A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on Mixed Integer Genetic Algorithm

By Melika Hamian, Ayda Darvishan, M. Hosseinzadeh, M. Lariche, Noradin Ghadimi, A. Nouri. Engineering applications of artificial intelligence, 2018

297 citations recorded by the source corpus.

Abstract In this paper, a methodology has been proposed to decrease the solution time of the joint energy-reserve market clearing problem under Payment Cost Minimization (PCM) mechanism. PCM mechanism is recently proposed to replace the available economically-inefficient clearing mechanisms in electricity markets. While the exact methods have been successfully applied to solve the market clearing problem under available mechanisms, their computational efficiency is poor when applied to solve this problem under PCM mechanism due to special characteristics of PCM formulation. The proposed solution methodology uses linear programming along with Mixed Integer Genetic Algorithm (MIGA) to minimize the payment cost. Most of efforts are focused on speeding up the applied optimization technique, since the available frameworks for solving the PCM problem suffer from very slow convergence. Different custom-designed functions have been added to the basic MIGA to decrease the solution time. The results are compared to those obtained using branch and cut technique.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c7cc9419d84590519bb3a815671824a27b802c14

Citations in the corpus

• High step-up interleaved dc/dc converter with high efficiency (2020, unknown): “…converters with interleaved techniques capable to decrease the tolerances of the input current, significantly so that it is suitable for renewable application such as it is easy to achieve more accurate measurements current of the PV panel at the maximum power point (MPP) (Hamian et al. 2018).”