Multi-Microgrid System Optimization Addressing the Uncertainties in Generation and Load Demand

A complete graph network imposes high costs for power routing in a multi-microgrid system when considering a separate connection between every two microgrids. Clustering highly interacting microgrids into subsets yields a more cost-effective substitute for the complete graph assumption. In this paper, we develop a mathematical optimization framework to capture the interactions between multiple microgrids in a multi-microgrid energy management system. The framework aims to achieve optimal profit while meeting the load demand for the system. Each microgrid is subject to various sources of energy generation that entail uncertainty. Moreover, this paper proposes a model that could cluster these microgrids through optimization, in which the microgrids can transact energy within their respective clusters. Our model has yielded savings that could prove to be beneficial in the long run. Also, this project provides a stepping stone into dynamic pricing, which could be implemented to manipulate the consumers' behavior in terms of energy consumption. This body of work has proven to come up with economical, environmentally, and more reliable energy transactions.