Editorial for the special issue: “Optimization in energy”

Abstract

The idea for this special issue of Energy Systems entitled "Optimization in Energy" was born following the Fourth Conference on Optimization and Practices in Industry (COPI) organized by the OSIRIS department of EDF R&D in October, 2014. The triannual COPI conference is aimed at bringing together academics and industry to share optimization modeling expertise and build collaboration. The 2014 conference was organized jointly with the Gaspard Monge Program for Optimization and Operations Research (PGMO). 1 The academic research on electricity optimization carried out through the PGMO, along with the applications of optimization in EDF R&D show the great importance that optimization has in the electricity industry. Modeling and computational improvements for traditional operational problems such as unit commitment are still being discovered for very large-scale systems. Other traditional problems involve the annual planning of hydroelectric energy, or the scheduling of maintenance for generating B Wim van Ackooij

Energy Economy and Energy Structure Optimization Countermeasures Based on Big Data Mining

“…It also reveals the mechanism of risk element generation in microgrid management, as well as the influence of mutual transmission among subsystems, providing a theoretical foundation for microgrid managers to optimize and make microgrid decisions in the context of big data on energy and reduce microgrid risks. According to the literature [15], DM is becoming increasingly important in decision support activities in all walks of life as a new interdisciplinary application technology. In literature [16,17], how to identify risk elements in microgrid operation is studied from two aspects of power generation and electricity consumption, against the backdrop of energy big data and based on risk element transfer theory.…”

“…Literature [19] analyzes big data of wind power generation, big data of photovoltaic power generation, big data of power supply and demand, and so on and points out the dynamic change phenomenon of risks in microgrid during microgrid operation. In literature [20], according to the data characteristics of the energy internet, the mathematical model of abnormal disturbance signals is established, and the abnormal disturbance signals are decomposed by wavelet transform. Literature [21] puts forward a virtual machine scheduling algorithm based on the genetic algorithm, which is simulated by cloud computing simulation software CloudSim.…”