Search

Grid dynamics and control mechanisms have improved as smart grids have used more inverter-based renewable energy resources (IBRs). Modern converter technologies try to improve converters’ capacities to compensate for grid assistance, but their inertia still makes them heavily dependent on synchronous generators (SGs). Grid-following (GFL) converters ensure grid reliability. As RES penetration increases, the GFL converter efficiency falls, limiting integration and causing stability difficulties in low-inertia systems. A full review of grid converter technologies, grid codes, and controller mechanisms is needed to determine the current and future needs. A more advanced converter is needed for integration with more renewable energy sources (RESs) and to support weak grids without SGs and with low inertia. Grid-forming (GFM) inverters could change the electrical business by addressing these difficulties. GFM technology is used in hybrid, solar photovoltaic (PV), battery energy storage systems (BESSs), and wind energy systems to improve these energy systems and grid stability. GFM inverters based on BESSs are becoming important internationally. Research on GFM controllers is new, but the early results suggest they could boost the power grid’s efficiency. GFM inverters, sophisticated smart inverters, help maintain a reliable grid, energy storage, and renewable power generation. Although papers in the literature have compared GFM and GFL, none of them have examined them in terms of their performance in a low-SCR system. This paper shows how GFM outperforms GFL in low-inertia and weak grid systems in the form of a review. In addition, a suitable comparison of the results considering the performance of GFM and GFL in a system with varying SCRs has been depicted in the form of simulation using PSCAD/EMTDC for the first time.

The power transmission system of Afghanistan is witnessing a significant shortage in terms of capacity, reliability, flexibility, and energy security. The goal of this paper was to identify and examine the associated issues, challenges, and opportunities for domestic transmission grid and power imports in the country. On these bases, proposals and recommendations were provided that can help the Afghan energy sector strategically enhance its transmission capacity and make the country's existing massive renewable energy potential exploitation feasible. The focus was on the expansion of high-voltage direct current transmission technology. The findings of the paper revealed that the utilization of high-voltage direct current, hybrid high-voltage alternating current, and high-voltage direct current transmission technologies can facilitate bulk power transmission and trade between south and central Asia, as well as regional energy sustainability, development, and security.

Optimum operation of power system network becomes of great importance to handle the problem of power shortage. Connecting renewable energy (RE) based distributed generators (DG) into electrical networks (EN) are spreading rapidly to earn more benefits environmentally and economically. This paper aims to optimally operate DGs in order to minimize daily power loss in case of uncertainty of wind and PV system. Three different types of DGs, photovoltaic DG (PVDG), wind DG (WDG) and conventional DG (CDG) are connected to IEEE 33-bus system. Monte Carlo simulation (MCS) is used to generate probable scenarios for wind and photovoltaic power plants based on actual data for wind speed and solar radiation for one day. Fast forward scenario reduction (FFSR) is used to reduce the number of scenarios generated by MCS. Particle swarm optimization (PSO) is used to minimize daily power losses through determining the optimum operating point (operating power and power factor (PF)) for CDG to cope with PVDG and WDG uncertainties.

This study proposes the Higher Rotational Speed Maintaining (HRSM) control algorithm that is suitable for large wind speed fluctuations. One of the challenges faced by wind generators is operating with significant wind speed oscillations. A wind generator with a large moment of inertia increases the time constant in rotational speed control and delays the tracking of the optimal operating point. This delay could reduce generated power by up to 40% or more. There are numerous regions in Japan’s inland areas where average wind speeds exceed 6.5 m/s. However, frequent fluctuations in wind speeds make wind power generation challenging. Therefore, this paper proposes a control algorithm suitable for wind turbines operating under unstable wind conditions. Higher rotational speeds are required to convert sudden high wind speeds into higher power output, especially when wind speed oscillations are large. Hence, the proposed algorithm focuses on maintaining the rotational speed during periods of unstable wind conditions to improve generation efficiency. The effectiveness of the proposed control method for wind turbines is verified through numerical simulation results. The novel control algorithm aims to promote the spread of wind turbines in areas with unstable wind conditions by overcoming these disadvantages.

Hybrid Renewable Energy Systems (HRES) have recently been proposed as a way to improve dependability and reduce losses in grid-connected load systems. This research study suggests a novel hybrid optimization technique that regulates UPQC in order to address the Power Quality (PQ) problems in the HRES system. The load system serves as the primary link between the battery energy storage systems (BESS), wind turbine (WT), and solar photovoltaic (PV) components of the HRES system. The major objective of the study is to reduce PQ issues and make up for the load requirement inside the HRES system. The addition of an Optimized Fractional Order Proportional Integral Derivative (O-FOPID) controller improves the efficiency of the UPQC. The Crow-Tunicate Swarm Optimization Algorithm (CT-SOA), an enhanced variant of the traditional Tunicate Swarm Optimization (TSA) and Crow Search Optimization (CSO), is used to optimize the control parameters of the FOPID controller. Utilizing the MATLAB/Simulink platform, the proposed method is put into practice, and the system’s performance is assessed for sag, swell, and Total Harmonic Distortion (THD). The THD values for the PI, FOPID, and CSA techniques, respectively, are 5.9038%, 4.9592%, and 3.7027%, under the sag condition. This validates the superiority of the proposed approach over existing approaches.

Applications for alternative energy sources range from very small scale to large scale grid-coupled hybrid energy systems. In addition, hybrid energy systems are safer to generate power and cheaper to produce than single-source energy systems. The main goal of this study is to determine whether renewable energy hybrid system with horizontal axis wind turbine (HAWT) or vertical axis wind turbine (VAWT) is more efficient and cost effective in terms of energy, economics, and environmental performance. The use of the Multi Objective Genetic Algorithm (MOGA) in MATLAB software for the sizing of hybrid sustainable energy system with wind turbine (horizontal and vertical axis), solar photovoltaic, and grid connection is evaluated in this study. The results revealed that the cost of energy, NPC and system total cost for the case where HRES consists of HAWT is $0.02 /kWh, $85,905, and $332,240, respectively, while for the case when VAWT is used, these values are $0.06 /kWh, $129,932 and $502,511, respectively. The renewable fraction and CO2 emission saving are 80.5% and 73.2% for cases 1 and 2, respectively. The use of renewable energy sources will spread more widely and there will be less air pollution as a result of less reliance on grid electricity. The findings from both situations show that adopting HAWTS-based HRESs is more cost effective and efficient for electrifying rural areas. This study paves the way for researchers to focus on types of wind turbines while designing HRESs.

The abundance of fossil fuels and their negative environmental effects, together with the substantial reduction in their investment prices, have made solar-biomass hybrid plants an increasingly appealing choice for supplying the world’s energy needs. This study evaluates the performance of a PV/biomass hybrid renewable energy system (HRES) that incorporates three distinct biomass processes, including pyrolysis, direct combustion, and gasification. The hybrid system is modeled employing the multi-objective genetic algorithm (MOGA). The most excellent layout is tabbed based on factors such as the largest proportion of green energy and the least amount of noxious emissions, as well as the minimum cost of energy (COE) and net present cost (NPC). The COE in the pyrolysis system is 17% and 38% lower than in scenarios 1 and 2, respectively. The decrease in NPC and overall system cost, which demonstrates 17% and 65% drops in NPC and 15% and 37.5% decreases in total system cost, respectively, as compared to scenarios 1 and 2. After comparing all the essential aspects, it is revealed that the HRES incorporating biomass pyrolysis is preferable to the most cost-effective option for making hybrid systems than other HRESs executed up of gasifier or direct combustion biomass technologies. This idea would improve the use of biomass resources in HRES by including the foremost biomass power production technology, making it simpler for researchers to identify the paramount hybrid renewable energy systems and create decisive HRES using biomass as the main source.

Single-inductor 3x multilevel boost (SI-3xMLB) converters, recognized for their potential to supply multiple output voltage levels, are gaining much attention in microgrids due to their capability to diverse sub-systems with changing voltage requirements. However, some loads, such as constant power loads (CPLs), threaten the stability of SI-3xMLB converters due to their negative impedance properties. Thus, advanced regulation mechanisms are necessitated to reduce the destabilization effect of such loads in the microgrids. In response to the issue, in this paper, an adaptive data-driven controller with an observer is designed to mitigate the effect of non-ideal CPLs in dc/dc SI-3xMLB converters. The suggested data-driven controller can stabilize the system outputs using the input-output (I/O). In particular, the multi-agent fuzzy approximate Q-learning (MAFQ-learning) is employed to adjust the coefficients embedded in the data-driven controller dynamically. To do this, a reward function is defined according to the voltage control requirements of the dc/dc SI-3xMLB converter. By interacting the agent of MAFQ-learning with the environment, tunable control coefficients are adaptively adjusted. This process finds optimal policy, allowing the controller to dynamically respond to the effects of CPLs. The real-time responses based on the Arduino platform revealed the superior performance of the suggested controller to stabilize the dc/dc SI-3xMLB boost converter feeding CPL than the discrete time-averaged model predictive control (DTA-MPC), the optimal PI control based on state-action-reward-state-action (SARSA) technique.

Electric vehicle (EV) drivers aim to charge their vehicles cost-effectively and with minimal charging time. Meanwhile, the ever-increasing number of EVs without charging control strategies could result in a massive surge in peak demand, potentially overloading distribution equipment and violating voltage constraints. To tackle this challenge, this paper introduces a transactive energy market (TEM) framework for an EV parking lot (EVPL) equipped with photovoltaic (PV) panels and battery systems (BSs), considering the preferences of both EV drivers and the EVPL operator. In this framework, EVs parked in the EVPL participate in the TEM by submitting their charging flexibility through response curves, which indicate the compensation required for different values of flexibility. Furthermore, the proposed model allows the EVPL operator to utilize the flexibility of EVs in the vehicle-to-grid (V2G) program by incentivizing EV drivers, considering their preferences and the degradation cost of EV batteries. The study employs the stochastic programming method to model uncertainties in PV output, electricity prices, and EV availability. It also incorporates BS degradation costs and carbon emissions constraints into the EVPL scheduling problem. Linearization techniques are then applied to transform the non-linear optimization problem into a mixed-integer linear programming (MILP) model. Finally, applying the model to a case study validates its superiority in satisfying the preferences of both EV drivers and EVPL.

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.

The widespread use of green energy sources creates a significant demand for energy storage. Hybrid floating photovoltaic (FPV) and pumped hydro storage (PHS) represent one of the most dependable and cost-effective solutions, which uses the PV system on the water body combined with a pair of lakes with different heights. This study focuses on the load side as well as the PHS capacity factor and aims to lower the cost of energy (COE) by raising the PHS capacity factor. Since building a PHS requires a significant upfront investment, doing so will also lower the COE and enable the acquisition of PHS for large-scale power production, which will guarantee power access in large cities. To simulate the FPV-PHS system, the multi-objective genetic algorithm (MOGA) is employed. Sufficient power management is a prerequisite for achieving system reliability in the best possible hybrid energy system design and implementation. Considering the 60-year system lifespan, the net present cost (NPC) analysis shows that, out of all the communities evaluated, the FPV-PHS system has the lowest NPC and COE. For the optimal configuration (FPV (Block A) 105 MW, PHS 80 MW, FPV (Block B) 357 MW, and the current hydropower plant), the expected NPC and energy costs for implementing the hybrid energy system (HRS) at the chosen location are $44,737,613 and $40/MWh, respectively. The existing FPV system spans 4.65 km2 and lowers the evaporation fraction by 17,279,400 m3. The hybrid FPV-PHS system reduces annual CO2 emissions by 581,830 tons. Our research reveals that a hybrid floating PV and pump storage hydropower system offers more steady clean electricity, implying a great significance for power grid infrastructure.

Maintaining a continuous supply of power with a sustainable microgrid is still a difficult task for the off-grid area in a developing country. Using HOMER software, the techno-economic potentiality of various system configurations is demonstrated, and a PV/WT/battery/DG-based optimal system is chosen for the selected off-grid area based on Net Present Cost (NPC). The result of this optimal system is compared with the result obtained by Mixed Integer Linear Programming (MILP). The NPC ($1087520) and Renewable Fraction (RF) (90.7%) of MILP-based systems are 3.7% lower and 29.1% higher than HOMER-based systems. The amount of carbon emissions in the HOMER-based system is 79.7% larger than the MILP-based system, even though the difference in NPC is not excessive. Due to lower NPC and carbon emissions, MILP-based systems are more cost-efficient and eco-friendly. A sensitivity analysis of uncertain parameters, including annual load, discount rate, fuel price, and solar radiation, has been added to evaluate the effect of the variation of these parameters.Additionally, a few linguistic factors, such as land usage, future of technology, employment, comfortable living, and biological diversity, are also examined by normalization to demonstrate the efficiency of the optimal system. The optimal system achieved top ranking for its outstanding preference values in all criteria except land usage and is recommended in the environmental, technological, and social categories. This evaluation approach can aid researchers, decision-makers, and financiers in better planning microgrids focusing on sustainable features.

The introduction of advanced technologies has led to an unprecedented rise in automated appliances in the housing sector. Building new administrative structures to satisfy electrical needs has grown more crucial to ensure the safety of residential devices. One of the approaches to achieve this is Demand Side Management (DSM), a key component of both micro-grid and Smart Grid technology. DSM can be accomplished by carefully controlling requirements while upholding the trust of clients. Most of the DS Management which has been covered in the research is aimed at helping households manage their power plan. The innovative HBA+DMO technique inherits Honey Badger Optimization (HBA) and Dwarf Mongoose Optimization (DMO) for executing the DSM program. The groundwork for the proposed framework implemented in this investigation is provided by the Critical-Peak-Price (CPP) and Real-Time-Price (RTP) payment processes. Two operational instances (60 min and 12 min) are being taken into consideration to evaluate client requirements and behavior over the suggested strategy. In accordance with the results from simulations, the suggested strategy arranges the devices in the best possible way, leading to fewer energy expenses while maintaining user comfort (UC). Customers sometimes pay a premium as a result of gadget waiting periods in order to gain the most comfort. As equipment is turned on in response to user comfort, the amount of time spent waiting during an unscheduled situation is close to zero. Tools for lowering energy expenditures and consumption for buildings, communities, or enterprises are frequently provided through energy management software. The three main uses of the energy data that EMS collects are reporting, monitoring, and engagement. The computational time of the proposed approach is (∼213.42). Future testing involving different conditions and control methods for study into HRES microgrid infrastructure may be done on the medium of the experiment bench.

Remote island power systems are difficult to install renewable energy sources (RES) because of the small system capacity and the large impact of intermittency of RES. Virtual synchronous generators (VSG) can equip inverters with the inertia of synchronous generators and can suppress frequency and voltage fluctuations. This paper proposes a VSG control method for dynamic voltage stability in remote island power systems. In conventional VSG control methods, the command of active and reactive power is determined by the Droop characteristics of frequency and transmission line voltage. However, the command are not based on dynamic voltage stability indices. Therefore, the compensation amount of active and reactive power is inappropriate. The proposed method determines the command of active and reactive power based on dynamic voltage stability indices. The dynamic voltage stability index considers the critical boundary index (CBI). The CBI is defined as the distance between the operating point of active and reactive power flowing through a transmission line and the stability limit curve calculated from the power flow between two bus lines. The proposed method improves dynamic voltage stability by determining power command so that the operating points of active and reactive power are closer to the origin direction. Simulation verification is performed for load increase and three-phase ground fault. In the three-phase ground-fault simulation, FRT requirements are considered to verify continued operation and the case of gate blocking. The conventional Droop control and the proposed method are compared for the case of continued operation with FRT requirements during load increase and ground fault. Simulation verification results show that the proposed method improves the dynamic voltage stability compared to the conventional method. Furthermore, it is shown that the proposed method is capable of stable continuous operation of the system even after a transmission line failure considering the FRT requirement.

This paper proposes a coordinated voltage control by three-phase step voltage regulators (3ϕSVRs) and photovoltaic (PV) units with smart inverters. An optimization problem is formulated to improve the voltage profile of distribution networks and reduce the active power curtailment of PVs. The tap positions of 3ϕSVRs and the active and reactive power output of PVs are coordinated by whale optimization algorithm. The effectiveness of the proposed approach is verified by case studies on the IEEE 123 node test feeder. The results show that the proposed approach achieves lower voltage unbalance while avoiding excessive PV curtailment, improving utility, consumer, and environmental benefits.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Recent years have seen a surge in interest in DC microgrids as DC loads and DC sources like solar photovoltaic systems, fuel cells, batteries, and other options have become more mainstream. As more distributed energy resources (DERs) are integrated into an existing smart grid, DC networks have come to the forefront of the industry. DC systems completely sidestep the need for synchronization, reactive power control, and frequency control. DC systems are more dependable and productive than ever before because AC systems are prone to all of these issues. There is a lot of unrealized potential in DC power, but it also faces some significant challenges. Protecting a DC system is difficult because there is no discrete location of where the current disappears. DC microgrid stability that is dependent on inertia must also be considered during the planning stage. The problems that DC microgrids have include insufficient power quality and poor communication. The power quality, inertia, communication, and economic operations of these value streams, as well as their underlying architectures and protection schemes, are all extensively discussed in this paper. This review paper examines the pros and cons of both grid-connected and isolated DC microgrids. In addition, the paper compares the different kinds of microgrids in terms of power distribution and energy management agency, such as the prerequisites for a DC microgrid’s planning, operation, and control that must be met before state-of-the-art systems can be implemented.

The power quality (PQ) has been significantly affected by the integration of intermittent non-conventional sources (NCS) into the local distribution system in addition to the adoption of power electronic technologies to regulate non-linear loads. This article combines the H-bridge cascade five-level unified power quality conditioner (5L-UPQC) with the wind power generation system (WPGS), solar photovoltaic power generation system (SPVGS), and battery storage system (BSS) as an effective approach to address PQ problems. The utilization of the Levenberg–Marquardt backpropagation (LMBP)-trained Artificial neural network controller (ANNC) in the UPQC is recommended for generating appropriate reference signals for the converters. This eliminates the requirement for conventional complex conversions, such as abc, dq0, and αβ. Moreover, the artificial neuro-fuzzy interface system (ANFIS) is recommended for achieving a DC-link balance. Football game optimization (FBGO) is utilized to determine the optimal shunt and series filter characteristics. The major objectives of the proposed system are to reduce the current waveform irregularities, resulting in a decrease in the total harmonic distortion (THD), an enhancement in the power factor (PF), the mitigation of supply voltage imbalances and disturbances, and the maintenance of a steady direct-current link capacitor voltage (DLCV), despite the variations in the load, solar irradiation, and wind velocity. The efficiency of the suggested strategy is assessed using four case studies that involve different loads, variable wind velocities, and source voltage balancing conditions. Based on the simulation studies and obtained results, the suggested method significantly decreases the THD to values of 2.91%, 3.63%, 3.75%, and 3.50%. Additionally, it achieves a power factor of unity, which is considerably lower compared to other multilevel schemes that use the traditional symmetrical reference frame (SRF) and instantaneous reactive power (pq) methods. This design has been executed using the MATLAB/Simulink program.

For a car that is propelled by an armature-controlled DC motor This study proposes an adjustable linear positioning control. In this paper, to optimize the parameters of the car's position controller the sine cosine optimization algorithm (SCA) is utilized, with support from the Balloon effect (BE), The BE is incorporated to enhance the responsiveness of the traditional sine cosine optimization algorithm when faced with external disturbances and variations in system parameters. In the proposed approach, the determined value of the open loop transfer function of the motor and the updated values of the controller gains serve as the basis for the modified sine cosine algorithm's objective function (OF). Under the influence of changes in motor parameters and step load disturbances, the system using the suggested controller is evaluated. Results from simulations and experiments show that the proposed adaptive controller, which implements the modified sine cosine algorithm, enhances the system's overall performance in the presence of load disturbances and parameter uncertainties.

A distribution system's network reconfiguration is the process of altering the open/closed status of sectionalizing and tie switches to change the topological structure of distribution feeders. For the last two decades, numerous heuristic search evolutionary algorithms have been used to tackle the problem of network reconfiguration for time-varying loads, which is a very difficult and highly non-linear efficiency challenge. This research aims to offer an ideal solution for addressing network reconfiguration difficulties in terms of a system for power distribution, to decrease energy losses, and increase the voltage profile. A hybrid Genetic Archimedes optimization technique (GAAOA) has also been developed to size and allocate three types of DGs, wind turbine, fuel cell and PV considering load variation. This approach is quite useful and may be used in many situations. This technique is evaluated for loss reduction and voltage profile on a typical 33-bus radial distribution system and a 69-bus radial distribution system. The system has been simulated using MATLAB software. The findings suggest that this approach is effective and acceptable for real-time usage.

This paper compares the differences in power generation and resident experience between S-shape and M-shape photovoltaic (PV) systems at the same location with the same number of modules. First, the sky anisotropy model (Hay-Davies-Klucher-Reindl, HDKR) is used to simulate solar irradiance (direct, diffuse, and reflection radiation) received by solar modules based on the laws of movement between the Earth and the Sun in the solar system. Next, the annual output power of two different PV system arrangements is calculated based on the solar irradiation received. Furthermore, because the solar module receives three types of solar radiation, interference is added to each of the three parts one by one to simulate shadows cast by moving objects such as birds, leaves, or clouds. Subsequently, off-grid and grid-connected power consumption models are established, and two different photovoltaic power generation systems are used at the power generation end. Lastly, the photovoltaic systems were tested based on residents’ electricity consumption habits, considering both off-grid and grid-connected households. Among them, the grid-connected model adopts the Particle Swarm Optimization (PSO) algorithm to generate an intelligent and flexible power consumption strategy. The results, based on data from Naha, Japan, show that with the same number of modules, the M-shape arrangement generates less annual power than the S-shape. In summer, though, the M-shape can generate more power, and the two structures have similar immunity to disturbances. The S-shape and M-shape have little difference in the power consumption experience. However, the general applicability of these results to other regions remains uncertain, and further testing is needed to account for environmental variations.

Electrical microgrids (EMGs) are positioned to play an important role in the future distribution grid as technology advances and distributed energy resources (DERs) emerge. In the event of major natural disasters, the functioning capabilities of active distribution systems (DS) face ongoing problems. To assess the resilience of a distribution system, a thorough methodology must be established. This study proposes a methodology that demonstrates how the use of EMGs and DERs, in conjunction with line hardening, can improve resilience in extreme operating situations. The framework examines four separate scenarios, each with its own set of restoration procedures and critical loads. A combination of battery electric vehicles (BEVs), solar photovoltaic distributed generation (SPV-DG), battery energy storage systems (BESS), and distribution static compensators (DSTATCOMs) is being integrated into practical Indian distribution systems consisting of 28 and 52 buses to improve resilience. The goal is to improve the newly specified resilience indices and restore all of the loads that have been affected by the faults. The bald eagle search algorithm (BESA) is used to identify the appropriate allocation of SPV-DG and solve the objective function within the system. The results of our tests show that our proposed technique has the capacity to enhancement the resilience and successfully restore all damaged loads in a distribution system.

The main aim of this paper is to design a local controller for DC/DC converter in a battery energy storage system (BESS) and a controller based on a virtual synchronous generator (VSG) for photovoltaic (PV) converter connected to AC grid. In the DC/DC converter design, the state feedback method is used so that the voltage and current control loops are combined, leading to higher flexibility and improved damping. This flexibility, which maintains the state of charge (SOC) of BESS, is supported by the design of virtual resistance and virtual capacitor statically and dynamically. In this design method, the BESS can be independently connected to each DC bus node and detect disturbances through local measurements. In addition, the improved Pre-Parallelism (PREP) method has been used to improve the transient performance caused by the disconnection and connection of additional loads, the existence of faults, and the inherent inertia difference in parallel operation between VSG and synchronous generator (SG). In this method, the problem of phase jump caused by transient disturbances is solved by considering a cosine function in VSG design. Also, to solve the problem of inertia difference between the units, a small signal model has been presented, in which, by considering the capacity ratio of the units on the AC side, the necessary inertia for VSG can be included in the design. The proposed method is simulated by considering different scenarios in MATLAB software, so the results demonstrate the superiority of the proposed controller compared to other existing methods.

This paper aims to increase the power production of gearless permanent magnet synchronous generator (PMSG) based wind energy conversion systems (WECS) in strong (greater than 25 m/s) wind speed regions. In general, most wind turbines cut off when wind speed is over the cut-out rate; as a result, the power generation of the wind farm decreases. This study introduces the look-up table pitch angle and rotational speed control to generate extra power in a strong wind region. Consequently, WECS can produce electric power at a reduced level until the wind speed reaches 35 m/s and the possibility of shutting down the wind turbines decreases. The proposed algorithm decreases the mechanical stress of wind turbine by decelerating rotational speed rather than turbine torque in strong wind regions. Furthermore, the model predictive control (MPC) is proposed to replace the PI controller in the inner current loop of the rotor speed control loop, thereby improving the performance of the stator current track. The proposed control method is validated in Matlab/SimPowerSystem software. The simulation result confirms that the proposed control method reduces the possibility of shutdown and guarantees temporally power production in strong wind conditions.

Abstract This paper presents a new phase-shift modulation for isolated dual active bridge (DAB) direct current–direct current (DC–DC) converter. The proposed technique aims to minimize the maximum current stress of the converter, which could directly increase the efficiency and reduce the device losses. This modulation technique controls the converter power through only two phase-shift angles or two degrees of freedom; one phase shift is used between the legs of its first bridge and the other one between the legs of the second bridge. Although the traditional single-phase shift (SPS) technique has only one degree of freedom, it suffers from many drawbacks in terms of high current stress and reverse circulating power flow, which decrease the converter efficiency. On the other hand, increasing the number of phase-shift angles can enhance the system performance but also increase the control complexity. Thus, a comparative analysis between the proposed modulation technique and the traditional SPS was conducted; the new method showed better performance in terms of current stress reduction, along with implementation simplicity.

With the widespread use of lithium-ion batteries in a wide range of consumer electronics products, the CE industry has undergone a dramatic shift. The Li-ion battery has emerged as the heart of electric cars, and the focus has now shifted to the automotive sector. Liquid crystal displays have evolved over time to meet the demands of automobiles. International research groups and the performance of production electric vehicles are used to discuss and inform vehicle-driven battery targets. There is still a lot of room for improvement in terms of energy, life expectancy, cost, safety, and fast-charging capabilities for LIBs suited for the automotive sector. In this study, a review of lithium-ion battery applications in electric vehicles is presented.

A Quasi Z-Source Indirect Matrix Converter (QZSIMC) is proposed in this paper for Permanent Magnet Generator (PMG) based Direct Drive Wind Energy Conversion Systems (DDWECS) to enhance the voltage transfer ratio and also to control the output voltage under various loading conditions. The QZSIMC comprises three Quasi Z-Source (QZS) networks connected between PMG and load through a three-phase Indirect Matrix Converter (IMC). Two different PWM control techniques such as Carrier Based Pulse Width Modulation Technique and Modified Space Vector Pulse Width Modulation Technique are proposed to analyze the performance of QZSIMC. By changing the shoot through time period, the duty ratio of quasi Z-Source network can be increased which in turn increases the boost factor of both QZSIMC without affecting the efficiency of the converter. A closed-loop control system has also been designed to sustain constant parameters at the output. The proposed QZSIMC for PMG based DDWECS along with two different PWM Control Schemes are to be modeled using MATLAB/SIMULINK environment. To validate the simulation results, a 1 kW, 415 V, 50 Hz experimental setup with an FPGA SPARTAN-6 processor has been developed in the laboratory for QZSIMC and the results are investigated based on the parameters such as output voltage, output current, Switching Stress, Boost Factor, inductor current, capacitor voltage, Total Harmonic Distortion.

Model predictive control (MPC) is recently emerging as an efficient and promising technique for the control of power converters. In the conventional MPC algorithm, the control objectives are usually estimated and evaluated for a large/definite number of switching states. Since prediction and evaluation are done for all possible states, massive amounts of estimations are needed, moreover, the computational burden is more challenging with the increase of control objectives. In this paper, a computationally efficient version of the finite control set-MPC (FCS-MPC) is proposed to decrease the calculation effort of the MPC algorithm likewise minimizing its execution time to enforce its vast application for the control of three-phase power converters. The suggested procedure is to eliminate the current predictions as well as reduce the number of available switching states that need to be estimated by the algorithm which reduces considerably the amount of time consumed by these computations. The studied techniques achieved nearly the same performance with an interesting reduction in the algorithm execution time accomplished by the proposed modified FCS-MPC algorithms.

The energy sector is enduring a momentous transformation with new technological advancements and increasing demand leading to innovative pathways. Artificial intelligence (AI) is emerging as a critical driver of the change, offering new ways to optimize energy systems operations, control, automation, etc. Developing a competitive policy framework aligned with circular economy practices to adapt to the trends of the rapid revolution is crucial, shaping the future of energy and leading the sector in a sustainable, equitable, and impartial direction. This study aims to propose an AI-driven policy framework that aligns with the circular economy business model to address the transformation trend in the development of energy policies through a multidisciplinary approach. The study identifies key trends, various approaches, and evaluates the potential of AI in addressing the challenges. The AI-driven policy paradigm outlines a comprehensive framework and roadmap to harness the potential of AI through a forward-looking policy framework that considers the rapidly changing landscape and the essence of the circular economy. The proposed novel framework provides a roadmap for researchers, governments, and other stakeholders to navigate the future of energy and unlock the potential of AI for a sustainable energy future.

With the increase in the integration of renewable energy resources in the grid and ongoing growth in load demand worldwide, existing transmission lines are operating near their loading limits which may experience voltage collapse in a small disturbance. System stability and security can be improved when the closeness of the system to collapse is known. In this research, voltage stability of IEEE 30 bus test network is analyzed and assessed under continuously increasing load condition, utilizing the Critical Boundary Index (CBI); and improved with continuous integration of battery energy storage system (BESS). BESS is considered to be a hybrid combination of storage units and voltage source converter to have a controllable real and reactive power output. Security constraint optimal power flow is utilized for optimally sizing the installed BESS. It is evident from the outcome of the research that the voltage stability of the system is controlled to be above the acceptable range of 0.3 pu CBI in all lines and the system voltage is kept within the acceptable and constrained range of 0.9–1.1 pu.

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability.

Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.

The rapidly increasing demand for electrical power and difficulties in providing the same using traditional power generating sources provide a motivation to integrate distributed generator (DG) in the radial distribution network. In this work, the optimal arrangement of DG using genetic algorithm (GA) is obtained based on fixed penetration level (PL). The state of charge (SoC) value for each hour before placing the battery based on the demand was estimated, then battery will be placed in the optimal location of the fixed capacity. This will help in reducing the power loss and simultaneous improvement of voltage profile of the network. Furthermore, electric vehicle (EV) is incorporated in the system. In the presence of EV, the loads are variable due to the charging and discharging of batteries in the EV. The variation of network power losses in the presence of EVs along with DGs and battery have been investigated on standard 33 and 69 bus systems for 2 different topologies and 2 different loading scenarios.

A Stable and highly reliable DC link voltage represents an important factor for efficient power transfer in high voltage direct current (HVDC) networks. In this framework, this paper investigates the control and stability analysis of voltage source converter (VSC) for DC link voltage regulation. To separately achieve the independent active and reactive power control, the system voltages and currents are represented in the synchronous reference frame. In order to optimally design the parameters of proportional-integral (PI) controller, the inner and outer loops’ transfer functions are thoroughly derived/developed. In addition,/moreover, in order to attain satisfactory/certain system performance, modulus optimum, symmetrical optimum pole placement control approaches are studied and implemented for the purpose of tuning the voltage/current controller gain parameters. In particular for the symmetrical optimum control method, the gain parameters of the DC-bus voltage are determined under different values of network impedance. Furthermore, the impact/influence of changing gain parameters on the DC-link voltage and poles/zeros movement are investigated. MATLAB/Simulink model is built and simulation studies are carried out to verify the introduced concepts.

Stochastic nature of load demand has a great impact on the performance of electrical power system. As a result, planning of electrical power system considering load uncertainties became inevitable. This paper presents Monte Carlo simulation based different bio-inspired algorithms, grey wolf optimization (GWO), manta ray foraging optimization (MRFO), satin bower bird optimization (SBO) and whale optimization (WOA) to optimize locations of three DG units under load uncertainties considering 500 scenarios. Each scenario includes 50 iterations which means that for each run we have 25,000 iterations and 500 characteristics for different load value. Two objectives are achieved. Firstly, statistically finding the optimal probabilistic location of three DG units under load uncertainties in IEEE 33-bus and IEEE 69-bus radial distribution system based on Monte Carlo simulation integrated with different bio-inspired algorithms. Secondly, comparing between the performances of four different bio-inspired algorithms. Three objective functions are considered, minimizing active power loss, minimizing voltage deviation and maximizing voltage stability index. The active and reactive power demand are normally distributed using normal distribution function. The optimal probabilistic location is investigated considering two cases under load uncertainties, optimizing location of three DG units generally and optimizing location of one DG unit assuming two optimum locations for the other two units extracted from case I. The obtained results (after placing DG units) are compared to the base case (DG units are not connected) and compared to each other according to the optimization technique. The results show that, SBO algorithm superiors other algorithms almost in all cases. Comes next GWO which provide good results generally. However, the good performance obtained by MRFO, it consumes twice the time of other algorithms. WOA however fast convergence, it provides results worse than other algorithms. The system is applied to the well-known IEEE 33-bus and IEEE 69-bus radial distribution system.

A very critical and integral part of the power system is the distribution networks. The final component of the power system, including transmission systems or consumers, is the distribution system (DS). This leads to the highest energy loss that happens. So improving the performance of distribution networks is required not only to provide the reliability of power supply but also to achieve the most economic cost. By optimizing the power flow and simultaneously minimizing the total emission cost and generation cost and taking into account the power losses, these objectives can be achieved. In recent years, heuristic methods are widely employed for solving such complex problems, and The main modern optimization techniques are genetic algorithm(GA). The most important issue in Evolutionary Algorithms is exploration vs. exploitation. Maybe GA is restricted for exploration features, what causes slow convergence and poor robustness Therefore, using the hybridization strategy, the main reason behind this is that such a hybrid approach is expected to create swape between the exploration and exploitation. This work presents performance improvement of a radial distribution networks using a new hybrid optimization technique of Genetic Algorithms (GA) with Equilibrium optimizer (EO) algorithm called Hybrid Genetic Algorithm Equilibrium optimizer (GAEO). It is used for optimum location and size of Renewable Energy Sources (wind energy, photovoltaic, fuel cell) on distribution systems. DG source locations and capacity have strongly influenced the improvement of the distribution network performance by reducing the entire system's power loss, enhancing the voltage profile, reducing fuel costs and emissions of contaminants.

To achieve carbon neutrality by 2050, additional measures must be taken, including the extensive incorporation of renewable energy sources (RESs) [...]

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility.

Energy shortage has always been a problem that the world needs to face. The combined cooling, heating, and power (CCHP) system, as a multi-level energy utilization system that can provide cooling, heating, and electric energy simultaneously, is considered to have good development prospects in alleviating energy problems. In addition, because of the rapid development of electric vehicles (EVs), using EVs as power supply devices has become a hot topic of research. In this paper, EVs are combined with the CCHP system as new power supply equipment, and the influence of the season on the user’s cooling, heating, and power demand is considered. Aiming at the minimum economic cost, the system is optimized by using the PSO algorithm in two operating modes: following electricity load (FEL) and following thermal load (FTL). The final results show that the participation of EVs can reduce costs in both operating modes, especially in FTL mode, which can reduce costs by 4.58%, 13.61%, 12.74%, and 3.57% in spring, summer, autumn, and winter, respectively. In addition, the FEL mode is more economical in spring and winter, and the FTL mode is more economical in summer and winter. In addition, the CO2 emissions in FEL mode are always less than in FTL mode.

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1.

This paper presents a novel single-phase modified Vienna rectifier (MVR) for the harmonic mitigation and power factor improvement of an induction heating (IH) system. The latter employs a high-frequency resonant inverter that is responsible for the generation of high-frequency harmonics, which, in turn, deteriorates the power quality. This mitigation must be done in accordance with harmonic regulations such as IEEE Std. 519-2014,IEC-555, and EN 61000-3-2, etc. Consequently, an MVR is placed between the power supply and the IH system. The proposed novel MVR topology overcomes the limitations of conventional Vienna rectifiers, such as their unbalanced voltage across output capacitors, high ripple at the output DC bus, and high THD in the supply current, etc. The efficacy of the proposed model has been verified by a series of simulations in PSIM. It is followed by a hardware validation using an Arduino Uno ATmega328 digital controller on a 1.2 kW experimental prototype of the IH system. The simulation and experimental results of the power quality indices comply with the IEEE-519 standards.

The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of electricity are recharged and a very high return on investment can be achieved, but with demand response, the return on investment is faster. The results provide a rationale for encouraging infrastructure development in areas that have not yet adopted electric vehicles.

The concept of introducing hybrid off-grid systems has made electricity accessible to areas that are far or have no access to grid network. This paper evaluates the techno-economic and environmental characteristics of a hybrid renewable energy system considering three different scheduling approaches, four different solar tracking systems, two different PV modules and eight scheduling scenarios to supply sustainable electricity to a rural community in Sierra Leone. Each scenario consists of a solar tracking system, a specific type of PV module and a scheduling approach. The aim is to find the most efficient and cost-effective scenario that meets the electrical demands of the village. Results revealed that the ‘Two axis tracking system’ generated the highest PV power, 28.8% additional power compared to the ‘No tracking system’ confirming the superiority of using a tracking system though it comes with initial cost repercussions. Also, systems that employed the use of Canadiasolar Dymond CS6K-285M-FG PV module tend to be more efficient and cost-effective than those that employed Sharp ND-250QCS PV module even with the same solar tracking technology and scheduling approach. From the best scheduling approach (third scheduling), Scenario 7 (SC#7) gives the lowest net present cost (NPC) of $ \$ $ 1.53M with $ \$ $ 0.173/kWh cost of energy (COE) and CO2 emission of 8.54 kg/yr making it the optimum scenario. A daily operation of the optimum scenario on both a sunny and rainy day confirms that the system is capable of supplying the required electricity for both rainy and dry seasons. Sensitivity analyses explain the high reliance of the system cost on the erratic inflation rate, discount rate and PV derating factor. Maintaining a healthy and sustainable environment depends on the minimum load ratio of both the biogas and diesel generators.

In this work we had presented a power system electricity frequency control approach of 100% renewable energy sources isolated power grid by applying model predictive control (MPC). As the PV panel and storage battery price had dramatically dropped in these years, large capacity of PV generation and storage battery had been introduced base on an original 100% renewable energy power system. MPCs had been design for each of the model (Photovoltaic Generation, Wind turbine Generation, Storage Battery, Fuel Cell and Seawater Electrolyzer) to suppress the consumption and generation balance. In addition, in order to suppress the load frequency, demand response (real-time pricing) also had been applied in this scheme by adjust the controllable loads. All of the control approach proposed in this work had been verified by Matlab@/Simulink@. And by discuss and analysis the simulation results, the stability, robustness, reliability and effectiveness of this scheme had been examined.

Background: Some firms with good growth opportunities and additional funds could have difficulties accessing external finance. One possible way to enhance their financial inclusion could be an exciting approach to planning the money reserve collected on a firm’s account. Methods: This article aims to disclose the introduction of financial logistics as the new theoretical field of management science. The authors present, in this paper, the key findings on the development of logistical models of an optimum money reserve calculation taking into account digital transformation and industry 4.0 technologies and optimization methods. Results: The monetary reserve models are analogies of models of storekeeping in supply chains. The specific area of the theoretical research of logistics is shown in this paper, which could be disclosed as the subject of financial logistics as a science. The authors consider the term “Financial Logistics” based on logistics theory and money demand. Conclusions: Authors suggest the methodology of studying the nature of both financial and material flows of resources by comparing the relevant formulas. From the researchers’ points of view, financial logistics could be defined as the theory of managing the cash flows based on the logistical models for calculating a corporation’s cash reserve. The authors find it interesting to expand the conditions for calculating financial flows since the uncertainty of external market conditions always influences actual commercial activity.

This paper proposes an explicit adaptive controller to damp oscillations and to enhance the single machine infinite bus SMIB stability. Owing to the increasing requests for renewable energy and operating conditions, the identification for power systems has been increased recently. Changes in the power system parameters cause to use an explicit self-tuning control. The controller structure consists of combined on-line identifier and a feedback controller as PID and a radial basis function neural network (RBFNN) which acts as an adaptive power system stabilizer for SMIB. An adaptive linear neural network (ADALINE) depending on the input and output of open loop system is employed as on-line model identification to mimic on-line the SMIB output. The difference between SMIB and the identified model responses is used to adjust the ADALANN model weights on-line depending on a recursive least squares principle RLS and a recursive least square with adaptive directional forgetting RLSMadf. The particles swarm optimization (PSO) beside RLS and RLSMadf assess the weights of (RBFNN) and coefficients of PID controllers depending on the on-line ADALINE model weights. The proposed controller is validated with several operating conditions under various disturbances. The simulation results show the proposed controller whose parameters depend on on-line tuning techniques provides better performance than a conventional PID controller.

Sustainable energy transition is generally understood as a concept of developing robust, effective and efficient energy sectors in a particular country or region without compromising the present and future socio-environmental security. In view of this, several countries have made remarkable efforts towards achieving this all-important objective in compliance with modern energy needs and supply requirements. Over half of the reported impoverished people globally reside in the sub-Saharan Africa (SSA) region and a sizeable portion of these people (up to 400 million) live in Nigeria, being the most populated country in Africa. The economic growth and social development of any nation depend remarkably on the sufficiency of its energy sector. Thus, the realization of the Sustainable Development Goals (SDGs) comes the year 2030, as projected by the united nations, in Nigeria and the entire SSA region depends heavily on energy. In this study, the importance of an equitable right to sustainable energy adoption in Nigeria and other countries in Africa towards meeting United Nations SDG’s deadline has been succinctly discussed. The reform activities towards improving the energy sector of Nigeria are discussed and appropriate recommendations are made taking clues from other developing countries’ successful energy transition efforts.

The power system voltage controls are developed by generator excitation control, and power system components such as loads and transformers that generally absorb reactive power. This paper presents implementation of thyristor control series capacitor (TCSC) and auxiliary control that can be deployed in the network to improve the voltage profile of a system-based reactive control. Also, TCSCs can be used as controllable devices in power flow and voltage control devices. Since voltage magnitudes in a system are closely related to the ability of various components to absorb or supply reactive power, TCSC with auxiliary controls are used for transients corresponding to rotor relative motion. Two-area system is used to examine the feasibility of the proposed TCSC, with auxiliary control to improve the grid voltage profile, and network performance. The simulation results prove the effectiveness of the proposed method for voltage profile improvement and network performance.

Recently, an explosive growth in the potential use of natural metaphors in modelling and solving large-scale non-linear optimization problems. Artificial neural network (ANN) is a potent gadget broadly utilized in many data classification tasks. Fundamentally, nature-inspired algorithms have demonstrated their effectiveness and ability over traditional algorithms for generating the optimal ANN parameters, rules and topology that provide the best classification performance with regarding to the quality of the solution, computational cost and avoiding local minima. The literature is vast and growing. This study provides a review on the basic theories and main recent algorithms for optimizing the ANN. Different types of nature-inspired meta-heuristic algorithms are presented; outlining the concepts and components that are used in order to give a summary and ease of the state-of-the-arts to find suitable methods in real world applications for the readers. Additionally, this survey covers the most used type of neural networks, feed-forward neural network (FFNN) in several optimized applications. The performances of FFNNs designed by nature-inspired algorithms have been explored in single and multi-dimensional optimization space; highlighting their models, features, objectives, constraints, etc. to analyse their differences and similarities. A comprehensive survey of the earliest works and recent modified in the last decade in addition to expect approaches has been investigated in details.