scholarly journals An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1231-1237
Author(s):  
S T Suganthi ◽  
D Devaraj
Keyword(s):  
Power Systems ◽  
Congestion Management ◽  
Test System ◽  
Photovoltaic System ◽  
Management Technique ◽  
Optimization Approach ◽  
Solar Photovoltaic ◽  
Teaching Learning Based Optimization ◽  
Solar Photovoltaic System ◽  
Teaching Learning

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

Customer-Operated Solar Photovoltaic System to Improve the System and Customer Reliability

2021 ◽  
pp. 175-185
Author(s):  
Atul Kumar Yadav ◽  
Lalit Tak ◽  
Vasundhara Mahajan
Keyword(s):  
System Reliability ◽  
Distribution System ◽  
Average Energy ◽  
Electrical Energy ◽  
Test System ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Reliability Parameters ◽  
Load Demand ◽  
Solar Photovoltaic System

In this chapter, the advantage of distributed generation can be seen in terms of system reliability and reliability of customer load. The solar photovoltaic (SPV) system is one of the distributed generations that may lead to the supply of electrical energy. The customer at the site of load demand mainly uses the SPV system. The installation of the SPV system is advantageous for the electrical load demand. Solar systems have greater efficiency for supplying both types of load (i.e., thermal and electrical) simultaneously. The modeling of two power system components (i.e., generation and distribution) can be performed using the Monte Carlo simulation (MCS) technique. The data used for generation modeling is taken from IEEE-RTS (reliability test system) and data for the distribution system is obtained from IEEE-RBTS (reliability busbar test system). The reliability parameters such as average energy not supplied (AENS) and loss of energy expectation (LOEE) are evaluated for the analysis of individual customer reliability and overall system reliability simultaneously.

scholarly journals A new meta-heuristic pathfinder algorithm for solving optimal allocation of solar photovoltaic system in multi-lateral distribution system for improving resilience

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Varaprasad Janamala
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Voltage Stability ◽  
Optimal Allocation ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Voltage Profile ◽  
Pv System ◽  
Solar Photovoltaic System ◽  
The Impact

AbstractA new meta-heuristic Pathfinder Algorithm (PFA) is adopted in this paper for optimal allocation and simultaneous integration of a solar photovoltaic system among multi-laterals, called interline-photovoltaic (I-PV) system. At first, the performance of PFA is evaluated by solving the optimal allocation of distribution generation problem in IEEE 33- and 69-bus systems for loss minimization. The obtained results show that the performance of proposed PFA is superior to PSO, TLBO, CSA, and GOA and other approaches cited in literature. The comparison of different performance measures of 50 independent trail runs predominantly shows the effectiveness of PFA and its efficiency for global optima. Subsequently, PFA is implemented for determining the optimal I-PV configuration considering the resilience without compromising the various operational and radiality constraints. Different case studies are simulated and the impact of the I-PV system is analyzed in terms of voltage profile and voltage stability. The proposed optimal I-PV configuration resulted in loss reduction of 77.87% and 98.33% in IEEE 33- and 69-bus systems, respectively. Further, the reduced average voltage deviation index and increased voltage stability index result in an improved voltage profile and enhanced voltage stability margin in radial distribution systems and its suitability for practical applications.

A High Gain Novel Double-Boost Converter for DC Microgrid Applications

2020 ◽  
Vol 29 (15) ◽  
pp. 2050246 ◽  
Author(s):  
B. N. Ch. V. Chakravarthi ◽  
G. V. Siva Krishna Rao
Keyword(s):  
Control Strategies ◽  
High Gain ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Dc Microgrid ◽  
Boost Converters ◽  
Power Point ◽  
Solar Photovoltaic System ◽  
Conversion Stage ◽  
And Control

In solar photovoltaic (PV)-based DC microgrid systems, the voltage output of the classical DC–DC converter produces very less voltage as a result of poor voltage gain. Therefore, cascaded DC–DC boost converters are mandatory for boosting the voltage to match the DC microgrid voltage. However, the number of devices utilized in the DC–DC conversion stage becomes higher and leads to more losses. Thereby, it affects the system efficiency and increases the complication of the system and cost. In order to overcome this drawback, a novel double-boost DC–DC converter is proposed to meet the voltage in DC microgrid. Also, this paper discusses the detailed operation of maximum power point (MPP) tracking techniques in the novel double-boost DC–DC converter topology. The fundamental [Formula: see text]–[Formula: see text] and [Formula: see text]–[Formula: see text] characteristics of solar photovoltaic system, operational details of MPP execution and control strategies for double-boost DC/DC converter are described elaborately. The proposed converter operation and power injection into the DC microgrid are verified through the real-time PSCAD simulation and the validation is done through the experiment with hardware module which is indistinguishable with the simulation platform.

A novel way of parameter estimation of solar photovoltaic system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rahul Bisht ◽  
Afzal Sikander
Keyword(s):  
Parameter Estimation ◽  
Mean Absolute Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Solar Panels ◽  
Content Type ◽  
Solar Photovoltaic System

Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

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