Guest Editorial for Special Issue on Control and Optimization in Renewable Energy Systems

I.I NTRODUCTION W ITH the advent of low-carbon economy,there has been a growing interest in harnessing renewable energy resources particularly for electricity generation.Renewable energy resources are advocated for the economic and environ-

A Review on Renewable Energy Systems for Irrigation in Arid and Semi-Arid Regions

The lack of water in arid and semi-arid regions has often limited agricultural production. Indeed, even where water is available for irrigation, the lack of electricity, as well as the high costs of diesel, has created constraints on small farmers. The purpose of this research is to review the renewable energy potential available in arid and semi-arid zones that can be used for irrigation as a substitute for fossil fuels. In this review, the solar thermal irrigation, solar photovoltaic (PV) irrigation, wind pumping and biomass pumping are discussed. The comparison of different hybrid pumping systems and analyses of renewable sources irrigation assessment in arid and semi-arid regions of Mozambique also are discussed. The results of this study showed that there are still certain technological limitations regarding the use of solar thermal energy for irrigation. As far as wind power is concerned, the analysis of the pumping water life cycle cost showed that the wind power water pumping system is more economical and viable compared to the diesel based system. However, the study concluded that photovoltaic solar energy has been shown to be more viable for pumping water for irrigation in arid and semi-arid regions.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

CT-NET: A Novel Convolutional Transformer-Based Network for Short-Term Solar Energy Forecasting Using Climatic Information

Photovoltaic(PV)systems are environmentally friendly,generate green energy,and receive support from policies and organizations.However,weather fluctuations make large-scale PV power integration and management challenging despite the economic benefits.Existing PV forecasting techniques(sequential and convolutional neural networks(CNN))are sensitive to environmental conditions,reducing energy distribution system performance.To handle these issues,this article proposes an efficient,weather-resilient convolutional-transformer-based network(CT-NET)for accurate and efficient PV power forecasting.The network consists of three main modules.First,the acquired PV generation data are forwarded to the pre-processing module for data refinement.Next,to carry out data encoding,a CNNbased multi-head attention(MHA)module is developed in which a single MHA is used to decode the encoded data.The encoder module is mainly composed of 1D convolutional and MHA layers,which extract local as well as contextual features,while the decoder part includes MHA and feedforward layers to generate the final prediction.Finally,the performance of the proposed network is evaluated using standard error metrics,including the mean squared error(MSE),root mean squared error(RMSE),and mean absolute percentage error(MAPE).An ablation study and comparative analysis with several competitive state-of-the-art approaches revealed a lower error rate in terms of MSE(0.0471),RMSE(0.2167),and MAPE(0.6135)over publicly available benchmark data.In addition,it is demonstrated that our proposed model is less complex,with the lowest number of parameters(0.0135 M),size(0.106 MB),and inference time(2 ms/step),suggesting that it is easy to integrate into the smart grid.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

Physics Insight of the Inertia of Power Systems and Methods to Provide Inertial Response

The growth of renewable energy reduces the moment of inertia for the synchronous AC grid,so the authors put forward two basic questions:1)What is the physics insight that a synchronous AC grid needs for mechanical inertia?2)How to provide inertial response for the power grid dominated with renewable energy?Based on Einstein’s special relativity and the Lorentz transformation,these papers illustrates that the nature of the inertia of the AC grid comes from the relativity of the electromagnetic field and motion,and from the strong coupling between them.According to their nature,the inertial response of the synchronous generator is self-proven.By contrast,the converter for the grid-connection of renewable energies used various algorithms in order to provide virtual inertia.But because algorithms do not rebuild the coupling between electromagnetic fields and motion,it is doubtful whether they can provide inertia and inertial responses.Therefore,the authors propose that there is a need to build extra electromagnetic fields and motion coupling for grids with high penetration rates of renewable energy.Therefore,a new grid-connection technology via Motor-Generator Pair(MGP)is discussed.The electromagnetic-motion coupling of the MGP is analyzed,and the results of simulation and experimental studies are also reported.