Although the distributed generator (DG) placement and distribution network (DN) reconfiguration techniques contribute to reduce power loss, obviously the former is a design problem which is used for a long-term purpose while the latter is an operational problem which is used for a short-term purpose. In this situation, the optimal value of the position and capacity of DGs is a value which must be not affected by changing the operational configuration due to easy changes in the status of switches compared with changes in the installed location of DG. This paper demonstrates a methodology for choosing the position and size of DGs on the DN that takes into account re-switching the status of switches on distribution of the DN to reduce power losses. The proposed method is based on the runner root algorithm (RRA) which separates the problem into two states. In State-I, RRA is used to optimize the position and size of DGs on closed-loop distribution networks which is a mesh shape topology and power is delivered through more than one line. In State-II, RRA is used to reconfigure the DN after placing the DGs to find the open-loop distribution network which is a tree shape topology and power is only delivered through one line. The calculation results in DN systems with 33 nodes and 69 nodes, showing that the proposed method is capable of solving the problem of the optimal position and size of DGs considering distribution network reconfiguration.

The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

The maximum power point tracking (MPPT) solutions improve power generation efficiency, quickly stabilizing the output waveform of photovoltaic (PV) systems under variable operating conditions. Along with new algorithms, improved and adjusted methods to exploit energy from PV systems are increasingly being researched and proposed. However, the proposed solutions based on the traditional algorithms and their improvements have poor performance, while the advanced algorithms or hybrid methods bring high performance but need to be simplified, and the response speed is higher. Moreover, a suitable PV configuration makes choosing a simple but highly efficient algorithm, especially in low-power PV system applications such as rooftop solar power, traffic lights, and moving vehicles…where the number of PV panels is insufficient to implement flexible configurations. This paper proposes a modified version of the Perturb and Observe (MPO) algorithm to improve MPPT performance and increase convergence speed in the parallel structure of PV panels. The Short-Circuit Current (I_sc) and Open-Circuit Voltage (V_oc) are calculated directly at specific operating conditions to quickly determine the potential maximum power point (MPP) that will reduce power interruptions and increase power generation efficiency compared to periodic updates. Therefore, the proposed solution converges faster, with higher efficiency, and the output signal in static and dynamic MPPT situations is more stable. The results show that the highest efficiency in simulation and experiment is 99.99% and 99.93%, respectively, while the convergence speed is 0.01 s and 0.03 s, respectively. They are better than the traditional Perturb and Observe (P&O) algorithm, the Variable Step Size Perturb and Observe (VSSP&O) method, and the Particle Swarm Optimization (PSO) technique under the same operating conditions. In addition, its performance and convergence speed are also compared with the latest introduced algorithms. The results show that it is valuable and reliable for parallel PV configuration.

This paper proposes the non-isolated DC-DC converter with high boost ratios and efficiency. We suggest boosting methods including voltage multiplier cells or coupled inductors. However, both techniques have a limited constant voltage multiplier coefficient according to the fixed configuration. Therefore, in the proposed method, we change the multiplier factor (as the number of activity levels) and combine it with the turn of the appropriate duty cycle; these factors are considered at the same time in order to increase the energy conversion efficiency and expand the control area of the duty cycle. A mathematical analysis of the losses of the components in operation modes shows that the boost ratio and efficiency are functions of the number of activity levels and duty cycle. Therefore, this paper proposes an algorithm for tracking the activity levels and duty cycle in order to obtain the maximum efficiency. The simulations produced with the Powersim-PSIM software, and the experiments with load capacities of 250 W, 500 W and 1000 W, boosting the ratio by more than ten times, were conducted in order to clarify the capabilities of the proposed configuration.

Under uniform operating conditions, the power–voltage (P-V) and current–voltage (I-V) curves of a photovoltaic (PV) system have only one maximum point, which facilitates the operation of maximum power point tracking (MPPT) algorithms. In practice, the PV systems often operate under heterogeneous environments due to partial shading conditions (PSCs). The P-V and I-V curves exhibit multiple extremes, and distinguishing between the global maximum power point (GMPP) and local maximum power point (LMPP) is a major challenge for algorithms aiming to improve performance and convergence speed. This paper presents a global maximum power point tracking (GMPPT) method based on simulating the behavior of the I-V curve of a PV system under the influence of PSCs. With only one initial parameter selected, the proposed solution quickly determines the LMPPs based on the characteristics of the PV type and the shading condition during operation. This work helps to limit the potential GMPP region to reduce the search time and improve efficiency by using a simple algorithm and a small tuning step size. The experimental results demonstrate that the proposed method provides superior MPPT performance and significantly reduces search time due to improved GMPP detection accuracy combined with small step sizes.