexible Distributed Optimization Framework for Service of Concurrent Tasks in Processing Networks

2019 ◽  
Author(s):  
Zai Shi ◽  
Atilla Eryilmaz
Keyword(s):  
Distributed Optimization ◽  
Optimization Framework ◽  
Concurrent Tasks ◽  
Processing Networks

A programmable distributed optimization framework for 5G

2020 ◽  
Vol 8 (1) ◽  
pp. 65-68
Author(s):  
Swaminathan Seetharaman ◽  
Dilip Krishnaswamy
Keyword(s):  
Distributed Optimization ◽  
Optimization Framework

scholarly journals Distributed Optimization Framework for In-Network Data Processing

2019 ◽  
Vol 27 (6) ◽  
pp. 2432-2443
Author(s):  
Sepideh Nazemi ◽  
Kin K. Leung ◽  
Ananthram Swami
Keyword(s):  
Data Processing ◽  
Distributed Optimization ◽  
Network Data ◽  
Optimization Framework

A distributed optimization framework for wind farms

2013 ◽  
Vol 123 ◽  
pp. 88-98 ◽  
Author(s):  
Maryam Soleimanzadeh ◽  
Rafael Wisniewski ◽  
Kathryn Johnson
Keyword(s):  
Distributed Optimization ◽  
Wind Farms ◽  
Optimization Framework

scholarly journals Towards online optimization for power grids

2021 ◽  
Vol 1 (1) ◽  
pp. 51-58
Author(s):  
Deming Yuan ◽  
Abhishek Bhardwaj ◽  
Ian Petersen ◽  
Elizabeth L. Ratnam ◽  
Guodong Shi
Keyword(s):  
Power Systems ◽  
State Of The Art ◽  
Distributed Optimization ◽  
Power Grid ◽  
Optimization Techniques ◽  
Online Optimization ◽  
Power Grids ◽  
Optimization Framework ◽  
Grid Applications ◽  
Grid Operators

In this note, we discuss potential advantages in extending distributed optimization frameworks to enhance support for power grid operators managing an influx of online sequential decisions. First, we review the state-of-the-art distributed optimization frameworks for electric power systems, and explain how distributed algorithms deliver scalable solutions. Next, we introduce key concepts and paradigms for online optimization, and present a distributed online optimization framework highlighting important performance characteristics. Finally, we discuss the connection and difference between offline and online distributed optimization, showcasing the suitability of such optimization techniques for power grid applications.

scholarly journals Fully Privacy-Preserving Distributed Optimization Based on Secret Sharing

2021 ◽  
Author(s):  
Nianfeng Tian ◽  
Qinglai Guo ◽  
Hongbin Sun ◽  
Xin Zhou
Keyword(s):  
Secret Sharing ◽  
Private Information ◽  
Data Exchange ◽  
Distributed Optimization ◽  
Critical Issue ◽  
Privacy Preserving ◽  
Collaborative Optimization ◽  
Third Party ◽  
Master Problem ◽  
Optimization Framework

With the increasing development of smart grid, multiparty cooperative computation between several entities has become a typical characteristic of modern energy systems. Traditionally, data exchange among parties is inevitable, rendering how to complete multiparty collaborative optimization without exposing any private information a critical issue. This paper proposes a fully privacy-preserving distributed optimization framework based on secure multiparty computation (SMPC) secret sharing protocols. The framework decomposes the collaborative optimization problem into a master problem and several subproblems. The process of solving the master problem is executed in the SMPC framework via the secret sharing protocols among agents. The relationships of agents are completely equal, and there is no privileged agent or any third party. The process of solving subproblems is conducted by agents individually. Compared to the traditional distributed optimization framework, the proposed SMPC-based framework can fully preserve individual private information. Exchanged data among agents are encrypted and no private information disclosure is assured. Furthermore, the framework maintains a limited and acceptable increase in computational costs while guaranteeing optimality. Case studies are conducted to demonstrate the principle of secret sharing and verify the feasibility and scalability of the proposed methodology. <br>

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