Using PSO algorithm for power flow management enhancement in PV-battery grid systems

ABSTRACT


INTRODUCTION
The problems caused by the use of fossil fuels, such as climate change, the shortage of production and the increase in prices, are among the most important reasons which have led to the trend towards renewable energies (solar and wind).In [1], [2] advances in technology especially in the field of power electronics and the evolution of the PV market over the last decade have contributed to giving a kind of reliability to photovoltaic energy.
Furthermore, the development of intelligent regulation in the PV industry has facilitated the quick advancement of PV applications, particularly PV systems connected to the energy grid, which have increased in size from a few kW to over 100 MW [3], [4].To do this, the photovoltaic system's converters must perform better in order to raise the system's efficiency.By suggesting an inverter control scheme with a boost chopper to connect the grid to the PV generator, which delivers optimal PV power and high-quality current injected into the grid, this work contributes to improving the efficiency of the PV-battery-grid connected inverter DC/AC.In the literature, various control techniques have been considered, like [5], an intelligent algorithm based on direct current and DC link voltage controllers for a three-phase grid-connected photovoltaic inverter is presented in this work.Lakshmi and Hemamalini [6] presented a unique approach for the global maximum photovoltaic power generated (MPPT) controller is put forth that solves the PSC shading problem by applying the moth-flame (MFO) optimization technique.Farhat et al. [7] suggested to use a fractional proportional-integral (FO-PI) controller to decouple control of a grid-connected PV system.According to the power produced by the solar systems and the power consumed by the electrical network, this decoupled control method enables separate control of the actual  [8] presented a PI-GA controller was used to improve the performance of a 3-phase grid-connected PV system by adjusting the KP and Ki values of the PI controller of the current regulator that was used to control the P and Q powers of the system .In this paper, the FLC tuned by PSO is introduced into two regulation loops.The first is the DC voltage regulation loop for the DC/AC inverter control, and the second is the battery charge/discharge current regulation loop for the buck-boost bidirectional converter control.The goal is to improve the stability and efficiency of the system and improve the quality of energy injected into the grid.In addition to that, we used the fuzzy logic controller to extract the MPPT of a PV generator surrounded by volatile weather conditions.Therefore, a comparative study of two methods (PI and PI-FLC PSO) has been proposed, which will be covered in more detail in the sections that follow.MATLAB/Simulink will be used to run the simulation.

SYSTEM DESCRIPTION
The proposed controller inverter DC-AC (CIDA) is responsible for ensuring flexibility in energy exchange between the DC source (GPV-storage battery) and the grid connexon via the HB bridge three-phase inverter DC/AC with boost converter that is used to extract the MPPT.The configuration of the PV system chosen in this study is shown in Figure 1.The power of the PVG is equal to 286 kW with a capacity battery of 1500 A/h.The existence of a bidirectional converter (BUCK-Boost DC/DC) is necessary to interconnect the battery with the other equipment of the system.Because of its dual functionality, it functions as a boost converter in charge mode and a buck converter in discharge mode [8] , [9].

PV generator (PVG)
The mathematical model of PVG is deduced from the mono-diode model of the PV cell.It is given by (1) [10], [7].(1)

Three-phase inverter (TPI) DC/AC
As shown in Figure 3 the inverter has three independent inputs, each consisting of a filter that eliminates electromagnetic interference and a boost chopper (only a single input is indicated in Figure 3) [11]− [13].The three Boost converters are connected in parallel on a three-phase bridge (3 half bridges), which then converts the direct current (DC) supplied by the DC/DC converter into alternating current (AC) using the PWM technique, the fundamental of which is at the frequency of 50 Hz [14], [15].The midpoint (B) of the capacitors is located just before the three-phase bridge is connected to the network neutral.A filter eliminates high frequency ISSN: 2088-8694  Using PSO algorithm for power flow management enhancement in PV-battery … (Benslimane Abdelkader) 415 Grid harmonics to obtain a sine wave [16], [17].The operation of this inverter requires that the switches of the same arm not be simultaneously blocked, and Table 1 shows the eight possible operating states of the TPI switches (K1, K2,..K6) shown in Figure 3 [5], [18].

Bidirectional DC-DC converter (BDC)
The buck-boost converter shown in Figure 4. Combines two properties: increasing and decreasing DC voltage to convert [19], [20].The BDC's potential operating scenarios are shown in Table 2.

Battery system
Figure 5 shows the equivalent circuit and the battery discharge characteristics (nickel-metal hybrid) used in the simulation.This work was carried out using the implementation of a parameterized dynamic model to represent this type of rechargeable battery.This type of battery is used in [21].The relationship between the parameters is noted in (2).

DC motor load
The Figure 6 illustrates the analog DC load circuit and their associated equations that connect the various parameters.the solution of these coupled differential equitation is difficult in this form.But when we apply the Laplace transform for these equations become there algebraic and the system linear [16].The architecture of the DC/AC controller is shown in Figure 7.It is based on two regulation loops, one for optimal regulation of the intermediate circuit voltage and the other for external control of the direct and quadrature currents (Id, Iq) given by the phase-locked loop.For the PVG to work in PPM, it is mandatory to have an MPPT command that acts on the converter boost incorporated into the TPI.In addition, a bidirectional buck-boost converter ensures both battery operation (charge/discharge) and motor power supply thanks to the existence of an energy management control (CSBD) [21].
In general, control loops mainly depend on PI controllers.Our goal in this work, is to wait for an optimal gain, that's why we replaced these controllers with others based on fuzzy logic and the meta-heuristic algorithm, which calls for particle swarm optimization (FLC-PSO) for the purpose of improving the dynamic performance of the proposed system.

Control approach for CSBC
To maximise efficiency, maintain the continuity of the power supply and manage the exchange of power flows with better stability, we have adopted in this work a bidirectional converter control strategy (CSBC) optimised by the PSO algorithm [22], [23].This strategy is shown in Figure 8.

PIFLC-PSO controller
In this work, we've proposed a method for adjusting the scaling factors for the PI-type FLC controller based on the PSO algorithm, which is applied in two control loops (current & DC link) of the DC/AC inverter as shown in Figure 9.This tuning technique was put out in references [24], [25].By ( 4), the FLC transforms into a time-varying PI controller, and its proportional equivalents, the control and integral control components, are, respectively,  2  and  2  The term "PI-FLC" refers to this fuzzy controller [26], [27].Practically speaking, the Fuzzy PI type of control is better at suppressing steady-state mistakes.However, because of the internal integration process, it performs poorly in the transitory response to the higher order process.Performance is enhanced by coupling a serial integrator to the PI-type fuzzy controller's output, whose inputs include errors and error rate of change.Figure 8 shows a block diagram in which fuzzy variables are multiplied by scaling factors (k1, K2) before being applied to the fuzzy block and fuzzy output variables in (5), with the adjustment of the output scaling factor β is performed using a PSO algorithm [27], [28].
Figures 10(a) and 10(b) display the surface maps (input and output) of the controllers (FLC-CSBC) and (FLC-TPI DC/AC) accomplished using a Mamdani fuzzy inference method.The technique of iteratively minimizing a cost function to estimate the ideal variables was done using the PSO algorithm, which scaled the output factor (β).In ( 6) and (7) shows, respectively, the formula for the continuous (Jc) and discontinuous (Jd) temporal performance indices.

− Using PSO algorithm for scaling β
The optimization of these scale factors is suggested as a potential remedy to cope with challenges and errors in selecting appropriate values for the scale factors of each PID type FLC structure by the test processes.Chettibi and Mellit [21] using PSO optimization, we were able to determine the best choice variables  * = ( 1 * ,  2 * ,  * )  to represent the scale factors of a certain FLC structure that is similar to a PID and minimises the desired cost function based on the maximum overshoot (MO) and integral of absolute error (IAE) performance criteria (IAE).Overshoot D, steady state error Ess, rise time tr and settling time ts of the system step response are among the time domain control requirements shown in (8), which also include other needs.PSO is a meta-heuristic algorithm that draws inspiration from migrating bird behaviour.We attribute the construction of artificial intelligence [29].The ( 9) and (10) show the standard formulations of PSO commonly used.

RESULTS AND DISCUSSION
The grid-connected PV system is depicted in Figure 11 and is developed in MATLAB/Simulink.It consists of the PV generator, HB bridge three-phase inverter DC/AC with boost converter, the bidirectional converter, storage battery, and DC load motor.In this study, we compared the effectiveness of conventional control to intelligent control (PIFLC-PSO), which depended on the PSO algorithm, in terms of how well the solar generator performed and how well the battery was charged and discharged.It was also determined how much of an improvement in energy quality the DC/AC inverter that was installed on the grid had made.Comparing the DC motor speed curve of the three control scenarios (PI, PI-FLC, and PI-FLC-PSO) is illustrate in Figure 16 reveals improved response time and stability when using a PI-FLC controller adjusted by the PSO algorithm.There is a 0.5-second gap between them.Figures 17 and 18 present the harmonic distortion rates of the voltages injected by the inverter to the connection transformer with the network and that of the network voltage in the three cases of the regulation (PI; PIFLC; PIFLC-PSO).In the case of the use of a PI-FLC-PSO, an improvement in the THD% of the order of (0.4%-0.34%=0.06%)for the voltage injected by the inverter and of (56.68%-46.65%=10.03%)for mains voltage was noticed.

CONCLUSION
An intelligent control strategy-based PIFLC adjusted by PSO has been introduced in the regulation of DC-AC Inverters and DC-DC bidirectional converters to improve the quality of energy injected into the network on the one hand and better manage the flow of energy between the different parts of the system (GPV-battery storage) grid-connected on the other hand.This change in regulation strategy is implemented and evaluated in the MATLAB/Simulink environment.First, the influence of the PIFLC-PSO controller on the external DC coupling voltage control loops and the direct current and internal quadrature control (Id, Iq) provided by the PLL of the DC/AC inverter is remarkable, according to the simulation results, which reveal that the PIFLC-PSO provides the best THD values.Secondly, the calculation of the optimal values of the PIFLC controller output scale factor of the battery charge/discharge current control loop of the bidirectional DC/DC converter by the PSO algorithm makes it possible to ensure an adaptive regulation of the output currents of the DC motor load and stabilises the photovoltaic power generation.This is what we observed through the results obtained.We say that the proposed control provided the system with the stability of the output power of the GPV and also contributed to improving the speed of response to the consumption required by the load of the direct current motor and the quality of energy injected into the network.

Figure 4 .
Figure 4.The circuit diagram of the BDC

Figure 5 .
Figure 5. Plot charging/discharging battery and equivalent circuit

()Figure 6 .
Figure 6.Modeling of a DC motor

Table 1 .
Operating states for a TPI

Table 2 .
The various BDC operational scenarios