Asignación óptima de recursos energéticos a través de algoritmo Húngaro y Bipartite Matching para respuesta a la demanda en microredes.

En el presente documento, se implementó dos modelo matemático para encontrar en forma óptima un despacho energético al menor costo, en base a un sistema de microred de distribución eléctrica, que permita reducir el costo de energía hacia la demanda, se considera analizar los resultados de dichos algoritmos para realizar una comparación de cual modelo matemático asigna de forma más óptima al menor costo posible en comparación a un despacho convencional, se plantea el problema en base a centrales eléctricas de energías renovables y no renovables, mediante una heurística en base al modelo húngaro y el modelo bipartite matching para tener una óptima respuesta a la demanda, en conclusión en este trabajo se pretendió conseguir la mejor heurística para la asignación de los recursos energéticos y que se observe un ahorro significativo en el consumo de energía. La solución se conseguirá en base a programación lineal implementando los algoritmos que serán desarrollados en la plataforma de MATLAB para obtener la mejor respuesta en optimización. La respectiva contribución es analizar y describir un algoritmo de asignación y equilibrio en base a costos de energía para consumidores.In this document, two mathematical models were implemented to optimally find an energy dispatch at the lowest cost, based on a microgrid reductive distribution system, which allows to reduce the energy cost towards the demand, it is considered to analyze the results of these algorithms to make a comparison of which mathematical model allocates more optimally at the lowest possible cost compared to a conventional dispatch, the problem arises based on renewable and non-renewable power plants, using a heuristic based on the Hungarian model and the bipartite matching model to have an optimal response to demand, in conclusion in this work we tried to achieve the best heuristic for the allocation of energy resources and to observe a significant saving in energy consumption. The solution will be achieved based on linear programming by implementing the algorithms that will be developed in the MATLAB platform to obtain the best response in optimization. The respective contribution is to analyze and describe an allocation and equilibrium algorithm based on energy costs for consumers

Computing Maximum Cardinality Matchings in Parallel on Bipartite Graphs via Tree-Grafting

It is difficult to obtain high performance when computing matchings on parallel processors because matching algorithms explicitly or implicitly search for paths in the graph, and when these paths become long, there is little concurrency. In spite of this limitation, we present a new algorithm and its shared-memory parallelization that achieves good performance and scalability in computing maximum cardinality matchings in bipartite graphs. Our algorithm searches for augmenting paths via specialized breadth-first searches (BFS) from multiple source vertices, hence creating more parallelism than single source algorithms. Algorithms that employ multiple-source searches cannot discard a search tree once no augmenting path is discovered from the tree, unlike algorithms that rely on single-source searches. We describe a novel tree-grafting method that eliminates most of the redundant edge traversals resulting from this property of multiple-source searches. We also employ the recent direction-optimizing BFS algorithm as a subroutine to discover augmenting paths faster. Our algorithm compares favorably with the current best algorithms in terms of the number of edges traversed, the average augmenting path length, and the number of iterations. We provide a proof of correctness for our algorithm. Our NUMA-aware implementation is scalable to 80 threads of an Intel multiprocessor and to 240 threads on an Intel Knights Corner coprocessor. On average, our parallel algorithm runs an order of magnitude faster than the fastest algorithms available. The performance improvement is more significant on graphs with small matching number