Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

Abstract

Abstract:In this work, we describe an approach that allows for optimizing the structure of a smart grid (SG) with renewable energy (RE) generation against abnormal conditions (imbalances between generation and consumption, overloads or failures arising from the inherent SG complexity) by combining the complex network (CN) and evolutionary algorithm (EA) concepts. We propose a novel objective function (to be minimized) that combines cost elements, related to the number of electric cables, and several metrics that quantify properties that are beneficial for SGs (energy exchange at the local scale and high robustness and resilience). The optimized SG structure is obtained by applying an EA in which the chromosome that encodes each potential network (or individual) is the upper triangular matrix of its adjacency matrix. This allows for fully tailoring the crossover and mutation operators. We also propose a domain-specific initial population that includes both small-world and random networks, helping the EA converge quickly. The experimental work points out that the proposed method works well and generates the optimum, synthetic, small-world structure that leads to beneficial properties such as improving both the local energy exchange and the robustness. The optimum structure fulfills a balance between moderate cost and robustness against abnormal conditions. Our approach should be considered as an analysis, planning and decision-making tool to gain insight into smart grid structures so that the low level detailed design is carried out by using electrical engineering techniques.

Analysis of the Effect of Parameter Variation on a Dynamic Cost Function for Distributed Energy Resources: A DER-CAM Case Study

“…The second category consists of energy sources that are renewed on a frequent basis (for, instance daily) such as wind turbines, solar photovoltaic (PV) panels, and stationary battery banks. The present increase of small-scale urban PV in buildings [3] and urban wind generators [4] can help home electricity consumers also become producers using the smart grid (SG) concept [5][6][7] and the microgrids approach [8,9].…”

“…A review of the key concepts of smart grids can be found in Cuadra et al, . Specifically, Cuadra et al [5] explore the optimization of distribution smart grids with distributed renewable generation using a novel approach based on complex network concepts [10] with evolutionary algorithms. The use of evolutionary algorithms and other computational intelligence techniques in energy can be found in Ardabili et al, Cuadra et al and Jiang et al [11][12][13].…”

“…Popular search strategies include stochastic, deterministic, and evolutionary algorithms [16]. The fundamental concepts of evolutionary algorithms (EAs) are inspired by two biological phenomena [12,17]: (1) the characteristics of living beings are encoded (represented) using genetic information; and (2) evolution is the result of the interaction between the random creation of new genetic information and the selection of those individuals that are best adapted to the ecosystem [5]. In an EA, candidate solutions ('individuals') for the OF are encoded in a way that simplifies the search for the optimal solution.…”

“…When fulfilling a stop criterion, the EA ends in providing the solution that optimizes the OF (the individual that is best adapted to the ecosystem, in the biological analogy). More details of EAs applied to DER can be found in Cuadra et al [5]. With respect to evolutionary algorithms, they are classified as single-objective, multi-objective, or many-objective.…”

Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event

“…Distributed Energy Resource (DER) generations and microgrids: they provide the option to self-supplies local area when their feeder is disrupted [4,[86][87][88].…”