MILP model for volt-var optimization considering chronological operation of distribution systems containing DERs

• Autor(a) principal: Ferraz, Bibiana Maitê Petry
• Data de Publicação: 2021
• Outros Autores: Resener, Mariana, Pereira, Luís Alberto, Lemos, Flávio Antônio Becon, Haffner, Sérgio Luís
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/225364
• topic: Energia elétrica : Distribuição; Programação linear; Operation planning of distribution system; Volt-var control; Mixed-integer linear programming; K-means clustering; Chronological operation

This paper presents a mixed-integer linear programming model for volt-var optimization considering the chro-nological operation of distribution systems containing distributed energy resources (DERs). The proposed model describes the operation problem of capacitor banks (CBs) and voltage regulators (VRs), and it is further based on the steady-state operation during each time interval contained in typical scenarios of distribution systems. A procedure using a K-means clustering algorithm is used to select the scenarios, thus preserving the simultaneity and chronological combination of different loads and DERs. According to the formulation that we developed, the regulation devices become sensitive to downstream load variations, since we use explicit current variables to control automatic CBs, and since we include means to compensate voltage drops along distribution lines in the control of VRs. The model is validated by comparing the results obtained during several tests of two typical cases with those obtained through nonlinear power flow. The typical case studies presented in the paper highlight the good agreement between the results obtained with the linearized model and with power flow method; further, the practical results confirm that the use of typical scenarios allows representing different levels of loads and DERs, while keeping the validity and performance of the proposed model.