In this article, interval type-2 fuzzy logic controller (IT2FLC) has been proposed as secondary load frequency controller (LFC) for hybrid two area power system instead of type-1 fuzzy logic controller (T1FLC) and PID based controllers. The investigated power system integrates both conventional and renewable power resources like large scale solar parks. These large scale solar parks in addition to demand load changes increases load frequency control problems due to their continuous and severe output power variation. In order to reduce the effect of solar irradiance variation on the power system, another type-2 fuzzy logic controller has been proposed to control the output for the solar park during cloudy days instead of maximum power point trackers. As one of the best energy storage systems utilized in modern power systems, Reduction oxidation flow battery (RFB) has been integrated in the investigated power system to act as fast active power source which absorbs and discharges power during disturbances caused by generation or demand changes. Power flow controller like thyristor controlled phase shifter (TCPS) has been proposed in this paper to control tie-line power shared between generating areas during system disturbances. In order to enhance the dynamic performance for the proposed controller, meta-heuristic nature inspired optimization algorithm like whale optimization algorithm (WOA) has been proposed for proposed controller gains off line tuning. The superiority of the proposed IT2FLC controller against T1FLC controller has been investigated by simulating their performance during severe demand load changes and solar irradiance variations, while WOA enhanced the dynamic performance of the proposed controller compared to other optimization algorithms like particle swarm optimization (PSO) and grey wolf optimization (GWO).INDEX TERMS Area control error (ACE), automatic generation control (AGC), flexible alternating current transmission system (FACTS), interval type-2 fuzzy logic system (IT2FLC), load frequency control (LFC), redox flow battery (RFB), solar park, thyristor controlled phase shifter (TCPS), whale optimization algorithm (WOA).

Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.

In order to achieve global optimal solutions, this paper proposes a new hybrid sperm swarm optimization and gravitational search algorithm (HSSOGSA), which is based on the idea of balancing the exploration and exploitation capabilities by combining sperm swarm optimization (SSO), which has a fast convergence rate, and a gravitational search algorithm (GSA), which can efficiently explore a search domain. Here, the suggested algorithm is utilized to assess how interconnected micro-grids (IMGs) should operate economically, including optimization energy management for charging the system of electric vehicles (EVs), where the optimal charging system for the EV is applied in one micro-grid of interconnected micro-grids. Each micro-grid (MG) consists of various distributed generation (DG) units, including solar photovoltaic (PV), wind turbine (WT), and micro-turbine (MT). The primary objective function of each micro-grid seeks to minimize the cost of total power generation while taking into account the power exchange between IMGs and utility with a focus particular emphasis on technical constraints. The suggested HSSOGSA algorithm is compared with another optimization technique based on sperm swarm optimization (SSO) and war strategy optimization (WSO) algorithms to demonstrate its effectiveness. Results obtained from the HSSOGSA algorithm demonstrate how to manage energy transfer between each MG and the utility. It has small electricity consumption for a variety of daily loads Including the loads for charging EVs as well as small the price of overall electricity output, small utility costs, raising micro turbine (MT) efficiency and smart regulation of EVs charging throughout the day.

In this paper, a proposed efficient solution for an important power quality problem focused on harmonic distortion in the source current is introduced by using Shunt Active Power Filter (SAPF) controlled by the sinusoidal source current control strategy which is based on the instantaneous active and reactive power theory (Sinusoidal PQ-theory). As a part of this controller, there is PI-Controller which enhances the APF controller performance when tuning its proportional and integral gains. The gains of the PI-Controller are optimized in this article based on an effective metaheuristic optimization algorithm known as Mayfly Optimization Algorithm (MOA), and its effectiveness is clear when compared with other algorithms such as Particle Swarm Optimization (PSO), Artificial Hummingbird Algorithm (AHA) and Archimedes Optimization Algorithm (AOA). The performance of the optimized controller is evaluated under load variation and distorted source voltage conditions. The results obtained using MATLAB/SIMULINK prove the ability of the MOA to get the optimal PI-Controller gains which lead to enhancement of the APF controller performance to reduce the total harmonic distortion in the drawing source current with minimum and acceptable values according to (IEEE-519) harmonics standard.

This paper addresses the problem of Micro-Grid (MG) Energy Management Control (EMC) including Electric Vehicle (EV) scheduling with considering a reduction in the overall operating cost of MG in a residential grid. The main motivation for this study is the impact of the daily load profile combined with electric vehicles (EVs) on the grid. Unless the EV integration with load is monitored and controlled, the MG may experience an unexpectedly high or low load. So, EMS is a trend in recent years for optimal planning of MG. On the other hand, the available energy stored in the energy storage Battery can be utilized to free the distribution system from some of the congested load at certain times or to allow the grid to charge more EVs at any time of the day, including peak hours. This work was implemented by using four metaheuristic algorithms (Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Hybrid population-based algorithm (PSOGSA), and Capuchin Search Algorithm (CapSA) for optimal operation with minimum total daily cost without and with EVs included in MG by two different daily profile of EV. The MG used in this paper consisted of a diesel generator (DG), Battery storage device, photovoltaic (PV) system, and Wind turbine unit (WT). For a more dispatchable practical MG, Emissions from DG and deterioration of storage devices in addition to the cost of charging the EVs have been taken into account. The results demonstrate that CapSA is a suitable method for generating robust models for EMS. This means that the proposed CapSA approach can be applied in a wide range of complex nonlinear systems.