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Every year, each solar panel suffers an efficiency loss of 0.5% to 1%. This degradation of solar panels arises due to environmental and electrical faults. A timely and accurate diagnosis of environmental faults reduces the damage caused by faults on the panel. In recent years, deep learning precisely convolutional neural networks have achieved wonderful results in many applications. This work is focused on finely tuning pretrained models of convolutional neural networks, especially AlexNet, GoogleNet, and SqueezeNet. Based on the performance metrics, SqueezeNet is used for training thermal images of solar panels and for the classification of environmental faults. The results obtained show that SqueezeNet has a significant testing accuracy of 99.74% and F1 score of 0.9818, which make the model successful in identifying environmental faults in solar panels and help users to protect the panels.

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In this manuscript, the energy trading model is proposed for investigating the cooperative benefits between several electric vehicle charging stations (EVCSs) and integrated energy systems (IES) based on hybrid system. The proposed hybrid system is combination of Radial‐Basis Function Neural Network (RBFNN) and artificial transgender longicorn algorithm (ATLA), hence it is called RBFNN‐ATLA technique. Initially, the RBFNN approach manages the energy between IES and EVCSs. After that, the original issue breaks down into the main energy trade and payment negotiation issue. The energy trading issue and payment negotiation problem can be solved using ATLA approach. This proposed structure may not only diminish the IES cost, however also enlarge the EVCS profit. The uncertainties in electricity and renewable energy prices are modeled using a robust optimization technique. Additionally, the integrated demand response is modeled for maximizing operational performance. The distributed algorithm depends on the proposed technique is evolved for solving the issue of energy trading, ensuring the privacy of players. The proposed algorithm may obtain the global optimal solutions devoid of adjusting the parameter compared with existing algorithm. The proposed system is performed by the matrix laboratory (MATLAB)/Simulink and the performance is evaluated with other existing methods. The RBFNN‐ATLA method reduces the cost up to 3.76%, 7.793%, 8.210% and 9.01% for independent and cooperative mode. The experimental outcomes demonstrate that the proposed system can accurately detect that optimal global solution.

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Demand response (DR) is a high priority smart grid technology yet, efficient implementation of the same at the circuit level is often overlooked. Technologies like DR is vital to the improvement, stability and reduction of congestion in the grid. At the distribution side, addition of photovoltaic (PV) systems with appropriate metering has made consumers to prosumers. Prosumers contribute to the grid supply and often meeting increased demand. PV installation has made surplus grid power available. Power from PV is an environment friendly approach, in-order to yield its maximum benefit, it should be appropriately connected with advanced metering infrastructure (AMI). Further, AMI enables other technologies like real time pricing (RTP), DR and demand side management (DSM). Pilot projects put forward by Government of India (GOI) has already deployed smart meters, transforming existing conventional meters into AMI. Hence, functional benefits of AMI are to be studied to its full usage potential. DR and RTP are more focused on consumer behaviour and involvement whereas, DSM is under the control of utility. Hence, novel layouts for energy efficient prosumers with net-metering, gross-metering, hybrid loads and renewable PV integration are analysed. Consumer feasibility of DR without compromising, basic needs of power availability and comfort is focused. Inference is made from both metering infrastructures, tariff schemes and its application in Puducherry locality of India.

A hybrid renewable energy system, which could provide a reliable energy alternative for conventional Battery systems is implemented in this paper. The primary requirement is that the hybrid energy system should be cost-effective while meeting the energy demand. The hybrid system is implemented to a area located in Uttarakhand, India using solar photovoltaic cells to supply power during hot and humid conditions and using wind turbine generators to supply power during windy conditions. The wind turbine generators and photovoltaic cells are used in a combined manner along with the diesel generators as in case if it fails to meet the demand. In order to meet the requirements, modified Differential Evolution (DE) Algorithm is being implemented. Moreover, the effectiveness of the performance is evaluated by comparing the results obtained from modified DE with other optimization algorithms. In comparison with other optimization algorithms, results indicate that the implementation of the modified DE algorithm helps in obtaining the best cost effective solution for the system along with meeting the energy demand.