This article provides a general overview of the field now known as "evolutionary multi-objective optimization," which refers to the use of evolutionary algorithms to solve problems with two or more (often conflicting) objective functions. Using as a framework the history of this discipline, we discuss some of the most representative algorithms that have been developed so far, as well as some of their applications. Also, we discuss some of the methodological issues related to the use of multi-objective evolutionary algorithms, as well as some of the current and future research trends in the area.

Evolutionary multi-objective optimization: a historical view of the field

Topology optimization of continuum structures: A review*

Concerns about climate change as well as fossil fuel usage restrictions motivate the energy transition to a sustainable energy sector requiring very high penetration level of renewable energy sources in the World energy matrix, including those heavily hydrocarbon-based as fuel for transportation. Some of these renewable sources have an uncontrollable output and managing the variability is challenging. The current upward trend in renewables participation will demand even more flexibility from the energy systems. Among several options for increasing flexibility, energy storage (ES) is a promising one considering the variability of many renewable sources. The purpose of this study is to present a comprehensive updated review of ES technologies, briefly address their applications and discuss the barriers to ES deployment. Methodology involves the description and the analysis of ES many existing and developing technologies. ES applications are discussed briefly using logistic and parametric classification logics. As result of this study, it will be pointed out that no ES technology outstands simultaneously in all technical characteristics and consequently, selection should be driven on a case base analysis. Economic feasibility of ES business models and establishment of a well-suited regulatory environment are major issues to unlock ES deployment. Regarding energy transition, Power-to-Gas, Power-to-Liquids and Solar-to-Fuel technologies are very promising and further studies about these technologies are required to better understand their possibilities and how to overcome the barriers to their practical usage.

Energy storage in the energy transition context: A technology review

The ever increasing penetration of renewable energy systems (RESs) in today deregulated intelligent power grids, necessitates the use of electrical storage systems. Energy storage systems (ESSs) are helpful to make balance between generation and demand improving the performance of whole power grid. In collaboration with RESs, energy storage devices can be integrated into the power networks to bring ancillary service for the power system and hence enable an increased penetration of distributed generation (DG) units. This paper presents different applications of electrical energy storage technologies in power systems emphasizing on the collaboration of such entities with RESs. The role of ESSs in intelligent micropower grids is also discussed where the stochastic nature of renewable energy sources may affect the power quality. Particular attention is paid to flywheel storage, electrochemical storage, pumped hydroelectric storage, and compressed air storage and their operating principle are discussed as well. The application of each type in the area of power system is investigated and compared to others.

Emergence of energy storage technologies as the solution for reliable operation of smart power systems: A review

https://cris.brighton.ac.uk/ws/files/5530179/Liquid_air_energy_storage_Analysis_and_first_results_from_a_pilot_scale_demonstration_plant.pdf.pdf

Liquid air energy storage – Analysis and first results from a pilot scale demonstration plant

Thermodynamic analysis of pumped thermal electricity storage

Real-world engineering optimisation problems are typically multi-objective and highly constrained, and constraints may be both costly to evaluate and binary in nature. In addition, objective functions may be computationally expensive and, in the commercial design cycle, there is a premium placed on rapid initial progress in the optimisation run. In these circumstances, evolutionary algorithms may not be the best choice; we have developed a multi-objective Tabu Search algorithm, designed to perform well under these conditions. Here we present the algorithm along with the constraint handling approach, and test it on a number of benchmark constrained test problems. In addition, we perform a parametric study on a variety of unconstrained test problems in order to determine the optimal parameter settings. Our algorithm performs well compared to a leading multi-objective Genetic Algorithm, and we find that its performance is robust to parameter settings.

Geoff Parks

Papers

Thermodynamic analysis of pumped thermal electricity storage

A multi-objective tabu search algorithm for constrained optimisation problems

Optimization of blade profiles for the Wells turbine

Multi-Objective Optimization of Perturbed Impulsive Rendezvous Trajectories Using Physical Programming

Life-cycle impacts from novel thorium-uranium-fuelled nuclear energy systems