Designing a Multi-objective Novel Strategy for Economic Dispatch solution using Fuzzy sets, Map-reduce, graph algorithms and machine-learning

This paper has a description of a new approach for finding the solution of economic dispatch problem using Fuzzy sets. The objective of the economic dispatch is to minimize the cost of generation. This solution has another objective for risk term, which represents uncertainties in the operation. The risk term is represented by Taguchi's loss function. A multi-objective formulation of the solution using Fuzzy sets is given here. The solution of the multi-objective economic dispatch based on Fuzzy sets, is performed using programming tools and results are presented, here. Next, the second objective for the risk term may be represented using Map-reduce method. The map-reduce method is a common term in context to Big data. Using this method for representing risk term in economic dispatch problem, is a novel strategy, which is described here. Lastly, the computation of risk term using graph algorithms is given and using a classification method of machine learning in the strategy is discussed. This strategy presented here, provides a tool to represent the economic dispatch problem as a multi-objective problem with objectives of cost and risk. The use of machine learning methods helps for its applicability in recent smart-grid scenario, where cloud-computing and Big data have significant impact.