Multi-objective optimization with post-pareto optimality analysis for the integration of storage systems with reactive-power compensation in distribution networks

摘要

The integration of energy storage systems in power distribution networks allows to obtain several benefits, such as, the minimization of energy losses, the improvement of voltage profile and the reduction of the energy costs. However, due to the high cost of these energy storage systems, this integration must be carefully applied. Thus, this work proposes the integration of energy storage systems based on a multiobjective optimization. The type of storage systems that is considered are the batteries. These systems require electronic power converters as an interface between the batteries and the grid. Thus, this work uses those converters to supply an ancillary service, more specifically, reactive power compensation. In this way, besides the peak shaving, the optimization approach will also consider the reactive-power compensation, allowing to improve the capital investment return of these systems. The reactive power compensation considers the maximum active power of the converter, to minimize the cost of the system. In consequence, when the energy storage system is at its maximum discharge mode, the reactive power compensation function will be inhibited. Since the multi-objective optimization generates a Pareto-optimal set with a large number of solutions, an approach to support the choice of the solution is also proposed. This approach considers a new post-Pareto analysis, which is based on the sum of the ranking differences. To demonstrate the applicability of the proposed approach, a case study using the 94-bus real test feeder is presented. Three scenarios tests are also presented for the post-pareto optimality analysis, each considering different weights for the objective functions. The results show that even for a specific case where the weights are assigned for each of the objective functions, more than one solution is obtained.