(8o) New Modeling and Decision-Making Paradigms in Systems Engineering

Systems engineering is an interdisciplinary field concentrated on the design and management of complex systems. The tools and perspectives of systems engineers are increasingly necessary to establish new links between research fields and drive inter-disciplinary innovation. This poster discusses my research vision in the context of three new paradigms in systems engineering.

Multi-Stakeholder Decision-Making

The design and control of engineering systems involves complex decision-making processes with multiple conflicting objectives of social, economic and environmental natures. Multi-objective optimization techniques have been applied to many chemical engineering domains, including separations, crystallization, chromatography, control systems, and chemical reactors, and have also been used to co-optimize economic and environmental metrics, quantified through life-cycle analysis. Most literature, however, assumes the perspective of a single decision-maker and considers only two or three competing objectives. 

We recently proposed a framework for multi-stakeholder decision-making that addresses these two fundamental issues. The conflicting opinions from multiple stakeholders are transformed into individual stakeholder dissatisfactions that are minimized. We generalize previous approaches using the conditional-value-at-risk norm, which provides statistical interpretations to the compromise solutions and guarantees Pareto efficiency. Most notably, the method is computationally scalable. This is critical, as many sustainability studies consider hundreds of conflicting metrics. As a demonstration, we determine locations for waste-to-biogas conversion facilities while considering safety, transportation, water quality and cost priorities. Future work includes developing alternate solution metrics to explicitly address fairness and explore the theoretical connections with stochastic programming. I envision novel design methods for sustainable chemical processes based on the proposed framework, where the ambiguity of prioritizing different suitability metrics and life-cycle analysis standards is addressed through a multi-stakeholder perspective.

Stochastic Programming and High Performance Computing

In many engineering systems, uncertainty is addressed through safety margins centered on deterministic analysis. Stochastic analysis helps identify overly conservative designs and operating policies, thus further improving the realistic performance. In stochastic programming, the goal is to optimize a statistic (e.g., expected value) of the objective function while considering the probabilistic nature of input data (e.g., weather, future prices) propagated through constraints. Parallelized algorithms tailored for high performance computing platforms are of increased interest, as complex uncertainty structures can result in stochastic optimization problems with millions of variables and constraints.

Many energy systems benefit from stochastic analysis, as their performance depends both on design decisions and the operating policy. Consider, for example, sizing an energy storage system to be coupled with a solar or wind farm. If only average annual weather is considered, the system will likely be under-designed for extreme events. We are exploring the use of new stochastic programming algorithms to design concentrated solar power systems with thermal storage and other energy systems, while considering large historical datasets for weather and energy prices. This analysis is essential to properly value the flexibility provided by storage.

Energy Markets

We are studying the incentives embedded in multi-scale price signals from wholesale electricity markets for a diverse set of energy and manufacturing systems. We have determined that 70% of the economic opportunities are only available at fast timescales (seconds to minutes). Most literature, however, focuses on slower timescales, such as hourly prices. These findings inform the design of energy and manufacturing systems, and emphasize the importance of dynamic flexibility. This presents enormous new opportunities for energy intensive industrial systems or facilities with onsite utility plants. For example, providing regulation energy services to the market with a combined heat and power system may introduce low amplitude, high frequency (seconds) variations in steam availability for the coupled chemical process. Large separation systems take hours to days to reach steady-state, however, and may be resilient to these additional disturbances. Such a system has inherent dynamic flexibility that is not well understood, valued or utilized. Future research goals focus on characterizing the dynamic flexibility of industrial systems and developing multi-scale optimization architectures to exploit such flexibility. Long-term goals focus on inter-disciplinary research to redesign electricity markets to more effectively incentivize industrial participation and leverage dynamic flexibility from existing infrastructure to cost effectively support wide-scale adoption of wind and solar intermittent generation.

Additional details and publication record are available at www.alexdowling.net.

Teaching Interests:

I am qualified to teach all undergraduate chemical engineering core courses, and especially interested in teaching control, design, thermodynamics, separations/unit operations, mathematics, engineering problem solving, and statistics. I am also excited to teach graduate courses in optimization, numerical methods and process design, and look forward to developing new courses related to energy systems and sustainability.

Presentations at AIChE Annual Meeting: 

A Framework for Multi-Stakeholder Decision-Making and Conflict Resolution
Session: Advances in Optimization I 

Economics and Dynamic Flexibility of Concentrated Solar Power Technologies
Session: Sustainable Electricity: Generation and Storage

Exploiting Dynamic Flexibility to Enable Participation in Multi-Scale Electricity Markets
Session: Energy Systems Design and Operations II