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Details of Grant 

EPSRC Reference: EP/T001577/1
Title: Algorithms for Industrial Demand-Side Management Under Uncertainty
Principal Investigator: Tsay, Mr C
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Lancaster University Praxair Inc Process Systems Enterprises Ltd
Department: Computing
Organisation: Imperial College London
Scheme: EPSRC Fellowship
Starts: 01 April 2020 Ends: 31 March 2023 Value (£): 350,543
EPSRC Research Topic Classifications:
Energy Efficiency
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
29 Jan 2020 Engineering Fellowship Interview Panel 29 and 30 January 2020 Announced
03 Dec 2019 Engineering Prioritisation Panel Meeting 3 and 4 December 2019 Announced
Summary on Grant Application Form
Uncertainties are present in many energy-related process design (e.g., how should a process be configured?) and operational (e.g., what is the best production schedule for a day/week?) optimisation problems of current industrial interest. The efficiency of an energy-intensive hydrogen production plant can be greatly improved by optimising the steam-methane reformer, but design decisions regarding the reformer are subject to uncertain catalyst performance. Likewise, an electricity-intensive air separation unit can derive economic savings and reduce peak power demand by engaging in demand-response; however, deciding optimal production schedules relies on uncertain forecasts of electricity supply and product demand. Regrettably, state-of-the-art software is not suitable for decision-making under these uncertain conditions, severely limiting the benefits of industrial demand-side management (DSM) towards national energy efficiency. Here, DSM refers to measures of improving the energy system at the side of consumption, ranging from reducing overall demand by increasing process efficiencies to smarter consumption patterns through demand response operation. Demand response (DR) operation aims to increase the systemic integration of volatile renewable energy sources by matching consumption to the short-term and long-term (daily to seasonal) fluctuations in supply.

Motivated by the above, this interdisciplinary project will introduce Algorithms for Industrial Demand-Side Management Under Uncertainty. The potential of curtailing carbon emissions through improving the efficiency of energy-intensive process industries is massive, with industrial entities comprising 17% of total energy consumption in the United Kingdom in 2017. DR operation in the electricity-intensive process industries further reduces carbon emissions by synchronising demand with renewable-based generation. Therefore, a complete DSM decision-making toolkit must consider uncertainty in both design and operational decisions of process systems. In modern environments, these tools must also be computationally scalable, synergise with the abundant available data, and accompany decisions with rationale. The proposed scientific advances have numerous immediate applications: optimising energy efficiency in manufacturing, balancing the power grid through DR, and mitigating negative effects of disturbances.

The primary observation of the proposed research is that modern markets and environments dictate a deviation from the accepted paradigm of deterministic (i.e., no uncertainty is modeled), local (i.e., risks sub-optimal decision-making) optimisation. The process industries require a new generation of decision-making algorithms that can solve, and re-solve, large-scale optimisation problems to global optimality, often in an online or recurring fashion. The proposed research introduces DSM technologies that: (1) automatically decompose process models for global optimisation, (2) exploit historical operating data for planning and scheduling, and (3) produce explainable results for user-friendly re-optimisation. The fellowship will be held at the Department of Computing at Imperial College, which has an outstanding reputation and provides an ideal environment for the proposed software advances. Imperial is also the birthplace of the field of process systems engineering (PSE) and thus is a premier forum for applied PSE research. By providing freely available software tools, we will contribute to the forefront of PSE, as well as relevant related domains of optimisation theory, data science, and artificial intelligence. Finally, promoting the algorithmic advancements by releasing and contributing to open-source software will spur new academic and industrial applications in computational decision-making for energy efficiency.

Key Findings
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Organisation Website: http://www.imperial.ac.uk