Title
District Energy System Optimization under Uncertainty

CoPED ID
6eec4204-ae15-4937-a600-5f0983c297ce

Status
Active

Funders

Value
No funds listed.

Start Date
Sept. 30, 2020

End Date
Sept. 29, 2024

Description

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Problem or Challenge
In district energy systems, economic risk, regulatory uncertainty, and technology lock-in all weigh on decision makers' choices. Accordingly, model parameters are coming under scrutiny for their inherent uncertainty. More recently, optimisation methods which account for uncertainty have been proposed. However, the quantification, propagation, and management of uncertainty in model parameters continues to pose challenges.
Furthermore, current models are fundamentally ill-suited to project future evolutions of energy demand. In the context of economic volatilities, organizational restructuring, and environmental vulnerabilities, we are likely to see shifts in historic trends of energy consumption. At the moment we have no mechanism to efficiently assimilate these shifts.
MRes/PhD project objectives
This project will investigate the role of digital technologies and new data sources to quantify uncertainties in district energy system optimization. It will explore hybrid-models that combine statistical and numerical modelling for improved management and reduction of uncertainties in model outcomes. Specific emphasis will be on communication of uncertainties for effective decision-making.
PhD project description
The PhD will extend a recent model of district energy optimization in the following aspects: (a) representation of additional components and technologies (for eg. EV charging within the district energy network), (b) develop a systematic methodology to quantify uncertainties in model inputs, especially those pertaining to resilience of the system. This will be carried out by exploiting digital technologies (c) propagation, management, and communication of uncertainties through novel combinations of statistical and numerical modelling.
MRes component
- A thorough literature review of the state-of-the-art in stochastic optimization
- The component (a) listed above, along with identification of relevant digital technologies.
PhD - Expected Outcomes, Contributions to Knowledge & Practice
- A tool for stochastic energy system optimization
- Data assimilation across the system lifetime to assess changes in system operation
- Efficient display and communication of model outputs through appropriate computational platforms.

University of Cambridge LEAD_ORG
BP International Limited STUDENT_PP_ORG
Arup Group Ltd STUDENT_PP_ORG

Ruchi Choudhary SUPER_PER
Teresa Irigoyen Lopez STUDENT_PER

Subjects by relevance
  1. Optimisation
  2. Uncertainty
  3. Energy
  4. Energy efficiency
  5. Modelling (creation related to information)
  6. Simulation
  7. Digital technology
  8. Technology
  9. Mathematical models
  10. Energy systems
  11. Energy consumption (energy technology)

Extracted key phrases
  1. District Energy System Optimization
  2. District energy system
  3. Stochastic energy system optimization
  4. District energy network
  5. Inherent uncertainty
  6. Regulatory uncertainty
  7. Model parameter
  8. Energy demand
  9. Energy consumption
  10. Current model
  11. Recent model
  12. Model outcome
  13. Model input
  14. Model output
  15. Challenge

Related Pages

UKRI project entry

UK Project Locations