Integrated technical paradigm based novel approach towards photovoltaic power generation technology

Feng, Jianghong; Xu, Su Xiu

doi:10.1016/j.esr.2020.100613

Cited by 11 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the problem lies on the other side of the coin. In terms of regulated power generation, demand PV and other renewable sources are still not the right choices [47]. Another drawback is its high capital upfront investment cost [48].…”

Section: Recent Progress On Power Dispatch With Pvmentioning

confidence: 99%

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Fahim,

Silva,

Hussain

et al. 2023

Sustainability

View full text Add to dashboard Cite

Fossil fuel is considered to be the primary power generation source. As this source is not that eco- and environmentally friendly, researchers are constantly searching for an alternative source for power generation. Renewable energy has drawn much attention in this regard in recent times. For solving economic load dispatch issues, numerous operational constraints must be considered. Due to the restructuring of the power sector, there is competition between different power systems organizations. Increasing fossil fuel costs drive power-producing utilities to adopt a cost-effective technique for dispatching actual power output. Due to the presence of nonlinearity and non-convexity in the fuel of cost function of generators, the economic load dispatch is often considered a complex optimization problem. Many researchers have been optimizing fuel costs to solve the economic power dispatch problem. This paper offers a critical analysis of ELD that takes into account both traditional and non-traditional energy sources. The review covers a variety of algorithms, including hybrid algorithms for integrating renewable energy sources (RES). The paper also focuses on several restricted optimization techniques and contemporary algorithms including PSO, Jaya, GWO, SMO, TLBO, Rao, MRao-2, and MFO to reduce the fuel cost of generation units using large-scale solar PV. Moreover, this paper provides a comprehensive overview of the current state of economic load dispatch and provides valuable insights for electricity researchers and practitioners. It also discusses future technologies and next steps in the field of ELD, emphasizing the need for more environmentally friendly and cost-effective power generation and distribution solutions. Overall, the paper demonstrates the benefits of renewable energy sources as well as optimization techniques for creating a more sustainable and efficient power generation system.

show abstract

Section: Recent Progress On Power Dispatch With Pvmentioning

confidence: 99%

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Fahim,

Silva,

Hussain

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In this research, we first determined the range of 4 search parameters. According to previous research, we set the ranges for the number of neurons, the number of network layers, the time step, and the time interval as [1,16], [1,5], [1,10], and [1,10], respectively. All target parameters are integers.…”

Section: Aga-lstm Modelmentioning

confidence: 99%

“…In recent years, the demand for energy consumption has increased, followed by a series of problems such as energy shortages and environmental degradation. In this regard, as a clean and renewable energy source, solar energy has huge potential and is garnering increasing attention [1]. However, the instability of solar energy has severely restricted the large-scale development of photovoltaic power generation.…”

Section: Introductionmentioning

confidence: 99%

Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm

Zhu

et al. 2022

Energies

View full text Add to dashboard Cite

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 minutes. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.

show abstract

“…As a result, in the last few years, attention has increasingly been switched from fossil fuels to renewable energy (RE) sources for electricity generation. Amongst these RE sources, the solar energy is considered as one of the promising sources [2]. Looking for new clean energy sources is not the only way to reduce the impact on energy consumption, but one of the most important ways to address this problem is to optimise energy use [3].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques

et al. 2023

View full text Add to dashboard Cite

The majority of energy sources being used today are traditional types. These sources are limited in nature and quantity. Additionally, they are continuously diminishing as global energy consumption increases as a result of population growth and industrial expansion. Their compensation is made from clean energy and renewable energy. Renewable energy is strongly dependent on climatic conditions; therefore, an aspect of energy management is needed, which is essential in distribution systems, because it enables us to calculate the precise energy used by the load as well as by its many components. It also helps us understand how much energy is required and its origin. The energy management aspect contains two main phases: forecasting and optimization. In this study, we are focused on the forecasting level using intelligent machine learning (ML) techniques. To ensure better energy management, it is very important to predict the production of renewable energy over a wide time period. In our work, several cases are proposed in order to predict the temperature, the irradiance, and the power produced by a PV system. The proposed approach is validated by an experimental procedure and a real database for a PV system. The big data from the sensors are noisy, which pose a major problem for forecasting. To reduce the impact of noise, we applied the multiscale strategy. To evaluate this strategy, we used different performance criteria, such as mean error (ME), mean absolute error (MAE), root mean square error (RMSE), nRMSE and the coefficient of determination (R2). The obtained experimental results show good performance with lower error. Indeed, they achieved an error for nRMSE criteria between 0.01 and 0.37.

show abstract

Integrated technical paradigm based novel approach towards photovoltaic power generation technology

Cited by 11 publications

References 44 publications

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations

Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm

Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques

Contact Info

Product

Resources

About