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exploratory modeling and analysis, an approach for model-based foresight under deep uncertainty

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2013年第3期80卷 419-431页

作者： Kwakkel, Jan H. Pruyt, Erik Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands

exploratory modeling and analysis (EMA) is an approach that uses computational experiments to analyze complex and uncertain issues. It has been developed mainly for model-based decision support. This paper investigates the extent to which EMA is a promising approach for future oriented technology analysis (FTA). We report on three applications of EMA, using different modeling approaches, in three different technical domains. In the first case, EMA is combined with System Dynamics (SD) to study plausible dynamics for mineral and metal scarcity. The main purpose of this combination of EMA and SD is to gain insight into what kinds of surprising dynamics can occur given a variety of uncertainties and a basic understanding of the system. In the second case, EMA is combined with a hybrid model for airport performance calculations to develop an adaptive strategic plan. This case shows how one can iteratively improve a strategic plan through the identification of plausible external conditions that would cause the plan to perform poorly. In the final case, EMA is combined with an agent-based model to study transition dynamics in the electricity sector and identify crucial factors that positively and negatively affect a transition towards more sustainable functioning of the electricity sector. This paper concludes that EMA is useful for generating foresights and studying systemic and structural transformations despite the presence of a plethora of uncertainties, and for designing robust policies and plans, which are key activities of FTA. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Future-oriented technology analysis exploratory modeling and analysis Deep uncertainty System dynamics Adaptive policymaking Agent-based modeling

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An exploratory analysis of the Dutch Electricity System in Transition

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JOURNAL OF THE KNOWLEDGE ECONOMY 2014年第4期5卷 670-685页

作者： Kwakkel, Jan H. Yucel, Gonenc Delft Univ Technol Fac Technol Policy & Management Delft Netherlands Bogazici Univ Ind Engn Dept Istanbul Turkey

Recent contextual developments constitute a backdrop of change for the Dutch electricity system. Institutional change driven by liberalization, changing economic competitiveness of the dominant fuels, new technologies, and changing end-user preferences regarding electricity supply are some examples of these developments. This paper explores plausible transition trajectories in the face of these developments given technological uncertainty about investment and operating costs, and fuel efficiency of various alternative technologies;political uncertainty about future CO2 abatement policies such as emission trading;and socio-economic uncertainty about fuel prices, investment decisions of suppliers, and load curves. Various alternative developments for these uncertainties are specified. The consequences of each of these alternative developments are assessed using an agent-based model of the Dutch electricity system. The outputs are analyzed using various data-mining and data visualization techniques in order to reveal arch-typical transition trajectories and their conditions for occurring. Policy recommendations are derived from this. The results indicate that most transition trajectories point towards a future energy supply system that is reliant on clean coal and gas. Under the explored uncertainties, only rarely a transition to renewables occurs. The various sustainable energy support programs appear to be ineffective in steering the energy supply system towards a more sustainable mode of functioning across the various uncertainties.

关键词： Electricity supply Transitions exploratory modeling and analysis Uncertainty Agent-based modeling

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Light-duty electric vehicles to improve the integrity of the electricity grid through Vehicle-to-Grid technology: analysis of regional net revenue and emissions savings

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APPLIED ENERGY 2016年第0期168卷 146-158页

作者： Noori, Mehdi Zhao, Yang Onat, Nuri C. Gardner, Stephanie Tatari, Omer Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Arizona State Univ Julie Ann Wrigley Global Inst Sustainabil Walton Sustainabil Solut Initiat Tempe AZ 85281 USA Vermont Law Sch Certificate Energy Law Environm Law & Policy Royalton VT USA

Vehicle to Grid technologies utilize idle EV battery power as a grid storage tool to meet fluctuating electric power demands. Vehicle to Grid systems are promising substitutes for traditional gas turbine generators, which are relatively inefficient and have high emissions impacts. The purpose of this study is to predict the future net revenue and life cycle emissions savings of Vehicle to Grid technologies for use in ancillary (regulation) services on a regional basis in the United States. In this paper, the emissions savings and net revenue calculations are conducted with respect to five different Independent System Operator/Regional Transmission Organization regions, after which future EV market penetration rates are predicted using an Agent-Based Model designed to account for various uncertainties, including regulation service payments, regulation signal features, and battery degradation. Finally, the concept of exploratory modeling and analysis is used to estimate the future net revenue and emissions savings of integrating Vehicle to Grid technology into the grid, considering the inherent uncertainties of the system. The results indicate that, for a single vehicle, the net revenue of Vehicle to Grid services is highest for the New York region, which is approximately $42,000 per vehicle on average. However, the PJM region has an approximately $97 million overall net revenue potential, given the 38,200 Vehicle to Grid-service available electric vehicles estimated to be on the road in the future in the PJM region, which is the highest among the studied regions. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Vehicle to Grid Emissions Net revenue Agent-Based modeling exploratory modeling and analysis Planning and forecasting

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A stochastic optimization approach for the selection of reflective cracking mitigation techniques

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2014年第Nov.期69卷 367-378页

作者： Noori, Mehdi Tatari, Omer Nam, BooHyun Golestani, Behnam Greene, James Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Florida Dept Transportat State Mat Off Gainesville FL 32609 USA

Purpose: In Hot Mix Asphalt (HMA) overlays, the existing cracks in the underlying pavements can propagate upward to the new added overlay and may cause Reflective Cracks (RC). These cracks allow water infiltration to the underlying layers and causes further moisture damage as well as weakening the unbound layers. Over the years, several methods have been developed for mitigating the RCs. This study aims to investigate the current reflective cracking mitigation methods and develop a methodology for the selection of appropriate mitigation technique. The developed model is then applied to a case study in the state of Florida. Method: To accomplish this goal, a nationwide literature review was conducted to better understand the current in practice methods in the United States. Moreover, a life cycle cost analysis (LCCA) in five different road types was performed to find the annuity of roadway rehabilitation for each of the mitigation methods. The uncertainty in the LCCA results is represented using exploratory modeling and analysis (EMA) method. Then through a Multi Criteria Decision Making (MCDM) model, a stochastic optimization model was developed to find the appropriate reflective cracking mitigation solution under Florida's climate and road conditions, based on different cost and performance weights. Results: Based on the available data for the state of Florida, the LCCA results indicate that the annuity of maintaining the roadway with Fabrics and ISAC are lower compared to other methods. However, the results of stochastic optimization model reveal that while looking at the performance and cost at the same time, different methods would be more feasible. For instance, while the cost of the used method does not matter at all and only performance matters, STRATA (R) is more probable to be the appropriate mitigation technique. The findings of this research are critical for decision makers to better understand the most cost-effective mitigation technique in different con

关键词： Hot Mix Asphalt Reflective cracking mitigation method Life cycle cost exploratory modeling and analysis Multi Criteria Decision Making

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An exploratory approach for adaptive policymaking by using multi-objective robust optimization

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SIMULATION MODELLING PRACTICE AND THEORY 2014年 46卷 25-39页

作者： Hamarat, Caner Kwakkel, Jan H. Pruyt, Erik Loonen, Erwin T. Delft Univ Technol NL-2600 AA Delft Netherlands GEN Nederland Utrecht Netherlands

Developing robust policies for complex systems is a profound challenge because of their nonlinear and unpredictable nature. Dealing with these characteristics requires innovative approaches. A possible approach is to design policies that can be adapted over time in response to how the future unfolds. An essential part of adaptive policymaking is specifying under what conditions, and in which way, to adapt the policy. The performance of an adaptive policy is critically dependent on this: if the policy is adapted too late or too early, significant deterioration in policy performance can be incurred. An additional complicating factor is that in almost any policy problem, a multiplicity of divergent and potentially conflicting objectives has to be considered. In this paper we tackle both problems simultaneously through the use of multi-objective robust simulation optimization. Robust optimization helps in specifying appropriate conditions for adapting a policy, by identifying conditions that produce satisfactory results across a large ensemble of scenarios. Multi-objective optimization helps in identifying such conditions for a set of criteria, and providing insights into the tradeoffs between these criteria. Simulation is used for evaluating policy performance. This approach results in the identification of multiple alternative conditions under which to adapt a policy, rather than a single set of conditions. This creates the possibility of an informed policy debate on trade-offs. The approach is illustrated through a case study on designing a robust policy for supporting the transition toward renewable energy systems in the European Union. The results indicate that the proposed approach can be efficiently used for developing policy suggestions and for improving decision support for policymakers. By extension, it is possible to apply this methodology in dynamically complex and deeply uncertain systems such as public health, financial systems, transportation, and housing

关键词： Adaptive policymaking Deep uncertainty Multi-objective robust optimization Simulation optimization Complex systems exploratory modeling and analysis

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Development of an agent-based model for regional market penetration projections of electric vehicles in the United States

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ENERGY 2016年 96卷 215-230页

作者： Noori, Mehdi Tatari, Omer Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

One of the most promising strategies recommended for increasing energy security and for mitigating transportation sector emissions is to support alternative fuel technologies, including electric vehicles. However, there is a considerable amount of uncertainty regarding the market penetration of electric vehicles that must be accounted for in order to achieve the current market share goals. This paper aims to address these inherent uncertainties and to identify the possible market share of electric vehicles in the United States for the year 2030, using the developed Electric Vehicle Regional Market Penetration tool. First, considering their respective inherent uncertainties, the vehicle attributes are evaluated for different vehicle types, including internal combustion engine, gasoline hybrid, and three different electric vehicle types. In addition, an agent-based model is developed to identify the market shares of each of the studied vehicles. Finally, market share uncertainties are modeled using the exploratory modeling and analysis approach. The government subsidies play a vital role in the market adoption of electric vehicle and, when combined with the word-of-mouth effect, may achieve electric vehicle market share of up to 30% of new sales in 2030 on average, with all-electric vehicles having the highest market share among the electric vehicle options. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Electric vehicle Market penetration Inherent uncertainty Agent-based modeling exploratory modeling and analysis

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Assessing Water Management Strategies in Data-Scarce Mountain Regions under Uncertain Climate and Socio-Economic Changes

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WATER RESOURCES MANAGEMENT 2024年第11期38卷 4083-4100页

作者： Munoz, R. Vaghefi, S. A. Drenkhan, F. Santos, M. J. Viviroli, D. Muccione, V. Huggel, C. Univ Zurich Dept Geog Winterthurerstr 190 CH-8057 Zurich Switzerland Univ Zurich Dept Banking & Finance Zurich Switzerland Eawag Swiss Fed Inst Aquat Sci Dubendorf Switzerland WMO World Meteorol Org Geneva Switzerland Pontificia Univ Catolica Peru Dept Humanities Geog & Environm Lima Peru Swiss Fed Res Inst WSL Birmensdorf Switzerland

Water management in mountainous regions faces significant challenges due to deep uncertainties arising from data scarcity, knowledge gaps, and the complex interplay of climate and socio-economic changes. While existing approaches focused on uncertainty reduction and water system optimization contribute to managing uncertainties, they often require probability distributions that can be difficult to obtain in data-scarce mountain regions. To address these challenges, we demonstrate the effectiveness of exploratory modeling and analysis (EMA) in assessing water management strategies and identifying operational ranges that avoid future water scarcity. Through a case study in the complex and data-scarce Peruvian Andes, we employed EMA to run 12,000 simulations by 2050, incorporating deep uncertainties from climate and socio-economic scenarios, and hydrological modeling parameters. This analysis identified specific policy combinations demonstrating greater robustness across diverse scenarios and uncertainties. EMA explicitly identifies operational ranges of policies to avoid water scarcity but also highlights the conditions that might trigger policy failure. We also delve into the roles of the different factors used in EMA and their significance in water management applications. Our research illustrates that an exploratory hydrological modeling approach based on robust decision-making can foster a more informed decision-making process for long-term water adaptation in rapidly changing mountain regions under data scarcity and deep uncertainties.

关键词： Climate change Socio-economic change Deep uncertainty exploratory modeling and analysis Water management Social-ecological system

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A model-based analysis of biomethane production in the Netherlands and the effectiveness of the subsidization policy under uncertainty

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ENERGY POLICY 2015年第Jul.期82卷 178-196页

作者： Eker, Sibel van Daalen, Els Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands

Biomethane is a renewable alternative to natural gas. It has the potential to increase the sustainability of the energy system and to help deal with supply problems. However, several factors make the future of biomethane production complex and uncertain, such as resource availability, demand, capacity installation, profitability and the competition between the biomethane and electricity sectors for sharing the available biogas and biomass resources. In this research, we study the dynamics of the Dutch biomethane production and analyze the effects of subsidization policy with a system dynamics model. The policy is tested under uncertainty with respect to three conflicting objectives, namely maximizing production and emission reduction, and minimizing costs. According to the results, the subsidization is crucial to develop biomethane production, and the performance of the policy is enhanced in terms of robustness and of meeting all three objectives satisfactorily when the policy is implemented for a long time, with relatively low subsidy prices. Besides, the subsidization policy is found to be most vulnerable to the producers' uncertain investment response to profitability. In future research, different policy options such as subsidizing other biomass-based renewable energy options and policies affecting the biomethane demand can be tested. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Biomethane Biogas System dynamics exploratory modeling and analysis Multi-objective robust optimization Uncertainty

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Scenario generation using adaptive sampling: The case of resource scarcity

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ENVIRONMENTAL MODELLING & SOFTWARE 2016年 79卷 285-299页

作者： Islam, Tushith Pruyt, Erik Delft Univ Technol Fac Technol Policy & Management NL-2600 GA Delft Netherlands

Static input-oriented sampling approaches are often used for generating model-based scenarios. However, for models of deeply uncertain and dynamically complex issues, there is no guarantee that such approaches reveal the total behavioral spectrum that could be generated by simulating them. In this paper, we present an adaptive output-oriented sampling approach for exploring the full behavioral spectrum that could be generated by computational models in view of generating interesting, even previously undiscovered, scenarios. In this paper, we use a resource scarcity model to illustrate the approach, show the difference between static sampling and adaptive sampling, and demonstrate the usefulness for scenario discovery of the latter combined with other methods. We show that this approach can be used for revealing the behavioral spectrum of models, identifying regions of the input space that generate particular behaviors, and selecting (sets of) scenarios that are representative in terms of output and input spaces. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Adaptive sampling Scenario discovery Uncertainty exploratory modeling and analysis Behavioral spectrum System dynamics

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Dynamic scenario discovery under deep uncertainty: The future of copper

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2013年第4期80卷 789-800页

作者： Kwakkel, Jan H. Auping, Willem L. Pruyt, Erik Delft Univ Technol Fac Technol Policy & Management Delft Netherlands The Hague Ctr Strateg Studies The Hague Netherlands

Scenarios are commonly used to communicate and characterize uncertainty in many policy fields. One of the main challenges of scenario approaches is that analysts have to try and capture the full breadth of uncertainty about the future in a small set of scenarios. In the presence of deep uncertainty, this is even more challenging. Scenario discovery is a model-based technique inspired by the scenario logic school that addresses this challenge. In scenario discovery, an ensemble of model runs is created that encompasses the various uncertainties perceived by the actors involved in particular decision making situations. The ensemble is subsequently screened to identify runs of interest, and their conditions for occurring are identified through machine learning. Here, we extend scenario discovery to cope with dynamics over time. To this end, a time series clustering approach is applied to the ensemble of model runs in order to identify different types of dynamics. The types of dynamics are subsequently analyzed to identify dynamics that are of interest, and their causes for occurrence are revealed. This dynamic scenario discovery approach is illustrated with a case about copper scarcity. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Scenario discovery exploratory modeling and analysis System dynamics Deep uncertainty Metal scarcity

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