A Two-Layer Energy Management Strategy for Networks Including Flexible Industrial Energy Hubs

• Autores: Sina Parhoudeh
• Directores de la Tesis: Pablo Eguía López (dir. tes.), Abdollah Kavousi Fard (codir. tes.)
• Lectura: En la Universidad del País Vasco - Euskal Herriko Unibertsitatea ( España ) en 2025
• Idioma: inglés
• Programa de doctorado: Programa de Doctorado en Sistemas de Energía Eléctrica por la Universidad de Málaga; la Universidad de Sevilla; la Universidad del País Vasco/Euskal Herriko Unibertsitatea y la Universidad Politécnica de Catalunya
• Menciones:
  • Internacional
• Calificación: Sobresaliente Cum Laude
• Enlaces
  • Tesis en acceso abierto en: ADDI
• Resumen

  • The main purpose of this thesis is to propose a comprehensive two-layer energy management strategy to optimize the operation of industrial energy hubs within multi-energy networks. The approach pursue minimizing energy losses in the energy networks while maximizing the financial gains of flexible industrial energy hubs through participation in energy, reactive power, and day-ahead reserve regulation markets. This is achieved via a bi-level optimization model including upper-level which model expected energy losses minimization in electrical, gas and heat networks under the constrain of optimal power flow of these networks, while the economic benefits maximization of industrial energy hubs in energy, reactive power and reserve markets is pursued in lower level problem. To enhance computational efficiency, a Karush-Kuhn-Tucker (KKT)-based method is employed to convert the bi-level problem into a single-level formulation, while combined stochastic-robust planning addresses uncertainties in load, market prices, renewable output, reservation demand, and mobile storages. An extension further incorporates renewable/non-renewable resources, hydrogen and thermal storages, and electric car chargers with a fuzzy decision-making process for compromise solutions.

    Validation through extensive numerical case studies confirms that the proposed methods yield substantial benefits—achieving up to 28.5% reduction in energy losses, 39% lower maximum temperature, and 27.8% decreased voltage drop—while maintaining robust profitability, full hub flexibility, and resilience against prediction errors.