RNN-K: A Reinforced Newton Method for Consensus-Based Distributed Optimization and Control Over Multiagent Systems

2020 ◽  
pp. 1-15
Author(s):  
Mou Wu ◽  
Naixue Xiong ◽  
Athanasios V. Vasilakos ◽  
Victor C. M. Leung ◽  
C. L. Philip Chen
Keyword(s):  
Multiagent Systems ◽  
Newton Method ◽  
Distributed Optimization ◽  
Optimization And Control ◽  
And Control

ADVANCES IN DISTRIBUTED OPTIMIZATION USING PROBABILITY COLLECTIVES

2006 ◽  
Vol 09 (04) ◽  
pp. 383-436 ◽  
Author(s):  
DAVID H. WOLPERT ◽  
CHARLIE E. M. STRAUSS ◽  
DEV RAJNARAYAN
Keyword(s):  
Monte Carlo ◽  
Objective Function ◽  
Distributed Optimization ◽  
Joint Probability ◽  
Expected Value ◽  
Joint Probability Distribution ◽  
Mathematical Framework ◽  
Optimization And Control ◽  
Full Rationality ◽  
And Control

Recent work has shown how information theory extends conventional full-rationality game theory to allow bounded rational agents. The associated mathematical framework can be used to solve distributed optimization and control problems. This is done by translating the distributed problem into an iterated game, where each agent's mixed strategy (i.e. its stochastically determined move) sets a different variable of the problem. So the expected value of the objective function of the distributed problem is determined by the joint probability distribution across the moves of the agents. The mixed strategies of the agents are updated from one game iteration to the next so as to converge on a joint distribution that optimizes that expected value of the objective function. Here, a set of new techniques for this updating is presented. These and older techniques are then extended to apply to uncountable move spaces. We also present an extension of the approach to include (in)equality constraints over the underlying variables. Another contribution is that we show how to extend the Monte Carlo version of the approach to cases where some agents have no Monte Carlo samples for some of their moves, and derive an "automatic annealing schedule".

Distributed Optimization and Control of Microgrid Considering Environmental Protection and Stability

2013 ◽  
Vol 732-733 ◽  
pp. 1297-1302
Author(s):  
Yu Chen Hao ◽  
Xiao Bo Dou ◽  
Zai Jun Wu ◽  
Min Qiang Hu ◽  
Tao Li ◽  
...  
Keyword(s):  
Environmental Protection ◽  
Real Time ◽  
Control Strategy ◽  
Distributed Optimization ◽  
Stability Control ◽  
Optimal Scheduling ◽  
Time Operation ◽  
Real Time Operation ◽  
Optimization And Control ◽  
And Control

In order to reduce pollutant emissions to improve environmental protection, and maintain microgrid stability during real-time operation, a distributed energy optimization scheduling and stability control strategy was proposed. According to the distributed nature of the microgrid, as well as operational objectives of different microsources, an optimal scheduling model for microgrid environmental protection was designed. Based on the proposed model, the tasks of each unit in optimal scheduling and stability control were described. Genetic algorithm (GA) and user datagram protocol (UDP) were used to implement distributed optimization and control of the microgrid. The simulation indicates that, compared with the traditional centralized optimization and control, the proposed distributed optimization and control strategy can clearly show the characteristics of each unit, and have a faster computation speed. Meanwhile, it can timely response once the voltage fluctuates due to power imbalance, so as to keep microgrid stability in real-time operation.

scholarly journals A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

2017 ◽  
Vol 8 (6) ◽  
pp. 2941-2962 ◽  
Author(s):  
Daniel K. Molzahn ◽  
Florian Dorfler ◽  
Henrik Sandberg ◽  
Steven H. Low ◽  
Sambuddha Chakrabarti ◽  
...  
Keyword(s):  
Power Systems ◽  
Electric Power ◽  
Distributed Optimization ◽  
Electric Power Systems ◽  
Control Algorithms ◽  
Optimization And Control ◽  
And Control
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