Search Results (2,350)

This research presents an innovative solution to optimise maintenance planning and integrity in offshore facilities, specifically regarding corrosion management. The study introduces a prototype for maintenance planning on offshore oil platforms, developed through the Design Science Research (DSR) methodology. Using a 3D CAD/CAE model, the prototype integrates machine learning models to predict corrosion progression, essential for effective maintenance strategies. Key components include damage assessment, regulatory compliance, asset criticality, and resource optimisation, collectively enabling precise and efficient anti-corrosion plans. Case studies on oil and gas platforms validate the practical application of this methodology, demonstrating reduced costs, lower risks associated with corrosion, and enhanced planning efficiency. Additionally, the research opens pathways for future advancements, such as integrating IoT technologies for real-time data collection and applying deep learning models to improve predictive accuracy. These potential extensions aim to evolve the system into a more adaptable and powerful tool for industrial maintenance, with applicability beyond offshore to other environments, including onshore facilities. Full article

Decentralized planning of renewable energy systems aims to address the substantial spatiotemporal variability, and thus uncertainty, associated with their underlying hydrometeorological processes. For instance, solar photovoltaic (PV) energy is driven by two processes, namely solar radiation, which is the main input, and ambient temperature, with the latter affecting the panel efficiency under specific weather conditions. The objective of this work is to provide a comprehensive investigation of the role of spatial scale by assessing the theoretical advantages of the distributed production of renewable energy sources over those of centralized, in probabilistic means. Acknowledging previous efforts for the optimal spatial distribution of different power units across predetermined locations, often employing the Modern Portfolio Theory framework, this work introduces the generic concept of spatial reliability and highlights its practical use as a strategic planning tool for assessing the benefits of distributed generation at a large scale. The methodology is verified by considering the case of Greece, where PV solar energy is one of the predominant renewables. Following a Monte Carlo approach, thus randomly distributing PVs across well-distributed locations, scaling laws are derived in terms of the spatial probability of capacity factors. Full article

The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh. Full article

In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management. Full article

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, often overlooking the potential of water transfers between rivers and basins. To address this gap, this article employs an improved mathematical model of hydropower production, considering the adjustment of the water transfer in the operation schedule as an additional optimization variable. A customized meta-heuristic, named the Evolutionary Socio-Bio Inspired Technique (ESBIT), has been tailored to integrate water transfer mechanisms into the operational planning model. The proposed model was validated through a case study at the Henry Borden Complex in São Paulo, Brazil, using real power plant parameters and inflow data from the Brazilian system. The results obtained from the test case, both with and without water transfer, demonstrate that the proposed methodology effectively captures the operational characteristics of a system that allows water transfers between rivers or basins to optimize the available water resources and system costs. Full article

The reconstruction of land spatial planning and the increasing severity of carbon emissions pose significant challenges to carbon peak and carbon neutrality strategies. To establish low-carbon and sustainable agricultural spatial planning while achieving dual carbon strategy goals, it is essential to accurately analyze the mechanisms of agricultural spatial transfer and their carbon emission effects, as well as the key factors influencing carbon emissions from agricultural spatial transfer. Therefore, this study, based on land use remote sensing data from 2000 to 2020, proposes a carbon emission accounting system for agricultural space transfer. The carbon emission total from agricultural space transfer in the Harbin-Changchun urban agglomeration over the 20-year period is calculated using the carbon emission coefficient method. Additionally, the spatiotemporal patterns and influencing factors are analyzed using the standard deviation ellipse method and the geographical detector model. The results indicate that: (1) The agricultural space in the Harbin-Changchun urban agglomeration has increased, with a reduction in living space and an expansion of production space. Among land type conversions, the conversion between cultivated land and forest land has been the most intense. (2) The conversion of agricultural space to grassland and built-up land has been the primary source of net carbon emissions. The carbon emission center has shown a migration path characterized by “eastward movement and southward progression,” with a high-north to low-south distribution pattern. Significant carbon emission differences were observed at different spatial scales. (3) Natural environmental factors dominate the carbon emissions from agricultural space transfer, while socioeconomic and policy factors act as driving forces. Elevation is the primary factor influencing carbon emissions from agricultural space transfer. Interactions between factors generally exhibit nonlinear enhancement, with the interaction between elevation, annual precipitation, and industrial structure showing a strong explanatory power. Notably, the interactions between elevation, average annual precipitation, and industrial structure demonstrate significant explanatory power. These findings highlight the necessity for government action to balance agricultural spatial use with ecological protection and economic development, thereby providing scientific references for optimizing future land spatial structures and formulating regional carbon balance policies. Full article

Remote agricultural regions in desert areas, such as Ghardaïa in southern Algeria, face significant challenges in energy supply due to their isolated locations and harsh climatic conditions. Harnessing solar energy through photovoltaic (PV) systems offers a sustainable solution to these energy needs. This study aims to identify suitable areas for PV power installations in Ghardaïa, utilizing a geographic information system (GIS) combined with the fuzzy analytical hierarchy process (AHP). Various environmental, economic, and technical factors, such as solar radiation, land use, and proximity to infrastructure, are incorporated into the analysis to create a multi-criteria decision-making framework. The integration of fuzzy logic into AHP enables a more flexible evaluation of these factors. The results revealed the presence of ideal locations for installing photovoltaic stations, with 346,673.30 hectares identified as highly suitable, 977,606.84 hectares as very suitable, and 937,385.97 hectares as suitable. These areas are characterized by high levels of solar radiation and suitable infrastructure availability, contributing to reduced implementation costs and facilitating logistical operations. Additionally, the proximity of these locations to agricultural areas enhances the efficiency of electricity delivery to farmers. The study emphasizes the need for well-considered strategic planning to achieve sustainable development in remote rural areas. Full article

Energy leaders around the world are constantly looking into feasibility and opportunities in renewable energy to diversify their energy sources. This study examines the reliability of a grid-connected microgrid consisting of solar energy, wind energy, and storage batteries to supply the required load and share the surplus with the grid. As the reliability of each component separately has an impact on system reliability, in this study, the loss of load expectation (LOLE) technique was used to estimate the peak load-carrying capability (PLCC) of the systems and the duration of outages as a means of analyzing the reliability of these systems and selecting the optimal combination among the cases. Moreover, this study used the load data of the area under study as the primary load and considered the grid as a secondary load to share the surplus after fulfilling the demand requirements. Furthermore, ten cases of grid-connected system configurations were considered to conduct this research, incorporating various combinations of solar panels, wind turbines (WTs), and batteries. The results revealed that, while maintaining an acceptable risk level represented by an LOLE of 0.1 days per year, the WT (850 MW) case emerged as the leading power producer compared to the other cases. It was able to produce 840.245 MW and 818.345 MW as the total power produced and the amount of surplus power that will be delivered to the grid after meeting the primary load needs in the area under study, respectively. This analysis can be informative for administrators in charge of planning and policy-making, helping them to take appropriate action. Full article

This article is devoted to planning water-energy regimes for hydropower plants, taking into account economic and ecologic criteria. A new methodology based on a probabilistic model of water inflow has been proposed. The probabilistic method requires the calculation of low-water and average-water year typical hydrographs based on the probability curve. This allows the determination of the guaranteed hydropower plant generation schedule with a month time-step. According to the method considered, the mathematical model of the reservoir filling and normal power station operation has been designed. The software for the automated water-energy mode calculation is presented in this paper. The economic feasibility of maximum replacement of thermal power plants in the energy system with more environmentally friendly hydropower plant is substantiated. The methodology of water resources cost calculation and economic efficiency assessment under various hydropower plant regime scenarios have been proposed in the paper. Using the data and characteristics of HPPs and TPPs, an assessment of energy efficiency will be obtained in accordance with the developed methodology to determine the price of water for HPPs and all participants in the water management complex. The results of the implementation of the developed approach indicate that the price of electricity sales in a competitive electricity market can be brought into line with the price of electricity sales generated by thermal power plants, which increases the economic feasibility of the maximum replacement of thermal power plant capacity in the system with more economical and environmentally friendly hydropower plant. The developed method allows for an increase in the efficiency of water resources use and the efficiency of hydropower plant participation in the energy balance, which makes it possible to displace part of the power generated by thermal power plants. Full article

Background: In agricultural supply chains, unequal bargaining power often leads to economic inequality, particularly for farmers. The fair profit distribution (FPD) approach offers a solution by optimizing supply chain flows (materials, information, and money) to promote economic equity among members. However, our literature review highlights a gap in applying the FPD approach to the facility location-allocation problem in supply chain network design (SCND), particularly in sugarcane-derived biofuel supply chains. Methods: Consequently, we propose a multi-period optimization model based on FPD to design a sugarcane biofuel supply chain. The methodology involves four steps: constructing a conceptual model, developing a mathematical model, designing a solution strategy, and generating insights. This model considers both investment (crop development, biorefinery construction) and operational phases over a long-term planning horizon, focusing on farm location and crop allocation. Results: By comparing the FPD model to a traditional centralized planning supply chain (CSC) approach, we examine the impact of the planning horizon, number of farms, and sugarcane prices paid by biorefineries on financial performance. While the FPD model results in lower overall system profits, it fosters a fairer economic scenario for farmers. Conclusions: This study contributes to economic justice in supply chains and offers insights to promote fair trade among stakeholders. Full article

The selection of the optimal 35 kV network structure is crucial for modern distribution networks. To address the problem of balancing investment costs and reliability benefits, as well as to establish the target network structure, firstly, the investment cost of the distribution network is calculated based on the determined number of network structure units. Secondly, reliability benefits are measured by combining the comprehensive function of user outage losses with the System Average Interruption Duration Index (SAIDI). Then, a multi-objective planning model of the network structure is established, and the weighted coefficient transformation method is used to convert reliability benefits and investment costs into the total cost of power supply per unit load. Finally, by using the influencing factors of the network structure as the initial population and setting the minimum total cost of the unit load as the fitness function, the DE algorithm is employed to obtain the optimal grid structure under continuous load density intervals. Case studies demonstrate that different load densities correspond to different optimal network structures. For load densities ranging from 0 to 30, the selected optimal network structures from low to high are as follows: overhead single radial, overhead three-section with two ties, cable single ring network, and cable dual ring network. Full article

Electric mobility is one of the main pillars of the global energy transition towards a more sustainable and environmentally responsible model. Greenhouse gas emissions could be drastically reduced with electric mobility massification. Public transport systems represent the first step of this massification due to government policies, but these electromobility projects should optimize their resources to be cost-effective. Furthermore, the implementation of charging stations could cause negative impacts on electrical distribution networks, which should be evaluated beforehand for the adequate planning of power grids. A methodology was developed and implemented herein for the technical and economic evaluation of electric bus-based transport systems. Full article

To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration. Full article

This study analyzes recloser operation in the South Korean distribution system to propose effective operational strategies for improving safety and efficiency. This research is based on actual data, such as recloser operation data and fault statistics provided by the Ministry of the Interior and Safety and the Korea Electric Power Corporation, without the use of simulation tools or experiments. Key operational elements, such as reclosure counts, sequence settings, and high-current interruption features, were analyzed. First, an analysis of reclosure counts revealed that over 73% of faults were cleared after the first reclosure, and when the second reclosure was included, more than 90% were successfully restored. This finding suggests that reducing the number of reclosures from the standard three to one or two would not significantly impact fault restoration performance while simultaneously reducing arc generation, thereby improving safety. Additionally, a review of recloser sequence settings highlighted the fact that the traditional 2F2D (two fast, two delayed) sequence often led to frequent instantaneous tripping, increasing the risk of arc generation. The 1F1D (one fast, one delayed) sequence, which applies a delayed trip after an initial fast trip, offers a better fault-clearing performance and reduces the risk of arc generation. Lastly, an analysis of the high-current interruption feature suggested that enabling this function for faults with low reclosing success rates, particularly in cases of short-circuit faults, and setting an immediate trip threshold for fault currents exceeding 3 kA would enhance both safety and efficiency. This operational strategy was implemented in the South Korean distribution system over a three-year period, starting in 2021. While there was a 2.1% decrease in reclosure success rates, this strategy demonstrated that similar success levels could be maintained while reducing the number of reclosures, thus mitigating equipment damage risks and improving safety measures. The refined recloser operation plan derived from this study is expected to enhance the overall stability and reliability of distribution systems. Full article

With the continuous penetration of flexible resources, the distribution network is gradually forming a two-way interactive supply and demand relationship with the transmission network and users. The deployment of soft open point (SOP) and energy storage represents a crucial strategy for voltage regulation and power flow control in distribution networks. This article puts forth a methodology for optimizing the configuration of SOP and energy storage based on the characteristics of the snowflake-shaped grid and sensitivity analysis. Firstly, the location of the SOP is determined based on the characteristics of the interconnection nodes between snowflake websites. Secondly, the voltage sensitivity analysis is employed to identify nodes that have a significant impact on the system voltage distribution, thereby enabling the selection of an optimal energy storage site. Subsequently, a multi-objective optimization configuration model for SOP and energy storage is established, taking into account the economic efficiency and load balancing of the power grid. Finally, the method is verified using a snowflake-shaped grid in Tianjin. In comparison with the plan that solely considers the economic aspects of the power grid, the method proposed in this article can reduce the degree of load balancing by 50.93% while simultaneously increasing the annual comprehensive cost by only 24.35%. Full article