Islanding detection in power systems poses a challenge that can result in technical problems and safety hazards. This study examines a new passive islanding detection method for photovoltaic‐based distributed generation (DG) units. The modal signal of the three‐phase current and its envelope are calculated for a targeted DG unit. Variance in the autocorrelation of the modal current envelope (VAMCE) is used as an islanding detection criterion in the proposed method. The proposed method employs autocorrelation function (ACF) of modal current envelope (derived by Hilbert‐transform‐based approach) to extract the transient content. Then, the variance of the ACF is calculated, called criterion VAMCE to distinguish between islanded and normal operating conditions. The main advantage of the proposed technique is more relevant to its more accuracy and smaller non‐detection zone in comparison with the other similar passive techniques. Since the VAMCE has significant variation during islanding conditions and negligible change in normal conditions, it can be used for detection of islanding condition effectively. The method was evaluated through several simulations under different islanded and normal operating conditions. The results confirm that the proposed method has better accuracy and speed compared to other similar methods.

The modern electric power system is foreseen to have increased penetration of controllable loads under demand response programs and renewable energy resources coupled with energy storage systems which can provide virtual inertia. In this paper, the conventional model of an electric power system is appended by considering, individually and collaboratively, the role of demand response and virtual inertia for the purpose of frequency analysis and control. Most existing literature on this topic either considers one of these two roles or lacks in providing a general model of power system with demand response and virtual inertia. The proposed model is presented in general form and can include/exclude demand response and/or virtual inertia. Further, power system operator can opt the power shares from conventional, demand response, and virtual inertia loops for frequency regulation and can also evaluate the impact of other parameters such as time delays and frequency deadbands on system frequency response. The mathematical formulation of steady-state values of frequency deviation and power contribution from all resources is provided and validated by simulation results under various scenarios including a case of wind intermittency.

Compelled by environmental and economic reasons and facilitated by modern technological advancements, the share of hybrid energy systems (HES) is increasing at modern smart house (SH) level. This work proposes an intelligent hybrid energy management system (IHEMS) for an SH connected to a power network that allows a bidirectional power flow. The SH has electrical and thermal power loops, and its main components include renewable energy from wind and photovoltaics, electric vehicle (EV), battery energy storage system, a fuel cell which serves as a micro-combined heat and power system, and a boiler. The proposed IHEMS models the components of the SH, defines their constraints, and develops an optimization model based on the real coded genetic algorithm. The key features of the developed IHEMS are highlighted under six simulation cases considering different configurations of the SH components. Moreover, the standard EV charging techniques are compared, and it is observed that the charging method which is flexible in timing and power injection to the EV is best suited for the economic operation of the SH. The simulation results reveal that the proposed IHEMS minimizes the 24-hour operational cost of the SH by optimally scheduling the energy resources and loads.

The present environmental and economic conditions call for the increased use of hybrid energy resources and, concurrently, recent developments in combined heat and power (CHP) systems enable their use at a domestic level. In this work, the optimal scheduling of electric and gas energy resources is achieved for a smart home (SH) which is equipped with a fuel cell-based micro-CHP system. The SH energy system has thermal and electrical loops that contain an auxiliary boiler, a battery energy storage system, and an electrical vehicle besides other typical loads. The optimal operational cost of the SH is achieved using the real coded genetic algorithm (RCGA) under various scenarios of utility tariff and availability of hybrid energy resources. The results compare different scenarios and point-out the conditions for economic operation of micro-CHP and hybrid energy systems for an SH.