Long-term Infrastructure Planning Research Articles

Assessing adaptation planning strategies of interconnected infrastructure under sea-level rise by economic analysis

This study assesses the commonly adopted adaptation planning strategies of infrastructures in Northwest Florida (USA) based on economic analysis under different objective years. Specifically, the economic analysis considers both direct and indirect impacts of sea level rise by deploying the interdependence of infrastructures. We demonstrate the difference and significance of considering indirect economic impacts in the process of cost-benefit analysis under sea level rise. Based on the results, we recommend that the most effective strategy is partial protection of land use plus inundated transportation network upgrade, even though the total shoreline protection can make more benefits. Furthermore, we compare the performance of objective planning year from two criteria: total benefits and cost-effectiveness. The result indicates that the year 2080 could be the most economical if it is set as the objective year for the long-term infrastructure planning. The result also highlights that the economic analysis of infrastructure should be conducted over time since the total costs are distributed over many years. It is not to say that the farther the year is, the more effectiveness the strategy would be, although the total benefits would be greater.

India’s path towards energy independence and a clean future: Harnessing india’s renewable edge for cost-effective energy independence by 2047

India’s heavy dependence on imported oil (90 %) and industrial coking coal (80 %) exposes the country to the volatility in global energy markets, impacting foreign exchange reserves & economy-wide inflation. This study assesses a pathway for India to meet its growing energy needs & achieve near-complete energy independence by 2047, focused on India’s three largest energy consuming sectors –power, transport, and industry — which collectively account for more than 80 % of energy consumption and energy-related CO2 emissions. We find that India can achieve energy independence through aggressive deployment of clean technology – renewables, electric vehicles, and green hydrogen - reducing the fossil energy imports by 90 % (or $240 billion). Clean energy deployment will inflation-proof India’s energy expenditure, create $2.5 trillion in net consumer savings, and avoid over 4 million air pollution related premature deaths by 2047. India’s electricity demand could increase nearly fivefold to over 6500 TWh/yr by 2047, while CO2 emissions from power, transport, and industrial sectors will peak in the early 2030 s before dropping to ∼800 million tons/year. Clean energy deployment will be more capital-intensive, needing a net additional investment of $1.5 trillion. Managing the clean energy transition would require significant policy support, including deployment mandates for cost-effective clean technologies, financial support for emerging technologies, long-term infrastructure planning, accelerating domestic manufacturing, and planning for a just transition.

Regional significance of historical trends and step changes in Australian streamflow

Abstract. The Hydrologic Reference Stations is a network of 467 high-quality streamflow gauging stations across Australia that is developed and maintained by the Bureau of Meteorology as part of an ongoing responsibility under the Water Act 2007. The main objectives of the service are to observe and detect climate-driven changes in observed streamflow and to provide a quality-controlled dataset for research. We investigate trends and step changes in streamflow across Australia in data from all 467 streamflow gauging stations. Data from 30 to 69 years in duration ending in February 2019 were examined. We analysed data in terms of water-year totals and for the four seasons. The commencement of the water year varies across the country – mainly from February–March in the south to September–October in the north. We summarized our findings for each of the 12 drainage divisions defined by Australian Hydrological Geospatial Fabric (Geofabric) and for continental Australia as a whole. We used statistical tests to detect and analyse linear and step changes in seasonal and annual streamflow. Monotonic trends were detected using modified Mann–Kendall (MK) tests, including a variance correction approach (MK3), a block bootstrap approach (MK3bs) and a long-term persistence approach (MK4). A nonparametric Pettitt test was used for step-change detection and identification. The regional significance of these changes at the drainage division scale was analysed and synthesized using a Walker test. The Murray–Darling Basin, home to Australia's largest river system, showed statistically significant decreasing trends for the region with respect to the annual total and all four seasons. Drainage divisions in New South Wales, Victoria and Tasmania showed significant annual and seasonal decreasing trends. Similar results were found in south-western Western Australia, South Australia and north-eastern Queensland. There was no significant spatial pattern observed in central nor mid-west Western Australia, with one possible explanation for this being the sparse density of streamflow stations and/or the length of the datasets available. Only the Tanami–Timor Sea Coast drainage division in northern Australia showed increasing trends and step changes in annual and seasonal streamflow that were regionally significant. Most of the step changes occurred during 1970–1999. In the south-eastern part of Australia, the majority of the step changes occurred in the 1990s, before the onset of the “Millennium Drought”. Long-term monotonic trends in observed streamflow and its regional significance are consistent with observed changes in climate experienced across Australia. The findings of this study will assist water managers with long-term infrastructure planning and management of water resources under climate variability and change across Australia.

Perencanaan Bak Penampungan Air yang Berasal Dari Mata Air di Lingkungan Universitas Jambi

Jambi University is one of the State Universities in Indonesia which is currently in the phase of sustainable development which is currently focusing on increasing development, especially water resource infrastructure. Planning for water reservoirs with reinforced concrete is an option in long-term infrastructure planning. The planned reservoir is in the form of a reservoir that functions as a container for storing clean water in the water treatment system at the Jambi University. Planning focuses on the design of the capacity and structural elements of the reinforced concrete reservoir. Planning refers to ACI-350M using the LRFD method, as well as SNI 7509:2011. The reservoir capacity is planned according to (Al-Layla et al., 1977)at 39% of the maximum water discharge, and the structural elements of the reservoir are planned in the form of beams, columns, upper, lower plates and walls. Based on the design, the reservoir capacity is 216 m3 with a length of 12 m, a width of 6 m and a height of 3.5 m. In planning the structural elements used concrete quality 21 MPa with the type of reinforcement BJTP-28. Based on the design results, the column size is 20 cm x 20 cm with 4D12 reinforcement details, then for beam sizes 30 cm x 20 cm using 4D12 for tensile reinforcement and 3D12 for compression reinforcement, and for plates using a thickness of 150 cm with reinforcement details D12-150 for top plate and D12-200 for bottom plate and wall plate.

Power Modeling for Effective Datacenter Planning and Compute Management

Over the past decade, there has been a global growth in datacenter capacity, power consumption and the associated costs. Accurate mapping of datacenter resource usage (CPU, RAM, etc.) and hardware configurations (servers, accelerators, etc.) to its power consumption is necessary for efficient long-term infrastructure planning and real-time compute load management. This paper presents two types of statistical power models that relate CPU usage of Google’s Power Distribution Units (PDUs, commonly referred to as power domains) to their power consumption. The models are deployed in production and are used for cost- and carbon-aware load management, power provisioning and infrastructure rightsizing. They are simple, interpretable and exhibit uniformly high prediction accuracy in modeling power domains with large diversity of hardware configurations and workload types across Google fleet. A multi-year validation of the deployed models demonstrate that they can predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse PDUs across Google fleet. This performance matches the best reported accuracies coming from studies that focus on specific workload types, hardware platforms and, typically, more complex statistical models.

Integrated design of unmanned aerial mobility network: A data-driven risk-averse approach

In this paper, we propose an integrated design problem of Unmanned Aerial Mobility Network (UAMN), which includes airport location selection (strategic decision) and routes planning (operational decision) to minimize the total cost, while guaranteeing flow constraints, capacity constraints, and electricity constraints. To facility expensive long-term infrastructure planning facing demand uncertainty, we develop a data-driven risk-averse two-stage stochastic optimization model based on the Wasserstein distance. The analysis of the numerical examples proves that our DRO framework provides a relatively robust solution for UAMN. Also, we find that the optimal network configuration is affected by the “pooling effects”, which is proved by the fact that the total infrastructure costs can be saved by pooling drone flows into a small number of high-capacity channels/transfer airports. Interestingly, a candidate node without historical demand records can be chosen to locate an airport, in case the demand surges up at this node. We demonstrate the application of our model for a real medical resources transportation problem with our industry partner, collecting donated blood to a blood bank in Hangzhou, China.

Increase in Daily Household Water Demand during the First Wave of the Covid-19 Pandemic in Germany

Precautionary measures and governmental regulations during the COVID-19 pandemic’s first wave have drastically altered daily activities and hence water consumption patterns. Many people had to change their working routines, the organization of childcare and hygiene practices. While first evidence appears on the impacts of COVID-19 on, e.g., carbon emissions, energy demand and water treatment, even indicating an unexpected increase in water consumption during that period, no study has investigated the pandemic’s sole and in-depth implications for water supply so far. This paper investigates hourly and daily water consumption volumes of a utility in northern Germany for the first wave of the pandemic. We performed a linear mixed model to compare the 2020 daily water consumption volumes with previous years. We eliminated the effects of climate using Bayesian statistic in order to carve out and estimate the original COVID-19 effect. We also compared hourly water consumption of similar time periods and calculated the percentage difference between 2020 and the two previous years. Our results reveal about 14.3% (3 968 m3) higher residential water consumption per day with higher morning and evening demand peaks during the day. We hypothesis that the reasons for the increasing water demand may be found in changed behavioral routines with an important question for water utilities, if and which of these new dynamics may persist after COVID-19 and hence pose a challenge for long-term infrastructure planning.

Delivering on the Sustainable Development Goals through long-term infrastructure planning

Through the provision of a range of essential services, infrastructure systems profoundly influence development. At a time of increasing global investment in infrastructure, there is a need to support practitioners in making informed choices in order to achieve progress toward sustainable development objectives. Using the 2030 Agenda for Sustainable Development (the SDGs) as a framework to structure infrastructure decision-making and trade-offs, this analysis develops a performance indicator system that assesses the potential development implications of a portfolio of infrastructure investments and policies. We develop metrics to assess the performance of infrastructure-linked targets. We then embed these metrics in a systems model that allows for the quantification of future infrastructure needs and the assessment of portfolios of infrastructure investments and policies that contribute to meeting these needs. These methods are applied to the small-island country of Curaçao, demonstrating the potential for meeting the SDGs through adoption of strategies of cross-sectoral infrastructure investments and policies in the energy, water, wastewater and solid waste sectors. In the face of growing demands for infrastructure services, we find that inaction with regard to infrastructure supply and demand will lead to a 28% decrease in average SDG achievement across these targets by 2030. We assemble a portfolio of interventions that provide infrastructure services across these four sectors that enable achievement of 19 SDG targets directly linked to infrastructure. These interventions imply scaling up of infrastructure where there are gaps in service provision, ranging from an overall 10% increase in the water sector to a 368% increase in waste sector infrastructure from current capacities by 2030. Achieving the SDGs does not necessarily imply more infrastructure: in the energy sector the sustainable policy implies demand reductions of 32% from current levels. Nearly 50% of the assessed targets require intervention in more than one sector, emphasising the interdependent nature of the infrastructure system. The analysis addresses future uncertainties around the key drivers of residential population and tourism growth on the island by modelling infrastructure needs for alternate scenario projections. Averaged across the four sectors, these needs range from −14% (low) to +5% (high) in relation to the moderate projection. The analysis provides the first step towards a practical means of utilising infrastructure to deliver the SDGs, using quantitative indicators to underpin effective decision-making.

Joint infrastructure planning and fleet management for one-way electric car sharing under time-varying uncertain demand

We propose an innovative framework to deploy a one-way Electric Vehicle (EV) sharing system that serves an urban area. For the first time, long-term infrastructure planning (charging station location and fleet distribution) and real-time fleet operations (relocation and charging decisions) are jointly optimized under time-varying uncertain demand. This substantially advances EV sharing system efficiency and yields a practical management strategy. We propose a multistage stochastic model to address the critical challenge of time-varying uncertain demand. An accelerated solution algorithm is developed to conquer the curse of dimensionality in integer infrastructure planning decisions. Meaningful insights are delivered through hypothetical numerical experiments and a realistic case study with EV sharing service in the New York City.

Multivariate simulation modelling for adaptive long-term infrastructure planning

Infrastructure asset managers are challenged by rapid changes around the world such as urbanisation and climate change. They have to deal with multiple complex and dynamic infrastructure systems that can influence each other and that can be influenced by various uncertain factors. Traditional planning methods are not well suited to complex and dynamic infrastructure environments. However, there are various alternative methods that can be used. This paper presents a simulation model for adaptive long-term infrastructure planning. For this model, a combination of planning and simulation methods is used that can cope with complex and dynamic (infrastructure) environments (i.e. system dynamics, exploratory modelling and analysis and adaptive pathways). This methodology is illustrated with a case consisting of a road system and a lock system in the Netherlands. The approach should be tested further in other cases, but it shows promise in improving the support of infrastructure decision-making in a complex, dynamic and uncertain infrastructure environment.

Building Foresight in Long-Term Infrastructure Planning Using End-Effect Mitigation Models

This paper applies the end-effect mitigation models to build foresight into long-term infrastructure planning problems. This paper describes the phenomenon of end effects; presents four different models that address it, namely extended simulation, salvage value, and primal and dual equilibria; and applies them to long-term energy system planning problems with finite resources for investments. The planning model is a simultaneous multiperiod linear optimization model that plans for energy system infrastructures at the national scale. Two instances of this model are considered: 1) a small-scale five-node model; and 2) a medium-to-large-scale 13-node model. These instances are used to assess and quantify: 1) the end effects of using investment solutions; and 1) the efficacy of each mitigation methods in terms of accuracy and computational time. The illustrations demonstrate that, without attributing the long-term infrastructure planning with some level of foresight about the future cost and performance of the technologies, the resulting portfolio will have end effects in terms of: 1) investment bias toward low-cost resources at the end years; and 2) adopting low-cost quick fixes (such as excess cofiring) to meet near-term emission targets, both of which may render the model to lose sight of long-term targets and economics.

Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves

Climate non-stationarity is a challenge for electric power infrastructure reliability; recordbreaking heat waves significantly affect peak demand [1], lower contingency capacities, and expose cities to risk of blackouts due to component failures and security threats. The United States’ electric grid operates safely for a wide range of load, weather, and power quality conditions. Projected increases in ambient air temperatures could, however, create operating conditions that place the grid outside the boundaries of current reliability tolerances. Advancements in long-term forecasting, including projections of rising air temperatures and more severe heat waves, present opportunities to advance risk management methods for long-term infrastructure planning. This is particularly evident in the US Southwest—a relatively hot region expected to experience significant temperature increases affecting electric loads, generation, and delivery systems. Generation capacity is typically built to meet the 90th percentile (T90) hottest peak demand, plus an additional reserve margin of least 15%, but that may not be sufficient to ensure reliable power services if air temperatures are higher than expected. The problem with this T90 planning approach is that it requires a stationary climate to be completely effective. In reality, annual temperature differences can have more than a 15% effect on system performance. Current long-term infrastructure planning and risk management processes are biased climate data choices that can significantly underestimate peak demand, overestimate generation capacity, and result in major power outages during heat waves.This study used downscaled global climate models (GCMs) to evaluate the effects of non-stationarity on air temperature forecasts, and a new high-level statistical approach was developed to consider the subsequent effects on peak demand, power generation, and local reserve margins (LRMs) compared to previous forecasting methods. Air temperature projections in IPCC RCPs 4.5 and 8.5 are that increases up to 6°C are possible by the end of century, with highs of 58°C and 56°C in Phoenix, Arizona and Los Angeles, California respectively. In the hottest scenarios, we estimated that LRMs for the two metro regions would be on average 30% less than at respective T90s, which in the case of Los Angeles (a net importer) would require 5GW of additional power to meet electrical demand. We calculated these values by creating a structural equation model (SEM) for peak demand based on the physics of common AC units; physics-based models are necessary to predict demand under unprecedented conditions for which historical data do not exist. The SEM forecasts for peak demand were close to straight-line regression methods as in prior literature from 25–40°C (104°F), but diverged lower at higher temperatures. Power plant generation capacity derating factors were also modeled based on the electrical and thermal performance characteristics of different technologies. Lastly, we discussed several strategic options to reduce the risk of LRM shortages; including implementing technology, market incentives, and urban forms that reduce peak load and load variance per capita as well as their tradeoffs with several other stakeholder objectives.

Robust decision making in data scarce contexts: addressing data and model limitations for infrastructure planning under transient climate change

In the face of deeply uncertain climate change projections, robust decision frameworks are becoming a popular tool for incorporating climate change uncertainty into water infrastructure planning. These methodologies have the potential to be particularly valuable in developing countries where extensive infrastructure development is still needed and uncertainties can be large. However, many applications of these methodologies have relied on a sophisticated process of climate model downscaling and impact modeling that may be unreliable in data-scarce contexts. In this study, we demonstrate a modified application of the robust decision making (RDM) methodology that is specifically tailored for application in data-scarce situations. This modification includes a novel method for generating transient climate change sequences that account for potential variable dependence but do not rely on detailed GCM projections, and an emphasis on identifying the relative importance of data limitations and uncertainty within an integrated modeling framework. We demonstrate this methodology in the Lake Tana basin in Ethiopia, showing how the approach can highlight the vulnerability of alternative plans across different time scales and identify priorities for research and model refinement. We find that infrastructure performance is particularly sensitive to uncertainty in streamflow model accuracy, irrigation efficiency, and evaporation rates, suggesting that additional research in these areas could provide valuable insights for long-term infrastructure planning. This work demonstrates how tailored application of robust decision frameworks using simple modeling approaches can provide decision support in data-scarce regions where more complex modeling and analysis may be impractical.

Integrated Planning for Multiple Types of Locomotive Work Facilities Under Location, Routing, and Inventory Considerations

In North America, thousands of locomotives operating in the railroad network require various types of mechanical work every day. This work may be unscheduled, periodic, or on demand and could be conducted at either a fixed facility or a movable facility. Each facility is characterized by the type of work it can provide. The long-term infrastructure planning of these facilities is vital to the efficiency of the railroad. In this paper, we develop a large-scale mixed-integer mathematical model for infrastructure planning. The model integrates and optimizes decisions about (1) Locations, capabilities, and capacities of fixed facilities, (2) Home locations and routing plans of movable facilities, and (3) Assignments of locomotive work demands to facilities. We propose a decomposition-based heuristic solution framework consisting of several underlying algorithms to solve this large-scale optimization model. Computational results on numerous scenario studies using field data from the railroad industry show that the proposed model and algorithms are capable of providing solutions that are significantly superior to the current practice. The successful application of our methodology to real-world railroad planning has clearly demonstrated the substantial cost-saving benefits of our models on infrastructure planning solutions.

Comment on “Philippine Infrastructure and Connectivity: Challenges and Reforms”

Llanto (2016) starts by highlighting the broad challenges for the Philippines by contextualizing the growth and success of the service industry, that is, the business process outsourcing (BPOs) industry as well as e-commerce. These new types of infrastructure and connectivities have certainly led the recent growth trajectory of the Philippines. It is important to note that such growth was achieved despite limited growth in foreign direct investment (FDI) due, in part, to constraints in the development of labor intensive manufacturing in the domestic market. Moreover, other infrastructure provision, such as transportation (roads, railways, ports), power, and water, have not progressed well enough in attracting FDI despite the various incentives that have been introduced. For example, the power sector experienced an initial development with public–private partnership (PPP) investments, but later on stagnated due to fiscal constraints imposed by the government through privatization as well as policy changes to inhibit such infrastructure provision. In addition, the development of the power sector has been constrained by other factors such as stagnating investment in the bottleneck infrastructures such as transmission lines or gas pipelines, a lack of economics of scale, and an incomplete market design and regulatory regime. Llanto also suggests the importance of overcoming regional disparities through regional convergence strategies such as the Road- Roll-On Roll-Off Transport System (RRTS). As described, infrastructure provision and improvements in logistics have a positive correlation with and impact on both macro- and micro economic dimensions. Given the fact that high poverty incidence in rural and conflict affected areas as well as regional disparities continues to persist in the Philippines, appropriate infrastructure provision to enhance intra-country connectivity should also be linked with policy considerations for renewed industrial policies to enhance mobility and employment opportunities by taking into account the comparative advantages of industrial clusters across the country. Moreover, how to deal with infrastructure provision in urban areas from the viewpoint of the development of cities as industrial or population clusters and centers of growth is also critical. Because urbanization is one of the major developmental challenges, the provision of quality and adequate infrastructure to provide intra- as well as inter-city connectivity in enhancing the productivity of the urban city areas should not be overlooked. On infrastructure financing, Llanto provides an important viewpoint by suggesting the more effective usage of Official Development Assistance (ODA). As the Philippine government increases its preference for utilizing local funds in infrastructure spending while pursuing more PPP projects to be contracted out to the private sector, appropriate risk allocation and transparency of procedures in implementing infrastructure projects are becoming more important. In this context, the Philippines' government appreciates ODA resources if such assistance provides value for money through acquisition of quality sustaining appropriate technologies and services and transfer of expertise. ODA could play a vital role as a resource for providing effective technical and financial solutions in the areas of urban planning and land use plans, modal integration in the transportation sector, PPPs, building resilient infrastructure etc. The necessity of providing disaster resilient infrastructure cannot be over-stressed in the context of the Philippines. Llanto rightly points to the global and local attention towards the provision of policies as well as physical and institutional infrastructure conducive to disaster resiliency as emphasized in the principle of Build Back Better. What is crucial is not only how the government/localities rebound quickly with subsequent assistance ex-post of disasters, but also how to prepare and implement adequate policies, planning, and financing instruments which enable the government to provide appropriate budgetary provision, public spending, and capacity development conducive to disaster resiliency ex-ante to disasters. Being one of the most hazard prone countries in the world, the strong initiative by the Philippine government to pursue a disaster risk reduction, management, and financing strategy provides a clear policy direction reflecting this viewpoint. Last, continuity and coherence of government policies and reforms also cannot be stressed more. Sustaining policy coherence over the long-term provides positive signals to the improvement of investment climate and strong incentives to attracting FDI. In terms of long-term infrastructure planning and implementation, the “Road Map for Transport Infrastructure Development for Metro Manila and Its Surrounding Areas” which was approved by the National Economic Development Authority (NEDA) Board in 2014, for example, has provided the Philippine Government with a single robust framework wherein various project proposals in the transportation sector to be implemented by various government agencies in Metro Manila are identified and monitored (see NEDA 2014). This framework should serve as a strong basis to create expectations for the continuity of project planning and design, as well as enhancing coordination among oversight as well as implementing agencies over the long term, thereby enhancing the country's connectivity and competitiveness to improve the investment climate as a whole.

Special Issue on Challenges and Opportunities in the 21st Century Energy Infrastructure

Special Issue on Challenges and Opportunities in the 21st Century Energy Infrastructure

Infrastructure Planning in an Uncertain Environment

Long-term infrastructure planning is an ongoing challenge to all public and private sector organizations and can be made even more difficult if one is responsible for a complex that has an uncertain future and erratic funding. The authors apply five long-term capital asset planning principles to a major infrastructure project under consideration at the U.S. Department of Energy’s former nuclear weapons production site at Savannah River Site, raise critical issues and questions that need to be addressed as part of this process, and provide a framework for planning and managing a facilities and infrastructure program in an uncertain environment. The objective is to use this project as a means of identifying the many issues and questions that need to be addressed within the scope of each of the planning principles put forth.

More tunnel projects to rely less on state funds: M. Page, Tunnels & Tunnelling, 27(10), 1995, pp 18–22

This article discusses plans for extensive tunnelling in Germany, Switzerland and Austria, and how they will be financed. Thousands of km of tunnels and microtunnels are being planned in these countries, for a variety of reasons. For example, growth in prosperity and environmental issues will create a demand, not only for rail and road tunnels, but also for: (1) water and sanitary systems expansion and rehabilitation; and (2) transport and storage of hazardous goods. Germany has a very active urban rapid transit tunnelling programme for light and heavy rail, together with plans for some underground urban ring roads and car parks. A significant Berlin 2000 consortium has been established. Hamburg will have a fourth road tunnel under the River Elbe. Swiss Federal Railways (SBB) plans about 120km of railway tunnels under the Alps, to relieve increasing congestion on trans-Alpine rail routes. Austria is planning several major tunnelling works as part of a programme to upgrade its rail routes. Although all three countries have had long-term infrastructure planning, with extensive public funding of rail and road tunnels, tunnel construction financing is changing there, and several new approaches to private participation in funding are emerging.