(8p) Management of Energy Supply Chains Under Uncertainty

Energy demand, including electricity demand, is directly linked to factors such as economic development, population growth, and urbanization. However, meeting energy demands entails a critical challenge to use the Earthâ??s energy resources in a sustainable fashion, enhancing quality of life, and facilitating human development. Specifically, a number of environmental impacts have been identified associated with the development of energy systems. In particular, emissions of Greenhouse Gases (GHG), the depletion and degradation of water sources, as well as the potential for underground water contamination, are major concerns associated with these systems. Thus, the design and planning of energy systems is a challenging problem where economic and environmental aspects need to be considered at both the process and supply chain levels.

Even though both levels have received significant attention from the engineering research community, there has been comparatively less research on the development of decision-making support tools for the investigation of integrated energy supply chains. For instance, only a few studies have been devoted to the integration of water management and shale gas supply chains design as well as to the integrated planning of generation and transmission expansion in power systems, which are systems that have been playing and will continue to play important roles in supplying our energy needs. I am especially interested in attacking this gap, through the development of optimization tools for supporting the decision-making processes in these two aforementioned energy systems at both national and regional scales. Specifically, I have targeted the development of decision-support optimization frameworks for the strategic design and planning of shale gas supply chains integrated with water management as well as generation and transmission expansion in power systems. In [1] we present a methodology for the simulation and evaluation of different well-pads designs for the exploitation of shale gas resources. Then, in [2] we present the mathematical formulation of a optimization framework for the integration of well-pad design and water management with shale gas supply chain design. The mathematical formulation consists of a novel large-scale Mixed Integer Linear Programming (MILP) model and a Mixed Integer Nonlinear Programming (MINLP) model. Furthermore, the analysis of the results reveals strong interactions between water management and the design of the shale gas supply chain. For instance, it was found that both the Total Dissolve Solid (TDS) concentration in wastewater and the fresh water availability are key factors not only for the selection of the water management strategy but also for the drilling scheme that should be implemented. Additionally, we demonstrated that a more accurate formulation, which results in a MINLP model, could lead to improved designs which increase by about 71% the economic benefit of the exploitation of shale gas plays in comparison with the MILP formulation. While the MILP is much more efficient from a computational viewpoint it is an approximation of the real problem and thus fails to capture some important synergies.

In addition to the shale gas project, I have been working on the development of an optimization framework for the integrated planning of power generation and transmission in interconnected power systems. In [3] we presented the mathematical formulation, MILP models, of the optimization framework as well as some revealing applications including â??business as usualâ? and CO₂ mitigation policy scenarios. The novelty of this framework stems from the integration of power generation and transmission planning along with spinning and non-spinning reserve constraints as well as CO₂ emission constraints and mitigation options. In the case of CO₂ emission mitigation options, we consider the penetration of renewable energies, the integration of Carbon Capture and Sequestration (CCS) technologies, and the implementation of Demand Side Management (DSM) strategies. The results from the â??business as usualâ? scenario revealed that fuel prices have a direct impact not only on the allocation of electricity generation resources but also on the selection of the fossil power plant technologies. Additionally, the analysis of the results reveals an interdependency between the transmission constraints and the decisions regarding the investment schedule and installation of new power plants. Moreover, the results from the CO₂ mitigation policy scenario demonstrated that the reconfiguration of existing power generation technologies is the most cost-effective alternative for the abatement of CO₂emissions. Furthermore, the integration of renewable energy sources was demonstrated to be preferred over the retrofitting of coal power plants with CCS technologies.

My ongoing research is concentrated on the characterization and modeling of the uncertainties inherent to the shale gas supply chain and the power generation and transmission planning problems, as well as on the development of stochastic optimization models. Specifically, global sensitivity analysis is being carried out in order to identify the most impactful among the many parameters in each model. This analysis will allow us to focused on parameters with the most impact on the optimal solution, thus increasing the computational effectiveness when modeling the uncertainty space. Moreover, two-stage stochastic models will be developed in order to solve the optimization problems under uncertainty.

Teaching Interests:

My academic and industrial experiences have offered me the opportunity to not only strengthen my knowledge of mathematical modeling, simulation, and optimization of processes and systems but also to develop deep understanding on the applicability of such tools to deal with real world design and planning problems in different domains, i.e. refinery operations, shale gas supply chains, and power systems. Therefore, my teaching interest pertains to decision making theory and applications, i.e. courses on computer aided process design and planning or optimization theory with applications to industrial processes and systems. However, I am well prepared to teach courses in the chemical engineering core, e.g., material and energy balances, separation processes, and process economics, design, and evaluation, as well as courses on mathematical and numerical methods.

References:

[1] Calderón AJ, Guerra OJ, Papageorgiou LG, Siirola JJ, Reklaitis G V. Preliminary Evaluation of Shale Gas Reservoirs: Appraisal of Different Well-Pad Designs via Performance Metrics. Ind Eng Chem Res2015;54:10334â??49. doi:10.1021/acs.iecr.5b01590.

[2] Guerra OJ, Calderón AJ, Papageorgiou LG, Siirola JJ, Reklaitis G V. An Optimization Framework for the Integration of Water Management and Shale Gas Supply Chain Design. Comput Chem Eng 2016; 92:230â??255. doi:10.1016/j.compchemeng.2016.03.025

[3] Guerra OJ, Tejada DA, Reklaitis G V. An optimization framework for the integrated planning of generation and transmission expansion in interconnected power systems. Appl Energy 2016;170:1â??21. doi:10.1016/j.apenergy.2016.02.014.