Wind-diesel hybrid power system integration in the south Algeria

Abstract

In most isolated sites situated in south Algeria, the diesel generators are the major source of electrical energy. Indeed, the power supply of these remote regions still poses order problems (technical, economical and ecological). The electricity produced with the help of diesel generators is very expensive and responsible for CO₂ emission. These isolated sites have significant wind energy potential. Hence, the use of twinning wind-diesel is widely recommended, especially to reduce operating deficits. The objective of this paper is to study the global modeling of a hybrid system which compounds wind turbine generator, diesel generator and storage system. This model is based on the control strategy to optimize the functioning of the hybrid system and to consolidate the gains to provide proper management of energy sources (wind, diesel, battery) depending on the load curve of the proposed site. The management is controlled by a controller which ensures the opening/closing of different power switches according to meteorological conditions (wind speed, air mass, temperature, etc).

Graphical abstract

Keywords

wind-diesel / storage system / isolated site / management / simulation

1 Introduction

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 led to a rapid growth in greenhouse gas emissions and an increase in fuel prices [1], which is the world climate change and global warming issues greatly debated by researchers and environmentalists. This is caused by the growth of industrialization and world population which results in the increase of electricity demand especially the conventional electricity generation using the natural resources such as oil, gas and coals. This, in turn, contributes to the increase in the emissions of green house gasses (GHGs), carbon dioxide (CO₂) that bring hazard to the atmosphere [2]. Seeing vulnerable, Algeria has acceded since Kyoto policies on climate change. The problems of climate change observed in recent years and the imminent arrival of the end oil era pushes Algeria to invest in the field of new and renewable energy.

Today, the sustainable development and energy renewable have aroused the interest of several research teams. Therefore, the development of wind turbines is a major investment in research technology. The systems that produce electrical energy from the wind can be an alternative to technological and economical exhaustible energy sources [3]. Currently, several countries have already resolutely turned toward wind energy which is the fastest growing energy sector in the world, according to the annual report published by global wind energy council (GWEC) in the end of 2013: China is the largest producer of wind power, with a cumulative capacity of 91.424 MW, followed by the USA with a capacity of 61.091 MW, Germany with a capacity of 34.250 MW, Spain with a capacity of 22.959 MW, and India with a capacity of 20.150 MW [4].

Most of the remote and isolated communities such as the Algerian Sahara (Adrar, Bechar, In Salah, Timimoun, Tindouf, Amenas, etc.) which are not connected to national electric distribution grids rely on diesel engines to generate electricity. Diesel generated electricity is more expensive and, on top of that, should be added the transport and environmental cost associated with this type of energy [5]. Moreover, the electricity production by the diesel is ineffective, presents significant environmental risks (spilling), contaminates the local air and largely contributes to GHG emission. In all, it is estimated that16086 kg/a GHG of emission result from the use of diesel generators for the subscribers of the autonomous networks in Algeria [5,6].

Compared to the conventional diesel power system, the wind-diesel power system is one of the best alternative solutions that has many advantages such as more efficiency, relatively low maintenance cost and based on wind energy which is a clean, economic, and inexhaustible energy source. Furthermore, the wind-diesel power system is more environmentally friendly that has less CO₂ emission. The isolated areas in south Algeria have significant wind energy potential. Therefore, the wind-diesel power system is widely recommended.

The combination of diesel generators (DG) with wind turbine generators (WTG) has been studied to provide continuous high quality electric power. The main goal with the wind diesel hybrid system (WDHS) is to reduce fuel consumption and in this way to reduce system operating costs for economic purpose and environmental impact.

The system is equipped with a control system. A proper control strategy has to be developed to take full advantage of the wind energy during the period. In this paper, the problems in using diesel generators were analyzed, the wind energy resource in Algeria was estimated, the WDHS was presented, the modeling of the WDHS components was discussed, the control system strategy and energy management were elaborated on and the simulation results were presented and discussed.

2 Hybrid wind-diesel systems in isolated sites

The diesel power generating units, while requiring relatively little investment, are generally expensive to exploit and maintain, particularly when they are functioning regularly at partial load [7]. The use of diesel power generators under weak operating factors accelerates wear and increases fuel consumption. Therefore, the use of hybrid systems, which combines renewable sources and diesel generators, reduces the total diesel consumption, decreases the operation cost, and has many environmental benefits. Among all renewable energies, the wind energy experiences the fastest growing rate, at more than 30 % annually for the past 5 years. Presently, wind energy offers cost effective solutions for isolated grids when coupled with diesel generators. The wind-diesel system represents a technique of generation of electrical energy by using in parallel one or several wind turbines with one or several diesel groups [5].

3 Wind energy resource in Algeria

3.1 Winds map

The wind energy resource in Algeria varies greatly from one place to another mainly due to a very diverse topography and climate. The annual average wind speed is approximately 2 m/s to 6.5 m/s. The south is characterized by higher wind speeds than the north, especially in the south-west, with greater speeds than 4 m/s, exceeding 6 m/s at Adrar region. In the north, the overall average speed is low [8].

3.2 Geographical situation of the studied site

Adrar state situated in the extreme south-west of Algeria extends over 427 968 km², accounting for about one-fifth of the country. There are 399 714 estimated inhabitants which represent more than 76 % of the rural population. The studied site is located in Adrar region with geographical coordinates (27°88′N, – 0°28′E, 263 m).

3.3 Meteorological data of Adrar region

The climate data of wind speed, ambient temperature and humidity at Adrar region (Algeria) recorded at the weather station of the Renewable Energy Research Unit in Saharian Medium (URER-MS) Adrar in 2011 is listed in Table 1 [9].

4 Wind-diesel hybrid system

4.1 System description

The underlying hybrid wind-diesel system is illustrated in Fig. 1. The hybrid generation system is composed of a WTG, a DG, a battery bank, a consumer load, power electronic converters (AC/DC rectifier, DC/AC inverter, and DC/DC converter), a monitoring system, a distributed control system, switches and relays, a controller and other accessory devices and cables [10,11].

4.2 Operation modes

The WDHS is classified as being high penetration (HP) [10]. HP-WDHS have three modes of operation as depicted in Fig. 2.

1) Weak winds ( v_w\le 3\mathrm{m}/\mathrm{s}): diesel only (DO).

2) Moderate winds ( ): wind and diesel (WD) in service.

3) Strong winds ( v_w>\mathrm{10}m/s): wind only (WO).

where v_w is the wind speed upstream of the turbine.

In the DO mode, the DG supplies the active and reactive power demanded by the consumer load (in this mode the WTG is turned off). The DG includes a friction clutch which allows the diesel engine (DE) to be engaged (DO and WD modes)/disengaged (WO mode) to the synchronous machine (SM). In the WD mode, both the DG and WTG supply the active and reactive power demanded by the consumer load. Load sharing modules and speed governors controlling each diesel engine perform frequency regulation and voltage regulation is performed by the automatic voltage regulators in each synchronous machine [12].

In the WO mode, only the WTG supplies the active power (DG is disconnected). However, in an HP-WDHS, the power produced by the WTG (P _T) can be greater than that consumed by the consumer load. In this case, the surplus wind energy will be stored in the battery energy storage system (BESS). So, the distributed control system (DCS) must order transmit power to the BESS [12]. The stored energy is used during peak periods, also when the DG or WTG breaks down.

5 Modeling of hybrid system components

The schematic diagram of the isolated hybrid wind-diesel generation system with the proposed DCS is demonstrated in Fig. 3. The high penetration WDHS in Fig. 3 comprises of a DG, a WTG and a BESS [13].

5.1 Modeling of wind turbine system

5.1.1 Wind turbine model

A wind turbine can only convert a certain percentage of the captured wind power. This percentage is represented by C _p which is a function of the wind speed, the turbine speed and the pitch angle of any specific wind turbine blades [14,15].

The mechanical power P _mec extracted from the wind is mainly governed by three quantities, namely, the area swept by turbine blades (S), the wind speed (v _w) and the rotor power coefficient (C _p), expressed by [16].

(1) P_{\mathrm{m}\mathrm{e}\mathrm{c}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\rho S{v}_{\mathrm{w}}^{\mathrm{3}}{C}_{\mathrm{p}}(\lambda ,\beta ),

where \rho is the air density (kg/m³).

In this paper, C _p is the power coefficient of rotor which has been defined by Eq. (2) [16]. The power coefficient is a function of the tip speed ratio \lambda defined by Eq. (3) and the pitch angel β. The wind turbine considered in this paper is stall controlled, so the pitch angel is kept constant and is considered zero where the C _p value is 0.48 and C _p is a function of λ [14,15].

(2) C_p=(\mathrm{0.44}-\mathrm{0.0167}\mathit{\beta })\sin \left({\displaystyle \frac{(\pi /\mathrm{2})(\mathit{\lambda }-\mathrm{3})}{\mathrm{7.5}-\mathrm{0.15}\mathit{\beta }}}\right)-(\mathit{\lambda }-\mathrm{3})(\mathrm{0.00184}\mathit{\beta }),

(3) \mathit{\lambda }={\displaystyle \frac{{\mathrm{\Omega }}_{\mathrm{r}}R}{v_{\mathrm{w}}}},

where Ω_r is the rotor rotational speed and R is the wind turbine rotor radius.

Maximum wind power extraction The C_p-λ characteristics, for different values of the pitch angle β, are illustrated in Fig. 4. The maximum value of C_p^{\max } = 0.48 is achieved for β = 0 ° and for λ = 8.1. This particular value of λ is defined as the nominal value λ_nom [17].

If λ is maintained at its optimum value (λ ^opt), the power coefficient is always to its maximum value ( C_p^{\max }). Therefore, the mechanical power of wind is also its maximum value (see Fig. 5) [18,19].

(4) P_{\mathrm{m}\mathrm{e}\mathrm{c}}^{\mathrm{o}\mathrm{p}\mathrm{t}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\mathit{\rho }S{C}_{\mathrm{p}}^{\max }({\mathit{\lambda }}^{\mathrm{o}\mathrm{p}\mathrm{t}})v_{\mathrm{w}}^{\mathrm{3}}.

On the other hand, if λ is supposed to maintain the optimum value, and the wind speed Eq. (5) is isolated and replaced in Eq. (4), Eqs. (5) and (6) can be obtained.

(5) {\mathit{\lambda }}^{\mathrm{o}\mathrm{p}\mathrm{t}}={\displaystyle \frac{{\mathrm{\Omega }}_{\mathrm{r}}R}{v_{\mathrm{w}}}}\Rightarrow v_{\mathrm{w}}={\displaystyle \frac{R}{{\mathit{\lambda }}^{\mathrm{o}\mathrm{p}\mathrm{t}}}}{\mathrm{\Omega }}_{\mathrm{r}},

(6) P_{\mathrm{m}\mathrm{e}\mathrm{c}}^{\mathrm{o}\mathrm{p}\mathrm{t}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\mathit{\rho }S{C}_{\mathrm{p}}^{\max }{\left({\displaystyle \frac{R}{{\mathit{\lambda }}^{\mathrm{o}\mathrm{p}\mathrm{t}}}}\right)}^{\mathrm{3}}{\mathrm{\Omega }}_{\mathrm{r}}^{\mathrm{3}}.

Equation (6) is used to calculate the optimal value of the mechanical torque.

(7) T_{\mathrm{m}\mathrm{e}\mathrm{c}}^{\mathrm{o}\mathrm{p}\mathrm{t}}={\displaystyle \frac{\mathrm{1}}{\mathrm{2}}}\rho \cdot S\cdot C_{\mathrm{p}}^{\max }{\left({\displaystyle \frac{R}{{\lambda }^{\mathrm{o}\mathrm{p}\mathrm{t}}}}\right)}^{\mathrm{3}}\cdot {\mathrm{\Omega }}_{\mathrm{r}}^{\mathrm{2}}

Figure 6 shows the mathematical model of wind turbine developed in Matlab/Simulink. Equations (1)−(7) have been used in developing this model. The function f (u) in Fig. 6 is expressed as [17]

(8) f(u)={\displaystyle \frac{{\mathit{\lambda }}^{\mathrm{o}\mathrm{p}\mathrm{t}}{v}_{\mathrm{w}}}{R}}.

For the turbine used in the simulation, the maximum value of C_p ( C_p^{\max } = 0.48) reaches the optimum tip speed ration λ^opt = 8.1 and β = 0°.

5.1.2 PMSG model

During the previous years, permanent magnet synchronous generators (PMSGs) were greatly used in wind turbine applications because of their advantages such as low weight and velocity, high efficiency and gear-less structure. Extracting maximum power of turbine and delivering an appropriate energy to grid are two important purposes in wind turbines [20].

The model of PMSG is composed of two electrical differential equations and a mechanical differential equation. The electrical equations, expressed in direct (d) quadrature (q) coordinate are given by [21]

(9) U_d=-R_s{i}_d-L_d{\displaystyle \frac{d{i}_d}{dt}}-{\mathit{\omega }}_r{L}_q{i}_q,

(10) U_q=-R_s{i}_q-L_q{\displaystyle \frac{d{i}_q}{dt}}+{\mathit{\omega }}_r{L}_d{i}_d+{\mathit{\omega }}_r{\mathit{\varphi }}_f,

(11) T_e-T_{\mathrm{mec}}-{\mathrm{\Omega }}_{r}F=J_r{\displaystyle \frac{d{\mathrm{\Omega }}_r}{dt}},

where U denotes voltage, i denotes current, ϕ represents magnetic flux, R _s denotes resistance and L inductance, indexes d and q stand for the direct and quadrature components, ω _r is the generator electrical speed (ω _r = pΩ _r) and ϕ _f is the magnetic flux of the permanent magnets. T _mec is the mechanical torque from the generator shaft, T _e is the generator electromechanical torque, J _r is the aerodynamic rotor inertia, Ω _r is the generator angular velocity, F is the viscous friction coefficient from the generator shaft, and p is the number of pair poles.

The flux linkages are expressed as [21]

(12) {\mathit{\varphi }}_d=L_d{i}_d+{\mathit{\varphi }}_f,

(13) {\varphi }_q=L_q{i}_q.

The generator electromechanical torque is calculated by [21]

(14) T_{\mathrm{e}}={\displaystyle \frac{\mathrm{3}}{\mathrm{2}}}p[({\mathit{\varphi }}_d{i}_q-{\mathit{\varphi }}_q{i}_d)]={\displaystyle \frac{\mathrm{3}}{\mathrm{2}}}p[(L_d-L_q)i_d{i}_q+{\mathit{\varphi }}_{\mathrm{f}}{i}_q].

5.2 Modeling of diesel generator

The DG consists of a diesel engine (DE), a synchronous machine (SM), and a friction clutch, as shown in Fig. 3. The SM generates the voltage waveform of the isolated grid and its automatic voltage regulator keeps the system voltage to be within the prescribed levels during the three modes of operation. The DE provides mechanical power to the SM and its speed governor (speed regulator+ actuator) controls the DE speed. In this paper, the DE speed control is isochronous, so the diesel speed governor commands the necessary fuelling rate to make the DE run at a constant speed (ω _D = ω _SM) [13].

The hourly fuel consumption of the diesel generator Q(t) (L/h) were modeled by the linear law based on the output power required by the load [22].

(15) Q(t)={\mathit{\alpha }}_D{P}_D{(t)}_{\mathrm{gen}}+{\mathit{\beta }}_D{P}_D{(t)}_{\mathrm{rat}},

where {\mathit{\alpha }}_D (L/kWh) and {\mathit{\beta }}_D (L/kWh) are the coefficients of the consumption curve provided by the manufacturer while P_D{(t)}_{\mathrm{gen}} (kW) and P_D{(t)}_{\mathrm{rat}} (kW) are the power generated and the rated power of the DG. The values allocated to {\mathit{\alpha }}_D and {\mathit{\beta }}_D are 0.246 L/kWh and 0.08145 L/kWh, respectively [22].

The DG efficiency (kWh/L) is expressed as [23]

(16) {\mathit{\eta }}_D=\left[{\displaystyle \frac{P_D{(t)}_{\mathrm{gen}}}{Q(t)}}\right].

The efficiency in percentage (%) of the lower heating value (LHV) (kWh/L) of gas-oil can be defined as

(17) {\eta }_D(\%)={\displaystyle \frac{P_D{(t)}_{\mathrm{gen}}}{Q(t)\cdot {\mathrm{LHV}}_{gas-oil}}}\times \mathrm{100},

where LHV_gas-oil ranges between 10 kWh/L and 11.6 kWh/L.

5.3 Modeling of battery storage system

Ni-MH batteries have very similar properties to Ni-Cd batteries, using hydrogen absorbing alloy for the negative electrode instead of cadmium. Ni-MH batteries have shorter useful lifetime, but more power capability than Ni-Cd type [24]. Cadmium is a toxic heavy metal with mandatory provisions for disposal not so easy to fulfill in remote areas. Therefore, this environmental concern recommends selecting Ni-MH batteries for this present paper. The 240 V Ni-MH battery model [25] consists of a DC voltage source function of the state of charge (SOC), based on the discharge characteristic of the battery, and an internal resistance of assumed constant value.

The Ni-MH battery model was taken from Tremblay et al. [25] and its simulink schematic is illustrated in Fig. 7. The model consists of the controlled variable voltage source of value E in series with an internal constant resistance R. E depends on the capacity extracted from the battery {\displaystyle \int idt} (Ah) and the maximum theoretical battery capacity Q (Ah) (generally equal to 105 % of the rated capacity) according to Eq. (18).

(18) E=E_{\mathrm{0}}-K{\displaystyle \frac{Q}{Q-{\displaystyle \int i\mathrm{d}t}}}+A{\mathrm{e}}^{-B\int i\mathrm{d}t},

where E ₀ is the constant voltage, K is the polarization constant or polarization resistance, i is the battery current, and Q is the maximum battery capacity.

The equation parameters E₀, K, A and B values in the Appendix are obtained from the discharge characteristic of the battery. The SOC of the battery is zero when the battery is empty and 100 % when fully charged and is calculated by

(19) \mathrm{S}\mathrm{O}\mathrm{C}(\%)=\mathrm{100}\times \left(\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{Q}}{\displaystyle \int i\mathrm{d}t}\right).

6 Control system strategy and energy management

Energy management describes the process of managing both the generation and the consumption of energy. The purpose of energy management is to provide reliable and high quality energy for consumers. In this paper, the control strategy integrates the control system and energy management system applied in the generation. The main functions of the control system main functions are stabilizing the operations and controlling the system in a proper condition during various events. In addition, it is able to plan and manage energy resources and energy conversions to provide energy for consumers. Actually, the proposed energy management is one of the main results of the control strategy [16]. The power management strategy used in this paper is based on the flowchart shown in Fig. 8.

7 Simulation results and discussion

From Table 1, it is observed that the average wind speed ranges is between 4.7 m/s and 7.4 m/s. The highest wind speed occurs in June and the lowest in September. Figure 9 shows the average daily wind speed for the considered site in June and September.

The average daily load profile of the studied area is shown in Fig. 10. This profile is considered to be the same for all the days of the year and corresponds to the load profile usually encountered in the secluded regions in Algeria. The daily energy consumption of load is 1500 kWh/d which is equivalent to 547.5 MWh/a. The load profile is obtained from Sonelgaz group society (www.Sonelgaz.dz).

From the histogram of Fig. 11, it is observed that on June 9th the supply of consumers is ensured by the DG due to insufficient wind speed which is less than 3 m/s. On the contrary, on June, 12th and 28th, the DG all alone produces power to the consumers load, the wind speed exceeds 10 m/s (see Fig. 9), and there is a surplus of wind energy that can be stored in the battery for future use (succor). On September 1st, 20th and 22nd, only the diesel works, but on other days, the wind and diesel are hybridized.

The fuel consumption for the power generated in June and September is shown in Fig. 12. June has the highest fuel consumption, particularly on June, 9th, with an average of 39.29 L/h, due to wind deficit. On June the 12th and 18th, the wind speed is sufficient, therefore the fuel consumption is null. In September, the consumption reaches its maximum (39.29 L/h) on Sept. 1st, 20th and 22nd.).

The relationship between fuel consumption by the DG and CO₂ emissions for the months studied is shown in Fig. 13. Note that the pollution threshold is maximum on June, 9th, because the CO₂ emission reaches 98.23 kg/d and only diesel is running. Contrariwise, in September, this peak is repeated in 3 days as shown in Fig. 13.

The analysis of the histogram in Fig. 14 illustrates the cost of kWh supplied by the DG and WTG in those two months. The energy supplied by the wind turbine is greater than that by the diesel which costs 12792 DA/month for the WTG and 8808 DA/month for the DG, due to the number of the days of operation of each which has an influence on CO₂ emissions, making it less than the emissions in September. This is beneficial to the environment, since Algeria is a signatory to the Kyoto treaty on climate change policies.

The different economic costs are recapitulated according the Sonelgaz energetic indices in Table 2. The higher fuel consumption in September affects the diesel fuel cost and involves considerable CO₂ emissions than those of June. On the other hand, the cost of kWh supplied by the WTG during the year is cheaper than that of the DG (see Table 2). Therefore, this paper makes the effect of wind energy on the economy and environment evident.

8 Conclusions and perspectives

This paper presents the results of investigations on wind-diesel power system application in south Algeria for isolated areas. The simulation is based on real meteorological data. The obtained results and theoretical operation confirm the reliability and the superior performance of the proposed hybrid system. The system used a high control technique to manage different energy sources (wind, diesel, battery). This technique depends on the analysis of wind speed and the required power by load for reducing the consumption of diesel fuel, CO₂ emissions and system operating costs, etc. Therefore, the wind-diesel power system is widely recommended in isolated areas that have significant wind energy potential. In this way, the Algerian Sahara open high investment prospects in renewable energy sources in future.

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