  In this paper, we develop novel mathematical models to optimize utilization
of community energy storage (CES) by clustering prosumers and consumers into
energy sharing communities/microgrids in the context of a smart city. Three
different microgrid configurations are modeled using a unifying mixed-integer
linear programming formulation. These configurations represent three different
business models, namely: the island model, the interconnected model, and the
Energy Service Companies model. The proposed mathematical formulations
determine the optimal households' aggregation as well as the location and
sizing of CES. To overcome the computational challenges of treating operational
decisions within a multi-period decision making framework, we also propose a
decomposition approach to accelerate the computational time needed to solve
larger instances. We conduct a case study based on real power consumption,
power generation, and location network data from Cambridge, MA. Our
mathematical models and the underlying algorithmic framework can be used in
operational and strategic planning studies on smart grids to incentivize the
communitarian distributed renewable energy generation and to improve the
self-consumption and self-sufficiency of the energy sharing community. The
models are also targeted to policymakers of smart cities, utility companies,
and Energy Service Companies as the proposed models support decision making on
renewable energy related projects investments.
