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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]

[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]

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]

The European Union aims to deploy a high share of renewable energy sources in Europe's power system by 2050. Large-scale intermittent wind and solar power production requires flexibility to ensure an adequate supply–demand balance. Green hydrogen (GH) can increase power systems' flexibility and decrease renewable energy production's curtailment. However, investing in GH is costly and dependent on electricity prices, which are important for operational costs in electrolysis. Moreover, the use of GH for power system flexibility might not be economically viable if there is no hydrogen demand from the hydrogen market. If so, questions would arise as to, what would be the incentives to introduce GH as a source of flexibility in the power system, and how would electrolyzer costs, hydrogen demand, and other factors affect the economic viability of GH usage for power system flexibility. The paper implements a European power system model formulated as a stochastic program to address these questions. The authors use the model to compare various instances with hydrogen in the power system to a no-hydrogen instance. The results indicate that by 2050 deployment of approximately 140 GW of GH will pay off investments and make the technology economically viable. We find that the price of hydrogen is estimated to be around €30/MWh. • Green hydrogen (GH) increases the flexibility of the European power systems. • Scenarios with GH in the power system were compared to a no-hydrogen scenario. • By 2050, the deployment of 140 GW of GH might pay off investments. • The economic viability of GH for flexibility use depends on its participation in other hydrogen markets. • The break-even point for GH is estimated at €30/MWh, approximately. [ABSTRACT FROM AUTHOR]

Expanding the capacity of an existing reservoir hydropower plant, with the primary aim of enhancing its flexibility, poses a complex optimization problem that need consider current operational constraints. This study's focus on hydropower capacity expansion to enhance flexibility contrasts with the traditional goal of primarily promoting electricity generation. To address this challenge, a novel Capacity Expansion Optimization Model (CEOM) is proposed. This model is designed to estimate the optimal expansion capacity of existing hydropower infrastructure, ultimately maximizing the flexibility that hydropower can provide to the power system. This study explores the true potential of existing reservoir hydropower flexibility. Based on an analysis of the principles and characteristics of hydropower flexibility, a mathematical expression is developed to more accurately represent the upward flexibility of hydropower. The CEOM integrates reservoir operation simulation and flexibility mathematical expression for application in the secondary design stage of existing hydropower. Two regimes of the CEOM are considered: one focusing solely on the optimal design for expanding the capacity of a single reservoir hydropower plant, and the other considering the joint expansion of cascade hydropower, taking into account hydraulic connections. The proposed CEOM is validated on two real cases: Goupitan hydropower with an annually regulated reservoir and Silin hydropower with a daily or weekly regulated reservoir along the Wujiang River in Guizhou, China. The models are solved using Particle Swarm Optimization (PSO). The results indicate that annually regulated hydropower provides an average of 0.80 units of upward flexibility per unit of capacity expansion, while daily or weekly regulated hydropower provides a corresponding upward flexibility of 0.59 units. Jointly expanding cascade hydropower offers a broader range of upward flexibility compared to individual hydropower expansion. Furthermore, impact analysis suggests that the optimal upward flexibility provided by annually regulated hydropower capacity expansion increases sharply with reservoir inflow due to larger reservoir storage. However, the optimal upward flexibility provided by daily or weekly regulated hydropower capacity expansion does not increase with inflow. This implies that, for daily or weekly regulated hydropower, the limiting factor of flexibility provided by capacity expansion comes from the size of the reservoir capacity. Additionally, this paper highlights the significant untapped potential of existing conventional hydropower and aims to provide guidance for scientific and engineering organizations on the optimal utilization of capacity expansion. • Propose a method for estimating the flexibility of existing hydropower plant capacity expansion. • Integrate a novel Capacity Expansion Optimization Model (CEOM) to maximize power system flexibility for existing hydropower. • An impact analysis of existing hydropower expansion using real-world case studies to demonstrate varying flexibility levels. [ABSTRACT FROM AUTHOR]

The implementation of solar coupled with daytime electric vehicle (EV) charging, aligns seamlessly with the broader goal of transitioning to a decarbonized grid and clean energy future. This paper explores how fixed-tilt solar arrays coupled with daytime EV charging would affect a future renewable-energy-driven grid in California. We use capacity-expansion modeling to identify the lowest-cost grid design for 40 scenarios that compare two solar panel mounting configurations (east–west versus south-facing), two EV charging profiles, five tilt angles ranging from 15°to 35°, and two assumptions about a long-duration energy storage (LDES) candidate resource. We then evaluate and discuss the mix of generators selected by the model for the various scenarios, the associated curtailment, and the implications about the best approaches for adding fixed-tilt solar arrays as part of transitioning to a new energy system. Although the east–west-facing solar orientation is expected to reduce the need for diurnal storage by being able to charge EVs early and late in the day, it appears that the better matching between seasonal supply and demand for the south-facing orientation is more important. • South-facing orientation boosts solar and storage adoption rates. • East–west-facing increases the selection of wind and geothermal. • Daytime EV charging length affects storage build for south—more than E-W-facing. • South-facing chooses more long-duration energy storage (LDES) capacity expansion. • With no LDES, solar curtailment could be greater for east–west-facing. [ABSTRACT FROM AUTHOR]

Parallel operation is a significant and feasible method to compensate the insufficient capacity of single reciprocating-switcher energy recovery device (RS-ERD) in membrane desalination system. To grasp the operating characteristics in capacity expansion, three parallel schemes of RS-ERD, single-operation scheme, two-parallel scheme and three-parallel scheme, are investigated through the numerical simulation, experimental verification and theoretical parameterization. Results indicate that, the two-parallel scheme and three-parallel scheme achieve the flowrate continuity of low-pressure seawater and the flowrate fluctuation amplitudes in parallel RS-ERDs decrease by 50 % and 83 % respectively when compared with single-operation scheme. Parallel operation schemes also broaden the flowrate range with high efficiency for RS-ERD. To ensure the 95 % energy recovery efficiency, two-parallel and three-parallel schemes expand the maximum flowrates to 140 m3/h and 210 m3/h in contrast with the 70 m3/h of single-operation scheme. In addition, the two-parallel and three-parallel schemes of RS-ERD realize the much lower specific energy consumption (SEC) of 2.11 kWh/m3 and 2.09 kWh/m3 than the 2.23 kWh/m3 in single-operation scheme which contributes to the operating costs savings about 63,830 CNY and 75,778 CNY in one year. This research provides a new perspective in capacity expansion through the parallel operation of energy recovery devices. [Display omitted] • Three parallel schemes of RS-ERD are investigated in simulation and validation. • A parameterization method is introduced to evaluate the operating characteristics. • The flow continuity of LP fluids is achieved in the parallel operating schemes. • The parallel schemes broaden the flowrate range with high efficiency for RS-ERD. • The SEC and operating costs are quantified and evaluated for desalination system. [ABSTRACT FROM AUTHOR]

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales. • Both ORDCs and capacity markets can achieve resource adequacy targets. • Capacity markets can reduce wind and solar buildout due to suppressed energy prices. • Static capacity demand curves achieve reliability targets with stable price signals. • ORDCs increase generation commitment but at the expense of higher system costs. • Achieving high clean energy targets requires cost-competitive flexible technologies. [ABSTRACT FROM AUTHOR]

Equilibrium capacity expansion is an effective tool to help regulators predict the generation evolution of power systems by analyzing the decision-making of generation companies (Gencos). With the development of carbon markets (CMs) and electricity markets (EMs), Gencos' decisions in these two markets (encompassing investment and bidding decisions related to carbon emission allowances and electricity) are deeply intertwined through market clearing results. However, existing studies mainly focus on equilibrium capacity expansion with fixed carbon prices and usually ignore the realistic clearing process of CMs, which cannot simulate Gencos' coupled decision-making and accurately predict generation evolution under the CMs and EMs. This paper proposes a two-level equilibrium capacity expansion model for power systems which considers the coupled decisions of Gencos between CMs and EMs. At the upper level, multiple Gencos' coupled decision models are proposed based on their interaction with market clearing results. At the lower levels, CMs and EMs are respectively cleared to determine the varying carbon and electricity prices. Furthermore, the developed equilibrium capacity expansion model is reformulated as an equilibrium problem with equilibrium constraints (EPEC), which is solved by a diagonalization-based solution procedure. Finally, a provincial power system of southeast China is used to validate the performance of the proposed framework. The results illustrate that under CMs and EMs, non-fossil energy would account for 81.3% of total capacity and 73.88% of total generation in 2050. Compared to the existing model using fixed carbon prices, the consideration of realistic carbon market clearing will promote the installation of renewable energies and improve social welfare. The proposed model can provide beneficial references for regulators to perceive the evolution of power systems in CMs and EMs. • A two-level equilibrium capacity expansion model for power systems is proposed. • A novel coupled decision model for Gencos in CMs and EMs is developed. • The proposed model is solved by a diagonalization based solution procedure. • The results can guide the transition of power systems under the CMs and EMs. [ABSTRACT FROM AUTHOR]

• Provide a 50th anniversary bibliometric review of the bottleneck model research;. • Identify influential papers, top contributing authors and leading topics;. • Review the studies in terms of travel behavior, demand-side and supply-side strategies;. • Discuss potential directions for further studies. The bottleneck model introduced by Vickrey in 1969 has been recognized as a benchmark representation of the peak-period traffic congestion due to its ability to capture the essence of congestion dynamics in a simple and tractable way. This paper aims to provide a 50th anniversary review of the bottleneck model research since its inception. A bibliometric analysis approach is adopted for identifying the distribution of all journal publications, influential papers, top contributing authors, and leading topics in the past half century. The literature is classified according to recurring themes into travel behavior analysis, demand-side strategies, supply-side strategies, and joint strategies of demand and supply sides. For each theme, typical extended models developed to date are surveyed. Some potential directions for further studies are discussed. [ABSTRACT FROM AUTHOR]

Decarbonization of the electricity sector is one of the major measures in slowing down the pace of climate change. In this paper, we analyze the impacts of energy storage systems (ESS) and year-to-year variability and uncertainty in the hourly profiles of variable renewable energy (VRE) on power system decarbonization in 2050. We perform this analysis through capacity expansion optimization based on technology cost projections and CO 2 emission restrictions based on 11 years of wind, solar, and load data variations in Italy's power system, with a particular focus on how ESS changes the optimal generation portfolio and system performance. We also explore the impact of ESS duration on the renewables' adoption and system costs. To quantify the impact of VRE variability in different years, we present a comparative analysis of capacity expansion optimization based on multiple-year and single-year data. Our results indicate a high RES penetration even in the absence of decarbonization policies, due to expected declines in future technology costs. In the transition to a zero-carbon electricity system, carbon capture and storage (CCS) with less than 100% carbon capture efficiency will play a role only in intermediate low-emission scenarios unless future technology advancements improve its capture efficiency. ESS investments contribute to lower total system costs by replacing more expensive flexibility resources. However, the value of ESS changes by its duration, with longer ESS durations having lower marginal value per added kWh storage capacity. Variability in VRE profiles leads to substantial variation in the system's configuration and energy cost, depending on what year is used in the optimization. Decision making based on single-year data can therefore lead to substantial increases in the systems' operational costs in other years due to increased probability of capacity shortages and load curtailments. In contrast, optimizing over multiple VRE and load years provides a more robust and cost-effective generation expansion strategy. • Results show high RES penetration levels even without decarbonization policies. • Decarbonization is less expensive with ESS, under given cost assumptions. • The marginal value of EES drops rapidly as more capacity is added. • Annual variations in VRE significantly change the system configuration and costs. • Planning with single-year data can lead to high operational costs in other years. [ABSTRACT FROM AUTHOR]

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. • Addressing a frequent blackout caused by electricity demand surpassing infrastructure, particularly during summer peaks. • Targets 25 % renewable electricity by 2030, focusing on solar and wind amid economic and environmental concerns for Iraq. • The abundant solar and wind resources play a central role in the country renewable energy transition. • Potential renewable capacity addition could reach to 10 GW by 2030, increasing to 14 GW by 2035. [ABSTRACT FROM AUTHOR]

• Synergistic compensation is introduced to reinforce RGB-based image watermarking. • The SC enables the watermarks in RGB channels to withstand color JPEG compression. • DCT-based RM watermarking is implemented to demonstrate the efficiency of SC. • The incorporation of SC leads to an expansion of threefold payload capacity. • No noticeable difference is perceived for the watermarked images with SC deployed. Color image watermarking has achieved great success in applications such as copyright protection and content authentication. Nonetheless, color image watermarking individually implemented in RGB channels cannot survive JPEG compression attacks. This study introduces an auxiliary scheme that enables RGB -based watermarking methods to overcome this difficulty. The reason why a promising grayscale watermarking method suffers a severe setback from color image JPEG compression is first explained and the countermeasure used to remedy the deficiency is then delivered. The solution turns out to be the application of synergistic compensation (SC) to channels other than that used to embed the watermark. The benefit of the SC scheme is demonstrated through a well-developed blind watermarking method known as relative modulation implemented in the discrete cosine transform (DCT) domain. The experimental results confirmed that the incorporation of SC enabled RGB-based watermarking to triple the payload capacity and halve the bit error rates under JPEG compression with quality factors in the range of 40–90. Nonetheless, the gain in the capacity and the robustness against JPEG compression was accompanied by a minor drop of 1.174 dB in terms of peak signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Natural gas is a green fossil fuel with the highest demand growth. Natural gas industry is composed of different sectors that form a complex and large supply chain. These sectors make their own decisions individually, resulting in implementation of non-optimal decisions. The aim of this study is to design and optimize an integrated natural gas supply chain (NGSC) formulated as a mixed integer linear programming (MILP) model. The model minimizes total cost and optimizes gas flow between supply chain nodes through pipelines, location-allocation of facilities and pipeline routes along with their capacities, number of pipelines, capacity expansion, extraction, production, storage, exports and imports. The proposed model is applied to a real world case study based on information derived from Iran's NGSC. Finally, the change effect of the most important parameters on the optimal solution is investigated and three actual scenarios are analyzed. The results indicate that the parameters of operating costs and demand volume have a significant effect on the optimal solution and the largest share of supply chain costs is related to operating expenditures. Moreover, the expansion of joint fields and zero import capacity will lead to a remarkable increase in total supply chain costs. • A novel optimization framework for natural gas supply chain design is proposed.. • The integrated strategic and tactical planning of gas supply chain is performed. • Location-allocation and capacity expansion of facilities and pipeline routes are considered. • Gas storage process in the supply chain is carried out using underground reservoirs. [ABSTRACT FROM AUTHOR]

This work proposes a many objective approach where a schedule of new dams is optimised along-side dam selections and operating rules. We investigate the extent to which changing management rules during infrastructure system expansion increases the ability to identify best performing plans. The approach links river basin simulation to many objective robust optimisation, so that expansion plans are optimised across multiple scenarios and considering multiple metrics. The approach is demonstrated using the Blue Nile hydropower reservoir system, identifying dam activation schedules that achieve efficient trade-offs for various conflicting objectives including discounted net benefits, reliable downstream releases and energy generation. Multi-reservoir system expansion scheduling formulations with three different levels of operating rule responsiveness to expansion are compared for performance and computational requirements. Results show benefits increase when release rules change as the multi-reservoir system expands, with failure to optimally adapt operating rules loosing up to 25% of NPV. • Filling period performance may impact acceptability of new dams in large systems. • Selection, operating rules, & expansion schedule of new dams optimised across many criteria. • If release rules are allowed to change during system expansion, performance improves. • Not updating rules during expansion loses up to 25% in financial benefits in our Blue Nile case-study. • Visual analytic parallel plots can help communicate performance tradeoffs and expansion schedules. [ABSTRACT FROM AUTHOR]

Efficient provision of electricity requires timely expansions of power transmission capacity. However, regulation does not always send the right signals to generate the required (and timely) investments. Therefore, it is important to evaluate the effect of alternative regulations on investment on transmission capacity. In this paper, considering regulated remuneration, we perform this evaluation with a behavioral simulation model of the transmission capacity expansion, in which capacity is endogenously determined by the demand/supply relation. Two planning approaches were considered: centralized planning where the investments are fully coordinated by a central organism, and decentralized planning where the capacity expansions are driven by the investors’ rationality on the power market evolution. The model is applied to the Colombian case. The decentralized approach has lower costs (usage charges) than centralized expansion, but lower transmission capacity margins. As low transmission capacity margins create supply risks in high demand periods, regulators can increase coordination in decentralized planning by directly promoting investments that increase security of supply. [ABSTRACT FROM AUTHOR]

Abstract This Case Study considers generation capacity expansion planning in Bosnia and Herzegovina (BiH) including a range of issues related to renewable, energy efficiency, local emission and carbon reduction commitment policies. In addition, our analysis considers an outlook of wholesale electricity prices in the region. We have also compared our findings with the country's current official plan ("Indicative Plan"), which includes development of as many as four additional lignite-based plants. Our findings suggest that BiH, as a low-cost producer in the region, has significant opportunities to modernize and expand its current generation system with nearly €3 billion in new investments. This investment level is much lower than the €5 billion investment program promulgated in the Indicative Plan. These differences primarily have to do with the treatment of lignite plants. Since the end of the war roughly twenty years ago, the country has progressed quickly. Aspirations to join the EU compose a significant part of the authorizing environment for the power sector. Highlights • This Case Study considers generation capacity expansion planning in Bosnia and Herzegovina. • Our analysis considers an outlook of wholesale electricity prices in the region. • We have also compared our findings with the country's current official plan. [ABSTRACT FROM AUTHOR]

Hydrogen will be important in decarbonized energy systems. The primary ways to produce low emission hydrogen are from renewable electricity using electrolyzers, called green hydrogen, and by reforming natural gas and capturing and storing the CO 2 , known as blue hydrogen. In this study, the degrees to which blue and green hydrogen are complementary or competitive are analyzed through a sensitivity analysis on the electrolyzer costs, and natural gas price. This analysis is performed on four bases: what is the cost-effective relative share between blue and green hydrogen deployment, how their deployment influences the price of hydrogen, how the price of CO 2 changes with the deployment of these two technologies, and whether infrastructure can economically be shared between these two technologies. The results show that the choice of green and blue hydrogen has a tremendous impact, where an early deployment of green leads to higher hydrogen costs and CO 2 prices in 2030. Allowing for blue hydrogen thus has notable benefits in 2030, giving cheaper hydrogen with smaller wider socioeconomic impacts. In the long term, these competitive aspects disappear, and green and blue hydrogen can coexist in the European market without negatively influencing one another. • Green and blue hydrogen are competitive in 2030. • In 2050, green and blue hydrogen can coexist. • Early green hydrogen deployment increases price of both CO 2 and hydrogen. • Blue hydrogen can enable significant savings in the European power market. • Deployment of blue hydrogen is highly sensitive to the natural gas price. [ABSTRACT FROM AUTHOR]

• A Mixed-Integer Nonlinear Programming model for the strategic investment planning of large-scale hydrogen economy envelopes is formulated. • Production and carbon capture units are represented using surrogate models derived from rigorous simulation models. • Purity requirements for hydrogen use in transportation as well as the industrial processes are considered as part of the model formulation. • Case study: California's 10-year infrastructure plan achieves a 90 % carbon cut, $6.9B investment, $1.4B NPV. This work presents a novel Mixed-Integer Nonlinear Programming (MINLP) modeling and optimization framework for the investment planning of large-scale hydrogen economy envelopes. The scope of the model includes the design, synthesis, and long-term capacity expansion of hydrogen supply chain networks (HSCN) that include hydrogen production, purification, storage, transportation, and distribution, subject to environmental sustainability considerations. Production and carbon capture units are represented using surrogate models derived from rigorous simulations to capture important process non-linearities. This framework provides an optimal roadmap for the 10-year plan of hydrogen infrastructure development in the state of California, that demonstrates the important trade-offs between investment decisions, economic incentives, and regulatory carbon emissions constraints. It is shown that the development of the hydrogen production and distribution networks can be achieved over a 10-year horizon in an economically profitable way, while achieving a 90 % reduction in carbon emissions and satisfying all state regulatory mandates. [ABSTRACT FROM AUTHOR]

Capacity expansion strategy consists of fulfilling the demand with capacity portfolio via alternative configurations with long lead time for procurement and installation. Sequential dependent decisions including demand planning and cost structure of capital expenditure shall be taken into consideration for achieving enterprise profitability. Though a number of studies have been done to address related issues, limitations of existing approaches can be traced in part to the lack of a systematic framework in which multiple objectives of related total resource management problems can be considered and integrated. Little research has been done to address the present problem for capacity expansion for matured fabs from the perspective of total resource management. To fill the gaps, this study applies the concept of total resource management to integrate operational strategies and the overall usage of resources. This study develops a capacity expansion model with multiple objectives including minimizing resource costs, maximizing overall return, maximizing revenue, and minimizing capacity risk. Since the formulation of the multi-objectives can be non-linear and the size of the problem is increasing, it is difficult to solve the problem in reasonable time for practical use. A subpopulation preference adjective non-dominated sorting genetic algorithm (SPANS-GA) is developed to solve the decision problem. An empirical study in a leading semiconductor company in Taiwan is conducted for validation. The results have shown practical viability of the developed solution for multi-objective capacity planning for matured wafer fabs for total resource management. [Display omitted] • Novel capacity expansion model for total resource management is developed. • Subpopulation preference adjective non-dominated sorting GA is developed. • Multi-objective capacity expansion decision is structured for "More than Moore". • The developed solution is validated by empirical study in Taiwan. • This solution is implemented in a leading semiconductor company. [ABSTRACT FROM AUTHOR]

Using real-time load data and HOMER Pro's 'multi-year' optimization tool, this paper investigates the long-term cost optimal capacity expansion planning (CEP) for an overloaded photovoltaic (PV) mini-grid (MG) with storage batteries in off-grid rural Ethiopia over a 20-year planning horizon. Three distinct annual energy demand growth scenarios were considered: 0 % (fulfils the minimum load requirement), 5 %, and a 15 % from productive users only. In all scenarios, the generation mix consists of only solar energy and the maximum allowable capacity shortage (MACS) is limited to 10 %. The findings reveal that, in all scenarios, the largest capacity expansion is performed on the battery and PV systems, covering up to 73 % and 35 % of the total expansion costs, respectively. The annual unmet load fraction of the expanded MG system ranges from 5.9 % in scenario-3 to 9.4 % in scenario-1, and the cost of electricity (LCOE) ranges from $0.404/kWh in scenario-3 to $0.887/kWh in scenario-1. The results indicate that the scenario-3 expansion path is comparatively cost-effective and has the highest reliability; but it still falls short of fully satisfying the required load demand and is not financially viable. Surprisingly, increasing the reliability of the scenario-3 capacity expansion from 94 % to 100 % raises the MG's Net Present Cost by 37 %. The sensitivity analysis shows that the MACS, ambient temperature, and battery's depth of discharge significantly affect the cost and performance of the capacity expansion. The study demonstrates (a) there are significant trade-offs between minimizing MG expansion costs and maximizing reliability levels; (b) capacity expansion based solely on cost-minimization without considering key constraints and uncertainties (demand, cost, PV, and battery degradations) may not provide a practical and robust solution to severe reliability issues, (c) capacity expansion that supports demand from productive users increases the cost-effectiveness and bankability of isolated MGs. • Long-term capacity expansion planning for an off-grid PV-battery mini-grid performed • 100% renewable source and 10% maximum allowable capacity shortage imposed • Battery expansion cost accounted for 52-73% of total capacity expansion costs • Trade-off between minimizing expansion costs and maximizing reliability is high • Increased demand from productive use increases bankability of off-grid MGs [ABSTRACT FROM AUTHOR]

Highlights • Proposed a multistage stochastic programming model for bitumen upgrading plant capacity expansion planning. • Solved the stochastic programming problem through linear decision rule based method. • Addressed synthetic crude oil price uncertainty through time series model and constructed uncertainty set. • Results show that the effect of CO 2 tax rate uncertainty on the expansion planning is not significant. Abstract Expansion development of upgrading plants is an important decision to make for the oil sands industry. In this paper, we propose a multistage stochastic expansion development method to tackle uncertain synthetic crude oil (SCO) and CO 2 tax prices. The linear decision rule based technique is applied to solve the proposed stochastic optimization model. Various analyses are conducted based on optimization results: (i) effects of the uncertainty set size, (ii) comparison of solutions for selected pessimistic, realistic, and optimistic scenarios, (iii) effects of different operating modes for an upgrading plant, and (iv) cost distribution. Results of this work demonstrate that the stochastic model provides a more flexible, economical, and robust solution compared to the deterministic solution. In addition, the CO 2 tax price affects the optimal solution negligibly compared to the SCO price. Finally, expansion development of the studied upgrading plant is economically beneficial even at the current market state. [ABSTRACT FROM AUTHOR]

In the context of recent dramatic solar energy cost reductions, the U.S. Department of Energy set new levelized cost of energy goals for photovoltaics (PV) to achieve by 2030 to enable significantly greater PV adoption: $0.03/kWh for utility-scale, $0.04/kWh for commercial, and $0.05/kWh for residential PV systems. We analyze the potential impacts of achieving these “SunShot 2030” cost targets for the contiguous United States using the Regional Energy Deployment System (ReEDS) and Distributed Generation (dGen) capacity expansion models. We consider the impacts under a wide range of future conditions. We find that PV could provide 13%–18% of U.S. electricity demand in 2030 and 28%–64% of demand if the SunShot 2030 goals are achieved, with PV deployment increasing in every state. The availability of low-cost storage has the largest impact on projected deployment, followed by natural gas prices and electricity demand. For comparison, PV deployed under a business-as-usual scenario could provide only 5% of generation in 2030 and 17% in 2050. We find that the high levels of PV deployment explored here lead to lower electricity prices and system costs, lower carbon dioxide emissions, lower water consumption, increased renewable energy curtailment, and increased storage deployment compared with the business-as-usual scenario. [ABSTRACT FROM AUTHOR]

Nowadays, the disordered caused by terrorist attacks is considered as a major treat by governments and international comities. Meanwhile, different types of defensive systems are to develop and to focus on protecting the critical system services in recent years. By this motivation and contrary to previous works, this study firstly proposes a bi-objective, bi-level formulation for the partial interdiction problem considering different defensive systems. In the developed model, defender as a leader tries to optimize the two objectives i.e . different types of defensive systems to protect the facilities as well as the total cost for supply the demands of customers and outsourcing, simultaneously. Besides, the attacker seeks to maximize destroy for the customers' service system. Due to the nature of the problem, the solution approach is based on a nested approach, namely, bi-level metaheuristic. Accordingly, this study not only uses the old and the recent algorithms, but also utilizes two new hybrid algorithms according to the benefits of both groups. The performance of the algorithms is enhanced by Response Surface Method (RSM) to set the proper values for algorithms’ parameters. Finally, the approaches are evaluated by different criteria to identify the performance of algorithms as well as a set of sensitivity analyses are done to approve the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

This study develops a model for calculating the optimal amount of transmission, wind, and solar capacity that should be built in a grid's different regions. It also presents a framework for choosing CO 2 prices by balancing increasing system cost and flexibility requirements with CO 2 emissions reductions. In a simulation of the ERCOT grid, the model suggests a 60 $/ton CO 2 price and an optimal investment of 27.0 GW of transmission capacity to five different regions. These regions install a total of 26.6 GW of wind and 11.1 GW of solar, representing a grid with about 60% thermal and 40% renewable capacity. This renewable mix produces 110 TWh of energy per year, 34% of the total electricity demand. The grid emits 82.2 million tons of CO 2 per year under this scenario, a 65% reduction from the 237 million tons produced when no renewable capacity is installed. At the optimal renewable development solution, all coal and natural gas boiler generators have capacity factors less than 20% with many of them not being dispatched at all. While these results are specific to ERCOT, the methods and model can be used by any grid with an aim for renewable energy capacity expansion. [ABSTRACT FROM AUTHOR]

We present a real options model for capacity expansion that introduces uncertainty about potential future regulation (regulatory uncertainty) and key characteristics of capacity decisions: investment with time to build, divestment, the option to charter, and operating flexibility. Regulatory uncertainty is modelled as a possible jump in operating, investment, and charter costs during the simulation horizon. We find that regulatory uncertainty with grandfathering (the extant fleet is exempt from compliance) promotes high up-front investment leading to excess capacity and increased emissions. However, regulatory uncertainty without grandfathering reduces investment and emissions and the use of more flexible capacity options, such as chartering. [ABSTRACT FROM AUTHOR]

A general spatial optimization framework that relies on the use of a modified state-task network representation for design and planning problems in material and energy supply chain networks is presented. In brief, the proposed optimization framework considers for the tasks and states of the network: (i) the optimal selection and sizing of conversion, transfer and storage technologies, (ii) the capacity expansion for each technology over time, (iii) the inventory levels for storable states, (iv) the quantities of states converted or transferred through tasks, and (v) the optimal energy mix. Several variations of an illustrative design and planning problem of a mixed material and energy supply chain network have been solved effectively to study the trade-off between costs and emissions levels and different emissions regulation policies. A sensitivity analysis study with respect to alternative emissions caps and a multi-objective optimization example considering the conflicting objectives of total cost and emissions are also presented. The case studies showed that a more efficient way for emissions reductions is through regulation and emissions caps rather than increased emissions costs (i.e., 3.3% emissions reductions). Overall, the proposed optimization framework could be used to integrate various types of material and energy supply chain operations using a unified modeling representation towards the more efficient management of such interdependent networks under techno-economic and environmental aspects. [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. • A mix of hydro-wind-solar-battery can enable a sustainable low-carbon transition. • A 90% emissions cut is feasible, but could increase system costs by 11%–28%. • Reducing heavy reliance on natural gas may require policy interventions. • Early retirement of existing hydropower can increase low-carbon transition costs. • Existing interconnection lines support most of the future sub-regional power trade. [ABSTRACT FROM AUTHOR]

In light of offshore wind expansions in the North and Baltic Seas in Europe, further ideas on using offshore space for renewable-based energy generation have evolved. One of the concepts is that of energy islands, which entails the placement of energy conversion and storage equipment near offshore wind farms. Offshore placement of electrolysers will cause interdependence between the availability of electricity for hydrogen production and for power transmission to shore. This paper investigates the trade-offs between integrating energy islands via electricity versus hydrogen infrastructure. We set up a combined capacity expansion and electricity dispatch model to assess the role of electrolysers and electricity cables given the availability of renewable energy from the islands. We find that the electricity system benefits more from connecting close-to-shore wind farms via power cables. In turn, electrolysis is more valuable for far-away energy islands as it avoids expensive long-distance cable infrastructure. We also find that capacity investment in electrolysers is sensitive to hydrogen prices but less to carbon prices. The onshore network and congestion caused by increased activity close to shore influence the sizing and siting of electrolysers. • Distance to shore and investment in offshore electrolysis are mutually dependent. • Offshore electrolysis is more valuable for far-away energy islands. • Offshore grids are beneficial for large-scale offshore wind integration. • Electrolyser capacity is sensitive to hydrogen prices but less to carbon prices. • Onshore grid developments influence offshore development significantly. [ABSTRACT FROM AUTHOR]

China is now facing pressures of emission reduction both from greenhouse gases (GHGs) and local air pollutants (LAPs). Considering power generation system contributes a great share of CO 2 and PM 2.5 emissions, a regional power generation system modelling and optimization framework using Long-range Energy Alternatives Planning System (LEAP) is conducted in order to explore the cost-effective cleaner capacity expansion pathways under various emissions constraints. A case study of Shanghai follows, focusing on analysis of capacity additions for different power generation technologies, as well as energy inputs, reduction co-benefits and system costs. The results indicate that: i) the clean production pathways differentiate along with stricter CO 2 constraints; ii) Ultra-supercritical coal-fired units have significant advantage under no emission constraint, which are substitute by cleaner power generation units such as wind power, solar power and Integrated Gasification Combined Cycle (IGCC) under the CO 2 constrained scenario and the combined CO 2 -PM 2.5 constrained scenario; iii) CO 2 constraint displays stronger reduction co-benefits than that of PM 2.5 ; iv) Emission constraints are conductive to energy savings that will increase system cost. Finally, policy recommendations are made through sensitivity analysis that only when the price of natural gas declines sharply can natural gas units become one of the alternatives that reduce coal consumption and the related CO 2 emissions. [ABSTRACT FROM AUTHOR]

This paper builds a two-stage, stochastic model to study capacity expansion problem in logistics under cap-and-trade and carbon tax regulations. The optimal capacity expansion and production decisions are obtained, and the effects of carbon emission regulations on capacity expansion are studied. Through analytical study and a real case numerical analysis, we find that the carbon tax exhibits different impacts on optimal capacity expansion decisions in low tax rate and high tax rate, and the volatility of capacity investment cost has a larger impact on optimal capacity expansion than that of production cost. [ABSTRACT FROM AUTHOR]

• A generic methodology for capacity and production planning is presented. • Mathematical models created based on system characterization and translation. • Two approaches are described to determine capacity changes. • Case studies of real-world primary pharmaceutical production networks. • Effects of operational objectives vs. uncertain parameters are studied. Production planning is widely studied, and mathematical formulations have been proposed for various problems. The capacity level is usually fixed which requires a separate solution of the capacity planning problem. In this paper, a methodology is proposed for setting up and solving integrated capacity and production planning problems for a given manufacturing network. Mixed-integer linear programming problems are assembled from a constraint and objectives database based on a system characterization and their solution determines whether capacity levels should be changed. An indirect approach to solving the problem is suggested, in which the required capacities are determined with no knowledge of specific ways on how the capacity could be changed corresponding to an early stage of capacity planning. This contrasts with the traditional direct approach, in which a set of options are given and a subset of these chosen through optimization. The methodology is applied to three case studies from primary pharmaceutical manufacturing. A literature example is used to validate the method and two larger case studies show the method's ability to solve problems of industrial scale and relevance. [ABSTRACT FROM AUTHOR]

Hydrogen will be a central cross-sectoral energy carrier in the decarbonization of the European energy system. This paper investigates how a large-scale deployment of green hydrogen production affects the investments in transmission and generation towards 2060, analyzes the North Sea area with the main offshore wind projects, and assesses the development of an offshore energy hub. Results indicate that the hydrogen deployment has a tremendous impact on the grid development in Europe and in the North Sea. Findings indicate that total power generation capacity increases around 50%. The offshore energy hub acts mainly as a power transmission asset, leads to a reduction in total generation capacity, and is central to unlock the offshore wind potential in the North Sea. The effect of hydrogen deployment on power prices is multifaceted. In regions where power prices have typically been lower than elsewhere in Europe, it is observed that hydrogen increases the power price considerably. However, as hydrogen flexibility relieves stress in high-demand periods for the grid, power prices decrease in average for some countries. This suggests that while the deployment of green hydrogen will lead to a significant increase in power demand, power prices will not necessarily experience a large increase. • Green hydrogen economy requires 50% more European power generation in 2050. • North Sea is developed with at least 100 GW of offshore wind power capacity. • Hydrogen economy raises yearly average power price in Norway in 2050 by 50%. • Energy hub increases North Sea wind capacity by ca. 60%. • Hydrogen is mainly produced onshore, with total European capacity of 460 GW in 2050. [ABSTRACT FROM AUTHOR]

A petroleum supply chain is a large complex supply chain composed of several sub-problems. Numerous studies have focused on solving a portion of these problems, which led to a non-optimal solution. This study addresses a new multi-period multi-echelon and multi-transportation integrated petroleum supply chain model to obtain a global optimal solution. The main feature of this paper is to design an integrated supply chain model that considers both installation and capacity expansion of pipeline routes and facilities simultaneously, and optimizes location-allocation facilities and routes, capacity expansion, inventory, production, exportation and importation, as well as routing and transportation modes over a vast geographical area. To achieve this, a deterministic mixed-integer linear problem was developed and applied to a real world problem based on the information derived from Iran's petroleum chain. Numerous scenarios and sensitivity analyses have been presented for different cases to deepen more in the model. They showed that the optimal solution is less sensitive to variations of the cost parameters, but changes in the amount of demands and injected crude oil change the objective value remarkably. In general, the analysis showed that the developed model has the ability to present the best strategy in complex market situations. [ABSTRACT FROM AUTHOR]

Planning for power systems generation expansion follows environmental policies incorporating technologies based on renewables to reduce CO 2 emissions. These policies are susceptible to unpredictable changes, given dynamic economic and political contexts. This paper analyzes the impact of changes in energy policies, motivated by different environmental objectives. The analysis is done through a novel approach coupling Dynamic Programming and Multi-objective programming to generate several energy policy scenarios and their trade-offs, representing plausible policy changes in the different stages of the planning horizon. The results indicate a clear Pareto front and that energy policy scenarios with abrupt changes should be avoided in favor of scenarios with gradual changes. “Greener” energy policies in a given planning stage are not necessarily the best ones considering the full planning horizon, considering the unfolding impacts of current decisions into the future. The approach is useful in improving planners' future vision from myopic into a perspicacious one. [ABSTRACT FROM AUTHOR]

Private provision of public roads signifies co-existence of free, public-tolled and private-tolled roads. This paper investigates the Pareto-improving transportation network design problem under various ownership regimes by allowing joint choice of road pricing and capacity enhancement on free links. The problem of interest is formulated as a bi-objective mathematical programming model that considers the travel cost of road users in each origin-destination pair and the investment return of the whole network. The non-dominated Pareto-improving solutions of toll and/or capacity enhancement schemes are sought for achieving a win-win situation. A sufficient condition is provided for the existence of the non-dominated Pareto-improving schemes and then the properties of those schemes are analyzed. It is found that, under some mild assumptions, the optimal capacity enhancement is uniquely determined by the link flow under any non-dominated Pareto-improving scheme. As a result, the joint road pricing and capacity enhancement problem reduces to a bi-objective second-best road pricing problem. A revenue distribution mechanism with return rate guarantee is proposed to implement the non-dominated Pareto-improving schemes. [ABSTRACT FROM AUTHOR]

Cogeneration of water and power in integrated cogeneration production plants is a common practice in the Middle East and North Africa (MENA) countries. There are several combinations of water desalination and power technologies which give significant adverse environmental impact. Renewable and alternative energy technologies have been recently proposed as alternative power production paths in the water and power sector. In this study, we examine the optimal capacity expansion of water and power infrastructure over an extended planning horizon. A generic mixed integer linear programming model is developed to assist in the decision making process on: (1) optimal installation of cogeneration expansion capacities; (2) optimal installation of renewable and alternative power plants; (3) optimal operation of the integrated water and power supply chain over large geographical areas. Furthermore, the model considers the installation of carbon capture methods in fossil-based power plants. A case study will be presented to illustrate the mathematical programming application for the Emirate of Abu Dhabi (AD) in the United Arab Emirates (UAE). The case study is solved reflecting different scenarios: base case scenario, integration of renewable and alternative technologies scenario, and CO 2 reduction targets scenario. The results show that increased carbon tax values up to 150 $/ton-CO 2 gives a maximum 3% cost increase for the supply chain net present value. The installation of carbon capture methods is not an economical solution due to its high operation energy requirements in the order of 370 kW h per ton of captured CO 2 . Thus, the Cplex solver in GAMS chooses optimal solutions without installation of carbon capture processes. In addition, higher degree of alternative and renewable energy technologies penetration within the energy mix reduces the overall net present value of the network, and the carbon emissions by 40%, and 12%, respectively. [ABSTRACT FROM AUTHOR]

This paper investigates how the European electricity and heating system is impacted when medium-scale energy communities (ECs) are developed widely across Europe. We study the response on the capacity expansion of the cross-border transmission and national generation and storage within the European electricity and heating system with and without ECs in selected European countries. The representation of ECs has a special focus on flexibility, and we analyze the difference between flexibility responses by ECs towards local versus global cost minimization. Results show that EC development decreases total electricity and heating system costs on the transition towards a decarbonized European system in line with the 1.5 °C target, and less generation and storage capacity expansion is needed on a national scale to achieve climate targets. We also identify a conflict of interest between optimizing EC flexibility towards local cost minimization versus European cost minimization. • We study investments in the European energy market with energy community roll-out. • We compare flexibility responses with local versus European perspective. • Energy communities partly replace centralized generation and storage capacity. • Energy community roll-out shifts investments from onshore to offshore wind. • There is conflicting interest between local and European flexibility utilization. [ABSTRACT FROM AUTHOR]

This paper studies the capacity and pricing choice of two congestible airports in a multi-airport metropolitan region, under transition from a pure public, centralized airport system to partial or full privatization. We develop analytical models to investigate three privatization scenarios: public–private duopoly, private–private duopoly, and private monopoly. We find that, airports follow the same capacity investment rule as prior to privatization when capacity and pricing decisions are made simultaneously. Pricing rule after privatization becomes more complicated, with additional factors having an upward effect on the privatized airport(s) and a downward effect on the remaining public airport. [ABSTRACT FROM AUTHOR]

We adopt in this paper a perspective of portfolios of real options, to propose a mixed integer linear programming model for multistage Transmission Network Expansion Planning. The model is then used to analyze three fundamental network building blocks – an independent design, a radial design, and a meshed design – seeking to develop network design insights, in particular regarding the joint value of postponement and other sources of operational flexibility. The results clearly point to the importance of explicitly incorporating uncertainty, adopting a multistage perspective, and addressing complex interactions between different sources of flexibility, in the design of transmission networks. [ABSTRACT FROM AUTHOR]

In this article, we introduce an optimization model aimed to assist aviation authorities in their strategic decisions regarding the long-term expansion of a network of airports. The objective is to maximize total system throughput for a given budget taking into account the capacity of the airports and the impact of travel costs upon demand. The results that can be obtained through the application of the model are first illustrated for a hypothetical small-size network and then in a study regarding the evolution of the network of the principal airports of the United States. [ABSTRACT FROM AUTHOR]

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 number of consecutive days for energy arbitrage changes the operation of storage. • The optimization model selects longer-duration storage when the modeled time horizon is increased. • Seasonal energy storage is deployed when R&D efforts reduce CapEx to $1.3/kWh. • Long duration energy storage will decrease overbuilding renewable energy by up to 10%. • We use a capacity expansion model with high resolution for Western North America. [ABSTRACT FROM AUTHOR]

China has experienced off-seasonal power shortages in recent years, where the growth in capacity supply exceeds the power demand. This paper investigates the mechanism of power shortages in China by estimating the capacity expansion and capacity utilisation models with the annual data of 30 provinces from 2006 to 2011. With the theoretical and empirical evidence, we find that: (1) the mechanism of the power shortages in China could be twofold: the capacity shortage and the capacity under-utilisation, where the two can have a combined effect on shaping the power supply pattern in China and (2) the ‘coal-price-inflation-internalising’ policy employed by the state to solve the ‘coal-electricity’ conflict leads to the short-run power shortage and also distorts the market demand signal of the capacity utilisation for the power firms to make long-run investment decisions. [ABSTRACT FROM AUTHOR]

This study presents the retrofit of water supply chain problem through multi period mixed integer linear program (MILP) model. The major strategic decisions include the determination of optimal new facility locations and capacity expansions of water desalination supply chain infrastructure assets which consists of water desalination plants, pipelines, and storage tanks, over a long time planning horizon. Other strategic decisions deal with the optimal selection of desalination technologies for existing and new desalination plants. In addition, the model provides decisions to define the pipeline network configuration for water transportation among several sites. The operation decisions are modelled to optimize the water production from desalination plants, energy consumption, brine disposal, as well as CO 2 emissions. In addition, water transportation through the pipeline network and water storage at every site are optimized to satisfy water demand at every time period, and to minimize the net present value of the supply chain network. Finally, the proposed approach is solved for a case study to illustrate the application of the proposed mathematical programming model. The results show economic and environmental benefits if one considers full site integration and water production coordination in the supply chain network through the planning time horizon. [ABSTRACT FROM AUTHOR]

Abstract: Fossil fuel importing as well as exporting countries of the MENA region have recognized their enormous potential of power generation by renewable energy (RE) technologies and the associated benefit for their national budgets, and therefore have formulated ambitious targets for RE deployment. However, only few countries have a detailed master plan that shows how RE technologies can be integrated efficiently into their existing power plant portfolio. Capacity expansion of RE and conventional technologies must be adjusted and optimized in order to minimize total generation costs of the entire system while maintaining security of supply. Within the last three years, DLR has developed the capacity expansion and unit commitment optimization model REMix-CEM (Renewable Energy Mix – Capacity Expansion Model) in order to support authorities of the MENA region in the process of integrating RE technologies efficiently in the short-term and transforming their strongly growing fossil-fuel dominated power systems of today towards higher RE shares. REMix-CEM optimizes the capacity expansion of conventional and RE technologies from a state-owned utility perspective starting from the existing power plant portfolio by modeling the hourly performance of each single existing and candidate unit. The paper presents an overview of the developed methodology as well as the characteristics and capabilities of REMix-CEM by presenting a case study for the electricity sector of the Hashemite Kingdom of Jordan for the years 2013 – 2020. [Copyright &y& Elsevier]

Abstract: In this study, simulation methods of a system dynamics model for activated-sludge wastewater treatment plants were developed. A wastewater treatment plant in South Korea was chosen for these analyses. For existing conditions, the analyses were performed by varying activated-sludge return rate to observe changes in effluent water quality and treatment efficiency. The inflow rate beyond which aeration tank size needs to be increased was calculated while satisfying the effluent water quality requirement. The corresponding sizes of aeration tank were also calculated for different activated-sludge return rates under the maximum inflow rate which does not require expansion of the aeration tank. [Copyright &y& Elsevier]

Highlights: [•] We quantify the benefits of controlled charging of plug-in hybrid electric vehicles. [•] Costs are determined using an economic dispatch and unit commitment model. [•] The model is based on New York ISO and allows for capacity expansion. [•] We find controlled charging can significantly lower system costs. [•] Controlled charging benefits are larger with high wind penetration. [Copyright &y& Elsevier]

The transition towards decarbonized energy systems requires the expansion of renewable and flexibility technologies in power sectors. Many powerful tools exist to find optimal capacity expansion. In a stylized comparison of six models, we evaluate the capacity expansion results of basic power sector technologies. The technologies under investigation include base- and peak load power plants, electricity storage, and transmission. We define four highly simplified and harmonized test cases that focus on the expansion of only one or two specific technologies to isolate their effects on model results. We find that deviating assumptions on limited availability factors of technologies cause technology-specific deviations between optimal capacity expansion in models in almost all test cases. Fixed energy-to-power ratios of storage can entirely change optimal expansion outcomes, especially shifting the ratio between short- and long-duration storage. Fixed initial and final-period storage levels can affect the seasonal use of long-duration storage. Models with a pre-ordered dispatch structure substantially deviate from linear optimization models, as missing foresight and limited flexibility can lead to higher capacity investments. A simplified net transfer capacity approach underestimates the need for grid infrastructure compared to a more detailed direct current load flow approach. We further find deviations in model results of optimal storage and transmission capacity expansion between regions, and link them to variable renewable energy generation and demand characteristics. We expect that the general effects identified in our stylized setting also hold in more detailed model applications, although they may be less visible there. • Systematic comparison of six power sector capacity expansion models. • The analysis is based on simplified scenarios and fully harmonized input data. • Four stylized test cases focusing on the expansion of power plants, storage, and transmission. • Modeling of plant availability, storage designs, and transmission drives deviations. • Pre-defined dispatch order causes large deviations from deterministic optimization. [ABSTRACT FROM AUTHOR]