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DOI:10.35833/MPCE.2022.000400
A Mixed-integer Linear Programming Model for Defining Customer Export Limit in PV-rich Low-voltage Distribution Networks
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Author: Pedro P. Vergara1, Juan S. Giraldo2, Mauricio Salazar3, Nanda K. Panda1, Phuong H. Nguyen3

Author Affiliation: 1.Intelligent Electrical Power Grids (IEPG) Group, Delft University of Technology, Delft 2628CD, The Netherlands
2.Energy Transition Studies Group, Netherlands Organisation for Applied Scientific Research (TNO), Amsterdam, 1043 NT, The Netherlands
3.Electrical Energy Systems (EES) Group, Eindhoven University of Technology, Eindhoven, The Netherlands

Foundation:
Abstract: A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Keywords:
Low-voltage distribution network ; photovoltaic (PV) curtailment ; optimal power flow ; Monte Carlo simulations
Received:July 07, 2022               Online Time:2023/01/28
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