High Penetration Levels Research Articles

Digital Marketing Ecosystem Perspective of Regional Featured Product in North Sulawesi Province, Indonesia

The industrial sector that develops along with technological developments certainly has an impact on the economy of a country. Indonesia is a country that has great potential for the development of the digital economy with a high level of digital media penetration and a large market share. Strengthening local products based on the digital economy seems to be one way to maintain the positive momentum for post-pandemic national economic improvement. This is done by encouraging the marketing digitalization ecosystem to SMEs. This article is constructed based on literature studies that have been carried out previously by mapping articles, research journals, and related data to be able to provide a real picture regarding the role of the digital marketing ecosystem in encouraging marketing penetration of regional superior products. Some of the aspects that are used as barometer like consumer ecosystem, producer ecosystem, and core communication. These three things are collaborated with the facilitation of stakeholders such as the government, suppliers, consumers and business owners, who work together to encourage the smooth running and optimization of digital marketing operations in North Sulawesi Province, Indonesia. However, several obstacles remain, namely not all regional superior products have been reached in digital marketing. As a result, the role of actors in the local digital marketing ecosystem still needs to be carried out with various empowerment efforts. Stakeholders like the government and banking must creating more synergy with local business owners to maximize regional superior products that have enormous potential.

Research on the impact of green finance on the high quality development of the sports industry based on statistical models

<abstract> <p>The significant impact of high levels of green finance penetration on the development of the sports industry is evident, contributing to its comprehensive and high-quality growth across multiple dimensions. After analyzing the current state of the sports industry in China, data from 31 provincial-level administrative regions between 2008 and 2021 were carefully selected. An evaluation index system was established to assess the development of green finance and the high-quality development of the sports industry. The entropy method was applied to measure the levels of green finance and the quality of sports industry development in different regions. Empirical research demonstrates a statistically significant and positive relationship between green finance and the high-quality development of the sports industry, reaching a significance level of 1%. Nonetheless, there are still several challenges and areas for improvement within the realm of green finance and the sports industry's high-quality development. To propel the sports industry's growth under the umbrella of green finance, efforts should be focused on expediting the enhancement and construction of green finance, utilizing it effectively to empower the sports industry, staying attuned to the trends and dynamics of green finance development, as well as fostering a congenial environment for its further advancement.</p> </abstract>

Probabilistic operational costs assessment of combined PV–PEV connections in LV distribution networks

Decarbonization stimulates the increased penetration of photovoltaic (PV) systems and plug-in electric vehicles (PEV) in low-voltage (LV) distribution networks. However, the individual or combined PV and PEV massive connections may incur significant economic costs for distribution utilities due to technical issues caused by these connections. This paper proposes a probabilistic approach for operational costs assessment of combined PV–PEV penetration in LV distribution networks catering to the uncertainties associated with PV, PEV and households. In addition, to identify the relationships between the technical issues with each other and the increased combined PV and PEV penetration levels, a correlation analysis is developed to help utility engineers to estimate the operational costs of combined PV and PEV connections. The proposed methodology is applied to a real Brazilian LV distribution network showing that the costs of real power losses decrease in reduced penetration levels of combined PV–PEV. However, they increase under high combined penetration levels representing financial losses to utilities. Also, operational costs related to steady-state voltage level compliance indicators increase with PV connection, and the combined PV–PEV connections have beneficial results, reducing these costs. Further, the proposed methodology can be used by utility engineers responsible for studies of LV distribution network reinforcement actions. • Proposed methodology converts technical parameters into operational costs. • Operational costs in utility’s point-of-view due to combined PV–PEV connection. • Proposed approach considering stochastic uncertainties of PV, PEV and households. • Identifies high-risk consumer units, allowing LV network reinforcements. • Proposed methodology uses technical correlation analysis and Monte Carlo Simulation.

Day-ahead Network-constrained Unit Commitment Considering Distributional Robustness and Intraday Discreteness: A Sparse Solution Approach

Quick-start generation units are critical devices and flexible resources to ensure a high penetration level of renewable energy in power systems. By considering the wind uncertainty and both binary and continuous decisions of quick-start generation units within the intraday dispatch, we develop a Wasserstein-metric-based distributionally robust optimization model for the day-ahead network-constrained unit commitment (NCUC) problem with mixed-integer recourse. We propose two feasible frameworks for solving the optimization problem. One approximates the continuous support of random wind power with a finite number of events, and the other leverages the extremal distributions instead. Both solution frameworks rely on the classic nested column-and-constraint generation (C&CG) method. It is shown that due to the sparsity of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> -norm Wasserstein metric, the continuous support of wind power generation could be represented by a discrete one with a small number of events, and the rendered extremal distributions are sparse as well. With this reduction, the distributionally robust NCUC model with complicated mixed-integer recourse problems can be efficiently handled by both solution frameworks. Numerical studies are carried out, demonstrating that the model considering quick-start generation units ensures unit commitment (UC) schedules to be more robust and cost-effective, and the distributionally robust optimization method captures the wind uncertainty well in terms of out-of-sample tests.

Which country recorded the highest digital penetration in 2022-2023?

This paper explores country penetration levels of major Internet applications replacing physical activities. This exploration first aims to learn whether high penetration levels typify large or small countries. Second, it seeks to see whether wealthier countries, among the largest ones by population, lead penetration levels for major Internet applications. Previous data for 2010 showed that smaller countries led globally in their penetration levels, and it was not for wealthier countries to lead among the most prominent countries worldwide. The 2023 data for Internet penetration, for the adoption of smartphones, as well as for the penetration levels for the leading networking platforms (Facebook, Twitter (X), WhatsApp, Instagram, and TikTok) all reveal similar trends: high penetration levels typified both developing and developed countries and small, and usually wealthy, countries achieved the highest penetration scores worldwide. Thus, the trends studied in 2010 were not temporary. Thus, Digital networking is globally and freely available, possibly encouraging interaction and literacy. As far as e-mails are concerned, they are also automatically used by private, institutional, business, and IOT entities. Therefore, the list of leading countries for e-mail use for 2023 consists of developed countries. Online shopping levels depend on the economic status of individuals, so it was for developed countries to lead the 2023 list of online shopping levels.

THE IMPACT OF FOREIGN NGO-S ON FOSTERING ENGLISH AS THE OPPORTUNITY LANGUAGE - CHALLENGES AND TRENDS AMONG YOUNG ADULTS

In the last decade, many studies have focused their attention on the status of English in different countries, world English(es), and the usage of English. Nevertheless, none of the study was conducted in Kosovar context in the Balkan’s, although the position of English in Kosovo is extremely important. Therefore, this paper aims to review other studies and to shed light on the abovementioned issue by discussing and reflecting on national, historical, social, political, educational, cultural, and economic issues that contribute to the use of English language in everyday Kosovar lives. Taking into consideration historical points of views, the English-speaking counties’ impact in Kosovo’s independence, the high level of penetration of English loanwords, and Kosovars’ aspiration to accept and integrate these loanwords, it can be understood the high regard for English language among Albanian/Kosovar society which has also affected the everyday communication and lexis in different fields which is evident in different group ages, especially young adults. However, educational factors play the most important part in English learning. The institutional support in enhancing pedagogical practices, continuous teacher training programs, provision of desired books, native teacher mobility are considered to be the main factors that contribute to authentic English language acquisition.

Recursive Estimation in the Moving Window: Efficient Detection of the Distortions in the Grids with Desired Accuracy

The development of fast convergent and computationally efficient algorithms for monitoring waveform distortions and harmonic emissions will be an important problem in future electrical networks due to the high penetration level of renewable energy systems, smart loads, new types of power electronics, and many others. Estimating the signal quantities in the moving window is the most accurate way of monitoring these distortions. Such estimation is usually associated with significant computational loads, which can be reduced by utilizing the recursion and information matrix properties. Rank two update representation of the information matrix allows the derivation of a new computationally efficient recursive form of the inverse of this matrix and recursive parameter update law. Newton-Schulz and Richardson correction algorithms are introduced in this paper to prevent error propagation and for accuracy maintenance. Extensive comparative analysis is performed on real data for proposed recursive algorithms and the Richardson algorithm with an optimally chosen preconditioner. Recursive algorithms show the best results in estimation with ill-conditioned information matrices.

Multi-area distribution grids optimization using D-FACTS devices by M-PSO algorithm

This paper presents a model for the optimization of a multi-area distribution grid considering the high penetration levels of photovoltaic (PV) resources and electric vehicle charging station (EVCSs) based on distributed flexible AC transmission system (D-FACTS) devices including on-load tap changer (OLTC) transformers and step voltage regulator (SVR) tap control and shunt capacitor (SC) and shunt reactor (ShR) to reduce losses and voltage deviation. The overarching research aim was to avoid the overvoltage caused by power injection by PV sources and the voltage drop caused by charging EVs in distribution grids. The proposed model was designed based on distribution network load flow and was optimized using a modified particle swarm optimization (M-PSO) algorithm. Two different test networks were tested under different scenarios to analyze the proposed model, revealing the efficiency of the proposed model.

Optimization of Process Variables for Prediction of Penetration Depth of HSLA Steel Welds Using Response Surface Methodology

The statistical model is created for predicting penetration depth in an alternating current-based additional axial magnetic field controlled shielded metal arc welding of ASTM A 516 Gr.70 steel. The design for the trials is developed using the Placket-Burman design and response surface methodology. The created model determines the optimum process variables for getting excellent penetration depth. The input variables (current, magnetic field density, and magnetic frequency) are chosen for a response like penetration depth. This model can predict the main effects and the interacting effects of three process variables. The findings reveal that a higher current value with a low magnetic field density leads to deeper penetration and vice versa. Furthermore, a greater penetration depth is achieved at lower magnetic field density and higher magnetic frequency. With a desirability of 98.8%, the optimum process variables are 110 A, 0 mT, and 60 Hz. The predicted response values produced from the regression equation based upon process variables are extremely similar to the observed output, demonstrating the usefulness of second-order regression equations. For improved joint efficiency, a high level of penetration is needed.

A new protection algorithm for tackling the impact of fault‐resistance and cloud energy storage on coordination of recloser‐fuse protection

AbstractPresenting cloud energy storage system (CESS) in the landscape of storage devices exposes microgrids (MGs) to a substantial change. Employing a specific type of inverter namely synchronverter as the connecting interface for CESS gains high inertial response in MGs with high penetration level of inverter‐based distributed generations (IBDGs). However, high fault contribution of synchronverters can lead to challenging issues. Nevertheless, presence of fault resistance which results in malfunctioning of the recloser‐fuse pair intensifies the cruciality of the dilemma. In this paper, at the outset, the effects of contribution of synchronverter‐based CESS and also fault resistance on recloser‐fuse protection are explored and then, a new algorithm is proposed for tackling these bottlenecks. To overcome these hurdles, the conventional recloser‐fuse scheme is applied at first. Then, by using the obtained schemes for fast and slow operations of the recloser, a specific part of them is set in a manner that the fuse‐saving task is performed properly even when the synchronverter‐based CESS is deployed. On the other hand, the other part of the recloser scheme is altered in a way that the maloperation of the recloser‐fuse pair owing to the fault resistance is alleviated. Adequate numerical analyses are conducted to evaluate the proposed scheme.

Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region

Photovoltaic (PV) energy source generation is becoming more and more common with a higher penetration level in the smart grid because of PV energy’s falling production costs. PV energy is intermittent and uncertain due to its dependence on irradiance. To overcome these drawbacks, and to guarantee better smart grid energy management, we need to deal with PV power prediction. The work presented in this paper concerns the study of the performance of the fuzzy MPPT approach to extract a maximum of power from solar panels, associated with PV power estimation based on short time scale irradiance forecasting. It is particularly applied to a case study of a tropical insular region, considering extreme climatic variability. To validate our study with real solar data, measured and predicted irradiance profiles are used to feed the PV system, based on solar forecasting in a tropical insular context. For that, a spatio-temporal autoregressive model (STVAR) is applied. The measurements are collected at three sites located on Guadeloupe island. The high variability of the tropical irradiance profile allows us to test the robustness and stability of the used MPPT algorithms. Solar forecasting associated with the fuzzy MPPT technique allows us to estimate in advance the produced PV power, which is essential for optimal energy management in the case of smart energy production systems. Simulation of the proposed solution is validated under Matlab/Simulink software. The results clearly demonstrate that the proposed solution provides good PV power prediction and better optimization performance: a fast, dynamic response and stable static power output, even when irradiation is rapidly changing.

Polymeric Chloroquine as an Effective Antimigration Agent in the Treatment of Pancreatic Cancer.

Hydroxychloroquine (HCQ) has been the subject of multiple recent preclinical and clinical studies for its beneficial use in the combination treatments of different types of cancers. Polymeric HCQ (PCQ), a macromolecular multivalent version of HCQ, has been shown to be effective in various cancer models both in vitro and in vivo as an inhibitor of cancer cell migration and experimental lung metastasis. Here, we present detailed in vitro studies that show that low concentrations of PCQ can efficiently inhibit cancer cell migration and colony formation orders of magnitude more effectively compared to HCQ. After intraperitoneal administration of PCQ in vivo, high levels of tumor accumulation and penetration are observed, combined with strong antimetastatic activity in an orthotopic pancreatic cancer model. These studies support the idea that PCQ may be effectively used at low doses as an adjuvant in the therapy of pancreatic cancer. In conjunction with previously published literature, these studies further undergird the potential of PCQ as an anticancer agent.

Wind power forecasting in distribution networks using non-parametric models and regression trees

Renewable resources provide viable and advantageous solutions up to a certain integration share. At higher penetration levels, they violate the conventional generation constraints, leading to decentralized uncertainty with respect to bi-directional power flows. This generates an increasing need for smart tools able to predict their output with high accuracy, based on easily accessible input data for forecasting. Based on actual data with respect to load demand and wind power generation, this work presents a realization of decision trees that target on a continuous response, also known as regression trees. Utilizing the speed and direction of wind, the ambient temperature, relative humidity, renewable capacity and renewable energy source curtailment as predictors in distribution networks of different regions, the proposed configuration is able to predict the generated power with high accuracy. According to the obtained results under distinguished scenarios, the inclusion of temperature and humidity to the predictive list greatly improves the accuracy in terms of mean square error, root mean square error and mean absolute range normalized error, whereas the renewable availability offer no relevant changes. However, in the forthcoming de-carbonized power systems, the impact of curtailed energy will play an important role in expert forecasting systems where the input/output association must be modelled with high resolution.

Sizing capacities of renewable generation, transmission, and energy storage for low-carbon power systems: A distributionally robust optimization approach

To decrease carbon dioxide emission, a high penetration level of renewable energy will be witnessed over the world in the future. By then, energy storage will play an important role in power balancing and peak shaving. This paper considers the capacity sizing problem during the transition to a low-carbon power system: the retirement plan of conventional fossil-fuel generators and the growth of demands are given. The renewable generation capacities at given sites are to be determined in coordination with the upgrade of transmission lines and installation of energy storage units. In order to capture the inaccuracy of empirical probability distributions for uncertain renewable output and load profiles, a novel distributionally robust bi-objective sizing method using Wasserstein-metric-based ambiguity sets is proposed. The total investment cost and expected carbon dioxide emission subject to operating conditions and a load shedding risk constraint are minimized. The distributionally robust shortfall risk of load shedding and the worst-case expectation of carbon dioxide emission are reformulated into computable forms based on calculating the Lipschitz constants. The final problem comes down to solving mixed-integer linear programming problems. The numerical results demonstrate the effectiveness of the proposed method and the necessity of using distributionally robust optimization.

Economic-environmental convex network-constrained decision-making for integrated multi-energy distribution systems under electrified transportation fleets

Considering the high penetration rates of electrified transportation fleets, their impact on the integrated thermal/electrical/natural-gas multi-energy distribution systems (IMEDS) will be distinguishable. This study proposes a hybrid robust-stochastic convex optimization model for optimal scheduling of an IMEDS under the network constraints. Moreover, a high penetration level of smart plug-in hybrid electric vehicle (PHEV) fleets, as well as the renewable energy sources (RES) in the active electricity distribution system (AEDS) is regarded. A multi-objective optimization approach is taken to minimize the operation expenditures and the greenhouse gas emissions. The natural gas network’s (NGN) linepack storage capability along with the district heating network’s (DHN) thermal flexibility are formulated with the objective to enhance the flexibility and avoid overlapping in the peak demand of various energy forms under severe uncertainty condition (e.g., considering uncertain input parameters such as RES production, electrical loads, natural gas loads, drivers’ behaviour. To handle such uncertainty parameters, a scenario-based stochastic programming (SP) method is adopted, combined with robust optimization (RO) ambiguity sets, when dealing with volatilities of the market price. The network-constrained IMEDS was embodied by the standard IEEE 69-bus AEDS, 40-node NGN, 59-bus DHN systems. The proposed convex model is a second-order cone programming (SOCP) model, which was solved by the MOSEK solver. The functionality of the proposed method was confirmed through different case studies.

Electricity storage requirements to support the transition towards high renewable penetration levels – Application to the Greek power system

This paper investigates the electricity storage requirements to support the transition towards a high renewable energy source (RES) penetration in a cost-optimal manner. The achieved reduction of renewable energy curtailments and the decrease in the total generation cost of the system are quantified against a counterfactual scenario without storage. A methodology is presented to determine the optimum mix of short- and medium-duration storage needed to support system operation at increased RES penetration levels, using the mixed integer linear programming mathematical optimization. The Greek power system serves as a realistic study case, in its planned development for the year 2030, with a targeted annual RES energy penetration in the order of 60%. Li-ion batteries and pumped-hydro are selected as the representative technologies to include in the storage mix, assuming energy-to-power ratios of up to 6 h for the former and 10 h for the latter. It is shown that the introduction of a suitable mixture of storage facilities may improve renewable energy integration and, at the same time, reduce system cost to the extent that entirely compensates for the full cost of storage, thus allowing for a net economic benefit for the system. The optimum storage portfolio for the study case system and the targeted RES penetration level combines 2-h batteries and 6-h pumped-hydro stations, with an aggregate capacity of new facilities between 1250 MW and 1750 MW, on top of the existing 700 MW of open-loop pumped hydro plants. The optimum storage requirements vary with the targeted RES penetration and with the balance of RES technologies in the generation mix, particularly the level of PV integration.

Droop-based active voltage regulation control and robust stability analysis for VSCs in the large-scale RES-integrated power system

New features like high penetration level, low inertia, and weak damping are decreasing voltage support capacity in the power system as it sees large-scale renewable energy source (RES) integration continuously. The reasonable solution to this challenge is developing an additional active voltage regulation function for voltage source converters (VSCs). This study proposes a droop-based active voltage control strategy for VSCs in large-scale RES-integrated power systems to make VSCs continue to operate and regulate the voltage at the point of common coupling (PCC) during a fault without causing overcurrent. In order to meet the requirements of RESs and grid code, all control boundary conditions are designed based on practical engineering data. Meanwhile, plant uncertainty and parameter disturbance are introduced into a robust stability margin analysis model based on the component connection method (CCM) and structured singular value (SSV) to improve the engineering application prospect. The effectiveness and feasibility of this control strategy are proved by logical deduction and practical simulation based on a generalized small-signal model.

An adaptive fuse-saving protection scheme for active distribution networks

Electricity generation is one of the major concerns of today’s world due to increasing dependence on this energy. Continuity in power supply and service to network consumers may be disrupted by various disturbances; thus, the power grid requires a reliable and fast protection system. The protection of distribution networks is conventionally performed by overcurrent protective devices which preserving their coordination is a complex task due to bidirectional power flow resulting from the presence of distributed generation (DG) units. This paper presents an adaptive scheme to restore protection coordination in the distribution networks with a high penetration level of DG units. Using the multi-agent system (MAS) structure, the main protection technique monitors the current of the lateral fuse by an intelligent electronic device (IED) to estimate the operating time of the fuse and sends this data to the upstream recloser IED. Then, the recloser IED determines the recloser operating time to preserve the protection coordination. In the case of failing the main technique, the backup technique protects the distribution feeder by using a definite time piecewise linear curve for the recloser based on the proposed recloser current coefficient. The backup technique is independent of communication links. The reliable performance of the developed scheme is assessed on the ETAP simulation model of the IEEE 33 bus test system.

Impacts of Photovoltaics in Low-Voltage Distribution Networks: A Case Study in Malta

Photovoltaic systems (PVs) are promising low-carbon technologies playing a major role in the electricity business. In terms of voltage variation and feeder usage capacity, high PV penetration levels have significant technical implications for grid stability, particularly in Low Voltage (LV) networks. This paper presents a comprehensive PV integration analysis on real-life residential LV networks in Malta using recorded smart metering data. The methodology framework and tools developed are highlighted through step-by-step results on their usefulness. First, at the substation level, an LV network with seven LV feeders is analyzed using Monte Carlo simulations and OpenDSS. Then, Cumulative Distribution Functions (CDFs) are extracted to establish the likelihood of LV network challenges. Afterwards, 95 multi-feeder analyses assess the impact assessment on the first occurrence of LV network challenges and predominant issues. Finally, a Regression Analysis Tool, considering the regression’s standard error, is built for seven feeder characteristics to predict the impacts. The stochastic processes reveal strong relationships with feeder characteristics that are helpful for network planning and operations. However, the Maltese grid currently has less than 20% PV penetration at any LV feeder. Hence, significant technological hurdles are absent.

Decentralized Active Power Management in Multi-Agent Distribution Systems Considering Congestion Issue

Recently, due to the restructuring of power systems and the high penetration level of local renewables, distribution systems have encountered with the complexity of power management. Therefore, the modern systems would be operated in a multi-agent structure which facilitates the power management as well as privacy protections of independent entities. In this structure, the distribution system is assumed to compose of several agents who independently schedule their local resources in order to maximize their own profits. Consequently, this paper provides an efficient peer-to-peer (P2P) active power management framework in a multi-agent distribution system while considering network constraints (i.e., line loadings and losses). In this context, in the proposed P2P scheme, the distribution system operator (DSO) model the network constraints in the form of line-usage costs within the transactive signals. Respectively, the developed transactive control signals enable the DSO to model the power loss as well as alleviate the congestion in the grid. Therefore, the agents automatically consider the network constraints in their power transactions management procedure without any direct interferences of the DSO in their resource scheduling. Finally, the proposed model is implemented on the modified-IEEE-37-bus-test system in order to investigate its effectiveness in the energy management of multi-agent systems.