Multistage Planning Model for Active Distribution Systems and Electric Vehicle Charging Stations Considering Voltage-Dependent Load Behavior

• Autor(a) principal: Mejia, Mario A. [UNESP]
• Data de Publicação: 2022
• Outros Autores: Macedo, Leonardo H. [UNESP], Munoz-Delgado, Gregorio, Contreras, Javier, Padilha-Feltrin, Antonio [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/TSG.2021.3125786; http://hdl.handle.net/11449/234229
• topic: Active distribution systems; electric vehicle charging stations; mixed-integer linear programming; multistage planning; voltage-dependent load model

This work proposes a novel mixed-integer linear programming model for the medium-term multistage planning of active distribution systems and electric vehicle charging stations (EVCSs). Investment alternatives include the installation of conductors, capacitor banks, voltage regulators, dispatchable and nondispatchable distributed generation, energy storage units, and EVCSs. Hence, the model identifies the best size, location, and installation time for the candidate assets under the uncertainty associated with electricity demand, energy prices, renewable energy sources, and EVCSs' load profiles. Unlike classical planning approaches, conventional load is modeled as voltage-dependent. Besides, EVCSs are planned by zones to optimize the coverage of the service provided to users of electric vehicles and to reduce the discrepancy between the geographical requirements and the optimal locations for the installation of EVCSs in the system. EVCSs' load profiles are calculated using a travel simulation algorithm based on real travel patterns that consider fast, slow, and residential chargers. Moreover, as another salient feature, constraints for CO2 emissions are incorporated into the model. The resulting model is formulated as a stochastic scenario-based program, which is driven by the minimization of the total expected cost. Tests are conducted using a 69-node system to demonstrate the effectiveness of the proposed model.