Ali Naci Celik

Energy output estimation for small-scale wind power generators using Weibull-representative wind data

Abstract Estimation of energy output for small-scale wind power generators is the subject of this article. Monthly wind energy production is estimated using the Weibull-representative wind data for a total of 96 months, from 5 different locations in the world. The Weibull parameters are determined based on the wind distribution statistics calculated from the measured data, using the gamma function. The wind data in relative frequency format is obtained from these calculated Weibull parameters. The wind speed data in time-series format and the Weibull-representative wind speed data are used to calculate the wind energy output of a specific wind turbine. The monthly energy outputs calculated from the time-series and the Weibull-representative data are compared. It is shown that the Weibull-representative data estimate the wind energy output very accurately. The overall error in estimation of monthly energy outputs for the total 96 months is 2.79%.

Techno-economic analysis of autonomous PV-wind hybrid energy systems using different sizing methods

Abstract The sizing and techno-economic optimisation of an autonomous PV-wind hybrid energy system with battery storage is addressed in this article. A novel sizing method is introduced. It is a developed version of similar earlier sizing methods, taking into account a further design parameter. The techno-economic optimisation of autonomous energy systems should include the following design parameters at the same time: the level of autonomy, i.e. the fraction of time for which the specified load can be met, and the cost of the system. Without one of these, the techno-economic optimisation would be incomplete. New concepts, which combine the system autonomy and cost, are also introduced to be used in the techno-economic optimisation process. The sizing of a PV-wind hybrid system on a yearly basis requires a detailed analysis of the solar radiation and wind speed on a monthly basis. It is common to size such renewable systems for the worst month. It is, however, shown that the worst month scenarios lead to too costly and, therefore, non-optimal a system in terms of techno-economics. It is, therefore, suggested that alternative solutions be sought, rather than using the worst month scenario. An alternative method is applied in the present article. It suggests a third energy source (auxiliary source) be incorporated into the system instead of increasing the hardware sizes excessively for the worst month. It is shown that this leads to techno-economically more optimum systems.

Optimisation and techno-economic analysis of autonomous photovoltaic-wind hybrid energy systems in comparison to single photovoltaic and wind systems

A techno-economic analysis for autonomous small scale photovoltaic–wind hybrid energy systems is undertaken for optimisation purposes in the present paper. The answer to the question whether a hybrid photovoltaic–wind or a single photovoltaic or wind system is techno-economically better is also sought. Monthly analysis of 8 year long measured hourly weather data shows that solar and wind resources vary greatly from one month to the next. The monthly combinations of these resources lead to basically three types of months: solar-biased month, wind-biased month and even month. This, in turn, leads to energy systems in which the energy contributions from photovoltaic and wind generators vary greatly. The monthly and yearly system performances simulations for different types of months show that the system performances vary greatly for varying battery storage capacities and different fractions of photovoltaic and wind energy. As well as the system performance, the optimisation process of such hybrid systems should further consist of the system cost. Therefore, the system performance results are combined with system cost data. The total system cost and the unit cost of the produced electricity (for a 20 year system lifetime) are analysed with strict reference to the yearly system performance. It is shown that an optimum combination of the hybrid photovoltaic–wind energy system provides higher system performance than either of the single systems for the same system cost for every battery storage capacity analysed in the present study. It is also shown that the magnitude of the battery storage capacity has important bearings on the system performance of single photovoltaic and wind systems. The single photovoltaic system performs better than a single wind system for 2 day storage capacity, while the single wind system performs better for 1.25 day storage capacity for the same system cost.

Weibull representative compressed wind speed data for energy and performance calculations of wind energy systems

Abstract The present article presents the development of compressed wind speed data to be used in wind energy and performance calculations of stand alone or hybrid wind energy systems. Compressed data attempts to represent the important statistics of an entire month of days with d selected days, where d is less than the actual number of days in the month. In the present article, compressed wind speed data are generated based on the Weibull wind speed distribution model. Two different sets of wind speed data are generated, three- and four-day month, each month being represented by 72 and 96 h of wind speed, respectively. The three- and four-day month wind speed data are then used to calculate the monthly energy yields, which, in turn, are compared to those calculated from the measured hourly time series data. A total of eight years long of measured hourly time series data from five different locations throughout the world are used to validate the method presented. The overall errors in estimation of the wind energy yield using the Weibull representative compressed wind speed data are 3.67% and 3.21% for the three- and four-day months, respectively.

Assessing the suitability of wind speed probabilty distribution functions based on wind power density

Three functions have so far predominantly been used for fitting the measured wind speed probability distribution in a given location over a certain period of time, typically monthly or yearly. In the literature, it is common to fit these functions to compare which one fits the measured distribution best in a particular location. During this comparison process, parameters on which the suitability of the fit is judged are required. The parameters that are mostly used are the mean wind speed or the total wind energy output (primary parameters). It is, however, shown in the present study that one cannot judge the suitability of the functions based on the primary parameters alone. Additional parameters (secondary parameters) that complete the primary parameters are required to have a complete assessment of the fit, such as the discrepancy between the measured and fitted distributions, both for the wind speed and wind energy (that is the standard deviation of wind speed and wind energy distributions). Therefore, the secondary statistical parameters have to be known as well as the primary ones to make a judgement about the suitability of the distribution functions analysed. The primary and secondary parameters are calculated from the 12-month of measured hourly wind speed data and detailed analyses of wind speed distributions are undertaken in the present article.

A simplified model for estimating the monthly performance of autonomous wind energy systems with battery storage

This paper presents a simplified algorithm to estimate the monthly performance of autonomous small-scale wind energy systems with battery storage. The novel model is drawn based on the simulation results, using eight-year long hour-by-hour measured wind speed data from five different locations throughout the world. An hourly constant load profile is used. The renewable energy simulation program (ARES) of the Cardiff School of Engineering is used. The ARES simulates the battery state of voltage (SoV) and is able to predict the system performance.

The system performance of autonomous photovoltaic–wind hybrid energy systems using synthetically generated weather data

The yearly system performance of autonomous photovoltaic–wind hybrid energy systems with battery storage is the subject of this article. The yearly system performance is simulated using synthetically generated solar radiation and wind speed data and compared to that simulated using measured hour-by-hour data. Two different synthetic weather data sets are generated: 3-day month and 4-day month, in which 3 and 4 days represent a month, resulting in a total of 36 and 48 days for a year. The hourly varying solar radiation data are synthesised from the clearness index value for each month. The daily constant wind speed data are synthesised using the Weibull wind speed distribution model, on a monthly basis. Using two different synthetic weather data sets, the effect of number of synthetic days on the system performance estimation is studied. Different sequences of synthetic solar and wind days lead to 36 and 576 combinations for 3- and 4-day months, respectively. Three predetermined combinations for both the 3- and 4-day months are chosen and the system performance of an autonomous photovoltaic–wind hybrid energy system with battery storage is simulated using these predetermined combinations. It is shown that the yearly system performance predicted from the 3- and 4-day synthetic data closely agrees with that obtained from the measured data, varying only slightly for different combinations.

A Techno-Economic Analysis of Wind Energy in Southern Turkey

Turkeys total primary energy production covered 33% of the total energy consumption in 2005. It is predicted that this ratio of 33% will decrease further in the coming years. Therefore, Turkey has to make better use of her renewable energy resources, such as wind and solar. Studies show that Iskenderun (36.35°N, 36.10°E), located on the Mediterranean coast of Turkey, is amongst the possible wind energy generation regions. In the present study, the wind energy potential of the region is statistically analysed based on five year hourly time-series wind-speed data at two different wind-turbine heights. Economically usable power generation is hypothetically analysed for various wind turbines employing a typical year, using a model of quadratic power output function. A life-cycle cost analysis is carried out over a 20-year system lifetime. The wind turbine generators considered are of various nominal powers ranging from 0.6 to 500 kW. It was shown that the wind turbine with the nominal power of 500 kW provided the lowest cost of electricity at

CRITICAL EVALUATION OF WIND SPEED FREQUENCY DISTRIBUTION FUNCTIONS

0.15 per kWh. This is very close to

Modeling and Experimental Verification of Solar Radiation on a Sloped Surface, Photovoltaic Cell Temperature, and Photovoltaic Efficiency

0.13, which is the cost of electricity per kWh from the utility in Turkey. The cost of wind electricity per kWh can be significantly reduced if the components of wind energy systems were exempted from taxes and subsidies were introduced.