Optimal Location Research Articles

A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network

AbstractThe adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.

A method for siting adsorption-based direct air carbon capture and storage plants for maximum CO2 removal

Adsorption-based direct air carbon capture and storage (DACCS) is an emerging approach to mitigate climate change by removing CO2 from the atmosphere. Recent studies show separately that thermodynamic and environmental performance strongly depend on regional ambient conditions and energy supply but neglect regional CO2 storage potentials. To assess DACCS performance holistically, a detailed global analysis is needed that accounts for the interplay of regional ambient conditions, energy supply, and CO2 storage potential. Hence, we present a novel method for the optimal siting of DACCS plants derived from optimising a dynamic process model that uses global hourly weather data and regionalised data on electricity supply and CO2 storage potential. The carbon removal rate (CRR) measures the climate benefit and describes the speed at which a DACCS plant generates net negative emissions. First, we assume that CO2 storage is possible everywhere. For four electricity supply scenarios, we show that the optimal siting of DACCS significantly increases the CRR when comparing the best and worst locations in each scenario: For a DACCS plant with a nameplate capture capacity of 4 kt CO2 y−1, the CRR can be increased by 63% from 2.16 to 3.53 kt CO2 y‑1 when using photovoltaic, and by 39% from 2.95 to 4.1 kt CO2 y‑1 when using wind power. Assuming a carbon-free electricity supply, the CRR varies between 3.17 and 4.17 kt CO2 y‑1 (32%). Second, we significantly narrow down optimal locations for DACCS considering regional CO2 storage potential through CO2 mineralisation. Overall, accounting for the interplay of regional DAC performance, energy supply, and CO2 storage potential can significantly improve DACCS siting.

Mapping Kinetic Energy Hotspots in the Persian Gulf and Oman Sea Using Surface Current Derived by Geodetic Observations and Data Assimilation

Harnessing ocean kinetic energy has emerged as a promising renewable energy solution in recent years. However, identifying optimal locations for extracting this energy remains a significant challenge. This study presents a novel scheme to estimate the total surface current (TSC) as permanent surface current by integrating geodetic data and in-situ measurements. The TSC is typically a combination of the geostrophic current, derived from dynamic topography, and the Ekman current. We utilize NOAA’s Ekman current data to complement the geostrophic current and obtain the TSC. To further enhance the accuracy of the TSC estimates, we employ a 3DVAR data assimilation method, incorporating local current meter observations. The results are verified against two control current meter stations. The data-assimilation process resulted in an improvement of 4 to 15 cm/s in the precision of calculated TSC. Using the assimilated TSC data, we then assess the kinetic energy potential and identify six regions with the most significant promise for marine kinetic energy extraction. This innovative approach can assist researchers and policymakers in targeting the most suitable locations for harnessing renewable ocean energy.

The aeroelastic stability characteristics of a ring-stiffened conical three-layered sandwich shell with an FG auxetic honeycomb core utilizing zig-zag shell theory

This paper is devoted to the supersonic flutter analysis of a ring-stiffened conical three-layered sandwich shell consisting of an auxetic honeycomb (AH) core fabricated from functionally graded material (FGM). The face sheets are manufactured of FGM as well in which the volume fraction of the ceramic increases from zero at the inner surface to one at the outer surface based on a power-law function. The sandwich shell is mathematically modeled based on the Murakami's zig-zag shell theory and the aerodynamic pressure is modeled utilizing piston theory. The derivation of governing equations along with compatibility and boundary conditions are performed using Hamilton's principle and are solved through a semi-analytical solution consisting of an exact solution in the circumferential direction followed by an approximate solution in the meridional direction. The flutter boundaries are attained to investigate the variations of the natural frequencies and damping ratios to find the critical aerodynamic pressure (CAP). The impacts of several factors on the CAP are examined such as the power-law index, geometric characteristics of the cells in the FGAH, thickness of the honeycomb core, location of the ring, and boundary conditions. It is observed that an optimal location can be found for the ring support somewhere between the middle length and large radius of a conical shell that provides the best aeroelastic stability.

Smart grid management: Integrating hybrid intelligent algorithms for microgrid energy optimization

The increasing global demand for power drives the need for advancements in generation, control, and supply technologies. While bulk power generation remains cost-effective, it depends on transmission and distribution systems for delivery. Integrating distributed generators into the grid enhances reliability and direct power supply to consumers. Micro Grids (MGs) are a promising solution, offering smoother and more reliable operations. This study explores MGs, incorporating the latest loads and distributed generators to minimize operating costs for electrical power providers. This work utilizes intelligent algorithms, including the Firefly algorithm, Spider Monkey Optimization (SMO), and a hybrid approach combining both. The Firefly algorithm, inspired by swarm intelligence, mimics fireflies' light emission to optimize solutions. SMO, based on spider monkeys' social behavior, balances exploration and exploitation to achieve optimal results. A novel hybrid algorithm is developed to minimize MG operating costs by combining SMO's exploration with the Firefly algorithm's neighborhood optimization; enhancing navigation through the complex constraint solution space of MGs. Case studies on a modified IEEE 33-bus test feeder are conducted. The study compares three metaheuristic methods with non-convex solution spaces to MG operational algorithms. The results demonstrate the hybrid method's efficacy in determining the optimal location and size of real and reactive power support devices for MG operation. Numerical results show the hybrid algorithm's superiority over existing methods, reducing operating costs by 7.5 %, real power loss by 36.5 %, and reactive power loss by 1.7 %, and voltage deviation by 16.895. By considering MG constraints, the hybrid algorithm consistently delivers better outcomes, optimizing operating costs and ensuring efficient power supply. This study presents a novel MG optimization approach, enhancing future energy systems with improved solutions.

About the improvement in Mars Polar Motion determination from radio tracking of two landers

The polar motion of Mars is defined as the movement of the rotation axis with respect to a body-fixed frame tied to the crust of the planet. It is composed of forced motion at annual and sub-annual frequencies caused by the seasonal mass redistribution, formation of the polar ice caps and angular momentum variations of the atmosphere, and of the free mode called the Chandler wobble.Radio-tracking data from landers offers the most suitable means to measure the rotation of Mars, including its polar motion. The latter, however, has not yet been achieved using lander data alone. In this study, we assess the uncertainties associated with Mars polar motion estimation using Direct-To-Earth Doppler, range and Same-Beam Interferometry (SBI) observables between multiple landers on the surface of Mars. We evaluate the improvement enabled by combining data from multiple landers with respect to one-lander scenarios, and identify the optimal mission architectures for polar motion estimation by considering the influence of respective mission parameters on the estimation uncertainty. In particular, we consider the effects of absolute and relative locations of the landers and of mission scheduling. We re-evaluate the possibility of estimating the polar motion using data from landers in proximity to the equator, and apply our considerations to simulated data consistent in number and accuracy with that collected by past Martian missions. We notice and explain a strong longitude dependence of the formal errors when the polar motion parameters are estimated concurrently with the seasonal spin variation parameters, making it impossible to properly determine all components of polar motion with a single lander regardless of its location. However, the use of two or more landers in optimal locations with respect to each other eliminates those limitations. We evaluate the influence of latitudinal and longitudinal separation on polar motion determination in such cases. In particular, we are able to determine polar motion well even in cases where the longitudes of the two landers make determination from each single lander impossible.

Optimal location to land banking practices in urban-rural informal land market continuum of Ghana

Land banking practices in complex informal land markets are growing in developing countries. However, the land banking (LB) literature predominantly focuses on publicly driven land banks operating within formal land markets. Against this backdrop, this study investigates optimal locations for LB projects in Ghana's complex informal land markets from the perspective of private and semi-public real estate developers. Utilising a two-stage research process, first, the study developed a conceptual framework by using: (1) suppositions regarding space under economic geography; and (2) theoretical suppositions on the use of LB and its influence on LB locational choices uncovered from an interpretive hermeneutic literature review. The second stage focused on an empirical assessment of the conceptual framework by taking four urbanised regions in Ghana. The case study stage uses primary data from 30 interviewees selected using purposive and snowball sampling, while secondary data comprised land bank inventories from the regional Lands Commissions of the case study regions. Results revealed land title security as the primary factor determining optimal locations for land banks. There are significant challenges related to land title security in urban and inner parts of peri-urban areas. These challenges are aggravating the transformation of agricultural lands into residential lands in developers' preferred land bank locations. Based on the ongoing land transformation occurrences, the study underscores the need for policy responses that enhance title security to encourage developers to diversify their land banking locational preferences beyond solely greenfield sites to a mix of green and urban brownfield sites.

GIS-based management of a campus tree inventory for assessing optimal shade tree locations: a case study of National Cheng Kung University, Taiwan

GIS-based management of a campus tree inventory for assessing optimal shade tree locations: a case study of National Cheng Kung University, Taiwan

Intelligent hybrid deep learning models for enhanced shipboard solar irradiance prediction and charging station

Electrifying marine routes with solar energy significantly reduces ocean carbon emissions, playing a vital role in climate regulation. Solar irradiance forecasting ensures reliable power despite unpredictable sea weather, necessitating innovative model development. This research presents a forecasting model designed for the optimal placement of solar charging stations, providing hour-ahead solar irradiance predictions for onboard solar vessels. India (Jawaharlal Nehru Port Trust)– United States (Port of New York and New Jersey) sea route is selected to test our proposed forecasting model, as it aligns with busy shipping operations and long-distance trade requirements for electrification. Two distinct procedures by two case studies are used to select optimum data to predict the suitable locations for solar charging stations along the navigation route. In the first case study (Medium data analysis – MDA), the data are selected based on a statistical analysis of twenty years of hourly solar irradiance data from 201 locations. In the second case study (Large data analysis – LDA), two years of seasonal data from 201 locations are chosen and combined to form a large amount of hourly data. For both case studies, a hybrid solar irradiance forecasting model (ARIMA – Bi – LSTM) is developed by integrating the deep learning model with the time series model and fine-tuning them using optimization algorithms (PSO and GA). In MDA, the PSO optimization achieves mean absolute error (MAE) values of 0.32, 0.85, 1.83, 1.40, and 1.33 W/m2 for locations 1, 2, 3, 4, and 5, respectively, significantly outperforming the GA, which has MAE values of 1.36, 1.65, 3.25, 0.92, and 1.41 W/m2 for the same locations. In LDA, the PSO model achieves MAE values of 0.79, 0.91, 0.28, and 0.39 W/m2 for winter, rain, summer, and spring, respectively, also outperforming the GA, which has MAE values of 1.54, 2.17, 0.99, and 1.46 W/m2 for the same seasons. Comparatively, the LDA reinforces its best alignment to predict optimal charging station locations.

Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm

Optimal location and sizing of battery energy storage system using grasshopper optimization algorithm

Brain targeting for focused ultrasound essential tremor ablation: proceedings from the 2023 Focused Ultrasound Foundation workshop.

Essential tremor (ET) is the most common movement disorder globally and has negative impacts on quality of life. While medical treatments exist, approximately 50% of patients have tremor that is refractory to medication or experience intolerable medication side effects. Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy is an option for these patients and while incisionless, it is still invasive, although less so than other surgical treatments such as deep brain stimulation and radiofrequency thalamotomy. Despite MRgFUS being FDA-approved since 2016, there is still no current consensus on the best approaches for targeting, imaging, and outcome measurement. A 2-day workshop held by the Focused Ultrasound Foundation in September of 2023 convened experts and critical stakeholders in the field to share their knowledge and experiences. The goals of the workshop were to determine the optimal target location within the thalamus and compare best practices for localizing the target and tracking patient outcomes. This paper summarizes the current landscape, important questions, and discussions that will help direct future treatments to improve patient care and outcomes.

Exergetic investigation of the influence of injector location in HCCI engines utilizing DEE as pilot fuel and biogas as primary fuel

To address the global demand for sustainable energy, integrating biogas into internal combustion engines is becoming more important. Homogeneous Charge Compression Ignition (HCCI) engines, known for high efficiency and low emissions, offer a promising solution. This study investigates the optimal injector location for using biogas in HCCI engines, with diethyl ether (DEE) as the pilot fuel, evaluating three positions: (i) at the port, (ii) 6 cm away (Manifold 1), and (iii) 10 cm away (Manifold 2). Through experiments and simulations, the impact of injector location on engine performance is analyzed across various parameters, including methane fractions, engine loads, and exhaust gas compositions. Results show that port injection achieves the highest first law and exergy efficiencies but increases emissions of hydrocarbons (HC), carbon monoxide (CO), and smoke. At 15 Nm load, Manifold 1 shows a 27.34 % reduction in exergy efficiency compared to port injection, while Manifold 2 exhibits an 18.49 % decrease at higher loads. Despite lower efficiencies, Manifold 1 effectively reduces harmful emissions. The study also considers exergo-economic and sustainability aspects, highlighting that while port injection is optimal for efficiency, Manifold 1 excels in minimizing HC and CO emissions, with a 50 % reduction in HC and 71.43 % reduction in CO emissions at 15 Nm load compared to port injection. Manifold 2 achieves the lowest smoke emissions across all loads. This investigation provides crucial insights into optimizing HCCI engines for biogas utilization, emphasizing injector location, fuel composition, and operating parameters to enhance performance and reduce environmental impact.

Erratum for “Optimal Sampling Locations to Reduce Uncertainty in Contamination Extent in Water Distribution Systems”

Erratum for “Optimal Sampling Locations to Reduce Uncertainty in Contamination Extent in Water Distribution Systems”

Numerical Modeling the Rock Mass Stress-Strain State Near Vertical Excavations in Combined Mining

In recent years, the development of the mining industry in the Republic of Kazakhstan has been accompanied by the commissioning of new underground levels for many existing mineral deposits, which were initially developed through open-pit mining. As the depth of open-pit mining increases, the volume of overburden rises sharply, making open-pit mining unprofitable due to the significant amount of additional mining work required. For this reason, most open-pit mines in Kazakhstan are transitioning to underground mining, or combined mining. Many researchers have examined the timing of this transition and have worked on optimizing it to determine the best economic efficiency and manage risks. However, there is limited information available on how to determine the optimal location for a vertical mine shaft when transitioning from open-pit to underground mining. The purpose of this study is to identify a safe location for a vertical shaft in combined mining operations. Specifically, the study assesses the impact of the open-pit mine on the selection of the mine shaft’s location, considering the stress-strain state of the rock mass during combined mining methods. To address these objectives, numerical modeling of the stress-strain state around vertical excavations during combined mining was performed. The results provide a solution to the critical issue of determining the location of the mine shaft in combined geotechnology and lay the groundwork for further research on shaft placement in Kazakhstan. The novelty of this study lies in identifying the shaft location by considering the geometric shape of the open-pit mine and the depth of development. Doi: 10.28991/CEJ-2024-010-09-010 Full Text: PDF

Optimal location and sizing of renewable distributed generations and electric vehicle charging stations

Many countries are aiming to replace gasoline-based vehicles with electric vehicles (EVs). The increasing adoption of EVs has led to a surge in the number of charging stations, significantly impacting the electrical grid with issues such as power quality degradation, increased losses, and voltage fluctuations. In response to these challenges, there is a growing interest in integrating distributed generation from unconventional and renewable sources into the grid to power EV Charging Stations (EVCSs). However, this integration poses new complexities, including increased power losses and voltage instability. Consequently, the optimal allocation and sizing of Renewable Distributed Generations (RDGs) and EVCSs have become critical planning considerations. This paper addresses these issues by formulating a multi-objective optimization problem aimed at minimizing power losses and improving the voltage profile of distribution systems. The study introduces several constraints, including the placement of EVCSs and RDGs at separate buses, and employs particle swarm optimization and cuckoo search algorithm to simultaneously determine the optimal locations and sizes of the RDGs and the optimal locations of the EVCSs. The effectiveness of the proposed techniques is demonstrated through simulations on IEEE 33 radial and meshed distribution systems, illustrating their capability to identify optimal configurations for integrating RDGs and EVCSs.

Modern Methods and Solutions for Water Resources Management in the Wadi-Watir Region (Egypt)

The main problem of water resources in Egypt is the increasing demand and decreasing supply, especially after the construction of the Ethiopian Renaissance Great Dam. One of the best solutions is the use of unusual alternative management techniques, including as a major one, rainwater harvesting and distribution. This paper examines the use of unconventional approaches to water management in arid regions based on rainfall accumulation. Using Geographic Information Systems (GIS) and hydrological modelling, the suitability of the Wadi-Watir catchment (Egypt) for rainwater storage was assessed and optimal locations for the construction of storage structures were identified. The results of the study showed that 19% (666 km2) of the study area is highly suitable for the aims and objectives; 16% (573 km2) of the catchment area has limited realisability. Twelve sites were found to be suitable for the construction of storage dams. Fourteen sites are identified as optimal for the placement of percolation (filtration) reservoirs along watercourses. The area considered ideal for above-ground percolation (filtration) tanks is 25.9 km2. The optimum area for farm ponds is 1.34 km2. As a result, the construction of water storage structures is proposed to improve water management in the Wadi-Watir region. The implementation of the rainwater harvesting structures (SRF) was divided into three phases depending on the severity of the flash flood hazard. The first, second and third phases can meet 62.24% (34.24 mil m3) of the water demand. This approach is a new and modern solution to the problem of water scarcity under socio-economic and environmental pressures while achieving the goals of sustainable development in Egypt. The article is based on the dissertation research of Mohamed Mostafa Ezzeldin Abdelrahim (MGSU, dissertation council № 24.2.339.07, March 2024).

Floating solar photovoltaics in the Mediterranean Sea: Mapping and sensitivity analysis of the levelised cost of energy

In this work, the levelised cost of energy (LCOE) from floating solar photovoltaics (PV) is calculated and mapped in the Mediterranean Sea. A breakdown of the factors that influence the LCOE is presented, including site-specific aspects such as distance to shore, water depth and solar resources. To address the uncertainties inherent to any novel technology, a sensitivity analysis is conducted, in which the effects on the LCOE of variations in its factors are quantified. With respect to the numerator of the LCOE (the costs), the results indicate that installation costs, which increase rapidly with distance to shore, play the main role. With respect to the denominator (the energy output), the substantial solar resources of the Mediterranean Sea translate into large annual energy productions overall – a clear advantage for the development of floating solar PV. Going into greater detail, LCOE values are found to be particularly sensitive to: (i) parameters directly linked to the production of energy, e.g., panel capacity and number of panels per structure; (ii) general project and project finance parameters, e.g., number of structures, project lifetime and discount rate; (iii) sources of costs, e.g., structure weight and installation of components. The lowest LCOEs occur in nearshore areas of Libya and Egypt. The Iberian Peninsula presents LCOE values in the range 340–380 €/MWh, and Italy, in the range 380–400 €/MWh. In the Aegean Sea there is high spatial variability, with LCOE values ranging from 320 €/MWh to 500 €/MWh. These results help to identify optimal locations for the deployment of floating PV in the Mediterranean Sea and indicate the key parameters for reducing its cost in future.

Maximum Self-Consumption Planning Framework Considering Energy Management System for Optimal Photovoltaic-Battery System in Distribution Network

Power production from photovoltaic (PV) has advanced as Renewable Energy Resources (RES) power technology and is now a worthwhile alternative to conventional power generating. To effectively stabilize the process of connecting to the grid and increase power controllability owing to changing PV power production, energy storage systems (ESS), with their flexible charging and discharging capabilities, are most often used to cooperate with RES power generation. However, ESS's rapid rate of charging and discharging might cause it to age more quickly than it should and reduce its lifespan. Therefore, this study proposed an effective planning framework of optimal penetration of PV-ESS system associated with effective energy management systems (EMS). The proposed framework, the optimal PV size, PV location and maximum State of Charge (SOCmax) are determined. The fixed minimum State of Charge (SOCmin) based on load conditions is applied to minimize the degradation while maximizing PV self-consumption. The result shows that when applying different SOCmin during at off-peak and peak time load demand, the degradation cost is reduced to 19.40%.

Low-Damage Trimming of Micro Hemispherical Resonators by Chemical Etching.

To enhance the performance of micro-hemispherical gyroscopes, achieving low-damage and high-surface-quality trimming is essential. This enables greater stability and reliability for the gyroscopes. Current methods for reducing frequency split often come with drawbacks such as high cost, adverse effects on the Q-factor, or surface damage. In this paper, a chemical etching trimming method is proposed to reduce frequency split in micro-hemispherical resonators. This method allows for trimming with minimal damage, while also being cost-effective and easy to implement. The theoretical basis of this method was analyzed, followed by a simulation to determine the optimal trimming range and location on the resonator. The simulated Q-factor analysis before and after trimming preliminarily validated the method's low-damage characteristics. Ultimately, the frequency split of the resonator was reduced to below 1 Hz. Additionally, test results of the Q-factor and surface quality before and after trimming further confirmed that chemical etching offers effective low-damage trimming capabilities. The proposed method holds significant potential for improving the performance of micro-hemispherical resonator gyroscopes.

Optimizing Defibrillator Deployment with Bus-Mounted Automated External Defibrillator

Objectives Early defibrillation with an automated external defibrillator (AED) can effectively improve the survival rate of patients with out-of-hospital cardiac arrest (OHCA). Placing AEDs in public locations can reduce the defibrillation response interval from collapse to defibrillation. Most public AEDs are currently placed in a stationary way (S-AED) with limited coverage area. Bus mounted AED (B-AED) can be delivered directly to the demand point. Although B-AEDs are only available during bus operating hours, they provide greater coverage area. When the number of available AEDs is insufficient, better coverage may be achieved by placing a portion of AEDs as B-AEDs. Our purpose is developing a model to determine the optimal locations of B-AEDs and S-AEDs with a predetermined number of available AEDs. The goal is to maximize the total coverage level of all demand points. Methods We proposed a joint location model to place B-AEDs and S-AEDs based on the p-median problem (JPMP). Using data from Chang’an District, Xi’an City, China, we determined the optimal AED deployment. The performance of JPMP was compared with several other models. The coverage results of JPMP are analyzed in details, including the quantity assignment, coverage level, and geographical location of B-AEDs and S-AEDs. The impact of the bus departure intervals on coverage was also discussed. Results The use of B-AEDs results in an average 98.43% increase in the number of covered demand points, and an average 74.05% increase in total coverage level. In optimal AED deployment, B-AEDs coverage follows an inverted U-shaped curve with increasing number of available AEDs. It begins to decrease when all demand points during the operating hours are covered. With a constant number of available AEDs, the total coverage level increases and then decreases as the bus departure interval increases. The larger the number of available AEDs, the smaller the optimal departure interval. Conclusions With a given number of available AEDs, combinational deployment of B-AEDs and S-AEDs significantly improves the coverage level. B-AEDs are recommended when AEDs are insufficient. If more AEDs are available, better coverage can be obtained with reasonable location of S-AEDs and B-AEDs.