Upgrade Costs Research Articles

Future cloud: spherical processors for realizing low-cost upgrade in underwater data centers

Underwater data centers are appealing due to their ability to benefit from the ocean cooling. However, upgrading the computing capability of underwater data centers is challenging because they are not easily accessible. Nevertheless, underwater data centers should be able to process increasing workloads. This paper addresses the challenge of upgrading underwater data centers by proposing the geometric processor paradigm (GPP). In GPP, underwater data centers use spherical processors with a higher surface area than conventional processors and host more processing units. GPP also incorporates the processor pause feature (PPF) that enables to consider the power available to an underwater data center. The research evaluates how the use of spherical processors enhances computational capability and reduces upgrade costs. The evaluation shows that the use of spherical processors enhances computational capability and reduces data centers interrupt epochs by up to 95% and 79% on average, respectively.

Multi-Path Routing in Green Multi-Stage Upgrade for Bundled-Links SDN/OSPF-ECMP Networks

This paper considers the novel problem of upgrading a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> network into a Software Defined Network (SDN) over multiple stages and saving energy in the upgraded network, or hybrid SDN. That is, in each stage, the problem at hand is to select and replace <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">legacy</i> switches with SDN switches and reroute traffic to power off as many unused cables as possible to save energy. Also, the operator must consider: (i) the available budget at each stage, (ii) maximum path delays, (iii) maximum link utilization, (iv) per-stage increase (decrease) in traffic size (upgrade cost), and (v) the Open Shortest Path First - Equal Cost Multi-Path protocol. This paper addresses two multi-path routing scenarios: 1) non-link-disjoint and 2) link-disjoint. It outlines a Mixed Integer Program and a heuristic algorithm for each scenario. The experimental results show that: (i) both solutions produce only up to 0.63% higher energy saving in scenario-1 than in scenario-2, (ii) the mixed integer program (heuristic algorithm) for both scenarios give an energy saving up to 71.93% (71.64%), (iii) using a larger budget and/or number of stages can increase the energy saving, and (iv) the saving achieved by the heuristic solution for each scenario is within 4% from the optimal saving.

Going Beneath the Surface: Petroleum Pollution, Regulation, and Health

This paper quantifies the health impacts of petroleum leaks from underground storage tanks, the effectiveness of tank regulation, and the role of information as a policy tool in the same setting. Exposure to a leaking underground storage tank during gestation increases both the probability of low birthweight and preterm birth by 7–8 percent. Compliance with regulations requiring the adoption of preventative technologies mitigated the entire effect of leak exposure on low birthweight, and information increased avoidance and moving among highly educated mothers. Back-of-the-envelope calculations suggest the health benefits of preventative regulations exceed the upgrade cost to facilities. (JEL I12, K32, L71, L78, Q35, Q51, Q53)

EFRADIR: An Enhanced FRAmework for DIsaster Resilience

This paper focuses on how to increase the availability of a backbone network with minimal cost. In particular, the new framework focuses on resilience against natural disasters and is an evolution of the FRADIR/FRADIR-II framework. It targets three different directions, namely: network planning, failure modeling, and survivable routing. The steady state network planning is tackled by upgrading a sub-network (a set of links termed the spine) to achieve the targeted availability threshold. A new two-stage approach is proposed: a heuristic algorithm combined with a mixed-integer linear problem to optimize the availability upgrade cost. To tackle the disaster-resilient network planning problem, a new integer linear program is presented for the optimal link intensity tolerance upgrades together with an efficient heuristic scheme to reduce the running time. Failure modeling is improved by considering more realistic disasters. In particular, we focus on earthquakes using the historical data of the epicenters and the moment magnitudes. The joint failure probabilities of the multi-link failures are estimated, and the set of shared risk link groups is defined. The survivable routing aims to improve the network’s connectivity during these shared risk link group failures. Here, a generalized dedicated protection algorithm is used to protect against all the listed failures. Finally, the experimental results demonstrate the benefits of the refined eFRADIR framework in the event of disasters by guaranteeing low disconnection probabilities even during large-scale natural disasters.

Upgrading locally-regulated green buildings to LEED standards: a Dubai case study

With Dubai aiming to be one of the greenest cities in the world, the local government has set various requirements and regulations to achieve this goal. The Dubai Municipality has already established and implemented its own locally-developed green building regulation, Green Building Regulations & Specifications (GBR&S), to address major concerns associated with buildings’ resource efficiency. This study proposes a comparison between the GBR&S system with USGBC’s LEED v4 rating to identify where GBR&S falls short and how much it would cost to upgrade to the various levels of LEED certification. The authors also hypothesize that access to efficient, mass public transportation systems is a major factor when determining the cost of LEED certification. With this in mind, proximity to a public transportation network and its effect on the cost of a LEED upgrade were investigated.

Lebanon’s electricity from fuel to solar energy production

Since 1924, Lebanon planned to use renewable energy and in particular hydraulic energy to produce the national need of electricity. Until the beginning of the 70, many steps have been achieved by the government where in 1974 around 41.5% of the Lebanese electricity was produced from hydroelectric stations. Unfortunately since then and due to the instability in the political regimen, electric plants and networks suffered from destruction, aging, increased demand, hacking and many other deterioration factors. Therefore and based on all the last mentioned troubles the electric sector in Lebanon transformed to be the major economic and environmental problem.This paper aims at presenting and analyzing the actual state of the electric sector in Lebanon emphasizing on its impact on air pollution and on the development of factories and small enterprises. Therefore, it will be shown that the problem with the electrical sector is essentially due to the old networks easily hacked, strong reliance on fuel-based electricity, which leads to increased air pollution without meeting the energy consumption demand. This underproduction of electrical energy leads to the development of a parallel electrical private sector also based on fuel burning what worsened air pollution problems despite all the measures taken by the governments to switch to greener electricity production.A case study on electricity consumption in different locations in Beirut is also presented and used to prove that shifting from fuel to solar production will decrease the economic impact of electricity. Hydroelectric energy was not considered due to a decrease in the precipitation level that made it difficult to produce enough energy from rivers and due to the high cost of rehabilitation and upgrading.

Optimal versioning strategies for software firms in the competitive environment

Software-as-a-service (SaaS) is gaining tremendous popularity in the enterprise software releases, which has brought the growing challenge for the incumbent used to adopt the on-premise licensing model. In this paper, we focus on the optimal versioning strategy problem of the incumbent when he faces the competition from the entrant who releases the SaaS software. He can choose from providing the upgraded on-premise software, turning to offer the SaaS version of the software, and releasing both the versions. We indicate that releasing both versions won't be the optimal choice in the competitive environment. When the upgrade cost is above a certain value, and the gap of value is below a threshold positively linear correlated with the upgrade cost, the incumbent prefers the SaaS strategy. Otherwise, the on-premise strategy generates the highest profit. A special case is that in the medium switching cost environment, the SaaS strategy can be optimal for all possible values of upgrade cost. Finally, We consider the problem in the presence of network effect and in the uncovered market setting. The results provide effective managerial insights into the strategy selection for the incumbent.

Maximum shortest path interdiction problem by upgrading edges on trees under weighted $$l_1$$ norm

Network interdiction problems by deleting critical edges have wide applicatio ns. However, in some practical applications, the goal of deleting edges is difficult to achieve. We consider the maximum shortest path interdiction problem by upgrading edges on trees (MSPIT) under unit/weighted $$l_1$$ norm. We aim to maximize the the length of the shortest path from the root to all the leaves by increasing the weights of some edges such that the upgrade cost under unit/weighted $$l_1$$ norm is upper-bounded by a given value. We construct their mathematical models and prove some properties. We propose a revised algorithm for the problem (MSPIT) under unit $$l_1$$ norm with time complexity O(n), where n is the number of vertices in the tree. We put forward a primal dual algorithm in $$O(n^2)$$ time to solve the problem (MSPIT) under weighted $$l_1$$ norm, in which a minimum cost cut is found in each iteration. We also solve the problem to minimize the cost to upgrade edges such that the length of the shortest path is lower bounded by a value and present an $$O(n^2)$$ algorithm. Finally, we perform some numerical experiments to compare the results obtained by these algorithms.

A stochastic cost–benefit analysis framework for allocating energy storage system in distribution network for load leveling

Increasing peak demand, retirement of conventional generation capacity, and high cost of constructing new generation capacity and network reinforcements are the challenges faced by utilities in urban cities. To address the above challenges, this paper proposes a stochastic cost–benefit analysis (CBA) framework, named CBA-LL, for allocating centralized energy storage system (ESS) in distribution network for load leveling. CBA-LL is built on a scenario-based stochastic optimization approach that integrates principal component analysis and k-means clustering for effective probabilistic load modeling. In CBA-LL, a genetic algorithm (GA) based optimization approach is utilized over a multi-year planning horizon for ESS sizing. Within the GA, the optimal operation of ESS for load leveling is determined using fmincon solver of MATLAB with the objective of minimizing load variance. The benefits of integrating ESS in terms of distribution system upgrade cost, distribution energy loss cost, arbitrage benefit, and generation plant upgrade cost are considered. In CBA for ESS allocation, the financial objective chosen is generally maximizing net savings (NS) (also known as net present value (NPV)). However, the limitation of NS objective function is that it does not consider the scale of investment required to achieve the objective. ROI and NS are two important and complimentary criteria in investment decision making. However, to the best of our knowledge, ROI has never been investigated in the literature as a planning objective function for sizing ESS. Hence, in this paper, we investigate maximizing ROI as a planning objective function and compare it with maximizing NS objective function. The proposed CBA-LL approach is implemented on a 33-bus distribution system, and two types of ESS technologies, namely, Li-ion and Vanadium Redox Flow (VRF) are compared. The simulation results demonstrate the complementary nature of NS and ROI objective functions. Furthermore, the simulation results highlight that ROI objective function may be more preferable when there is a lack of investment budget or high opportunity cost of investment. Our analysis suggests that the decision makers (DM) can investigate both NS and ROI objective functions for ESS sizing and then select a solution based on a more informed decision. The comparison of battery technologies demonstrate that the Li-ion battery is significantly superior to the VRF battery for the load leveling application.

Research on Design Technology of Safety Facilities in Highway Traffic Engineering

In highway traffic, setting up the necessary traffic safety facilities can provide a guarantee for the safe driving of vehicles. To a certain extent, proper and reasonable setting of traffic safety facilities is an important indicator to measure the safety design of highway traffic engineering. In order to do a good job in highway traffic safety from the source, people gradually pay attention to how to improve the rationality of the design of highway traffic safety facilities. As people pay more attention to highway traffic safety facilities, their design technology has become an important research content to effectively avoid safety problems in highway traffic design, construction and operation, and reduce the high cost of subsequent upgrades. This paper takes the design technology of highway traffic engineering safety facilities as the research object, and analyzes the main structure of highway traffic road safety facilities and the main causes of safety problems on the side of highway traffic roads. On this basis, with the main purpose of effectively avoiding road traffic safety accidents and guaranteeing road traffic safety, targeted safety protection measures are provided. From the aspects of guardrail design technology, visual illusion deceleration marking design technology, warning signs and other aspects, research and discussion are carried out to provide a reference for effectively improving the overall level of traffic safety facilities design.

The hidden costs of environmental upgrading in global value chains

Sustainability has become important in the operation of the global economy and its regulatory structure, leading significant shifts in the way powerful ‘lead firms’ in global value chains approach sustainability. In this paper, I argue that private, value chain-oriented forms of sustainability governance are not addressing the environmental problems they are putatively designed to solve. Through the analysis of how lead firms stimulate environmental upgrading along their value chains, I show that the mainstreaming of sustainability in business operations has allowed global buyers to accumulate ‘green’ profits and capital in ways that extract value from suppliers – especially those based in the global South. Drawing from analyses of the wine and coffee value chains, I show how lead firms push the hidden costs of sustainability compliance and related risks upstream towards producers. These processes have important redistributive repercussions as they raise entry barriers for smaller, less organized and/or more marginalized actors. Under the mantle of achieving environmental sustainability, lead firms in value chains stealthily capture value for themselves, while extracting more demands from their suppliers and promoting a further consolidation of their supply base. In the meanwhile, serious environmental challenges remain unaddressed.

Assessing local costs and impacts of distributed solar PV using high resolution data from across Great Britain

Abstract Highly spatially resolved data from across Great Britain (GB) are combined with a distribution network modelling tool to assess impacts of distributed photovoltaic (PV) deployment up to 2050 on local networks, the costs of avoiding these impacts, and how these depend upon context. Present-day deployment of distributed PV, meter density, and network infrastructure across GB are found to be highly dependent on rurality, and data on these are used to build up three representative contexts: cities, towns, and villages. For each context, distribution networks are simulated, and impacts on these networks associated with PV deployment and growth in peak load up to 2050 calculated. Present-day higher levels of PV deployment in rural areas are maintained in future scenarios, necessitating upgrades in ambitious PV scenarios in towns and villages from around 2040, but not before 2050 in cities. Impacts of load growth are more severe than those of PV deployment, potentially necessitating upgrades in cities, towns, and villages from 2030. These are most extensive in cities and towns, where long feeders connect more customers, making networks particularly susceptible to impacts. Storage and demand side response are effective in reducing upgrade costs, particularly in cities and towns.

Using BIM for Optimizing the Upgrading Cost to Convert the Traditional Buildings to Sustainable Buildings in Iraq

A high percentage of existing buildings in Iraq are traditional buildings, yet there is approximately no such green building in Baghdad or other governorates. Most of these buildings require urgent upgrading to increase their performance (operationally, economically, and environmentally), also the building owners looking for identifying and implementing many of the green building measures to reduce the operational and maintenance costs of their buildings. The decision-makers need to support the possibility of achieving sustainable measures of existing building rating systems such as LEED or BREEAM, and that would require an optimization model. The goal of this study is to maximize the building’s operational efficiency, health, and comfort of the building to its occupants through minimizing upgrading cost and reducing the environmental impacts of the building. An existing building will be used as a case study to illustrate the optimization process and demonstrate the plans and processes required to achieve the green building measures, the capability of reducing the required upgrade costs. For this type of problem, the building drawings are represented Using Building information modeling (BIM) (Revit program), and the life cycle costing and environmental impact can be found using analytical tools (ONE CLICK LCA STUDENT VERSION online software). The results show that up to (15.4 and 23.2) % reduction in discounted and nominal costs respectively could be achieved, the CO2 emissions also had decreased from 34233 Ton to 19299 Ton.

Surplus forest biomass – The cost of utilisation through optimised logistics and fuel upgrading in northern Sweden

In order to achieve a net zero emission economy by 2045, Sweden’s inland biomass resources are under examination. Large amounts of forest biomass in the north of the country are not utilised. In this study, resource mapping of surplus forest biomass, (logging residues, tree stumps and mill by-products) is carried out according to ecological harvesting restrictions to determine the annual potential of this resource. Using a network of terminals, optimised supply chains are used to determine the cost of feedstock supply and upgrading of biomass to value-added torrefied pellets. The results indicate that the total biomass surplus is 5.75 Mt (dry mass), representing a forested procurement area half the size of Sweden (224 500 km2). The surplus represents two per cent of Sweden’s annual energy consumption and 60 % of its coal and coke use. Aggregation of the existing terminal network results in 274 to 1611 kt of biomass (dry mass) supplied to ten terminals. Supply and upgrading costs at the terminals reflect a trade-off between the size of procurement area and the scale of upgrading; a large plant capacity corresponds to a low upgrading cost but a large supply cost and vice versa. The lowest supply cost is 79 € t−1 (dry mass) of feedstock and the upgrading capacity ranges from 86 to 1310 kt (dry mass). Utilisation of the surplus would require an effective energy cost range of 27–37 € MWh−1 exclusive of delivery cost to the end user and operational profits – corresponding to a minimum torrefied pellet price of 131 € t−1. Though much higher than other solid fuels, the energy price is comparable to that of natural gas. The results of this study have importance for legislation supporting EU and Swedish energy policy objectives.

Comprehensive impact analysis of electric vehicle charging scheduling on load-duration curve

Within the next decade, the number of electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) will increase exponentially owing to the environmental benefits related to their utilization. In an ideal framework condition, the vehicle-to-grid (V2G) system provides advantages such as subordinate administration of load leveling and peak shaving guidelines, and minimized upgrade costs. In this paper, deterministic and probabilistic methods of scheduling PHEV charging/discharging are analyzed, and their performances are compared based on a case study. In the case of the deterministic method, an optimization model is proposed to reduce the overall grid output power consumption for utilities and operate a smart charging/discharging schedule for PHEV users. Quadratic programming (QP) is used to solve the optimization cost function. In contrast, the probabilistic method uses Monte Carlo simulation (MCS) to analyze the impact and determine a coordinated time-of-use (TOU) structure for the PHEV charging/discharging schedule. Both methods suggest V2G feasibility for the smart grid system.

Multi-objective model for simultaneous distribution networks reconfiguration and allocation of D-STATCOM under uncertainties of RESs

This paper proposes an approach for simultaneous placement of distribution static compensator (D-STATCOM) and renewable energy sources (RESs) and distribution system reconfiguration (DSR) based on stochastic multi-objective modelling. Associated uncertainties of RESs in generating power, market price errors and load forecasting are modelled in this problem. The problem is optimised by non-dominated sorting genetic algorithms-II (NSGA-II) and then the best compromise solution is specified with fuzzy set theory. The aim of optimisation is to minimise installation and operation costs of distributed generation (DG) units, cost of D-STATCOM installation, the cost of procured energy from the transmission grid, costs of reactive and active power losses, line upgrade cost and DSR expense. A risk-based model of energy not supplied (ENS) as a reliability index is considered in the objective function to improve the reliability of the system. The proposed approach is validated on the IEEE 33-bus test grid.

Citizen Scientist: Frank von Hippel’s Adventures in Nuclear Arms Control PART 8. Nuclear-reactor Safety Again

ABSTRACT The Fukushima accident of 2011 pulled von Hippel back into the reactor-safety world. He was invited to join a Congressionally-mandated four-year study of the lessons that could be learned from the Fukushima accident to improve the safety of US nuclear plants. During that study he learned more about both a much worse accident that almost happened at Fukushima, and about how the US Nuclear Regulatory Commission had come to use “risk-informed regulation” as a way to avoid requiring costly safety upgrades opposed by US nuclear utilities. He campaigned unsuccessfully at both the national and state levels to move the nuclear utilities away from densely packed spent fuel pools by moving fuel that had cooled for more than five years to dry-cask storage. He also promoted on-site dry-cask storage in Japan and South Korea as an alternative to reprocessing. This last chapter ends with some stories of his father, also a physicist, who would not be intimidated and who was a leader in the development of the field of materials science and engineering.

Keeping infrastructure reliable under climate uncertainty

Characterizing infrastructure vulnerability to climate change is essential given the long asset lives, criticality of services delivered and high costs of upgrading and maintaining these systems. Reconciling uncertainty from past infrastructure design decisions with future uncertainty of climate change will help prioritize limited resources to high risk assets.

Service differentiation-based spectrum defragmentation in mixed grid optical networks

Abstract As the most promising candidate for upgrading today’s fixed-grid networks, the concept of flexible grid has been introduced into elastic optical networks (EONs), which enables on-demand spectrum assignment and adaptive modulation formats. Considering the cost of network upgrade, it is practical and economical for network operators to adopt the gradual migration from fixed grid to flexible grid optical network. Thus, a mixed grid optical network is a typical network scenario in the near future where fixed grid technology coexists with flexible grid technology. Spectrum fragmentation is regarded as a significant issue in flexible grid optical network. Compared with flexible grid, with heterogeneous spectral widths, more spectrum fragmentation is induced in mixed grid optical networks. Therefore, spectrum fragmentation in mixed grid optical network is actually much more challenging than flexible grid optical network. This paper first analyzes the resource allocation and proposes a novel spectrum defragmentation algorithm based on service differentiation in mixed grid optical networks. The performance of the proposed algorithm is evaluated in simulation with different traffic profiles. Simulation results verify that the proposed algorithm can effectively reduce the blocking probability by consolidating spectrum fragmentation in mixed grid optical networks.

A cost-effective viable strategy for gradually transitioning Egypt’s cities into truly IOT-enabled smart cities

A smart city is a city that uses information and communication technologies (ICT), smart sensors, actuators and video cameras to better support and optimize the delivery of urban services and to address challenges within city communities. A smart city collects and analyzes data from smart IoT sensors (IoT devices/nodes) and video cameras. The data collected by the sensor nodes can be used to develop applications and systems that benefit the city and the community. Smart city industry is projected to be a $400 billion market by 2021, with 600 cities around the globe expected to generate 60% of the world's GDP by 2025, according to McKinsey research. Many cities have adopted at least one smart city project. However, the majority of cities around the globe are not yet smart cities. The key hurdle is securing the huge fund needed to start a smart city project, and ensuring that there are sufficient resources to sustain the project over time. Because implementing new smart city infrastructure is a large and complex financial undertaking that poses a financial hurdle, specifically, for most of the under-developed countries, a viable and cost-effective strategy is needed to tackle this problem and conquer the digital divide. Building upon and leveraging existing infrastructure is the key to address this problem. This minimizes the cost of technology upgrades and allows for an easier integration process. In addition, leveraging existing infrastructure can give cities a better and quicker return on investment. The main objective of this work is to devise an innovative research and development initiative, which builds upon and leverages ongoing global deployment of smart city and outdoor lighting infrastructures trend, to explore the potential and assess the feasibility of gradually transitioning existing Egypt’s cities infrastructure into truly IoT-enabled smart cities. Specifically, we propose and develop an innovative cost-effective, future-proof, scalable and modular end-to-end device-to-cloud connected outdoor Lighting network infrastructure solution that is based on P2P 4G LTE cellular technology to provide the direct connectivity between lighting poles (IoT devices) and the cloud. The proposed intelligent Lighting solution includes three key building blocks:1) light pole-mounted smart control nodes; 2) cloud-based smart city software solution (NetServ) to remotely monitor, manage, and control the entire lighting infrastructure; and 3) P2P 4G LTE cellular network. In the proposed architecture, smart streetlights serve as the framework for a high- bandwidth, low-latency wireless sensor network, capable of transporting large amounts of data in real-time, while concurrently supporting deployment of a wide range of smart city services.