A Distributed Randomized Gradient-Free Algorithm for the Non-Convex Economic Dispatch Problem

Jun Xie, Qingyun Yu and Chi Cao
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Jun Xie: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Qingyun Yu: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Chi Cao: College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Energies, 2018, vol. 11, issue 1, 1-15

Abstract: In this paper, a distributed randomized gradient-free algorithm (DRGF) is employed to solve the complex non-convex economic dispatch problem whose non-convex constraints include valve-point loading effects, prohibited operating zones, and multiple fuel options. The DRGF uses the Gauss approximation, smoothing parameters, and a random sequence to construct distributed randomized gradient-free oracles. By employing a consensus procedure, generation units can gather local information through local communication links and then process the economic dispatch data in a distributed iteration format. Based on the principle of projection optimization, a projection operator is adopted in the DRGF to deal with the discontinuous solution space. The effectiveness of the proposed approach in addressing the non-convex economic dispatch problem is demonstrated by simulations implemented on three standard test systems.

Keywords: distributed randomized gradient-free algorithm; non-convex economic dispatch; randomized gradient-free oracles (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
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