This paper proposes a highly efficient model predictive control (MPC) technique for primary-level control of a hybrid storage system (HSS) in a microgrid with renewables. Generally, active set (AS)-based MPCs are used to control HSS-based microgrid systems, which are computationally complex. The high-complexity prevents making control decisions within the sampling time, which negatively affects the robustness of the control system. Moreover, high complexity necessitates powerful controllers, which increase the system's overall cost and energy consumption. On the other hand, less complex controls, such as supervisory control, reduce the storage system's stability, reliability, and lifetime. The proposed method is a closed-form AS-based MPC that shares the mutual advantages of both AS-based MPC and supervisory controllers with additional robustness. It thus significantly reduces the computational complexity while being stable and reliable, improving storage lifetime, and having higher robustness compared to MPC and supervisory controllers. The proposed method is compared to modified AS based MPC, Hildreth quadratic programming (HQP), and sequential quadratic programming (SQP) based MPC recently employed for an HSS microgrid application. The analysis showed that the proposed method could significantly outperform the conventional MPC methods in computational complexity and robustness, as well as the supervisory controller in terms of reliability, stability, enhanced battery lifetime, and robustness.

As any alternating current (AC) load, plug-in electric vehicle (PEV) battery when powered by a photovoltaic (PV) source is subject to the power decay problem. To optimize the PV power extraction for a given non-uniform irradiance and temperature, the PV power must be managed by a maximum power extraction (MPE) system. The PV MPE coupled with an inverter and a PEV battery includes a proportional integral (PI) control system which negatively impacts the MPE controller performance. This is unlike a straightforward MPE system without a PI control where the PV power is exclusively controlled by the MPE controller. Metaheuristic algorithms are usually employed to optimize the PV MPE systems for a non-PI controlled MPE system, This study proposes a low exploration metaheuristic-based algorithm to mitigate the problems with the MPE system coupled with a PI control system. The proposed algorithm is contrasted with the low burden narrow search (LBNS) (low exploration) and the Jaya algorithms (high exploration). The findings show that the proposed algorithm performed significantly better than LBNS and Jaya in addressing the aforementioned problems.

Due to their abundance, affordability, and clean energy production, Renewable Energy Sources (RESs) have emerged as crucial sources of electricity production. As a result, considerable effort has been made to integrate renewable energy sources into the grid to help it meet energy demands. One of the challenges is the Maximum Power Extraction (MPE), where the maximum power needs to be extracted from each RES. The cost of implementation rises linearly with the number of RESs if the MPE arrangements are being made for each RES individually. Additionally, the MPE through multiple RES systems gets more challenging, especially when several PhotoVoltaic (PV) strings are connected and each PV string receive non-uniform irradiance. This paper proposes an integrated MPE control system for a multi-RES grid-connected system. The proposed solution uses a single microcontroller to maximize power from all sources to overcome the linearly growing cost problem. Moreover, we propose an improved Multi-Dimensional Cuckoo (MDC) algorithm for MPE to tackle the non-uniform irradiation problem with multi-string PV, in contrast to prior works with grid-connected multiple sources. The proposed technique is first put up against the individual MPE control system, and then it is put up against the Jaya algorithm that is documented in the literature for a typical one-dimensional MPE.

This study proposes a new algorithm for parameter estimation of the electric circuit model of Lithium (Li)-ion battery. The first-order battery-electric circuit model is considered in this work that resembles battery charging and discharging behaviors. The battery circuit element values have been modeled as polynomial equations with unknown coefficients. An accurate estimation of the battery circuit element values is profound to accurately find the battery State of Charge (SoC), an immeasurable quantity required in battery management systems (BMS). The ElectroStatic discharge algorithm (ESDA) is used in this study to estimate the unknown polynomial coefficients and, in turn, the values of the battery circuit elements. The accuracy of the proposed ESDA in estimating the battery circuit element values is compared to the recently proposed Artificial Hummingbird Optimization Technique (AHOT), Chameleon Swarm Algorithm (CSA), and Tuna Swarm Optimization (TSO). The results demonstrate the superiority of the proposed algorithm for charging and discharging in battery parameters estimation over the other algorithms with an accuracy gain of at least 10%.

Photovoltaic (PV) forecasting plays a major role in residential and industrial PV installation as well as penetration with the grid. An inaccurate PV power forecasting may result in increased monetary and energy losses. This study proposes a metaheuristic-based strategy for accurate PV power forecasting using a heuristic-based data-driven PV model. The proposed algorithm integrates a dense explorative strategy with the existing PV equation knowledge by a multilayer perceptron (MLP) network with Sigmoid activation functions to predict the best coefficients for the inputs of the data-driven PV model. The proposed method is compared to a recently proposed metaheuristic algorithm, the artificial hummingbird optimizer algorithm (AHOA). The comparison is performed for inside distribution (ID) and out-of-distribution (OOD) irradiance datasets and with varying temperatures. The results prove that the proposed NN-based algorithm achieves higher accuracy in PV power parameter prediction and hence forecasting.

Photovoltaic (PV) arrays, when subjected to Partial Shading (PS), exhibit several power losses due to diminished current across the array. Therefore, bypass diodes are connected across array modules to avoid the PS effect. Although they reduce the PS effect, the bypass diodes make the Power versus Voltage (P- V) relation of a PV non-convex. This paper investigates the Maximum Power Point Tracking (MPPT) problem under PS conditions to track the PV array's Maximum Power Point (MPP). Because previously proposed algorithms for this problem either failed to track the MPP or were computationally expensive, we propose a modified version of the Bat metaheuristic algorithm with dynamically narrowing search space (DNSS) exploration to avoid exploring low-power regions. The results show around 35 % gain in terms of rapidity and efficiency of the proposed metaheuristic approach in mitigating power losses compared to other existing algorithms.

Electricity generation using photovoltaic (PV) technology has become highly popular recently. However, natural barriers such as trees, buildings, bird drops, etc., cause partial shading (PS) on the PV surface resulting in high power losses. Bypass diodes used to mitigate the PS effect cause multiple peaks in the PV power delivery. The tracking of the optimal power peak can be considered an optimization problem with a continuously changing objective function due to different insolation conditions. All optimization strategies applied in previous works spanning from mathematical programming techniques to Machine Learning and the recently proposed Nature-inspired algorithms led to either sub-optimal maximum power or required extensive computations. This work presents an algorithm that combines the advantages of the previous works and avoids their loopholes. Experimental results indicate the superiority of the proposed algorithm over the state-of-the-art algorithm for the Maximum Power Peak Tracking problem.

The necessity for clean and sustainable energy has shifted the energy sector’s interest in renewable energy sources. Photovoltaics (PV) is the most popular renewable energy source because the sun is ubiquitous. However, PV’s power transfer efficiency varies with different load’s electrical characteristics, temperatures on PV panels, and insolation conditions. Based on these factors, Maximum Power Point Tracking (MPPT) is a mechanism formulated as an optimization problem adjusting the PV to deliver the maximum power to the load. Under full insolation conditions, varying solar panel temperatures, and different loads MPPT problem is a convex optimization problem. However, when the PV’s surface is partially shaded, multiple power peaks are created in the power versus voltage (P-V) curve making MPPT non-convex. Unfortunately, all optimization strategies for MPPT under partial shading applied in previous works, from traditional techniques to Machine Learning and the recently proposed Nature-inspired algorithms, were either computationally expensive or/and led to extensive power losses. To this end, this work presents an algorithm that builds upon metaheuristic optimization algorithms to reduce their complexity further and mitigate the power losses during power tracking. Our experimental results demonstrated that the proposed algorithm converges faster to maximum power point with lower power losses during tracking compared to two very recently proposed MPPT algorithms under partial shading conditions.

This paper proposes an improved Maximum Power Point Tracking (MPPT) control for the inverter's photovoltaic (PV) string connection. This is achieved by replacing MPPT control microcontrollers across each PV string with a single central microcontroller controlling all PV strings' Maximum Power Point (MPP). The proposed system, in addition to making the PV string system more power efficient, also reduces the overall system cost. However, replacing multiple controllers across each string with a single central controller makes the MPPT problem multi-dimensional, increasing the complexity. To solve this, we first used the Jaya algorithm for solving the multi-dimensional MPPT problem. Then, the proposed Multi-Dimensional MPPT (MDMPPT) Jaya is compared to another metaheuristic, the Particle Swarm Optimization (PSO), and a conventional algorithm, the Perturb and Observe (P&O). The results prove the superiority of the proposed Jaya algorithm over PSO and P&O.

Photovoltaic (PV) systems are becoming one of the most emerging systems for power generation owing to their low cost and clean operation. Due to the high demand for PVs in several small and large-scale applications, their accurate modeling is essential in power systems simulation to obtain reliable results. In this work, an improved Electrostatic Discharge Algorithm with Dense Explorative Search (ESDADES) is proposed that estimates the parameters of the most popular PV cell models with more accuracy than previous works. More specifically, the dense explorative search proposed in this work enhances the ESDA capability to search around the regions closer to the best solution found by ESDA more extensively, thus improving convergence accuracy. The proposed ESDADES was compared to two recently proposed optimization algorithms, namely the Self-adaptive Ensemble-based Differential Evolution (SEDE), the Directional Permutation Differential Evolution (DPDE), and the simple ESDA. The experimental results demonstrated that the proposed algorithm arrives faster at more accurate estimates of the examined PV cell models parameters

Due to its clean and abundant availability, solar energy is popular as a source to generate electricity. Solar photovoltaic (PV) technology converts sunlight incident on the solar PV panel or array directly into non-linear DC electricity. However, the non-linear nature of the solar panels' power needs to be tracked for its efficient utilization. The problem of non-linearity becomes more prominent when the solar PV array is shaded, even leading to high power losses and concentrated heating in some areas (hotspot condition) of the PV array. Bypass diodes used to eliminate the shading effect cause multiple peaks of power on the power versus voltage (P-V) curve and make the tracking problem quite complex. Conventional algorithms to track the optimal power point cannot search the complete P-V curve and often become trapped in local optima. More recently, metaheuristic algorithms have been employed for maximum power point tracking. Being stochastic, these algorithms explore the complete search area, thereby eliminating any chance of becoming trapped stuck in local optima. This paper proposes a hybridized version of two metaheuristic algorithms, Radial Movement Optimization and teaching-learning based optimization (RMOTLBO). The algorithm has been discussed in detail and applied to multiple shading patterns in a solar PV generation system. It successfully tracks the maximum power point (MPP) in a lesser amount of time and lesser fluctuations.

This paper proposes an improved grid-connected system with PhotoVoltaic (PV) and battery storage under non-uniform irradiance conditions. We first develop an implementation of the system while considering non-uniform Partial Shading (PS) conditions which several literatures do not consider. Next, we propose an improved PV Maximum Power Point Tracking (MPPT) algorithm, which have shown its superiority for non-uniform irradiance conditions compared to another MPPT algorithm specifically in terms of power convergence efficiency. Finally, we validate the performance of the grid-connected architecture by showing battery charging and discharging in situations of excess and deficient PV supply and also the grid supplying to the load in case of deficiency in both battery State of Charge (SoC) and PV supply.

Photovoltaic (PV) arrays are gaining popularity for electricity generation due to their simple and green energy production. However, the power transfer efficiency of PV varies depending on the load’s electrical properties, the PV panels’ temperature, and the insolation conditions. Maximum Power Point Tracking (MPPT) is a method formulated as an optimization problem that adjusts the PV output voltage to deliver maximum power to the load based on these criteria (maximum power in the P-V curve). MPPT is a convex optimization problem when the Sun’s rays completely cover the PV surface (full insolation). Several power points are formed in the Power vs. Voltage (P-V) curve, rendering MPPT as a non-convex problem during incomplete insolation (partial shadowing) on the PV surface due to barriers such as passing clouds or trees in the path of the Sun and the PV’s surface. Unfortunately, mathematical programming techniques, such as gradient ascent and momentum, are not good optimization candidate algorithms because they cannot distinguish between the local and global maximum of a function (the case of non-convex problems). On the other hand, metaheuristic algorithms have better search space exploration capability, making it easier to discern the P-V curve’s local and global power peaks. However, due to their pseudorandom search space exploration (random with some intuition), there is plenty of room for improving their performance. In this work, we elaborate on the Advanced Limited Search Strategy (ALSS), a technique we proposed in one of our previous works on MPPT. We prove its universal usefulness by applying it to other MPPT algorithms to enhance their performance. The ALSS first finds the direction where it is most probable to discover the MPP using the finite difference between two candidate duty cycles and then computes a duty cycle between two bounds designated by the previous direction. After that, the resulting duty cycle is further updated according to the metaheuristic update equation. Therefore, the single solution update is another advantage of ALSS that further improves the computational cost of the MPPT algorithms.

This article presents a high-gain DC-to-DC converter with a single switch, called the cubic converter, which provides very high voltage gain compared to the existing topologies such as the quadratic converter and conventional boost converter. The operation of the proposed converter at a lower duty ratio ensures lesser conduction losses. Various mathematical approaches are employed to confirm the higher voltage gain and improved efficiency of the converter. The proposed cubic boost converter (CBC) is compared with the quadratic boost converter (QBC) and other converters discussed in the literature. A generalized n th -order boost converter is also derived. To test the effectiveness of the QBC and CBC circuits, the Hardware-In-the-Loop (HIL) validation is performed using Typhoon HIL 402 real-time emulator machine. Moreover, the proposed topology is tested and compared with other topologies for maximum power point tracking (MPPT) of a solar photovoltaic (PV) array to show its effectiveness in a real-world scenario. A detailed comparison between conventional boost, QBC and CBC is presented for dynamic partial shading conditions in real-time mode using Typhoon HIL 402 real-time emulator machine.

Inclusion of bypass diodes at the output terminal of the PV array mitigates the effect of partial shading (PS) but causes multiple peaks of power at the output. The conventional hill climbing and perturb and observe algorithms cannot track the optimal point during partial shading phenomena for multiple peaks corresponding to the different shading pattern on the Power-Voltage (P-V) curve. Fuzzy logic controller and artificial neural network-based methods for Maximum Power Point Tracking (MPPT) provide satisfactory results but at the cost of increased memory and computational burden. Recent work to incorporate exploration and exploitation phenomena of nature-inspired algorithms to track optimal power point have shown encouraging results by preventing convergence to local maxima and posing less burden on the processor. However, due to performance variation between different algorithms of this category newer algorithms with improved performances are still a requirement. In this paper, a novel most valuable player algorithm (MVPA) has been used to track the optimal operation point for extracting maximum power from a solar PV system. The algorithm's performance is compared with the commonly employed particle swarm optimization (PSO) and the recently proposed Jaya algorithm's modified form. It is observed that the proposed algorithm outperformed both the algorithms with a considerable improvement in terms of tracking speed, power tracking efficiency, robustness, faster decision for convergence after tracking the maximum power and lesser number of power fluctuations for different shading patterns.

The use of solar power is on the rise as energy demand increases and fossil fuels are under increasing stress. Solar energy is an eco-friendly and cheap source of energy as opposed to traditional energy sources. The solar photovoltaic (PV) cells utilize solar radiation to harness energy, but not in all cases the solar PV array operates in ideal conditions. The sun may not always be in direct line-of-site of the solar PV array as the direction of sun changes both annually and diurnally coupled with local blockade from nearby trees, buildings, natural barriers, clouds, etc. which ultimately leads to partial shading. The bypass diodes that are inculcated in order to reduce this partial shading effect create different power points in the P–V curve. The PV array cannot harvest the maximum power out of all available peaks. The conventional algorithms although worked well under full insolation conditions and failed under partial shading conditions. Hence, in this chapter the metaheuristic algorithms have been used in order to track the maximum power among various available peaks. Also, the comparison between different metaheuristic algorithms is done to show the best algorithm for Maximum Power Point Tracking (MPPT) applications.

When subjected to partial shading (PS), photovoltaic (PV) arrays suffer from the significantly reduced output. Although the incorporation of bypass diodes at the output alleviates the effect of PS, such modification results in multiple peaks of output power. Conventional algorithms—such as perturb and observe (P&O) and hill-climbing (HC)—are not suitable to be employed to track the optimal peak due to their convergence to local maxima. To address this issue, various artificial intelligence (AI) based algorithms—such as an artificial neural network (ANN) and fuzzy logic control (FLC)—have been employed to track the maximum power point (MPP). Although these algorithms provide satisfactory results under PS conditions, a very large amount of data is required for their training process, thereby imposing an excessive burden on processor memory. Consequently, this paper proposes a novel optimization algorithm based on stochastic search (random exploration of search space), known as the adaptive jaya (Ajaya) algorithm in which two adaptive coefficients are incorporated for maximum power point tracking (MPPT) with a rapid convergence rate, fewer power fluctuations and high stability. The algorithm successfully eliminates the issues associated with existing conventional and AI-based algorithms. Moreover, the proposed algorithm outperforms other state-of-the-art stochastic search-based techniques in terms of fewer fluctuations, robustness, simplicity, and faster convergence to the optima. Extensive analysis of results obtained from MATLAB® is done to prove the above performance parameters under static insolation conditions (using a three, four and a five-module series-connected PV system), under dynamically varying insolation (using a four-module series connected system), by changing the PV module rating (using a four-module series connected system) and using an IEC standard.

Solar PV system has become one of the most important aspect for power generation due to the abundant supply of rays coming from the sun. But due to partial shading effects, the power at the output of a Solar array becomes limited. Hence, this paper has proposed a comparison among various available techniques for tracking maximum power at the output of the PV array. Different particle swarm optimization (PSO) based algorithms have been used to track the maximum power and the comparison is done on the basis of convergence rate for different shading patterns. The standard version of PSO was found to be the most effective way for tracking maximum power for most of the shading patterns. However, for very few cases other variants of PSO performed better. [GRAPHICS] .

This paper focusses on three phase Cascaded H bridge Multi-level Inverter (MLI) topology fed by solar Photovoltaic (PV) module. For maintaining maximum power as well as constant voltage for MLI, Perturb and Observe (P&O) technique is applied. Sine Pulse Width Modulation (SPWM) is implemented along with unipolar and bipolar switching scheme. Performance parameters like Total Harmonic Distortion (THD), switching loss, switching stress, Root Mean Square (RMS) fundamental voltage and efficiency of MLI output voltage is calculated and graphically compared for both unipolar as well as bipolar schemes. It is found that unipolar scheme outperforms bipolar scheme in all parameters and hence is better than the later. The control algorithm is also validated in Hardware-in-the-Loop using Typhoon HIL 402 emulator and results are presented and discussed.

In today’s competitive world, the advancements in technology are taking place at a very rapid rate. Due to this advancement in technology and higher population growth, the existing energy-producing sources are depleting very fast. In order to eliminate such issues, there comes into consideration the use of non-renewable energy systems. But the adoption of these systems requires their proper and efficient utilization. In this paper, we have proposed a method to utilize a solar PV system efficiently with a better convergence time and efficiency of convergence to the maximum power. A modified PSO algorithm known PSO with constriction factor (PSO-CF) is used for tracking maximum power point (MPP) of a solar PV array.