Using blockchain technology as one of the new methods to enhance the cyber and physical security of power systems has grown in importance over the past few years. Blockchain can also be used to improve social welfare and provide sustainable energy for consumers. In this article, the effect of distributed generation (DG) resources on the transmission power lines and consequently fixing its conjunction and reaching the optimal goals and policies of this issue to exploit these resources is investigated. In order to evaluate the system security level, a false data injection attack (FDIA) is launched on the information exchanged between independent system operation (ISO) and under-operating agents. The results are analyzed based on the cyber-attack, wherein the loss of network stability as well as economic losses to the operator would be the outcomes. It is demonstrated that cyber-attacks can cause the operation of distributed production resources to not be carried out correctly and the network conjunction will fall to a large extent; with the elimination of social welfare, the main goals and policies of an independent system operator as an upstream entity are not fulfilled. Besides, the contracts between independent system operators with distributed production resources are not properly closed. In order to stop malicious attacks, a secured policy architecture based on blockchain is developed to keep the security of the data exchanged between ISO and under-operating agents. The obtained results of the simulation confirm the effectiveness of using blockchain to enhance the social welfare for power system users. Besides, it is demonstrated that ISO can modify its polices and use the potential and benefits of distributed generation units to increase social welfare and reduce line density by concluding contracts in accordance with the production values given.

To achieve low‐carbon sustainable energy development, new technologies such as Internet of Energy (IoE), intelligent systems and Internet of Things (IoT) as well as distributed energy generations via smart grids (SG) are gaining attention. The interoperability between intelligent energy systems, realised through the web, enables automatic consumption optimisation and increases network efficiency and intelligent management. IoE is an intriguing topic in close connection with the IoT, communication systems, SG and electrical mobility that contributes to energy efficiency to achieve zero‐carbon technologies and green environments. Furthermore, nowadays, the widespread growth and utilisation of processors for mining digital currency in homes and small warehouses are some other factors to be considered in terms of electric energy consumption and greenhouse gas emission. However, research on the use of the Internet for evaluating the misallocation of energy and the effect it can have on CO ₂ emissions is often neglected. In this study, the authors present a detailed overview regarding the evolution of SG in conjunction with the employment of IoE systems as well as the essential components of IoE for decarbonisation. Also, mathematical models with simulation are provided to evaluate the role of IoE in reducing CO ₂ emission.

Regarding the complex behaviour of price signalling, its prediction is difficult, where an accurate forecasting can play an important role in electricity markets. In this paper, a feature selection based on mutual information is implemented for day ahead prediction of electricity prices, which are so valuable for determining the redundancy and relevancy of selected features. A combination of wavelet transform (WT) and a hybrid forecast method is presented based on a neural network (NN). Furthermore, an intelligent algorithm is considered for a prediction process to set the proposed forecast engine free parameters based NN. This optimisation process improved the accuracy of the proposed model. To demonstrate the validity of this model, the Pennsylvania-New Jersey-Maryland (PJM) electricity market is considered as a test case and compared with some of the most recent price forecast methods. These comparisons illustrate the effectiveness of the proposed strategy.

In this paper, a new feature selection and forecast engine is presented for day ahead prediction of electricity prices, which are so valuable for both producers and consumers in the new competitive electric power markets. In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, non-stationary, and time variance. Also, an appropriate feature selection is crucial for accurate forecasting. In this paper, a two-step approach that identifies a set of candidate features based on the data characteristics proposed and then selects a subset of them using correlation and instance-based feature selection methods, applied in a systematic way. Then, a combination of wavelet transform (WT) and a hybrid forecast method is presented based on neural network (NN) and an optimization algorithms. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

A common gynecological disease in the world is breast cancer that early diagnosis of this disease can be very effective in its treatment. The use of image processing methods and pattern recognition techniques in automatic breast detection from mammographic images decreases human errors and increments the rapidity of diagnosis. In this paper, mammographic images are analyzed using image processing techniques and a pipeline structure for the diagnosis of the cancerous masses. In the first stage, the quality of mammogram images and the contrast of abnormal areas in the image are improved by using image contrast improvement and a noise decline. A method based on color space is then used for image segmentation that is followed by mathematical morphology. Then, for feature image extraction, a combined gray-level cooccurrence matrix (GLCM) and discrete wavelet transform (DWT) method is used. At last, a new optimized version of convolutional neural network (CNN) and a new improved metaheuristic, called Advanced Thermal Exchange Optimizer, are used for the classification of the features. A comparison of the simulations of the proposed technique with three different techniques from the literature applied on the MIAS mammogram database is performed to show its superiority. Results show that the accuracy of diagnosing cancer cases for the proposed method and applied on the MIAS database is 93.79%, and sensitivity and specificity are obtained 96.89% and 67.7%, respectively.