Abstract
Virtual resistance-based droop control is widely adopted as secondary-layer control for grid-connected converters in DC microgrids. This paper presents an alternative usage of the virtual resistances to minimize the total operating cost of DC microgrids under real-time pricing. The total operating cost covers the running cost of utility grids, renewable energy sources (RES), energy storage systems (ESS), fuel cells, and power loss on the distribution lines. An adaptive Differential Evolution (ADE) algorithm is adopted in this paper to optimize the virtual resistances of the droop control for the grid-connected converters of dispatchable units, such that the power flow can be regulated. The performances of the proposed strategy are evaluated by the case studies of a 12-bus 380 V DC microgrid using Matlab and a 32-bus 380 V DC microgrid using a Real-Time Digital Simulator (RTDS). Both results validate that the ADE can significantly reduce the operating cost of DC microgrids and outperform the conventional Genetic Algorithm (GA) in terms of cost saving. Comparisons among the microgrids with different numbers of dispatchable units reveal that the cost saving is more prominent when the expansion of dispatchable units.
Highlights
With high penetrations of renewable energy sources (RES), and energy storage systems (ESS), DC microgrids with on-site power generations are widely adopted in electric ships, data centers, smart buildings, island grids, and some residential communities, etc. [1]–[9]
The total operating cost of DC microgrids is minimized at different operating conditions. (ii) This paper reveals that the operating cost reduction is more prominent when more dispatchable units are connected to the DC microgrid, which has never been reported
The adaptive Differential Evolution (ADE) optimizes the virtual resistances of dispatchable units, such that the power flow of DC microgrids can be regulated to minimize the total operating cost
Summary
With high penetrations of renewable energy sources (RES), and energy storage systems (ESS), DC microgrids with on-site power generations are widely adopted in electric ships, data centers, smart buildings, island grids, and some residential communities, etc. [1]–[9]. Power flow between the utility grids, RES, ESS, fuel cells, loads and DC microgrids, respectively, and the power loss on the distribution lines are regulated by the control of virtual resistances. Considering the cost of the utility grids, RES, ESS, fuel cells, and the distribution power losses, electricity prices of DC microgrids can be minimized under real-time pricing and load profiles via the optimization of virtual resistances. An economic dispatch strategy under real-time pricing based on an adaptive Differential Evolution (ADE) algorithm is proposed to reduce the operating cost of DC microgrids with different numbers of buses. The main contributions of this paper include: (i) This might be the first paper to globally optimize the total operating cost of RES, ESS, fuel cells, loads, and distribution power loss in DC microgrids. Where PFC is the measured power flow between the DC microgrid and the fuel cell; aFC, bFC, and cFC are constant coefficients
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