Distributed energy resources network connection considering reliability optimization using a NSGA-II algorithm

Abstract

Abstract-Distributed Energy Resources (DER) has been widely introduced in distribution networks in response to the increase of environmental awareness of the consumers. The benefits with the use of DER are increased with network reconfiguration, but in some countries exists the impossibility of island operation as well as the obligation to buy all the electricity generated. These limitations, in a network fault situation, causes a waste of resources, because of the unavailability of the DER. On other hand, the location of DER connection under the context of the improvement of the reliability indexes has not been studied. Thus, this paper will propose a multi objective optimization of the location of DER connection considering the switching devices placement to increase network reliability and availability of DER, minimizing at the same time the investment in equipment in a no island operation environment. For the resolution of the proposed formulation, it will be used the multi objective algorithm NSGA-II (Fast Non-dominated Sorting Genetic Algorithm). The formulation of the problem also considers a composite index made up of DER and interruption duration. This approach is applied to a real utility distribution network, with the results presented and discussed.

Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation

“…In [6], the reconfiguration strategy of grid switches was considered for simultaneously optimizing power losses, voltage deviation, and service outage. In [7], optimization of equipment cost and service reliability was achieved by optimizing the allocation of distributed generation and reconnecting switches.…”

Multi-Criteria Analysis for decision making applied to active distribution network planning

“…From each evaluation of (2), the higher overall score and the label of the related alternative being collected. This final score is calculated as the sum of the overall score obtained by the best alternative on each AHP run, as shown in (4).…”

“…This approach does not allow minimising/maximising the OFs independently; with the aim to find a number of optimal planning alternatives without introducing any a priori subjective preferences, a "true" multi-objective algorithm can be more effective [1]. The multi-objective a posteriori methods based on evolutionary algorithms have been widely studied for devising Active Distribution Network (ADN) planning alternatives [4][5][6][7]. The main advantage of multi-objective evolutionary algorithms used for network planning is the devising of a set of Pareto optimal alternatives of abstruse planning problems.…”

Model of a Composite Energy Storage System for Urban Rail Trains

“…The Pareto optimal solution set of this kind of integrated optimisation problem can be obtained by optimising the control strategy parameters with both consumption and emission as the optimisation objectives. This solution can provide various options for setting control strategy parameters [11][12][13][14][15].…”