Flexible dynamic boundary microgrid operation considering network and load unbalances

Yu Su, Dingrui Li, Fred Wang, Mohammed Olama, Maximiliano Ferrari, Ben Ollis and Yilu Liu

Applied Energy, 2024, vol. 371, issue C, No S030626192401016X

Abstract: Flexible microgrids with dynamic boundaries have recently been introduced in the literature. With the ability to reconfigure the topology of the microgrids dynamically through remotely controlled switches, flexible microgrids with dynamic boundaries can further improve the resiliency and energy efficiency of microgrids with distributed energy resources (DERs). This paper focuses on the optimal operation considering one of the predominant characteristics of microgrids and distribution systems – unbalanced networks and loads. In existing literature, balanced modeling of microgrids is more common due to its attractive simplicity. The three-phase power unbalance has not been considered as a constraint on the generation units in a microgrid. Negative sequence constraints have also been neglected. In this article, we propose a set of constraints that is specifically related to the capabilities of inverter interfaced resources to supply unbalanced current/power when the microgrid is islanded from the main distribution grid. We incorporate the new set of constraints into two optimization formulations leveraging two convex relaxations of the three-phase power flow equations: mixed-integer linear programming (MILP) and mixed-integer semidefinite programming (MISDP) that optimize the dispatch of controllable switches and DERs in the microgrid. The algorithms are then extended to networked microgrids with grid-forming sources. We test the algorithms on a realistic community microgrid model in Puerto Rico as well as standardized IEEE distribution test feeders. The testing results demonstrate the performance of the proposed algorithms. The MILP is fast and scalable, and the MISDP enforces the negative sequence voltage constraints.

Keywords: Microgrids; Dynamic boundaries; Grid resilience; Mixed-integer linear programming; Mixed-integer semidefinite programming; Inverter interfaced sources; Unbalanced load (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123633

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