Capacity Expansion Research Articles

Risk assessment and optimal scheduling of serial projects

The valuation and planning of complex projects are becoming increasingly challenging with rising market uncertainty and the deregulation of many industries, which have also raised the need for efficient risk management. We take the perspective of a private firm interested in sequential capacity expansion of a project and develop a framework for measuring the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we present an accurate representation of the project’s net present value (NPV) distribution based on a Pearson curve fit, leading to closed-form expressions for the associated risk measures. We then assess the impact of duration variability on the value at risk and demonstrate its role in stochastic project scheduling. We also account for the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. It becomes obvious that both the duration variability of each stage and the decision-makers’ risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers.

The impact of economic development of primary and secondary industries on national CO2 emissions: The case of Russian regions

In today's world, where economic development and environmental sustainability are becoming increasingly important aspects of national strategy, attention to the impact of different economic sectors on climate change is becoming an integral part of scientific research. This article focuses on analyzing the impact of primary and secondary economic sectors development on carbon dioxide (CO2) emissions at the sub-national level in Russia from 2005 to 2019. The aim of the study is to provide an in-depth understanding of the relationships between the dynamics of these sectors and CO2 emission levels in different regions of the country. Weighted regression and panel data methods were applied to better identify the patterns of the impact. The results show that the size of population and electricity consumption have the highest impact on CO2 emissions. So that, the expansion of nuclear and gas generation capacity, as well as significant improvement of energy efficiency, are of crucial importance to reduce the emissions. Other sectors have a heterogeneous impact and requires more differential approaches, considering the specifics of regions. Taking into account the significant differences between the Russian constituent entities, this paper emphasizes the low informativeness of assessments at the national level and their inadequacy in terms of improving the efficiency of domestic management, including decarbonization policies.

Utilising a hybrid DMAIC/TAM model to optimise annual maintenance shutdown performance in the dairy industry: a case study

PurposeOutput from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.Design/methodology/approachCombining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.FindingsApplication of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.Practical implicationsThe results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.Originality/valueTo the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

(Invited) Challenges in Fabricating and Scaling up Silicon Carbide Wafers and Devices

Silicon Carbide is rapidly growing in the power electronics industry. This is fueled to a great degree by the adoption of electric cars and power electronics needed both inside the vehicle and to support the charging infrastructure. This attention from automotive industry comes with a clear spotlight on the cost, quality, and efficiency of SiC power devices. This in turn results in intense pressure and scrutiny to improve yields and reduce defects in every part of the manufacturing chain. There is a wide release and proliferation of Diode and MOSFET devices from various companies in the marketplace. Several companies are investing billions of dollars in capacity expansions driving exponential growth of wafers and devices.In this review, we will discuss the steps and processes of fabricating a SiC wafer including crystal growth, wafering, epitaxy and device fabrication. The process challenges associated with the steps unique to SiC will be highlighted with a perspective on scaling up volumes and the transition to 200mm wafers. New innovations at some of the steps are enabling higher efficiency and output. The impact of these innovations will be presented. In addition, defects at each step of the process will be reviewed along with how they interact with the following steps [1]. Finally, the effect of various processes and defects on devices and reliability will be presented [2,3].

SAN ANTONIO TRANSPORTATION PLANNING SYSTEM: MAPPING INFRASTRUCTURE CAPACITY AND COORDINATION

Abstract Ride Connect Texas and its partnering agencies seek to develop an expanded senior mobility management system supported by a One Call One Click transportation planning system. Its purpose is to enhance on-demand transportation system coordination and the application of technology, providing consumers, service providers, and mobility managers with direct access to comprehensive transportation information. GIS mapping is used to help develop a spatial awareness of the transportation challenges among older people and individuals with a disability and the current system capacity. Interviews with transit, non-transit, and health and human service agencies will reveal factors that influence coordination among providers and opportunities for capacity expansion. Surveys and focus groups of older adults and individuals with disability will show differentiating characteristics of riders and transportation needs and challenges. GIS mapping will allow us to geocode these findings relative to the user’s street address and zip code, illustrating variations in specialized client needs, service availability, travel behavior, and available amenities for basic daily living. Mapping will identify gaps in transportation services, hot zones revealing high user demand and low system capacity, and the ability to reduce fragmentation of service through seamless technology-based coordination. The mapping element informs the measurement of a community’s transportation system capacity for this user group. The value proposition of this tool is its ability to help any community measure its readiness toward developing a comprehensive implementation strategy for coordinated demand response transportation, including resource mobilization strategies, capacity augmentation, phasing of the system, and implementation timeline.

Green Hydrogen Production: Process Design and Capacity Expansion Integrating Economic and Operational Autonomy Objectives

Green Hydrogen Production: Process Design and Capacity Expansion Integrating Economic and Operational Autonomy Objectives

A large capacity expansion and robust recognition method of dot-dispersing coded targets with binary step-response serial encoding

In close-range photogrammetry, it is difficult to meet the measurement requirements of large scenes in actual engineering due to the limited capacity of coded targets. To expand the capacity of the coded target, we propose a binary step-response serial-coded target (BSSCT). The BSSCT introduces periodic binary wave information as an additional feature in the dot-dispersing coded target. Also, a robust recognition algorithm for the BSSCT is developed, the P2-Invariant, and the step period is used for decoding. The capacity of the coded target reaches 7 magnitudes without increasing the auxiliary points. Under different lighting conditions and viewing angles, the measurement experiments show that the BSSCT outperforms other state-of-the-art coded targets. Our BSSCT is a promising standard method for large-field system calibration and object measurement.

A two-stage stochastic game model for elective surgical capacity planning and investment

Waiting for elective procedures has become a major health concern in both rich and poor countries. The inadequate balance between the demand for and the supply of health services negatively affects the quality of life, mortality, and government appraisal. This study presents the first mathematical framework shedding light on how much, when, and where to invest in health capacity to end waiting lists for elective surgeries. We model the healthcare system as a two-stage stochastic capacity expansion problem where government investment decisions are represented as a non-symmetric Nash bargaining solution. In particular, the model assesses the capacity requirements, optimal allocation, and corresponding financial investment per hospital, region, specialty, and year. We use the proposed approach to target Chile’s elective surgical waiting lists (2021–2031), considering patients’ priorities, 10 regional health services, 24 hospitals, and 10 surgical specialties. We generate uncertain future demand scenarios using historical data (2012–2021) and 100 autoregressive integrated moving average prediction models. The results indicate that USD 3,331.677 million is necessary to end the waiting lists by 2031 and that the Nash approach provides a fair resource distribution with a 6% efficiency loss. Additionally, a smaller budget (USD 2,000 million) was identified as necessary to end the waiting lists in a longer planning horizon. Further analysis revealed the impact of investment in patient transfer and a decline in investment yield.

Government GDP targets and corporate capacity expansion – Empirical evidence based on A-share listed companies

The phenomenon of Chinese-style capacity expansion is widespread and closely tied to the interventionist behavior of local governments. Using data from A-share listed companies between 2010 and 2021, we find a significant correlation between economic growth targets and corporate capacity expansion. The impact of growth targets on capacity expansion is more pronounced during the National Party Congress, when officials use stricter constraints and when the municipal party secretary is around 55 years old. Officials influence capacity decisions by allocating key resources such as land and finances. We provide valuable insights into Chinese-style capacity expansion, offering reference significance for promoting China's supply-side structural reform.

Evidence-Based Policymaking: Insights and Recommendations for the Implementation of Clean Energy Transition Pathways for Kenya’s Power Sector

With ambitious targets to drastically increase economic activity over the next decade, Kenya’s future is undoubtedly energy-intensive. Current power capacity expansion plans will see Kenya considerably ramp up fossil fuel generation, significantly increasing emissions. Therefore, Kenya is at a crucial stage of its national development, with critical decisions to make regarding its future power expansion and production. OSeMOSYS modelling software (clicSAND version v1.1) is employed to produce a series of possible clean energy transition pathways to increase renewable power production under rapidly intensifying demand. This study integrates existing national priorities and policies into six modelled scenarios to provide insights into their generation, total production, and costs, which can assist future policymaking and capacity-building efforts. The high-level insights gained in this research were employed to suggest key recommendations for Kenya’s power sector. Most notably, policy alignment, increased wind power production, energy-efficiency penetration, finance and investment securement, the development of storage technologies, power transmission, and distribution improvements should be prioritised.

ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer – study protocol

IntroductionFifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%–40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall...

Controle democrático e segurança Nacional Serviços de inteligência nos Estados Unidos (1972-1980)

Abstract This article delves into the vicissitudes of democratic control of government intelligence activities in the United States between 1972 and 1980. The previous phase of the Cold War (1947-1971), characterized by the intense systemic polarization between the United States and the Soviet Union, coupled with the expansion of state capacity and internal social conflicts within the US, contributed to the establishment of complex national systems of intelligence organizations and activities in both countries. In the 1970s, the strategic stabilization of US-USSR relations (détente) depended, in part, on the technological advancements in intelligence gathering from communications, signals, and imagery via satellites. Police and military surveillance of internal dissidents and the growing political crisis towards the end of the Nixon administration created the conditions for an unprecedented and consistent attempt to exert democratic external control over intelligence operations by the Legislative and Judiciary branches. The resurgence of the Cold War from 1980 onwards and the election of Reagan marked the beginning of a partial reversal of the controls. In less than a decade, a highly institutionalized democracy such as the US made great efforts and experienced difficulties regulating and controlling intelligence activities.

Fossil gas lock-in risks: analysis of Algeria’s electricity sector and implications for a renewable energy transition

Algeria's electricity sector is dependent on fossil gas. Domestic gas demand is growing, and gas-fired power plants are projected to make up 84% of total installed capacity by 2028. This brings the country into a Sustainability Nexus dilemma: on the one hand, Algeria is committed to reduce its greenhouse gas emissions and other environmental impacts of the gas sector, whereas on the other hand, the energy and export revenue derived from gas would need to be substituted in order to ensure social and economic development in the country. Socioeconomic tradeoffs and alternatives must therefore be understood and managed in a way that ensures continued political and public support for the country's energy transition and fossil fuel exit. This paper aims to (a) identify fossil gas lock-in factors and (b) explore the opportunities to overcome these obstacles in order to facilitate a transition into renewable energy. Using a deductive approach, we draw on theoretical assumptions regarding carbon lock-in theory and the 2020 framework of Trencher et al. to study (1) technological and infrastructural conditions, (2) actors and agency, (3) formal and informal institutions, knowledge and competences, and (4) exogenous context. Between November 2021 and March 2022, a total of 23 semi-structured interviews were conducted in Algeria with stakeholders from the government, power and industry organizations, civil society organizations, and scientific institutions. The interview transcripts were then thematically analyzed to get a picture of recurrent themes and patterns. Our analysis identified several factors contributing to the fossil gas lock-in in Algeria's electricity system: economic dependence on oil and fossil gas; the significant influence of oil and gas actors, and the governmental support for fossil gas; gas and electricity subsidies, which lock out all other energy alternatives; and an overall lack of attention to climate change in energy policy formulation and in political and societal debate, despite the major capacity expansion plans prompted by heatwaves. The paper ends with a brief discussion of how Algeria's gas lock-in may worsen due to the war in Ukraine.

Multistage Charging Station and Distributed Generator Capacity Expansion in Decentralized Power Distribution and Transportation Systems

Multistage Charging Station and Distributed Generator Capacity Expansion in Decentralized Power Distribution and Transportation Systems

Multi-objective Configuration Method for Mobile Energy Storage Aimed at Enhancing the Resilience of Regional Power Grid

Mobile energy storage has the advantages of high mobility, environmental friendliness, and wide application scenarios. It is widely used in important load protection, outdoor emergency power supply, and distribution network capacity expansion scenarios. This paper proposes a multi-objective configuration method for flexibly supporting the optimal operation of regional distribution networks. It aims to address the configuration problem of multi-mobile energy storage supporting regional power grid operation. A multi-objective optimization operation model for renewable energy distribution networks has been established. A multi-objective optimization method combining the normalized normal constraint method and improved composite method is proposed. It can achieve optimization decision-making of mobile energy storage positions, charging and discharging states, and power at different periods. Finally, simulation analysis is conducted on the IEEE 33 node distribution system to verify the effectiveness of the control method and algorithm.

Flexible facility requirements for strategic planning of airport passenger terminal infrastructure

Facility requirements determine how and when the capacity of airport passenger terminal facilities is adjusted over time to meet expected demand. Given high levels of uncertainty inherent in long-term airport planning, under and over provision of capacity is a recurrent risk, as conventional strategic planning methods fail to adapt dynamically to changing circumstances. This paper introduces a novel flexible capacity expansion model for airport terminals that considers simultaneously real options ‘on’ and ‘in’ systems. The model is validated for the provision of check-in facilities at Zurich Airport. Results confirm suggestions in the literature that incorporating flexibility creates planning and financial advantages over conventional alternatives. Indeed, for the case of Zurich, the financial value of the flexible alternative is approximately 5% higher than the best conventional phased plan. This also suggests that phasing developments can be carefully devised to produce satisfactory outcomes that enable ex-post application of flexibility ‘on’ systems.

Implications of a smart grid-integrated renewable distributed generation capacity expansion strategy: The case of Iraq

The rapid growth of electricity demand in Iraq has consistently outstripped the country's electricity infrastructure, leading to frequent blackouts, especially during peak summer demand. With a heavy reliance on oil 93 % and gas 7 % for electricity generation, the nation is exposed to economic fluctuations associated with global oil prices and environmental impacts from burning fossil fuels. This study explores the implications of integrating smart grids and expanding renewable distributed generation capacity in the country's energy system. Drawing upon the projection of the energy demand by 2035 across four scenarios (Business as Usual, Sustainable Development, High Growth, and Low Carbon), the study underscores solar energy, backed by abundant solar and wind energy, as a key player in the country's renewable transition. However, challenges persist, including the need for technical expertise in renewable energy technologies, financing hurdles, and concerns about policy stability. With the country's policies targeting 25 % renewable electricity by 2030 and the establishment of significant renewable projects, there tangible momentum. The results have drawn out a roadmap for an estimated addition of up to 10 GW of renewable energy capacity by 2030 and 14 GW by 2035. In addition, strategies for distributed generation capacity expansion were highlighted, underscoring the crucial role of solar and wind energy and other renewables in the country's sustainable energy future.

Sociopolitical and financial goals in state-owned manufacturers' expansion of production capacity: evidence from China

PurposeThe rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.Design/methodology/approachThe authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.FindingsThe authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.Originality/valueThe authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

High temporal resolution generation expansion planning for the clean energy transition

As power systems integrate increasing quantities of wind, solar and energy storage resources, it is important to revisit power system capacity expansion modeling methods and assumptions that have been utilized in thermal-dominated systems. We conduct a series of case study analyses using a simplified representation of the Electric Reliability Council of Texas (ERCOT) system to demonstrate how least-cost capacity expansion outcomes are impacted by changes in model resolution across two temporal dimensions: 1) the number of considered representative periods, and 2) the system dispatch interval. First, we find that the least-cost generation portfolio can differ significantly for small changes in the number of representative days, but largely converges to the 365-day result once 104 representative days are considered. Furthermore, systems with wind, solar and storage resources were more sensitive to changes in the number of representative days than a thermal-dominated system. Second, we find that considering five-minute dispatch resolution consistently results in least-cost generation portfolios with less solar capacity and more energy storage capacity than corresponding scenarios with hourly dispatch intervals. This suggests that hourly dispatch representation fails to capture the intra-hour volatility of solar generation, and therefore also overlooks opportunities for storage resources to provide system value by balancing this volatility. Collectively these results indicate that capacity expansion modelers should revisit conventional approaches to temporal representation when conducting analyses of deeply decarbonized power systems to ensure that such analyses are robust and actionable. To our knowledge, this is the first study to analyze capacity expansion outcomes with five-minute dispatch resolution in this manner.

A Review on Congestion Mitigation Techniques in Ultra-Dense Wireless Sensor Networks: State-of-the-Art Future Emerging Artificial Intelligence-Based Solutions

The Internet of Things (IoT) and wireless sensor networks (WSNs) have evolved rapidly due to technological breakthroughs. WSNs generate high traffic due to the growing number of sensor nodes. Congestion is one of several problems caused by the huge amount of data in WSNs. When wireless network resources are limited and IoT devices require more and more resources, congestion occurs in extremely dense WSN-based IoT networks. Reduced throughput, reduced network capacity, and reduced energy efficiency within WSNs are all effects of congestion. These consequences eventually lead to network outages due to underutilized network resources, increased network operating costs, and significantly degraded quality of service (QoS). Therefore, it is critical to deal with congestion in WSN-based IoT networks. Researchers have developed a number of approaches to address this problem, with new solutions based on artificial intelligence (AI) standing out. This research examines how new AI-based algorithms contribute to congestion mitigation in WSN-based IoT networks and the various congestion mitigation strategies that have helped reduce congestion. This study also highlights the limitations of AI-based solutions, including where and why they are used in WSNs, and a comparative study of the current literature that makes this study novel. The study concludes with a discussion of its significance and potential future study topics. The topic of congestion reduction in ultra-dense WSN-based IoT networks, as well as the current state of the art and emerging future solutions, demonstrates their significant expertise in reducing WSN congestion. These solutions contribute to network optimization, throughput enhancement, quality of service improvement, network capacity expansion, and overall WSN efficiency improvement.