Strategic Real Option and Flexibility Analysis for Nuclear Power Plants Considering Uncertainty in Electricity Demand and Public Acceptance

Nuclear power is an important energy source especially in consideration of CO2 emissions and global warming. Deploying nuclear power plants, however, may be challenging when uncertainty in long-term electricity demand and more importantly public acceptance are considered. This is true especially for emerging economies (e.g., India, China) concerned with reducing their carbon footprint in the context of growing economic development, while accommodating a growing population and significantly changing demographics, as well as recent events that may affect the public's perception of nuclear technology. In the aftermath of the Fukushima Daiichi disaster, public acceptance has come to play a central role in continued operations and deployment of new nuclear power systems worldwide. In countries seeing important long-term demographic changes, it may be difficult to determine the future capacity needed, when and where to deploy it over time, and in the most economic manner. Existing studies on capacity deployment typically do not consider such uncertainty drivers in long-term capacity deployment analyses (e.g., + 40 years). To address these issues, this paper introduces a novel approach to nuclear power systems design and capacity deployment under uncertainty that exploits the idea of strategic flexibility and managerial decision rules. The approach enables dealing more pro-actively with uncertainty and helps identify the most economic deployment paths for new nuclear capacity deployment over multiple sites. One novelty of the study lies in the explicit recognition of public acceptance as an important uncertainty driver affecting economic performance, along with long-term electricity demand. Another novelty is in how the concept of flexibility is exploited to deal with uncertainty and improve expected lifecycle performance (e.g. cost). New design and deployment strategies are developed and analyzed through a multistage stochastic programming framework where decision rules are represented as non-anticipative constraints. This approach provides a new way to devise and analyze adaptation strategies in view of long-term uncertainty fluctuations that is more intuitive and readily usable by system operators than typical solutions obtained from standard real options analysis techniques, which are typically used to analyze flexibility in large-scale, irreversible investment projects. The study considers three flexibility strategies subject to uncertainty in electricity demand and public acceptance: 1) phasing (or staging) capacity deployment over time and space, 2) on-site capacity expansion, and 3) life extension. Numerical analysis shows that flexible designs perform better than rigid optimal design deployment strategies, and the most flexible design combining the above strategies outperforms both more rigid and less flexible design alternatives. It is also demonstrated that a flexible design benefits from the strategies of phasing and capacity expansion most significantly across all three strategies studied. The results provide useful insights for policy and decision-making in countries that are considering new nuclear facility deployment, in light of ongoing challenges surrounding new nuclear builds worldwide

Flexibility and real options analysis in power system generation expansion planning under uncertainty

Over many years, there has been a drive in the electricity industry towards better integration of environmentally friendly and renewable generation resources for power systems. Such resources show highly variable availability, impacting the design and performance of power systems. In this paper, we propose using a stochastic programming approach to optimize generation expansion planning (GEP), with explicit consideration of generator output capacity uncertainty. Flexibility implementation - via real options exercised in response to uncertainty realizations - is considered as an important design approach to the GEP problem. It more effectively captures upside opportunities, while reducing exposure to downside risks. A decision-rule based approach to real options modeling is used, combining conditional-go and finite adaptability principles. The solutions provide decision makers with easy-to-use guidelines with threshold values from which to exercise the options in operations. To demonstrate application of the proposed methodologies and decision rules, a case study situated in the Midwest United States is used. The case study demonstrates how to quantify the value of flexibility, and showcases the usefulness of the proposed approach

Development of a Waste-to-Energy Decision Support System (WTEDSS)

International audienceRapid increase in urban population has created the need for the development of efficient Decision Support Systems (DSS) guiding municipal planners to mitigate urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. To ensure sustainable urban planning, a DSS must provide not only an optimal planning solution based on input assumptions, but must also help to identify concrete city challenges, determine available resources (e.g., land and energy sources) and highlight any implementation constraints. It must support the creation of flexible interactive scenarios for urban development and their realistic representation in an urban context. This paper presents a Waste-to-Energy Decision Support System (WTEDSS) that identifies the optimal long-term deployment strategy for waste-to-energy infrastructures under future uncertain operational conditions and then directly assesses its feasibility and integration into an urban environment using 3D visualization. The WTEDSS is designed as an interactive and analytical waste management planning tool integrating four modules: data analytics, optimization, simulation and a user-friendly graphical interface. Emphasis is placed on the development and integration of the optimization module and 3D urban simulation, which provides users with decision support based on 3D visualized optimum facilities deployment plans. The optimization module receives calibrated data and solves a model based on inputs obtained from the user interface. The simulation platform developed in Unity 3D provides a friendly real-world environment for studying and understanding the facility deployment process over time and space, while also considering uncertainty

Quantitative performance-based evaluation of a procedure for flexible design concept generation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 152-163).This thesis presents an experimental methodology for objective and quantitative design procedure evaluation based on anticipated lifecycle performance of design concepts, and a procedure for flexible design concept generation. The methodology complements existing evaluation methodologies by measuring anticipated performance via efficient computer modeling techniques. The procedure, in contrast to others, stimulates flexible design concept generation by packaging a short lecture on flexibility, and a prompting ideation mechanism. Controlled collaborative experiments had participants suggest alternative solutions to a design problem under different treatment conditions. Experimental conditions used the procedure for flexibility, while control conditions relied on prior training in science and engineering only, and free undirected ideation. Measures included the quantity of flexible design concepts generated, anticipated economic performance improvements compared to a benchmark design, participants' subjective impressions of satisfaction with the process and results, and results quality assessments. Seventy-one designers divided among twenty-six teams performed the experiments involving a simplified real estate infrastructure design problem. Application of the methodology demonstrated effective and efficient evaluation of the design procedure based on anticipated performance of design concepts. The lecture and prompting mechanism significantly improved anticipated performance compared to the benchmark design, by nearly thirty-six percent. The prompting mechanism significantly improved generation of valuable flexible design concepts. Lecturing improved significantly user satisfaction with the process and results, as well as results quality assessments. Even though prompting demonstrably improved anticipated performance and concept generation, it had no effect on participants' satisfaction with the process and results - unless combined with the lecture. Also, prompting did not lead participants to expect better results quality. This demonstrates the need for thorough and rigorous procedure evaluations based both on subjective user impressions and objective quantitative measurements. A preliminary analysis suggests that the proposed experimental platform can be used to study the influence of uncertainty and flexibility related words on discussion content, although more work is necessary to fully validate the approach.by Michel-Alexandre Cardin.Ph.D

Design Catalogs: A Systematic Approach to Design and Value Flexibility in Engineering Systems

This paper proposes design catalogs as an efficient systematic process for identifying and evaluating improved designs in engineering systems by exploiting ideas of flexibility. Standard design and evaluation approaches typically do not cope well with a range of possible operating conditions. They often simplify considerations of uncertainty, which may lead to designs that do not perform as well as those responding dynamically to changing conditions. The proposed process addresses the complexity of the design problem under uncertainty, recognizing that it is impossible to analyze all possible combinations of evolutions, and the flexible ways in which the system could adapt over time. The process creates a small subset of designs that collectively perform well over a range of scenarios. It bundles representative scenarios and their flexible responses to enable a more thorough analysis that accounts explicitly for uncertainty—and enable considerations of improved designs. Each element consists of combinations of design variables, parameters, and management decision rules carefully selected, and referred as operating plans. In the example analysis, the process improves economic performance by 37% as compared to standard methods in an infrastructure system case study, while exploring only 3% of the design space. It reaches 86% of the stochastically optimal solution while being 183 times faster computationally in the example numerical study. The systematic property aims for practical applications in industry. In each phase, it gives the freedom to rely on the designer's expertise with the system, or to consider analytical tools already in use at the design organization.National University of Singapore (MOE AcRF Tier 1 Grant WBS R-266-000-061-133)Massachusetts Institute of Technology. Engineering Systems DivisionMassachusetts Institute of Technology. Center for Real Estat

Design and management of flexible engineering systems

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 108-112).This thesis proposes a practical approach to defining flexible design and development strategies for maximizing the expected value of engineering systems. Specifically, the approach deals with the fact that it is generally computationally impractical to explore all the possible ways a system might be developed and operated, given the large number of possible scenarios in which the system might evolve. To make the analysis tractable within the computational resources available, it proposes that designers and program managers use a catalog of representative operating plans built from combinations of design elements and management decision rules. These are associated with a range of possible scenarios of uncertain variables that might affect the system's expected value and performance. This work develops the novel methodology introduced by (de Neufville, 2006) to guide the search for catalogs of operating plans while aiming at minimizing computational effort. It assumes a model of the engineering system is available, together with several value/performance metrics such as Expected Net Present Value (ENPV) and Value At Risk and Gain (VARG). It uses an algorithm based on statistical experiment design, Adaptive One-Factor-At-a-Time (OFAT) (Frey and Wang, 2006; Wang, 2007), to search the combinatorial space in light of system's responses to a limited set of uncertain variable scenarios.(cont.) Two case studies demonstrate the benefits of the analysis methodology. One is inspired from the development of a parking garage near the Bluewater commercial center in the United Kingdom. The other relates to the development of a real estate project in the United States. Results from case studies show improvement compared to inflexible design of engineering systems while still requiring minimal computational effort. This, together with appropriate policy recommendations, provides incentives for dissemination of the analysis methodology in industry and government. The simplicity of the methodology and use of tools already familiar to the firm and government agency alleviate political barriers to implementation. It allows designers and program managers to remain within established framework, rules, and management constraints. It favors transparent presentation and efficient application to design and management of engineering systems, thus allowing program managers to present the natural evolution of decisions to senior decision-makers.by Michel-Alexandre Cardin.S.M

Design Catalogs: A Practical Real Options Valuation Tool for Real Estate Design and Development Planning

IRES Working Paper Serie

A Stochastic Programming Approach for the Design of Multi-Storey Recycling Facility

International audienceA rapid increase in urban population creates major challenges related to urban sprawl, pollution and waste generation, unsustainable production and consumption patterns. These challenges become even more crucial in the case of land-constrained urban territories, such as Singapore and Hong Kong, and require the development of decision-making methodologies for flexible long-term land use planning. The paper explores the possible relocation of decentralized companies with similar work processes to relocate towards centralized Multi-Storey Factories (MSF) for a higher density of land use. The developed decision-making methodologies aim, on the one hand, to maximize land savings and, on the other hand, to decrease each company's operational budget evaluated under uncertainties in future operational conditions, such as transportation costs. The optimization problem addressed has been formulated as a two-stage stochastic problem and tested for the application case of Multi-Storey Recycling Facility (MSRF). Optimization under uncertainty shows a 16.46% increase in estimated land savings in comparison with the solution obtained under deterministic conditions

Policy Design, Planning, and Management in Global Systems Science

Policy Design is defined to be a new area of inquiry that takes the methods of design into the world of social, economic and environmental policy. Policy exists at many levels and it is increasingly recognized that policies applied to one system may impact on policies applied to other systems. The European Commission suggest a ‘science of global systems’ to improve the way that science can help inform policy and societal responses to global challenges such as climate change, global financial crises, global pandemics, city growth and migration patterns. The new science requires radically novel ideas and thinking to embed scientific evidence into the policy and societal processes. It is here argued that Policy Design in the context of planning and management is an essential part of the methodology of Global System Science

A Wireless-Assisted Hierarchical Framework to Accommodate Mobile Energy Resources

The societal decarbonisation fosters the installation of massive renewable inverter-based resources (IBRs) in replacing fossil fuel based traditional energy supply. The efficient and reliable operation of distributed IBRs requires advanced Information and Communication Technologies (ICT) , which may lead to a huge infrastructure investment and long construction time for remote communities. Therefore, to efficiently coordinate IBRs, we propose a low-cost hierarchical structure, especially for remote communities without existing strong ICT connections, that combines the advantages of centralised and distributed frameworks via advanced wireless communication technologies. More specifically, in each region covered by a single cellular network, dispatchable resources are controlled via a regional aggregated controller, and the corresponding regional information flow is enabled by a device-to-device (D2D) communication assisted wireless network. The wireless network can fully reuse the bandwidth to improve data flow efficiency, leading to a flexible information structure that can accommodate the plug-and-play operation of mobile IBRs. Simulation results demonstrate that the proposed wireless communication scheme significantly improves the utilization of existing bandwidth, and the dynamically allocated wireless system ensures the flexible operation of mobile IBRs