Long-term Investment Planning Research Articles

A Surrogate Model Approach to Predict Operations and Maintenance Cost for Combined Cycle Power Plant

Abstract In the transition toward carbon neutrality by 2050, renewable energy sources are gaining prominence. The variability of renewable energy poses challenges for power grid stability. Gas turbine combined cycle (GTCC) power plants play a crucial role in grid stabilization, especially during peak loads. In GTCC power generation, operations and maintenance (O&M) cost is the next most important after fuel and initial investment cost, and it is necessary to predict O&M cost according to changes in the operating environment for efficient and stable operation of GTCC power plant. However, various uncertainties exist when estimating O&M costs because actual design information is limitedly disclosed due to maintenance contracts between operators and manufacturers, and it is difficult to obtain accurate variables according to the operation condition. In this paper, the correlation was analyzed considering the uncertainty of the factors affecting the O&M cost of the GTCC power plant through sensitivity analysis, and the main factors were defined. In addition, a surrogate model was proposed that can quickly calculate fixed and variable O&M cost according to changing operating scenarios such as shortening startup time, enhancing ramp rate, or lower minimum load. Through the case study, short-term and long-term O&M costs can be estimated in the operating environment of the GTCC power plant currently being operated. Additionally, the model facilitates cost projections for new hybrid GTCC facilities (combining gas turbines with energy storage systems), aiding long-term investment planning.

Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality

This work introduces a framework to address the computational complexity inherent in Mixed Integer Programming (MIP) models by harnessing the potential of deep learning. By employing deep learning, we construct problem-specific heuristics that identify and exploit common structures across MIP instances. We train deep learning models to estimate complicating binary variables for target MIP problem instances. The resulting reduced MIP models are solved using standard off-the-shelf solvers. We present an algorithm for generating synthetic data enhancing the robustness and generalizability of our models across diverse MIP instances. We compare the effectiveness of (a) feed-forward neural networks (ANN) and (b) convolutional neural networks (CNN). To enhance the framework's performance, we employ Bayesian optimization for hyperparameter tuning, aiming to maximize the occurrence of global optimum solutions. We apply this framework to a flow-based facility location allocation MIP formulation that describes long-term investment planning and medium-term tactical scheduling in a personalized medicine supply chain.

A dataset for multi-faceted analysis of electric vehicle charging transactions

This study discloses a dataset of electric vehicles’ (EVs’) charging transactions at a scale for multi-faceted analysis from both EV charger and user perspectives. The data comprises whole sessions that occurred during a charging operation company’s annual commercial operation period, specifically including identifiers and charger location categories. For data acquisition, machine-to-machine wireless communication system with proper retransmission for interruption is utilised. The entire dataset is newly collected and is available with 72,856 sessions from 2,337 EV users and 2,119 chargers. The dataset can be used in a variety of ways for the functioning of power systems and markets, including EV charging service businesses, charger installation siting, demand transaction market design, and long-term investment planning of EV-related infrastructure.

Recent Developments in Research on the Impact of ESG on Corporate Value

ESG (Environmental, Social, and Governance) has become an increasingly prominent topic in the investment field. Questions such as whether ESG can create value for companies, the credibility of ESG investments, and whether ESG should be incorporated into my long-term investment plan are crucial considerations. Many scholars have conducted research from various perspectives to address these questions. This article summarizes relevant literature and answers the impact of ESG on corporate value and the factors influencing ESG's impact on corporate value. The research findings are as follows: 1.ESG has a generally positive impact on corporate value, with some factors showing inhibitory effects or no significant correlation.2.ESG primarily affects corporate value through aspects such as market valuation, financing costs, innovation capability, and reputation. 3. The impact of ESG on corporate value varies depending on the ownership structure and industry of the company. Based on these findings, companies should decide whether to enhance their ESG performance based on various factors such as their operational situation, industry composition, and ownership structure.

Sustainability and Resilience of Engineering Assets

The frequency and severity of natural or human-induced disaster events, such as floods, earthquakes, hurricanes, fires, pandemics, hazardous material spills, groundwater contamination, structural failures, explosions, etc., as well as their impacts, have greatly increased in recent decades due to population growth and extensive urbanization, among other factors. The World Bank estimates that the total cost of cities’ and communities’ vulnerability to these types of disasters could reach more than USD 300 billion per year by 2030. However, it has been argued that investment to improve the quality and resilience of engineered physical assets that are the backbone of modern societies, such as critical infrastructure, industrial facilities, and buildings, could significantly contribute to more sustainable and prosperous societies. Engineered assets are key to the delivery of essential services, such as transport, food, water, electricity supply, health and safety, etc. Some of these physical assets are integrated into asset systems and national or regional networks, with life cycles of several decades or even centuries. It is, therefore, of great importance that strategies and life cycle decisions, such as those related to short- and long-term capital investment planning, maintenance strategies, operational plans, and asset disposal, lead to the maximization of the value derived from these assets. Moreover, it is essential that the achievement of these goals is sustainable over time. Organizations dealing with engineering assets, both public and private, must, therefore, integrate sustainability and resilience concerns into everyday operations, using budgets that are often restricted, while also meeting demanding performance requirements in risky and uncertain environments. This Special Issue collates a selection of papers reporting the latest research and case studies regarding the trends and emerging strategies used to address these challenges, with contributions discussing how asset management principles and techniques can help to push the boundaries of sophistication and innovation to improve the life cycle management of engineered assets to ensure more sustainable and resilient cities and societies.

Selling the jewels: patient capital, state-business relations, and the privatization of strategic utilities in Italy and Spain

The privatization of public utilities marked a turning point in European capitalism, reshaping the relationship between the public and private spheres of the economy. However, the extent of state disinvestment varies greatly from country to country. While in some countries direct state ownership has disappeared, in others the state still acts as a reference shareholder in strategic companies. Despite their institutional similarities, Italy and Spain provide a puzzling example of this divergence. While Spain completed the privatization of all public utilities, the Italian state retains a controlling stake in many of them. Through historical case studies based on official documents, legal texts, archival research of newspaper articles, secondary sources and memoirs, this paper explains this divergence. Contributing to recent debates on patient capital and state-business interactions, it is argued that Spain completed the privatization process because the state was able to orchestrate the creation of shareholder alliances among private investors. Crucially, these investors were willing to ensure that management prioritizes long-term investment plans over the distribution of short-term financial profits. In the absence of domestic private providers of patient capital, the Italian state had to keep the role of anchor investor for itself after unsuccessfully experimenting with various privatization strategies.

Assessment of Construction Competitiveness through Knowledge Management Process Implementation

In the turbulent construction market, the knowledge management process (KMP) is one of the most valuable tools of sustainability to help construction companies deal with dynamic changes and enhance their construction competitiveness (CC). To effectively utilize KMP in construction, this study aims to explore the interrelationships between key KMP factors and their influences on the CC, utilizing the structural equation modeling (SEM) approach. The objectives include extracting key KMP factors necessary for the CC enhancement, identifying direct and indirect relationships between the KMP and CC factors, and developing a self-assessment form to assist construction companies in evaluating their KMP performance and planning for long-term improvement. The results show that the five key KMP factors, namely knowledge utilization (KU), knowledge dissemination (KD), knowledge responsiveness (KR), knowledge storage (KS), and knowledge acquisition (KA), have direct and indirect effects on CC and that the feedback of CC is sent back to KMP factors for continuous improvement. The KU factor is crucial for short-term improvement. Construction companies should utilize stakeholders’ current practices and experiences to solve problems, conclude lessons learned, and pinpoint practices for future uses. The KS factor, on the other hand, should be emphasized for long-term plans to enhance KMP implementation and CC achievements. A long-term investment plan should be initiated in the database system to properly and effectively implement digital transformation in the 4.0 Era. The self-assessment form developed from the study results assists construction companies in assessing their KMP implementation and planning for sustainable development.

Optimal decarbonization strategies for existing districts considering energy systems and retrofits

Integrated energy system planning at the district level can contribute towards the sustainable transformation of the building sector by unlocking solutions beyond individual buildings. This is particularly true for existing districts, whose older buildings have a low energy performance and for which measures to reduce the energy demand and ensure a low-emission energy supply must be implemented. In the urban context, district heating networks (DHN) are a promising way of doing the latter, especially with carbon capture and storage (CCS) on the horizon. However, investment decisions for both types of measures – energy supply and demand reduction – must consider individual buildings as part of district-scale considerations as building-level demand-side interventions affect energy demand patterns and densities. These can in turn affect energy supply decisions at the district-level.This study presents a comprehensive methodology for determining optimal decarbonization strategies for existing districts while considering building-level energy supply and retrofitting investment decisions and the expansion of existing DHN. We do so by extending the MANGOret (Multi-stAge eNerGy Optimization - retrofitting) optimization framework for the long-term investment planning of building-level multi-energy systems and envelope retrofits. In addition, the study presents an approach for modeling CCS as an emission-reduction technology for the waste incinerator supplying the marginal expansion of the district heating network.The developed optimization model considers a long-term time horizon with multiple investment stages, allowing it to represent economic and technological developments over the model time horizon. It incorporates a multi-objective perspective, capturing the trade-offs between the total system costs and emissions. The model is applied to two existing case study neighborhoods in the city of Chur, Switzerland. The long-term energy system design and operation for the two neighborhood typologies – mixed-use and residential – are analyzed as part of the DHN expansion investment decision.The results show that retrofitting is the main cost driver of any decarbonization strategy. Therefore the choice and the size of the energy systems offer better leverage for reducing emissions with a moderate increase in costs. In dense inner-city neighborhoods with high heating demands, district heating is the cost-optimal heating choice. Together with a low-emission district heating source such as a waste incinerator with CCS or biomass, it also becomes the CO2-optimal choice. Furthermore, the case studies demonstrate that a combination of heat pumps, hot water thermal storage, and solar PV is not only the CO2-optimal but also the cost-optimal decentralized heating option if no DHN is available. This highlights the attractiveness of energy-efficient heating systems already today.

Resilience improvement model of distribution network based on two-stage robust optimization

Frequent natural disasters have a serious impact on the power system. It is urgent to ensure the normal operation of the distribution network under disaster events. This paper divides the stages of improving the resilience of the distribution network into two aspects: long-term investment planning and short-term response. We combine the decision-making process with the chronological order of natural disasters, and a N-k uncertainty set is applied to describe the uncertainty of distribution network damage. We propose a line hardening and distributed generations configuration strategy for the purpose of enhancing the resilience of distribution networks and countering extreme natural disasters. The problem is expressed as a tri-level robust optimization formulation in order to minimize the load shedding under the worst line damaged scheme. The first level corresponds to the long-term investment planning and preparation of pre-disaster stage. The second level represents the stage of line damage, and the third level matches the optimal scheduling during the post-disaster period. On the basis, this mixed integer linear programming problem is transformed into a solvable form, which is calculated by a modified Column and Constraint Generation (C&CG) algorithm. Case studies show that our proposed model can reduce load shedding to 70% of the original level, and the running time of the algorithm is less than 60 s, which verifies the model can effectively enhance the resilience of the distribution network and enhance its ability to respond to natural disasters.

Selection of Manufacturing Technologies in the Context of Digital Transformation: A Systematic Review

Currently, the digital transformation phenomenon is evident in all decision areas within modern firms and directly impacts the technology management process. For manufacturers, the management and selection of appropriate manufacturing technologies are relevant processes for the achievement of strategic objectives, including a digital strategy. This work is characterized as a review paper, based on a systematic literature review approach. It is derived from the observation of a real problem in a recognized world-class automaker, regarding the selection of manufacturing technologies. The identified problem consists of the lack of a structured and consolidated decision-aiding procedure to support managers in selecting manufacturing technologies in the context of digital transformation, from defining performance objectives to recommending technologies according to the decision objective. The main theoretical implications is the proposition of a research agenda for the development of a decision-aiding procedure concerning two main decision objectives: (i) develop a technology roadmap for a long-term investment plan, and (ii) address specific issues to improve manufacturing performance. The main managerial implication is the synthesis of decision methods and decision criteria observed in the set of related works that can be used for future applications regarding the selection of manufacturing technologies.

An integrated framework for optimal infrastructure planning for decarbonising heating

This paper presents the HEGIT (Heat, Electricity and Gas Infrastructure and Technology) model for optimal infrastructure planning for decarbonising heating in buildings. HEGIT is an optimisation model based on Mixed Integer Linear Programming. The model co-optimises the integrated operation and capacity expansion planning of electricity and gas grids as well as heating technologies on the consumer side while maintaining the security of supply and subject to different environmental, operational and system-wide constraints. The three main features of the HEGIT model are:•It incorporates an integrated unit commitment and capacity expansion problem for coordinated operation and long-term investment planning of the electricity and gas grids.•It incorporates the flexible operation of heating technologies in buildings and demand response in operation and long-term investment planning of gas and electricity grids.•It incorporates a multi-scale techno-economic representation of heating technologies design features into the whole energy system modelling and capacity planning.These features enable the model to quantify the impacts of different policies regarding decarbonising heating in buildings on the operation and long-term planning of electricity and gas grids, identify the cost-optimal use of available resources and technologies and identify strategies for maximising synergies between system planning goals and minimising trade-offs. Moreover, the multi-scale feature of the model allows for multi-scale system engineering analysis of decarbonising heating, including system-informed heating technology design, identifying optimal operational setups at the consumer end, and assessing trade-offs between consumer investment in heating technologies and infrastructure requirements in different heat decarbonisation pathways.

Green bargains: leveraging public investment to advance climate regulation

ABSTRACT Climate policy has entered a new era as public investment is increasingly moving to center stage, including recovery spending and long-term climate investment plans. While essential for decarbonization, public investment is not enough – the carrots of investment need to go hand in hand with regulatory sticks. Public investment in clean technologies and infrastructure does not guarantee decarbonization outcomes. Yet, climate regulation, particularly high and comprehensive carbon prices, has remained elusive in the United States and many other parts of the world. Against this backdrop, the big public investment push offers a political opportunity of government leverage. Governments can tie climate requirements to public investment as part of a 'green bargain.’ At the core of these arrangements is reciprocity – linking carrots to sticks – to ensure public investment leads to technological change and emission reductions. Drawing on the experience with ‘reciprocal control mechanisms’ in public investment among late industrializers, this paper advances our understanding of green bargains. It examines the historical precedents of green bargains, introduces a typology, and offers an empirical overview based on an original dataset of implemented and proposed green bargains. We then focus on the design choices related to leverage, scope, and accountability to make green bargains effective at technological learning and advancing decarbonization. The article concludes with a discussion of US and international policy fora that can serve as platforms to develop green bargains as public climate investments expand. Key policy insights Scaling public investment for decarbonization is essential but not sufficient. Climate regulation needs to complement investment to ensure decarbonization outcomes. Yet governments, in particular the US, are falling short of needed climate regulation. Public investment offers leverage to tie climate requirements to government spending on clean technology and infrastructure. Linking carrots to sticks represents a ‘green bargain’ between governments and firms and industries, with reciprocity at its core. This resembles the ‘reciprocal control mechanisms’ of late industrializers. To make green bargains effective, their design needs to facilitate broad scope, high government leverage, and high accountability.

A long-term capacity investment and operational energy planning model with power-to-X and flexibility technologies

In this research, we present a new long-term energy planning model that considers endogenous capacity investment, energy dispatch, Power-to-X, and demand response technologies. A thorough literature review of existing energy planning models is also presented, allowing to present the distinctive characteristics of the proposed model. The proposed model considers an energy system with the objective of minimizing the total capacity investment cost, throughout all technologies, and the operational cost faced by the system in satisfying energy demand. The model also considers the links among different demand sectors, including the links between the electricity, industry, heat, transport, and electro-fuels (e.g., Hydrogen) sectors. The proposed model is used to study the decarbonization of the Croatian energy system under distinct policies associated to RES levels and CO2 emissions goals. We demonstrate that Power-to-X technologies can certainly provide the flexibility that is required by new capacity investments in variable renewable energy sources, obtaining systems with lesser levels of critical excess of energy production. Higher usage of battery storage and Power-to-heat technologies are adopted primarily for variable renewable shares and CO2 reductions of close to 80%, while below such levels, the adoption of such technologies is limited. Additionally, Power-to-heat flexibility options become the major technologies when limits on CO2 emissions from the heating sector are imposed and, particularly, when the variable renewable energy shares in the electricity sector gets close to levels of 60%.

Equipment Reliability Process in Krško NPP

To ensure long-term safe and reliable plant operation, equipment operability and availability must also be ensured by setting a group of processes to be established within the nuclear power plant. Equipment reliability process represents the integration and coordination of important equipment reliability activities into one process, which enables equipment performance and condition monitoring, preventive maintenance activities development, implementation and optimization, continuous improvement of the processes and long term planning. The initiative for introducing systematic approach for equipment reliability assuring came from US nuclear industry guided by INPO (Institute of Nuclear Power Operations) and by participation of several US nuclear utilities. As a result of the initiative, first edition of INPO document AP-913, ‘Equipment Reliability Process Description’ was issued and it became a basic document for implementation of equipment reliability process for the whole nuclear industry. The scope of equipment reliability process in Krško NPP consists of following programs: equipment criticality classification, preventive maintenance program, corrective action program, system health reports and long-term investment plan. By implementation, supervision and continuous improvement of those programs, guided by more than thirty years of operating experience, Krško NPP will continue to be on a track of safe and reliable operation until the end of prolonged life time.

A novel approach for incorporating incentive-based and price-based demand response programs in long-term generation investment planning

Today, power system planning requires simultaneous attention to supply and demand side management. It has gained remarkable attention due to the advent of new technological paradigms such as smart grid concepts and renewable energy resources. In this study, the effects of Demand Response (DR) as an emerging strategy of demand side management have been investigated on generation investment planning for achieving a technical, flexible, economical, and reliable solution for the supply side in the long-term. To this end, taking the advantage of system dynamics concepts and tools, the modeling of both principal DR types, i.e., price-based and incentive-based, is explored. In the first type, we formulate a novel market-based mutual relevance between market clearing price and willingness to pay of the price-based loads for accurately obtaining the electricity price as the primary signal for the investment process. The uncertain behavior of loads in these programs is expressed with a new concept called Certainty of Responsiveness (COR). A novel index, namely Loss of Load Duration Margin (LM), is proposed to incentivize the participants in incentive-based DR. Dispatching of these loads has been done using options contract. Numerical simulations are performed on a typical test supply system. Wind and solar photovoltaic units as the leading renewables in power systems are considered alongside the conventional thermal technologies. Different regulatory decisions about the adjustable parameters of DR programs are also analyzed. The results validate the effects of DR programs on total installed generation capacity, market prices, reliability indices, and outage costs in the long-term.

Long-term dynamic allocation and maintenance planning of modular equipment to enhance gas field production flexibility

Modular equipment is becoming popular in unconventional gas field development due to cost savings and flexibility in capacity adjustment by dynamic allocation. It is necessary to make long-term investment and maintenance planning to give full play to the advantages of modular equipment. This paper fills the gap of considering individual scheduling and the impact of utilization time on the value of the equipment in the unconventional gas field development by presenting a comprehensive method that considers the various costs of modular equipment and can facilitate tracking the usage of the equipment. A mixed integer linear programming model is developed to get the solution for the modular equipment, including the processing capacity selection, the installation and use plans, mobile scheduling, and depreciation sale plan, to enhance gas field production flexibility and achieve the best net present value. The changes of production and market demand, and the impact of utilization time on equipment depreciation are taken into account comprehensively. A real case study is used to prove the practicality and advantages of the proposed model. The results show the equipment utilization rate has been increased from 60% to 75%, achieving 10% higher economic benefits when using modular equipment compared with the traditional method in gas field production. In addition, the sensitivity analysis is implemented to investigate the influence of uncertain parameters such as output and market demand, equipment service life, and type of equipment on the equipment dynamic allocation and the economic performance. This study provides a powerful decision-making tool for gas field development to reduce costs, increase efficiency and promote clean practical production.

MANGOret: An optimization framework for the long-term investment planning of building multi-energy system and envelope retrofits

This study presents MANGOret (Multi-stAge eNerGy Optimization — retrofitting), a novel optimization framework and model for the long-term investment planning of existing building retrofits. MANGOret bridges the methodological gaps between energy system modeling and real estate management to present a scalable framework to optimize both energy and non-energy costs while considering building value. With a 2050 horizon, MANGOret is able to harness the strategic value of investment flexibility to optimally phase investments across the multi-objective cost and CO2 emission decision space considering both operational and embodied emissions.From the energy perspective, the model generates long-term investment strategies for decentralized multi-energy systems and envelope retrofits. The model considers the interdependent trade-offs between demand- and supply-side measures for a number of technologies across time. Technology scheduling is informed by condition degradation functions from utilizing the Schroeder method. From the real estate management perspective, the framework digitalizes the multi-year investment planning process. The model is supported by series of automated data retrieval and processing steps to consider each contextual building project. Importantly, we develop an archetypal energy demand database to reference demands of various retrofitting packages. By considering all retrofitting-relevant investments, the model incorporates the critical budgeting elements of rental revenues to calculate building value.We demonstrate the value of the MANGOret framework across various building types and sizes in different Swiss real estate markets. Our results demonstrate relevance for energy engineers and building owners relating to the long-term design, operation, and investment scheduling of existing buildings. We present multiple optimal strategies considering the trade-offs between cost, value, and CO2 emissions.Aligning with previous studies, our results show that higher investment costs are necessary to achieve low-CO2 retrofits relative to minimum cost strategies. Higher costs are, to a large extent, influenced by envelope retrofits and non-energy internal renovations while energy supply systems contribute to a smaller share of the budget. To achieve low-CO2, retrofits utilize lower embodied emission technology choices and are scheduled early on. Nevertheless, we show that these trade-offs do not necessarily have to be weighed at the extremes of the Pareto front, instead presenting ‘minimal regret’ solutions which reduce CO2 at negligible cost increases. Considering both embodied and operational CO2 emissions over the building life-cycle, our results demonstrate that optimal emission reductions necessitate subsequent reductions in energy consumption. Low-CO2 retrofitting strategies are typified by reducing energy demands as much as possible in order to self-consume as much renewable energy as possible, typically by solar PV and heat pump coupled systems with grid reliance.

Modelling a highly decarbonised North Sea energy system in 2050: A multinational approach

The North Sea region, located in the Northwest of Europe, is expected to be a frontrunner in the European energy transition. This paper aims to analyse different optimal system configurations in order to meet net-zero emission targets in 2050. Overall, the paper presents two main contributions: first, we develop and introduce the IESA-NS model. The IESA-NS model is an optimization integrated energy system model written as a linear problem. The IESA-NS model optimizes the long-term investment planning and short-term operation of seven North Sea region countries (Belgium, Denmark, Germany, the Netherlands, Norway, Sweden and the United Kingdom). The model can optimize multiple years simultaneously, accounts for all the national GHG emissions and includes a thorough representation of all the sectors of the energy system. Second, we run several decarbonisation scenarios with net-zero emission targets in 2050. Relevant parameters varied to produce the scenarios include biomass availability, VRE potentials, low social acceptance of onshore VRE, ban of CCUS or mitigation targets in international transport and industry feedstock. Results show a large use of hydrogen when international transport emissions are considered in the targets (5.6 EJ to 7.3 EJ). Electrolysis is the preferred pathway for hydrogen production (up to 6.4 EJ), far ahead of natural gas reforming (up to 2.2 EJ). Allowing offshore interconnectors (e.g. meshed offshore grid between the Netherlands, Germany and the United Kingdom) permits to integrate larger amounts of offshore wind (122 GW to 191 GW of additional capacity compared to reference scenarios), while substantially increasing the cross-border interconnection capacities (up to 120 GW). All the biomass available is used in the scenarios across multiple end uses, including biofuel production (up to 3.5 EJ), high temperature heat (up to 2.5 EJ), feedstock for industry (up to 2 EJ), residential heat (up to 600 PJ) and power generation (up to 900 PJ). In general, most of the results justify the development of multinational energy system models, in which the spatial coverage lays between national and continental models.

Monitoring efikasnosti hidroelektrana kroz parametre SKADA sistema

Modern approach to the modernization of the power generation capacities in the hydropower sector of PE Electric Power Industry of Serbia (EPS) is seen through intensive rehabilitations and building new hydropower plants (HPPs). Although these priorities are defined by long-term investment plan, there are also improvements that are seen through the optimization of the operational regimes of HPPs. EPS technical expert team, after several months of adjustment and investigation has prepared monthly reports with the operational parameters of the HPP. In this paper, as an example, investigated parameters and results for one particular aggregate of the HPP Iron Gate 1 (ser. Đerdap 1/ rom. Portile de Fier I) are presented. Generally, in the current phase of investigation, monthly report is prepared which includes selected parameters that are archived (such as headrace and tailrace water level, the aggregates’ power and discharge) and based on data from the hill charts, losses in pipeline, generator etc. output parameters are defined. Output parameters which are considered as important for the presentation are efficiency of the turbine, generator, hence aggregate, and losses in the operational regimes with the elevation lower than the maximum headrace level. Basic parameters of the aggregates, and generally HPPs’ operational regimes, are retrieved through SCADA system, averaged on minute time intervals, and presented for the one month period.

Container fleet renewal considering multiple sulfur reduction technologies and uncertain markets amidst COVID-19

The onset of 2020 is marked by stricter restrictions on maritime sulfur emissions and the spread of Coronavirus Disease 2019 (COVID-19). In this background, liner companies now face the challenge to find suitable sulfur reduction technologies, make reasonable decisions on fleet renewal, and prepare stable operation plans under the highly uncertain shipping market. Considering three sulfur reduction technologies, namely, fuel-switching, scrubber, and liquefied natural gas (LNG) dual-fuel engine, this paper develops a robust optimization model based on two-stage stochastic linear programming (SLP) to formulate a decision plan for container fleet, which can deal with various uncertainties in future: freight demand, ship charter rate, fuel price, retrofit time and Sulfur Emission Control Area (SECA) ratio. The main decision contents include ship acquisition, ship retrofit, ship sale, ship charter, route assignment, and speed optimization. The effectiveness of our plan was verified through a case study on two liner routes from the Far East to Northwest America, operated by COSCO Shipping Lines. The results from SLP model show that large-capacity fuel-switching ships and their LNG dual-fuel engine retrofits should be included in the long-term investment and operation plan; slow-steaming is an important operational decision for ocean liner shipping; if the current SECA boundary is not further expanded or the sulfur emission restrictions not further tightened, the scrubber ship will have no advantage in investment cost and operation. However, considering the probabilities of more flexible scenarios, the results from the robust model suggest that it is beneficial to install scrubber on medium-capacity fuel-switching ships, and carry out more LNG dual-fuel engine retrofits for large-capacity fuel-switching ships. Compared with SLP, this robust strategy greatly reduces sulfur emissions while slightly pushing up carbon emissions.