Xiaojun "Gene" Shan
Permanent URI for this collectionhttps://hdl.handle.net/10657.1/1656
Dr. Xiaojun (Gene) Shan is an Assistant Professor of Engineering Management at University of Houston-Clear Lake. Dr. Shan's research interests are in the areas of Healthcare systems engineering, modeling, applied operations research/optimization, continuous process improvement, health information systems, data mining and big data analytics, with emphasis on operational excellence; Mathematical modeling (with focus on game-theoretic modeling) of complex systems (e.g., health care delivery, defense and electricity systems); Risk management against man-made and natural disasters.
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Browsing Xiaojun "Gene" Shan by Author "Felder, F. A."
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Item Assessing the Policy Interaction Effect of Renewable Portfolio Standards (RPS) and Clean Power Plan (CPP) Emissions Goals for States in the U.S. Northeast(PowerEnergy2016, 2016-06-26) Chandramowli, S. N.; Felder, F. A.; Shan, XiaojunWith the proposed Clean Power Plan for regulating carbon emissions from the power sector in the U.S, policymakers are likely to use a cost optimization framework to plan for future scenarios and implementation strategies. The modeling framework introduced in this paper would help such policymakers to make the appropriate investment decisions for the power sector. This paper applies an analytical model and an optimization model to investigate the implications of coimplementing an emission cap and a Renewable Portfolio Standards (RPS) policy for the U.S. Northeast. A simplified analytical model is specified and the first order optimality conditions are derived. The results from the analytical model are verified by running simulations using LP-CEM, a linear programming-based supply cost optimization model. The LP-CEM simulation results are analyzed under the recently proposed Clean Power Plan emissions cap rules and RPS scenarios for the U.S. Northeast region. The marginal abatement cost estimates, derived from a limited set of LP-CEM runs, are analyzed and compared to the theoretical results. For encouraging renewables generation, an RPS instrument is costeffective at higher policy targets, while an emissions cap instrument is cost-effective at lower policy targets. For CO2 emissions reduction, an emissions cap instrument is found be cost-effective for all policy targets. There is a trade-off between emissions levels and supply costs when the two instruments are co-implemented.Item Game-theoretic Model for Electric Distribution Reliability from a Multiple Stakeholder Perspective(Institute for Operations Research and Management Science (INFORMS), 2014-11-09) Shan, Xiaojun; Felder, F. A.; Coit, D. W.Abstract not available.Item Game-theoretic Model for Electric Distribution Reliability with Government Intervention(4th International Engineering Systems Symposium, 2014-06-08) Shan, Xiaojun; Felder, F. A.; Coit, D. W.Abstract not available.Item Game-theoretic Model for Electric Distribution Reliability with Government Intervention(4th International Engineering Systems Symposium, Hoboken, 2014-06-08) Shan, Xiaojun; Felder, F. A.; Coit, D. W.Abstract not available.Item Game-theoretic Model for Electric Distribution Resiliency/Reliability from a Multiple Stakeholder Perspective(2017) Shan, Xiaojun; Felder, F. A.; Coit, D. W.We study decentralized decisions among resiliency investors for hardening electric distribution systems with governance, which could coordinate the achievement of social optimums. Significant investments are being made to build resilient infrastructure for society well-being by hardening electric distribution networks. However, whether independent investment decisions can reach social optimums is not well studied. Previous research has focused on optimization of system designs to improve resiliency with limited modeling efforts on the interactions of decentralized decision making. Within regulatory governance, we investigate interactions between two independent resiliency investors with a game-theoretic model incorporating detailed payoff functions. Moreover, we demonstrate the framework with typical data and sensitivity analyses. We find that the decentralized optimal solution is not a social optimum without governance and the government could subsidize grid hardening to achieve the social optimum. Additionally, we conduct Monte Carlo simulations by varying key parameters and find that a socially undesirable outcome could occur with the highest frequency. Therefore, it is important to narrow the uncertain ranges for particular benefits/costs and use policy instruments to induce the socially desired outcomes. These results yield important insights into the role of regulatory governance in supervising resiliency investors and highlight the significance of studying the interactions between independent investors.Item Multi-objective Framework for Evaluating Resiliency Measures for Electric Power Systems(FERC Workshop/Tran-Atlantic Infraday (TAI), 2014-11-06) Felder, F. A.; Shan, Xiaojun; Coit, D. W.Abstract not available.Item Role of Governance in Independent Decision Making for Building Electric Infrastructure Resilience(FERC Workshop/Tran-Atlantic Infraday (TAI), 2014-11-06) Shan, Xiaojun; Felder, F. A.; Coit, D. W.Abstract not available.