Optimal heat recovery using photovoltaic thermal and thermoelectric generator for solid oxide fuel cell-based polygeneration system: Techno-economic and environmental assessments

Optimization with traffic-based control for designing standalone streetlight system: A case study

Uncertain renewable energy supplies, load demands and the non-linear characteristics of some components of photovoltaic (PV) systems make the design problem not easy to solve by classical optimization methods, especially when relevant meteorological data are not available. To overcome this situation, modern methods based on artificial intelligence techniques have been developed for sizing PV systems. However, simple methods like worst month method are still largely used in sizing simple PV systems. In the present study, a method for sizing remote PV systems based on genetic algorithms has been compared with two classical methods, worst month method and loss of power supply probability (LPSP) method. The three methods have been applied to a PV lighting system with orientation due south and inclination angles between 0° and 90° in Adrar city (south Algeria). Because measured data for the chosen location were not available, a year of synthetic hourly meteorological data of this location, generated by PVSYST software, have been used in the simulation. Genetic algorithms and worst month methods give results close to each other between 0° and 60° but the system is largely oversized by the worst month method when the tilted angle is over 60°. The results obtained by LPSP method show that the system is very undersized. Hence, a proposition has been made to improve results obtained by this method.

/pdf/comparative-study-between-classical-methods-and-genetic-4jdqtk59rq.pdf

Comparative study between classical methods and genetic algorithms for sizing remote PV systems

This paper studies the generation to feed three stand-alone existing pumping systems in the province of Zaragoza (Spain), in the same area but with different consumption and irrigation needs. All of them are used only for half the year. We look for the optimal size of the generation mix, both economically and in reducing emissions. For simulation and optimization the program used is HOGA (Hybrid Optimization by Genetic Algorithms). It is considered diesel generation, photovoltaic and wind as well as storage. As renewable resources are almost identical in the three locations, the magnitude of energy demand and schedule are the distinctive parameters of each facility. We considered how to choose the optimum angle of the PV array, the preference between the solar and wind and the desirability of whether or not the diesel generator. The results show the importance of seasonality and time consumption profile to determine the generation mix and to choose the optimum inclination of the field of photovoltaic conversion. Results also show that most emissions reductions are not always obtained by removing the genset.

/pdf/sizing-stand-alone-hybrid-generation-for-seasonal-irrigation-35mmozobzi.pdf

Sizing stand-alone hybrid generation for seasonal irrigation pumping

Wireless Sensor Networks (WSNs) are widely used during the last years in various environmental and industrial applications and they are frequently installed in geographically isolated areas, where access to electricity is not available. In such cases, the WSN nodes are power-supplied by Renewable Energy Sources (RES), accompanied by energy-storage units. In this paper, a design optimization method is presented for deriving the optimal configuration of the RES-based energy production system of a WSN node, such that its total lifetime cost is minimized, while simultaneously it is guaranteed that the data-acquisition equipment of the WSN node is uninterruptedly power-supplied during the entire year. The design results verify that by applying the proposed optimization technique, RES-based power-supply structures with a lower lifetime cost are derived, compared to the non-optimally designed configurations. Experimental results are also presented, demonstrating the successful operation of a RES-based power-supply system, which has been optimally designed using the proposed method.

Design optimization of a RES-based power-supply system for Wireless Sensor Networks

Based on Genetic Algorithm (GA), which has the ability to attain the global optimum with relative computational simplicity, one optimum sizing method for solar lighting system with battery banks was recommended. The decision variables included in the optimization process are the PV module capacity, battery capacity and PV module slope angle. A case study is presented which has been working reliably in Hong Kong for several years, and useful experience in system optimization and operation/performance has been obtained. This paper also considers the shading effects which can influence the final sizing results significantly in some cases.

One Novel Optimum Sizing Method for Solar Lighting System by Using Genetic Algorithm

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One Novel Optimum Sizing Method for Solar Lighting System by Using Genetic Algorithm