**1. Introduction**

230 Renewable Energy – Trends and Applications

Even though the costs of installations producing electric energy with PV panels are high compared to the costs of conventional installations, the number of such systems is continuously increasing. It is very important to determine the output characteristics of the PV panels in order to achieve an accurate connection and operation of the device and reduce

Monitoring activities follow the operation analysis by periodical reports, papers, synthesis, with the precise aim to make the most accurate decisions to produce electric energy using

To quantify the potential for performance improvement of a PV system, data acquisition systems has been installed. The importance of this chapter consists in the presentation of a dedicated DAQ used in PV system analysis and real data measurements. The operation is

The information obtained by monitoring parameters, such as voltage, current, power and energies are fed to the PC via the DAQ for analysis. The control interface has been developed by utilizing LabVIEW™ software. The system has been in operation during the

Andrei, H.; Dogaru, V.; Chicco, G.; Cepisca, C. & Spertino, F. (2007). Photovoltaic Applications, *Journal of Materials Processing Technology,* 181 (1-3), 2007, 267-273 Andrei, H.; Cepisca, C.; Grigorescu, SD.; Ivanovici, T. & Andrei, P. (2010). Modeling of the

Awerbuch, S. (2002). Energy Diversity and Security in the EU: Mean -Variance Portfolio

Dogaru Ulieru,V.; Cepisca, C. & Ivanovici, T. (2009). Data Acquisition in Photovoltaic

Judd,B. (2008). Everything You Ever Wanted to Know about Data Acquisition, In: *United* 

Manea,F. & Cepisca, C. (2007). PHP+Apache+Testpoint -An original way for having remote

Szekely, I. (1997). *Systems for data acquisition and processing*, Ed. Mediamira, Cluj–Napoca Vasile, N. (2009). Players on the market in renewable energy, *Round Table - renewable sources of energy between the European Directive 77/2001 and reality*, Bucharest, Romania, May 2009

*Electronic Industries*, 2008, available from *www.ueidaq.com* 

Nawrocki, W. (2005). *Measurement System and Sensors*, Artech House, London

PV panels circuit parameters using the 4 - terminals equations and Brune's conditions, *Scientific Bulletin of the Electrical Engineering Faculty,* 10 (1), 2010, 63-67

Analysis of Electricity Generating Mixes, and the Implications for Renewable Sources, *Proceedings of EURELECTRIC Twin Conf. on DG*, pp. 120-125, Brussels, Belgium, 2002 Cepisca, C.; Andrei, H.; Dogaru Ulieru,V. & Ivanovici, T. (2004). Simulation and data

acquisition of the photovoltaic systems using LabVIEW™, *Proceedings of ICL 2004*,

Systems, *Proceedings of 13th WSEAS International Conference on Circuits, Systems, Communications and Computers*, pp. 234-238, Rodos Island, Greece, July 22-24, 2009 Ertugrul, N. (2002). *LabVIEW™ for electric circuits, machines, drives and laboratories,* Ed.

control over any type of automation, *Scientific Bulletin UPB, Series C Electrical* 

**5. Conclusion** 

energy losses.

**6. References** 

unconventional sources.

performed by simulations using LabVIEW™.

last five years and all its units have functioned well.

pp. 80-84, Villach, Austria, 2004

Prentice Hall, New York

*Engineering,* 69 (2), 2007, 85-92

Ambros, T., et.al. (2004). *Renewable energy*, TEHNICA-INFO, Kishinev

In Iran, 100% of the region populated with more than 20 families is electrified. For the other regions the electrification will be done. These regions almost are rural and remote areas. For utility company it is important that electrification be done with the least cost.

Many alternative solutions could be used for this goal (decreasing the cost). Using renewable energy system is one of the possible solutions. A growing interest in renewable energy resources has been observed for several years, due to their pollution free energy, availability, and continuity. In practice, use of hybrid energy systems can be a viable way to achieve trade-off solutions in terms of costs. Photovoltaic (PV) and wind generation (WG) units are the most promising technologies for supplying load in remote and rural regions [Wang et al., 2007]. Therefore, in order to satisfy the load demand, hybrid energy systems are implemented to combine solar and wind energy units and to mitigate or even cancel out the power fluctuations. Energy storage technologies, such as storage batteries (SBs) can be employed. The proper size of storage system is site specific and depends on the amount of renewable energy generation and the load.

Many papers are discussed on design of hybrid systems with the different components. Also, various optimization techniques are used by researchers to design hybrid energy system in the most cost effective way.

Rahman and Chedid give the concept of the optimal design of a hybrid wind–solar power system with battery storage and diesel sets. They developed linear programming model to minimize the average production cost of electricity while meeting the load requirements in a reliable manner, and takes environmental factors into consideration both in the design and operation phases [Chedid et al., 1997]. In [Kellogg et al, 1996], authors proposed an iterative technique to find the optimal unit sizing of a stand-alone and connected system. In 2006 is presented a methodology for optimal sizing of stand-alone PV/WG systems using genetic algorithms. They applied design approach of a power generation system, which supplies a residential household [Koutroulis et al, 2006]. In [Ekren, 2008], authors used the response surface methodology (RSM) in size optimization of an autonomous PV/wind integrated hybrid energy system with battery storage. In [Shahirinia, 2005], an optimized design of stand-alone multi sources power system includes sources like, wind farm, photovoltaic array, diesel generator, and battery bank based on a genetic algorithm is presented. Also, authors in [Koutroulis et al, 2006, Tina, 2006] used multi-objective genetic algorithm, in order to calculate reliability/cost implications of hybrid PV/wind energy system in small isolated power systems. Yang developed a novel optimization sizing model for hybrid solar–wind power generation system [Yang et al., 2007]. In [Terra, 2006] an automatic multiobjective optimization procedure base on fuzzy logic for grid connected HSWPS design is described. In some later works, PSO is successfully implemented for optimal sizing of hybrid stand-alone power systems, assuming continuous and reliable supply of the load [Lopez, 2008, Belfkira, 2008]. Karki and Billinton presented a Monte-Carlo simulation approach to calculate the reliability index [Karki et al., 2001] and Kashefi presented a method for assessment of reliability basis on binominal distribution function for hybrid PV/wind/fuel cell energy system that is used in this study [Wang et al., 2007].

As previous studies shown, renewable energies are going to be a main substitute for fossil fuels in the coming years for their clean and renewable nature [Sarhaddi et al., 2010]. Photovoltaic solar and wind energy conversion systems have been widely used for electricity supply in isolated locations that are far from the distribution network.

The future of power grids is expected to involve an increasing level of intelligence and integration of new information and communication technologies in every aspect of the electricity system, from demand-side devices to wide-scale distributed generation to a variety of energy markets.

In the smart grid, energy from diverse sources is combined to serve customer needs while minimizing the impact on the environment and maximizing sustainability. In addition to nuclear, coal, hydroelectric, oil, and gas-based generation, energy will come from solar, wind, biomass, tidal, and other renewable sources. The smart grid will support not only centralized, large-scale power plants and energy farms but residential-scale dispersed distributed energy sources [Santacana et al., 2010].

Being able to accommodate distributed generation is an important characteristic of the smart grid. Because of mandated renewable portfolio standards, net metering requirements and a desire by some consumers to be green, there is an increasing need to be prepared to be able to interconnect generation to distribution systems, especially renewable generation such as photovoltaic, small wind and land fill gas powered generation [Saint, 2009].

The future electric grid will invariably feature rapid integration of alternative forms for energy generation. As a national priority, renewable energy resources applications to offset the dependence on fossil fuels provide green power options for atmospheric emissions curtailment and provision of peak load shaving are being put in policy [Santacana et al., 2010].

Fortunately, Iran is a country with the adequate average of solar radiation and wind speed for setting up a hybrid power generation e.g. the average of wind speed and perpendicular solar radiation were recorded for Ardebil province is 5.5945 m/s and 203.1629 W/m2 respectively in a year.

In this study, an optimal hybrid energy generation system including of wind, photovoltaic and battery is designed. The aim of design is to minimize the cost of the stand-alone system over its 20 years of operation. The optimization problem is subject to economic and technical constraints. Figure1 show the framework of activities in this study.

The generated power by wind turbine and PV arrays are depended on many parameters that the most effectual of them are wind speed, the height of WTs hub (that affects the wind speed), solar radiations and orientation of PV panels. In certain region, the optimization variables are considered as the number of WTs, number of PV arrays, installation angle of PV arrays, number of storage batteries, height of the hub and sizes of DC/AC converter. The

order to calculate reliability/cost implications of hybrid PV/wind energy system in small isolated power systems. Yang developed a novel optimization sizing model for hybrid solar–wind power generation system [Yang et al., 2007]. In [Terra, 2006] an automatic multiobjective optimization procedure base on fuzzy logic for grid connected HSWPS design is described. In some later works, PSO is successfully implemented for optimal sizing of hybrid stand-alone power systems, assuming continuous and reliable supply of the load [Lopez, 2008, Belfkira, 2008]. Karki and Billinton presented a Monte-Carlo simulation approach to calculate the reliability index [Karki et al., 2001] and Kashefi presented a method for assessment of reliability basis on binominal distribution function for hybrid

As previous studies shown, renewable energies are going to be a main substitute for fossil fuels in the coming years for their clean and renewable nature [Sarhaddi et al., 2010]. Photovoltaic solar and wind energy conversion systems have been widely used for

The future of power grids is expected to involve an increasing level of intelligence and integration of new information and communication technologies in every aspect of the electricity system, from demand-side devices to wide-scale distributed generation to a

In the smart grid, energy from diverse sources is combined to serve customer needs while minimizing the impact on the environment and maximizing sustainability. In addition to nuclear, coal, hydroelectric, oil, and gas-based generation, energy will come from solar, wind, biomass, tidal, and other renewable sources. The smart grid will support not only centralized, large-scale power plants and energy farms but residential-scale dispersed

Being able to accommodate distributed generation is an important characteristic of the smart grid. Because of mandated renewable portfolio standards, net metering requirements and a desire by some consumers to be green, there is an increasing need to be prepared to be able to interconnect generation to distribution systems, especially renewable generation such as

The future electric grid will invariably feature rapid integration of alternative forms for energy generation. As a national priority, renewable energy resources applications to offset the dependence on fossil fuels provide green power options for atmospheric emissions curtailment and provision of peak load shaving are being put in policy [Santacana et al., 2010]. Fortunately, Iran is a country with the adequate average of solar radiation and wind speed for setting up a hybrid power generation e.g. the average of wind speed and perpendicular solar radiation were recorded for Ardebil province is 5.5945 m/s and 203.1629 W/m2

In this study, an optimal hybrid energy generation system including of wind, photovoltaic and battery is designed. The aim of design is to minimize the cost of the stand-alone system over its 20 years of operation. The optimization problem is subject to economic and technical

The generated power by wind turbine and PV arrays are depended on many parameters that the most effectual of them are wind speed, the height of WTs hub (that affects the wind speed), solar radiations and orientation of PV panels. In certain region, the optimization variables are considered as the number of WTs, number of PV arrays, installation angle of PV arrays, number of storage batteries, height of the hub and sizes of DC/AC converter. The

photovoltaic, small wind and land fill gas powered generation [Saint, 2009].

constraints. Figure1 show the framework of activities in this study.

PV/wind/fuel cell energy system that is used in this study [Wang et al., 2007].

electricity supply in isolated locations that are far from the distribution network.

variety of energy markets.

respectively in a year.

distributed energy sources [Santacana et al., 2010].

goal of this study is optimal design of hybrid system for the North West of Iran (Ardebil province). The data of hourly wind speed, hourly vertical and horizontal solar radiation and load during a year are measured in the region. This region is located in north-west of Iran and there are some villages far from the national grid. The optimization is carried out by Particle Swarm Optimization (PSO) algorithm. The objective function is cost with considered economical and technical constraints. Three different scenarios are considered and finally economical system is selected.

Fig. 1. The framework of activities

This study is organized as follows: section 2 describes the modeling of system components. The reliability assessment is discussed in section 3. Problem formulation and operation strategy are explained in section 4 and 5, respectively. In the next section, is dedicated to particle swarm optimization. Simulation and results are summarized in section 7. Finally, section 8 is devoted to conclusion.
