Particle Swarm (PSO) and Bacterial Foraging (BF) Optimizers are two widely used optimization
techniques. A proper combination of these two algorithms would improve their search
capability while minimizing their shortcomings, such as parameter dependency and premature convergence.
This paper presents a hybrid optimization algorithm that combines PSO and BF
(HPSBF) to ensure security and the system’s stability following faults and disturbances. The formulated
objective function is claimed to be innovative and straightforward.
The set objectives are to minimize the dropped load by shedding relays while maximizing the lowermost
swing frequency. The optimal operation of Under-Frequency Load-Shedding (UFLS)
Relays is driven by the HPSBF technique as a bounded optimization with bounds representing
the limits of the system’s state variables. The viability of the HPSBF is verified against
conventional-, PSO-, and BF-UFLS approaches. The standard IEEE 9-bus and IEEE 39-bus systems
are exploited to examine the response of the developed UFLS techniques. The tested systems
are exposed to various operational scenarios such as loss of power plants and a considerable abrupt
load increase. The DigSilent power factor software is used to simulate the IEEE 9- and 39-bus systems,
while MATLAB code was implemented to obtain optimal operational points for the implemented
algorithms. The HPSBF accomplished the uppermost swing frequency and the
lowermost quantity of the disconnected load. Furthermore, the computational times of HPSBF
are equivalent to those of the PSO.

This paper presents a design of voltage controller for  standalone self-excited induction generator (SEIG) driven by a variable speed wind turbine. Particle swarm optimization (PSO) algorithm has been applied to predict the value of capacitance necessary to maintain the generator terminal voltage at a preset value under specific load and speed conditions. The proposed model completely avoids the tedious and erroneous manual work of segregating the real and imaginary components of the complex impedance of the machine for deriving the specific model for each operating modes. The use of FACTS device called static synchronous compensator (STATCOM) to control the reactive power and keep the output voltage of standalone SEIG at rated value under normal and abnormal conditions such as, de-excitation due to over-loading under balanced and unbalanced load conditions, symmetrical fault, and variation of wind turbine speed is presented. The dynamic model of the system is developed and a methodology to decide  the  ratings  of STATCOM  components  such  as  the  DC  bus  capacitor,  AC  side  filter  and insulated gate  bipolar transistors  (IGBT)  is introduced. The proposed system is modeled and simulated using Matlab/Simulink software program to examine the dynamic characteristics of the system with proposed control strategy. Dynamic simulation results demonstrate the effectiveness of the proposed STATCOM voltage controller.

The Micro energy grids (MEGs) are expected to play a vital role in designing smart grids because it reduces energy expense and gas emissions by utilizing renewable and non-renewable distributed energy resources. However, currently, problems exist concerning the design and utilization of MEGs. In our previous work , we assume that the distributed generators put the priority on electricity production and the surplus/deficient electricity can be sold to/bought from the utility grid. The surplus/deficient heat can be stored/or supplied by thermal storage tank, boiler and/or electrical heater. In this study the distributed generators put the priority on heat production and the deficient heat can be supplied by the boiler and/or electrical heater. Multi-objective particle swarm optimization (MOPSO) algorithm is used to specify the optimal combination of components and optimal sizing of all MEG components for minimization of the total system cost and total carbon dioxide emissions simultaneously.

The present paper aims to determine the optimal size of Micro energy grid (MEG) resources and energy storage units taking into consideration the thermal and electrical load curves. The MEG under study consists of wind turbines (WT), photovoltaic panels (PV), combined heat and power units (CHP). Further, the MEG includes a thermal storage (TS) units, boiler, and electrical heater to satisfy the electrical and heat load demand within MEG. The optimization objective is to define the optimal capacity of different types of the above distributed generators (DG) and TS units that guarantee the MEG can operate economically and reliably over a wide range of thermal and electrical load conditions. The objective function is to minimize the net present cost (NPC) including capital, operating and maintenance cost, and total CO2 emission simultaneously. In the present study, DG units put the priority on electricity production and the surplus/deficient electricity can be sold to/bought from the utility grid. The surplus/deficient heat can be storage/or supplied by TS, boiler and/or an electrical heater. This paper proposed multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal combination and optimal sizing of MEG components. The results show that integration of CHP and TS units to the MEG has a significant enhancement in the system performance by minimizing NPC and total CO2 emission. Simulation results proved the effectiveness of proposed MOPSO to find the optimal sizing in contrast with traditional single objective methods in terms of less computation time, fewer calculation steps.

The present paper introduces a particle swarm optimization (PSO) algorithm to define the optimal combination of the energy hub infrastructures and the optimal scheduling of natural gas energy, wood chips biomass energy and electrical energy that guarantee economical operation of energy hub. Three objective functions are considered during the study: minimizing net present cost, minimizing total CO2 emission and minimizing both net present cost and CO2 emission simultaneously. Simulation results prove the effectiveness of proposed PSO to find the optimal energy hub scheduling. The results show that a natural gas turbine (NGT) is superior to biomass generation unit in reducing the total operating cost. On the other hand, biomass wood chips generator is superior to NGT in reducing total CO2 emission. The results show that using a mix of NGT and biomass generator can enhance the system performance of the energy hub by minimizing both total operating cost and CO2emissions simultaneously.

In this paper, a comparison of two renowned optimization approaches, Genetic Algorithm (GA) and Simulated Annealing (SA) are presented to pick the best fault correction strategy of a system using a scheme of Proportional-Integral-Derivative (PID) controller and Matrix Converter (MC). Monitoring the measured load currents is used to design the proposed fault detection and correction technique. PID control strategy provides real-time regulation in a closed-loop system. The proper design of MC circuits loaded by unbalanced passive RL load is performed. MATLAB/SIMULINK is used to evaluate the proposed fault correction methodology's success and prove the effectiveness of the proposed techniques. Regarding fault correction, the results show that employing a GA to select the best PID controller design has several superior features over SA in diminishing settling time and overshoot.

Isolated renewable energy sources (RESs) could be competitive as a reliable and robust electrical energy supply for remote and rural territories. An efficient energy storage system is required to guarantee the continuity of the supply from isolated RESs. This article advises reliable and robust off-line hybrid RESs with pumped hydro storage (PHS) to satisfy the electrical energy needs of a coastal city in Egypt, Hurghada (33°48'E, 27°15'N). The sizing procedure of the PHS-integrated hybrid Wind/Strilling dish system is investigated via using System Advisory Model (SAM) software. The minimal value of the levelized cost of energy (LCOE) is used to determine the proposed system optimal configuration. Data of solar irradiance components, temperature, wind speed, direction, and load are inputs for the sizing procedure. The results demonstrate that combining RESs and PHS is beneficial and trustworthy for offering sustainable electricity to metropolitan areas with suitable topographical features.

Rooftop Photovoltaic (RTPV) systems have gained more interest due to modularity and environmental friendliness. This article proposes an RTPV system for fulfilling the load demand of the main campus of Assuit University. The proposed system's economic and technical feasibility is comprehensively explored, including the expense and reliability. The system's sizing is implemented as a constrained optimization challenge. Particle Swarm Optimization (PSO) is employed to identify the proposed RTPV modules' optimal quantity, considering expanding Assuit University until 2025. Robust and reasonably accurate load forecasting models are developed and examined, including medium and long terms, to identify the monthly/annual load peaks from 2019 to 2025. The sizing procedure's PSO outcomes are validated via their comparison with Software such as PVsyst and PVGIS. The results indicated the economic efficacy of the proposed RTPV system and the ability of PSO to yield improved sizing than the other Software because of the well-formed objective function.

In this paper, a simple and efficient hybrid Simulated Annealing Particle Swarm (SAPS) algorithm is proposed to determine the optimal size of a Microgrid (MG) that guarantees economically and reliable operation of the off-line mode and/or on-line mode of the MG. The proposed SAPS optimizer considers the intermittent nature of renewable energy sources (RESs) while boosting the power supply security/stability. Batteries are resized to enhance the power supply reliability and acting as virtual synchronous inertia. During disturbances/fault circumstances, the primary frequency control is provided by batteries and diesel generators. The constraints are set to achieve higher levels of power supply security and frequency stability. Qena AL-Gadida (QA) city is the MG under concern. The SAPS results are validated versus Simulated Annealing (SA) and Particle Swarm Optimization (PSO). The results revealed the reliability and applicability of the proposed sizing procedure. Moreover, adopting the primary frequency regulation of batteries resulted in a slight increase in the overall costs.