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.

This paper 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 nth-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.

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.

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 HSSbased 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 ASbased 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 a modified AS-based MPC recently employed for an HSS microgrid application. The analysis showed that the proposed method could significantly outperform the conventional MPC method in computational complexity and robustness, as well as the supervisory controller in terms of reliability, stability, enhanced battery lifetime, and robustness.

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.

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.