Microgrids energy management systems: A critical review on methods, solutions, and prospects

A review on feedstocks, production processes, and yield for different generations of biodiesel

Biodiesel, derived from the transesterification of vegetable oils or animal fats, is composed of saturated and unsaturated long-chain fatty acid alkyl esters. In spite of having some application problems, recently it is being considered as one of the most promising alternative fuels in internal combustion engine. From scientific literatures, this paper has collected and analyzed the data on both advantages and disadvantages of biodiesel over conventional diesel. Since the aim of this study is to evaluate the biodiesel feasibility in automobiles, the first section is dedicated to materials compatibility in biodiesel as compared to that in diesel. The highest consensus is related to enhanced corrosion of automotive parts due to its compositional differences. In the subsequent sections, data on performance, emission and engine durability have been analyzed and compared. In this case, the highest consensus is found in reducing emissions as well as in increasing moving parts sticking, injector coking and filter plugging. This paper has also summarized the factors of biodiesel in contributing these technical performances.

Biodiesel feasibility study: An evaluation of material compatibility; performance; emission and engine durability

The effect of nano-additives in diesel-biodiesel fuel blends: A comprehensive review on stability, engine performance and emission characteristics

Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.

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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends

Unit commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads under different constraints and environments. Many algorithms have been invented in the past five decades for optimization of the UC problem, but still researchers are working in this field to find new hybrid algorithms to make the problem more realistic. The importance of UC is increasing with the constantly varying demands. Therefore, there is an urgent need in the power sector to keep track of the latest methodologies to further optimize the working criterions of the generating units. This paper focuses on providing a clear review of the latest techniques employed in optimizing UC problems for both stochastic and deterministic loads, which has been acquired from many peer reviewed published papers. It has been divided into many sections which include various constraints based on profit, security, emission and time. It emphasizes not only on deregulated and regulated environments but also on renewable energy and distributed generating systems. In terms of contributions, the detailed analysis of all the UC algorithms has been discussed for the benefit of new researchers interested in working in this field.

A solution to the unit commitment problem—a review

An assessment on the effects of 1-pentanol and 1-butanol as additives with Calophyllum Inophyllum biodiesel.

Renewable energy, particularly solar and wind energies are associated with high degree of uncertainty due to climatic conditions. A proper modelling and analytical treatment of these uncertainties play a key role in taking operational and financial decisions by the microgrid operator. A number of models are proposed and tested successfully in literature to model these energy uncertainties. This article presents a survey of some of the latest analytical and approximation techniques reported in literature to model the uncertainties in microgrid environment. The article mainly focuses on the methods which are applied in particular to the study of uncertainties in Renewable energy availability, heat demand and load demand. Different models, their main features, relative merits and demerits, application in literature, etc are reviewed and presented in form of a table for a quick view. The review shows the inadequacy of uncertainty modelling methods applicable to Renewable sources, both in terms of number and accuracy. It also envisages the scope and need for more flexible models for specific applications.

B. Saravanan

Papers

A solution to the unit commitment problem—a review

An assessment on the effects of 1-pentanol and 1-butanol as additives with Calophyllum Inophyllum biodiesel.

Recent techniques to model uncertainties in power generation from renewable energy sources and loads in microgrids – A review

Unit commitment problem solution using invasive weed optimization algorithm

Effect of next generation higher alcohols and Calophyllum inophyllum methyl ester blends in diesel engine.