Modelling and Simulation in Service of Energy Policy

AbstractModelling and simulation has long and well served the actors and various decision makers in the domain of energy policy. Various modelling approaches and models have been applied to address a variety of energy policy related issues. However, the journey continues. This paper provides an overview of these modelling approaches and models and identifies their key challenges in the face of emerging issues. The identified energy policy modelling related issues include the characterization of energy systems as complex, dynamic system with numerous uncertainties, non-linearities, time lags, and intertwined feedback loops. System dynamics modelling as a viable solution to address these issues is also suggested

System dynamics based learning environments: a technology for decision support and assessment

Traditionally decision support systems (DSS) are designed to help the users make better decisions. However, the empirical evidence concerning the impact of DSS on improved decision making and leaning in dynamic tasks is equivocal at best. In this article, we introduce a new type of DSS based system dynamics technology as tool not only to support users’ decision making and leaning but can also provide an effective assessment of the performance and learning as well

System dynamics advances strategic economic transition planning in a developing nation

The increasingly complex environment of today's world, characterized by technological innovation and global communication, generates myriads of possible and actual interactions while limited physical and intellectual resources severely impinge on decision makers, be it in the public or private domains. At the core of the decision-making process is the need for quality information that allows the decision maker to better assess the impact of decisions in terms of outcomes, nonlinear feedback processes and time delays on the performance of the complex system invoked. This volume is a timely review on the principles underlying complex decision making, the handling of uncertainties in dynamic envrionments and of the various modeling approaches used. The book consists of five parts, each composed of several chapters: I: Complex Decision Making: Concepts, Theories and Empirical Evidence II: Tools and Techniques for Decision Making in Complex Environments and Systems III: System Dynamics and Agent-Based Modeling IV: Methodological Issues V: Future Direction

Analysis of the Dynamic Relationships among Renewable Energy Consumption, Economic Growth, Financial Development, and Carbon Dioxide Emission in Five Sub-Saharan African Countries

This research investigates the relationships among renewable energy consumption, economic growth, and financial development in five sub-Saharan African nations utilizing panel data from 2000 to 2020. Econometric methods are used to ascertain the existence or absence of cross-sectional dependence and the short-run and long-run connections between the following factors: Pesaran cross-sectional dependence (CD) and cross-sectionally augmented IPS (CIPS) unit root tests, pooled mean group (PMG), and dynamic ordinary least squares (DOLS) estimations. The presence of cross-sectional dependence is found and represented with the CIPS unit root test. No significant short-run relationship is found between the variables of the study, yet a significant long-run relationship is present among them. A positive relationship exists between CO2 emissions and financial development, while financial development and renewable energy consumption are found to have negative relationships with CO2 emissions. The study also supports the scale effect of the environmental Kuznets curve hypothesis. Additionally, no causality is found among the variables, and impulse response and variance decomposition estimation are carried out to recommend future effects. Policy implications of findings are discussed, with accompanying suggestions

The impact of renewable energy consumption and environmental sustainability on economic growth in Africa

In line with the global call for alternative sources of energy rather than conventional fossil-based sources, research in the area of renewable energy, energy efficiency, and sustainability seems to have intensified in Africa in the last five years. As a form of a contribution to the existing body of knowledge, this study seeks to parametrically estimate the effects of renewable energy consumption and environmental sustainability on economic growth in Africa. Using panel data, for thirty-seven African countries, and employing the system Generalized Method of Moments estimation technique which more efficiently solves the problems of endogeneity and omitted variable bias than least squares and causal estimation method, this study found that renewable energy adoption and development will lead to an increase in economic growth in Africa, both in the long run and short run as a one percent increase in renewable energy consumption will lead to 0.07% and 1.9% increases in economic growth in both the short-run and long-run, respectively The study also found that environmental sustainability through a reduction of emission may not be Africa’s priority towards achieving an all-inclusive development at present because the coefficient of CO2 emission in the study is not statistically significant. Therefore, African countries’ governments should intensify efforts towards developing the renewable energy sector, especially using policy instruments, while also harnessing the already mature nonrenewable industry for more rapid growth in the continent and the attainment of Agenda 2063

QNP_SHELL: A computerized tool for improving decision-making skills for nuclear power plant operators Input Output (User Queries) (Fault Diagnosis) Inference Engine Program Execution Module Diagnostic Module -Control Strategy- Inferencing Modules -Knowled

Abstract: Decision-making in complex systems such as nuclear power plants (NPPs) is a difficult task at best. The safety and integrity of many such high-capital cost-intensive installations depend on the operator's capability to correctly diagnose and take appropriate measures to avoid any abnormal operations of an NPP. Therefore, the role of the expert systems in the offline training programs for the operators is ever increasing. In this paper, we describe the development of an expert system, "QNP_ SHELL," to assist, offline QNPP operators and plant personnel in a better familiarization to infer the anticipated and foreseen malfunctions from the observed symptoms. QNP_SHELL's inferencing mechanism is of the "Rule-based" type and to search the knowledge base it adopts the "Depth First" technique. The diagnostic performance of the trainee operators using QNP_SHELL on various accidents at QNPP has been found, through both the qualitative and quantitative evaluations, satisfactory. Complex, Dynamic Tasks (Springer: 2014), focuses on the design, development, and applications of computer simulation-based decision support systems. The research reported here, relating to his research on the thematic area of decision-making in complex tasks, describes the development and utility of an expert system for the training of nuclear plant operators. PUBLIC INTEREST STATEMENT Decision-making in complex systems such as nuclear power plants is a difficult task at best. The safety and integrity of many such highcapital cost-intensive installations depend on the operator's capability to correctly diagnose and take appropriate measures to avoid any abnormal operations of a nuclear power plant. Therefore, training of nuclear power plant operator takes the center stage among the possible initiatives for the safe and successful plant operations. It is imperative that training for this kind of complex tasks needs to be specific and focused to the needs of the particular power plant. How should we train these operators? Various tools and programs are used for this purpose. Use of computerized decision support is common. Among such tools, the knowledge-based expert systems are particular useful for the fault diagnosis training tasks. In this paper, we describe the design, development, and use of an expert system, "QNP_SHELL.&quot

Better decision making in complex, dynamic tasks: training with human-facilitated interactive learning environments

This book describes interactive learning environments (ILEs) and their underlying concepts. It explains how ILEs can be used to improve the decision-making process and how these improvements can be empirically verified. The objective of this book is to enhance our understanding of and to gain insights into the process by which human facilitated ILEs are effectively designed and used in improving users’ decision making in complex, dynamic tasks. This book is divided into four major parts. Part I serves as an introduction to the importance and complexity of decision making in dynamic tasks. Part II provides background material, drawing upon relevant literature, for the development of an integrated process model on the effectiveness of human facilitated ILEs in improving decision making in dynamic tasks. Part III focuses on the design, development, and application of FishBankILE in laboratory experiments to gather empirical evidence for the validity of the process model. Finally, part IV presents a comprehensive analysis of the gathered data to illustrate the lessons to be learned. Better Decision Making in Complex, Dynamic Tasks will be useful for managers and practitioners, researchers, and students of dynamic decision making. Praise for Better Decision Making in Complex, Dynamic Tasks: "...rich in content, full with unique insights, and a self-sufficient volume on the design, development and application of system dynamics based simulations that are implemented as interactive learning environments (ILEs). Practitioners and researchers interested in seeking evidence for the efficacy of ILEs’ use in decision-making will find this book a must read." Prof. Carmine Bianchi, Scientific Coordinator of CED4-System Dynamics Group, University of Palermo, Ital

Adoption and Growth of Fuel Cell Vehicles in China: The Case of BYD

Compared to battery electric vehicles (BEVs), fuel cell vehicles (FCVs) have been developing since the early 2000s due to their efficiency and environmental advantages. However, unlike the battery industry which has already achieved economies of scale, the scale of fuel cell manufacturing is still in its early stage in China. In this exploratory study, using the case of BYD, we identify and analyze the key economic and environmental factors that might facilitate and propel the adoption of FCVs in China. Utilizing quantitative (i.e., the statistically descriptive method) and qualitative (i.e., a semi-structured interview and Porter’s model) reasoning, this study finds that by systematically addressing two factors, (i) customers’ misperceptions about the safety and environmental friendliness of FCVs and (ii) lack of technical competencies in the upstream and downstream of the FCV industry’s value chain in general and for BYD in particular, the sustainable development and adoption of FCVs in China can be achieved

A Review and Analysis of Green Energy and the Environmental Policies in South Asia

This paper explores the challenges and opportunities for green energy and environment transition in South Asia, a region that faces the dilemma of meeting its growing energy demand while reducing its greenhouse gas emissions and environmental vulnerability. The region has rich renewable energy sources and potential for energy efficiency improvement, but it also relies heavily on fossil fuels and suffers from various barriers and constraints that hinder its green energy development. The region needs policies that can achieve economic growth, social welfare, and environmental sustainability coherently and effectively. Utilizing the thematic literature review approach, this paper examines the literature on four main topics: (1) the estimation of green energy resources potential and scenarios in South Asia; (2) the comparison of green energy targets and policies in the South Asian Association for Regional Cooperation (SAARC) countries; (3) the evaluation of green energy deployment and performance in different sectors; and (4) the identification of green energy transition challenges and opportunities in South Asia. This paper fills some research gaps in the literature by providing a comprehensive, comparative, holistic, and integrated analysis of green energy and environment policies in South Asia, using various data sources, methods, frameworks, criteria, indicators, scenarios, impacts, trade-offs, drivers, barriers, best practices, lessons learned, and policy recommendations. This paper also develops a conceptual model for the green energy transition in South Asia, which consists of five key variables: green energy potential, green energy policies, green energy deployment, green energy performance, and green energy transition. The main findings and implications of this paper are that South Asia has a huge opportunity to pursue a green energy and environment transition that can address its multiple challenges and aspirations, but this requires overcoming various obstacles and constraints that hinder its progress. This paper suggests some policy options and strategies to enhance the green energy and environment policies in South Asia, such as developing a clear and consistent policy framework, enhancing regional cooperation and collaboration, leveraging information technology and data analytics, emphasizing sustainability and resilience, and engaging with other stakeholders and partners

QNP_SHELL: A computerized tool for improving decision-making skills for nuclear power plant operators

Decision-making in complex systems such as nuclear power plants (NPPs) is a difficult task at best. The safety and integrity of many such high-capital cost-intensive installations depend on the operator’s capability to correctly diagnose and take appropriate measures to avoid any abnormal operations of an NPP. Therefore, the role of the expert systems in the offline training programs for the operators is ever increasing. In this paper, we describe the development of an expert system, “QNP_SHELL,” to assist, offline QNPP operators and plant personnel in a better familiarization to infer the anticipated and foreseen malfunctions from the observed symptoms. QNP_SHELL’s inferencing mechanism is of the “Rule-based” type and to search the knowledge base it adopts the “Depth First” technique. The diagnostic performance of the trainee operators using QNP_SHELL on various accidents at QNPP has been found, through both the qualitative and quantitative evaluations, satisfactory