A novel semi-quadratic buck-boost structures with continuous input current for PV application.

This paper recommends new design for non-isolated semi-quadratic buck/boost converter with two similar structure that includes the following features: (a) the continuous input current has made it reasonable for PV solar applications and reduced the value of the capacitors in the input filter reducing the input ripple as well as EMI problems; (b) the topology is simple, and consists of a few numbers of components; (c) the semiconductor-based components have lower current/voltage stresses in comparison with the recently recommended designs; (d) semi-quadratic voltage gain is D (2 - D) / (1 - D)2; (e) 94.6 percent from the theoretical relations and 91.8 percent from the experimental for the output power of 72W, the duty of 54.2 percent, and output voltage of 72 V are the efficiency values in boost mode; (f) 89.3 percent from the theoretical relations and 87.2 percent from the experimental for the output power of 15W, the duty of 25.8 percent, and output voltage of 15 V are the efficiency values in buck mode. One structure is the continuous output current and negative output polarity, and other structure is positive output polarity. The recommended topologies have been studied in both ideal and non-ideal modes. The continuous current mode (CCM) is the suggested mode for the proposed converters. Moreover, the requirements of CCM have been discussed. The various kinds of comparisons have been held for voltage gain, efficiency, and structural details, and the advantages of the suggested design have been presented. A small-signal analysis has been completed, and the suitable compensator has been planned. Finally, PLECS simulation results have been associated with the design considerations.

A new single switch universal supply voltage DC-DC converter for PV systems with MGWM-AFLC MPPT controller.

The present power generation government companies focus on Renewable Power Sources (RPS) because their features are zero carbon footprint, unlimited power source, fewer greenhouse pollutants, fewer output wastages, plus creatinga very healthy atmosphere. In this work, the sunlight source is utilized for the Photovoltaic (PV) standalone network. The merits of sunlight sources are very optimal human resources needed, unlimited natural sources, plus easy operation. However, the solar power resource is nonlinear fashion. As a result, the operating point of the sunlight network fluctuates concerning sunlight intensity. So, in this article, the Modified Grey Wolf Methodology with Adaptive Fuzzy Logic Controller (MGWM-AFLC) is introduced to maintain the operating point of the sunlight system at the global power point position of the PV array. This controller traces the MPP with very low fluctuations in the PV-produced voltage. The advantages of this proposed method arefewer sensing devices required, less difficulty in development, more useful for rapid changes inthe sunlight temperatures, simpler to realize operation, greater economic growth, plus highly useful for household applications. The sunlight set-up generation voltage is lowwhich is improved by introducing the new Wide Power Rating High Voltage DC-DC Boost Converter (WPRHVBC). The features of this WPRHV converter are low voltage strain on semiconductor devices, few passive elements are enough to develop the circuit, plus easy understanding.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency.

Economic development relies on access to electrical energy, which is crucial for society's growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia's Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95-1.025 p.u.). After reviewing the current distribution network's operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system's integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Fuzzy analytic hierarchy process based generation management for interconnected power system.

Decision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller's performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia.

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals.

In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.

A novel on high voltage gain boost converter with cuckoo search optimization based MPPTController for solar PV system.

Traditionally, isolated and non-isolated boost converters are used for solar photovoltaic systems (SPV). These converters have limitations such as low voltage gain, less voltage ripples, temperature dependence, high voltage stress across the switches, and being bulky in size. Besides, the solar PV system also has non-linear characteristics between I-V and P-V, and the energy yield potential is affected by partial shading phenomena. Therefore, maximum power point tracking (MPPT) is being added to the SPV system to get the maximum output power under steady and dynamic climate conditions. Although the conventional MPPT has drawbacks such as less accuracy in predicting the MPP under partial shading conditions, low tracking speed, and more ripples, Hence, the research proposes a stackable single switch boost converter (SSBC) with a Cuckoo search MPPT controller for the SPV system. The efficiency of the proposed circuit topology has been compared with conventional boost converters with various MPPTs. Subsequently, the accuracy of tracking true MPPT by CSO is compared with that of PSO and FPNA. The results show, that the CMPPT with CBC has produced more ripples, whereas the BMPPT with SSBC produces ripple-free power under steady conditions. It is also observed that SSBC with BMPPT produces more power than SSBC with TMPPT. The efficiency of SSBC with BMPPT is better than other combinations. Finally, a prototype model has been developed and verified.

Optimal sizing and location of grid-interfaced PV, PHES, and ultra capacitor systems to replace LFO and HFO based power generations.

The impacts of climate change, combined with the depletion of fossil fuel reserves, are forcing human civilizations to reconsider the design of electricity generation systems to gradually and extensively incorporate renewable energies. This study aims to investigate the technical and economic aspects of replacing all heavy fuel oil (HFO) and light fuel oil (LFO) thermal power plants connected to the electricity grid in southern Cameroon. The proposed renewable energy system consists of a solar photovoltaic (PV) field, a pumped hydroelectric energy storage (PHES) system, and an ultra-capacitor energy storage system. The economic and technical performance of the new renewable energy system was assessed using metrics such as total annualized project cost (TAC), loss of load probability (LOLP), and loss of power supply probability (LPSP). The Multi-Objective Bonobo Optimizer (MOBO) was used to both size the components of the new renewable energy system and choose the best location for the solar PV array. The results achieved using MOBO were superior to those obtained from other known optimization techniques. Using metaheuristics for renewable energy system sizing necessitated the creation of mathematical models of renewable energy system components and techno-economic decision criteria under MATLAB software. Based on the results for the deficit rate (LPSP) of zero, the installation of the photovoltaic field in Bafoussam had the lowest TAC of around 52.78 × 106€ when compared to the results for Yaoundé, Bamenda, Douala, and Limbe. Finally, the project profitability analysis determined that the project is financially viable when the energy produced by the renewable energy systems is sold at an average price of 0.12 €/kWh.

HARNet in deep learning approach-a systematic survey.

A comprehensive examination of human action recognition (HAR) methodologies situated at the convergence of deep learning and computer vision is the subject of this article. We examine the progression from handcrafted feature-based approaches to end-to-end learning, with a particular focus on the significance of large-scale datasets. By classifying research paradigms, such as temporal modelling and spatial features, our proposed taxonomy illuminates the merits and drawbacks of each. We specifically present HARNet, an architecture for Multi-Model Deep Learning that integrates recurrent and convolutional neural networks while utilizing attention mechanisms to improve accuracy and robustness. The VideoMAE v2 method ( https://github.com/OpenGVLab/VideoMAEv2 ) has been utilized as a case study to illustrate practical implementations and obstacles. For researchers and practitioners interested in gaining a comprehensive understanding of the most recent advancements in HAR as they relate to computer vision and deep learning, this survey is an invaluable resource.

A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm.

Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

Application of DSO algorithm for estimating the parameters of triple diode model-based solar PV system.

Solar Photovoltaic (SPV) technology advancements are primarily aimed at decarbonizing and enhancing the resiliency of the energy grid. Incorporating SPV is one of the ways to achieve the goal of energy efficiency. Because of the nonlinearity, modeling of SPV is a very difficult process. Identification of variables in a lumped electric circuit model is required for accurate modeling of the SPV system. This paper presents a new state-of-the-art control technique based on human artefacts dubbed Drone Squadron Optimization for estimating 15 parameters of a three-diode equivalent model solar PV system. The suggested method simulates a nonlinear relationship between the P-V and I-V performance curves, lowering the difference between experimental and calculated data. To evaluate the adaptive performance in every climatic state, two different test cases with commercial PV cells, RTC France and photo watt-201, are used. The proposed method provides a more accurate parameter estimate. To validate the recommended approach's performance, the data are compared to the results of the most recent and powerful methodologies in the literature. For the RTC and PWP Photo Watt Cell, the DSO technique has the lowest Root Mean Square Error (RMSE) of 6.7776 × 10-4 and 0.002310324 × 10-4, respectively.

Techno-economic optimization for isolated hybrid PV/wind/battery/diesel generator microgrid using improved salp swarm algorithm.

The main objective of this study is to develop a new method for solving the techno-economic optimization problem of an isolated microgrid powered by renewable energy sources like solar panels, wind turbines, batteries, and diesel generators while minimizing greenhouse gas emissions. An Improved Salp Swarm Algorithm (ISSA) with a position adaptation mechanism for the salp leader that involves a leader salp that moves about depending on both food availability and its previous position has been proposed to overcome the convergence problem. In the original SSA, as the approach converges, it can no longer find optimal solutions and becomes trapped in a local minimum. Three Microgrid System (MS) configurations are discussed: PV/WT/BESU/DG, PV/BESU/DG, and WT/BESU/DG. The proposed method seeks to find a middle ground between technical criteria and environmental concerns when deciding on PV, WT, BESU, and DG sizes. The findings indicate that the proposed ISSA approach gives superior results compared to other well-known algorithms like the original SSA, the Ant Lion Optimizer (ALO), the Dragonfly Approach (DA), and the Moth-Flame Optimization Algorithm (MFO), which, after significant investigation, has been proven to help determine the appropriate microgrid size. With PV sizes of 10, 9 WT, 24 BESU, and 3 DG, the PV/WT/BESU/DG configuration offers the highest level of cost-effectiveness with Cost of Energy (COE) of 0.2109 $/kWh, Net Present Cost (NPC) of 376,063.8 $, Loss of Power Supply Probability (LPSP) of 4%, Renewable Energy Fraction (REF) of 96%, and CO2 emission of 12.4457 tons/year. ISSA is brought up as a possible solution to both the problem of rising energy prices and the difficulties inherent in microgrid design.

An efficient energy management scheme using rule-based swarm intelligence approach to support pulsed load via solar-powered battery-ultracapacitor hybrid energy system.

This work presents an energy management scheme (EMS) based on a rule-based grasshopper optimization algorithm (RB-GOA) for a solar-powered battery-ultracapacitor hybrid system. The main objective is to efficiently meet pulsed load (PL) demands and extract maximum energy from the photovoltaic (PV) array. The proposed approach establishes a simple IF-THEN set of rules to define the search space, including PV, battery bank (BB), and ultracapacitor (UC) constraints. GOA then dynamically allocates power shares among PV, BB, and UC to meet PL demand based on these rules and search space. A comprehensive study is conducted to evaluate and compare the performance of the proposed technique with other well-known swarm intelligence techniques (SITs) such as the cuckoo search algorithm (CSA), gray wolf optimization (GWO), and salp swarm algorithm (SSA). Evaluation is carried out for various cases, including PV alone without any energy storage device, variable PV with a constant load, variable PV with PL cases, and PV with maximum power point tracking (MPPT). Comparative analysis shows that the proposed technique outperforms the other SITs in terms of reducing power surges caused by PV power or load transition, oscillation mitigation, and MPP tracking. Specifically, for the variable PV with constant load case, it reduces the power surge by 26%, 22%, and 8% compared to CSA, GWO, and SSA, respectively. It also mitigates oscillations twice as fast as CSA and GWO and more than three times as fast as SSA. Moreover, it reduces the power surge by 9 times compared to CSA and GWO and by 6 times compared to SSA in variable PV with the PL case. Furthermore, its MPP tracking speed is approximately 29% to 61% faster than its counterparts, regardless of weather conditions. The results demonstrate that the proposed EMS is superior to other SITs in keeping a stable output across PL demand, reducing power surges, and minimizing oscillations while maximizing the usage of PV energy.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization.

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

DGS-SCSO: Enhancing Sand Cat Swarm Optimization with Dynamic Pinhole Imaging and Golden Sine Algorithm for improved numerical optimization performance.

This paper introduces DGS-SCSO, a novel optimizer derived from Sand Cat Swarm Optimization (SCSO), aiming to overcome inherent limitations in the original SCSO algorithm. The proposed optimizer integrates Dynamic Pinhole Imaging and Golden Sine Algorithm to mitigate issues like local optima entrapment, premature convergence, and delayed convergence. By leveraging the Dynamic Pinhole Imaging technique, DGS-SCSO enhances the optimizer's global exploration capability, while the Golden Sine Algorithm strategy improves exploitation, facilitating convergence towards optimal solutions. The algorithm's performance is systematically assessed across 20 standard benchmark functions, CEC2019 test functions, and two practical engineering problems. The outcome proves DGS-SCSO's superiority over the original SCSO algorithm, achieving an overall efficiency of 59.66% in 30 dimensions and 76.92% in 50 and 100 dimensions for optimization functions. It also demonstrated competitive results on engineering problems. Statistical analysis, including the Wilcoxon Rank Sum Test and Friedman Test, validate DGS-SCSO efficiency and significant improvement to the compared algorithms.

Geometric trust-based secure localization technique for resiliency of GPS outage and location error in vehicular cyber-physical systems (VCPS).

Management of vehicle traffic is a challenging task as it is non-deterministic by nature. Vehicular Cyber-Physical Systems (VCPS) is the emerging field of dynamics of vehicle management. Vehicle localization is considered an important task in VCPS. Many researchers proposed methodologies for this based on the Global Positioning System (GPS) which poses few location identification errors. Also, there are more vulnerabilities to the existing vehicular positioning system due to Zig-Zag attacks and bad-mouth attacks. In this work, an error-free and secure environment for communication between dynamically moving vehicle models has been proposed. In our proposed model a localization technique based on mathematical geometry which is capable of GPS outages and encompasses the dynamism of vehicle and on-road trajectory has been developed. The proposed model includes Extended Kalman filter-based routing to predict the neighbouring vehicle position. To avoid vulnerabilities created by the malicious nodes, a trust-based computation is performed by each node on its neighbours perceiving the authenticity of received messages. To validate the methodology, NS2 tool has been used to simulate the VCPS and to test the efficiency with different scenarios such as erroneous location, GPS outage, and malicious attack. The result shows that the proposed approach is more optimal and secure than the existing methodologies.

Analysis and mitigation of PQ disturbances in grid connected system using fuzzy logic based IUPQC.

Renewable energy integration introduces grid instability due to variable and intermittent sources like solar and wind, impacting reliability. This paper provides a thorough discussion of recent advancements and emerging trends in grid-integrated wind energy systems (GIWES) and grid-integrated solar energy systems (GISES). More than 70 research articles have been rigorously assessed and listed the technological and economic challenges. The increase in installations of grid-Integrating systems gives rise to challenges like as grid strain, peak shaving impacts, unpredictability of renewable energy sources (RES), and power quality disturbances. A variety of custom power devices, such as dynamic voltage restorers (DVR), static synchronous compensators (STATCOM), active power filters (APF), and unified power quality conditioners (UPQC), have gained popularity in response to these challenges. Among the various challenges, power quality disturbances, including voltage sag, swell, current and harmonics pose significant issues. To address these disturbances this work present a novel approach utilizing fuzzy logic (FL) to develop multi-feeder interline unified power-quality conditioners (MF-IUPQCs). The MF-IUPQC has three legs and three levels, each of which has four diode-clamped inverters. Switching is carried out through the use of space vector pulse width/duration modulation (SVPWM). Total harmonic distortion (THD) induced by nonlinear loads is reduced by the FLC-based MF-IUPQC, which also improves dynamic performance and offers a smooth DC-link voltage. The proposed control mechanism is implemented using MATLAB/Simulink. The fuzzy-based controller is compared to the industry-standard proportional-integral (PI) controller to determine its efficacy. Among them, the MF-IUPQC based on FLC delivers the smoothest voltage profile and the lowest THD.

A cost-effective smart metering approach towards affordable deployment strategy.

Revamping the power grid into a smart grid and modernizing it with advanced metering infrastructure are essential steps in addressing ongoing energy challenges. Smart meters play a pivotal role in power grid modernization by providing real-time energy-related data which fuels the control activities of modern grid. While the advantages of smart meters are evident, their deployment necessitates a comprehensive redesign of the grid architecture, involving smart end devices for monitoring and communication networks for efficient data exchange. Yet, achieving cost-effective and widespread adoption of these technologies poses a challenge, particularly in developing and underdeveloped nations due to high capital costs, technological constraints and uneconomical deployment strategies. Moreover, the prevailing research often advocates a complete transition to new smart meters to achieve 'smartness,' neglecting the potential of existing metering infrastructure upgrades. To address these concerns, this study proposes and simulates the design of a low-cost Smart Network Meter. Notably, this meter upgrades the existing meter infrastructure while validating a cost-effective deployment strategy. Furthermore, a consumer opinion survey was also conducted to compelling evidence supporting the adoption of the proposed low-cost smart metering solution.

Feasibility and techno-economic analysis of PV-battery priority grid tie system with diesel resilience: A case study.

Ethiopia is close to the equator and has enormous potential as a solar energy resource that has yet to be realized. The country has some small-scale diesel-based power generation, and all universities and government agencies have installed standby generator sets for supplying power when the grid is interrupted. Ethiopia is a developing nation with a significant reliance on oil imports and insufficient rural electrification, which exacerbates the problem of poverty. The increased cost of oil, frequent hard currency expenditures for oil, and exaggerated maintenance costs for the generators are the main reasons to undertake this feasibility study. As the assessments show, the annual average interruption was more than 800 h in the past five years in the Debre Markos University distribution feeder. A preliminary study on the techno-economic feasibility of the existing diesel generator set and PV system with the same rated power of 500 kW is conducted in this work. As the break-even point of the economic comparison shows, the existing diesel generator is not economically feasible as compared to the proposed PV-battery priority grid tie system due to high running and service costs. The study also shows the technical feasibility of solar energy, as the P-V and I-V characteristic curves illustrated on a single standard solar module indicate. As the economic comparison break-even point shows, the diesel-based generator set is not economical after 5 years due to the high operational and maintenance costs. As the results show, the proposed PV/Battery Priority system with a diesel generator for resilience is more environmentally friendly, reducing the carbon footprint by 94%.

Hybrid GWO-PSO based optimal placement and sizing of multiple PV-DG units for power loss reduction and voltage profile improvement.

Distributed generation (DG) is integrated in a passive distribution system to reduce power loss, improve voltage profile, and increase power output. To reap the most benefits of the distribution system, the best location and appropriate DG size must be determined. This paper presents a hybrid Grey wolf Optimizer (GWO) and Particle swarm optimization (PSO) approach for determining the best placement and DG size while considering a multi-objective function that includes active and reactive power loss minimization as well as voltage profile enhancement. Dilla distribution system was used as a case study and the weighted technique was used to convert to a single objective function while taking into account multiple constraints such as bus voltage limit, DG output limit, and branch current limit. DG penetration is limited to up 60% of the total active load on the feeder and a forward-backward sweep load flow algorithm was used to generate the load flow solutions. The findings of the study show that combining three PV-DGs (Case 3) is the best way to improve voltage profile and minimize losses. In addition, the proposed hybrid GWO-PSO algorithm performed better compared to the other four algorithms (Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Particle swarm optimization (PSO), and sine cosine algorithm (SCA)) in terms of achieving the best multi-objective function (MOF) outcome.