When attempting to better understand the strengths and weaknesses of an algorithm, it is important to have a strong understanding of the problem at hand. This is true for the field of multiobjective evolutionary algorithms (EAs) as it is for any other field. Many of the multiobjective test problems employed in the EA literature have not been rigorously analyzed, which makes it difficult to draw accurate conclusions about the strengths and weaknesses of the algorithms tested on them. In this paper, we systematically review and analyze many problems from the EA literature, each belonging to the important class of real-valued, unconstrained, multiobjective test problems. To support this, we first introduce a set of test problem criteria, which are in turn supported by a set of definitions. Our analysis of test problems highlights a number of areas requiring attention. Not only are many test problems poorly constructed but also the important class of nonseparable problems, particularly nonseparable multimodal problems, is poorly represented. Motivated by these findings, we present a flexible toolkit for constructing well-designed test problems. We also present empirical results demonstrating how the toolkit can be used to test an optimizer in ways that existing test suites do not

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A review of multiobjective test problems and a scalable test problem toolkit

Current status of research on optimum sizing of stand-alone hybrid solar–wind power generation systems

Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

Optimum design of hybrid renewable energy systems: Overview of different approaches

Stand-alone hybrid renewable energy systems usually incur lower costs and demonstrate higher reliability than photovoltaic (PV) or wind systems. The most usual systems are PV–Wind–Battery and PV–Diesel–Battery. Energy storage is usually in batteries (normally of the lead-acid type). Another possible storage alternative, such as hydrogen, is not currently economically viable, given the high cost of the electrolyzers and fuel cells and the low efficiency in the electricity–hydrogen–electricity conversion. When the design of these systems is carried out, it is usually done resolve an optimization problem in which the Net Present Cost (NPC) is minimized or, in some cases, in relation to the Levelized Cost of Energy (LCE). The correct resolution of this optimization problem is a complex task because of the high number of variables and the non-linearity in the performance of some of the system components. This paper revises the simulation and optimization techniques, as well as the tools existing that are needed to simulate and design stand-alone hybrid systems for the generation of electricity.

Simulation and optimization of stand-alone hybrid renewable energy systems

Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified – technology design, building design, urban climate, systems design, and policy assessment – each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date. Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

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A review of urban energy system models: Approaches, challenges and opportunities

Multiobjective optimisation of integrated energy systems for remote communities considering economics and CO2 emissions

This paper introduces a multi-objective EA, termed the Clustering Pareto Evolutionary Algorithm (CPEA). The CPEA finds and retains many local Pareto- optimal fronts, rather than just the global front as is the case of most multi- objective EAs found in the literature. This has been achieved using a clustering technique commonly used in multivariate statistical analysis, which ensures that competition between individuals is local in variable space, allowing the population to grow to resolve as many Pareto-optimal fronts as necessary. The performance of the CPEA is evaluated on several test problems taken from the literature which have either single optima or multiple local optima and is shown to be extremely effective. The present clustering method is computationally expensive and will be replaced with an incremental method in the near future.

A New Clustering Evolutionary Multi-Objective Optimisation Technique

Vehicle drivetrains are complex integrated systems, which need to be designed for numerous thermodynamic, economic and environmental factors. As part of a project of the Alliance for Global Sustainability between the MIT, the SFITs and the University of Tokyo, a new evolutionary multi-objective optimisation algorithm has been developed and applied to various problems including vehicle drivetrain configurations. The simulation of a potential vehicle is complex and calculation intensive. The new algorithm, the Clustering Pareto Evolustionary Algorithm (CPEA) is attractive in this kind of problems because it allows an efficient use of each vehicle simulation - gathering more information about the solution domain for the same effort as a single point optimisation. In addition it allows optimisation of disparate objectives, for example, costs and emissions to be considered separately. Conventional, series electric hybrid and parallel electric hybrid have been evaluated over the ECE-EUDC and US06HWY drive cycles considering performance in terms of CO2, NOx, fuel economy, estimated investment, operating and pollution costs. Although some own technology models have been developed and used, the major part of the optimisation results are based on models from Advisor which are available on the web. The parallel hybrids were shown to be preferable to series hybrids in all cases studied, but were frequently beaten by conventional vehicles. In this first study, pollution costs considerations were limited to NOx (13.8 Frs/kg) and CO2, (0.03 Frs/kg) and diesel engine vehicles proved difficult to beat in terms of operating costs, and even overall investment and operating cost. Indeed it was found that a five fold increase in pollution costs traditionaly considered in the literature, would be needed before any difference would be observed. Of course the situation looks different if investment and operating cost, without any pollution costs, are evaluated only in comparison with the NOx emission levels or if a pollution cost of particulates is added. Among marketed hybrids the “Insight” was found to be highly attractive (keeping however in mind its limited seating capacity), although its exceptional performance is undoubtedly linked to the ultra light weight structure, which, in spite of the braking energy recovery of hybrids, is still shown to have a major effect. It is interesting to note that the pollution costs were found to be of low significance compared to the operating and investment costs and an average vehicle, mainly due to the significant level of tax already applied to fuel in the road transport domain. Politically it would seem that applying a uniform CO2 cost accross the board of all energy domains will have a much more limited effect on the road vehicle technologis, than an other technologies such as those used for building heating and air conditioning.

Multi-Objective Optimisation of Vehicle Drivetrains

This paper presents a methodology for optimisation of vehicle drivetrain configuration and their design with respect to multiple parameters. Some preliminary results from such optimisations are presented. Multi-objective optimisation algorithms have been developed to improve on earlier work on "environomic" optimisation, in which costs and environmental parameters are optimised using an agglomerated objective function. The vehicle component models are based on a combination of experimental data and theory, and in some cases neural net approximations of complicated subsystems have been used to replace slow theoretical models. The components developed at LENI have been incorporated into the vehicle simulation system ADVISOR and the system has been optimised with respect to component sizes, as well as three configurations (one conventional and two hybrid drivetrains) for such diverse objectives as emissions, costs and performance.

A New Multi-Objective Optimisation Technique Applied to a Vehicle Drive Train Simulation

The CEPA, an Evolutionary Algorithm that preserves diversity by finding clusters in the population, has had its convergence performance improved by a technique tentatively called ‘evolutionary operator selection’. Performance is compared to results found in the literature, though at the moment it is not entirely clear how the evolutionary operator selection mechanisms work. The resulting algorithm has been applied to a number of problems - including a hybrid vehicle configuration and coke production in Shanxi Province, China.

Geoffrey Basil Leyland

Papers

Multiobjective optimisation of integrated energy systems for remote communities considering economics and CO2 emissions

A New Clustering Evolutionary Multi-Objective Optimisation Technique

Multi-Objective Optimisation of Vehicle Drivetrains

A New Multi-Objective Optimisation Technique Applied to a Vehicle Drive Train Simulation

A Fast Multi-Objective Evolutionary Algorithm applied to Industrial Problems