System Power Loss Research Articles

Modelling of Power Semiconductor Devices Switching and Conduction Losses in a Power Electronic System

In power converters, power semiconductor devices, mainly Insulated-Gate Bipolar Transistor (IGBT), are generally used as they are less costly and capable of converting electrical energy into high frequency and voltage. It is critical to accurately determine the switching and conduction losses of power devices before they are assembled into an electronic system. The accurate values help to minimize power loss in a power converter system. With the aid of simulation, design problems are identified to help eliminate device destruction, and critical parameters are monitored. In addition, costly equipment for measuring purposes and testing prototypes is not required at the initial design stage. This research uses simulation work in a power converter system to predict IGBT and diode losses with varied frequencies and voltages. The predictive modelling is produced using regression analysis. The models have been validated by R square (R2) and Mean Absolute Percentage Error (MAPE) techniques. This work is aimed at providing a supervised learning technique mainly used in a machine learning environment, in line with the technological development in the semiconductor industry. With the simulation performed in this work, the efficiency of a boost converter system is enhanced as it shows the models have a good fit with an R2 value of almost 1, and MAPE values are noticed to be less than 5%.

Optimizing size and location of UPFC for enhanced system dynamic stability using hybrid approach

This paper proposed a novel hybrid optimization technique, combining the Enhanced Multi-Strategic Sparrow Search Algorithm (EMSA) and White Shark Optimizer (WSO), to determine the optimal location and size of a Unified Power Flow Controller (UPFC) for enhancing power system dynamic stability. The proposed method offers improved search capabilities, reduced randomness, and lower computational complexity compared to existing approaches. Generator faults can significantly impact system dynamic stability constraints, including voltage and power loss. EMSA algorithm is employed to identify optimal location for UPFC placement by selecting bus with minimum power loss. Subsequently, WSO algorithm is used to optimize UPFC's capacity, ensuring that affected system parameters and dynamic stability constraints are restored within safe limits. Optimized UPFC is then installed at identified location, and system's power flow is analyzed. Proposed method is implemented in MATLAB/Simulink environment and tested on both IEEE 30 and IEEE 14 standard benchmark systems. Proposed method's performance is evaluated by comparison with existing methods.

Enhancement of Active Distribution Network Performance with Multiple Distributed Generators and DSTATCOMs using Reptile Search Algorithm

The integration of multiple DGs can introduce power quality issues and instability in the (DN) due to their intermittent and fluctuating nature. Distributed Static Compensators (DSTATCOMs) are devices that effectively manage and regulate both active and reactive powers, thereby maintaining the desired levels of reactive power in the DNs. This paper investigates the problem of determining the optimal number and placement of multiple DSTATCOMs within active DNs with multiple Distributed Generators (DGs) connected to them. To achieve the optimal sizing and allocation of multiple DSTATCOMs, a novel heuristic method called the reptile search algorithm (RSA) is introduced in this study. A combination of the RSA method and the loss sensitivity factor (LSF) are utilized to identify the optimal number, sizes, and locations of DSTATCOMs to enhance the performance of active DNs with different types of DGs and loads. The desired improvements include mitigating voltage deviation at nodes, minimizing system power losses, and alleviating overloading in feeders. The effectiveness of the algorithm is evaluated using an IEEE-33 bus DN, which is modified to incorporate different DGs and load types. To assess the efficiency of the proposed method, a modified particle swarm optimization (MPSO) algorithm is used for comparison. The results demonstrate that the RSA approach presented in this paper is robust in obtaining optimal solutions, offers fast and easy implementation, can be applied to large DNs, and outperforms the MPSO algorithm.

Research on multi-time scale Volt/VAR optimization in active distribution networks based on NSDBO and MPC approach

To explore the potential of all-round and multiangle voltage and reactive power control to reduce losses in a new power system with a high proportion of distributed resources connected to a distribution network, this study proposes a Volt/VAR optimization method for distribution networks using a nondominated sorting dung beetle optimizer (NSDBO) and model predictive control (MPC). According to the characteristics of the various adjustable resources in the network, they are divided into day-ahead and intraday optimizations. The optimization process in the day-ahead stage uses the NSDBO to create a discrete equipment scheduling plan. The optimization process in the intraday stage combines the discrete equipment scheduling plan with the MPC to create the scheduling plan for photovoltaics, energy storage, and vehicle charging stations. Two-stage optimization scheduling is achieved with the lowest discrete equipment regulation cost and minimization of network loss and node voltage deviation penalty cost as the optimization goal. The feasibility of this method for coordinating various resources in the network, processing discrete and continuous variables, and coping with the volatility and uncertainty of high-proportion distributed resources is verified through case analysis. The effectiveness of this method in improving the security and economy of distribution networks is demonstrated. The superiority of the solution speed and quality is also confirmed.

Reducing Overall Active Power Loss by Placing Solar and Wind Generators in a Distribution Power System using Wild Horse Optimizer Algorithm

This study optimized the locations and sizes of wind-based distributed generators (WDGs) and solar photovoltaic-based distributed generators (PVDGs) to reduce the overall active power loss (OAPL) of an IEEE 85-bus distribution power system (DPS). Three meta-heuristic algorithms, including the Wild Horse Optimizer Algorithm (WHOA), the Archimedes Optimization Algorithm (AOA), and the Transient Search Optimization (TSO) algorithm, were applied and compared to each other to identify the most effective method for finding the best value of OAPL. Based on the analysis, WHOA outperformed the other methods in achieving the best value of OAPL according to different criteria. Additionally, the effectiveness of WHOA was compared with previous studies, while WHOA also proved its strength in reducing overall losses, decreasing grid power, and improving voltage profiles. Moreover, the effectiveness of WHOA was tested for a 24-hour period with varying loads and the addition of PVDGs and WDGs. The results indicated that WHOA could successfully determine the optimal positions of both PVDGs and WDGs in Case 3, Case 4.1, and Case 4.2, achieving the optimal value of OAPL in the selected DPS, decreasing grid power utilization, and improving the voltage profile. In conclusion, WHOA proved itself to be an effective optimization tool for dealing with large-scale optimization problems

Battery energy storage with renewable energy sources integration in unbalanced distribution network considering time of use pricing

The increasing demand for sustainable energy solutions and the escalating energy demand have facilitated the emergence of renewable energy sources (RES), such as photovoltaic (PV) sources. Combining RES with battery energy storage system (BESS) technology reduces peak hour demand and allows for economical charging and discharging for time-based energy pricing. Integrating PV and BESS in an unbalanced system with multiple RES sources is a challenging task. It requires a robust algorithm to minimise power loss in the distribution system and decrease the voltage unbalance factor (VUF). This paper presents a new multi-objective Pelican optimisation algorithm (MOPOA) for the optimal allocation of PV and BESS. The MOPOA helps find the best placement of PV and BESS in an IEEE-33 bus unbalanced radial distribution system (URDS). The proposed algorithm combines multiple benefits from a lower net present cost (NPC) and a higher voltage profile enhancement index (VPEI). The case studies and simulation results show that the proposed method places PV and BESS in IEEE 33-bus URDS optimally, satisfying all of the system’s requirements. The results indicate an improvement in the minimum VUF factor of 4.4% in a winter day and 4.3% in a summer day; a reduction in active power loss of 16% in a winter day and 7.1% in a summer day; and a reduction in reactive power loss of 7.5% in a winter day and 7.2% in a summer day. Thus, the recommended strategy effortlessly accelerates to a suboptimal solution.

Artificial immune systems (GA-AIS) enabled power loss mitigation in distributed generation: X3PAIS optimization approaches

The distribution network is a crucial component of the power system, with industrialization driving increased energy demand. Traditional power-generating techniques, such as thermal and hydroelectric are not enough to meet this demand, leading to the development of Distributed Generation (DG). DG requires an extensive re-evaluation of the current power system, as it modifies energy losses and line flows. Inadequate integration of DG can cause power outages, disruption of protection coordination, and lead to islanding. AI can help overcome this issue by determining the best system architecture. Researchers have been interested in the Artificial Immune System (AIS) algorithm, which has room for development and lacks a fixed template. In order to improve AIS, X3PAIS, a hybridization strategy that combines clonal selection with a three-parent crossover has been developed within the scope of the study. X3PAIS was pre-tested using applications in a planetary gear train, a wastewater treatment facility, and mathematical calculations, showcasing its robustness and versatility. In the context of power distribution, X3PAIS is used in the multiple DG architecture of the power distribution system, reducing power losses by placing DG units in the best locations and sizing them to match load profiles. The four DGs' experiment results show that X3PAIS can minimize power losses by more than 89 %. To optimize power losses in the power distribution system, X3PAIS may be improved with a three-parent multiple-point crossover operation.

A HYBRID MVO-BMO TECHNIQUE FOR PLUG-IN ELECTRIC VEHICLE CHARGING OPTIMIZATION

The electric vehicle (EV) market is expanding rapidly around the world due to technological advancements, decreasing cost of batteries, and supportive government regulations. It is both a challenge and an opportunity for distribution utilities to manage the additional power demand from EVs. Effective and optimal EV charging scheduling strategies are essential to avoid the adverse effects of large EV penetration in the power grid system. This paper proposes an optimal plug-in electric vehicle (PEV) charging scheduling in a distribution grid system using a hybrid algorithm approach that combines a multiverse optimizer (MVO) and also a barnacle mating optimizer (BMO) termed as HMVO-BMO. The optimization model is developed with the objective to minimize the grid power loss, considering overnight home charging. Random arrival times of PEVs are considered and charging is scheduled based on available power demand on the distribution grid. The proposed methodology is demonstrated on the IEEE 33-bus system with different PEV penetration levels. Comparisons are made between three optimization algorithm approaches, namely the standard MVO and BMO, and the proposed HMVO-BMO algorithms. The simulation results demonstrated that the proposed hybrid technique can achieve better and efficient results in terms of system power loss.

Optimizing power system efficiency and costs in smart buildings with renewable resources

The increasing industrial development and energy demand have necessitated the maximization of available energy use and the deployment of renewable resources. Effective energy management, optimal modeling, and efficient planning are essential to transform the power system into a high-efficiency, optimal model. This study focuses on the initial step of modeling smart buildings (SBs) equipped with non-responsive devices and renewable photovoltaic sources. A comprehensive energy management (EM) plan is formulated for these buildings, incorporating the KNX protocol for solar energy system management. Batteries are integrated into the building model to store energy during periods of low consumption and serve as generators during peak load conditions, with the primary goal of minimizing power system losses and related costs. To address the complexity of this model, whale optimization algorithm (WOA) is employed for optimization. Optimal candidate buses are selected for interconnected building management based on a suggested sensitivity analysis to minimize losses. Cost performance is then assessed, considering energy production and sales. The findings indicate that substantial control of operating costs can be achieved through strategic management of battery charging and discharging, as well as the utilization of photovoltaic units. The proposed model is evaluated across various scenarios using a test system comprising 30 modified system, demonstrating its effectiveness in enhancing energy efficiency and management.

Stochastic Optimal Operation of SOP-Assisted Active Distribution Networks with High Penetration of Renewable Energy Sources

This paper introduces a mixed-integer convex model for optimizing the scheduling of soft open points (SOPs) integrated with energy storage (ES) in active distribution networks (ADNs) with high proportions of photovoltaic sources, designed to ensure zero risk of constraint violations. A stochastic optimization model for ADNs is proposed to maximize the benefits of SOPs while simultaneously minimizing system power losses, SOP power losses, voltage deviations, PV power curtailment, battery energy storage system (BESS) operation cost, and utility power purchase. Uncertainties in PV generation and load demand are considered by Monte Carlo simulation and k-means technologies. Finally, simulation cases from a 21-bus distribution network show that the curtailment of PV sources is minimized and the power fluctuations of the BESS are reduced in comparison to the case without SOP. Constraints in the nodal voltages, power outputs, energy balance, and power flow are all satisfied.

A novel high-efficient lithium-ion battery serial formation system scheme based on partial power conversion

The formation of the battery is a crucial step in its manufacturing process. The existing battery formation system suffers from low efficiency and high energy consumption costs due to long energy flow paths, high DC bus line losses, and additional balancing circuit applications. Therefore, a novel high-efficient battery series formation system (BSFS) that combines partial power processing architecture (PPPA) with the modular converter is proposed to address this problem in this article. The PPPA-BSFS includes two-stage isolated sub-modules consisting of Buck-Boost converter and LLC converter with input-serial and output-parallel connection. The isolated converter only processes the serial compensation power, about 1/3 of the full power. Consequently, the converter capacity can be shrunk in the PPPA-BSFS, which can reduce the system's cost and power loss. Furthermore, the paper establishes the steady-state mathematical model and energy efficiency model of PPPA-BSFS based on analyzing the power compensation principle, and the key factors affecting the operational efficiency of PPPA-BSFS are studied. Finally, we construct the simulation models with 10 cells and 120 cells to compare and analyze energy efficiency between PPPA-BSFS and existing battery formation topology under various operating conditions. Our simulation results show that the highest operating efficiency of the PPPA-BSFS proposed is 99.37 % when 120 cells are formed normally, where there is an increment of 5.95 %, 2.36 %, and 0.85 % compared with others, respectively. The high efficiency of the PPPA-BSFS is verified, and the PPPA-BSFS shows great potential for application in battery formation scenarios.

Optimal energy management strategy for electric vehicles and electricity distribution system based on quantile deep learning with enhanced African vulture optimization

The increasing fluctuation in electricity prices due to unpredictable demand loads on the power grid has underscored the importance of finding optimal scheduling solutions for electric vehicles (EVs). Integrating EVs into the distribution system (DS) is critical for efficiently managing their energy needs. However, current EV scheduling approaches face challenges. Centralized solutions are often unreliable and complex, while existing decentralized methods struggle to efficiently schedule EVs in large networks with smart energy DS. To address these challenges, we propose a hybrid technique called Quantile Deep Learning with Enhanced African Vulture Optimization (Quantum VultureNet) for energy management systems (EMS) involving EVs and DS. This approach combines the African Vultures Optimization algorithm with elite mutation, learning based on dynamic opposition, and chaotic map-based population initialization. By enhancing exploration and exploitation capabilities, this hybrid method aims to improve the scheduling of EVs, particularly in dynamic environments. The primary goal of the Quantum VultureNet technique is to minimize system costs and power losses while optimizing energy management. The proposed system considers factors such as bidirectional energy trading, EV fleet arrival times in driving plans, Photo Voltaic uncertainty impacts on energy management systems, and factors affecting energy selling back to the grid. The model was implemented in MATLAB and compared with conventional methods. Simulation results demonstrate that the Quantum VultureNet technique effectively finds near-global optimum solutions with reduced computational complexity. This indicates the effectiveness of the proposed approach for EMS in EVs integrated with DS.

Efektivitas Mutasi Transformator Dalam Mengatasi Fluktuasi Beban Pada Trafo Distribusi ULP Bululawang

Transformer is a very important component in the distribution of electricity from the distribution substation to consumers. Overloading and underloading of transformers can lead to significant inefficiencies and increased losses in the electrical distribution system. Based on data measurement of the ULP Bululawang transformer in 2022, it was found that the C0177 transformer (100 KVA) was overloaded (88.4%) and the C0254 transformer (160 KVA) was underloaded (21.4%). Load forecasting for 2027 predicts that the C0177 transformer load will reach 107%, causing overheating, while the C0254 transformer will remain underloaded at 23.68%. To address these issues, a transformer mutation was proposed. This study aims to evaluate the effectiveness of transformer mutation in redistributing loads and improving efficiency. The method involved measuring transformer loads before and after mutation and using the Least Square method for load forecasting. Post-mutation measurements showed the C0177 transformer load reduced to 62.6% and the C0254 transformer load increased to 37%. These results indicate that the transformer mutation significantly balanced the load distribution. The study also adjusted the protection settings, including FCO, output cable, main switch, PHB-TR, and NH Fuse output cable. The findings suggest that transformer mutation can effectively address load imbalances, improve operational efficiency, and reduce power losses in the distribution system.

Investigation of performance reduction of PV system due to environmental dust: Indoor and real-time analysis

This paper analyzes the effect of different types of dust on the reduction of PV systems' performance. The PV system installed on the field gives maximum output. However, in a real-time scheme, the PV system fails to generate the desired power output due to various realistic and unavoidable factors like soiling. Different dust particles accumulating on photovoltaic (PV) modules result in shadowing and reduced irradiance, impacting power production. The study investigates eight dust samples (ash, dirt, cement, brick powder, putty, wood dust, sand, and salt) in indoor and outdoor environments. It evaluates the decrease in power at solar irradiance levels of 500, 700, and 1000 W/m². The soiling experiments conducted indoors show that the maximum% reduction in power is 39.21 % in the case of Ash, while a minimum of 8.32 % in the case of red soil. The outdoor experiments suggest that red soil, sand, and brick dust can create huge power losses in the PV system, followed by ash powder, wood dust, putty dust, salt, and cement powder. The advantages of this research lie in its thorough analysis of several forms of dust and their impact on photovoltaic (PV) systems under different circumstances. Furthermore, the study emphasizes the crucial need for implementing dust control measures to maximize system performance, thereby providing insightful information for system design and maintenance procedures. The novelty of this study is to investigate the performance impact of various kinds of dust on PV systems, offering significant insights for improving system efficiency in real-world circumstances. The research enhances the reliability and applicability of its results by thoroughly assessing outdoor and indoor conditions, bridging the gap between laboratory results and practical applications.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

ENERJİ SİSTEMİNİN PAYLAYICI ELEKTRİK ŞӘBӘKӘLӘRİNDӘ AVTOMATİK NӘZARӘTİN VӘ İDARӘETMӘ ÇEVİKLİYİNİN ARTIRILMASI

The accurate report of electricity losses in power systems is important and of great importance to increase the automatic control and management flexibility of power systems in distribution power grids. This issue is relevant in our country, as well and in the recent years the electricity losses reduction is in the wide spotlight. The reactive powers and their compensation which are the reasons of electric power losses with the construction of an innovative consumption system in the power distribution networks of the energy system have been shown in the article. By advancement of the power factor and its improvement, the specified reports on the reduction of electricity losses have been conducted. These researches lead to increasing the efficiency of the accounting system of "Azerishiq" OJSC, automatic control of its distribution networks, intellectual networks and management flexibility. In recent years, such measures implemented in the system of "Azerishiq" OJSC have resulted in energy saving by reducing the losses of electricity in overhead power transmission lines.

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

Estimation of Total Real and Reactive Power Losses in Electrical Power Systems via Artificial Neural Network

Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point. Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.

Embedded Real-Swarm Evolutionary Programming Technique for Intelligent Load Curtailment Strategy

Some traditional optimization techniques are inaccurate and failed to reach their optimal solutions since the solutions normally stuck at local optimal. Thus, any optimization technique cannot be generalized as a reliable optimizer since some optimization techniques are unique to solve optimization problems. This may also occur in power system optimization problem. Load curtailment is one of the important issues in power systems since its approach can help control the power system loss. In general, it is termed as loss minimization so that the delivery of electricity to the consumers can be smoothened. This paper proposes a new optimization technique, Embedded Real Swarm Evolutionary Programming (ERSEP) for identify contingencies occurrence in power system. ERSEP is the integration of real mutation swarm operator with the traditional evolutionary programming (EP) which aims to produce better results in terms of achieving lower optimal solution. Comparative studies were conducted to observe the advantages of ERSEP over the traditional. Results exhibited that the proposed ERSEP outperformed the traditional EP in achieving lower optimal solution validated on IEEE 30-Bus Reliability Test System (RTS). Significant results deduced from this study revealed that total transmission loss reduction worth 52.83% was achieved by EP, 54.09% solved by PSO and 74.09% by ERSEP in Case 1 for chosen load condition. In Case 2, ERSEP maintains to achieve the highest loss reduction worth 54.97%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. ERSEP maintains to achieve highest loss reduction worth 61.63%, while EP achieved 51.03% and PSO achieved 52.98% loss reduction. This implies that ERSEP is superior in all cases to reach the lowest minimized transmission loss.

Coordination of PSS and Multiple FACTS-POD to Improve Stability and Operation Economy of Wind-thermal-bundled Power System

Aims and Background: Focusing on the low-frequency oscillation and system power loss problem in wind-thermal-bundled (WTB) transmission systems and the interaction problem due to different controllers, this study aimed to improve the low-frequency oscillation characteristics of WTB transmission system while ensuring the economy of operation and suppressing the adverse interaction between controllers. Methods: For this purpose, a coordination and optimization strategy for a power system stabilizer (PSS), static synchronous series compensator with additional power oscillation damping controller (SSSC-POD), and static synchronous compensator with additional power oscillation damping controller (STATCOM-POD) is proposed based on multi-objective salp swarm algorithm (MSSA). The controller regulation characteristics are taken into account in the coordination method. Results: Several designed scenarios, including changing the transmission power of the tie line and increasing wind power output, are considered in the IEEE 4-machine 2-area to test the proposed method. The power flow analysis, characteristic root analysis, and time-domain simulation are used to analyze the simulation results. Conclusion: Simulation results demonstrate that the proposed approach can effectively suppress the low-frequency oscillation of the WTB system while reducing its net loss. The application in engineering issues for MSSA is supplemented.