Definition Of Scenarios Research Articles

Assessment of Strategies for Numerical Modeling of the APB in Oil Wells

This study aims to study and compare numerical modeling strategies for reproducing the phenomenon of Annular Pressure Build-up (APB) in vertical oil wells, in order to contribute to the development of procedures with higher accuracies. APB occurs due to the tendency of confined fluids filling the annular region between casings to expand in response to thermal variations within the well. This phenomenon is critical in the petroleum industry, especially in deepwater environments, where greater temperature and pressure differentials are present. APB leads to increased stresses on well casings, which can cause structural failures and, in extreme situations, could in human, environmental, and economic losses. Therefore, studying the origins and effects of this phenomenon and considering them during the well design phase are essential to ensure safety and efficiency. Motivated by the significance of the topic and the challenge of reproducing APB analytically, several authors have sought to model the phenomenon and its effects using finite element-based computational software like Abaqus. To achieve the proposed objective, the methodology adopted includes: a) literature review of existing strategies for APB modeling; b) definition of a simplified scenario for reproducing selected strategies; c) comparison of methodologies and results obtained from each; and d) discussion on discrepancies, gaps, and potential improvement opportunities. This study evaluates two modeling strategies in Abaqus, both utilizing fluid cavity interaction to model fluid behavior within a plane axisymmetric analysis. The difference lies in the approach to thermal expansion. While one calculates APB directly from thermal variation, the other does so by introducing an equivalent mass flow. Furthermore, the strategies will be compared not only in terms of results and accuracy, but also with regards to computational cost, aiming to identify the most efficient approach for modeling the phenomenon. Despite methodological differences, both approaches yield similar results, with the second providing the flexibility to model fluids with different behaviors. Thus, this study contributes to understanding and optimizing APB modeling, aiding in the development of more robust and efficient strategies for predicting the effects of this phenomenon.

Future prospectives in plantain agro-chain by foresight-by-scenarios and Delphi analysis

Purpose This paper aims to identify future scenarios and convergent technologies regarding the plantain chain in the region of Quindío, Colombia. It proposes the definition of key variables, convergent technologies, future objectives, future scenarios and hypotheses based on stakeholders’ and experts’ opinions collected through questionnaires, surveys and workshops. Design/methodology/approach The present analysis seeks to identify and anticipate the future routes for the improvement of scientific, technological, innovative and skills management of the plantain agroindustrial chain in the region of Quindío, Colombia using the foresight-by-scenarios and, Delphi methodologies and finally validating the results with artificial intelligence code and natural language processing. Findings After the analysis of 100 initial variables, the results suggested the identification of five key variables defined by the stakeholders and matrix-based multiplication applied to a classification (MICMAC) analysis such as “weather,” “financing and economy,” “grouping and associativity,” “crop” and “territorial planning and raw material.” Moreover, the definition of four future objectives along with the matrix of alliances and conflicts, tactics, objectives and recommendations (MACTOR) analysis suggested scenarios according to the probability. The most possible, probable and desirable scenario, was the cooccurrence of the five proposed hypotheses. Furthermore, the Delphi analysis allowed us to define nine subgroups from 116 subtopics including: “plantain varieties,” “plantain agroindustry,” “waste use” and “crop,” among others. Research limitations/implications Due to the specificity of the analyzed agro-chain, the study only encompasses the plantain and banana sectors. However, the implications are related to the generation of projects in the selected technologies. Practical implications This paper includes implications for the development of prospective studies combining two or more different methodologies such as foresight-by-scenarios and Delphi method at the same time and further comparing the results with artificial intelligence analysis. Social implications The generation of public policies in the sector and input for governmental analysis and tools for decision-making with a well-grounded, systematic and rational point of view. Originality/value This work describes for the first time, the implementation of the Delphi method regarding an important agroindustry sector such as plantain and banana prospective study. Furthermore, it explains the alignment of two methodologies; foresight-by-scenarios and the Delphi method related to the sectorial approaches, and convergent technologies and innovations, respectively. Moreover, a complementary bibliometric analysis with global terms related to the plantain or banana agroindustry was also included. In a novel way, we also applied an artificial intelligence code using Python software to contrast the results previously obtained in the foresight-by-scenarios method.

Evaluation of future wind climate over the Eastern Mediterranean Sea

The aim of this study is to evaluate wind climate characteristics in the Eastern Mediterranean Sea until the end of the 21st century using the wind fields simulated by the EURO-CORDEX Regional Climate Models (RCM), namely the Rossby Centre regional atmospheric model (version RCA4). Long-term averages, long-term trends, and long-term variability in wind characteristics under two Representative Concentration Pathway (RCP 4.5 and 8.5) scenarios were assessed for two future periods: the near future for the years 2021–2060 and the middle future for the years 2061–2100. Relative to the historical period, the most remarkable changes in the mean wind speeds were projected under the RCP 8.5 scenario in the middle future in the central and southern Aegean Sea (exceeding 5 % increase) and in the eastern and northern Levantine basin (up to 5 % decrease), compatible with the definition of the climate scenarios as the RCP8.5 scenario assumes a rising radiative forcing until 2100. The changes in extreme wind characteristics are more noteworthy in agreement with the indicators of climate change (i.e., an increase in the intensity and the frequency of extreme events). 100-year return period wind speed increases by about 29 % in the western Levantine basin under the RCP 8.5 scenario while the most drastic decrease in 100-year extreme wind speed was found in the central Levantine basin with a reduction of exceeding −29 % for the middle future period under the RCP 8.5 scenario. These decreasing and increasing behaviors in extreme wind speeds are expected in the study area as the Mediterranean is affected by many climate systems.

Myelofibrosis and anemia: A German claims data analysis to describe epidemiology and current treatment.

There is limited data on the incidence, prevalence, and treatments for myelofibrosis (MF) in Germany. This retrospective study examined claims data from 3.3 million insured individuals, spanning from 2010 to 2021. Four sensitivity scenarios were explored to identify cases of MF. Point prevalence and cumulative incidence of MF were determined as of December 31, 2021, and within 2021, respectively. A cross-sectional analysis used the main scenario definition of MF to identify cases and evaluate the period prevalence of patients receiving treatment for symptoms and/or splenomegaly, including first-line (1L) Janus kinase inhibitor (JAKi), second-line, or further (2L+) MF-related treatment therapies during 2021. The prevalence of anemia treatment was also reported. The estimated standardized point prevalence of MF on December 31, 2021, was 9.9-12.4 cases per 100 000 persons, and cumulative incidence in 2021 was 1.2-1.8 cases per 100 000 persons. Standardized period prevalence in 2021 for MF patients receiving 1L JAKi and/or 2L+ MF-related treatment was 4.0 cases per 100 000. Among these patients, 47.1%-53.7% required treatment for anemia, resulting in a period prevalence of 1.9-2.2 cases per 100 000 individuals. The data reveal gaps in MF treatments and the need to improve patient quality of life.

Conceptual approach for a holistic low‐flow risk analysis

AbstractLow‐flow events, characterized by a significant water deficiency in river systems, have profound impacts on various water users and river ecology. Recent low‐flow events in Europe have had severe economic and ecological consequences such as disruptions to hydropower production, irrigation bans, constraints on navigation and complete river drying. These events highlight the urgent need for effective low‐flow risk management and demand a holistic risk analysis as a basis. The existing approaches to low‐flow analysis often focus on hydrological aspects, utilizing indices such as the Standardized Runoff Index (SRI) or Low‐flow Index. However, these indices lack information regarding consequences and impacts. Other approaches consider parts of a risk approach but often focus on special aspects, such as the economy; in general, no holistic assessment is made. This study introduces a conceptual approach to a holistic low‐flow risk analysis. The approach provides a continuous long‐term simulation to capture the special long‐term behaviour of low‐flow events and therefore avoids the complex definition of scenarios. In this conceptual approach, the low‐flow risk is analysed using a combination of various analyses that cover all aspects from occurrence to consequences. Meteorological analysis is used to generate synthetic long‐term weather data time series, which are transformed into runoff time series in hydrological analysis. Based on these results, hydrodynamic analysis quantifies the water levels, water temperatures, and flow velocities along the river. The consequences are analysed in terms of socio‐economic and ecological consequences. The results represent a long‐term series of damage values. Finally, the damage values are summed in the risk analysis and divided by the number of years considered in the analysis. For testing and demonstration purposes, the presented conceptual risk approach is partly applied to a proof‐of‐concept at the Selke catchment, a small river catchment in Germany. Finally, the results are presented, evaluated, and discussed.

DIN 18009-2 – a New German Standard on Evacuation Simulation

After more than eight years of intensive work, the second part of the German DIN 18009 standard on "Simulation of evacuation and personal safety" was published in August 2022. The DIN 18009 series offers a guideline for fire protection engineering for Germany. The first part "Principles and application rules" was published in 2016, with further parts describing standards for smoke simulation and safety concepts to follow. This marks a milestone in the application of simulation models in the safety context for Germany. This article discusses the advantages and disadvantages of prescriptive guidelines and performance-based engineering methods in the context of building evacuation. Furthermore, aspects of the application of the DIN 18009-2 are highlighted, and what challenges are still to be tackled and how. This includes in particular the choice and definition of scenarios and performance criteria.

Smart City Scenario Editor for General What-If Analysis.

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project.

Data-driven mechanistic framework with stratified immunity and effective transmissibility for COVID-19 scenario projections

Scenario-based modeling frameworks have been widely used to support policy-making at state and federal levels in the United States during the COVID-19 response. While custom-built models can be used to support one-off studies, sustained updates to projections under changing pandemic conditions requires a robust, integrated, and adaptive framework. In this paper, we describe one such framework, UVA-adaptive, that was built to support the CDC-aligned Scenario Modeling Hub (SMH) across multiple rounds, as well as weekly/biweekly projections to Virginia Department of Health (VDH) and US Department of Defense during the COVID-19 response. Building upon an existing metapopulation framework, PatchSim, UVA-adaptive uses a calibration mechanism relying on adjustable effective transmissibility as a basis for scenario definition while also incorporating real-time datasets on case incidence, seroprevalence, variant characteristics, and vaccine uptake. Through the pandemic, our framework evolved by incorporating available data sources and was extended to capture complexities of multiple strains and heterogeneous immunity of the population. Here we present the version of the model that was used for the recent projections for SMH and VDH, describe the calibration and projection framework, and demonstrate that the calibrated transmissibility correlates with the evolution of the pathogen as well as associated societal dynamics.

Efficient AI-Physics hybrid model with productive capabilities to reduce the time of history matching and scenario assessment; a case study: Minagish oil field

This paper proposes a novel approach that combines physics-based numerical simulation with deep-learning neural networks to create an AI-Physics hybrid model for reservoir simulation. Our primary objective is to reduce the time of history match and achieve the best match between model and observation data using our hybrid model. We trained our model using historical data and created a reliable forecasting model that predicts field behavior and proposes new scenarios to improve production in the upcoming years. To test our model, we combined AI physics history training with blind test prediction calculations of the remaining oil map, and the AI physics model created a forecast scenario definition based on this map. Our proposed simulation method can reduce the time of history matching and scenario evaluation by 90-95%. We created three improved forecast scenarios based on predefined scenarios that can produce millions of standard barrels more oil over three years than the original development scenario. The results demonstrate the potential of our AI-Physics hybrid model in revolutionizing reservoir simulation in the oil and gas industry. Our approach can significantly reduce simulation time, improve forecasting accuracy, and optimize production strategies, leading to significant economic benefits.

Cellular automata-based simulators for the design of prescribed fire plans: the case study of Liguria, Italy

Abstract Background Socio-economic changes in recent decades have resulted in an accumulation of fuel within Mediterranean forests, creating conditions conducive to potential catastrophic wildfires intensified by climate change. Consequently, several wildfire management systems have integrated prescribed fires as a proactive strategy for land management and wildfire risk reduction. The preparation of prescribed fires involves meticulous planning, entailing the identification of specific objectives, verification of prescriptions, and the definition of various scenarios. During the planning phase, simulation models offer a valuable decision-support tool for the qualitative and quantitative assessment of different scenarios. In this study, we harnessed the capabilities of the well-established wildfire simulation tool , to identify areas where prescribed fires can be performed, optimizing the wildfire risk mitigation and the costs. We selected a case study in the Liguria region, Italy, where the model is utilized operationally by the regional wildfire risk management system in emergency situations. Results Initially, we employed the propagation model to simulate a historical wildfire event, showcasing its potential as an emergency response tool. We focused on the most significant fire incident that occurred in the Liguria region in 2022. Subsequently, we employed to identify optimal areas for prescribed fires with the dual objectives of maximizing the mitigation of wildfire risk and minimizing treatment costs. The delineation of potential areas for prescribed fires has been established in accordance with regional regulations and expert-based insights. The methodology put forth in this study is capable of discerning the most suitable areas for the implementation of prescribed burns from a preselected set. A Monte Carlo simulation framework was employed to evaluate the efficacy of prescribed burns in mitigating the spread of wildfires. This assessment accounted for a variety of conditions, including fuel loads, ignition points, and meteorological patterns. The model was utilized to simulate the progression of wildfire spread. Conclusions This study underscores the utility of in offering both quantitative and qualitative insights that can inform prescribed fire planning. Our methodology has been designed to involve active engagement with subject matter experts throughout the process, to develop scenarios grounded in their expert opinions. The ability to assess diverse scenarios and acquire quantitative information empowers decision-makers to make informed choices, thereby advancing safer and more efficient fire management practices.

Finding an optimal distribution strategy path in an unpredictable environment

Purpose: This article introduces an innovative method designed to optimize distribution strategies with respect to future uncertainty. It goes beyond the limitations of traditional scenario-based planning that often leads to suboptimal strategies due to the unpredictability of future developments and the challenge of accurately assigning probabilities to these scenarios. Consequently, the method allows selection of the most economically viable future strategy. Methodology: Our methodology diverges from conventional approaches by refraining from making rigid assumptions about the probabilities of future scenarios. Instead, it comprehensively explores the entire allowable probability space to identify an optimal strategy that works well in possible future developments. We employed this method in the case study of a real-world company based in Czechia, where we devised three viable distribution strategies and four model development scenarios. Results: The application of our method demonstrated its effectiveness in selecting the most advantageous strategy, as evidenced by the results of our case study. However, the applicability of the method is contingent upon the accurate definition of potential future scenarios and the evaluation of the performance of different strategies within these scenarios. Conclusion: Our findings suggest that this approach significantly enhances strategic planning under uncertainty. Future research will seek to refine this method further by integrating causal relationships to convey additional information across different model periods, thereby improving the robustness and applicability of the strategy selection process.

On the intersection between prospective LCA and patent analysis. A theoretical discussion

The prospective life cycle assessment (pLCA) methodology allows to estimate the future environmental impacts of emerging technologies and immature products not yet on the market. To make up for the lack of information on the product, the pLCA makes extensive use of secondary data, taken from various sources including patents which contain prospective information of industrial interest. To date, there have been attempts to show the potential of patent analysis in supporting pLCA, but never extensively and as proposed in this work with a truly systematic approach. In fact, this study aims to demonstrate how an accurate analysis of the information contained within patents can provide solutions to a number of open problems underlying the pLCA. More specifically, it was shown how patents can serve for: the support to experts in technological forecasting, the definition of scenario ranges, the support to the background and foreground systems modelling, the comparison of the future diffusion of a mature and an emerging technology, the support to the projections based on pathways to reach policy reductions to ensure time consistency. For each question, it was explained how to decline the patent analysis, i.e. which parts of the patents to analyse and how.

Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

The development of new assessment methods for the performance of automated vehicles is essential to enable the deployment of automated driving technologies, due to the complex operational domain of automated vehicles. One contributing method is scenario-based assessment in which test cases are derived from real-world road traffic scenarios obtained from driving data. Given the complexity of the reality that is being modeled in these scenarios, it is a challenge to define a structure for capturing these scenarios. An intensional definition that provides a set of characteristics that are deemed to be both necessary and sufficient to qualify as a scenario assures that the scenarios constructed are both complete and intercomparable. In this article, we develop a comprehensive and operable definition of the notion of scenario while considering existing definitions in the literature. This is achieved by proposing an object-oriented framework in which scenarios and their building blocks are defined as classes of objects having attributes, methods, and relationships with other objects. The object-oriented approach promotes clarity, modularity, reusability, and encapsulation of the objects. We provide definitions and justifications of each of the terms. Furthermore, the framework is used to translate the terms in a coding language that is publicly available.

The DYNAMO Initiative – Improving health system resilience through dynamic modelling of integrated health and care pathways in response to systemic shocks

Background: Unexpected systemic shocks such as pandemics or natural disasters challenge the absorptive capacity of health and care systems to maintain adequate service levels.[1] Strategies have focused on better coordination between services, with multi-disciplinary care pathways being one instrument to achieve coordinated responses.[2] But the co-design process for this type of pathways tends to be too long and involved for fast response required by crises.
 Targeted community and stakeholder engagement: Health and care service planners and providers, service payers, public health and civil protection agencies as well as regional and municipal governments are affected by this problem. They need methods and tools to support ad-hoc design and instantiation of cross-sectoral pathways to address different types of crises. The DYNAMO initiative has brought together an initial group of stakeholders from five countries to start a multi-staged co-design programme that will also include further stakeholders.
 Explanation of the initiative: DYNAMO will develop an innovative software tool enabling stakeholders to jointly adapt existing service delivery processes to systemic shocks and structural changes. This includes sharing and integrating cross-sectoral data to accurately forecast possible outcomes and impacts of alternative pathway configurations. By means of a public tendering process, the participating care providers are developing the software package together with commercial technology providers. Up to now, local pathway modelling groups have been set up at each site. These have identified several ""high pressure"" service scenarios (e.g. future pandemics widespread internet/power outages, recurrent heat waves). For each scenario, alternative pressure points were identified that could have a notable impact on the performance of current health and care systems (e.g. resource or staff shortages). In the next project phase, several technology providers will be selected to co-develop prototype solutions that meet the requirements of the "high-pressure" service scenarios.
 Achieved and expected impacts: The definition of the high pressure scenarios and associated pressure points are a first outcome and showed that priorities for disaster readiness vary considerably between regions. With the software tools and methodologies to be developed, DYNAMO expects to significantly improve the response time and quality of health care ecosystems to a wide variety of systemic threats.
 Learnings for an international audience: DYNAMO is in its early stages and there are as yet no formalized learnings. Experiences and observations shared between projects participants however highlighted two key issues: 1) Responses to universal threats still happen in a haphazard and uncoordinated manner and awareness of the precise issue of multi-disciplinary pathways to respond to such threats is still too low. 2) While DYNAMO focusses on the development of a software tool, there is agreement among participating regions that this needs to be embedded into a regional cooperation culture and structure (a kind of “readiness community”) in order to achieve its intended impacts.
 [1] Adger WN, Brown K, Fairbrass J. et al. Governance for sustainability: towards a ‘thick’ analysis of environmental decision making. Environ Plan A. 2003;35(6):1095–1110. doi: 10.1068/A35289.
 [2] Lewis, L. and N. Ehrenberg (2020). Realising the true value of integrated care: Beyond COVID-19. https://integratedcarefoundation.org/publications/realising-the-true-value-of-integrated-care-beyond-covid-19-2#report
 

Emissions offset incentives, carbon storage and profit optimization for Australian timber plantations

This article evaluates additional carbon storage that becomes profitable over a range of carbon credit prices through Australian Emissions Reduction Fund (ERF) methods introduced in 2017 for timber plantations. We found prices of $20 and $50 CO2-eq t−1 motivated switching 4% and 29% of southern Australian case study estate stands from short-rotation E. globulus to long-rotation P. radiata, resulting in 10% and 48% more estate carbon storage. We found no uptake of not for harvest environmental plantings to be profitable, even at carbon prices of $100 CO2-eq t−1. This appears to be because of the relatively faster initial growth and carbon gain for long-rotation timber as compared to environmental planting species and the detail of baseline and comparison scenario definitions used in credit calculations. We also evaluated carbon storage versus profit trade-offs over the full set of technically possible and efficient estate management options to achieve abatement, greater than levels that maximize profit. Key conclusions from this analysis were that significant carbon storage increases even beyond what is profitable are possible for the case study estate at relatively little reduced profit opportunity cost for carbon prices of $20 CO2-eq t−1 and less. In contrast, managing all stands in the estate for the sole objective of improving carbon storage could nearly double carbon storage but result in a significant 27% profit reduction under a $20 CO2-eq t−1 carbon price scenario.

A holistic solution to virtual coupling based urban rail train control system

Virtual coupling (VC) is an emerging concept that is gaining popularity in railway control systems. It has the potential to significantly enhance railway capacity and flexibility, and has attracted considerable research attention. This study explores several key aspects of VC research in the context of urban rail transit systems, including system structure, train platoon control method, scenario definition, and train scheduling. The paper presents three main contributions: Firstly, it proposes a potential system structure for VC and discusses infrastructure occupancy procedures within typical operational scenarios. Secondly, a VC platoon controller based on the adaptive back-stepping method is designed to realize the function of the operation layer. Thirdly, combining operational scenario definition and control methods, we propose a VC train scheduling method based on mixed integer linear programming (MILP). A case study demonstrated that the proposed solution could effectively achieve the primary functions of VC.

Synthetic data generation for the continuous development and testing of autonomous construction machinery

Abstract The development and testing of autonomous systems require sufficient meaningful data. However, generating suitable scenario data is a challenging task. In particular, it raises the question of how to narrow down what kind of data should be considered meaningful. Autonomous systems are characterized by their ability to cope with uncertain situations, i.e. complex and unknown environmental conditions. Due to this openness, the definition of training and test scenarios cannot be easily specified. Not all relevant influences can be sufficiently specified with requirements in advance, especially for unknown scenarios and corner cases, and therefore the “right” data, balancing quality and efficiency, is hard to generate. This article discusses the challenges of automated generation of 3D scenario data. We present a training and testing loop that provides a way to generate synthetic camera and Lidar data using 3D simulated environments. Those can be automatically varied and modified to support a closed-loop system for deriving and generating datasets that can be used for continuous development and testing of autonomous systems.

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium.

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

Stochastic-robust planning optimization method based on tracking-economy extreme scenario tradeoff for CCHP multi-energy system

The load tracking performance of combined cooling, heating, and power multi-energy system (CCHP-MES) greatly depends on the equipment capacity configuration. And the frequent fluctuations in the source-load uncertainty puts higher demands on the load tracking ability of CCHP-MES. For this reason, this paper proposes a novel tracking-economy extreme scenario tradeoff model by comparing the differences between tracking ability and economic benefit. The corresponding extreme scenarios based on these two objectives are characterized and the concept of uncertainty adjustment parameter is introduced to weigh the preference of extreme scenarios. On this basis, a multi-objective two-stage stochastic-robust optimization approach is developed, where the first stage optimizes the investment cost and the second stage optimizes the operating cost. Case study using typical load demand and renewable energy data in Nanjing verify the efficacy of the optimization model. The results indicate that the proposed tracking-economy extreme scenario can better characterize the frequent fluctuations of uncertainties compared to the traditional definition of extreme scenario. The equipment capacity configuration obtained under tracking-economy extreme scenario tradeoff model can better cope with the uncertainty fluctuations under extreme weather. Depending on different optimization preference, the economic robustness and tracking robustness can be improved by 13.80% and 36.06%, respectively.

Limpopo National Park (Mozambico): groundwater assessment as a tool for a sustainable management of the area

This paper deals with updated results coming from hydrogeological studies carried on the framework of the SECOSUD Phase II, called “Conservation and equitable use of biological diversity in the SADC region (Southern African Development Community), a project supported by the Italian Ministry of Foreign Affairs in the SADC, whose focus area includes South Africa Development Countries. The main goal of the SECOSUD Phase II Project is the definition and implementation of scenarios for sustainable development, aimed at an equitable conservation of biodiversity resources and, as a consequence of this target, the hydrogeological characterization, with the groundwater recharge assessment, of this area and its buffer zone. Limpopo National Park is one of the jewels in the crown of Mozambique’s protected areas. As a matter of fact, sustaining the conservation of biodiversity, due to its complexity and multiple drivers, which stress it, is on first a matter of water environment assessment, as most ecosystems are highly dependent on the hydrological cycle and groundwater availability. After gathering regional and local geological data, which let us set up a detailed geological map of the area under study, pointing out the main outcropping geological units, with their main hydrogeological properties, the methodological approach adopted has been to assess the potential infiltration, applying the Inverse Hydrogeological Budget Technique, performed for the focus area. Because of the lack of meteorological data referred to Limpopo National Park, it has been applied a spatial distribution of precipitation measurements, collected in many gauge stations, located in the Kruger National Park during the last 54 years, which represent an interesting rainfall historical series. The target of the study has been to assess a trend of meteorological data with the aim of understanding how precipitations could affect groundwater recharge, and their influence on groundwater availability. The estimation of groundwater recharge is the tool for suggesting better water management in the area, aimed to preserve as much biodiversity as people living in the buffer zone.