The tremendous advances in information and communications technology (ICT), as well as the embedded systems, have been led to the emergence of the novel concept of the internet of things (IoT). Enjoying IoT-based technologies, many objects and components can be connected to each other through the internet or other modern communicational platforms. Embedded systems which are computing machines for special purposes like those utilized in high-tech devices, smart buildings, aircraft, and vehicles including advanced controllers, sensors, and meters with the ability of information exchange using IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer networks. In this study, the application of IoT in smart grids is addressed. Hence, at first, an introduction to the necessity of deployment of IoT in smart grids is presented. Afterwards, the applications of IoT in three levels of generation, transmission, and distribution is proposed. The generation level is composed of applications of IoT in renewable energy resources, wind and solar in particular, thermal generation, and energy storage facilities. The deployment of IoT in transmission level deals with congestion management in power system and guarantees the security of the system. In the distribution level, the implications of IoT in active distribution networks, smart cities, microgrids, smart buildings, and industrial sector are evaluated.

Utilizing plug-in hybrid electric vehicles (PHEVs) is growing fast and booming nowadays. These vehicles, as portable loads and energy sources, may be connected to standard sockets at home. As a result, those extra electrical loads, grid to vehicle, generations, and vehicle to grid, have several impacts on distribution networks, e.g., network energy loss. This paper presents a two-stage optimization method to minimize the energy loss of microgrid with different penetration levels of PHEVs. In the first stage, a novel convex quadratic objective function for active power management of PHEVs is proposed, and daily required energy of PHEVs is calculated based on stochastic model of PHEV owners' behavior. It is supposed that PHEVs can be employed as distributed capacitors. Therefore, reactive power of PHEVs is specified in the second stage. Afterward, the proposed methodology is applied to a realistic distribution network. It will be illustrated that network energy loss is likely to rise considerably in the case of increasing penetration level of PHEVs without smart charging strategy; in order to minimize network energy loss, a smart management scheme will have to be considered. Also, the significant impact of PHEVs' active and reactive power management on energy loss reduction will be demonstrated.

The accelerated trend of development in the areas of information and communications technology (ICT), as well as energy systems, has been led to emersion of a new concept called the internet of energy (IoE). This concept is classified as a subsector of the internet of things (IoT), which means the utilization of advanced digital controllers, sensors, actuators, and meters with the ability of information exchange through IT infrastructures. The phrase "internet", in this context, does not exclusively refer to the World Wide Web rather than any type of server-based or peer-to-peer network. These communicational tools are able to receive information for local analyze and control, receive analyzed commands, send raw data, or send instructions. The incorporation of IoT in power systems conveys a brilliant future for overcoming power system operation hurdles and environmental challenges, although it incurs a large capital investment portfolio initially. However, it is hoped that the evolution of current technologies and the advent of new technologies with economically affordable prices can help to increase the pervasiveness of these state-of-the-art technologies. IoE covers the entire energy scope such as thermal or electrical energies. However, in this study, the applications of IoE in power systems, as the central core of the energy ecosystem, will be addressed. At first, the basic terminology of this topic such as IoE, IoT, plug & play capability, smart energy, future networks, smart grid 2.0, energy 4.0, prosumers, AMI, API, digitalization, and grid edge are described to make the rest of paper more intelligible. Then the role of IoE in supply-side and demand-side of power systems encompassing renewable generation section, large-scale energy storage section, thermal power plant section, system operation and protection as well as execution of demand response programs (DRPs), microgrids, integration of plug-in electric vehicles with V2G capability, and end-user residential consumption management in smart buildings are explained. In this study, it is tried to outline a different classification of IoE in power systems which has not been drawn by previous similar works.

Low frequency oscillations (LFOs) occur in a system of interconnected generators connected by weak interconnection. A power system stabilizer (PSS) is commonly used to improve the capacity of the power system dampening. Under a variety of operating conditions, traditional PSSs fail to deliver superior damping. To address this issue, a Farmland Fertility Algorithm (FFA-PSSs controller) was used to solve an optimization problem for optimal design of PSSs system parameters, and its performance efficiency was compared to GA and PSO-based PSSs controllers. In addition to PSS, flexible current transmission (FACTS) devices are widely used. PSSs controllers and FACTS devices are frequently constructed in tandem to improve the dampening efficiency of the system. In this study, an Interline Power Flow Controller (IPFC) FACTS device will be added to the PSSs controller to improve the power system’s oscillatory stability. PSSs optimal design and supplemental controller of power fluctuations for IPFC were conducted out on WSCC multi-machine test systems using a system linear model. Using time-domain simulations and quantitative analysis, the proposed IPFC model was compared to the FFA-PSSs controller in terms of performance and efficiency. The main disadvantage of this technique is the difficulty in designing a dynamic IPFC model in test systems, as well as the burden of IPFC coordinated PSSs optimization. In both PSSs design using FFA method and FFA-optimized PSS with IPFC cases, rise in the computational and simulation costs was found unavoidable. To compensate for these flaws and obtain the research contribution, this paper proposes a Neuro-Fuzzy Controller (NFC) developed as a damping controller that can take the place of the two controllers (research objectives three). The application of the NFC substitute the computational and simulation cost involved in designing multi-machine PSS and IPFC-FACTS systems simultaneously. With the availability of NFC in SIMULINK, a dynamic model of the WSCC three-machine system was developed under a variety of operating situations. Quantitative analysis results from the WSCC test system simulation show that when comparing the proposed NFC model to the IPFC model for the WSCC test system, the proposed NFC model was found to be 149 percent and 0 percent efficient in terms of the time to settle of rotor angle respond for G2 and G3, respectively, but 394 percent efficient when compared to the uncontrolled model. The decreased settling time values ensured the proposed NFC model’s efficacy in damping down the LFO and achieving superior stability over the two controllers. The proposed NFC model has shown significant performance improvement in both the transient and steady-state areas than when the system was design with the two damping controllers.