Path Catalog
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Path Catalog
列表数据实时取自 GET /api/v1/paths，仅包含存在已发布版本的 Path。
共 7800 条 Path · 第 203 / 780 页
A graduate-level learning path that teaches the application of game-theoretic models to analyze strategic behavior in energy markets. It covers foundational mathematical tools, core solution concepts, auction theory, and their application to electricity and natural gas markets.
This graduate-level learning path equips learners with the conceptual and mathematical tools of complexity science—nonlinear dynamics, emergence, self-organization, and complex adaptive systems—and applies them to analyze, model, and manage modern energy systems. It systematically builds from foundational mathematics and systems thinking through core complexity concepts to advanced applications in energy networks, markets, and infrastructure resilience.
This advanced graduate-level path equips learners with the analytical tools to assess how climate policies shape energy systems. It covers emissions accounting, policy instruments, energy systems modeling, and pathway analysis, culminating in a comprehensive capstone project.
This advanced graduate-level path explores the role of digitalization in modern energy systems, focusing on smart grids. It covers foundational power systems engineering, digital infrastructure, IoT, data analytics, automation, and grid modernization, culminating in a comprehensive understanding of how digital technologies transform energy systems.
This graduate-level path equips learners with the skills to apply discrete-event, Monte Carlo, and agent-based simulation methods to energy systems. It covers foundational modeling, programming, and statistical analysis, followed by specialized simulation techniques and their application to energy case studies.
This graduate-level learning path equips learners to analyze the dynamic behavior of energy systems using system dynamics principles. It covers feedback loops, stocks and flows, time delays, and dynamic simulation, culminating in the construction and validation of system dynamics models for energy systems.
A comprehensive graduate-level learning path covering advanced optimization techniques for energy systems. Learners will master mixed-integer programming, stochastic programming, multi-objective optimization, and decomposition methods, with applications to real-world energy problems.
This learning path provides a systematic introduction to Energy-Economy-Environment (E3) modeling frameworks. It begins with foundational concepts in economics, energy systems, and programming, then progresses through input-output models, CGE models, and integrated assessment models, emphasizing their interconnections and applications. Designed for undergraduate engineering students, the path balances theoretical understanding with practical implementation skills.
This learning path introduces undergraduate engineering students to the roles of stakeholders and governance frameworks in energy systems, integrating basic political science and economics. It covers key actors, governance structures, market regulation, and policy instruments, providing a foundation for understanding the socio-technical context of energy.
This learning path guides undergraduate engineering students through the dynamics of energy system transitions, covering historical transitions, key drivers and barriers, path dependency, and socio-technical change. It builds from foundational energy system concepts to advanced analytical frameworks.