A Mixed-Integer Programming Model for Reconfiguration of Active Distribution Systems Considering Voltage Dependency and Type of Loads and Renewable Sources - Mahdavi, Meisam; Schmitt, Konrad; Chamana, Manohar; Jurado, Francisco; Bayne, Stephen; Marfo, Emmanuel Attah; Awaafo, Augustine (IEEE transactions on industry applications, 2024)
Enhancing the efficiency of distribution systems can be effectively achieved through hybrid reconfiguration and the coordinated operation of distributed generators (DGs). This integrated approach proves to be highly effective in minimizing power losses within distribution networks, with a particular emphasis on the substantial impact of load power and grid voltage on distribution losses and the output of DGs. Load and DG powers are dynamic, varying with fluctuations in voltage levels. Furthermore, the correlation between power demand and renewable sources generation with voltage is contingent upon the specific type of load and DG. Nevertheless, scant attention has been given to these crucial factors in the research pertaining to network reconfiguration and the planning of DG. A limited number of papers have taken into account the type and voltage dependency of DG or load within their respective formulations. However, the reconfiguration models they proposed introduce significant nonlinearity without considering the simultaneous effects of grid voltage on load and DG power, necessitating computation through nonlinear solvers or metaheuristic algorithms. Alternatively, the implementation may require intensive linearization techniques for compatibility with linear solvers. Nonlinear solvers demand extensive computational time, while metaheuristic algorithms cannot ensure the attainment of optimal solutions. Hence, it is crucial to accurately model load behavior when undertaking the reconfiguration of active distribution systems. This paper presents a streamlined reconfiguration model, designed to be easily implemented with conventional optimization tools while ensuring accuracy in identifying appropriate solutions for the reconfiguration problem.