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.

In this paper, an adaptive multiobjective fractional-order fuzzy proportional-integral-derivative controller is proposed for the load frequency control (LFC) of islanded microgrids (MGs), while benefiting from the assets of electric vehicles (EVs) in this respect. Although the use of battery energy storage systems (BESS) can solve the unbalance effects between the load and supply of an isolated MG, their high cost and tendency toward degradation are restrictive factors, which call for the use of alternative power balancing options. In recent years, the concept of utilizing the BESSs of EVs, also known as vehicle-to-grid (V2G) concept, for frequency support of MGs has attracted a lot of attention. In order to allow the V2G controller operate optimally under a wide range of operation conditions caused by the intermittent behavior of renewable energy resources, a new multiobjective fractional-order control strategy for the EVs in V2G scenarios is proposed in this paper. Moreover, since the performance of the controller depends on its parameters, optimization of these parameters can play a significant role in promoting the output performance of the LFC control; hence, a modified black hole optimization algorithm is utilized for the adaptive tuning of the noninteger fuzzy PID controller coefficients. The performance of the proposed LFC is evaluated by using real world wind and solar radiation data. Finally, the extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller tracks frequency with lower deviation and fluctuation and is more robust in comparison with the prior-art controllers used in all the case studies.

Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine for training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method.

Due to the simultaneous development of DC-microgrids (DC-MGs) and the use of intelligent control, monitoring and operation methods, as well as their structure, these networks can be threatened by various cyber-attacks. Overall, a typical smart DC-MG includes battery, supercapacitors and power electronic devices, fuel cell, solar Photovoltaic (PV) systems, and loads such as smart homes, plug-in hybrid electrical vehicle (PHEV), smart sensors and network communication like fiber cable or wireless to send and receive data. Given these issues, cyber-attack detection and securing data exchanged in smart DC-MGs like CPS has been considered by experts as a significant subject in recent years. In this study, in order to detect false data injection attacks (FDIAs) in a MG system, Hilbert-Huang transform methodology along with blockchain-based ledger technology is used for enhancing the security in the smart DC-MGs with analyzing the voltage and current signals in smart sensors and controllers by extracting the signal details. Results of simulation on the different cases are considered with the objective of verifying the efficacy of the proposed model. The results offer that the suggested model can provide a more precise and robust detection mechanism against FDIA and improve the security of data exchanging in a smart DC-MG.

In restructured markets where transactions process is competitive, forecasting of electricity price is inevitably an important available tool for market participants. Due to the sensitivity of forecasting issues in market's performance, and high prediction error resulted from the behavior of price series, nowadays probabilistic forecasting highly attracted participants' attention. In this paper, a probabilistic approach for the hourly electricity price forecasting is presented. In the proposed method, the uncertainty of predictor model is considered as the uncertainty factor. The bootstrapping technique is used to implement the uncertainty and since the method is needed to be fast and of low computational cost in the daily forecasting, a generalized learning method is applied, which has high accuracy and speed. This newly presented learning method is based on generalized extreme learning machine approach to be used for improved wavelet neural networks. Also in order to reach more accommodation, the predictor model with the changes of price time series, the wavelet preprocessing is used. Effective performance of the proposed model is validated by testing on data of Ontario and Australian electricity markets.