Network Upgrade Research Articles

Modeling and upgrade of disaster-resilient interdependent networks using machine learning

Recent global emergencies emphasize the critical role of reliable communication networks. As dependence on critical infrastructures grows, the focus shifts from isolated failures to designing networks capable of withstanding disasters, taking into account their interdependence with infrastructures like the power grid. This paper investigates the problem of the disaster resilient upgrade of interdependent networks, focusing on enhancing network resilience during emergencies and ensuring a service-level agreement. We analyze how the interdependency between the networks affects the disaster resilience and propose heuristic methods for network operators to improve resilience against disasters. Furthermore, to address the challenge of hidden interdependencies, we present a novel approach using graph neural networks for predicting interdependency between networks based on historical data of failures. Using simulations with real networks and earthquake data, we demonstrate that limiting the number of interdependent edges per node significantly affects resilience. We show that if sufficient data is available graph neural networks can learn the connection between failures and interdependencies, and capable of predicting interdependencies. Additionally, we show that selecting appropriate upgrade methods can reduce network upgrade costs by up to 20%.

Collaborative operation of active distribution network considering PV-storage-charging-compensation

Abstract With the conjunction of clean energy generation and electric cars, the power distribution network is gradually shifting from a traditional one-way radiative network to an active network. Coordinated control of these controllable elements of “source-grid-load-storage” can improve the operating voltage level and economic benefits, and reduce the load peak and off-peak difference. A collaborative dispatch model for active power distribution networks including PV-storage-charging-compensation is constructed in this paper, with the goal of minimizing network losses and PV curtailment costs. The cone transformation is used to handle non-linear constraints of power flow. Compared with existing ordered charging control methods, the constructed model can maximize the control efficiency of the distribution grid, enhance the PV absorption capacity, and effectively slow down the upgrading and renovation of the distribution network.

External costs of high-voltage overhead transmission lines: Evidence from a causal approach in rural France

In light of climatic change and global warming, electricity transmission networks need to be developed for the integration of renewable sources. In North America and Europe, to achieve renewable electricity goals, massive investments in electricity transport infrastructure are planned to connect solar and wind generation sites to the grid network. However, local opposition to new installations of high-voltage overhead transmission lines (HVOTLs) is causing delays and cost overruns. Visual nuisance is an important basis of local stakeholders' opposition to a new project. Estimations of the local economic impact of HVOTLs can help in identifying the external costs of the electricity supply industry to improve both efficiency in electricity pricing and fairness in defining provision for host community compensation. Prior research has provided evidence that existing HVOTLs have negative but not substantial effects on their immediate neighbourhoods. However, these studies have applied a correlational approach. This paper revisits the external effects of HVTOLs, exploring the case of a 225 kV line connecting two medium-size cities (Saint-Privat-d'Allier to Saint-Etienne) in France. We take advantage of the public disclosure in 2009 of undergrounding 8 km out of a total of 87 km lines as a result of a network upgrade project, to develop a difference-in-difference specification of a hedonic regression model, by comparing house sale prices at different distances from existing pylons, before and after the announcement of the decision to upgrade the line. We show that undergrounding was accompanied by an increase of about 38% in the price of treated houses located within 200 m of old pylons. The implied environmental costs of proximity to high-voltage pylons are substantial and their reduction is internalized into house prices as soon as the decision to go underground is announced.

Distributed multienergy and low-carbon heating technology for rural areas in northern China

In the context of China's “double carbon” policy and “rural revitalization” strategy, clean and low-carbon heating in rural areas of the northern China has become a key problem that needs to be urgently addressed. Practice has proved that “coal-to-gas” and “coal-to-electricity” projects have various limitations, such as high operating cost, high carbon emission, and high cost of distribution network upgrade, making them unsuitable for nationwide promotion. To address this issue, this research proposes a distributed multienergy and low-carbon heating (DMLH) system, which integrates the photovoltaic power generation, combined heat and power (CHP) system, heat storage, and newly developed multisource efficient heat pump (MEHP). The MEHP operates by simultaneously absorbing heat from air and waste heat sources of the CHP generation, providing a stable and efficient heat output. The multienergy complementary operating strategy of the DMLH system has been presented, considering variations of heating demand and environmental temperature. Comprehensive case studies were performed under typical and extremely cold winter scenarios to examine the effectiveness of the DMLH system. Simulation results revealed that 66.7 % of the operating cost can be reduced by the proposed system in typical scenario and the heat supply stability of MEHP has been considerably enhanced compared to a single-stage heat pump system. Furthermore, the DMLH system has low-level requirement of distribution network capability, which is conducive to promotion in the rural areas of northern China.

Heat pumps to upgrade existing CHP-DHN systems towards new generation thermal networks

District heating networks with combined heat and power systems and renewable energies are one of the most promising solutions for efficient and sustainable energy supply. In many cases, however, especially for district heating networks prior to the 4th generation, significant renovations are required to meet decarbonization targets. In this paper a study is proposed to evaluate the integration of high temperature heat pumps in an existing combined heat and power - district heating plant to reduce fossil fuel consumption and increase the exploitation of renewable energy sources. The plant is currently operating in central Italy and connects more than 1250 users. The identified solution implies lowering the district heating networks operating temperature and supplying power peaks with a high temperature heat pump acting as a booster. Results showed significant improvements in system performance especially in the winter months, due to the greater impact of lowering the temperature level of the district heating network during these months. Overall, the updated scenario allows the overall demand and ground heat losses to be reduced annually by 5.3 % and 13.5 % respectively. This reduces natural gas consumption by 13.3 % and avoids the emission of about 836 tCO2. The analysis provides guidelines for the upgrade of 3rd generation district heating network that can be useful for planning improvements towards newest generation thermal networks.

Who Should Pay for Network Upgrades? [Viewpoint

Who Should Pay for Network Upgrades? [Viewpoint

Economic Viability of Distribution Network Upgrade Deferral through BESS Sizing from K-Means Clustered Annual Load Profile Data

Economic Viability of Distribution Network Upgrade Deferral through BESS Sizing from K-Means Clustered Annual Load Profile Data

GSNR-aware resource re-provisioning for C to C+L-bands upgrade in optical backbone networks

Efficient network management in optical backbone networks is essential to manage continuous traffic growth. To accommodate this growth, network operators need to upgrade their infrastructure at appropriate times. Given the cost constraint of upgrading the entire network at once, upgrading the network periodically in multiple batches is a more pragmatic approach to meet the growing demands. While multi-period, batch-upgrade strategies to increase network capacity from the conventional C band to C+L bands have been proposed, they did not consider so far the possibility to re-provision existing traffic. In this work, we investigate how to selectively re-provision connections from C band to L band during a batch upgrade. This is to ensure greater availability of C-band resources which can help to delay network upgrade and hence reduce upgrade cost, while limiting the number of disrupted connections in the network. This study proposes two re-provisioning strategies, namely, Budget-Based (BB) and Margin-Aware (MA) re-provisioning, which rely on the Quality of Transmission (QoT) of lightpaths. These strategies leverage the knowledge of Generalized Signal-to-Noise Ratio (GSNR) to choose which lightpaths to re-provision. We compare these strategies with a baseline distance-based strategy that uses path length to select and re-provision lightpaths. We also incorporate Machine Learning techniques for QoT estimation of lightpaths to reduce the computational time required for optical-path feasibility check. Numerical results show that, compared to distance-based strategy, BB and MA strategies reduce disruption by about 22% and 27%, respectively, in representative network topologies.

A geographically disaggregated approach to integrate low-carbon technologies across local electricity networks

Meeting climate targets requires widespread deployment of low-carbon technologies such as distributed photovoltaics, heat pumps and electric vehicles. Without mitigating actions, changing power flows associated with these technologies would adversely impact some local networks. The extent of these impacts, and the optimal means of avoiding them, remains unclear. Here we use local-level data and network simulation to estimate variation in future network upgrade costs in over 40,000 geographical regions comprising all of Great Britain. We find that costs vary substantially between localities, and are typically highest in urban areas, and areas with highest deployment of heat pumps and electric vehicles. We estimate reductions in required upgrades associated with local flexibility, which vary substantially between localities. We show that using geographically disaggregated data to inform flexibility deployment across the country could reduce network upgrade costs by hundreds of millions of pounds relative to an approach that treats localities as homogeneous.

Financial dynamics, green transition and hydrogen economy in Europe

The introduction of the Next Generation funding scheme provides an opportunity for the EU-25 to make significant financial progress towards a radical and fully sustainable Green economy. This study examines the financial implications of a complete transition to hydrogen energy. In this paper, we propose the Hydrogen Production Safe Supply and Storage energy model by analyzing the conversion of electricity from offshore wind farms to hydrogen, which serves as both an energy storage and fuel for a variety of sectors, including transportation, households, industries, and governments. We analyze the financial impact of the Green transition across the EU-25 economies, based on a panel auto-regressive distributed lag model and the simulation procedure proposed by Jordan and Philips (2018). In addition to electricity production and consumption, the variables used include greenhouse gas emissions, real output per capita, and total environmental tax revenue. According to the study's financial insights, increased renewable electricity production results in a significant reduction in household energy consumption, as well as an increase in consumption in the transportation, industrial, and public sectors. These findings underscore the economic implications of transitioning to renewable energy and highlight the financial imperatives, such as targeted state aid, the immediate expansion of the hydrogen supply network, further research on safe mobile hydrogen containment, and an extensive upgrade of the regional hydrogen supply network within the EU-25.

Enabling ultra-high bit rate transmission with CFBG as dispersion compensator in an OptiSpan 240 km DWDM network

Abstract In this paper, a dispersion compensation technique for an ultra-long haul optical network utilizing a chirped fiber Bragg grating (CFBG) is presented. A high bit-rate signal of 40 Gbps is inputted into each channel. The CFBG, employed for dispersion compensation, is positioned after demultiplexing, enabling effortless network upgrades. The grating parameters of the implemented CFBG are mathematically analyzed and optimized to counteract net dispersion of over 3153 ps/nm across the channel. The design is verified using the OptiSystem software, resulting in a successful transmission up to 240 km with an average Q-factor of 8.09, utilizing an amplification gain of 49.5 dB. Additionally, the achieved optical signal-to-noise ratio (OSNR) level using the symmetrical amplification technique surpasses the acceptable value, with an average bit error rate of 10−16.

The Impact of Digital Entrepreneurship on Digital Leadership to Improve the Project Management of Solar Projects

The current study aimed to research the impact of digital entrepreneurship on digital leadership to improve the project management of solar projects, The study relied on the descriptive approach. The study population consisted of all project managers in solar energy companies in Jordan, pproximately 2,000 project managers in solar companies in Jordan. the study sample suggestion based on Laplace's equation is to randomly select 385 project managers from the study population. The results of the study shows that the mean values of the study sample's estimations were high for the independent variable (digital entrepreneurship), the overall mean was (4.08) with a high estimation score, also the mean values of the study sample's estimations were moderate for the dependent variable (digital leadership), and the overall mean was (3.05) with a moderate estimation score, the mean values of the study sample's estimations were moderate for the (project management for solar projects) variable, the overall mean was (3.60). Based on the findings of this study, the following recommends to conduct needs assessments to identify required hardware, software, and network upgrades, allocate sufficient budget for IT infrastructure development, and ensure reliable internet connectivity to support digital solutions.

Physical-layer-aware multi-band optical network planning framework for rate-adaptive transceivers

Flexible-grid elastic optical networks (EONs) have recently been widely deployed to support the growing demand for bandwidth-intensive applications. For cost-efficient scaling of the network capacity, multi-band systems are a promising solution. Optimized utilization of EONs is required to delay cost-extensive network upgrades and to lower cost and power consumption. Next-generation bandwidth-variable transceivers (BVTs) will offer increased adaptivity in symbol rate and modulation through techniques such as probabilistic shaping (PS). In this work, we investigate the impact of increased configuration granularity on optical networks. We account for practical implementation considerations of BVT configurations for estimating the required signal-to-noise ratio. Additionally, an optimization algorithm is presented that selects the most efficient configuration for each considered data rate and bandwidth combination. We utilize advanced quality of transmission estimation modeling to evaluate PS configurations in multi-band systems with optimized launch power distributions. We present results of network planning studies for C-band systems in a national and a continental optical backbone network topology considering different granularities of the configurations. Our analysis confirms that finer modulation-based rate-adaptivity results in substantial resource savings, decreasing the number of necessary lightpaths by at most 13% in C-band EONs. Additional savings are observed in multi-band systems, showing further increased savings in the number of required lightpaths of up to 20%. In contrast, increased symbol rate granularity only results in minor savings.

An EV Charging Station Load Prediction Method Considering Distribution Network Upgrade

An EV Charging Station Load Prediction Method Considering Distribution Network Upgrade

ЕФЕКТИВНІСТЬ КРИПТОВАЛЮТ ЯК ЗАСОБУ МІЖНАРОДНИХ ПЛАТЕЖІВ: АНАЛІЗ ВАРТОСТІ, ШВИДКОСТІ ТА БЕЗПЕКИ ТРАНЗАКЦІЙ

This article focuses on the effectiveness of using cryptocurrencies as a means for international payments, with an emphasis on the analysis of cost, speed, and security of transactions. The aim of the study is to explore the potential of blockchain technologies to optimize international financial operations. This research considers the performance of cryptocurrencies in international payments, focusing on an analysis of cost, speed, and security of transactions, utilizing data from various sources and case studies. Blockchain technologiessuch as Ripple and Solana demonstrate high throughput, capable of processing up to 65,000 transactions per second (TPS).In comparison, traditional banking systems offer significantly lower processing speeds, withBitcoin handling around 7 TPS and Ethereum up to 15 TPS. The anticipated Ethereum 2.0 network upgrade projects an increase to 100,000 TPS, significantly enhancing their potential in international payments. Traditional banking transactions can incur fees of up to $50 USD, whereas blockchain networks can reduce costs to less than $0.01 per transaction. The implementation of distributed ledger technology could save up to $27 billion by 2030 on international transactions. According to research, the average cost of an international remittance is 6.4% of a $200 transaction, indicating the potential for significantsavingsthrough the adoption of cryptocurrencies. Cryptocurrency payments are protected using cryptographic methods and decentralization, reducing the risks of fraud and attacks. By adhering to "privacy by default" principles, transaction confidentiality can be further enhanced, including generating new addresses for each transaction. Regular security audits and multi-factor authentication provide an additional layer of protection. It can be asserted that blockchain technology offers significant advantages in speed, cost, and security of transactions, which could radically transform the landscape of international trade and financial transfers. The practical significance of the research lies in identifying the potential of cryptocurrencies as a reliable, cost-effective, and secure tool for international payments, which may alter the global money transfer system.

Electricity generation and China's GDP growth under dual control policy

To achieve the carbon neutralization plan by 2060, China implemented the “Dual Control” ( Neng Hao Shuang Kong in Chinese) policy in 2015 by limiting the energy intensity and consumption, leading to forced slowdown of energy-related production and consumptions, which in turn hampered China's economic growth. The economic impact of energy shortage has been discussed in such studies as Cheng et al., the economic consequences of the new policy remain unanswered. In this article, we select China's provincial GDP and electricity generation data from 2001 to 2021 to perform causality tests and a double log model which delineates the nexus between GDP growth and electricity generation. We find that (a) electricity generation growth Granger causes GDP growth but not vice versa, and (b) a 1% increase in electricity generation growth will lead to 0.17% increase in GDP growth. The significance of the finding is: as coal still primarily fuels electricity generation, dual control will further hinder China's economic development sans significant enhancement of electric system or expansion of renewable energy, our findings thus support an upgrade of current grid network by China Power International Development; an expansion of renewable capacity such as a proposal of 400 GW new energy project; and construction of energy storage devices such as the CGD Group thermal storage System and Yingcheng Compressed Air Energy Storage System.

Sanacija nivelmanske mreže Mestne občine Ljubljana

The article presents the renovation and upgrading of the urban leveling network of the municipality of Ljubljana. The old measurements in the of the municipality of Ljubljana area, the renovation project and the form of the new leveling network as well as the leveling measurement are presented. The pre-processing of the data is described. Based on the analysis of the stability of the 1st order benchmarks of the leveling network of Slovenia, the predetermined benchmarks for the adjustment of the leveling network were selected. The precision is analyzed based on the deviations of the both sides measured height differences, the deviations when closing the leveling loops and the corrections of the measured height differences after adjustment.

Transformer Outage Contingency in the Southern Interconnected Grid of Cameroon Based on Dynamic Power Flow Simulation

The power system is a network of components having specific operational limits such that any violation of these limits during their operation could lead to more damage in the grid. The grid in Cameroon has been facing several undesired loading events at some sections of the network and the utility company has over the years, employed measures to mitigate these issues. This study has used the ETAP software to conduct a contingency analysis on the largest independent grid in Cameroon, supplying 6 regions in Cameroon. Seventeen transformer outage events in the network were used to conduct the N‐1 scenario‐based contingency, so that the vulnerability of the network can be evaluated. The fast decoupled power flow method was continuously applied on the network, and the different contingency scenarios were ranked using a combination of 4 performance indices. These four performance indices gave information on the impact of the various transformer outage scenarios on the grid. The four indices were added to give a combined value, and the outages were ranked in order of severity based on this combined index. A major contribution of this paper is the use of four scalar indices to classify the critical elements in the grid of a developing country. The initial load flow simulation of the network showed that 12 nodes had under voltages, while 3 transformers and one transmission line were overloaded. The Ngousso corridor suffered one of the worst under voltage situation during the initial power flow study. This is an indication that the utility company needs to undertake intensive network upgrade and implore measures to increase power generation to support the growing loads. It was observed that all the contingency scenarios experienced convergence. The Oyomabang transformer 1 had the highest combined index of 71.20, showing a huge negative impact on the network as a result of the outage of the transformer. This was followed by the Oyomabang transformer 2 with a combined index of 48.63. The results will be very useful to utility operators in prioritizing transformers in the network with high risk of causing huge impact on the network in case of an outage.

Programmable device deployment for efficient network function offloading

Network functions (NFs) play an important role in ensuring network security and performance. To improve the NF throughput performance, an emerging method is to offload NFs on programmable devices, bringing orders-of-magnitude improvements. A primary task for efficient NF offloading is how to deploy programmable devices (e.g., programmable switches) for network upgrades. Although programmable switches have powerful computing resources, their memory resources are usually limited, which poses a challenge of offloading stateful NFs (e.g., load balancer, NAT) with a large-size memory requirement. A promising solution to break the memory limitation is using external memory (e.g., on commodity servers) with the help of remote direct memory access (RDMA) supported by SmartNIC. Therefore, this paper studies the problem of upgrading networks by replacing legacy switches with programmable switches and equipping commodity servers with SmartNICs so that NFs can be offloaded on programmable switches with external memory expansion. We prove that this problem is NP-Hard, and there is no polynomial-time algorithm with an approximation ratio of (1−ϵ)⋅lnh, where ϵ is an arbitrarily small value, and h is the total number of requests in the network. Then we design an efficient algorithm with an approximation ratio of 2.5⋅H(m⋅n), where m is the number of NF types, n is the maximum number of requests through a switch, and H(m⋅n) is the (m⋅n)-th harmonic number. The simulation results show that our solution can reduce the upgrade cost by about 70% compared with the state-of-the-art approaches while preserving the same system throughput.

Project Oriented Management (POM) and Logistical Challenges in Maintenance and Network Development Systems

Maintenance, Refurbishment and Network Upgrading Systems occupy a prominent place among service systems, especially due to their dispersed location and large territorial extent. For the efficient opertaion of these systems, it is essential to apply Project Oriented Management (POM), which aims at optimizing logistic processes and meeting project specific requirements.