Techno-Economic Optimization and Social Costs Assessment of Microgrid-Conventional Grid Integration using Genetic Algorithm and Artificial Neural Networks: A Case Study for Two US Cities


Through two case studies, a methodology is presented that assessed the techno-economic and environmental performance of microgrid-conventional grid integration scenarios for fifty homes located in US cities of Fargo and Phoenix. The microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H2 tanks. Firstly, mathematical models that predicted the hourly power generation were developed for every microgrid component. Secondly, Artificial Neural Networks were utilized to predict hourly electricity demand and its results were validated with actual available data. Thirdly, through an electricity dispatch strategy and a Genetic Algorithm optimization technique, microgrid configurations were determined that had lowest levelized cost of energy, $/kWh. From peak power standpoint, four microgrid-conventional grid integration scenarios were examined, namely, microgrid possessing penetration level of 25%, 50%, 75%, 100%. Based on the environmental life cycle assessment of power generation, three carbon taxes were imposed -$12, $48, $72/tonne carbon dioxide emitted. Microgrid's electricity cost was found to be $0.43-0.86/kWh. Imposing carbon taxes barely showed any effect on microgrid's electricity cost nor its optimum configuration, but conventional grid's electricity cost was found to increase by 7-33% as its carbon emissions were five times as that of microgrid.


Chemical and Biochemical Engineering

Research Center/Lab(s)

Center for Research in Energy and Environment (CREE)

Second Research Center/Lab

Center for High Performance Computing Research

Keywords and Phrases

Artificial neural network; Carbon tax; Environmental assessment (LCA); Genetic algorithm optimization; Microgrid; Techno-economic assessment

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2019 Elsevier Ltd, All rights reserved.

Publication Date

01 Aug 2019