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Three Network Design Problems for Community Energy Storage

Bissan Ghaddar, Ivana Ljubic, Yuying Qiu

Abstract

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.

Three Network Design Problems for Community Energy Storage

Abstract

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.
Paper Structure (30 sections, 18 equations, 12 figures, 14 tables)

This paper contains 30 sections, 18 equations, 12 figures, 14 tables.

Table of Contents

  1. Introduction
  2. Related work and contribution
  3. Contribution:
  4. Three Microgrid Models
  5. Island model
  6. Interconnected model
  7. ESCO model
  8. Summary of the proposed models
  9. Mathematical model
  10. Notations
  11. Island model
  12. Interconnected model
  13. ESCO model
  14. Benders Decomposition
  15. Case study: Cambridge, MA
  16. ...and 15 more sections

Figures (12)

  • Figure 1: A simple architecture of island model clusters
  • Figure 2: A simple architecture of interconnected model clusters. The architecture is taken from the battery storage perspective, not showing the consumers' or prosumers' connection to the grid.
  • Figure 3: One arbitrary prosumer's daily demand and power generation.
  • Figure 4: The electricity prices for June 18th, 2019 in Cambridge, MA
  • Figure 5: Battery deployment at candidate locations of three clustering methods
  • ...and 7 more figures