Renewable Energy Management Algorithm for Stand ...

Renewable Energy Management Algorithm for Stand–alone System M. Dahmane, student Member IEEE, J. Bosche, A. El-Hajjaji and M. Dafarivar MIS Laboratory, University of Picardie Jules Verne (UPJV) 33, rue Saint Leu, 80039, Amiens, FRANCE [email protected]

Abstract— This paper proposes an intelligent algorithm for optimal management power applied to hybrid renewable standalone system. This hybrid system focuses on the combination of wind turbine (WT), photovoltaic (PV) as the main sources of energy and storage batteries, in addition, it uses and diesel engine as an additional source of rescue and dump load to dissipate thee overproduction when there is. The load presented by the average consumption of a household of four people. At first, the paper presents the modeling of the various element involved in the system. After that, it present the power management strategy used to supply the load demand. Simulations were done on Matlab/Simulink to validate this algorithm. Keywords-component; renewable energy; power management; wind power; photovoltaic; battery storage.

I.

INTRODUCTION

The current global energy situation can be simply summarized: the demand increases so that it is more difficult to be satisfied by the offer. This increased energy demand is justified firstly, by a considerable technological development with the emergence of a multitude of systems that depend on energy, and secondly, by demographic changes. Renewable energy resources, called sustainable or alternative energy, are energies generated from natural resources such as wind, sunlight, tide, hydro, biomass and geothermal which are naturally replenished. since many isolated rural areas in all over the world are not electrified, one of the interesting utilization of the renewable energies is to electrify many remote villages and rural areas or rugged terrain located so far from power stations and distribution networks or utility lines which are uneconomical to install[3] [7]. In this paper, we present a power management strategy applied to hybrid stand-alone system. Using hybrid standalone system has become popular in recent years [14], [7]; this kind of system strongly depends on the geographical and meteorological conditions of the installed region [14]. However, using only wind and photovoltaic system with battery storage may not meet the energy demand, so another kind of sources must be added [1,], [11], [12]. In our case, a diesel engine is chosen as emergency source like shows in fig.1. The paper is structured as follow: In section II, a description Research supported by European Regional Development Fund and “le conseil regional de Picardie”.

of the global system is given. After, the modeling and control of the renewable sources and battery storage is given in the section III. In section VI, the power management strategy applied to the global system is presented and simulation illustrated in section V whereas conclusion s are summarized in section VI. II.

DESCRIPTION OF THE HYBRID SYSTEM

In this paper, the treated hybrid system is given by the figure below; it is composed of renewable energy generators (wind, photovoltaic panels) as the main energy sources, a diesel generator as a backup source. To ensure uninterrupted power supply, it uses storage batteries to store the energy excess and restore it when the production is deficit. It was also planned to use dissipative load (dump load) to be used in the event of overproduction and the batteries are fully charged. All the precedents elements involved in the global system are connected to direct continue (dc) bus through suitable power converters. The main objective of this system is to supply an isolated house and ensure energy production without interruption by using mainly the renewable energy sources, and therefore minimize the intervention of the conventional source (diesel generator). In addition to provide power without interruption, the system must take into account the operation mode of each element, particularly storage batteries, and must impose an optimal behavior to increase its lifespan.

Fig. 1. The multi-sources hybrid system.

III.

Before proceeding to management algorithm, we present the modeling of different renewable energy generators A.

I corresponding to the current at the maximum power I mp .

MODELING AND CONTROL OF RENAWABLE ENERGY SYSTEMS •

The “ROBUST CONTROL” block allows tracking the reference current I by generating a control signal u for the Boost Converter

Modeling and Control of photovoltaic system

A photovoltaic generator consists of a set of elementary photovoltaic cells connected in series and/or parallel to achieve the desired electrical characteristic, such as power, current short-circuit or open-circuit voltage. The electrical behavior of a photovoltaic cell is equivalent to the following circuit:

Fig. 3: Diagram of control strategy

For more detail about the control strategy of the photovoltaic system, please refers to [8]. Fig. 2: Equivalent circuit of a PV cell

The current generated by a photovoltaic cell is given by the following equation:

⎧ ⎡ q(Vcell + IRs ) ⎤ ⎫ Vcell + IRs I = I ph − I 0 ⎨exp ⎢ ⎥ − 1⎬ − R AkT ⎣ ⎦ ⎭ ⎩ sh

B. Modeling and control of wind turbine A wind turbine converts wind energy into electricity. The power from the kinetic energy of wind Pwind is given by the Betz relationship:

(1)

1 Pwind = .ρ .S .V 3 2

A photovoltaic panel is composed of N S cells in series and

N P in parallel, therefore, the current generated is written as follows: ⎧⎪ ⎡ 1 Vpv I pv Rs ⎤ ⎫⎪ Np ⎛ Vpv I pv Rs ⎞ )⎥ −1⎬ − ⎜ + I pv = Np I ph − Np I0 ⎨exp ⎢ ( + ⎟⎟ ⎜ ⎩⎪ ⎢⎣Vt Ns Np ⎥⎦ ⎭⎪ Rsh ⎝ Ns Np ⎠ (2) With : Vt =

q is the thermodynamic potential, V pv is N s AkT

the panel voltage, I 0 is the diode reverse saturation current, T is the cell temperature, k is the Boltzmann constant, q is the electron charge, A is the ideality factor of the n-p junction. To optimize the photovoltaic performance panel and extract the maximum power available, the MPPT (Maximum Power Point Tracking) algorithm is used. We find in the literature several types of MPPT algorithms, in our case we used a fuzzy logic algorithm that allows to, by measuring the temperature and irradiation, generate voltage panel at the maximum power point. The control strategy used in this case is based on our previous work [8]. It summarized in two steps bellow and is illustrated in the following figure: • From the weather conditions in terms of temperature of the panel surface’s T and irradiance G, the “G.A.” Block allows generating a reference current I ref

With:

(3)

ρ : Air density. S: The area swept by the blades. V: Wind speed.

However, just a part of this energy is captured by the wind generator, this power is given by: Pr =

1 CP ( β , λ ).ρ .S .V 3 2

(4) The power coefficient CP is a non-linear function depends on the pitch angle β and the reduced speed λ . This power factor is expressed by: ⎛ 116 ⎞ C p (λ , β ) = 0.5176 ⎜ − 0.4 β − 5 ⎟ 0.5e ⎝ λi ⎠ With: 1

λi

=

λ=

−16.5

λi

(5)

1 0.035 − 3 λ + 0.089β β + 1

ωR V

In the wind generation system, there is a windmill, a permanent-magnet synchronous generator (PMSG), a rectifier,

and a dc/dc converter to interface the generator with the dc bus. The converter is used to control indirectly the operating point of the wind turbine (and consequently its power generation) by commanding the voltage on the PMSG terminals.

u = − f1 g1 + 2Koptωe f3 (φsr g1 ) − iq f3 ( g1ωe ) + 2 ( γ Sw ( x) + ξmax ∂Sw ( x) ∂x ) × sign ( Sw (x)) ( 3φsrωe g1 )

(9)

With γ = 1000 and ξ = 0, 02 being design constant and 2

2

3 ⎛3 ⎞ ⎛ ⎞ ∂S w ( x) ∂x = ⎜ φsr ωe ⎟ + ⎜ 3K opt ωe2 − φsr iq ⎟ . 2 ⎝2 ⎠ ⎝ ⎠ In the control design shown in (9), Sw ( x) = Pw,max is the sliding surface and the mathematical model can be written as x = f ( x) + g ( x )u with: Fig. 4: Wind energy conversion system

The electrical behavior of this system can be described by the following mathematical equations [9][15]: π vb iq ⎧ Rs ωeφm − u ⎪iq = − iq − ωe id + L L 3 3L iq2 + id2 ⎪ ⎪ Rs π vb id ⎪ u ⎨id = − id − ωe iq − L 3 3L iq2 + id2 ⎪ ⎪ ⎪ω = p (T − 3 p φ i ) ⎪⎩ e 2 J t 2 2 m q (6) Where iq and id are the quadrature current and the direct

current in the rotor reference frame, respectively; Rs and L are the per phase resistance and inductance of the stator windings, respectively; ωe is the electrical angular speed; φm is the flux linked by the stator windings; vb is the voltage of the dc bus; u is the control signal (duty cycle of the dc/dc converter), p is the PMSG number of poles, J is the inertial of the rotating parts, and Tt is the wind turbine torque. Based on the previous equations system, we can express the power generated by the wind system into the dc bus as follows: πv Pw = b iq2 + id2 u 2 3 (7) For the wind subsystem controller, the objective is to track the maximum power when the management controller orders this. The maximum power delivered by the wind system can be written as follows: 3 Pw,max = K opt ωm3 − (iq2 + id2 )rs 2 (8)

Where K opt = Ct ( λopt ) ρ AR 3

( 2λ ) 2 opt

and λopt is the tip

speed ratio at which the coefficient C p ( λ ) = Ct ( λ ) λ reaches its maximum, and Ct is the torque coefficient of the wind turbine. We follow the controller design proposed using Sliding Mode control technique [9]:

⎛ ωeφm ⎞ ⎛ Rs ⎜− i ω i − − + ⎜ L q ed ⎟ 3 ⎜ L ⎟ ⎛ f1 ⎞ ⎜ ⎛ g1 ⎞ ⎜ ⎜ ⎟ ⎜ R ⎜ ⎟ ⎜ ⎟ f (x) = ⎜ f2 ⎟ = ⎜ − s id − ωeiq ⎟; g(x) = ⎜ g2 ⎟ = ⎜ − L ⎜f ⎟ ⎜ ⎜g ⎟ ⎜ 3 ⎟ ⎝ 3⎠ ⎝ 3⎠ 3 p p ⎜ (T − ⎟ ⎜0 ⎜ 2J t 2 2 φmiq ) ⎟ ⎝ ⎠ ⎜⎜ ⎝

π vbiq

⎞ ⎟ 3L i + i ⎟ ⎟ π vbid ⎟ 3L iq2 + id2 ⎟ ⎟ ⎟ ⎟⎟ ⎠ 2 q

2 d

C. Batteries Storage Battery is a crucial component in a stand-alone system; it allows to ensure an uninterrupted power supply to the load and to compensate the lack of production when weather conditions are unfavorable. Lead-acid batteries have been the most widely used energy storage units in stand-alone photovoltaic (PV) applications. They are cheap and most readily available. The battery behavior has been largely described in the literature by many authors [5][6]. The selected model is that elaborated by CIEMAT [2], this model is largely applied in to lead acid battery. This model is based on the following circuit:

Fig. 5: Equivalent circuit of a battery with nb cells

This model is based on the calculation of the instantaneous capability to determine the SoC [4] as illustrated in the equations bellow: ⎧ I ⎪ SoC (t ) = SoC (t − 1) + bat Cbat ⎪ ⎪ C 1, 67 ⎪ 10 (1 + 0, 005.ΔT ) ⎨Cbat = 0.9 ⎛ I bat ⎞ ⎪ 1 + 1, 67 ⎜ ⎟ ⎪ ⎝ I10 ⎠ ⎪ ⎪⎩ SoCmin ≤ SoC ≤ SoC max (10)

E. Dump load: The dump load (dissipative resistance), its role is to dissipate the excess production when storage batteries are fully charged.

800

The management algorithm designed for our system aims to satisfy the load demand regardless of the variation in the production of renewable energy and load. Indeed, the hybrid system is inherently dynamic since weather conditions (wind, radiation, temperature) vary greatly during the day time, Furthermore, the load demand is very fluctuating, which makes the algorithm design very delicate. In order to increase the system lifetime, the management algorithm must optimally use the renewable generators and the batteries (the charge level of the battery must be maintained between a predefined minimum and a maximum levels according to its technology) and the diesel generator which must be solicited as little as possible, which amounts to deplete the maximum in renewable generators to satisfy the load demand. The strategy of the management algorithm is summarized in the diagram illustrated in the fig.6. In this strategy, the photovoltaic system is considered as the principle source, and this system operate at it maximum power using Maximum power point tracking (MPPT) algorithm. The wind system is considered as auxiliary source, so it operates when the principal source can’t meet the load demand. Whenever the principle and auxiliary sources can’t satisfy load demand and the battery state of charge (SOC) is in the minimum level, the conventional source (diesel engine) will be enabled to meet load demand.

600 400 200 0

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0

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320 300 280 260

W in d s p e e d [ m / s ]

POWER MANAGEMENT STRATEGY

20

10

0

Time [h]

Fig. 7. Weather conditions (Irradiation, cell’s temperature and wind velocity).

For the consumption of a four person home, we took a profile of an average value of the order of 500 watts and a peak consumption of 900 watts, as shown in the following figure: 900

800

700 Load Profil [W]

IV.

SIMULATION RESULTS

For the simulation we used the profiles of irradiation, temperature and wind speed as given in the following figure:

I rra d ia t io n [ W / m 2 ]

D. The diesel fuel generator: In the literature, and in order to simplify the diesel generator is generally modeled as a system of first order with delay [10]. −τ K .e f H DE ( s ) = DE (11) 1 + s.τ DE

V.

T e m p e ra t u re [ ° K ]

Where I bat instantaneous current; Cbat the battery capacity ΔT Difference between the temperature of the battery and ambient temperature; C10 the rated capacity of the battery.

600

500

400

300

200

0

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25

Time [h]

Fig. 8. The load profile

Fig. 6: Power management strategy scheme

We simulated the system for 24 hours using the previous profiles, we got the results below. Figure 10 shows the evolution of the state of charge of the battery, and figure 9 shows the involvement of different sources (PV, wind and battery) during the day. Diesel engine and dump load not involved in the management if only necessary. This simulation was done under Matlab/Simulink.

1500 Pw+Ppv P,load P,bat

0.42

0.4

Battery State of Charge [%]

Power [W]

1000

500

0

-500

0.38

0.36

0.34

0.32

0.3

-1000

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Time [s]

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Time [s]

Fig. 10. Battery State of charge.

Fig 9. Power generated 15

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10 0.8 0.7

Power [W]

Current [A]

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-10 0.1

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15 Time [s]

Time [h]

Fig. 11. The battery current.

Fig. 12. Diesel engine activation.

Fig. 13. Power management strategy

20

25

Figures comments: The fig.9 shows the power generated by wind and photovoltaic system together according to the load power variations and the battery power behavior depending to the production and consumption variations. In fig.10, we present the battery state of charge (SOC), we can see that it varies between 30% and 60% of charge according to the imposed constraint and the current of charge and discharge does not exceed 15A in absolute value (fig.11) which represents the maximum current supported by this battery. We can see also that the battery current becomes zero between 18h and 23h because of the activation of the Diesel engine at this period as indicate in fig.12. We note that if it was excess in production, it was dissipated in dump load.

[3]

[4]

[5]

[6] [7]

[8]

[9]

VI.

CONCLUSIONS

In this paper, the PV/Wind/Diesel engine/Battery hybrid power system is designed and modeled for stand-alone residential users. The dump load added to the system aims to dissipate the power product excess. The propose power management strategy satisfy the load and battery bank SOC and it is tested in simulation using theoretical profiles of weather conditions. The battery maximum and minimum SOC level are chosen so as to increase its lifetime.

[10]

[11]

[12]

[13]

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