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The relentlessly depleting fossil-fuel-based energy resources worldwide have forbidden an imminent energy crisis that could severely impact the general population. This dire situation calls for the immediate exploitation of renewable energy resources to redress the balance between power consumption and generation. This manuscript confers about energy management tactics to optimize the methods of power production and consumption. Furthermore, this paper also discusses the solutions to enhance the reliability of the electrical power system. In order to elucidate the enhanced reliability of the electrical system, microgrids consisting of different energy resources, load types, and optimization techniques are comprehensively analyzed to explore the significance of energy management systems (EMSs) and demand response strategies. Subsequently, this paper discusses the role of EMS for the proper consumption of electrical power considering the advent of electric vehicles (EVs) in the energy market. The main reason to integrate EVs is the growing hazards of climate change due to carbon emissions. Moreover, this paper sheds light on the growing importance of artificial intelligence (AI) in the technological realm and its incorporation into electrical systems with the notion of strengthening existing smart grid technologies and to handle the uncertainties in load management. This paper also delineates the different methodologies to effectively mitigate the probability of facing cyber-attacks and to make the smart grids invulnerable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, a multiagent clustering-based distributed approach for the optimal planning of wind-distributed generators (DGs) and switched capacitor banks (SCBs) is proposed. First, electrical distance matrices for the power systems are constructed. Additionally, a constrained optimization problem, which includes several indices and a few constraints, for the optimal clustering of distribution networks is formulated and solved. After obtaining optimal clusters, agents are assigned to the clusters, and a second multiobjective optimization problem (MOOP) for the distributed planning of wind DGs and SCBs is formulated and assigned to a head agent. The number of objective functions in the MOOP is equal to the number of agents. The objective function of an agent consists of three indices: annual energy losses, investment costs, and voltage enhancement. Moreover, a deep neural network architecture is designed, and four independent networks are trained with six years of wind speed data for the seasonal wind speed forecasting. Two IEEE unbalanced test feeders, one with 37 nodes and the other with 123 nodes, and eight test cases are considered for simulations. The results show that losses and costs are optimized, and the voltage unbalance of the system is reduced. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The progression towards smarter grids, incorporating clean energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. The DGs often cause a rise in voltage at their points of common coupling. Ordinary voltage regulation devices such as on-load tap changers (OLTCs) are not capable of addressing these issues adequately without careful coordination with DGs. In this paper, a new supervisory control and data acquisition-based two-stage voltage control scheme for the coordination of an OLTC transformer, capacitor banks (CBs) and the DGs is presented. The proposed scheme sets forth a new criterion for the selection of tap positions of an OLTC. In the first stage, tap positions of the OLTC are changed optimally using the micro genetic algorithm, whereas in the second stage, a recursive genetic algorithm is run to minimise the power losses in order to find the optimal reactive powers for the distribution network. Stochastic modelling of wind speed and solar irradiance data is also performed. The scheme is verified by studying it under four different test cases. An OLTC, CBs, photovoltaic panels, wind power DGs and dispatchable DGs are installed in the distribution network, and an IEEE 37-node test feeder is used for the verification of the proposed scheme. The simulation results show that the scheme achieves the objective of voltage regulation. [ABSTRACT FROM AUTHOR]