This research addresses the problem of the optimal placement and sizing of (PV) sources in medium voltage distribution grids through the application of the recently developed Newton metaheuristic optimization algorithm (NMA). The studied problem is formulated through a mixed-integer nonlinear programming model where the binary variables regard the installation of a PV source in a particular node, and the continuous variables are associated with power generations as well as the voltage magnitudes and angles, among others. To improve the performance of the NMA, we propose the implementation of a discrete–continuous codification where the discrete component deals with the location problem and the continuous component works with the sizing problem of the PV sources. The main advantage of the NMA is that it works based on the first and second derivatives of the fitness function considering an evolution formula that contains its current solution (xit) and the best current solution (xbest), where the former one allows location exploitation and the latter allows the global exploration of the solution space. To evaluate the fitness function and its derivatives, the successive approximation power flow method was implemented, which became the proposed solution strategy in a master–slave optimizer, where the master stage is governed by the NMA and the slave stage corresponds to the power flow method. Numerical results in the IEEE 34- and IEEE 85-bus systems show the effectiveness of the proposed optimization approach to minimize the total annual operative costs of the network when compared to the classical Chu and Beasley genetic algorithm and the MINLP solvers available in the general algebraic modeling system with reductions of 26.89% and 27.60% for each test feeder with respect to the benchmark cases.

This article proposes a new approach to the operation of unbalanced Active Distribution Systems (ADS) using an economic dispatch optimisation model for Active Distribution Systems Management (ADSM). The model proposes a two-level control strategy. The first one poses an optimisation problem with the objective of minimising total active power losses in the ADS and the second one proposes an algorithm that controls the position of the taps of three-phase on-load tap-changer (OLTC) transformers to ensure compliance with the technical constraints imposed by the Distribution System Operator (DSO). The optimisation problem is solved by MATLAB and DIgSILENT PowerFactory for power systems static simulations. This paper includes a novel peer to peer communication framework between MATLAB /DIgSILENT . The control and optimisation strategy is validated on the IEEE 34-Node Distribution Test Feeder. This network incorporates balanced and unbalanced three-phase loads, single-phase loads in the different phases, and two-phase loads. In this scientific paper, photovoltaic (PV) and wind power generation (WT) have been integrated to test feeder operation, with the support of battery energy storage systems (BESS). The correct operation of the proposed ADSM is demonstrated using numerical simulation on five scenarios considering several configurations of the renewable generation units and the batteries. The strategy has also been validated in a more extensive distribution network, proving its good performance.

This paper discusses the power loss minimization problem in asymmetric distribution systems (ADS) based on phase swapping. This problem is presented using a mixed-integer nonlinear programming model, which is resolved by applying a master–slave methodology. The master stage consists of an improved version of the crow search algorithm. This stage is based on the generation of candidate solutions using a normal Gaussian probability distribution. The master stage is responsible for providing the connection settings for the system loads using integer coding. The slave stage uses a power flow for ADSs based on the three-phase version of the iterative sweep method, which is used to determine the network power losses for each load connection supplied by the master stage. Numerical results on the 8-, 25-, and 37-node test systems show the efficiency of the proposed approach when compared to the classical version of the crow search algorithm, the Chu and Beasley genetic algorithm, and the vortex search algorithm. All simulations were obtained using MATLAB and validated in the DigSILENT power system analysis software.

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