Energy Management of Hybrid Power System PV Wind and Battery Based Three Level Converter

Renewable energy sources have received greater attention during the past few decades and considerable efforts have been made to develop efficient renewable energy conversion system. The major objectives of these approaches are to have reduced environmental damage, conservation of energy, exhaustible sources and increased safety. The renewable energy systems can be used to supply power either directly to a utility grid or to an isolated load. The stand-alone systems find wider applications in isolated areas which are far away from the utility grid [1].

The integration of Energy storage systems (ESSs) have some significant applications in operations like grid stabilization, stable power quality, load shifting, grid operational support and smooth power injection to grid. Several power smoothing methods have been introduced in literature [2], battery energy storage system (BESS) is selected as energy storage and incorporated into wind, PV to maintain power and energy balance as well as to improve power quality.

In our research, the proposed Energy Management of Hybrid Power System PV/Wind and Battery Based Three Level Converter consists of a wind energy conversion system (WECS)/PV generator as the primary energy source and a battery energy storage system (as short and medium, time storage devices).

They are all connected to a DC voltage link, and a three phase three level Neutral Point Clamping (NPC) converter is used to connect the whole system to the load. The DC coupled structure makes the overall system more flexible since the number and the types of energy sources can be freely chosen and it requires fewer controllers as no synchronization is needed to integrate the energy sources.

For the control of the battery using the information compared between the reference DC voltage connection and the measured DC voltage, a reference battery current

Regarding the control of the three-level inverter connected to the load, we used fuzzy logic control (FLC) to ensure the RMS value of the load voltage.

The structure of the presented work is organized as follow: The description of the proposed hybrid system approach and the physical modeling different part of our system with their equations model is set in section 2. The control of different component is set in section 3. The simulations results of the studied are presented in section 4. Section 5 summarizes the work done in the conclusion.

The hybrid system shown in Figure 1 contains control strategies that will be developed in this section.

3.1 Modeling and control of the battery

(1) Battery modeling

Several authors have proposed models for the battery and the results of experiments carried out on lead/acid batteries deduce a model named “CIEMAT model” representing the battery operation during the charge, discharge and overcharge processes. In our case study, from the carried out experiments, a validated model is proposed with respect to the battery capacity for any size and type of Nickel Metal Hydride battery [7].

This model represented by an equivalent circuit model contains a voltage source which is the open circuit voltage V, in series with an internal resistance R. Thus, the output voltage of the battery is:

${{V}_{bat}}=V-R{{I}_{bat}}$  (8)

where, the both V_bat  and  I_bat  depend on the battery state of charge (SOC), temperature and internal resistance variations R .

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Figure 4. The battery controls

In this study, this simple model based on the CIEMAT model for the battery is considered as enough accurate to assess power management objectives and to compare performance of several strategies. During the charging and discharging process, the state of charge (SOC) in terms of time (t) can be described by [8]

$SOC\left( t \right)=\left\{ \begin{matrix}   SOC\left( t-\Delta t \right)+{{P}_{bat}}.\frac{{{\eta }_{ch}}}{{{C}_{n}}.{{V}_{dc}}}.\text{ }\!\!\Delta\!\!\text{ }t  \\   SOC\left( t-\Delta t \right)+{{P}_{bat}}.\frac{1}{{{\eta }_{dis}}.{{C}_{n}}.{{V}_{dc}}}.\Delta t  \\\end{matrix} \right.$    (9)

where, Δt is the time step,  P_bat  represents the battery power,  C_n  is the nominal capacity of the battery,  ${{\eta }_{ch}}$ and ${{\eta }_{dis}}$  are respectively the battery efficiencies during charging and discharging phase.  V_dc  denotes the nominal DC bus voltage. At any time, step Δt, the SOC must comply with the following constraints

$~SO{{C}_{\min }}\le SOC\left( t \right)\le SO{{C}_{\max }}$    (10)

where, SOC_min and SOC_max are maximum and minimum allowable storage capacities, respectively.

(2) Battery control

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Figure 5. The battery controls

The objective of the control system is to regulate the battery current in order to obtain the required power. Charging and discharging current limits and maximum SOC limitations are also included in the model. The BESS is connected to the DC grid via a bi-directional Buck-Boost DC/DC converter, as shown in Figure 5.

The BESS will operate in charging, discharging or floating modes depending on the energy requirements and these modes are managed according to the DC bus voltage at the BESS point of coupling. Consequently, the BESS is required to provide necessary DC voltage level under different operating modes of the microgrid. When charging, switch S2 is activated and the converter works as a boost circuit; otherwise, when discharging, switch S1 is activated and the converter works as a buck circuit. When the voltage at the DC link is lower than the voltage reference, switch S1 is activated. Alternatively, when the voltage at the DC link is higher than the voltage reference, switch S2 is activated. The PV-battery system response to transient variations is characterized by an inherent time constant. In such cases, capacitors along the DC grid can act as virtual inertia to supply the shortfall or absorb the surplus of energy [9-11].

The DC-link power balance can be expressed by the following differential equation:

${{V}_{dc}}{{i}_{dc}}={{P}_{PV}}+{{P}_{W}}+{{P}_{bat}}-{{P}_{load}}$    (11)

Neglecting the losses in the power converters, battery, filtering inductors and transformer and also the harmonics due to switching actions, the power balance of the integrated hybrid distributed generation system (DGS) with energy storage is governed by:

${{V}_{dc}}{{i}_{dc}}=C{{V}_{dc}}\frac{d{{V}_{dc}}}{dt}={{P}_{PV}}+{{P}_{W}}+{{P}_{bat}}-{{P}_{load}}$   (12)

The objective of the battery converter is to maintain constant voltage at the DC link, so the ripple in the capacitor voltage is much lower than the steady-state voltage.

If the powers injected by the two back-to-back voltage source converters (VSC) are assumed constant at any particular instant, the power from the battery is responsible for adjusting the capacitor voltage.

3.2 Three-level inverter modeling

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Figure 6. Three-level inverter

A three-level inverter differs from a conventional two-level inverter in that it is capable of producing three different levels of output phase voltage. The structure of a three-level neutral point clamped inverter is shown in Figure 6. When switches 1 and 2 are on the output is connected to the positive supply rail. When switches 3 and 4 are on, the output is connected to the negative supply rail. When switches 2 and 3 are on, the output is connected to the supply neutral point via one of the two clamping diodes [12, 13].

The functions  $F_{km}^{b}$  of connection are given by:

$\left\{ \begin{matrix}   F_{k1}^{b}={{F}_{k1}}.{{F}_{k2}}  \\   F_{k0}^{b}={{F}_{k3}}.{{F}_{k4}}  \\\end{matrix} \right.$    (13)

where,  m=1:  the upper half arm and m=0: the lower half arm.

The phase voltage  ${{V}_{AO}},{{V}_{BO}},{{V}_{CO}}$  can be written as:

$\left\{ \begin{matrix}   {{V}_{AO}}=F_{11}^{b}{{V}_{c1}}-F_{10}^{b}{{V}_{c2}}  \\   {{V}_{BO}}=F_{21}^{b}{{V}_{c1}}-F_{20}^{b}{{V}_{c2}}  \\   {{V}_{CO}}=F_{31}^{b}{{V}_{c1}}-F_{30}^{b}{{V}_{c2}}  \\\end{matrix} \right.$    (14)

Simple output voltages are written as:

$\left[ \begin{matrix}   {{V}_{A}}  \\   {{V}_{B}}  \\   {{V}_{C}}  \\\end{matrix} \right]=\frac{1}{3}\left[ \begin{matrix}   2 & -1 & -1  \\   -1 & 2 & -1  \\   -1 & -1 & 2  \\\end{matrix} \right]\left\{ \left[ \begin{matrix}   F_{11}^{b}  \\   F_{21}^{b}  \\   F_{31}^{b}  \\\end{matrix} \right]{{V}_{c1}}-\left[ \begin{matrix}   F_{10}^{b}  \\   F_{20}^{b}  \\   F_{30}^{b}  \\\end{matrix} \right]{{V}_{c2}} \right\}$    (15)

3.3 Fuzzy logic control (FLC)

Fuzzy logic as a control theory is introduced in the control strategy of multilevel inverters, as for adaptive control, neural networks, fuzzy control and neuro-fuzzy control [14, 15]. Fuzzy control is simple and easy, does not require modeling, it is suitable for nonlinear systems that present variables and parameters variation.

Fuzzy logic and classical PI controllers are designed and applied to achieve reasonable rise time, settling time, overshoot and steady state error. The proposed method minimizes the error of the reference motor speed. Fuzzy logic has an advantage over other control methods due to the fact that it is not sensitive to plant parameter variations.

The three-level inverter causes distortion of voltage and current at the output of load which deteriorate the system performance. Therefore, to overcome the problem mentioned above, fuzzy logic control FLC is used to optimize the whole system. In the FLC, there is no need the mathematical model of system and the choosing of rules depends on the knowledge experts. In this method, the three-phase input voltage and current a, b, c are converted to direct and quadrature current d, q frame based on park transformation for more simplicity, and to convert from non-linear to linearity. The switching signal of three level inverters is determined based on the error between the reference load voltage reference and actual voltage output. In this case, the FLC has two inputs, the first input is error and the second input are change of error with one output the magnitude of RMS voltage load as shown in Figure 8.

The fuzzy controller rules are given in Table 1. The function forms are chosen optimally.

Table 1. Fuzzy logic rules (Df)

where, LP: Large Positive, MP: Medium Positive, SP: Small Positive, Z: Zero, SN: Small Negative, MN: Medium Negative, LN: Large Negative.

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Figure 7. Specification of a membership function

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Figure 8. The schematic of proposed FLC of Three-level inverter

3.4 Power management

The proposed chart of the power management system is described in Figure 9, and it takes into consideration the following steps:

Step 1: define the different powers involved in our hybrid system,

Step 2: If the power supplied is greater than the requested power, in this case the excess power will be stored in the battery,

Step 3: If the requested power is greater than that generated, the following two cases are distinguish:

a) If the state of charge SOC is greater than 50 %, the storage battery devices are switched on.

b) In the opposite case, the battery stops working, which forces the delesting.

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Figure 9. Flow chart of the supervisory controller

In this work a multi-source energy system for hybrid wind/solar energy and battery have been presented. Dynamic modeling and simulations of the hybrid system is proposed using SIMULINK. A hybrid energy system and its supervisory-control system was developed and tested. Load demand is met from the combination of PV array, wind turbine and the battery. A three-level inverter is used to convert output from solar and wind systems into AC power output. Circuit Breaker is used to connect and disconnet an additional load in the given time. This hybrid system is controlled to give maximum output power under all operating conditions to meet the load. Either wind or solar system is supported by the battery to meet the load. Also, simultaneous operation of wind and solar system is supported by battery for the same load.

These results show the efficiency of the management and the controls used for this hybrid system and can be implemented easily with DSP or Dspace platform.