The Energy Internet paradigm is the evolution of the Internet of Things concept in the power system. Microgrids (MGs), as the essential element in an Energy Internet, are expected to be controlled in a corporative and flexible manner. This paper proposes a novel decentralized robust control strategy for multi-agent systems (MASs) governed MGs in future Energy Internet. The proposed controller is based on a consensus algorithm applied with the connected distributed generators (DGs) in the MGs in the energy internet paradigm. The proposed controller’s objectives are the frequency/voltage regulation and proportional reactive/active power-sharing for the hybrid DGs connected MGs. A proposed two-level communication system is implemented to explain the data exchange between the MG system and the cloud server. The local communication level utilizes the transmission control protocol (TCP)/ internet protocol (IP) and the message queuing telemetry transport (MQTT) is used as the protocol for the global communication level. The proposed control strategy has been verified using a hypothetical hybrid DGs connected MG such as photovoltaic or wind turbines in MATLAB Simulink environment. Several scenarios based on the system load types are implemented using residential buildings and small commercial outlets. The simulation results have verified the feasibility and effectiveness of the introduced strategy for the MGs’ various operating conditions.

Microgrids (MGs) suffer from unpredictable faults on the feeders for various random reasons. These faults could obstruct the stability of the MG operation and damage the components. Moreover, many uncertainties elements affect the MG’s response to faults, such as faults’ types and locations and resistances, MG operation modes, DG penetration levels, load variations, and system topologies. Therefore, fault detection, classification, and location are vital for the MGs as they provide rapid restoration and protect the components. This paper proposes an adaptive protection (AP) scheme for the future renewable electric energy delivery and management (FREEDM) system. The proposed scheme is based on the convolution neural network (CNN), in which the measured current and voltage at buses are processed in multidimensional arrays for the images’ identification and classification. The gorilla troops optimization (GTO) technique has been used to improve the CNN by acquiring the optimal architecture and hyperparameters of the proposed CNN. The proposed protection scheme can detect the system fault, classify the fault type, and determine the fault location using three proposed CNN-GTO protection scheme models. A communication channel has been performed to transfer the data, information, and tripping signals between the different devices in the FREEDM system. The proposed method is tested using a hypothetical FREEDM microgrid system under different fault conditions. The results show that the proposed CNN-GTO models can detect, classify, and location feeder faults in the FREEDM system with high accuracy. A comparison with the existing schemes such as Support vector machine, Fuzzy logic, conventional CNN, and wavelet-based CNN is performed. The optimized CNN-based GTO models can achieve an overall accuracy for fault detection, classification, and location of 99.37, 99, and 98.2%, respectively.

The electrical demand and generation in power systems is currently the biggest source of uncertainty for an electricity provider. For a dependable and financially advantageous electricity system, demand response (DR) success as a result of household appliance energy management has attracted significant attention. Due to fluctuating electricity rates and usage trends, determining the best schedule for apartment appliances can be difficult. As a result of this context, the Improved Cockroach Swarm Optimization Algorithm (ICSOA) is combined with the Innovative Apartments Appliance Scheduling (IAAS) framework. Using the proposed technique, the cost of electricity reduction, user comfort maximization, and peak-to-average ratio reduction are analyzed for apartment appliances. The proposed framework is evaluated by comparing it with BFOA and W/O scheduling cases. In comparison to the W/O scheduling case, the BFOA method lowered energy costs by 17.75%, but the ICSA approach reduced energy cost by 46.085%. According to the results, the created ICSA algorithm performed better than the BFOA and W/O scheduling situations in terms of the stated objectives and was advantageous to both utilities and consumers.