Bio-Inspired Optimization of Sustainable Energy Systems: A Review

Abstract

Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm

“…These results were obtained for a desired LLP of 0.01 or less. Using equations (27)(28)(29)(30), the obtained PV module range was (7, 95) while the battery range calculated was (10,542,158,125) Wh. According to [39], the recommended values of the mutation factor (F) and crossover rate (CR) are 0.75 and 0.5, respectively.…”

“…In these researches, a similar methodology was followed whereas three main objective functions were adapted, which are LSPS, TNPC, and the CO2 emission.A recent review highlights the need for new, fast, and accurate sizing methods [27]. Other recent reviews [28][29][30] demonstrate the increased attention given to AI techniques' use in sizing as they have been proven to be more successful than conventional optimization methods. These reviews stated that the most two important issues to be considered when optimizing a SPAV are system models and the efficiency and speed of the utilized AI algorithm.…”

Optimization in microgrids with hybrid energy systems – A review

“…Future research may pave the way for hybridization and multi-objective implementation of bio-inspired solutions. Attempts to exploit advantages and disadvantages of different algorithms have been made by implementing hybrid algorithms [84][85][86][87]126]. These approaches have been proved to be more fast, accurate and powerful than individual systems.…”

Effects of nanostructure on clean energy: big solutions gained from small features

“…Advanced materials and nanotechnology related to clean coal and fossil fuels, biofuels and biomass conversion, biosystem, and bioinspired system for energy conversion, piezoelectric conversion, and CO 2 capture, storage, and utilization, as well as hydrogen storage, have been researched intensively for clean energy applications in recent decades [48,[61][62][63][64][65][66][67][68][69]. For example, synthesis of methane (CH 4 ) from syngas (CO ?…”