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Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling. Plain Language Summary: Over the past few years, large power outage events in California and Texas have underscored the vulnerability of our power systems to extreme weather. By increasing the intensity and frequency of extreme weather, climate change could lead to more power outages. In response, power system planners are grappling with how to plan for extreme weather and climate change when making investment decisions, such as in wind and solar power. In our research, we build and apply a new analytical framework for making power system investment decisions under climate change. Our framework draws on a hundred realizations of future climate, and integrates weather in those realizations with power system models that make investment decisions and explore the risk of power outages. We find five alternative investment pathways all could suffer from moderate to significant power outages under possible climate realizations by 2040. But by identifying what realizations drive outage risk in these pathways, we construct a new pathway that does not exhibit outage risks to our future climate realizations. Overall, these insights demonstrate the value of our new analytical framework for making better investment decisions under uncertainty posed by climate change. Key Points: We identify a decarbonization pathway for the power system that is robust to future climate realizationsOur framework is extensible to long‐term planning by utilities, regions, and regulatorsLarge climate ensembles expose significant resource adequacy vulnerabilities in alternative decarbonization pathways [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Refinery Engineering is the property of Petroleum Refinery Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fracture-cavity carbonate reservoirs provide a large area, fracture development, high productivity, long stable production time, and other characteristics. However, after long-term exploitation, the lack of energy in the formation leads to a rapid decrease in production, and the water content in crude oil steadily increases, thereby disrupting normal production. To recover normal production, it is necessary to connect the cracks and pores that have not been affected during the original production, so as to allow the crude oil inside to enter the production cracks and replenish energy through methods such as hydraulic expansion of fracture-cavity carbonate rock. Accordingly, we propose hydraulic expansion techniques based on the following four processes for implementation: (1) applying high pressure to prevent a nearby fracture network from opening the seam, (2) connecting a distant fracture-cavity body, (3) breaking through the clay filling section of a natural fracture network, and (4) constructing an injection production well pattern for accelerating injection and producing diversion. Hydraulic fracturing involves closing or partially closing the original high-permeability channels, which usually produce a large amount of water, while opening previously unaffected areas through high pressure to increase crude oil production. We also introduce two composite techniques: (1) temporary plugging of the main deep fractures, followed by hydraulic expansion; and (2) capacity expansion and acidification/pressure processes. Hydraulic expansion allowed us to recover and supplement the formation energy and efficiently increase production. We tested various wells, achieving an effective hydraulic expansion rate of up to 85%. In addition, the productivity of conventional water injection and hydraulic expansion after on-site construction was compared for one well, clearly indicating the effectiveness of water injection and the remarkable crude oil increase after hydraulic expansion. [ABSTRACT FROM AUTHOR]

Purpose: The 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/approach: The 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. Findings: The 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/value: The 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. [ABSTRACT FROM AUTHOR]

Abstract Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

For the expansion problem of the maximum capacity spanning arborescence in networks (EMCSA), NP-hardness is proved by constructing an instance of EMCSA from 0-1 knapsack problem, and a heuristic algorithm is designed. Finally, one special version of EMCSA, the expansion problem of the minimum arc number on the maximum capacity spanning arborescence in networks (NEMCSA), is considered. For NEMCSA, a strongly polynomial algorithm, in O(mn) time, is provided by substituting the arc with the minimum weight difference. [ABSTRACT FROM AUTHOR]

The increasing demand for freight services and the use of larger vessels to meet this demand has led to challenges related to storage space and logistics activities, highlighting the need for improvements in port infrastructure for better logistics management. At a crucial phase in its growth, the Port of Trois-Rivières in Canada is planning a major expansion, including the construction of a new terminal to enhance its hosting capacities and freight services. This expansion faces potential access congestion problems during the planned construction, exacerbated by the port's urban setting. In response to the needs identified by the port authorities for this event, the study's objective is to assess the implications of increased construction and freight truck flows on access gate fluidity and the impact of additional access infrastructure investment to mitigate potential congestion. These evaluations aim to define effective access management strategies throughout the construction period of the new terminal. To address these complexities, our approach is based on scenario analysis in variants co-constructed with the partner. These scenarios are evaluated using simulation models, configured according to parameters calibrated with a granularity that allows congestion detection. The results enabled an evaluation of the capability of existing and potential gates to manage access. Subsequently, recommendations were shaped in accordance with the expected objectives to manage access traffic effectively. These recommendations concern the optimization of construction activity planning, the layout and planning of access, and the importance of enhanced collaboration between municipal and port authorities for more controlled road traffic management. Recognizing the importance of synchromodality, road network centrality management, and the outsourcing of capacity through inter-port cooperation and with dry ports to manage congestion, these tools will be discussed in this work. The study proposes an approach that reconciles scientific rigor with the implementation constraints of the proposed solutions, allowing this study wider applicability in various port contexts facing challenges in this field of study. [ABSTRACT FROM AUTHOR]

British Columbia (BC) is committed to transitioning to a low-carbon energy system to meet its CO2 emission reduction targets, but this shift towards renewable energy sources may have significant implications for land use. This paper investigates the land-use impacts of different electrification pathways and technology choices in BC's energy system using the BC Nexus model. Our analysis highlights the potential increase in land-use requirements associated with transitioning from fossil fuels to renewable energy sources, with the occupied land of the power system potentially increasing up to six times larger than the current total build-up land (depending on the scale of electrification and technology choice). These findings have important implications for policymakers in terms of balancing the trade-offs between energy security, economic development, and environmental sustainability. By understanding the physical footprint of the energy transition, decision-makers can develop more effective climate policies and sustainable development strategies.

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

The development of the hydrogen-fueled vehicle industry needs a mature hydrogenation network to provide a guaranteed source of hydrogen in the future. We propose a dynamic hydrogen supply network optimization model under demand uncertainty. The model incorporates the dynamic characteristics of the network, takes into account the linkages of units within the network as well as the hydrogenation network between cycles. Hydrogenation networks can be planned over multiple cycles and allow for capacity expansion of hydrogen production and refueling stations, and shifts in the way hydrogen is transported between sites. Robust optimization is used to deal with demand uncertainty. In the results of the case study obtained, resources within the network were fully utilized, costs within the network show economies of scale; the average resource utilization rate at each site reached 85.6 %; and the rate of meeting user demand reached more than 98.5 %. A hydrogenation network with sustainable operations was formed. [Display omitted] • The dynamic characteristics of the network over time is considered. • A hydrogenation network with sustainable operations is formed. • Stations and hydrogen transportation demonstrate economies of scale. • A robust optimization model is established to deal with uncertainty. • Maximize network resources while hydrogen demand is effectively met. [ABSTRACT FROM AUTHOR]

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Education / Tiyu Xuekan is the property of Journal of Physical Education Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work considers the incorporation of renewable ammonia manufacturing sites into existing ammonia supply chain networks while accounting for ammonia price uncertainty from existing producers. We propose a two-stage stochastic programming approach to determine the optimal investment decisions such that the ammonia demand is satisfied and the net present cost is minimized. We apply the proposed approach to a case study considering deploying in-state renewable ammonia manufacturing in Minnesota's supply chain network. We find that accounting for price uncertainty leads to supply chains with more ammonia demand met via renewable production and thus lower costs from importing ammonia from existing producers. These results show that the in-state renewable production of ammonia can act as a hedge against the volatility of the conventional ammonia market. [ABSTRACT FROM AUTHOR]

This paper adopts a holistic approach to the integrated water supply chain system, considering water-related aspects including production, technology selection, distribution routes, and storage, among others. It also introduces a multi-period comprehensive mathematical model that assists decision makers in jointly optimizing a multitude of decisions pertaining to the water supply chain, ranging from strategic to tactical ones. In particular, the former address the installation of new desalination plants along with their operational capacity, capacity expansion of existing plants and infrastructure assets (storage and distribution), as well as the selection of the best-suited desalination technologies. On the other hand, tactical decisions consider issues such as the allocation of water resources among end users, production level of desalinated water at the different plants, and storage levels across various demand zones. All these decisions are being optimized in every period towards minimizing financial and environmental costs throughout the planning horizon. To illustrate the model's practical relevance and assess its validity, a case study focused on an Emirate in the United Arab Emirates (UAE). Results highlight the need for increased desalination capacity over the next 15 years to match growing demand. The study pinpoints the timings for desalination facility expansions, compensating for decreased capacity due to degradation or retirement. The study underscores the superiority of reverse osmosis technology over other desalination options. [ABSTRACT FROM AUTHOR]

This paper adopts a holistic approach to the integrated water supply chain system, considering water-related aspects including production, technology selection, distribution routes, and storage, among others. It also introduces a multi-period comprehensive mathematical model that assists decision makers in jointly optimizing a multitude of decisions pertaining to the water supply chain, ranging from strategic to tactical ones. In particular, the former address the installation of new desalination plants along with their operational capacity, capacity expansion of existing plants and infrastructure assets (storage and distribution), as well as the selection of the best-suited desalination technologies. On the other hand, tactical decisions consider issues such as the allocation of water resources among end users, production level of desalinated water at the different plants, and storage levels across various demand zones. All these decisions are being optimized in every period towards minimizing financial and environmental costs throughout the planning horizon. To illustrate the model’s practical relevance and assess its validity, a case study focused on an Emirate in the United Arab Emirates (UAE). Results highlight the need for increased desalination capacity over the next 15 years to match growing demand. The study pinpoints the timings for desalination facility expansions, compensating for decreased capacity due to degradation or retirement. The study underscores the superiority of reverse osmosis technology over other desalination options.

Fracture-cavity carbonate reservoirs provide a large area, fracture development, high productivity, long stable production time, and other characteristics. However, after long-term exploitation, the lack of energy in the formation leads to a rapid decrease in production, and the water content in crude oil steadily increases, thereby disrupting normal production. To recover normal production, it is necessary to connect the cracks and pores that have not been affected during the original production, so as to allow the crude oil inside to enter the production cracks and replenish energy through methods such as hydraulic expansion of fracture-cavity carbonate rock. Accordingly, we propose hydraulic expansion techniques based on the following four processes for implementation: (1) applying high pressure to prevent a nearby fracture network from opening the seam, (2) connecting a distant fracture-cavity body, (3) breaking through the clay filling section of a natural fracture network, and (4) constructing an injection production well pattern for accelerating injection and producing diversion. Hydraulic fracturing involves closing or partially closing the original high-permeability channels, which usually produce a large amount of water, while opening previously unaffected areas through high pressure to increase crude oil production. We also introduce two composite techniques: (1) temporary plugging of the main deep fractures, followed by hydraulic expansion; and (2) capacity expansion and acidification/pressure processes. Hydraulic expansion allowed us to recover and supplement the formation energy and efficiently increase production. We tested various wells, achieving an effective hydraulic expansion rate of up to 85%. In addition, the productivity of conventional water injection and hydraulic expansion after on-site construction was compared for one well, clearly indicating the effectiveness of water injection and the remarkable crude oil increase after hydraulic expansion.

[Introduction] This article aims to study the optimal planned capacity setting of large-scale offshore wind farm. [Method] A series of numerical experiments were conducted on a single planned site with a capacity of 1 GW using three models with different unit capacities and three offshore wind farm wake models, combined with an offshore wind resource atlas, to gradually expand it to 2 GW. Power generation, wake effects and marginal utility of the whole site were analyzed. [Result] The results show that employing the wind turbine with larger single-unit capacity, the higher gaining of the power generation and slower growth of wake loss appear during the capacity expansion. Meanwhile, the larger effective expansion range which taking into account the safety of the wind turbine and the marginal utility exists. However, evaluation of effective expansion range may be affected by wake model selection. [Conclusion] This article shows that the optimal planned capacity of a single offshore wind farm can be slightly higher than the existing benchmark when multiple constraints, such as utilization of sea area, wind turbine safety and economical efficiency are met. Individual site needs to be subdivided scientifically and the optimal planned capacity should be reasonably set in the future planning of offshore wind farm bases with capacities in the tens of millions of kilowatts.

We study the problem of determining the locations and capacities of COVID-19 specimen collection centers to efficiently improve accessibility to polymerase chain reaction testing during surges in testing demand. We develop a two-echelon multi-period location and capacity allocation model that determines optimal number and locations of pop-up testing centers, capacities of the existing centers as well as assignments of demand regions to these centers, and centers to labs. The objective is to minimize the total number of delayed appointments and specimens subject to budget, capacity, and turnaround time constraints, which will in turn improve the accessibility to testing. We apply our model to a case study for locating COVID-19 testing centers in the Region of Waterloo, Canada using data from the Ontario Ministry of Health, public health databases, and medical literature. We also test the performance of the model under uncertain demand and analyze its outputs under various scenarios. Our analyses provide practical insights to the public health decision-makers on the timing of capacity expansions and the locations for the new pop-up centers. According to our results, the optimal strategy is to dynamically expand the existing specimen collection center capacities and prevent bottlenecks by locating pop-up facilities. The optimal locations of pop-ups are among the densely populated areas that are in proximity to the lab and a subset of those locations are selected with the changes in demand. A comparison with a static approach promises up to 39% cost savings under high demand using the developed multi-period model. [ABSTRACT FROM AUTHOR]

In this research, we investigate a supply chain consisting of a downstream firm who purchases a component from an upstream firm and then transforms it into a final product. The downstream firm has a production capacity constraint and considers to raise capital from an investor through equity financing. The raised capital not only enables the downstream firm to expand the capacity but also allows the investor to hold equity shares in the downstream firm. We derive the optimal pricing and production decisions of the two firms and discuss the optimal equity financing strategy of the downstream firm. We consider two distinct models: the external equity financing model, where the capital is raised from an outside institution, and the internal equity financing model, where the capital is raised from the upstream firm. We show that because the cooperative relationship between the two firms can be improved in the internal equity financing model, the production quantity in this model may be even higher than that in a benchmark model with no capacity constraint and no equity holding by the investor in the downstream firm. In addition, the original shareholder of the downstream firm gets more benefit from the internal equity financing activity than from the external equity financing activity. We also analyze the impacts of the key model parameters on the equity financing strategy and find that the dependence of the financing strategy on the initial asset of the downstream firm is quite different in the two models. Moreover, when the production cost of the downstream firm is changed, less capital raised for expanding capacity may create more value for the original shareholder of the downstream firm in each model. Finally, we show that the key finding remains unchanged when deterministic demand is changed to stochastic demand. [ABSTRACT FROM AUTHOR]

In this study, we investigate a firm's optimal investment timing and capacity decisions in the presence of uncertain time-to-build. The firm raises revenue from the investment after an uncertain amount of time elapses, while the time-to-build of the follow-up investment is expectedly shorter than that of the initial investment due to learning by doing. We derive the optimal investment strategies and examine the impact of time-to-build on the investment dynamics. We show that both the initial and follow-up investment can be made earlier in the presence of time-to-build than they would in the absence of the lags, especially in a volatile market. This is in contrast to the case of a single investment, whose timing is always delayed by the time-to-build. Furthermore, the capacity of the follow-up project dominates that of the initial project in the presence of time-to-build, whereas the latter dominates the former in the absence of lags. The capacity choice of each project, however, is nonmonotone with respect to the size of the lags. We also show that uncertainty delays investment even when the investment involves lags but the negative impact softens in the presence of time-to-build and learning effects. [ABSTRACT FROM AUTHOR]

Reasonable capacity expansion optimization of determined charging stations is important for the popularization of electric vehicles (EVs). In this article, Gaussian two-step floating catchment area method and temporal clustering are adopted to study the unbalanced spatial and temporal distribution of charging station accessibility, as well as to determine candidate sites for capacity expansion optimization. Then, a two-level optimization model for capacity expansion of determined charging stations is proposed to reduce the computational complexity caused by the non-linearity and non-convexity of choice probability formulas when the discrete choice model is applied to statistically describe and analyze discrete behaviors such as users' EV purchase preferences and charging station heterogeneity. In the proposed method, the lower-level model is described as a maximal coverage location problem by expressing the error term of the utility function as a linear combination of the random vectors from IID normal distribution and IID Gumbel distribution, which effectively simplifies the computing process. Finally, the two-level model can be transformed into an integer linear programming problem to optimize the capacity of determined charging stations. Experimental results show that the purchase rate of EVs is significantly improved, and the accessibility of charging stations during rush hours is more balanced than before. [ABSTRACT FROM AUTHOR]

We propose a general modular approach to support decision‐makers' response in the early stages of a pandemic with resource expansion, motivated by the shortage of Covid‐19‐related intensive care units (ICU) capacity in 2020 in Italy. Our approach uses (1) a stochastic extension of an epidemic model for scenarios of projected infections, (2) a capacity load model to translate infections into scenarios of demand for the resources of interest, and (3) an optimization model to allocate this demand to the projected levels of resources based on different values of investment. We demonstrate this approach with the onset of the first and second Covid‐19 waves in three Italian regions, using the data available at that time. For epidemic modeling, we used a parsimonious stochastic susceptible‐infected‐removed model with a robust estimation procedure based on bootstrap resampling, suitable for a noisy and data‐limited environment. For capacity loading, we used a Cox queuing model to translate the projected infections into demand for ICU, using stochastic intensity to capture the variability of the patient arrival process. Finally, we used stochastic dynamic optimization to select the best policy (when and how much to expand) to minimize the expected number of patients denied ICU for any level of investment in capacity expansion and obtain an efficient frontier. The frontier allows a trade‐off between investment in additional resources and the number of patients denied intensive care. Moreover, in the panic‐driven early days of a pandemic, decision‐makers can also obtain the time until which they can postpone action, potentially reducing investment costs without increasing the expected number of denied patients. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Exacta is the property of Exacta - Engenharia de Producao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Dynamic multi-period EoL traction battery recycling network with capacity expansions. • Location decisions are taken on two levels of the supply chain, namely for disassembling centers and recycling plants. • Model is applied to an OEM in Germany with data derived from cost estimation. • Literature investigation and demonstration of their limitation. • Trade-off between economy of scale and transport costs exists. The growth of the battery powered vehicle market will lead to an increasing amount of End of Life (EoL) electric vehicle battery systems (EVBSs) in the future. Although pointed out as a future challenge by research as well as industry, the analysis and design of EoL traction batteries' recycling networks have not been conducted extensively. Existing quantitative optimization models do not contain dynamic characteristics that are of importance for a growing market. We present a dynamic EoL battery reverse supply chain optimization model that allows planning over multiple periods and multiple supply chain layers while including capacity expansions of disassembling centers and recycling plants. The model is applied to a case study of an original equipment manufacturer (OEM) of battery electric vehicles that handles all EoL recycling activities for its batteries in a single stakeholder-driven network in Germany. The average EoL costs per EVBS were estimated to decrease by over 35% from 2030 to 2044 due to using larger processing facilities that benefit from economy of scale and lower transportation costs because more locations exist. The network change is driven by the growth of EoL EVBS supply. [ABSTRACT FROM AUTHOR]

We explore sustainable electricity system development pathways in South America’s MERCOSUR sub-region under a range of techno-economic, infrastructural, and policy forces. The MERCOSUR sub-region includes Argentina, Brazil, Chile, Uruguay, and Paraguay, which represent key electricity generation, consumption, and trade dynamics on the continent. We use a power system planning model to co-optimize investment and operations of generation, storage, and transmission facilities out to 2050. Our results show that, under business-as-usual conditions, wind and solar contribute more than half of new generation capacity by 2050, though this requires substantial expansion of natural gas-based capacity. While new hydropower appears to be less cost-competitive, the existing high capacity of hydropower provides critically important flexibility to integrate the wind and solar and to avoid further reliance on more expensive or polluting resources (e.g., natural gas). Over 90% emission cut by 2050 could be facilitated mostly by enhanced integration (predominantly after 2040) of wind, solar, and battery storage with 11%–28% additional cost, whereas enhanced expansion of hydropower reduces the cost of low-carbon transition, suggesting trade-off opportunities between saving costs and environment in selecting the clean energy resources. Achieving high emission reduction goals will also require enhanced sub-regional electricity trade, which could be mostly facilitated by existing interconnection capacities.

Multi-day energy storage (MDS), a subset of long-duration energy storage, may become a critical technology for the decarbonization of the power sector, as current commercially available Lithium-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models (CEMs)), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale CEM. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: (1) modeling MDS with linked representative periods is crucial to capturing its full value, (2) MDS value is highly sensitive to the cost and availability of other resources, and (3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

Globally, sales of battery electric vehicles (BEVs) are surpassing records every year, and their growing charging needs will ultimately reshape power infrastructure planning practices. While studies have analyzed the impact of light-duty BEVs on the electricity sector, they have overlooked the prospective evolution in charging profiles. We developed a framework for analyzing passenger vehicle electrification futures accounting for the evolution in charging infrastructure, BEVs technical features, and socio-demographics. We soft-link a BEV charging profiles generator with an energy system optimization model to analyze a light-duty vehicle electrification scenario in the U.S. from 2020–2050. Compared to static charging profiles, common in prior work, evolving profiles lead to substantial differences in projected power plant installed capacity (up to ∼300 GW more solar PV) and activity (up to ∼460 TWh more solar PV generation). Hence, future studies should consider not only different charging profiles (e.g. day, night, uncontrolled) but also how these evolve over time.

Runtime and memory requirements for typical formulations of energy system models increase non-linearly with resolution, computationally constraining large-scale models despite state-of-the-art solvers and hardware. This scaling paradigm requires omission of detail which can affect key outputs to an unknown degree. Recent algorithmic innovations employing decomposition have enabled linear increases in runtime and memory use as temporal resolution increases. Newly tractable, higher resolution systems can be compared with lower resolution configurations commonly employed today in academic research and industry practice, providing a better understanding of the potential biases or inaccuracies introduced by these abstractions. We employ a state-of-the art electricity system planning model and new high-resolution systems to quantify the impact of varying degrees of spatial, temporal, and operational resolution on results salient to policymakers and planners. We find models with high spatial and temporal resolution result in more realistic siting decisions and improved emissions, reliability, and price outcomes. Errors are generally larger in systems with low spatial resolution, which omit key transmission constraints. We demonstrate that high temporal resolution cannot overcome biases introduced by low spatial resolution, and vice versa. While we see asymptotic improvements to total system cost and reliability with increased resolution, other salient outcomes such as siting accuracy and emissions exhibit continued improvement across the range of model resolutions considered. We conclude that modelers should carefully balance resolution on spatial, temporal, and operational dimensions and that novel computational methods enabling higher resolution modeling are valuable and can further improve the decision support provided by this class of models.

Power systems have evolved following a century-old paradigm of planning and operating a grid based on large central generation plants connected to load centers through a transmission grid and distribution lines with radial flows. This paradigm is being challenged by the development and diffusion of modular generation and storage technologies. We use a novel approach to assess the sequencing and pacing of centralized, distributed, and off-grid electrification strategies by developing and employing the grid and access planning (GAP) model. GAP is a capacity expansion model to jointly assess operation and investment in utility-scale generation, transmission, distribution, and demand-side resources. This paper conceptually studies the investment and operation decisions for a power system with and without distributed resources. Contrary to the current practice, we find hybrid systems that pair grid connections with distributed energy resources (DERs) are the preferred mode of electricity supply for greenfield expansion under conservative reductions in photovoltaic panel (PV) and energy storage prices. We also find that when distributed PV and storage are employed in power system expansion, there are savings of 15%-20% mostly in capital deferment and reduced diesel use. Results show that enhanced financing mechanisms for DER PV and storage could enable 50%-60% of additional deployment and save 15 /MWh in system costs. These results have important implications to reform current utility business models in developed power systems and to guide the development of electrification strategies in underdeveloped grids.

Hydrogen is considered a solution to decarbonize the transportation sector, an important step to meet the requirements of the Paris agreement. Even though hydrogen demand is expected to increase over the next years, the exact demand level over time remains a main source of uncertainty. We study the problem of where and when to locate hydrogen production plants to satisfy uncertain future customer demand. We formulate our problem as a two-stage stochastic multi-period facility location and capacity expansion problem. The first-stage decisions are related to the location and initial capacity of the production plants and have to be taken before customer demand is known. They involve selecting a modular capacity with a piecewise linear, convex short-term cost function for the chosen capacity level. In the second stage, decisions regarding capacity expansion and demand allocation are taken. Given the complexity of the formulation, we solve the problem using a Lagrangian decomposition heuristic. Our method is capable of finding solutions of sufficiently high quality within a few hours, even for instances too large for commercial solvers. We apply our model to a case from Norway and design the corresponding hydrogen infrastructure for the transportation sector. [ABSTRACT FROM AUTHOR]

This research aims to investigate the potential impact of national policies on the attainment of Europe's goal of achieving net-zero greenhouse gas emissions by 2050. Specifically, it analyses the effects of policies on the power sector, by evaluating capacity expansion portfolios, import reliance, and costs by 2050. A linear programming model, the IESA-EUPS, is utilized to optimize the expansion and operation of the power system, considering 28 nodes and hourly temporal resolution. The study includes five scenarios from 2020 to 2050, with varying levels of biomass and nuclear penetration based on existing member state policies. Results show that by 2050, changes mainly occur in the interplay between firm capacities and cross-border transmission levels. Limiting biomass can significantly increase nuclear energy generation, while enforcing all policies leads to a 40 % rise in cross-border transmission by 2050, due to imbalances between countries. Some member states, such as Spain and Finland, are less affected, whereas others are heavily reliant on firm nuclear capacities. Western European countries with strict biomass and nuclear restrictions may see a boost in nuclear installations in countries allowing it. Member states without both nuclear and biomass may rely more on variable renewables, resulting in surplus electricity and increased LCOE. [Display omitted] • We analyse the EU's member state policy impact on power system capacity expansion towards a net-zero 2050. • We apply a novel hourly brownfield power system optimization model IESA-EUP up to 2050. • Policies have the greatest impact on firm capacities and cross-border transmission. • Overall cross-border transmission in the EU increases by 40 % when policies are applied. • Nuclear generation burden shifts to nations bordering strict policy neighbours. [ABSTRACT FROM AUTHOR]

We propose a nodal stochastic generation and transmission expansion planning model that incorporates the output from high-resolution global climate models through load and generation availability scenarios. We implement our model in Pyomo and perform computational studies on a realistically-sized test case of the California electric grid in a high performance computing environment. We propose model reformulations and algorithm tuning to efficiently solve this large problem using a variant of the Progressive Hedging Algorithm. We utilize the parallelization capabilities and overall versatility of mpi-sppy , exploiting its hub-and-spoke architecture to concurrently obtain inner and outer bounds on an optimal expansion plan. Initial results show that instances with 360 representative days on a system with over 8,000 buses can be solved to within 5% of optimality in under 4 h of wall clock time, a first step towards solving a large-scale power system expansion planning problem across a wide range of climate-informed operational scenarios. • Including climate projections into power system expansion plans helps resiliency. • Joint transmission, storage and generation expansion increases size of problem. • Stochastic optimization addresses uncertainty, but is computationally challenging. • Decomposition via Progressive Hedging Algorithm makes this problem tractable. • Parallel computing implementation allows quick solution times. [ABSTRACT FROM AUTHOR]

• The integrated capacity and production planning problem is studied. • A methodology is proposed which increases problem complexity stepwise. • Deterministic and stochastic MILP models are proposed. • Local (OAT) and global (Sobol') sensitivity analyses are performed. • The methodology is applied to industry derived case studies of multi-stage networks. This work presents a methodology for capacity planning under uncertainty in general multi-stage manufacturing networks. Multiple manufacturing lines are available and the production time on each line must be divided between a set of materials. The methodology generates mixed-integer linear programming models that represent capacity expansions with economy of scale costs functions and production planning details. A deterministic model is solved to create a baseline and the impact of uncertainty is investigated by sensitivity analysis and stochastic programming. The applicability of the methodology is exemplified through two case studies derived from industrial pharmaceutical manufacturing. The methodology identifies bottlenecks that limit supply and, where required, activates, and assigns capacity expansion projects for satisfying demand subject to uncertainty. The methodology determines the best use of existing resources and the location and size of capacity expansions thereby generating a portfolio of recommendations for decision making on integrated planning of capacity and production. [ABSTRACT FROM AUTHOR]

There are over 600 million people without access to electricity in Sub-Saharan Africa. The design of sustainable electricity systems needed to fuel development in the region requires context-specific power system modeling. Modeling data requirements however can be challenging for researchers and other stakeholders to access. To address this, an open-access cost optimization capacity expansion model, known as PROGRESS (Programmable Resource Optimization for Growth in Renewable Energy and Sustainable Systems), has been built to explore generation expansion pathways for countries with low availability and access to power systems data. The model has relatively low data requirements and is easily adaptable to enable comparative scenario analysis. This paper focuses on a first order proof-of-concept to show how PROGRESS works and then presents preliminary results for generation capacity expansion using the case of Kenya.

There are over 600 million people without access to electricity in Sub-Saharan Africa. The design of sustainable electricity systems needed to fuel development in the region requires context-specific power system modeling. Modeling data requirements however can be challenging for researchers and other stakeholders to access. To address this, an open-access cost optimization capacity expansion model, known as PROGRESS (Programmable Resource Optimization for Growth in Renewable Energy and Sustainable Systems), has been built to explore generation expansion pathways for countries with low availability and access to power systems data. The model has relatively low data requirements and is easily adaptable to enable comparative scenario analysis. This paper focuses on a first order proof-of-concept to show how PROGRESS works and then presents preliminary results for generation capacity expansion using the case of Kenya.

Water sector planning and policy making in arid and semi-arid regions are challenging because many drivers and decision criteria require consideration. In this study, a multi-period mixed-integer linear programming model was developed to integrate and economically evaluate water management options for water supply in arid regions. The applicability of the proposed approach was demonstrated through a case study in the Emirate of Abu Dhabi (EAD), United Arab Emirates. The model was programmed in general algebraic modeling system (GAMS) and solved using the Cplex solver. The model determined the optimal economic and environmental costs, capacity expansion of treatment plants and water transmission systems, and other environmental aspects including the carbon footprint and brine discharge. Results show that the capacity contribution of reverse osmosis for desalination is expected to increase from 5.1% in 2021 to 18.1% in 2050. Based on the model's results for the case study, it was concluded that even with moderate consideration of environmental aspects, desalination plants in the EAD need a major technology transformation from thermal desalination to reverse osmosis The proposed model is proved to be effective for integrated water resources management and infrastructure planning in the EAD, and has the potential for effective application in other arid or semi-arid countries.