Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.

Distributed optimization methods have been vastly investigated and approved by the researchers due to their major advantages including high accuracy, secured performance and low time-consuming structure compared to the centralized frameworks. This paper aims to provide a novel method based on fuzzy primal-dual method of multipliers (PDMM) to manage the optimal energy scheduling problem in the smart grids. The proposed method illustrates some unrivaled points of interest which are more preferable than the conventional alternating direction method of multipliers (ADMM) in terms of preciseness and convergence speed. The proposed smart grid is constructed of different components such as generators, wind park and storage devices as two of the most profitable and applicable energy sources in the power grids. In order to model the uncertainty effects, a stochastic method based on fuzzy cloud theory is developed to capture the high-dimension uncertainty in a more realistic way. The units are scheduled to exchange energy in the smart grid in a fully distributed manner when meeting the active/reactive generation and demand balance. Such an energy exchanging process continues until a proper solution would be found through which all the agents in the system are satiated. The simulation results on the IEEE 24-bus test system indicate that the proposed stochastic distributed energy management framework yields an error of less than 0.018% compared to the centralized approach.

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods.

The target of the current study was to review and analyze the research activities of previous studies on cooling techniques for thermal photovoltaic (PV) systems using phase-change materials. These materials have the ability to absorb and release certain amounts of potential heat energy by changing their state from phase to phase (solid–liquid) within a small temperature range. These materials have been used to regulate and lower the temperature, increase the efficiency, and extend the life of solar cells. A host of improvements have been made to phase-changing materials through the combined utilization of phase-change materials and fins in addition to nanoscale fluids to enhance electrical efficiency. When using PCMs, the thermal, electrical, and overall efficiency improved by 26.87%, 17.33%, and 40.59%, respectively. The addition of nanomaterials increased phase-change materials’ specific heat capacity and thermal conductivity, thus reducing the plate temperature and increasing the electrical efficiency. It was found that using of nanoparticles together with a microcapsule had better performance in terms of energy efficiency. Studies indicated that variable phase materials were not used because of their high cost and lack of stable operational design. Therefore, the effect of phase-change materials on PV/thermal (PVT) system performance needs further investigation and study.

The development opportunities and high-performance capacity of offshore wind energy project depends on the selection of the suitable offshore wind power station (OWPS) location. The present study aims to introduce a decision-making model for assessing the locations for OWPS from multiple criteria and uncertainty perspectives. In this regard, the concept of interval-valued intuitionistic fuzzy set (IVIFS) is utilized to express uncertain information. To quantify the degree of difference between IVIFSs, an improved distance measure is proposed and further utilized for deriving the objective weights of criteria. Numerical examples are discussed to illustrate the usefulness of introduced IVIF-distance measure. The RANking COMparison (RANCOM) based on interval-valued intuitionistic fuzzy information is presented to determine the subjective weights of criteria. With the combination of objective and subjective weights of criteria, an integrated weighting tool is presented to find the numeric weights of criteria under IVIFS environment. Further, a hybrid interval-valued intuitionistic fuzzy Weighted integrated Sum Product (WISP) approach is developed to prioritize the OWPS locations from multiple criteria and uncertainty perspectives. This approach combines the benefits of two normalization tools and four utility measures, which approves the effect of beneficial and non-beneficial criteria by means of weighted sum and weighted product measures. Further, the developed approach is applied to the OWPS location selection problem of Gujarat, India. Sensitivity and comparative analyses are presented to confirm the robustness and stability of the present WISP approach. This study provides an innovative decision analysis framework, which makes a significant contribution to the OWPS locations assessment problem under uncertain environment.