Loop-based microgrids are signified by their high reliability in islanded and grid-connected operations. This paper proposes an iterative procedure for the optimal design of a microgrid topology in active distribution networks, which applies graph partitioning, integer programming, and performance index for the optimal design. The proposed approach avoids infeasible and non-optimal designs of microgrid structures and provides remedial solutions for enhancing our previous topology design method. The numerical results for a microgrid test system show that the proposed designated steps can optimize a loopbased microgrid structure in an active distribution network.

Microgrids have presented themselves as an effective concept to guarantee a reliable, efficient and sustainable electricity delivery during the current transition era from passive to active distribution networks. Moreover, microgrids could offer effective ancillary services (AS) to the power utility, although this will not be possible before the traditional planning and operation methodologies are updated. Hence, a probabilistic multi-objective microgrid planning (POMMP) methodology is proposed in this paper to contemplate the large number of variables, multiple objectives, and different constraints and uncertainties involved in the microgrid planning. The planning methodology is based on the optimal size and location of energy distributed resources with the goal of minimizing the mismatch power in islanded mode, while the residual power for AS provision and the investment and operation costs of the microgrid in grid-connected mode are maximized and minimized, respectively. For that purpose, probabilistic models and a true multi-objective optimization problem are implemented in the methodology. The methodology is tested in an adapted PG&E 69-bus distribution system and the non-dominated sorting genetic algorithm II (NSGA-II) optimization method and an analytic hierarchy process for decision-making are used to solve the optimization problem.

Increased use of renewable energies in smart microgrids (SMGs) present new technical challenges to system operation. SMGs must be self-sufficient and operate independently; however, when more elements are integrated into SMGs, as distributed energy resources (DER), traditional explicit mathematical formulations will demand too much data from the network and become intractable. In contrast, tools based on optimization with metaheuristics can provide near optimal solutions in acceptable times. Considering this, this paper presents the variable neighborhood search differential evolutionary particle swarm optimization (VNS-DEEPSO) algorithm to solve multi-objective stochastic control models, as SMGs system operation. The goal is to control DER while maximizing profit. In this work, DER considered the bidirectional communication between energy storage systems (ESS) and electric vehicles (EVs). They can charge/discharge power and buy/sell energy in the electricity markets. Also, they have elements such as traditional generators (e.g., reciprocating engines) and loads, with demand response/control capability. Sources of uncertainty are associated with weather conditions, planned EV trips, load forecasting and the market prices. The VNS-DEEPSO algorithm was the winner of the IEEE Congress on Evolutionary Computation/The Genetic and Evolutionary Computation Conference (IEEE-CEC/GECCO 2019) smart grid competition (with encrypted code) and also won the IEEE World Congress on Computational Intelligence (IEEE-WCCI) 2018 smart grid competition (these competitions were developed by the group GECAD, based at the Polytechnic Institute of Porto, in collaboration with Delft University and Adelaide University). In the IEEE-CEC/GECCO 2019, the relative error improved between 32% and 152% in comparison with other algorithms.

This study seeks to determine a suitable arbitrage strategy that allows a battery energy storage system (BESS) owner to obtain the maximum economic benefits when participating in the Colombian electricity market. A comparison of different arbitration strategies from the literature, such as seasonal, statistical, and neural networks-based models, is performed. To determine BESS’s optimal operation, a Mixed Integer Linear Programming (MILP) optimization problem is formulated, including a battery degradation model based on an upper piecewise linear approximation method. A financial evaluation of the different arbitrage strategies is carried out, resulting, for all the analyzed cases, in a negative net present value (NPV); thus, the results show that the income obtained from BESS when only performing energy arbitrage in the Colombian market do not compensate the investment costs. Results have also shown that strategies based on statistical and prediction models have a better performance than seasonal strategies, especially in atypical circumstances such as COVID-19.

One of the main problems in electric vehicles is the volume of their electrical systems because their bulky components carry additional mass and high cost to the total system. On this topic, interleaving‐phases and magnetic coupling techniques have been reported as effective methods for increasing the power density of the DC–DC converters that work between the storage unit and the motor inverter. In that sense, a volume assessment of these topologies would provide a better understanding of the problems to be faced when an electric power train is designed. In this paper, a volume modelling methodology is introduced with the purpose of comparing four different DC–DC converters: Single‐Phase Boost, Two‐Phase Interleaved with non‐coupled inductors, Loosely Coupled Inductor (LCI), and Integrated Winding Coupled Inductor (IWCI). The analysis considers the volume of magnetic components, power devices (conventional and next‐generation), cooling devices and capacitors. The methodology can be used as a part of an optimization procedure to minimize the volume of DC–DC converters. Conclusively, LCI and IWCI were found effective to miniaturize power converters with a power density of 8.4 W/cc and 9.66 W/cc, respectively. Moreover, a maximum efficiency of 98.04% and 97.61% was obtained for a 1kW LCI and IWCI prototypes, respectively.