S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 11 Article Hybrid boost-cuk converter with bat-chicken swarm-optimized PI controller for photovoltaic grid systems S. Hema1*, S. Sreedevi2, K. Harinath Reddy3, Siddheswar Kar4, Ananthan Nagarajan5, S. Sengottaian6 1Department of Biomedical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai-600062, India 2Department of Electrical and Electronics Engineering, Sriram Engineering College, Perumalpattu - 602 024, India 3Department of Electrical and Electronics Engineering, Annamacharya University, Rajampet, India 4Department of Electrical Engineering, Medi-Caps University, Indore, Madhya Pradesh, India 5Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 62, India 6Department of Electrical and Electronics Engineering, Viswam Engineering College, Madanapalle-517 325, Andhra Pradesh A R T I C L E I N F O Article history: Received 10 February 2025 Received in revised form 14 March 2025 Accepted 26 March 2025 Keywords: PV system, Hybrid Boost-Cuk converter, Bat-Chicken swarm optimized PI controller, BESS, 3ϕ VSI *Corresponding author Email address: hemas04517@gmail.com DOI: 10.55670/fpll.futech.4.2.2 A B S T R A C T Recently, the reduction of greenhouse gas emissions and fuel consumption has been attended to by adopting the Photovoltaic (PV) system. Due to their intermittent nature, energy generated by PV systems is unpredictable for microgrid operation. Therefore, in this research, a novel hybrid Boost-Cuk converter is developed to efficiently increase the low voltage received from the PV system. Subsequently, the Bat-Chicken swarm optimized Proportional Integral (PI) controller is exploited to adjust the PI controller's parameters. Furthermore, the intermittency and instability of PV systems are addressed by adding a Battery Energy Storage System (BESS) to the microgrid to provide a steady and continuous power supply. This output is delivered to the grid via a Three Phase Voltage Source Inverter (3ϕ VSI), and the grid synchronization is accomplished with the aid of a PI controller. In order to validate the efficacy of the developed work, it is executed using the MATLAB/Simulink tool and compared with traditional topologies. The outcomes reveal that the developed research attains an efficacy of 93%, ensuring effective grid synchronization. 1. Introduction More fossil fuels are used to produce power, which increases environmental pollution. In addition to being the primary source of electrical production worldwide, fossil fuels are the main contributors to environmental degradation [1]. To reduce the negative effects of using fossil fuels, renewable energy systems (RES) like solar, wind, biomass, and hydraulic energies have received much attention [2]. Clean energy, such as PV, has drawn more and more attention as the environmental pollution issues brought on by conventional fossil fuels become more noticeable [3-5]. Effectively converting solar into affordable power without wasting energy is the main goal of all PV systems. Due to its plentiful resources and pollution-free benefits, grid-tied PV power generation technology has experienced rapid expansion [6-7]. Large-scale PV power-generation networks' ongoing grid integration results in a decline in the system's short-circuit capacity, which lowers the voltage support capacity [8]. However, Energy Storage Systems (ESSs) have been installed to guarantee a reliable power supply due to the variable nature of PV production systems during the day and their unavailability at night [9-10]. Numerous meteorological factors, including solar radiation, temperature, wind speed, precipitation, humidity, dust deposition, air pressure, and technical factors like inverter loss and PV array losses, affect the performance of solar PV power stations [11-12]. Future Technology Open Access Journal https://doi.org/10.55670/fpll.futech.4.2.2 May 2025| Volume 04 | Issue 02 | Pages 11-21 Journal homepage: https://fupubco.com/futech ISSN 2832-0379 mailto:hemas04517@gmail.com https://doi.org/10.55670/fpll.futech.4.2.2 https://fupubco.com/futech https://fupubco.com/ S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 12 Since PV systems depend on ambient temperature and solar irradiance, their output power typically varies during the day [13, 14]. The Quadratic Boost Converter, which achieves high voltage gain with a single switch, is represented by S. Chitra Selvi et al. [15]. However, QBC's high-frequency switching results in better switching losses, which reduces the system's effectiveness. Ahmed et al. [16] presented an interleaved Boost Converter with high conversion efficiency and lower switching losses. However, this converter requires more components, enhancing the overall cost and complexity. A transformerless boost converter that achieves high voltage gain without requiring high-duty cycles is designed by Ahmed et al. [17]. However, its increased input current ripple impacts the associated load's performance. Haider et al. [18] presented the high-gain Cuk converter sustaining a smooth current waveform because of the capacitors and inductors at both input and output sides. However, the operation and implementation of this converter are complex, and dynamic operation leads to high current stress on switches. Figure 1. Block diagram of PV-based grid system A quadratic Cuk converter was developed by H. Gholizadeh et al. [19] that reduces electromagnetic interference by maintaining a stable input current with minimal ripple. Nevertheless, the converter is effective; there are some power losses because of the extra components and switching operations. Therefore, this paper uses a hybrid Boost-Cuk converter to enhance the low voltage of the PV system. The PI controller is exploited to manage the function of the hybrid Boost-Cuk converter, and its parameters are tuned by the optimization algorithm. In order to address nonlinear, non-differentiable, and multi-modal optimization issues, the particle swarm optimization approach was introduced by Demir et al. [20]. However, it cannot ensure that the best solution will be found, especially for extremely complicated or multi-modal issues. In Shamseldin et al. [21] research, a Harmony Search (HS) optimization algorithm is developed, which sustains a good balance between exploration and exploitation. Nevertheless, the performance of HS is significantly affected by the initial harmony memory, which leads to suboptimal solutions. The Cuckoo Search Optimization technique improves accuracy and efficiency in optimization tasks while exhibiting reasonable convergence rates. Nevertheless, the algorithm's effectiveness is reduced by the initial fixed parameters, necessitating improvements for improved performance [22]. In Amador-Angulo et al. [23], a Chicken search optimization algorithm is developed that has shown competitive performance on a wide range of benchmark problems. The computational cost is high for large and complex problems, limiting its practical applications. As a result, the Bat-Chicken swarm optimization algorithm is exploited to fine-tune the PI controller’s parameters. The main motivations of this research are: • Implementing the hybrid Boost-Cuk converter that effectively enhances the low output voltage of the PV system. • The parameters of the PI controller are fine-tuned with the aid of the Bat-Chicken swarm optimization algorithm. • The battery is exploited to store the surplus energy from the PV system, and a bidirectional DC-DC converter is implemented to perform the battery's charging and discharging operations. Abbreviations AC Alternating Current BA Bat Algorithm BESS Battery Energy Storage System CN Chick Group CSO Chicken Swarm Optimization DC Direct Current ESS Energy Storage System G2V Grid to Vehicle HN Hen Group HS Harmony Search LPF Low Pass Filter PI Proportional-Integral PV Photovoltaic PWM Pulse Width Modulation QBC Quadratic Boost Converter RES Renewable Energy System RN Rooster Group SRF-PLL Synchronous Reference Frame-Phase Locked Loop V2G Vehicle to Grid VSI Voltage Source Inverter S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 13 2. Proposed methodology Figure 1 reveals the block diagram of the developed PV- based grid system. The PV system generates low voltage because of varying environmental conditions, which is boosted with the aid of a hybrid boost-Cuk converter. Nevertheless, the voltage of the developed converter, which is regulated by a PI controller, is unstable, and its parameters are tuned by a Bat-Chicken swarm optimization algorithm. After that, the Pulse Width Modulation (PWM) generator produces PWM pulses to better operate the developed converter. A Bidirectional DC-DC Converter enables charging and discharging operations, and battery voltage is regulated by a PI Controller. Subsequently, the DC power from the converter is transformed into AC power with the aid of 3ϕ VSI. Then, the PI controller is employed to regulate the function of the inverter, and the PWM generator is exploited to improve the functioning of the inverter. Furthermore, the obtained AC power is delivered to the 3ϕ grid with the aid of an LC filter, which provides harmonic less power to the grid. 2.1 PV system Solar cells are semiconductors that generate Direct Current (DC) over PV panels by absorbing solar energy on frontal surfaces. As seen in Figure 2, the PV panel circuit is built with a diode, series resistance(RS), a photocurrent(Iph), and resistance that are connected in parallel (Rp) to indicate a current leakage. Figure 2. Circuit of PV system By applying Kirchhoff’s law, the current equation becomes, 𝐼 = 𝐼𝑝ℎ − 𝐼𝐷 − 𝐼𝑝 (1) 𝐼𝑝 = 𝑉+𝑅𝑆𝐼 𝑅𝑝 (2) The current via the diode is denoted as 𝐼𝐷and the magnitude of the diode current is denoted as, 𝐼𝐷 = 𝐼𝑠𝑑 (𝑒𝑥𝑝 ( 𝑞.(𝑉+𝑅𝑆.𝐼) 𝑛.𝐾.𝑇 ) − 1) (3) Where 𝐾 stands for the Boltzmann constant and 𝐼𝑠𝑑reverse saturation current. Climate circumstances cause the obtained PV voltage to decrease, so a converter is required to enhance the voltage to power the grid. The hybrid Boost-Cuk converter approach used in this work is explained in the following part. 2.2 Hybrid boost-cuk converter The hybrid Boost-Cuk converter (Figure 3) integrates both Boost and Cuk converters to enhance the PV system's low voltage. The number of switches and diodes in a converter directly impacts its complex operation and control. Since each switch needs a gate drive signal to be controlled, this increase takes place. The developed converter has two stages of operation. Stage 1: When switch 𝑆 is active at 𝑡𝑜, this stage is initiated (Figure 4). Considering that twice the input voltage is the same as the voltage across 𝐶1and input voltage is similar to 𝑉𝐶2. Then, half of the output voltage is the same as the voltage across 𝐶3 and 𝐶4. During this mode, the voltage across 𝐿1 is positive. Consequently, the current flowing via the inductor is linearly increased. Figure 3. Hybrid boost-cuk converter 𝑉𝐿1 = 𝑉𝑃𝑉 + 𝑉𝐶1 + 𝑉𝐶2 (4) 𝑉𝐶2(𝑡𝑜) = 1 2 𝑉𝐶1(𝑡𝑜) = 𝑉𝑃𝑉 (5) 𝑉𝐿1 = 4𝑉𝑃𝑉 (6) ∆𝐼𝐿1 = 4𝑉𝑃𝑉 𝐿1 (𝑡1 − 𝑡𝑜) = 4𝐷𝑉𝑃𝑉 𝐿1𝑓𝑠 (7) In this mode, the inductor 𝐿1 receives energy from the input source and 𝐶1 and𝐶2. The voltage across the inductor 𝐿2 is, 𝑉𝐿2 = 𝑉𝑃𝑉 + 𝑉𝐶2 = 2𝑉𝑃𝑉 (8) ∆𝐼𝐿2 = 2𝑉𝑃𝑉 𝐿2 (𝑡1 − 𝑡0) = 2𝐷𝑉𝑃𝑉 𝐿2𝑓𝑠 (9) Inductor 𝐿2's current rises linearly as a result of the positive voltage across it. In this mode, 𝐿2 receives energy from the input source and capacitor 𝐶2. 𝑉𝐿3 = 𝑉𝑃𝑉 (10) ∆𝐼𝐿3 = 𝑉𝑃𝑉 𝐿3 (𝑡1 − 𝑡0) = 𝑉𝑃𝑉𝐷 𝐿3𝑓𝑠 (11) ∆𝐼𝐿1 = 2∆𝐼𝐿2 (12) 𝑡1 − 𝑡0 = 𝐷𝑇 (13) Where 𝐷𝑇 is the amount of time it takes for the switch to turn on and 𝐷 is its duty cycle. A higher anode voltage activates a diode because diodes 𝐷1 and𝐷2share a cathode. Diode 𝐷1's anode voltage is equal to 𝑉𝑃𝑉 , while diode 𝐷2′s anode voltage is equal to−(𝑉𝐶1 + 𝑉𝐶2). As a result, 𝐷2is reverse-biased. The voltage across the diode 𝐷3is calculated as follows because the switch is on. 𝑉𝐷3 = 𝑉𝑂 − 𝑉𝐶4 = 𝑉𝑂 2 (14) Furthermore, diode 𝐷5 is reverse-biased. In this mode, the output capacitor 𝐶𝑂 is charging capacitors 𝐶3 and 𝐶4 and diode 𝐷4 is forward-biased. However, the load is also delivered by the output capacitor. The nominal power determines the maximum input current in the developed converter. For a 100% efficiency assumption, the following expressions are obtained: 𝑉𝑂(𝑚𝑎𝑥) × 𝐼𝑂 = 𝑉𝑖𝑛 × 𝐼𝑖𝑛(𝑚𝑎𝑥) (15) 𝐼𝑖𝑛(𝑚𝑎𝑥) = 𝑉𝑂(𝑚𝑎𝑥)×𝐼𝑂 𝑉𝑖𝑛 (16) 𝑉𝐶3 + 𝑉𝐶4 = 𝑉𝑂 (17) As 𝐶3 = 𝐶4 and its voltage is half the output voltage. S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 14 Figure 4. Stages of developed converter Figure 5. Switching the waveform of the converter Stage 2: When the switch is turned off at 𝑡1, this mode is started. As a result, diode 𝐷2 is forward-biased and diode 𝐷1 is reversed- biased. Consequently, diodes 𝐷2, 𝐷3 and 𝐷5 provide the necessary path to move the energy stored in inductors 𝐿1 and 𝐿2 to capacitors 𝐶1 and 𝐶2, and the output. The energy held in capacitors 𝐶3 and 𝐶4 is transferred to the load when diodes, 𝐷3and 𝐷5 are turned on, while the diode 𝐷4is reverse-biased. 𝑉𝐿1 = 𝑉𝑃𝑉 + 𝑉𝐶1 + 𝑉𝐶2 + 𝑉𝐶4 − 𝑉𝑂 = 4𝑉𝑃𝑉 − 𝑉𝑂 2 (18) Owing to the negative voltage through𝐿1, it's current reduces linearly ∆𝐼𝐿1 = (4𝑉𝑃𝑉 − 𝑉𝑂 2 ) × (𝑡2−𝑡1) 𝐿1 = (4𝑉𝑆 − 𝑉𝑂 2 ) (1−𝐷) 𝐿1𝑓𝑠 (19) Based on the volt-second balance for the inductor 𝐿1, the following expression is obtained: 4𝑉𝑃𝑉𝐷𝑇 = ( 𝑉𝑂 2 − 4𝑉𝑃𝑉) (1 − 𝐷)𝑇 (20) When diode 𝐷2 is active, the subsequent expressions are calculated, 𝑉𝐿2 = −𝑉𝐶1 = −2𝑉𝑆 (21) ∆𝐼𝐿2 = −2𝑉𝑃𝑉(1−𝐷) 𝐿2𝑓 (22) In mode 2, capacitors 𝐶3 and 𝐶4 are connected in parallel, 𝑉𝐶3 = 𝑉𝐶4 (23) Figure 5 displays the switching waveform of the developed converter. After that, the PI controller is exploited to stabilize the voltage of the developed converter, and its parameters are tuned by the Bat-Chicken swarm optimization algorithm. 2.3 Bat-chicken swarm optimized PI controller A Bat-Chicken swarm-optimized PI controller integrates the BA and CSO to tune the PI controller’s parameters. This hybrid approach combines the exploration efficacy of BA and adaptive hierarchy-based dynamics of CSO to optimize the PI controller’s performance. Figure 6 reveals the flowchart of the Bat-Chicken swarm-optimized PI controller. It initializes a population of potential solutions that represent distinct 𝐾𝑃 and 𝐾𝐼 pairs. BA highlights the phenomenon of echolocation by approaching the prey that has been identified, as bats search for it separately. This implies that a single random person is persuading the entire swarm to deviate in search of food. Figure 6. Flowchart of Bat-Chicken swarm optimized PI controller By shifting their current position, the entire swarm converges to the optimal solution across generations. The flight of a bat is: 𝑄𝑖 (𝑡) = 𝑄𝑚𝑖𝑛 (𝑡) + (𝑄𝑚𝑎𝑥 (𝑡) − 𝑄𝑚𝑖𝑛 (𝑡) ) . 𝛽 (24) 𝑉𝑖 (𝑡+1) = 𝑉𝑖 (𝑡) + [𝑋𝑖 (𝑡) − 𝑋𝑏𝑒𝑠𝑡 (𝑡) ] . 𝑄𝑖 (25) S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 15 𝑋𝑖 (𝑡+1) = 𝑋𝑖 (𝑡) + 𝑉𝑖 (𝑡) (26) Where 𝑋𝑖 (𝑡+1) is the position of𝑖𝑡ℎbat at generation,𝑡, 𝑉𝑖 (𝑡) is the velocity of a single bat and𝑄𝑖 (𝑡) is the actual pulse frequency. Within the interval𝑄𝑖 (𝑡) 𝜖[𝑄𝑚𝑖𝑛, 𝑄𝑚𝑎𝑥], the output pulse frequency is fluctuating. The output pulse is specified by the random number 𝛽 ∈ [0, 1], and the current best solution at the moment is shown by 𝑋𝑏𝑒𝑠𝑡 (𝑡) . The two halves of the BA search process are exploitation and exploration. While exploitation guides the search in the vicinity of the current solutions, exploration refers to the discovery of new solutions. Since both processes usually rely on the variation operators, they can’t be carried out concurrently. However, striking a balance between exploration and exploitation sets the control parameters. More ideal parameter configurations exist. Two exploration strategies and the parameter 𝑟𝑖 (𝑡) are used in the BA to balance the exploration and exploitation parts of the search process. The first exploration strategy is more exploratory in character, whereas the second method, which is given as: 𝑋𝑛𝑒𝑤 = 𝑋𝑜𝑙𝑑 + 𝜖. 𝐴 (𝑡) (27) It employs the random walk, which is a type of local search that is more concerned with taking advantage of the best solution available at the moment. Let's observe that 𝑋𝑜𝑙𝑑 in the equation displays the current best solution, whereas 𝑋𝑛𝑒𝑤 presents the new best solution. 𝐴 (𝑡) is the average loudness, while 𝜖 is the random number in the range (−1,1). Chickens are a unique species of poultry animal due to their sociable character, and they usually work together to obtain food. Hens, chicks, and roosters are the three distinct groups of individuals that make up chicken flocks. Based on varying foraging hierarchies, the group exhibits a distinct foraging capacity. In this hierarchy, hens chase after roosters because they are better at foraging than they are, and chicks follow suit since they are less skilled at foraging. The intelligent optimization algorithm's optimization object is the objective function that requires an optimal solution. Its independent variable parameters are composed of 𝑛 𝑗 −dimensional space vectors 𝑋, where 𝑛 is any positive integer, and 𝑗 is the dimensionality. The Chicken Optimization Algorithm is divided into 3groups based on the number of vectors 𝑋. The first RN individuals with the lowest fitness value are assigned to the rooster group 𝑅𝑖; the Chick Group (CN) individuals with the highest fitness value are assigned to the chick group 𝐶𝑖; and the remaining Hen Group(HN) individuals are allocated to the hen group 𝐻𝑖. The corresponding numbers of individuals in each group within the colony are thus denoted by the letters Rooster Group (RN), HN, and CN. 𝑅𝑖 = {𝑅1, 𝑅2, ⋯ , 𝑅𝑅𝑁} (28) 𝐶𝑖 = {𝐶1, 𝐶2, ⋯ , 𝐶𝐶𝑁} (29) 𝐻𝑖 = {𝐻1, 𝐻2, ⋯ , 𝐻𝐻𝑁} (30) The rooster group’s location succession is: 𝑅𝑖,𝑗 𝑡+1 = 𝑅𝑖,𝑗 𝑡 [1 + 𝑟𝑎𝑛𝑑𝑛(0, 𝛿2)] (31) 𝛿2 = { 1, 𝑓𝑖 ≤ 𝑓𝑠 𝑒 𝑓𝑠−𝑓𝑖 |𝑓𝑖|+𝜖 , 𝑓𝑖>𝑓𝑠 𝑆𝜖[1, 𝑛], 𝑠 ≠ 𝑖 (32) Where 0, 𝛿2 is a Gaussian-distributed random number that obeys a 0 mean and variance of 𝛿2 and 𝑅𝑖,𝑗 𝑡 is the position of the 𝑖𝑡ℎ rooster in the 𝑗𝑡ℎ dimension following 𝑡 iterations, the random rooster index, or 𝑆, is a small but significant integer that prevents the denominator from 0, while the individual's fitness value is denoted by 𝑓. The hen group’s location succession is: 𝐻𝑖,𝑗 𝑡+1 = 𝐻𝑖,𝑗 𝑡 + 𝑘1 ∗ 𝑟𝑎𝑛𝑑 ∗ (𝑅𝐻𝑖 𝑡 − 𝑀𝑖,𝑗 𝑡 ) + 𝑘2 ∗ 𝑟𝑎𝑛𝑑 ∗ (𝑅𝐻𝑡 − 𝐻𝑖,𝑗 𝑡 ) (33) 𝑘1 = 𝑒 𝑓𝐻𝑖−𝑓𝑟𝐻𝑖 |𝑓𝐻𝑖|+𝜖 (34) 𝑘2 = 𝑒𝑓𝑅𝐻−𝑓𝐻𝑖 (35) The position of the 𝑖𝑡ℎ hen in the 𝑗𝑡ℎ dimension following t iterations is denoted by 𝐻𝑖,𝑗 𝑡 . A random number among 0 𝑎𝑛𝑑 1 is called a rand. 𝑅𝐻𝑖 𝑡 is the position of the 𝑖𝑡ℎ hen's leader rooster after 𝑡 iterations;𝑅𝐻𝑡 is the position of the randomly chosen individuals between the other roosters and hens, excluding the hen and leader cock, after 𝑡 iterations; 𝑘1denotes the rooster's influence factor and 𝑘2 represents the random individual effect factor. Where 𝑓𝐻𝑖 represents the 𝑖𝑡ℎ hen's fitness value. The rooster leading the hen has a fitness value of 𝑓𝑟𝐻𝑖 . Where Eq. contains 𝑓𝑅𝐻 , the random person's fitness value. Location succession of the hen groups is: 𝐶𝑖,𝑗 𝑡+1 = 𝐶𝑖,𝑗 𝑡 + 𝐹 ∗ (𝐻𝑖𝑗 𝑡 − 𝐶𝑖,𝑗 𝑡 ) (36) The 𝑖𝑡ℎ chick's position in the 𝑗𝑡ℎdimension after t iterations is denoted by 𝐶𝑖,𝑗 𝑡 ; the matching hen's position after 𝑡 iterations is denoted by 𝐻𝑖𝑗 𝑡; and 𝐹 is a random number between 0 𝑎𝑛𝑑 2.The hybrid algorithm iteratively refines 𝐾𝑝 and 𝐾𝑖 to minimize a fitness function defined by the control system's performance metrics. After reaching convergence, the best solution is selected as the optimized 𝐾𝑃and 𝐾𝐼values for the PI controller. This hybrid optimization approach is appropriate for complex and nonlinear systems, where traditional tuning methods are become infeasible because of dynamic variability or high dimensionality. 2.4 Battery The battery is represented by the electrical circuit, shown in Figure 7. The electrolyte, plate grids, separator porosity, and connecting conductors develop the equivalent series resistance (𝑅𝐵). The battery capacitance is represented by𝐶𝐵, whereas the equivalent parallel resistance (𝑅𝑝) is a representation of the impurities in the electrolyte and plates that cause the battery to gradually discharge when it is left disconnected (self- discharge resistance). Figure 7. Circuit diagram of battery S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 16 Buck mode: While the switch 𝐾2 remains open(𝑢2 = 0), the switch 𝐾1 is controlled by a PWM signal {𝑢1𝜖{0,1}}in this mode. After that, the DC bus transfers the electrical energy to the battery. The entire system (charger-battery) functions in G2V mode. However, the dc-dc converter functions in buck mode. Considering that 𝑢1is accept either1 𝑜𝑟 0, the subsequent switching model is derived: 𝐿 𝑑𝑖𝐿 𝑑𝑡 = −𝑟𝑖𝐿 − 𝑣𝐵 + 𝑢1𝑉𝐷𝐶 (37) 𝐶 𝑑𝑣𝐵 𝑑𝑡 = 𝑖𝐿 − 1 𝑅𝐵 𝑣𝐵 + 1 𝑅𝐵 𝑣𝐶 (38) 𝐶𝐵 𝑑𝑣𝐶 𝑑𝑡 = 1 𝑅𝐵 𝑣𝐵 − ( 1 𝑅𝐵 + 1 𝑅𝑝 ) 𝑣𝐶 (39) Boost mode: In this mode, the switch 𝐾1 remains open(𝑢1 = 0), but only switch 𝐾2 is controlled by a PWM signal{𝑢2𝜖{0,1}}. After that, the battery transfers its electrical energy to the DC bus. While the entire system (charger-battery) functions in V2G mode, the DC-DC power converter functions in boost mode. Considering that 𝑢2accept either 1 𝑜𝑟 0, the following switching model is derived. 𝐿 𝑑𝑖𝐿 𝑑𝑡 == −𝑟𝑖𝐿 − 𝑣𝐵 + (1 − 𝜇2)𝑉𝐷𝐶 (40) 𝐶 𝑑𝑣𝐵 𝑑𝑡 = 𝑖𝐿 − 1 𝑅𝐵 𝑣𝐵 + 1 𝑅𝐵 𝑣𝑐 (41) 𝐶𝐵 𝑑𝑣𝐶 𝑑𝑡 = 1 𝑅𝐵 𝑣𝐵 − ( 1 𝑅𝐵 + 1 𝑅𝑝 ) 𝑣𝐶 (42) Then, the output of the converter is fed into the VSI that transforms the DC to AC voltage, and grid synchronization is discussed below. 2.5 𝟑𝛟 Grid Synchronization A passive Low-pass filter (LPF) filters the output voltage of a controlled VSI, as shown in Figure 8. At the point of common coupling, the filtered voltage that is almost harmonic-free is synchronized with the grid voltage. The connection is guaranteed by the coupling breaker. The filtered VSI and grid voltage are converted into the inverter's rotating orthogonal frame. It is presumed that the inverter's frame and the reference signal frame are arranged so that the inverter's quadrature component,𝑈𝑞𝑖𝑛𝑣denotes its magnitude and its direct component, 𝑈𝑑𝑖𝑛𝑣, equals zero. Since 𝑈𝑞𝑓 and 𝑈𝑞𝑔 stand for the quadrature components, 𝑈𝑑𝑓 and 𝑈𝑑𝑔 are the filtered inverter voltage and the grid voltage’s direct components. Because of this arrangement, the direct components are ideal indicators of the phase shift among the inverter reference signal and the filtered and grid voltages. The initial and preliminary stage of synchronization is accomplished by regulating the inverter voltage to match the grid's magnitude. The phase-angle matching procedure is the second and most important stage. Where 𝑈𝑓 and 𝑈𝑔 are the inverter-filtered and grid voltage magnitudes, respectively, The inverter reference signal's phase angle is represented by𝜃𝑖𝑛𝑣 , the filtered VSI voltage by 𝜃𝑓 and the grid voltage by 𝜃𝑔. 𝑈𝑑𝑓 = −𝑈�̂�𝑠𝑖𝑛(𝜃𝑖𝑛𝑣 − 𝜃𝑓) (43) 𝑈𝑑𝑔 = −𝑈�̂�𝑠𝑖𝑛(𝜃𝑖𝑛𝑣 − 𝜃𝑔) (44) 𝑈𝑞𝑓 = −𝑈�̂�𝑐𝑜𝑠(𝜃𝑖𝑛𝑣 − 𝜃𝑓) (45) 𝑈𝑞𝑔 = −𝑈�̂�𝑐𝑜𝑠(𝜃𝑖𝑛𝑣 − 𝜃𝑔) (46) Figure 8. Grid synchronization Utilizing the phase-shift representative value, the inverter frequency is adjusted to ensure that the grid and the inverter direct components are identical to achieve a zero phase-shift among the voltages. 𝑒𝜃 = (𝑈𝑑𝑓 × 𝑈𝑞𝑔 − 𝑈𝑞𝑓 × 𝑈𝑑𝑔) (47) 𝑒𝜃 = 𝑈�̂�. 𝑈�̂�𝑠𝑖𝑛(𝜃𝑓 − 𝜃𝑔) (48) When the voltage of the grid 𝜃𝑔 and the VSI voltage phase- angle 𝜃𝑓 are equal, leading the equation (48) to be zero. While both voltage magnitudes are identical, this results in 𝑈𝑑𝑓 = 𝑈𝑑𝑔. 𝑒𝜃 = 𝑈�̂�. 𝑈�̂�(𝜃𝑓 − 𝜃𝑔) (49) This is the same as the SRF-PLL's controlled voltage value. The distinction is that, in this case, the phase shift is calculated between two measured voltages that need to be synchronized rather than between a measured signal and the internal PLL frame. To guarantee a zero phase-shift among the voltages, the value is regulated to zero is denoted by 𝑒𝜃 . Since the phase control is solely reliant on the constancy of the voltage magnitudes, it is crucial to begin controlling the voltage magnitude before controlling the phase. Since the phase control is based on actual voltage levels, any disruption brought on by the magnitude control has a significant impact on it. 3. Results and discussion This part analyzes the outcomes of a developed PV-based grid system using MATLAB/Simulink software. It also includes a comparison of conventional approaches with developed work. Table 1 displays the parameters of the developed research. Case 1: Constant temperature and intensity Figure 9 represents the characteristics of solar panels in constant temperature and intensity conditions. The temperature is sustained at a stable value of 35 ℃ without distortions. Also, the intensity value of solar panels is sustained to a value of 1000(W⁄(Sq.m)) without any fluctuations. Subsequently, the voltage of the solar panel is stabilized at a stable value of 310 V throughout the system. Likewise, the current of the solar panel gradually decreased S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 17 in the starting stage, and it maintained a value of 50 A in the entire system. Table 1. Parameters of developed work Parameter Specification PV System Rated Power 10kW No. of Panels in Parallel 3 Open Circuit Voltage 37.25V Cell linked in Series 36 No. of Panels in series 12 Short Circuit Current 8.95A Hybrid Boost-Cuk Converter C1, C2, C3and C4 22 μF Switching frequency 10KHz L1, L2 4.7mH CO 2200μF Figure 9. Characteristics of solar panel Figure 10 displays the developed converter’s waveform. The output voltage of the developed converter is linearly changed in the initial period, and it stabilizes at 600 V with the aid of the BCSO-PI controller. In the initial period, the output current slowly varied, and then it was sustained at a value of 24 A with little distortions. Figure 11 reveals the battery's waveform. The battery's State of Charge (SOC)value is maintained at a stable 80 % throughout the system. The battery voltage is sustained at 50V without any oscillations. Consequently, the battery current is maintained at a constant 2A with few fluctuations. Figure 12 illustrates the grid waveform. The grid voltage is stabilized at a constant value of 420 V without any fluctuations. Likewise, the grid current is maintained at a stable value of 12 A throughout the system. The Real power waveform remains stable at approximately 8000 W, indicating steady energy consumption or generation. Meanwhile, the reactive power waveform is constant at around 600 VAR, reflecting steady reactive power demand, as seen in Figure 13. Case 2: Varying temperature and intensity Figure 14 represents the characteristics of solar panels in varying temperatures and irradiation conditions. Initially, the solar panel's temperature is varied, and it is stabilized at a value of 35 ℃without any fluctuations. Similarly, the intensity of the solar panel is changed, and it maintains a constant value of 1000(W⁄(Sq.m))in the entire system. Likewise, the solar panel voltage is changed and sustained at a stable value of 310V without any oscillations. Initially, the input current is randomly varied and gets sustained at a stable value of 50 A. Figure 10. Waveform of the developed converter with BCSO-PI controller Figure 11. Waveform of battery Figure 12. Waveform of grid Figure 13. The waveform of real and reactive power The waveform of the developed converter with the BCSO-PI controller is revealed in Figure 15. At the starting stage, the output voltage is linearly changed, and it is increased to a stable value of 600 V. Consequently, the output current is gradually raised and settled to a value of 24A without distortions. The R phase's THD value of 0.58% is within acceptable limits, representing a high-quality power signal with negligible harmonic interference. Also, the Y phase (0.68%) has slightly higher harmonic distortion than the R phase. Subsequently, the B phase has the lowest THD of 0.51% S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 18 among the three phases, indicating a smoother power signal, as seen in Figure 16. Figure 14. Characteristics of solar panel Figure 15. Waveform of the developed converter with BCSO-PI controller Figure 16. Waveform of THD Figure 17 illustrates an analysis of efficiency for four different converters: Buck-Boost [24], High Gain Cuk [25], Z Source Boost [26], and the developed converter. The proposed converter attains the highest efficiency at 93%, highlighting its superior performance compared to the other converters. This makes it a promising choice for applications requiring high efficiency in power conversion. A comparative analysis of converter performance in terms of switching loss, inductor loss, capacitor loss, and diode loss for interleaved step-down converter [27] and Hybrid Boost-Cuk converter is displayed in Figure18. The Interleaved Step-Down Converter suffers more from inductor losses, making it less efficient in handling energy conversion. The Hybrid Boost-Cuk Converter demonstrates improvements in reducing inductor losses but experiences higher diode losses. Figure 19 shows the voltage gain for three types of converters: Transformerless [28], Interleaved Bi-directional [29], and the developed converter. The transformerless converter attains the highest voltage gain at all duty ratios, demonstrating superior performance in boosting voltage. The Interleaved Bi-directional Converter offers a balanced performance, providing moderate voltage gain. The Proposed Converter prioritizes other design objectives, such as reduced complexity, cost, or improved efficiency in specific operational conditions, over-achieving maximum voltage gain. Figure 20 depicts a comparative analysis of control approaches based on settling time and rise time for GWO-PI, Modified LOA-PI, and proposed PI controller. The BAT-CSO PI approach is the most efficient control method, achieving both the shortest settling time of 0.023 s and a rise time of 0.021 s. The BAT-CSO PI approach offers a balanced performance with results that are not as fast as GWO-PI [30] but considerably better than Modified LOA-PI [31]. S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 19 Figure 17. Analysis of efficiency Figure18. Analysis of converter performance Figure 19. Analysis of voltage gain Figure 20. Analysis of the control approach 4. Conclusion This research presents the importance of a PV-based hybrid Boost-Cuk converter for an efficient energy generation process. The hybrid Boost-Cuk converter linked to the PV system’s output side increases output voltage while lowering switching loss. With less iteration before convergence, the applied Bat-Chicken swarm-optimized PI controller yields a higher gain, providing better control performance. Additionally, a steady power supply is guaranteed by the BESS, which is connected to the microgrid via a battery converter that ensures grid stability by supplying extra energy to the grid during periods of peak demand. Finally, the obtained DC supply harnessing 3ϕ VSI transforms the DC supply to AC, resulting in effective grid synchronization. The system's dependability and effectiveness are confirmed by the MATLAB/Simulink tool, which shows an efficiency of 93 % with maximum voltage gain. Ethical issue The authors are aware of and comply with best practices in publication ethics, specifically with regard to authorship (avoidance of guest authorship), dual submission, manipulation of figures, competing interests, and compliance with policies on research ethics. The authors adhere to publication requirements that the submitted work is original and has not been published elsewhere. Data availability statement The manuscript contains all the data. However, more data will be available upon request from the authors. Conflict of interest The authors declare no potential conflict of interest. References [1] M. Liaqat, M. G. Khan, M. R. Fazal, Y. Ghadi and M. Adnan, “Multi-Criteria Storage Selection Model for Grid-Connected Photovoltaics Systems,” in IEEE Access, vol. 9, pp. 115506-115522, 2021.https://doi.org/10.1109/ACCESS.2021.3105592 [2] A. Laib, A. Krama, A. Sahli, A. Kihal and H. Abu-Rub, “Reconfigurable Model Predictive Control for Grid Connected PV Systems Using Thirteen-Level Packed E- Cell Inverter,” in IEEE Access, vol. 10, pp. 102210- 102222, 2022.https://doi.org/10.1109/ACCESS.2022.3208106 [3] W. Dong, H. Diao, J. Xu, J. Kang and Z. Lv, “Analysis and Parameters Design of Grid-Forming Converter for Enhancing the Stability of Photovoltaic Storage System Under Weak Grid,” in IEEE Access, vol. 12, pp. 134273- 134284, 2024. https://doi.org/10.1109/ACCESS.2024.3449340 [4] A. Almarshoud, “A techno-economic investigation for utilizing solar energy in irrigation of palm trees in Saudi Arabia," Future Sustainability, vol. 2, no. 1, pp. 35-46, 2022. https://doi.org/10.55670/fpll.fusus.2.1.4 [5] Y. Kassem, H. Gökçekuş and Q. A. Furaiji, “Applicability of solar systems with various technologies and sun- tracking: A case study of Baghdad, Iraq,” Future Energy, vol. 1, no. 2, pp. 03-08, 2022. https://doi.org/10.55670/fpll.fuen.1.2.2 [6] F. Wu, B. Yang, A. Hu, Y. Zhang, W. Ge, L. Ni, C. Wang and Y. Zha, “Inertia and Damping Analysis of Grid-Tied Photovoltaic Power Generation System With DC Voltage Droop Control,” in IEEE Access, vol. 9, pp. S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 20 38411-38418, 2021.https://doi.org/10.1109/ACCESS.2021.3059687 [7] C. Subramani, K. Dhineshkumar and P. Palanivel, “Design and implementation of 13 levels multilevel inverter for photovoltaic system,” In Journal of Physics: Conference Series, vol. 1000, no. 1, pp. 012047. IOP Publishing, 2018. [8] I. Hassan, I. Alhamrouni, Z. Younes, N. H. Azhan, S. Mekhilef, M. Seyedmahmoudian and A. Stojcevski, “Explainable Deep Learning Model for Grid-Connected Photovoltaic System Performance Assessment for Improving System Reliability,” in IEEE Access, vol. 12, pp. 120729-120746, 2024.https://doi.org/10.1109/ACCESS.2024.3452778 [9] J. Luo, K. Panchabikesan, K. Lai, T. O. Olawumi, M. C. Mewomo, Z. Liu, “Game-theoretic optimization strategy for maximizing profits to both end-users and suppliers in building rooftop PV-based microgrids,” Energy, vol. 313, pp.133715, 2024.https://doi.org/10.1016/j.energy.2024.133715 [10] K. S. Kavin, P. S. Karuvelam, M. Matcha and S. Vendoti, “Improved BRBFNN-based MPPT algorithm for coupled inductor KSK converter for sustainable PV system applications,” Electrical Engineering, pp. 1-23. 2025. https://doi.org/10.1007/s00202-025-02952-9 [11] K. P. Joshua, L. V. Rangasamy, C. V. Reddy and R. Veeruchinnan, “Energy management of solar photovoltaic fed water pumping system based BLDC motor drive using NBO–SDRN approach,” Electrical Engineering, vol. 106, no. 3, pp. 3045-3059, 2024.https://doi.org/10.1007/s00202-023-02102-z [12] Q. Zhong, Y. Qiu, Y. Zhao, H. Li, G. Wang and F. Wen, “Interharmonic Analysis Model of Photovoltaic Grid- connected System with Extended Dynamic Phasors,” in Journal of Modern Power Systems and Clean Energy, vol. 9, no. 6, pp. 1540-1547, 2021.https://doi.org/10.35833/MPCE.2020.000241 [13] S. M. Said, M. Aly and H. Balint, “An Efficient Reactive Power Dispatch Method for Hybrid Photovoltaic and Superconducting Magnetic Energy Storage Inverters in Utility Grids,” in IEEE Access, vol. 8, pp. 183708- 183721, 2020. https://doi.org/10.1109/ACCESS.2020.3029326 [14] K. S. Kavin, P. Subha Karuvelam, M. Devesh Raj and M. Sivasubramanian, “A Novel KSK Converter with Machine Learning MPPT for PV Applications,” Electric Power Components and Systems, pp. 1-19, 2024.https://doi.org/10.1080/15325008.2024.23468 06 [15] S. Chitra Selvi, R. Govindaraj, S. Chowdhury, S. Singh and B. Khan, “Embedded-Based Quadratic Boost Converter With Sliding-Mode Controller for the Integration of Solar Photo-Voltaic Source With Microgrid,” in IEEE Journal of the Electron Devices Society, vol. 12, pp. 831-841, 2024.https://doi.org/10.1109/JEDS.2023.3340249 [16] N. A. Ahmed, B. N. Alajmi, I. Abdelsalam and M. I. Marei, “Soft Switching Multiphase Interleaved Boost Converter With High Voltage Gain for EV Applications,” in IEEE Access, vol. 10, pp. 27698-27716, 2022. https://doi.org/10.1109/ACCESS.2022.3157050 [17] H. Y. Ahmed, O. Abdel-Rahim and Z. M. Ali, “New high- gain transformerless dc/dc boost converter system,” Electronics, vol. 11, no. 5, pp. 734, 2022.https://doi.org/10.3390/electronics11050734 [18] Z. Haider, A. Ulasyar, A. Khattak, H. S. Zad, A. Mohammad, A. A. Alahmadi and N. Ullah, “Development and analysis of a novel high-gain CUK converter using voltage-multiplier units,” Electronics, vol. 11, no. 17, pp. 2766, 2022.https://doi.org/10.3390/electronics11172766 [19] H. Gholizadeh and S. Hasanpour, “A New Quadratic CUK-Based Step-Up DC/DC Converterwithout Right Hand Plane Zero,” International Journal of Industrial Electronics Control and Optimization, vol. 8, no. 1, pp.25-35, 2025.https://doi.org/10.22111/ieco.2024.48683.1565 [20] M. H. Demir and M. Demirok, “Designs of particle- swarm-optimization-based intelligent PID controllers and DC/DC Buck converters for PEM fuel-cell-powered four-wheeled automated guided vehicle,” Applied Sciences, vol. 13, no. 5, pp. 2919, 2023.https://doi.org/10.3390/app13052919 [21] M. Shamseldin, M. A. Ghany and Y. Hendawey, “Optimal nonlinear PID speed control based on harmony search for an electric vehicle,” Future Engineering Journal 2, no. 1, pp.4, 2021. [22] C. Esakkiappan, “Soft Computing Based Tuning of PI Controller With Cuckoo Search Optimization For Level Control of Hopper Tank System,” 2021.https://doi.org/10.21203/rs.3.rs-920228/v1 [23] L. Amador-Angulo, O. Castillo, C. Peraza and P. Ochoa,“An efficient chicken search optimization algorithm for the optimal design of fuzzy controllers,” Axioms, vol. 10, no. 1, pp. 30, 2021.https://doi.org/10.3390/axioms10010030 [24] S. Saravanan, P. Usha Rani and M. P. Thakre, “Evaluation and Improvement of a Transformerless High-Efficiency DC–DC Converter for Renewable Energy Applications Employing a Fuzzy Logic Controller,” Mapan - Journal of Metrology Society of India, vol. 37, no. 2, pp. 291–310, 2022, https://doi.org/10.1007/s12647-021-00530-5 [25] Z. Haider, A. Ulasyar, A. Khattak, H. S. Zad, A. Mohammad, A. A. Alahmadi and N. Ullah, “Development and analysis of a novel high-gain CUK converter using voltage-multiplier units,” Electronics, vol. 11, no. 17, pp.2766, 2022.https://doi.org/10.3390/electronics11172766 [26] M. Saranya and G.G. Samuel, “Energy management in hybrid photovoltaic–wind system using optimized neural network,” Electr Eng, vol. 106,pp. 475–492, 2024. https://doi.org/10.1007/s00202-023-01991-4 [27] C. L. Shen, P. H. Chen, H. Q. Liu, Y. S. Liang, “Interleaved step-down converter with capacitor-diode voltage splitter and minimum switches for low current ripple and extra-low voltage ratio,” Discover Applied Sciences, vol. 6, no. 6, pp. 293, 2024.https://doi.org/10.1007/s42452-024-05987-y [28] L. Yu, L. Wang, C. Yang and M. Wu, “Analysis and implementation of a single-stage transformer-less converter with high step-down voltage gain for voltage S. Hema et al. /Future Technology May 2025| Volume 04 | Issue 02 | Pages 11-21 21 regulator modules, IEEE Trans Ind Electron, vol. 68, no. 12, pp. 12239–49, 2021. https://doi.org/10.1109/TIE.2020.3045592 [29] Y. Zhang, W. Zhang, F. Gao, S. Gao and D. J. Rogers, “A switched-capacitor interleaved bidirectional converter with wide voltage-gain range for super capacitors in EVs. IEEE Trans Power Electron, vol. 35, no. 2, pp. 1536–47, 2020. https://doi.org/10.1109/TPEL.2019.2921585 [30] G. Vasumathi, V. Jayalakshmi and K. Sakthivel, “Efficiency analysis of grid tied PV system with KY integrated SEPIC converter,” Measurement: Sensors, vol. 27, pp. 100767, 2023.https://doi.org/10.1016/j.measen.2023.100767 [31] R. Ramani and A. Nalini, “Enhanced EV Battery Monitoring Using IoT with Improved SEPIC-ZETA Converter and Modified Lion Optimization for Photovoltaic Systems,” Journal of Electrical Systems, vol. 20, no. 6s, pp. 3005-3018, 2024. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). https://creativecommons.org/licenses/by/4.0/