Robust Dynamic Transmission and Renewable Generation Expansion Planning: Walking Towards Sustainable Systems

Recent breakthroughs in Dynamic Transmission Network Expansion Planning (DTNEP) have demonstrated that the use of robust optimization, while maintaining the full temporal dynamic complexity of the problem, renders the capacity expansion planning problem considering uncertainties computationally trac...

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Detalhes bibliográficos
Autores: Roldán, Cristina, Sánchez de la Nieta, Agustín Alejandro, García Bertrand, Raquel, Mínguez, Roberto
Formato: artículo
Fecha de publicación:2018
País:España
Recursos:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/16411
Acesso em linha:https://hdl.handle.net/10578/16411
Access Level:acceso abierto
Palavra-chave:Power systems
Renewable generation expansion planning
Robust optimization
Transmission network expansion planning
Descrição
Resumo:Recent breakthroughs in Dynamic Transmission Network Expansion Planning (DTNEP) have demonstrated that the use of robust optimization, while maintaining the full temporal dynamic complexity of the problem, renders the capacity expansion planning problem considering uncertainties computationally tractable for real systems. In this paper an adaptive robust formulation is proposed that considers, simultaneously: i) a year-by-year integrated representation of uncertainties and investment decisions, ii) the capacity expansion lines have and iii) the construction and/or dismantling of renewable and conventional generation facilities as well. The Dynamic Transmission Network and Renewable Generation Expansion Planning (DTNRGEP) problem is formulated as a three-level adaptive robust optimization problem. The first level minimizes the investment costs for the transmission network and generation expansion planning, the second level maximizes the costs of operating the system with respect to uncertain parameters, while the third level minimizes those operational costs with respect to operational decisions. The method is tested on two cases: i) an illustrative example based on the Garver IEEE system and ii) a case study using the IEEE 118-bus system. Numerical results from these examples demonstrate that the proposed model enables optimal decisions to be made in order to reach a sustainable power system, while overcoming problem size limitations and computational intractability for realistic cases.