Operating Cost Reduction of DC Microgrids Under Real-Time Pricing Using Adaptive Differential Evolution Algorithm

Abstract

Virtual resistance-based droop control is widely adopted as secondary-layer control for gridconnected 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.

Distributed Convex Optimization-Based Control for Distribution Power Loss Minimization of Islanded Three-Phase AC Microgrids

“…The energy storage system is introduced to compensate power and alleviate bus voltage fluctuations. (1) According to the analysis in [3], [4], and [7], the converter loss can be simplified as a quadratic function of the output currents of DER as…”

“…For these voltage-based models, the best reference voltage [4] and the optimal power flow [5], [6] for each der can be obtained to reduce online power loss. To reduce loss, an offline-optimization method [7] is proposed to find the best operating point of the load shedding machine. However, this offline solution only works under very ideal working conditions, with little or no change in parameters.…”

Optimal Control Strategy for Distributed Energy Resources in a DC Microgrid for Energy Cost Reduction and Voltage Regulation

“…There have been various optimization approaches applied to microgrids; these include classic and artificial intelligence techniques, such as particle-swarm optimization (PSO) [6], genetic algorithms [7], or adaptive differential evolution algorithms [8]. In [7], the authors formulated an optimal power flow problem using a genetic algorithm to minimize the total operation cost of a DC microgrid while considering real-time pricing.…”

“…The total operating cost covered the operation cost of utility grids, renewable energy resources, energy-storage systems, and fuel cells. Similarly, the authors in [8] suggested an adaptive differential evolution algorithm to minimize the operating cost of DC microgrids under real-time electricity prices. In [9], the authors suggested an optimal design and model predictive control (MPC) of a standalone hybrid renewable energy system (HRES).…”

Distributed Higher Order Differentiator-Based Distributed Secondary Control for DC Microgrids Under Cyber-Attacks

“…ith a high penetration of power electronics interfaced distributed energy resources (DERs) in DC microgrid, the distribution loss is an increasingly prominent problem, especially in mediumand long-distance low-voltage networks [1]- [6]. In DC microgrids, distributed secondary control has been widely adopted to achieve voltage restoration [7][8][9], load sharing [9,10], energy balancing [11,12], and power loss reduction [12][13][14], and economic dispatch [15] for DERs However, those conventional distributed secondary control are based on the communication between the two neighboring units, which are vulnerable under cyberattacks [16]. Typical cyber-attacks on the communicationbased hierarchical control are took place in steady-state control variables [17].…”