A sizing procedure is developed for hybrid system with the aid of mathematical models for photovoltaic cell, wind turbine, and battery that are readily present in the literature. This sizing procedure can simulate the annual performance of different kinds of photovoltaic-wind hybrid power system structures for an identified set of renewable resources, which fulfills technical limitations with the lowest energy cost. The output of the program will display the performance of the system during the year, the total cost of the system, and the best size for the PV-generator, wind generator, and battery capacity. Security lightning application is selected, whereas system performance data and environmental operating conditions are measured and stored. This hybrid system, which includes a PV, wind turbine, inverter, and a battery, was installed to supply energy to 24 W lamps, considering that the renewable energy resources of this site where the system was installed were 1700 Wh/m2/day solar radiation and 3.43 m/s yearly average wind speed. Using the measured variables, the inverter and charge regulator efficiencies were calculated as 90% and 98%, respectively, and the overall system’s electrical efficiency is calculated as 72%. Life cycle costs per kWh are found to be $0.89 and LLP = 0.0428.
Renewable energy resources like solar and wind offer clean and economically competitive alternatives to conventional power generation where high wind speed and high solar radiation are available. For meeting the energy demand, PV-wind hybrid power generating systems can be beneficial in enhancing the economic and environmental sustainability of renewable energy systems. Growing public concerns over global warming as an impending outcome of greenhouse gas emissions initiated by energy resources based on fossil fuels have encouraged to study cleaner energy options, like PV, biomass, wind, and micro hydro systems for several applications. The record of worldwide photovoltaic (PV) market installations extended up to 27.4 GW in 2011 [
Renewable resources such as solar and wind energy which change randomly are individually less reliable. However, in many regions, when solar and wind resources are combined for power generation, they complement each other by means of daily and seasonal variations. Combining these two renewable energy sources could make the system more reliable, and the system costs might slightly decrease depending on the regional conditions. However, the energy system sizing procedure and operation control strategies are getting more complex due to the nonlinear components’ physical characteristics.
Photovoltaic-wind hybrid power systems are categorized as extraordinary complex in sizing and optimization process, where renewable energy resources and storage components must be sized to match the given load profile and the estimated ease of use of solar radiation and wind speed. Many PV-wind hybrid systems are unique in design, whereas the complete dynamic testing of hybrid system takes very long time and also its cost is very high. In some way, whether necessary time and budget are provided for the dynamic tests, it is very hard to test all the situations that will be met during the life cycle of the hybrid system. It is clear that if the individual performance does not match with the expected simulation outcomes, it leads the user to uncertainty. Without exact high-level comparisons between real objective performance and projected computer simulations, it is very hard to focus on enlightening the performance of PV-wind hybrid system. A reliable technique for assessing the performance of a hybrid power system at a specific location is an essential prerequisite for boosting investment in hybrid power systems. Such a technique is also suitable for comparing the performance of two hybrid power systems, specific conditions at a certain location [
Several economic viability and technical availability studies are carried out to assess choice of PV-wind hybrid power systems configurations that serve power to the load with the certain reliability criteria [
In this paper, a hybrid system model that covers costing and system performance variables is identified. Decision variables, objective function, and constraints are determined for this model. Using simple iterative technique, sizing variable and performance variables are optimized according to the objective function under constraints.
The solar-wind meteorological station is located on the roof of the Solar Energy Institute Building in Ege University for determining the local potentials of both solar and wind energy [
Main characteristics of the Solar-Wind Meteorological Station at the Solar Energy Institute.
Module | Parts | Specification/description |
---|---|---|
Wind speed | Accuracy 0.5 m/s | |
Wind direction | Precision 0.8 m/s | |
Sensor | Pyronometer | 4.7 |
Ambient temperature | −40 to 56°C, accuracy < 0.5°C | |
Relative humidity | 12–100%, accuracy < 3% | |
| ||
Signal input | 12 channels | |
Data logger | Memory | 2 Mbytes |
Computer connector | RS-232 | |
| ||
Power | PV module | 15.1 V, 10 WPeak |
Tripod | Stainless steel | For right setting the sensors |
PV-wind hybrid energy system’s main components are shown in Figure
Hybrid energy system components.
A PV module consists of a number of solar cells connected in series and parallel to obtain the desired voltage and current output levels. Each solar cell is basically a p-n diode. As sunlight strikes a solar cell, the incident energy is converted directly into electrical energy. Single-diode mathematic model is applicable to simulate silicon photovoltaic cells, which consists of a photocurrent source
Single-diode mathematical model of a PV cell.
The operating temperature of the cell, which differs from the ambient temperature, determines the open-circuit voltage. The operating temperature of a cell can be calculated using (
The power output of the PV array at time
Characteristic curves for wind turbines are given as power output versus wind speed at the hub height. Wind turbines are never connected in series [
Batteries in a hybrid system are connected in series to obtain the appropriate nominal bus voltage. Therefore, the number of batteries connected in series for the same type of battery in a battery bank is calculated as follows
The inverter characteristics can be described by the inverter input-output relationship. Some of the power supplied to the inverter will be lost due to transformation losses that are named inverter efficiency losses,
It is stated by Seeling-Hochmuth in 1998 that in life cycle costing equipment and operation costs are compiled and discounted over the assumed project life. The hybrid system life cycle costs (LCCs) are defined as the initial investment and future discounted operation costs:
The hybrid system operation costs are in general nonlinear and depend largely on the component size and type, and the way the system is operated. As they depend on future operations, they can only be estimated roughly [
The sizing variables are sizes of component types and their number is to be installed. From the PV module, wind turbine, battery, battery charger, and inverter performance models sizing variables are defined as follows:
Hybrid system must include operation strategies that describe the energy flow between the generator and the load. The operation decision variables to be optimized represent routing and operation decisions that are based on the power flow modeled for the hybrid system. The main operation decision variables of the hybrid system model are minimum battery stage of charge,
This section demonstrates the effectiveness of the proposed methodology by means of an implication of PV-wind hybrid system. The developed methodology is used to project a PV-wind hybrid power system for security lighting in the Solar Energy Institute Building. Inputs of the project are load profile, and hourly average of solar radiation, ambient temperature, and wind speed resource data for whole of the year. The load on the system is an experimental lighting of Solar Energy Institute. Load demand changes according to the period of the night. For the purposes of validating the model performance, the proposed algorithm is coded and simulated with MATLAB V 7.7 [
Monthly means of daily load profile of PV-wind hybrid system.
Hourly average of solar radiation data for 12 months of the year.
Hourly average of wind speed data for the 12 months of the year.
Using MATLAB code, the initial values of sizing variables referred to as hybrid system components and type of system components were defined and shown in Table
Hybrid system component’s initial values of sizing variables, and type of system components.
Variables | Symbol | Initial value or type |
---|---|---|
Number of series connected modules |
|
2 |
DC bus voltage |
|
24 volts |
Battery cell voltage |
|
2 volts |
Number of series connected batteries |
|
12 |
PV module type |
|
SM50 |
Wind turbine type |
|
3 blades, PMSG |
Inverter type |
|
True sine wave |
Battery cell capacity |
|
110 Ah |
Wind turbine power |
|
400 W at 10 m/s |
PV module power |
|
50 Wpeak |
The optimized hybrid system configuration.
PV system | Wind |
Inverter | Battery capacity | Load |
---|---|---|---|---|
500 W | 400 W | 300 W | 2.64 kWh |
|
LCC versus
Simulation result of battery SOC.
Configured PV-wind hybrid power system with battery backup was installed with the monitoring system in January 2009; wind turbine and PV array are shown in Figure
PV-wind hybrid system installed on the roof of Solar Energy Institute.
Datalogger, battery bank, DC-DC converters, and inverters in the laboratory.
For the assessment, the system was monitored for one-year period logged from January 2009 to January 2010. Battery voltage, load voltage, load current, battery current, PV output, and wind generator output currents have been measured with a sampling rate of 1 sec and recorded as 10 sec average values. Recorded data was uploaded to a computer, and thereafter daily, monthly, and yearly performance parameters have been calculated using these values of components and the hybrid system.
The data were analyzed following the International Electrotechnical Commission Standard (IEC) 61724 [
Results measured and calculated according to IEC 61724 for January 2009 to January 2010.
Parameter | Symbol | Measured | Units |
---|---|---|---|
Meteorological | |||
Global direct irradiation |
|
4.88 | kWh m−2d−1 |
Global available wind |
|
0.36 | kWh m−2d−1 |
Electrical energy quantities | |||
Net energy from the PV |
|
811 | kWh |
Net energy from the WT |
|
311 | kWh |
Energy fraction from the |
|
0.71 | Dimensionless |
Energy fraction from the |
|
0.29 | Dimensionless |
Total energy in the system |
|
1142 | kWh |
Total energy used |
|
822 | kWh |
Net energy to the load |
|
822 | kWh |
BOS component performance | |||
BOS efficiency |
|
72 | % |
System performance indices | |||
PV array yield |
|
4.4 | hd−1 |
Final PV system yield |
|
3.2 | hd−1 |
Wind turbine yield |
|
0.57 | hd−1 |
Final wind turbine yield |
|
0.41 | hd−1 |
Normalized losses | |||
PV array capture losses |
|
34.2 | % |
PV BOS losses |
|
28 | % |
Performance ratio for the |
|
41.4 | % |
Wind turbine capture losses |
|
27.7 | % |
Wind BOS losses |
|
37 | % |
Performance ratio for the |
|
46 | % |
System efficiencies | |||
Average PV efficiency |
|
9.3 | % |
Global PV efficiency |
|
6.72 | % |
Average WT efficiency |
|
9.46 | % |
Global WT efficiency |
|
6.86 | % |
Inverter and charge regulator efficiencies were calculated, 93% and 98%, respectively. The battery bank efficiency was derived from measured parameters and calculated as 82%, which is 3 points less than the expected value. The overall system efficiency was calculated as 72%. Life cycle costs per kWh was calculated as $0.89 and
Measured results of SOC.
PV-wind hybrid power system directly converts solar and wind resources to electricity. This energy conversion process is void of gas emission. Consequently, one can say that it is entirely clean. But throughout the PV cell and wind turbine manufacturing processes and the transportation phase, they essentially consume a huge amount of energy; as a result, they emit considerable volume of greenhouse gases. PV systems emit 61 g CO2-equiv./kWh [
The emissions caused by PV-wind hybrid system and grid-fed system.
System | Electricity prices |
Emissions |
Daily load |
Total load |
Total emissions |
---|---|---|---|---|---|
PV-wind hybrid | 0.89 | 82 | 3.0275 | 1142 | 92502 |
Turkey grid | 0.23 | 493 | 3.0275 | 1142 | 563006 |
A sizing procedure of a hybrid PV-wind energy system was presented. The outlined technique defines optimum hybrid energy system configuration and control criteria. It needs hourly changing meteorological data as input and contributes cost-effective hybrid system configuration with highest reliability. The procedure was applied for the sizing of PV-wind hybrid energy system that is considered to lighting of the Solar Energy Institute Building. Configured hybrid system was installed in January 2009 and the system variations were measured every 1 minute during one year. From the measured values, life cycle costs per kWh were calculated as $0.89, LLP = 0.0428, and also hourly changes of SOC are shown in Figure
Capital cost of hybrid system
Operation cost
PV battery charger control switch
WT battery charger control switch
Inverter control switch
Load control switch
Mismatch factor for different types of modules
Reference irradiation
Ambient irradiation
Reference short-circuit current
Short-circuit current of cell
Short-circuit current of module
Module current
PV array current
Wind turbine current
Battery discharge current
Load current
Battery charge current
Equivalent serial resistance of module
Equivalent serial resistance of cell
Efficiency for the battery charger
Inverter efficiency
Number of series connected batteries
Number of series connected cells
Number of parallel connected cells
Number of series connected modules
Number of parallel connected modules
Number of parallel connected batteries
The output power of the battery charger
Self-discharge losses of the battery
Battery stage of charge
Minimum SOC
Maximum SOC
Voltage of a battery
Thermal voltage,
Wind speed (m/s)
Air density.