System Level Optimization Techniques for Solar Energy Technologies

Editors

Ahmed Bilal Awan

College of Engineering and Information Technology, Ajman university

Texas A and M University

Impact

Original Research

22 July 2022

Hill Climbing Artificial Electric Field Algorithm for Maximum Power Point Tracking of Photovoltaics

Mohana Alanazi

, 3 more and

Amir Mosavi

Proposed combined HC-AEFA method for solving the MPPT problem.

In this paper, maximum power point tracking (MPPT) of a photovoltaic (PV) system is performed under partial shading conditions (PSCs) using a hill climbing (HC)–artificial electric field algorithm (AEFA) considering a DC/DC buck converter. The AEFA is inspired by Coulomb’s law of electrostatic force and has a high speed and optimization accuracy. Because the traditional HC method cannot perform global search tracking and instead performs local search tracking, the AEFA is used for a global search in the proposed HC-AEFA. The critical advantage of the HC-AEFA is that it is desirable performing local and global searches. The proposed hybrid method is implemented to derive an MPP by tuning the converter duty cycle, considering the objective function for maximizing the PV system extracted power. Its capability is evaluated and compared with well-known particle swarm optimization (PSO), considering standards, PSCs, and radiation changes conditions. The tracking efficiency for the most challenging shading pattern (third pattern) using the HC-AEFA, HC, AEFA and PSO is obtained at 99.93, 90.35, 98.85, 99.80%, respectively. The analysis of the population-based optimization process for different algorithms proved the HC-AEFA faster convergence at lower iterations than the other methods. So, the superiority of the proposed HC-AEFA subjected to different patterns is confirmed with higher tracking efficiency and global power peak, fewer fluctuations, higher convergence speed, and higher dynamic and Static-efficiency compared to the other methods.

2,513 views

9 citations

Layout of the experimental setup of the solar assisted yogurt processing unit.

Original Research

01 June 2022

Energy and Exergy Based Thermal Analysis of a Solar Assisted Yogurt Processing Unit

Syed Nabeel Husnain

, 2 more and

Oliver Hensel

2,541 views

4 citations

Original Research

18 October 2021

Optimization and Comparative Economic Analysis of Standalone and Grid-Connected Hybrid Renewable Energy System for Remote Location

Siddharth Jain

and

Yashwant Sawle

Due to rising population growth and economic development, there is indeed a growing demand for electricity. Both in aspects of generation and transmission, conventional power firms are striving to manage these demands. Moreover, the ubiquitous utilization of electricity and other power generators, which are mainly driven from fossil fuels, seems to have some limitations, like declining performance and restricted energy production. As a result, use of renewable energy sources is incredibly important. Decentralized power generation in remote regions has become the primary requirement of society, based on renewable energy. Particularly in comparison with the electrification of urban areas, rural electrification is quite expensive. Microgrid’s development utilizing hybrid power is a potential solution for the electrification of rural regions where the transmission chain of network’s extension is unfeasible or inefficient. This research aims to structure a power generation model associated with different HRES combinations using a HOMER software application at a location in India. In the findings of this research, it has been observed that NPC, O&M, COEs, and RF of on-grid energy systems are better than off-grid energy systems. In the study, between eight hybrid system combinations, the lowest COE of 0.034 $/kWh is obtained with the PV-WT-MH-GRID-CT system in the on-grid scenario. This analysis shows NPC, COE, O&M, and renewable fraction are sensitive to the variation in all the considered sensitivity parameters¹^,²^,³^,⁴.

4,699 views

24 citations

Original Research

31 January 2022

Research on Advanced Control Method of Multiple Photovoltaic Strings Input Type Recognition

Yiwang Wang

, 2 more and

Xiaogao Chen

1,340 views

0 citations

Original Research

23 August 2021

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

Muhammad Arsalan Ilyas

, 3 more and

Muhammad Babar Rasheed

The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.

3,517 views

9 citations