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Distributed secondary control stands out for its flexibility and expandability in microgrids (MGs) control, in where communication network plays a fundamental and critical role. The communication topology and link-weights have significant impact on the attack-resilience and dynamic performance of MGs, which should be appropriately designed. However, the joint optimization problem of communication topology and link-weights in the existing literature of consensus-based secondary voltage control of MGs is usually neglected. To bridge this gap, we propose a novel two-stage optimization approach for the communication network design, which jointly optimizes the topological structure to enhance the structural survivability and link weights to improve the dynamic performance (including the speed of convergence and robustness to time-delay). The first stage problem is formulated into a mixed-integer semi-define programming (MISDP) model based on convex relaxation technique, which is then converted equivalently into an integer quadratic programming (IQP) problem and then a MISDP feasibility problem to facilitate the solution. The second stage problem is formulated into a bi-objective SDP model to compromise between the convergence performance and robustness to time-delay. Simulations based on a microgrid with 10 distributed generation (DG) units under different scenarios are implemented to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Obtaining high‐quality solutions to the minimum label spanning tree problem is of crucial importance to efficient communication network design, since such solutions can reduce both the construction cost and the complexity of the ultimate architecture. However, the corresponding optimization task was shown to be hard even for complete graphs. As a consequence, no computationally efficient method for solving this problem exactly in a reasonable time is known to exist and one has to rely on approximation techniques such as heuristic and evolutionary algorithms. In this study, we investigate the performance of a different method called the Cross‐Entropy algorithm which relies on rigorous developments in the fields of information theory and stochastic simulation. Our findings indicate that the mathematical soundness of the Cross‐Entropy method makes it very reliable and robust as compared to its counterparts. In particular, the obtained results suggest that the Cross‐Entropy method is not sensitive to different graph models and that the proposed algorithm can obtain optimal or near‐optimal solutions while using a reasonable computational effort. [ABSTRACT FROM AUTHOR]

This paper explores the use of the meta-heuristic search algorithm Simulated Annealing for solving a minimum cost network synthesis problem. This problem is a common one in the design of telecommunication networks. The formulation we use models a number of practical problems with hop-limit, degree and capacity constraints. Emphasis is placed on a new approach that uses a knapsack polytope to select amongst a number of pre-computed traffic routes in order to synthesise the network. The advantage of this approach is that a subset of the best routes can be used instead of the whole set, thereby making the process of designing large networks practicable. Using simulated annealing, we solve moderately large networks (up to 30 nodes) efficiently. [ABSTRACT FROM AUTHOR]

This article presents the application of steady state genetic algorithms (SSGA) to minimize the total installation cost of a communication network by optimally designing the topology layout and assigning the corresponding capacities (TDCA problem). This highly constrained optimization problem is shown to be better solved using GAs. A binary representation of links between node pairs is developed and tested on a network of 20 nodes. Improved results, both in terms of network cost, performance and computation speed, are obtained when comparing with existing heuristic approaches [ABSTRACT FROM PUBLISHER]