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SUMMARY:Modelling and algorithms for energy system investment planning und
 er uncertainty -  Dr. Hongyu Zhang (Norwegian University of Science and Te
 chnology)  
DTSTART:20240311T110000Z
DTEND:20240311T120000Z
UID:TALK212767@talks.cam.ac.uk
CONTACT:Communications\, CEB
DESCRIPTION:we will welcome you via Microsoft Team: https://teams.microsof
 t.com/l/meetup-join/19%3ameeting_ZTU3OWMyYmYtMzU4Mi00MmY0LWE4NTItYmQ0ZmI5O
 DdiNmJk%40thread.v2/0?context=%7b%22Tid%22%3a%2249a50445-bdfa-4b79-ade3-54
 7b4f3986e9%22%2c%22Oid%22%3a%22026c67ed-9f3a-40b8-9234-48eb6576dfe8%22%7d\
 n\nWe propose the REORIENT (REnewable resOuRce Investment for the ENergy T
 ransition) model for energy systems planning with the following novelties:
  (1) integrating capacity expansion\, retrofit and abandonment planning\, 
 and (2) using multi-horizon stochastic mixed-integer linear programming wi
 th short-term and long-term uncertainty. We apply the model to the Europea
 n energy system considering: (a) investment in new hydrogen infrastructure
 s\, (b) capacity expansion of the European power system\, (c) retrofitting
  oil and gas infrastructures in the North Sea region for hydrogen producti
 on and distribution\, and abandoning existing infrastructures\, and (d) lo
 ng-term and short-term uncertainty. We utilise the structure of multi-hori
 zon stochastic programming and propose enhanced Benders decomposition meth
 ods to solve the model efficiently. We propose: (1) stabilising Adaptive B
 enders with the level method and adaptively selecting the subproblems to s
 olve per iteration for more accurate information\, (2) a centre point stab
 ilisation approach when the level set problem is hard to solve\, and (3) d
 ynamic level set management to improve the robustness of the algorithm by 
 adjusting the level set per iteration. \n\n \n\nWe first conduct a sensiti
 vity analysis on retrofitting costs of oil and gas infrastructures. We the
 n compare the REORIENT model with a conventional investment planning model
  regarding costs and investment decisions. Finally\, four algorithms are i
 mplemented for solving LP instances with up to 1 billion variables and 4.5
  billion constraints\, and two algorithms are implemented for MILP instanc
 es with high degeneracy. The results show that: (1) when the retrofitting 
 cost is below 20% of the cost of building new ones\, retrofitting is econo
 mical for most of the existing pipelines\, (2) compared with a traditional
  investment planning model\, the REORIENT model yields 24% lower investmen
 t cost in the North Sea region\, and (3) for a 1.00% convergence tolerance
 \, the enhanced Benders is up to 6.8 times faster than the reference algor
 ithm for MILP instances\, and is up to 113.7 times faster than standard Be
 nders and 2.14 times faster than unstabilised Adaptive Benders for LP inst
 ances. Also\, for a 0.10% convergence tolerance\, the enhanced Benders is 
 up to 45.5 times faster than standard Benders for LP instances\, and unsta
 bilised Adaptive Benders cannot solve the largest instance to convergence 
 tolerance due to severe oscillation. Finally\, the dynamic level set manag
 ement makes the algorithms more robust and is very helpful for solving lar
 ge problems.\n\n \n\nSpeaker bio: \n\nDr. Hongyu Zhang is a Researcher (pe
 rmanent position) at the Department of Industrial Economics and Technology
  Management\, Norwegian University of Science and Technology. He received 
 a PhD degree in Operational Research from Norwegian University of Science 
 and Technology in 2024\, an MSc degree in Operational Research with Data S
 cience from The University of Edinburgh in 2020\, and a BSc degree in Math
 ematics and Applied Mathematics from Huaqiao University in 2019. His resea
 rch interests include: (1) stochastic programming in the investment planni
 ng of energy systems\,  (2) decomposition algorithms for large-scale optim
 isation problems\, and (3) large-scale system analysis regarding power\, n
 atural gas\, hydrogen\, and carbon capture and storage among others. More 
 information can be found at www.hongyuzhang.com.
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