Power Loss Research Articles

The First Optimisation of a 16 kV 4H-SiC N-Type IGCT

A challenge in the development of Silicon carbide (SiC) gate turn-off thyristors lie in an uneven transient behaviour, necessitating expensive snubbers. To address these limitations and simplify circuit topology we present an optimized 16 kV n-type SiC integrated gate commutated thyristor (IGCT) design, which utilises a novel highly doped base strip (HDBS). A particular focus is on optimizing the gate commutation of the GCT during switching, and the trade-offs in the HDBS base design were investigated. The findings reveal that compared with conventional GCT design, the HDBS design under high current conditions recorded a 11.8% reduction in turn-off power losses. When simulating the device in a high-voltage scenario, the HDBS IGCT demonstrated a 3.9% reduction in turn-off power losses and an improved turn-on power loss performance. This resulted in a reduction of power losses by 12.1% and 2.3% in high current and high voltage conditions, respectively. In summary, the novel SiC HDBS IGCT design paves the way towards a secure, high current density, and low loss switching SiC thyristor device.

A new hybrid fixed and adaptive gains control algorithm to reduce power losses on LLCL filter-based renewable energy conversion systems with systematic parametrization using Grey Wolf optimizer

Currently, there is a transition in the energy matrix around the world, where traditional sources of energy generation are continually being replaced by energy generation systems based on renewable sources to mitigate the climate crisis. In this bias, this work presents the mathematical modeling of an LLCL filter, used to connect power generation systems based on renewable energy sources to the electrical grid, and presents a novel hybrid fixed-and adaptive gains control strategy for current injection into the grid using this system. The developed hybrid controller is composed of a proportional–integral controller and a direct robust adaptive controller. The first term of the controller guarantees the reference current, while the second term of the controller is used for disturbance rejection. Furthermore, a systematic procedure for the controller’s parametrization based on Grey Wolf Optimizer is also provided. The control of the current injected into the grid is carried out considering the LLCL filter without passive damping resistors in the filter structure to avoid power losses due to the passive filter elements. Additionally, the LLCL filter model considers minimal parasitic resistances to evaluate the controller’s performance and optimize it for the application of interest, aiming to maximize the system performance by ensuring a short transient regime due to the fast closed-loop system response. Simulation results indicate high performance of this optimized control strategy with small tracking error even considering grid impedance variations.

Mission-oriented dynamic reconfiguration of airborne photovoltaic array based on multidisciplinary model

The solar-powered stratospheric airship is demonstrated overwhelming superiority because of affordable cost, recyclable material and low carbon emission. Progress has been made in the schemes of energy system consisting of curved photovoltaic array, storage, and energy management devices. However, research on various flight conditions and the resulting presence of non-uniform irradiance on the airborne solar array is rare. Moreover, the methods to reduce the power losses of on-board solar array are not investigated. This paper proposed a dynamic reconfiguration method to improve the output performance of on-board solar array in different mission conditions. The partial shading conditions of airborne and ground-based solar arrays are compared. The multiple traveling salesman problem with solar array is introduced. The Simulated annealing algorithm is adopted. The real-time optimization of solar array is obtained with the dynamic reconfiguration method according to the operating condition consisting of various parameters. The results show the proposed method effectively improves the output power of solar array compared with common total-cross-tied connections. The improvements of daily output energy of the airborne array are more than 13.7 %. The work provides a conducive reference for realizing the utilization prospect of solar energy system on aerial vehicles in a low carbon future.

Evaluation of techno-economic design andimplementation of solar-wind hybrid microgridsystem for a small community

Pakistan is faces significant challenges in meeting its energy demand and consumption needs for consumers. This country's energy production from primary sources such as petroleum and natural gasses is incompetent in fuel-use and hence unable to meet feasibility cost. With an increasing population, Pakistan's energy consumption per capita has been steadily rising. This behaviour is leading to critical energy issues, especially in remote rural areas. This trend in rising energy costs and demand factors are similar to those in the energy markets in the South and South-East Asia. The primary energy sources in Asia continent, including fossil fuels, are insufficient supply to meet this growing demand in production and thus resulting in frequent electricity blackouts. Consequently, renewable energy sources such as solar photovoltaic (PV) and wind power have substantially started to produce energy and to provide a huge portion of Pakistan's daily energy needs apart in conventional energy currently. However, these sources are not yet as reliable, conventional energy bases have a challenge for sustainable energy production. As a result, renewable energy factors nonetheless initial started have effectively stabilized energy consumption, particularly for green electricity with net-zero carbon emissions. The aim of this study is to evaluate the feasibility and cost-effectiveness of integrating a microgrid hybrid system with combined (solar PV/wind power) renewable energy as well as conventional fossil fuel generators. This evaluation focuses on predicting energy production and its costs using Hybrid Optimization of Multiple Energy Resources (HOMER) software, and to enhance the electricity standards at NUST (National University for Sciences and Technology), Pakistan. The proposed methodology of microgrid hybrid system, when evaluated using HOMER software, shows a significant improvement in energy stability and cost efficiency. Moreover, this proposed system can reduce reliance on fossil fuels by a substantial percentage, enhances the predictability of energy production, and optimizes its energy consumption. These can achieve better performance metrics in terms of reliability, cost, and environmental impact; feasible solution for Pakistan and the developing countries. This proposed methodology offers a novel approach by integrating renewable energy sources with conventional generators to create a balanced and efficiency factor by microgrid system. This hybrid system goals as an investigation is to optimize this energy production, reduce carbon emissions, and provide a more stable and cost-effective energy supply.

Optimized the locations and sizes of FACTS devices on electrical network involving wind power using a new hybrid stochastic algorithm

This article focuses on utilizing a new hybrid optimization algorithm called the Fitness-Distance Balance-based Archimedes Optimization Algorithm (FDB-AOA) to solve the Optimal Power Flow (OPF) problems within a recently adopted electrical transmission grid, specifically the modified IEEE-30 bus system. This system integrates thermal and wind -based generating units, including various types of Flexible AC Transmission System (FACTS) devices. Several tests are performed where the stochastic wind energy is modeled using probability density functions. The optimization goal takes into account the cost of thermal generation, the direct cost of scheduled wind power, and the penalty cost for underestimating wind power. The locations and sizing of FACTS devices are optimized with aim of reducing several fitness functions. The optimization results achieved by the proposed method in solving single objective functions were more effective in finding the optimal solution compared to several well-known algorithms. The results show the superiority of the proposed method in the majority of case studies, as it achieved a better optimum solution with a total generation cost ( Cgen ) value of 806.9817 $/h, and a real power loss ( Ploss ) value 1.7619 MW, also yields a competitive gross cost ( Cgross ) value of 1104.6652 $/h compared to those obtained by the other algorithms. In contrast, the statistical analysis proven the superiority of this algorithm where the standard deviations (SD) required in solving the single objective problem ( Cgen ) is 0.0996, which are better compared to other techniques. the simulation results demonstrate that the FDB-AOA optimizer is robust than other approaches, like the Success history based-adaptive differential evolution (SHADE) algorithm, MSA (Moth Swarm Algorithm), and ABC (artificial bee colony) integrated with the SF (superiority of feasible solutions) approach, in solving OPF problems involving the integration of both thermal and wind power plants along with FACTS devices.

Pseudo-Core-Shell Permalloy (Supermalloy)@ZnFe2O4 Powders and Spark Plasma Sintered Compacts Based on Mechanically Alloyed Powders.

Soft magnetic composite cores were produced by spark plasma sintering (SPS) from Ni3Fe@ZnFe2O4 and NiFeMo@ZnFe2O4 pseudo-core-shell powders. In the Fe-Ni alloys@ZnFe2O4 pseudo-core-shell composite powders, the core is a large nanocrystalline Permalloy or Supermalloy particle obtained by mechanical alloying, and the shell is a pseudo continuous layer of Zn ferrite particles. The pseudo-core-shell powders have been compacted by SPS at temperatures between 500-700 °C, with a holding time of 0 min. Several techniques were used for the characterisation of the powders and sintered compacts: X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, magnetic hysteresis measurements (DC and AC), and electrical resistivity. The electrical resistivity is stabilised at values of about 7 × 10-3 Ω·m for sintering temperatures between 600-700 °C and this value is three orders of magnitude higher than the electrical resistivity of sintered Fe compacts. The best relative initial permeability was obtained for the Supermalloy/ZnFe2O4 composite compacts sintered at 600 °C, which decreases linearly for the entire frequency range studied, from around 95 to 50. At a frequency of 2000 Hz, the power losses are smaller than 1.5 W/kg. At a frequency of 10 kHz, the power losses are larger, but they remain at a reduced level. In the case of Supermalloy/ZnFe2O4 composite compact SPS-ed at 700 °C, the specific power losses are even lower than 5 W/kg. The power losses' decomposition proved that intra-particle losses are the main type of losses.

Usability evaluation of electronic health records at the trauma and emergency directorates at the Komfo Anokye teaching hospital in the Ashanti region of Ghana

BackgroundElectronic health records (EHRs) are currently gaining popularity in emerging economies because they provide options for exchanging patient data, increasing operational efficiency, and improving patient outcomes. This study examines how service providers at Ghana's Komfo Anokye Teaching Hospital adopt and use an electronic health records (EHRs) system. The emphasis is on identifying factors impacting adoption and the problems that healthcare personnel encounter in efficiently using the EHRs system.MethodA quantitative cross-sectional technique was utilised to collect data from 234 trauma and emergency department staff members via standardised questionnaires. The participants were selected using the purposive sampling method. The Pearson Chi-square Test was used to examine the relationship between respondents' acceptability and use of EHRs.ResultsThe study discovered that a sizable number of respondents (86.8%) embraced and actively used the EHRs system. However, other issues were noted, including insufficient system training and malfunctions (35.9%), power outages (18.8%), privacy concerns (9.4%), and insufficient maintenance (4.7%). The respondents' comfortability in using the electronic health record system (X2=11.30, p=0.001), system dependability (X2=30.74, p=0.0001), and EHR's ability to reduce patient waiting time (X2=14.39, p=0.0001) were all strongly associated with their degree of satisfaction with the system. Furthermore, respondents who said elects increase patient care (X2= 75.59, p = 0.0001) and income creation (X2= 8.48, p = 0.004), which is related to the acceptability of the electronic health records system.ConclusionThe study revealed that comfort, reliability, and improved care quality all had an impact on the EHRs system's acceptability and utilization. Challenges, including equipment malfunctions and power outages, were found. Continuous professional training was emphasized as a means of increasing employee confidence, as did the construction of a power backup system to combat disruptions. Patient data privacy was highlighted. In conclusion, this study highlights the relevance of EHRs system adoption and usability in healthcare. While the benefits are obvious, addressing obstacles through training, technical support, and infrastructure improvements is critical for increasing system effectiveness.

Enhancing acceleration data cleaning through time–frequency feature integration using deep learning

In recent years, the safety issues of bridge has garnered significant attention from various sectors of society. Bridge structural health monitoring (SHM) systems have emerged as essential tools for real-time monitoring and assessing the operational safety of bridges. Despite the substantial implementation and operation of monitoring systems, challenges have arisen from abnormal data caused by equipment malfunctions, power outages, and environmental interference. These anomalies are challenging to manually remove within the vast dataset. To address the challenge of automated detection and classification of anomalies, this paper focuses on acceleration data and proposes a data cleaning method that integrates time–frequency features and one-dimensional convolutional neural network (1D CNN). The method utilizes extreme value envelopes and wavelet scattering transforms to extract time–frequency features from acceleration data, thereby achieving data dimensionality reduction and feature extraction goals. Subsequently, these features are employed to train and test a 1D CNN. The proposed method’s validation is conducted using acceleration monitoring data obtained from a large-span arch bridge. Through extensive testing, the model demonstrates a classification accuracy exceeding 98% on both the test set and unseen data, showcasing high classification precision and generalization capabilities. This approach provides an effective solution for automated data cleaning and anomaly identification within the context of bridge health monitoring systems.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model’s better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Experimental test of optimizing maximum power point tracking performance in solar photovoltaic arrays based on backstepping control and optimized by genetic algorithm

Maximum Power Point Tracking (MPPT) is a key component of modern solar photovoltaic (PV) systems. It improves the efficiency of PV power generation systems by inevitably reducing power losses. The most developed MPPT methods adopted like hybrid approaches combine algorithms with nonlinear controllers, such as backstepping controller (BSC). Despite their prevalence, there is still a lack of standardized procedures for tuning the parameters of these nonlinear controllers. In response to this challenge, a proposed method is adopted to identify the optimal gains using Genetic Algorithm (GA) for an efficient power tracking. The advantage of this strategy is established by real-time testing with the GA-BSC technique, which validates its capacity to generate appropriate controller gains for improved tracking performance. Experimental comparisons reveal that the modified GA-BSC approach significantly outperforms the conventional GA-BSC strategy. The conventional GA-BSC method falls short due to its use of non-optimal gains, leading to less effective reference voltage tracking and lower overall performance. In contrast, the proposed method benefits from optimal gains, resulting in enhanced stability and faster response times—achieving maximum power point tracking in 0.2 s compared to 0.6 s for the P&O-BSC method. Another experiment was conducted to test the robustness of the proposed method under variable irradiation profile. The results demonstrated the efficiency of the improved GA-BSC approach compared to other methods, which was more stable and faster. These improvements highlight the capability of the proposed method to enhance hybrid method efficiency, thereby leading to increased efficiency and reliability in photovoltaic systems.

Strengthening energy system resilience planning under uncertainty by minimizing regret

This study applies the concept of regret in decision-making under uncertainty to an energy system optimization model to identify optimal robust and stochastic solutions amongst several design options. The approach is demonstrated on the case study of Accra, Ghana, considering uncertainties pertinent to the city, particularly under climate change. The evaluated uncertainty scenarios consider volatile fossil fuel supply, reduced hydropower generation, rising demand due to climate change-driven rural-urban migration and global warming, unplanned power outages due to increasing natural disasters, and currency depreciation. The evaluated systems include Pareto-optimal system solutions typically under consideration by planners, which balance costs and CO2 emissions. The regret performance is evaluated for each system subject to each uncertainty scenario. A near-CO2-minimized system is the optimal robust and stochastic least-regret solution. Two factors drive this result: (1) a diverse technology set, which provides generation and cross-sectoral flexibility for adaptation under uncertainty, and (2) effectively balancing rising investment and operation costs with decreasing unmet demand costs. The demonstrated method provides energy planners and policymakers with a pragmatic, effective and fast approach, which offers new insights into long-term energy system planning to improve resilience under uncertainty, supporting the aims of the United Nations Sustainable Development Goals 7 and 11.

Power converters design and experimental verification for electromagnetic contactors to reduce the impact of the voltage sag in the power system

This research aims to offer a power supply system based on power converters to supply electromagnetic contactors from the power distribution grid. Its primary purpose is to minimise the voltage sag impact on the contactor, eliminating the possibility of contacts tripping or excessive vibration. In addition to this, the contactor's inherent low power factor must be compensated, reducing the contactor's impact on the power grid. To complete these requirements, the power supply system comprises a Totem-pole PFC converter, a Buck converter stabilising the supplied voltage, and an H-Bridge, decreasing the switch-off commutation time. The research focuses on the converters' design methodology, supported by a case study and experimental verification. Considering the electromagnetic contactor characteristics, the system design requirements are systemised. The analytically and experimentally obtained data shows that based on the chosen converters' topologies, the suggested power supply system provides contactor stable operation in a wide input voltage range, acceptable power losses and high efficiency.

Selection of sectionalising switch to address any switching failure in a droop controlled islanded microgrid

This paper presents a heuristic approach to address the restoration of droop-controlled islanded microgrids (DCIMGs) in the event of a sectionalising switch (SS) failure. The proposed approach consists of two-step procedures to ensure the smooth operation of the network. First, a faulty SS is isolated by replacing it with a tie switch (TS) that acts as an SS. Then, the switching configuration is adjusted to improve the performance of DCIMGs. To assess the efficacy of the proposed approach, the 33-bus and 118-bus DCIMG test networks are considered. For fulfilling the load demand in each DCIMG, droop-controlled DGs (DDGs) and renewable DGs (RDGs) are employed. Along with satisfying load demand, DGs decrease the power loss and voltage drop. Additionally, shunt capacitor banks (SCBs) are also incorporated into the DCIMG to improve the voltage profile. In conjunction with the load flow analysis, the power injection of the DDGs are determined using the modified Newton–Raphson method. For determining the power injection of RDGs and the size of SCBs, particle swarm optimisation method is implemented. The paper investigates the effectiveness of the proposed method for resolving SS faults in DCIMGs over a 24 hour period amidst hourly demand and renewable power generation variation. By employing Hong’s 2m+1 point estimate method, the uncertainties associated with load demands and renewable power generation are also considered. The effectiveness of the issue formulation is verified using MatLabR2023b. With the proposed method, the voltage and frequency can be effectively maintained after the network is restored following a fault.

Performance optimization of triple tube heat exchanger using aluminum metal foam: A comprehensive numerical investigation

Metal foam is a porous medium with high porosity, convoluted flow pathways, strength-to-weight ratio, and thermal conductivity employed in heat exchanges and other engineering applications because of its properties. Therefore, a novel circular tube filled with aluminum metallic foam is proposed to improve hydraulic thermal performance and reduce pumping power losses and metal foam volume. The current work considers the Brinkman-Forchheimer Extended Darcy model for forced convection flow and the local thermal equilibrium (LTE) model for heat transfer under a constant thermal boundary condition. The governing equations are solved using the finite volume method (FVM). The variable parameters are the pore per inch (PPI) of 10–50, the porosity εrange of 0.79–0.95, and the Reynolds number range of 2500–7000. The results show that the performance index (PI) has its maximum value of 6.8 at 10PPI and ε =0.95 in the heat exchanger; this enhancement at 10PPI is 13% and 34.48% more as compared to the PI value at ε =0.85 and ε =0.79 and at a fixed inner annulus Re of 7000. The average Nusselt number (Nu) increased by 81.63%, 104.3%, and 120% compared to the foamless tube at 10, 20, and 30PPI andε =0.95, respectively, in an intermediate tube. Heat transfer coefficients (HTC) considerably increase compared to foamless tubes nearly 3.2 times, 3.8 times, and 4.2 times at 10, 20, and 30PPI, respectively, at 2500 Re and 144%, 175% and 200% more at 7000 Re. The maximum reduction ratio in normalized friction factor ratio (fP/fNoP) at 10PPI is approximately 50% compared to (fP/fNoP) at 30PPI and 25% at 20PPI; it occurs at 7000 Re. The normalized area goodness factor ratio has the maximum value at 10PPI and ε =0.95, which is 5.8, and for 20 and 30PPI, it is 3.5 and 2.3, respectively. This shows that aluminum metal foam in triple tube heat exchanger's intermediate tube at 10PPI and porosity of 0.95 can be the optimized results for performance enhancement.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

Improved photovoltaic energy production under partial shading using an innovative MPPT controller based on Flying Squirrel Search Optimization algorithm

This paper explores the issue of partial shading (PS) in photovoltaic systems and proposes a solution using the Flying Squirrel Search Optimization (FSSO) algorithm. PS results in power losses, and our study aims to enhance photovoltaic energy production by introducing a new Maximum Power Point Tracking (MPPT) controller based on the FSSO algorithm. This paper presents a controller designed to efficiently track the maximum power in the presence of partial shading. The controller uses the Modified ODD-EVEN (MOE) configuration, the Modified Symmetrical Array (MSA), and the Total-Cross-Tied (TCT). The performance of the FSSO algorithm was compared with that of GWO and PSO. A bidirectional DC-DC converter was integrated to connect the PV system to a battery. The proposed methods were also applied to an electric vehicle powered by a PV battery. The FSSO proposed in this study demonstrates an efficiency of over 99%, outperforming other methods in tracking the maximum power point and converging faster to the overall maximum power point. The results indicate that convergence time can be improved from 16% to over 60%. Furthermore, the proposed MPPT technique effectively minimizes over 80% of random oscillations. The proposed method reduces losses and maximizes fill factor compared to alternative algorithms, based on two observed shading cases. When connecting the system to a battery, the FSSO algorithm outperforms the PSO by more than 34.78% and the GWO by 49.02%. Although slightly inferior to the PSO, the FSSO still performs significantly in the context of an electric vehicle powered by a photovoltaic battery.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.