Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

Energy is vital for sustainable development of any nation – be it social, economic or environment. In the past decade energy consumption has increased exponentially globally. Energy management is crucial for the future economic prosperity and environmental security. Energy is linked to industrial production, agricultural output, health, access to water, population, education, quality of life, etc. Energy demand management is required for proper allocation of the available resources. During the last decade several new techniques are being used for energy demand management to accurately predict the future energy needs. In this paper an attempt is made to review the various energy demand forecasting models. Traditional methods such as time series, regression, econometric, ARIMA as well as soft computing techniques such as fuzzy logic, genetic algorithm, and neural networks are being extensively used for demand side management. Support vector regression, ant colony and particle swarm optimization are new techniques being adopted for energy demand forecasting. Bottom up models such as MARKAL and LEAP are also being used at the national and regional level for energy demand management.

Energy models for demand forecasting—A review

Finding energy sources to satisfy the world's growing demand is one of society's foremost challenges for the next half-century. The challenge in converting sunlight to electricity via photovoltaic solar cells is dramatically reducing $/watt of delivered solar electricity. In this context the sun trackers are such devices for efficiency improvement. The diurnal and seasonal movement of earth affects the radiation intensity on the solar systems. Sun-trackers move the solar systems to compensate for these motions, keeping the best orientation relative to the sun. Although using sun-tracker is not essential, its use can boost the collected energy 10–100% in different periods of time and geographical conditions. However, it is not recommended to use tracking system for small solar panels because of high energy losses in the driving systems. It is found that the power consumption by tracking device is 2–3% of the increased energy. In this paper different types of sun-tracking systems are reviewed and their cons and pros are discussed. The most efficient and popular sun-tracking device was found to be in the form of polar-axis and azimuth/elevation types.

A review of principle and sun-tracking methods for maximizing solar systems output

Ti2 O3 nanoparticles with high performance of photothermal conversion are demonstrated for the first time. Benefiting from the nanosize and narrow-bandgap features, the Ti2 O3 nanoparticles possess strong light absorption and nearly 100% internal solar-thermal conversion efficiency. Furthermore, Ti2 O3 -nanoparticle-based thin film shows potential use in seawater desalination and purification.

High-Performance Photothermal Conversion of Narrow-Bandgap Ti2O3 Nanoparticles

Selection of suitable heat transfer fluid for heat dissipation is an important consideration in the design of heat exchanging systems. Nanofluid, a colloidal mixture made of a base fluid and a nanoparticle, is a new generation of heat transfer fluids becoming a high potential fluid in heat transfer applications due to enhanced thermal conductivity. Research studies about nanofluids are on the rise owing to the mounting interest and demand for nanofluids as heat transfer fluids in a wide variety of applications. Recently, nanofluid technology has a new dimension of impregnating two or more nanoparticles in base fluids, namely hybrid or composite nanofluids. This paper reviews the preparation of metal and metal oxides nanofluids and hybrid nanofluids and the various techniques used to study the physical and chemical characteristics of nanofluids. Thermo-physical and heat transfer properties of nanofluids including the improved thermal conductivity, viscosity and specific heat models for nanofluids are presented. Finally, various application areas of nanofluids, such as transportation, electronic cooling, energy storage, mechanical applications etc. are discussed.

Omar Badran

Papers

The effect of using different designs of solar stills on water distillation

Sun tracking system for productivity enhancement of solar still

The effect of coupling a flat-plate collector on the solar still productivity

Experimental study of the enhancement parameters on a single slope solar still productivity

Effect of various absorbing materials on the thermal performance of solar stills