Improving efficiency with sustainable radial distribution networks (RDNs) is challenging for larger systems and small grid-connected RDNs. In this paper, the optimal placement of DGs with the Harris hawks optimizer (HHO) under seasonal load demands is proposed to simultaneously reduce total active and reactive power losses and minimize bus voltage drops with the consideration of operational constraints of RDNs. HHO is a newly inspired metaheuristic optimization algorithm primarily based on the Harris hawks’ intelligent behaviors during the chasing of the prey. Furthermore, the authors have investigated four stages of DGs. The first stage involves the optimal allocation of one DG. The second stage includes an investigation with two DGs, the third stage considers three DGs, and the fourth stage investigates the integration of four DGs. The effectiveness of the applied HHO is validated on IEEE 33 and 69 bus RDNs, and results are analyzed by comparing with the standard optimization methods. The Big-O test is also executed for statistical analysis with standard algorithms. The simulation results reveal the better performance of the applied HHO under different circumstances than other algorithms. Furthermore, the total active and reactive power losses and bus voltage drops are improved by adding more DGs into IEEE 33 and 69 bus RDNs.

The reliability of power converters in photovoltaic systems is critical to the overall system reliability. This paper proposes a novel active thermal-controlled algorithm that aims to reduce the rate of junction temperature increase, therefore, increasing the reliability of the device. The algorithm works alongside a normal perturb and observe maximum power point tracking algorithm, taking control when certain temperature criteria are met. In conjunction with a neural network, the algorithm is applied to long-term real mission profile data. This would grant a better understanding of the real-world trade-offs between energy generated and lifetime improvement when using the proposed algorithm, as well as shortening study cycle times. The neural network, when applied to 365 days of data, was 28 times faster than using standard electrothermal modeling, and the lifetime consumption was predicted with greater than 96.5% accuracy. Energy generated was predicted with greater than 99.5% accuracy. The proposed algorithm resulted in a 3.3% reduction in lifetime consumption with a 1.0% reduction in the total energy generated. There is a demonstrated trade-off between lifetime consumption reduction and energy-generated reduction. The results are also split by environmental conditions. Under very variable conditions, the algorithm resulted in a 4.4% reduction in lifetime consumption with a 1.4% reduction in the total energy generated.

Remote areas require reliable solutions for fulfilling electricity needs. PV-wind energy utilization is increasing worldwide with wind-battery-load topology as a cost-effective solution for remote area electrification and standalone hybrid microgrid clusters. Intelligent control of voltage source inverter (VSI) ensures high power quality with efficient voltage and frequency profile for sensitive and critical loads. While maximum PV-wind power extraction provides reliable solution with battery energy storage (BSS). In this paper, finite control set model predictive voltage control (FCS-MPVC) is proposed for wind-battery based standalone microgrid to regulate the terminal load voltage in terms of amplitude and frequency. Control hierarchy consists of DC boost convert, DC-DC buck boost converter (bidirectional) and VSI. PV-wind maximum power is extracted by using DC-boost converter and DC bus voltage is stabilized by using bidirectional battery converter. FCS-MPC strategy is used for primary control, which includes power droop control, three phase reference generator, and inner control loop. Fluctuating wind under variable AC load conditions are simulated.

Global warming, high carbon emission and increasing energy demand requires high penetration of green and clean renewable energy resources in existing microgrid systems. Remote areas need reliable solutions for fulfilling electricity needs for their daily life activities across the globe. Wind energy utilization is increasing worldwide with wind-battery-load topology as a cost-effective solution for remote area electrification and standalone hybrid microgrid clusters. Intelligent control of voltage source inverter (VSI) ensures high power quality with efficient voltage and frequency profile for sensitive and critical loads. While maximum wind power extraction provides reliable solution with battery energy storage system (BESS). In this paper, finite control set model predictive voltage control (FCS-MPVC) is proposed for wind-battery based standalone microgrid to regulate load voltage and frequency. Control hierarchy consists of dc-dc converters and VSI. Wind maximum power is extracted by using dc-dc boost converter and DC bus voltage is stabilized by using bidirectional battery converter. MPC strategy is used for primary control, which includes power droop control, three phase reference generator, and inner control loop. Fluctuating wind under variable ac load conditions are simulated. Results show the effectiveness of MPC as compared to PI controller in obtaining load voltage with THD less than 1%.

This paper investigates the performance of radial distribution networks (RDNs) in developing South Asian regions where load shedding is common. It compares the operational performance of RDNs under load shedding and normal conditions and examines the combined effects of load shedding and optimal deployment of distributed generators (DGs) on overall network efficiency. To address these challenges, a multiobjective optimization framework based on the Battle Royale Optimization (BRO) algorithm is proposed. Four DG configurations are analyzed: (1) active power injection similar to solar PV units, (2) reactive power injection as in capacitor banks or synchronous motors, (3) complex power injection as in synchronous generators, and (4) negative reactive power injection as in induction generators. These configurations are evaluated under four cases: base network, load shedding only, DGs only, and combined DGs with load shedding. The optimization objectives include minimizing total active and reactive power losses and voltage deviation while maximizing a multiobjective index. Simulations on IEEE 33-bus and 69bus systems show that BRO achieves higher loss reduction and voltage profile improvement compared with the Artificial Bee Colony (ABC) algorithm. In the IEEE 33-bus network, active power losses decreased by 55.95, 55.95, 78.26, and 94.89 percent for DG types 1 through 4, while reactive power losses decreased by 85.35, 55.94, 67.11, and 93.57 percent. Voltage deviations improved by up to 13.02 percent. Similar results were observed in the IEEE 69-bus system, confirming the superior performance of the proposed BRO-based DG planning approach.