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Microgrids (MGs) are small scale energy unit networks that can offer an adequate energy supply to cover local demand by incorporating renewable energy and storage technologies. The system capacity is generally between several kW to several MW. They work in terms of low voltage (LV) level or medium voltage (MV) level. They can also be connected/disconnected from main grid whenever it is necessary. This paper presents a comparison of two soft computing (SC) techniques fuzzy logic (FL)/artificial neural network (ANN) over a conventional proportional integral (PI)-based voltage frequency controllers used for improving the performance of MG under islanding mode. Microgrid is formed by using three 7.5kW, four pole, 50Hz, self-excited induction generators (SEIGs) driven by small hydro turbine feeding three-phase four-wire consumer load. The proposed topology functions excellently in maintaining phase angle, voltage and frequency (VF) regulation of the micro sources (MSs) in islanded mode as well as in resynchronization when one of the MSs is turned off due to fault or unavailability of resources. The conventional PI controller is replaced by a controller based on SC techniques, as it has disadvantages like explicit description of mathematical model, affected by variations in consumer loads and sources, thus the proposed SC techniques enhance the performance of VF controller. A comparative analysis of PI/FL/ANN controller is also carried out to highlight the superiority of AI controller. The performance of controller with proposed configuration is verified for balanced/unbalanced non-linear load. Microgrid and control schemes are simulated in MATLAB Sim Power Systems environment.

In this paper, the proposed hierarchical control scheme adds new control loop to control the reactive power reference by a fuzzy logic controller to have the benefit of increasing the system stability margins and moreover, eigenvalue, robustness and time delay analysis of proposed control scheme are presented. The reported droop-based control methods of VSI-based microgrids including hierarchical droop-based control scheme are limited to primary and secondary control levels while the proposed control scheme is completely analyzed so that the three hierarchical control levels modeled for both grid-connected mode and islanded mode. This scheme maintains the stability of microgrids not only for the small-signal events, but also for large-signal disturbances such as three phase and single phase to ground faults, heavy motor starting, etc. However, power sharing to loads and network is sufficiently done. To demonstrate the effectiveness of the proposed hierarchical controller, simulation studies have been performed on a microgrid consisting of four units of distributed generation with local loads and in presence of main grid using MATLAB/SIMULINK software and validated using OPAL RT real-time digital simulator.

The principal aim of Sustainable Development Goals (SDGs) or Global Goals is to attain a much better and more sustainable future for all. In recent times, microgrids have attracted much attention, given the transformation of software systems and raising the bar for customers in terms of sustainability, reliability and cost predictability. Consequently, there is a need for microgrid development in remote areas. Our paper proposes a hybrid renewable energy microgrid (HREM) that focuses on the affordable, efficient, reliable and sustainable growth of energy systems. This paper presents an optimized off-grid HREM for a remote locality in the south Indian state of Tamil Nadu. The proposed approach employs a configuration of photovoltaic (PV) arrays, AC loads, a diesel generator set, a wind turbine and a battery energy storage system (BESS) connected to AC/DC buses and designed to satisfy the power requirements of a remote, rural community residential area. The primary objective was to carry out a cost optimization of the designed system. A sensitivity analysis was also conducted to obtain a feasible solution from the optimized results and evaluate the robustness of the design. The primary objective was to carry out a cost optimization of the designed system. A sensitivity analysis was also conducted to obtain a feasible solution from the optimized results and evaluated the robustness of the design. A detailed analysis was made by comparing the $\mathrm{\text{PV}}+\mathrm{\text{DG}}$$\mathrm{\text{set}}+\mathrm{\text{battery}}$ and $\mathrm{\text{PV}}+\mathrm{\text{wind}}+\mathrm{\text{DG}}$$\mathrm{\text{set}}+\mathrm{\text{battery}}$ and it was found that the former offered better results for the particular location. Overall, it was observed that our proposed system offers renewable energy for residential loads that is relatively inexpensive, reliable and sustainable.

Power quality issues and their effective mitigation invariably play a crucial role in a microgrid system. Such power quality problems are often resolved by employing multiple power electronics-based components in the utility grid. This paper is focused on the optimal enhancement of power quality under islanded mode of operation in a microgrid, with a deep Convolutional Neural Network (CNN) with Long Short-term Memory (LSTM) algorithm using distribution static compensator (DSTATCOM). The objective of the research is centered on the reactive power control in DSTATCOM using deep CNN with LSTM for voltage enhancement, minimization of current distortion and reduction of harmonics on a microgrid. This objective can be achieved by the proposed Simulink design model of DSTATCOM intended for improving the power quality in a microgrid. The renewable energy-based power compensator is used for an enhanced and effective control strategy like voltage and current control of the microgrid circuit and uses LSTM-based deep CNN for achieving superior time consumption indicators. Due to varying loads in the microgrid, the reactive power and harmonic voltage and current may be distorted. This problem can be rectified by controlling the microgrid using the LSTM-based deep CNN. This approach consequently reduces the negative-sequence frequency range with the aid of this filtering method in the proposed microgrid circuit. The microgrid is thereafter subjected to different testing conditions and the corresponding simulation results are discussed in relation to existing approaches. The proposed framework was observed to have successfully accomplished harmonic substance and voltage profile enhancement.

This paper proposes the hybrid approach for increasing the profit of prosumers and reducing the energy cost of the residents. The hybrid method is the combination of chaotic maps and the Henry gas solubility optimization algorithm (HGSO), hence it is called chaotic Henry gas solubility optimization (CHGSO). The proposed approach is the community-based microgrid that incorporates energy storage systems (ESS), electric vehicles (EVs) and photovoltaics (PV). Both ESS and EV are conserved as alternative sources of energy. The stored EV energy is used as a power source for homes and the grid by utilizing the Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) modes. The power flow of EV/ESS is controlled depending on the rate of charging and discharging. Here, the battery degradation cost is considered. The proposed approach solves the optimization problems, like battery degradation of EV/ESS, regulation of charge/discharge and provides optimal economic benefits. The proposed approach increases the prosumer profit through encouraging EV energy transactions through incentive-based pricing. Finally, the CHGSO method is performed in MATLAB and is compared with existing methods. From the simulation analysis, it can be concluded that the cost of CHGSO method is less and the prosumer participation has increased.

In this paper, a dynamic parameters lightning search algorithm (DP-LSA) is proposed for optimal operation management of electric vehicle (EV) parking lots (PLs) and distributed generations (DGs) in a microgrid (MG). The MG comprises distributed generation sources such as diesel generators and fuel cells. The batteries of electric vehicles parked in the PL are also used for energy storage. A proposed objective function combines operating, maintenance, and replacement costs, considering inflation, motivation, and annual growth. Simulations are done in two scenarios, and the proposed algorithm results are compared with particle swarm optimization (PSO) algorithm results. Simulation results prove the better performance of the proposed LSA algorithm than the PSO algorithm in reducing the microgrid operation cost.