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Electric vehicles (EVs) are universally recognized as an incredibly effective method of lowering gas emissions and dependence on oil for transportation. Electricity, rather than more traditional fuels like gasoline or diesel, is used as the main source of energy to recharge the batteries in EVs. Future oil demand should decline as a result of the predicted rise in the number of EVs on the road. The charging infrastructure is considered as a key element of EV technology where the recent research is mostly focused. A strong charging infrastructure that serves both urban and rural areas, especially those with an unstable or nonexistent electrical supply, is essential in promoting the global adoption of EVs. Followed by different EV structures such as fuel-cell- and battery-integrated EVs, the charging infrastructures are thoroughly reviewed in three modes, specifically—off-grid (standalone), grid-connected, and hybrid modes (capable of both standalone and grid-connected operations). It will be interesting for the readers to understand in detail several energy-source-based charging systems and the usage of charging stations for different power levels. Towards the improvement of the lifetime and efficiency of EVs, charging methods and charging stations in integration with microgrid architectures are thoroughly investigated. EVs are a multi-energy system, which requires effective power management and control to optimize energy utilization. This review article also includes an evaluation of several power management and control strategies followed by the impact assessment of EVs on the utility grid. The findings and the future research directions provided in this review article will be extremely beneficial for EV operators and research engineers.

This paper introduces a hybrid fuzzy logic control-based proportional-integral (FLC-PI) control strategy designed to enhance voltage stability, power quality, and overall performance of central inverters in photovoltaic power plants (PVPPs). The study is based on a real-world PVPP with an installed capacity of 26.136 MWp, connected to the Egyptian national grid at Fares City, Kom Ombo Centre, Aswan Governorate. A user-friendly MATLAB/SIMULINK environment is developed, incorporating eleven distinct blocks along with a modelled national utility grid, utilizing actual operational data from the PVPP. To optimize the FLC-PI control scheme, several artificial intelligence (AI)-based metaheuristic optimization techniques (MOTs) are employed to simultaneously tune all control parameters—namely Grey Wolf Optimization (GWO), Harris Hawks Optimization (HHO), and the Arithmetic Optimization Algorithm (AOA)—are employed. These techniques are used to simultaneously fine-tune all the gain parameters of FLC-PI control, based on four standard error-based objective functions: Integral Absolute Error (IAE), Integral Square Error (ISE), Integral Time Absolute Error (ITAE), and Integral Time Square Error (ITSE). The optimized gains are applied to both voltage and current regulators of the central inverters, enabling the identification of optimal values. Among the tested methods, the HHO algorithm combined with the ISE objective function delivered the best performance, achieving a total harmonic distortion (THD) of 3.88%—well below the IEEE 519–2014 limit of 5.00%. The results confirm that the proposed FLC-PI controller significantly enhances the integration of high-penetration PVPPs into the utility grid by reducing power losses and inverter-induced harmonics, especially during maximum power point tracking (MPPT). Moreover, employing MOTs for controller tuning proves to be an effective solution for adapting to dynamic solar irradiance conditions. Ultimately, the optimized FLC-PI control approach enhances voltage stability, improves power quality, and boosts the overall efficiency of grid-connected PV systems.

The partially shaded photovoltaic (PSPV) condition reduces the generated power and contributes to hot spot problems that may lead to breakdown of shaded modules. PSPV generates multiple peak, one global one and many other local peaks. Many efficient, accurate and reliable maximum power point tracker (MPPT) techniques are used to track the global peak instead of local peaks. The proposed technique is not limited to global peak tracking, but rather it is capable of tracking the sum of all peaks of the PV arrays using an interleaved boost converter (IBC). The proposed converter has been compared with the state of the art conventional control method that uses a conventional boost converter (CBC). The converters used in the two PSPV systems are interfaced with electric utility using a three-phase inverter. The simulation findings prove superiority of the PSPV with IBC compared to the one using CBC in terms of power quality, reliability, mismatch power loss, DC-link voltage stability, efficiency and flexibility. Also, IBC alleviates partial shading effects and extracts higher power compared to the one using CBC. The results have shown a remarkable increase in output generated power of a PSPV system for the three presented scenarios of partial shading by 61.6%, 30.3% and 13%, respectively, when CBC is replaced by IBC.

As a decentralized energy management paradigm, micro-energy grid (MEG) clusters enable synergistic operation of heterogeneous distributed energy assets, particularly through multi-energy vector coupling mechanisms that enhance distributed energy resource (DER) utilization efficiency in next-generation power networks. While individual MEGs demonstrate limited capability in responding to upper-grid demands using surplus energy after fulfilling local supply/demand balance, coordinated cluster operation significantly enhances system-wide flexibility. This paper proposes a two-stage robust optimization model that systematically addresses both the synergistic complementarity of multi-MEG systems and renewable energy uncertainty. First, the basic operation structure of MEG, including distributed generation, cogeneration units, and other devices, is established, and the operation mode of the MEG cluster responding to host utility grid flexibly is proposed. Then, aiming to reduce operation expenses, an optimal self-scheduling plan is generated by establishing a MEG scheduling optimization model; on this basis, the flexibility response capability of the MEG is measured. Finally, to tackle the uncertainty issue of wind and photovoltaic power generation, the two-stage robust theory is employed, and the scheduling optimization model of MEG cluster flexibility response to the host utility grid is constructed. A southern MEG cluster is chosen for simulation to test the model and method’s effectiveness. Results indicate that the MEG cluster’s flexible response mechanism can utilize individual MEGs’ excess power generation to meet the host utility grid’s dispatching needs, thereby significantly lowering the host utility grid’s dispatching costs.

Penetration level of renewable energy (RE) in the utility grid is continuously increasing to minimize the environmental concerns, risk of energy security, and depletion of fossil fuels. The uncertain nature and availability of RE power for a short duration have created problems related to the protection, grid security, power reliability, and power quality. Further, integration of RE sources near the load centers has also pronounced the protection issues, such as false tripping, delayed tripping, etc. Hence, this paper introduces a hybrid grid protection scheme (HGPS) for the protection of the grid with RE integration. This combines the merits of the Stockwell Transform, Hilbert Transform, and Alienation Coefficient to improve performance of the protection scheme. The Stockwell Transform-based Median and Summation Index (SMSI) utilizing current signals, Hilbert Transform-based derivative index (HDI) utilizing voltage signals, and Alienation Coefficient index (ACI) utilizing voltage signals were used to compute a proposed Stockwell Transform-, Hilbert Transform-, and Alienation-based fault index (SAHFI). This SAHFI was used to recognize the fault conditions. The fault conditions were categorized using the number of faulty phases and the proposed Stockwell Transform and Hilbert Transform-based ground fault index (SHGFI) utilizing zero sequence currents. The fault conditions, such as phase and ground (PGF), any two phases (TPF), any two phases and ground (TPGF), all three phases (ATPF), and all three phases and ground (ATPGF), were recognized effectively, using the proposed SAHFI. The proposed method has the following merits: performance is least affected by the noise, it is effective in recognizing fault conditions in minimum time, and it is also effective in recognizing the fault conditions in different scenarios of the grid. Performance of the proposed approach was found to be superior compared to the discrete wavelet transform (DWT)-based method reported in the literature. The study was performed using the hybrid grid test system realized by integrating wind and solar photovoltaic (PV) plants to the IEEE-13 nodes network in MATLAB software.

The recent few years have seen renewable energy becoming immensely popular. Renewable energy generation capacity has risen in both standalone and grid-connected systems. The chief reason is the ability to produce clean energy, which is both environmentally friendly and cost effective. This paper presents a new control algorithm along with a flexible energy management system to minimize the cost of operating a hybrid microgrid. The microgrid comprises fuel cells, photovoltaic cells, super capacitors, and other energy storage systems. There are three stages in the control system: an energy management system, supervisory control, and local control. The energy management system allows the control system to create an optimal day-ahead power flow schedule between the hybrid microgrid components, loads, batteries, and the electrical grid by using inputs from economic analysis. The discrepancy between the scheduled power and the real power delivered by the hybrid microgrid is adjusted for by the supervisory control stage. Additionally, this paper provides a design for the local control system to manage local power, DC voltage, and current in the hybrid microgrid. The operation strategy of energy storage systems is proposed to solve the power changes from photovoltaics and houses load fluctuations locally, instead of reflecting those disturbances to the utility grid. Furthermore, the energy storage systems energy management scheme will help to achieve the peak reduction of the houses’ daily electrical load demand. Also, the control of the studied hybrid microgrid is designed as a method to improve hybrid microgrid resilience and incorporate renewable power generation and storage into the grid. The simulation results verified the effectiveness and feasibility of the introduced strategy and the capability of proposed controller for a hybrid microgrid operating in different modes. The results showed that (1) energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption, and (2) the newly developed energy management system proved to provide more robust and high performance control than conventional energy management systems. Also, the results demonstrate the effectiveness of the proposed robust model for microgrid energy management.

There will be a dearth of electrical energy in the world in the future due to exponential increase in electrical energy demand of rapidly growing world population. With the development of Internet of Things (IoT), more smart appliances will be integrated into homes in smart cities that actively participate in the electricity market by demand response programs to efficiently manage energy in order to meet this increasing energy demand. Thus, with this incitement, the energy management strategy using a price-based demand response program is developed for IoT-enabled residential buildings. We propose a new EMS for smart homes for IoT-enabled residential building smart devices by scheduling to minimize cost of electricity, alleviate peak-to-average ratio, correct power factor, automatic protective appliances, and maximize user comfort. In this method, every home appliance is interfaced with an IoT entity (a data acquisition module) with a specific IP address, which results in a wide wireless system of devices. There are two components of the proposed system: software and hardware. The hardware is composed of a base station unit (BSU) and many terminal units (TUs). The software comprises Wi-Fi network programming as well as system protocol. In this study, a message queue telemetry transportation (MQTT) broker was installed on the boards of BSU and TU. In this paper, we present a low-cost platform for the monitoring and helping decision making about different areas in a neighboring community for efficient management and maintenance, using information and communication technologies. The findings of the experiments demonstrated the feasibility and viability of the proposed method for energy management in various modes. The proposed method increases effective energy utilization, which in turn increases the sustainability of IoT-enabled homes in smart cities. The proposed strategy automatically responds to power factor correction, to protective home appliances, and to price-based demand response programs to combat the major problem of the demand response programs, which is the limitation of consumer’s knowledge to respond upon receiving demand response signals. The schedule controller proposed in this paper achieved an energy saving of 6.347 kWh real power per day, this paper achieved saving 7.282 kWh apparent power per day, and the proposed algorithm in our paper saved $2.3228388 per day.

The smart grid technology permits the revolution of the electrical system from a conventional power grid to an intelligent power network which has led the improvements in electrical system in terms of energy efficiency and sustainable energy integration. This study presents the energy management/coordination scheme for domestic demand using the key strategy of smart grid energy efficiency modelling. The structure consists of combining renewable energy resources, photovoltaic (PV) and wind power generation connected to the utility grid with energy storage system (ESS) in an optimal control manner to coordinate the power flow of a residential home. Based on the demand response schemes in the framework of real-time electricity pricing, this work designs a closed-loop optimal control strategy that is created by the dynamic model of the ESS to compute the system performance index, which is formulated by the cost of the energy flows. A dynamic distributed energy storage strategy (DDESS) is implemented to optimally coordinate the energy system, which reduces the total energy consumption from the main grid of more than 100% of the load demand. The designed model introduces a payback scheme while robustly optimising the energy flows and minimising the utility grid's energy consumption cost.

Due to their small size, minimum cost, and great efficiency, photovoltaic (PV) grid-connected transformerless inverters have been developed and become famous around the world in distributed PV generators systems. One of the most efficient topologies of the transformerless inverter family is H5 topology. This inverter extracts a discontinuous current from the PV panel, which conflicts with the operation at maximum power point tracking (MPPT) conditions while the utilization factor of the PV degrades. This paper proposes improved H5 topology featuring a boost converter inserted in the middle between the PV panels and the H5 inverter. The design of the boost converter is planned to operate at continuous conduction mode to guarantee MPPT conditions of the PV. A new and simple off line MPPT algorithm is introduced and performance factors like efficiency and utilization factors of the proposed and convention H5 topology are compared. The simulation results indicate that the proposed system provides a preferable utilization factor and a simpler MPPT algorithm.

This paper introduced a method using hybrid combination of network restructuring and optimal placement of optimally sized distributed generators (DG) to reduce loss and improve voltage profile in a practical transmission network for scenario of high load demand for a period of ten years. A study is performed for four study cases which includes the test transmission network without considering optimal DG placement and network restructuring, considering network restructuring, optimal placement of DG units using proposed grid parameter oriented harmony search algorithm (GPOHSA) and considering hybrid combination of network restructuring and DG placement using GPOHSA. Network restructuring is achieved by addition of a new 400 kV Grid-substation (GSS) and a 220 kV GSS along with associated transmission system. GPOHSA is obtained by a modification in the conventional harmony search algorithm (HSA) where grid coordinates are used for locating the individuals in an objective space. Performance Improvement Indicators such as real power loss reduction indicator (SPLRI), reactive power loss reduction indicator (SQLRI) and summation of node voltage deviation reduction indicator (SNVDRI) are proposed to evaluate performance of each case of study. The period of investment return is assessed to evaluate the pay back period of the investments incurred in network restructuring and DG units. It is established that hybrid combination of network restructuring and DG units placement using GPOHSA is effective to meet the increased load demand for time period of ten years with reduced losses and improved voltage profile. Investment incurred on the network restructuring and DG units placement will be recovered in a time period of 4 years. Effectiveness of the GPOHSA is better relative to the conventional genetic algorithm (GA) for DG unit placement. The study is performed using the MATLAB software on a practical transmission network in India.