Power Loss Reduction Research Articles

Multi-agent deep reinforcement learning with enhanced collaboration for distribution network voltage control

Due to the increasing high penetration of Photovoltaic (PV), it brings great challenge for voltage control issue of distribution network. To address this problem, this paper presents an improved collaborative Multi-Agent Reinforcement Learning (MARL) approach for proactive voltage control, aimed at mitigating voltage violations and minimizing power losses. The self-attention mechanism is embedded into the multi-agent soft actor-critic (MASAC) algorithm to enhance the collaboration of multi-agent system, which can well improve the learning efficiency to ensure the voltage safety of Distribution Network. In addition, the proposed learning approach is implemented on IEEE 33-bus, 141-bus and 322-bus systems, and the simulation results reveal that the proposed approach can control the voltage into safety domain as well as reduce power losses.

Design of higher gain linearly polarized 2×2 microstrip patch array antenna for wireless communication

The fifth-generation technology is popular and best as far as data rates are concerned for wireless applications. To meet the increased demand of the modern world for faithful communication, this research work is carried out. In this approach, a single patch, 1×2 array, and 2×2 array are designed using a low-cost FR4 dielectric substrate ( =4.4) covering the 3.3–3.8 GHz frequency band. Due to the lack of gain from arrays in the present research, an attempt is made to achieve excellent gain from arrays with a minimum number of patches. First, the gain of a single inset-fed antenna is compared with another normal single patch of the same thickness but with an additional air dielectric medium. Next, the second single patch is extended to obtain a rectangular 1×2 array and a 2×2 array antenna. A second single patch measuring 50×32.5×0.8 mm is designed assuming an infinite ground plane. It has 3mm air gap between the patch strip and the ground. Air acts as a second dielectric layer that reduces power loss. This allows a maximum gain of 15.2 dB with a return loss of -22 dB. Also, antenna efficiency and bandwidth are 91% and 267.69 MHz, respectively.

A hybrid optimization approach for strategically placing electric vehicle charging stations in a radial distribution IEEE-33 bus system

The main objective of this study is to demonstrate the effectiveness of using Hybrid Particle Swarm Optimization (HPSO), a unique methodology, to optimize an IEEE-33 bus system for optimal placement of electric vehicles. This methodology has been specifically designed to effectively regulate voltage fluctuations while reducing power dissipation. The exploration capabilities of PSO and the exploitation capabilities of SA make HPSO a robust algorithm for a specific objective function. HPSO has shown superior performance compared to current traditional methods after careful study. Simulation results illustrate power loss reductions ranging from 1.01% to 8.21% compared to various metaheuristic methodologies. The inclusion of this approach demonstrates that combining the capabilities of PSO and SA facilitates the development of a highly efficient optimization framework by combining their stochastic search and fast convergence capabilities. This methodology not only sets innovative benchmarks for power system development, but also represents a significant paradigm shift in managing and improving durable and efficient electrical networks.

A Multi-Objective Approach for Optimal Sizing and Placement of Distributed Generators and Distribution Static Compensators in a Distribution Network Using the Black Widow Optimization Algorithm

This paper presents a new optimization technique for the locations and sizes of Distributed Generators (DGs) and distribution static compensators (DSTATCOMs) in a radial system of a distribution network based on a multi-objective approach. It uses black widow optimization to improve voltage profile and power loss reduction. The black widow optimization simulates the mating behaviour of black widow spiders. The optimum size and placement of DGs and DSTATCOMs are deemed to be decision variables that are defined by using black widow optimization. The proposed technique is implemented in selected IEEE bus systems to evaluate its performance. The simulation results indicate reduced power losses and voltage profile enhancement as sizes and locations of integrated DGs and DSTATCOMs are adjusted based on optimization. The number of DGs and DSTATCOMs required to achieve the objectives is reduced. Furthermore, the results of the black widow algorithm are compared to existing techniques in the literature.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

Optimizing network reconfiguration to reduce power loss and improve the voltage profile in the distribution system: A practical case study

The electricity provider incurs a high costs and investments to transfer the electrical power to customers in the distribution network. Hence, the reliability of the distribution network (DN) is particularly important in the lives of customers. A distribution system faces many issues due to its direct impact of the voltage profile (VP) and power losses (PL) problems. The aim of this study is to provide an effective scheme to address the of VP and PL problems. In this research, a Selective Particle Swarm Optimization (SPSO) algorithm was used to find combination of switches to enhance VP and reduce PL. The proposed method has been tested in the distribution network system, 125-buses Sigeri-Makassar Indonesia. There are three kinds of considered load scenarios, namely: nominal load; active power load increase; active and reactive power load increase. The research results indicated that the VP of the system can increased from 0.918 pu to be 0.922 pu and decreased PL from 0.6482 MW to be 0.6052 MW; the VP can increased from 0.895 pu to be 0.901 pu and reduced PL from 1.0469 MW to be 0.97532 MW, the VP can increased from 0.895 pu to be 0.900 pu and reduced PL from 1.1929 MW to be 1.0758 MW, for scenarios 1, 2 and 3, respectively. The simulation results showed that the proposed method provides optimal solution, which can enhance the VP and reduce PL. This is indicated that the proposed method is an effective strategy for enhancing voltage stability and solve PL problem.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

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.

Design and implementation of a discontinuous SVM applied for a quasi Z-source inverter with power loss reduction

Design and implementation of a discontinuous SVM applied for a quasi Z-source inverter with power loss reduction

Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.

Analysis and design of an efficient soft‐switching inverter with asymmetric auxiliary circuits

SummaryThe paper designs an efficient three‐phase soft‐switching inverter with asymmetric auxiliary circuits, which is beneficial for further miniaturization and efficiency optimization. Only two auxiliary switches, one resonant inductor, and three resonant capacitors make up simple asymmetric auxiliary circuits of each phase, contributing to the reduction of power loss and hardware cost. As soon as the simple auxiliary circuits get involved in the operation of the designed inverter, main switches can realize zero‐voltage switching within the entire load range. The reduction of power loss of auxiliary circuits and main switches is conducive to further efficiency optimization. The paper expounds every operating status during one switching period. The relevant experimental waveforms make known that switching devices can realize soft‐switching. The efficiency reaches 99% at rated operation status of the designed inverter, confirming that the designed inverter has superiority over comparison objects in the experiment with regard to the efficiency. Thus, the structural simplification of auxiliary circuits is an effective method for further miniaturization and efficiency optimization.

Optimization of dielectric response in BiFeO3–CoFe2O4 nanocomposites for high frequency energy storage devices

The rapid development of smart and sophisticated miniaturized electronic devices put a high demand for high permittivity material. In the current report, we analyze structural, morphological, and dielectric response of spinel-perovskite composites consisting of CoFe2O4 (CFO) as the spinel phase and BiFeO3 (BFO) as the perovskites phase. Here pristine BFO and CFO are fabricated using Sol—Gel auto-combustion route and their composites with a recipe (1-x) BiFeO3 - (x) CoFe2O4 (x = 0.0, 0.1, 0.2 and 0.3) using solid-state reaction technique. Structural investigation identifies presence of rhombohedral perovskite phase of BFO and cubic spinel phase of CFO and thus guarantees the formation of the composite. The crystallite size increases from 16 to 36 nm for BFO and 39.5–54 nm for CFO. The distribution of well-shaped particles with a reduction in grain size in range of 50–400 nm is witnessed in morphological analysis with the increment of CFO contents in composites. Elemental purity and the presence of various elements according to their stoichiometric ratio are witnessed during elemental investigation. A detailed analysis of the dielectric permittivity, electric modulus, and impedance of these compositions is carried out to investigate their suitability for electronic and energy storage devices. The nanocomposites under study exhibit a dielectric constant of 1126 at 100 Hz for x = 0.3, which decreases to 102 at 1 MHz. Concurrently, the tangent loss for x = 0.3 is observed to be 3.37 at 100 Hz, decreasing to 0.31 at 1 MHz. However, a decrease in dielectric loss and increase in impedance is noticed when the concentration of CFO in BFO nano-composite is increased which reflects the reduction in power losses and better control in electronic conductivity and thus highlights the role of these compositions for advanced energy storage electronic devices.

A novel heap-based optimizer for allocation of shunt capacitors in radial distribution network

Due to power losses, a sizeable portion of the electrical power produced is lost, which causes voltage deviation and instability as a result of rising load demand at the radial distribution network (RDN). Shunt capacitor penetration will enhance the RDN’s efficiency by reducing power losses, improving the voltage profile and stability. For the best sizing and allocation of shunt capacitors (SCs) in RDN, a novel human-based metaheuristic heap-based optimizer (HBO) influenced by the ideas of corporate rank hierarchy (CRH) of standard organization is presented. The real power loss, voltage variation, and voltage stability index are all minimized while equality and inequality criteria are met by the HBO optimal allocation of SCs. One, two, and three numbers of SCs were investigated when installing the reactive compensating devices. Using typical IEEE 33-bus and 69-bus RDNs, covering a range of scenarios in the form of single- and multi-objective functions, the effectiveness of the HBO technique was examined. The real power loss for the conventional IEEE 33-bus and 69-bus RDN was reduced by 34.46% and 35.50%, respectively compared to their respective base cases. By contrasting the outcomes of the power loss with well-established and new optimization approaches, the efficacy and superiority of the suggested HBO were demonstrated.

Integration of multiple distributed solar PV (DSP) into the grid: The case of the distribution network in Freetown, Sierra Leone

The optimal integration of distributed solar photovoltaic (DSP) is a transformative engineering method that is key in contributing to a sustainable and resilient energy future, especially when countries seek to increase the proportion of renewables in their energy mix to reduce carbon emissions. This paper presents a power flow-based approach that makes use of Newton Raphson's method in the ETAP tool to integrate multiple DSPs into both the existing and expanded Freetown distribution networks. The study determined the network's hosting capacities and optimal points of injection for the reduction of active power loss and improvement of bus voltage profiles. The study showed that the existing Freetown distribution network had a hosting capacity of 34.6 MW with an active power loss reduction of 0.967 MW while the expanded Freetown distribution network had a hosting capacity of 59.57 MW with an active power loss reduction of 5.12 MW. Before the injection of the DSPs into both networks, most of the bus voltages were not within acceptable limits. However, with the intervention of the injected DSPs, bus voltages considerably improve. The study showed that the expanded Freetown distribution network is better for DSPs integration compared to the existing Freetown distribution network. To evaluate the impacts of the injected DSPs and to validate the model used, four network scenarios were considered. The study used an analytical approach, considering future load growth and an evolving grid to integrate DSPs for long-term planning. The study will inform policymakers, utilities, etc., about the potential of integrating DSPs.

Multi-objective design optimization of polymer spur gears using a hybrid approach

Polymer gears are used in applications requiring small to moderate loads to effectively transmit power and use the limited place available as possible. Various commercial standards have been provided designers with the rating criteria and acquaintance of different polymer material properties for the process of design. However, the result was unsatisfactory in terms of economy, time, and the optimality of the product. Thus, classic and stochastic algorithms have been embraced to reach the best design of polymer gears. Taking advantage of the former and latter algorithm’ methods, optimal design of gears could be attained with an increased gear life span and decreased failure modes. In this study, polyoxymethylene (POM) spur gear set has been optimized combining the mathematical model from plastic standards and hybrid optimization approach of multi-objective genetic algorithm (MOGA) and sequential quadratic programming (SQP). Weight and power loss were the objective functions. Five design variables were optimized with the satisfaction of bending and contact stresses, temperature, wear, and deformation as constraints. Solutions of the problem were formulated as Pareto optimal set. The results of multi-objective were compared with previously published single-objective optimization. The results favored multi-objective optimization (82.67%, 31.39% reduction in weight and power loss respectively) as it gave the best applicable solution fitting in real life situations. The results also went hand in hand with literature confirming the efficiency of the proposed algorithm. With the variation of operating parameters, various optimal designs could be obtained where the designers can choose the design that is suitable for a particular application.

Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6% for future times of up to 48 h.

Clustered Voltage Control and Modulation Scheme With Switching Loss Reduction for High Power Hybrid Cascaded Converter

The hybrid cascade converters (HCC) is a potential solution to achieve harmonic current compensation in medium and high voltage applications due to the characteristics of having high frequency module. However, the high switching frequency inevitably leads to an increase of undesirable switching loss. In addition, since the reduction of power loss and the balance of clustered voltage are mutually interrelated, the existing modulation and control schemes cannot cope with them decently. To address this problem, the HCC is divided into a high-frequency module (HFM) and a low-frequency module (LFM), and a corresponding hybrid modulation scheme is proposed to reduce the power loss while maintaining a high harmonic compensation performance. Then, based on the analysis of the system voltage constraints, the dc-link voltage and threshold voltage under different parameters are redesigned. Furthermore, since the power flow between modules has been changed with the hybrid modulation scheme, the conventional clustered voltage control strategy is not capable of clustered control. Therefore, based on the re-modeling of the power flow, a negative-sequence voltage injection control strategy is proposed to achieve voltage balance. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed scheme.

An Improved Hot Spot Mitigation Approach for Photovoltaic Modules Under Mismatch Conditions

The reduction of photogenerated current in photovoltaic (PV) cells due to various degradation mechanisms leads to hot spot (HS) generation, resulting in serious safety and reliability concerns. This article presents a novel hot spot mitigation circuit (HSMC), which effectively counters HS formation in cells under mismatch situations, improving the reliability of the entire PV arrangement. The effectiveness of the proposed HSMC is compared extensively with the conventional bypass diode (BPD) through experimentation on a 3×1 PV string and a 3×3 series-parallel (SP) PV array in an outdoor setting. Detailed thermographic analysis showed that the maximum temperature of the PV module under low shading levels reached 119.16% and 54.11% higher with the traditional BPD than with the proposed HSMC when tested on a 3×1 PV string and 3×3 SP PV array, respectively. This indicates that the mismatched cell was subjected to elevated temperatures with BPD. Moreover, the experimental results confirm that the proposed HSMC resulted in enhanced power output and a significant reduction of power loss in the bypass path of the shaded module under increased shading levels compared to the traditional BPD. The proposed HSMC reduced mismatch loss by up to 89.82% when tested on a 3×1 PV string.

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

AbstractThis paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

Requirement on the Capacity of Energy Storage to Meet the 2 °C Goal

The inherent power fluctuations of wind, photovoltaic (PV) and bioenergy with carbon capture and storage (BECCS) create a temporal mismatch between energy supply and demand. This mismatch could lead to a potential resurgence of fossil fuels, offsetting the effects of decarbonization and affecting the realization of the Paris target by limiting global warming to below 2 °C in the 21st century. While application of energy storage is widely recommended to address this limitation, there is a research gap to quantify the impacts of energy storage limitation on global warming. Here, we analyzed the hourly variation of global wind and PV power during the period 1981–2020 and the monthly capacity of biomass production in 2019, and thus quantified the impact of decreasing the capacity of energy storage on global warming using a state-of-the-art Earth system model. We found that global warming by 2100 in the SSP1-2.6 scenario would increase by about 20% and exceed 2 °C without deploying energy storage facilities. Achieving the 2 °C target requires reducing power losses of wind and PV by at least 30% through energy storage. This requirement delivers to a cumulative storage capacity of 16.46 TWh using batteries during the period 2021–2100, leading to the international trade of cobalt and manganese across countries due to deficits of minerals at a country level. In the context of energy security, we highlight the importance of considering the limitations of energy storage and mineral shortage in the forthcoming policies of decarbonization.