Use of Energy Storage to Reduce Transmission Losses in Meshed Power Distribution Networks

Mikulski, Stanisław; Tomczewski, Andrzej

doi:10.3390/en14217304

Cited by 11 publications

(7 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-dominated Sorting Genetic Algorithm II is a multi-objective optimization algorithm, and it starts with a temporary solution in each iteration (

) containing the population (

) and the offspring population of the same size (

) [ 41 ]. The offspring population is generated by using genetic operations of mutation and crossover.…”

Section: Methodsmentioning

confidence: 99%

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

Jadidi

Sikström

et al. 2022

Sensors

View full text Add to dashboard Cite

Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.

show abstract

“…Non-dominated Sorting Genetic Algorithm II is a multi-objective optimization algorithm, and it starts with a temporary solution in each iteration (

) containing the population (

) and the offspring population of the same size (

) [ 41 ]. The offspring population is generated by using genetic operations of mutation and crossover.…”

Section: Methodsmentioning

confidence: 99%

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

Jadidi

Sikström

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In this approach, the optimisation task boils down to determining the so-called Pareto front [30,31], which is a set of solutions from which, using expert knowledge, one final solution can be determined. The optimisation problem indicated above can be expressed mathematically as [30]…”

Section: Multicriteria Optimisation Of a Hybrid Generation System Wit...mentioning

confidence: 99%

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

Tomczewski

Mikulski

Piotrowski³

et al. 2023

Energies

Self Cite

View full text Add to dashboard Cite

This article focuses on determining the optimum structure for a hybrid generation and storage system designed to power a single-family housing estate, taking into account the different number of electric vehicles in use and an assumed level of self-consumption of the generated energy. In terms of generation, two generation sections—wind and solar—and a lithium-ion container storage system will be taken into account. With regards to energy consumption, household load curves, determined on the basis of the tariff for residential consumers and modified by a random disturbance, will be taken into account, as well as the processes for charging electric cars with AC chargers, with power outputs ranging between 3.6 and 22 kW. Analyses were carried out for three locations in Poland—the Baltic Sea coast (good wind conditions), the Lublin Uplands (the best insolation in Poland) and the Carpathian foothills (poor wind and insolation conditions). The mathematical and numerical model of the system and the MOPSO (multiobjective particle swarm optimisation) algorithm were implemented in the Matlab environment. The results include Pareto fronts (three optimisation criteria: minimisation of energy storage capacity, minimisation of energy exchanged with the power grid and maximisation of the self-consumption rate) for the indicated locations and three electromobility development scenarios with determined NPVs (net present values) for a 20-year lifetime. The detailed results relate to the inclusion of an additional expert criterion in the form of a coupled payback period of no more than 10 years, a maximum NPV in the last year of operation and a self-consumption rate of at least 80%. The economic calculations take into account the decrease in PV installation capacity as a function of the year of operation, as well as changes in electricity and petrol prices and variations in energy prices at purchase and sale.

show abstract

“…Latest developments in material sciences and technology have enabled to reduce the material costs of solar panels, with instances of Levelized Cost of Energy (LCOE) of solar power dropping below fossil fuel generation (Kennedy, 2023). This is primarily due to novel fabrication techniques (Joseph et al, 2022), high-efficiency solar cells (Asim et al, 2012), engineering in transmission lines to reduce power losses (Mikulski and Tomczewski, 2021), etc. However, costs of SPV installation affected by social factors, such as land, labor and supply chain costs have only recently been explored in literature.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Optimization of Location-Dependent Costs for Gravity Energy Storage Plants in a Mountainous Suburban Area: The Case of Fukuoka City, Japan

Hoshino,

Basu,

Ogawa

et al. 2024

ETE

View full text Add to dashboard Cite

<p class="MsoNormal" style="margin-top: 10pt; text-align: justify;"><span lang="EN-US" style="font-size: 14pt; font-family: 'times new roman', times, serif;">Gravity Energy Storage (GES) systems are recently being considered as a viable solution for storing intermittent renewable energy power, specifically in high curtailment zones. While a few studies have analyzed the material costs of GES systems, there is a paucity of literature on analyzing the socioeconomic costs of GES systems. This study analyzes the location-dependent costs of GES plants using a multi-factor spatial parameterization model for evaluating the existence of a point of minimum cost in a suburban mountainous geography. A case study of 500x500 points in a 50x50km2 area in the suburban area of Fukuoka city in Japan is performed. It is found that the cost of material transportation and transmission is more dominant in determining the position of an optimal cost location than factors of excavation and land costs. The position of the minima is also related to the principal urban area in that the line connecting the Center Business District (CBD) and suburban flat areas (line 1) is where the potential minima lie. The intersection point of an orthogonal to the line connecting the CBD with a substation nearest to the flat area with line 1, is the potential zone of minima location. The findings of this study are critical for urban energy planners and reveals how socioeconomic cost factors can aid in geolocation a suitable GES installation site.</span></p>

show abstract

Use of Energy Storage to Reduce Transmission Losses in Meshed Power Distribution Networks

Cited by 11 publications

References 43 publications

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

Multicriteria Optimisation of the Structure of a Hybrid Power Supply System for a Single-Family Housing Estate in Poland, Taking into Account Different Electromobility Development Scenarios

Geospatial Optimization of Location-Dependent Costs for Gravity Energy Storage Plants in a Mountainous Suburban Area: The Case of Fukuoka City, Japan

Contact Info

Product

Resources

About