Multi-Objective Finite-Time Control for the Interlinking Converter on Hybrid AC/DC Microgrids

Abstract

Hybrid AC/DC microgrids are of special interest for energization due to their flexibility, low infrastructure investments, reduced conversion losses, and reliability against failures on the utility grid. In these systems, global economic dispatch needs communication between the AC and DC subgrids to achieve a near-optimal solution since the incremental costs of all generators need to be equalized. Hence, this paper proposes a distributed coordination between generators by means of a finite-time controller for the microgrid's interlinking converter, which ensures an economic operation while taking care of the microgrid power utilization. The latter implies that a multi-objective control is performed by the interlinking converter, which uses shared variables of incremental costs and average powers from distributed generators in AC and DC sides. Also, an adaptive weighting method is proposed to adjust the control effort regarding the average power utilization of a side microgrid. The controller's performance is verified through simulations and experimental setups. Results show that the proposed strategy is able to perform a trade-off between the two control objectives while achieving a finite-time convergence even though communication delays exist.INDEX TERMS Economic dispatch, distributed control, finite-time control, hybrid AC/DC microgrid, multi-objective control.

Dynamic Average Consensus With Anti-Windup Applied to Interlinking Converters in AC/DC Microgrids Under Economic Dispatch and Delays

“…For the distributed control design, a communication network is required. A communicated AC/DC MG involves agents which may be DGs or ILCs [6], [7], [11]. Then, the communications allow the AC/DC MG to be viewed and analyzed as a multi-agent system with a graph G sys := G AC ∪ G DC ∪ G ILC [11].…”

“…A communicated AC/DC MG involves agents which may be DGs or ILCs [6], [7], [11]. Then, the communications allow the AC/DC MG to be viewed and analyzed as a multi-agent system with a graph G sys := G AC ∪ G DC ∪ G ILC [11]. The subgraphs G AC and G DC can operate independently when the ILC cluster (G ILC ) is disconnected, or when one side stops communicating to the ILC cluster.…”

“…A. Distributed control using dynamic average consensus For economic dispatch, the control over the power flow of an ILC can be described as compensation of average received incremental costs (ICs), supporting the IC consensus implemented by the subgrids [11]. When multiple ILCs are considered, a balancing control should be added.…”

“…When multiple ILCs are considered, a balancing control should be added. Then, inspired by [6], [7] and [11], the following protocol is proposed:…”

“…from a network operator), u L is the local error estimation for power transfer, u C is the error from the balancing of the cluster of ILCs, u R is the local error from the hard constraint, G P PI (s) = k P p +k P i /s is a PI controller. k P p and k P i can be determine according to the hierarchical control in both subgrids [11]. The parameters c L , c C , c P , c Ri regulate the convergence speed of their corresponding compensation loop.…”