Constrained Scheduling of Step-Controlled Buffering Energy Resources with Ant Colony Optimization

2020 ◽  
pp. 68-81
Author(s):  
Jörg Bremer ◽  
Sebastian Lehnhoff
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Energy Resources

scholarly journals Ant colony optimization for feasible scheduling of step-controlled smart grid generation

2021 ◽  
Author(s):  
Jörg Bremer ◽  
Sebastian Lehnhoff
Keyword(s):  
Ant Colony Optimization ◽  
Combinatorial Problem ◽  
Electrical Energy ◽  
Intermediate Energy ◽  
Ant Colony ◽  
Energy Resources ◽  
Artificial Ants ◽  
Decentralized Energy ◽  
Feasible Solutions ◽  
Energy Buffer

AbstractThe electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized energy resources will have to responsibly perform control tasks like frequency or voltage control. For many use cases, scheduling of energy resources is necessary. In the multi-dimensional discrete case–e.g.,  for step-controlled devices–this is an NP-hard problem if some sort of intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization, hence, becomes a difficult task. We prove the NP-hardness for the example of co-generation plants and demonstrate the multi-modality of systematically designing feasible solutions. For the example of day-ahead scheduling, a model-integrated solution based on ant colony optimization has already been proposed. By using a simulation model for deciding on feasible branches, artificial ants construct the feasible search graphs on demand. Thus, the exponential growth of the graph in this combinatorial problem is avoided. We present in this extended work additional insight into the complexity and structure of the underlying the feasibility landscape and additional simulation results.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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