Optimization Active and Reactive Power Flow for PV Connected to Grid System Using Newton Raphson Method

Nazir, Refdinal; Kanada, Kiki; Syafii, .; Coveria, P.

doi:10.1016/j.egypro.2015.03.235

Cited by 15 publications

(5 citation statements)

References 4 publications

(4 reference statements)

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“…In particular, the optimum PV module tilt angle value changes according to electrical energy demand of the domestic utility. In [22], an optimization of the power flow of the PV system connected to the grid was presented. It was performed by calculat-ing the root of the active and reactive power equation using Newton's Raphson method.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Load Flow Analysis using Conventional method and modern method

Al Shaltouni,

Ismail

2022

IJADT

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The optimal power flow for networked microgrids with different renewable energy sources (PV panels and wind turbines), storage systems, generators, and load is investigated in this study. A conventional method and an Artificial Intelligence method are applied to solve the OPF problem. The performance of MGs system with renewable energy integration was investigated in this study, with a focus on power flow studies. The power flow is calculated using the well-known Newton-Raphson method and the Neural Network method. The power flow calculation is used to assess grid performance parameters like voltage bus magnitude, angle, and real and reactive power flow in system transmission lines. under given load conditions. The standard test system used was a benchmark test system for Networked MGs with four MGs and 40 buses. The data for the entire system has been chosen as per the IEEE Standard 1547-2018. The results showed minimumlosses and higher efficiency when performing OPF using NN than the Newton-Raphson method. The efficiency of the power system for the networked MG is 99.3% using Neural Network and 97% using the Newton- Raphson method. The Neural Network method, which mimics how the human brain works based on AI technologies, gave the best results and better efficiency in both cases (Battery as Load/Battery as Source) than the conventional method.

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Section: Literature Reviewmentioning

confidence: 99%

Smart Load Flow Analysis using Conventional method and modern method

Al Shaltouni,

Ismail

2022

IJADT

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“…The paper in [14] proposed a new optimal operation of microgrids (MGs) in a distribution system with solar photovoltaic (PV) energy systems and battery energy storage (BES) systems. The active and reactive power flow optimization for PV connected to the grid system using Newton Raphson method has been reported in [15]. This optimization system has increased the reliability and efficiency of the use of solar energy sources.…”

Section: Arduino Based Load Management Automation Systemmentioning

confidence: 99%

Design of arduino-based loading management system to improve continuity of solar power supply

Muhardika

Syafii

2020

IJEECS

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<p>Solar power plants using environmentally friendly technology in the process of harvesting energy from the sun can be a solution to the future electricity crisis so that it has been the most widely developed and reliable alternative. However, the conversion of solar energy depends on the availability and conditions of sunlight. In sunny conditions, the PV system can serve large loads while charging the battery to the maximum. While in cloudy weather conditions or at night, the PV system serves the load and without charge of the battery. The battery will discharge the stored energy until it runs out, and the supply to the load will be cut off before the desired time. Therefore, research on the PV system loading management system is needed to increase the amount of electricity from solar energy and maintain the continuity of electricity supply to the load. The load power management strategy follows the conditions of sunny, cloudy, rainy, or night time by considering the remaining capacity of the battery that can be used. Load installations are designed to consist of low, medium, and high load installations. Simulation results show that the use of PV loading management strategies can increase the operating time of the PV system. When the remaining less than 10% battery capacity and PLN supply is available, the supply will be switched to PLN. The remaining 10% of PV battery capacity could be used to maintain electricity supply to a low load if the PLN supply interrupted. Thus, the use of a loading management strategy will increase the electricity supply from renewable energy and improve the sustainability of electricity supply.</p>

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“…The methods for solving reactive power optimization problems can be roughly divided into two categories: mathematical optimization methods and intelligent optimization algorithms. The traditional mathematical methods include the linear programming method [5], nonlinear programming method [6], simplified gradient method [7], sequential quadratic programming method [8], Newton method [9], interior point method [10], etc. A second-order cone programming model was established in reference [11] to transform non-convex optimization problems into convex optimization problems to make more effective use of distributed generation and reduce network losses and voltage fluctuations, and its effectiveness was verified with the IEEE33 system.…”

Section: Introductionmentioning

confidence: 99%

Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm

Zhang,

Liu

2023

Energies

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This paper presents an improved osprey optimization algorithm (IOOA) to solve the problems of slow convergence and local optimality. First, the osprey population is initialized based on the Sobol sequence to increase the initial population’s diversity. Second, the step factor, based on Weibull distribution, is introduced in the osprey position updating process to balance the explorative and developmental ability of the algorithm. Lastly, a disturbance based on the Firefly Algorithm is introduced to adjust the position of the osprey to enhance its ability to jump out of the local optimal. By mixing three improvement strategies, the performance of the original algorithm has been comprehensively improved. We compared multiple algorithms on a suite of CEC2017 test functions and performed Wilcoxon statistical tests to verify the validity of the proposed IOOA method. The experimental results show that the proposed IOOA has a faster convergence speed, a more robust ability to jump out of the local optimal, and higher robustness. In addition, we also applied IOOA to the reactive power optimization problem of IEEE33 and IEEE69 node, and the active power network loss was reduced by 48.7% and 42.1%, after IOOA optimization, respectively, which verifies the feasibility and effectiveness of IOOA in solving practical problems.

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Optimization Active and Reactive Power Flow for PV Connected to Grid System Using Newton Raphson Method

Cited by 15 publications

References 4 publications

Smart Load Flow Analysis using Conventional method and modern method

Smart Load Flow Analysis using Conventional method and modern method

Design of arduino-based loading management system to improve continuity of solar power supply

Research on Reactive Power Optimization Based on Hybrid Osprey Optimization Algorithm

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