Coupling Neighboring Microgrids for Overload Management Based on Dynamic Multicriteria Decision-Making

Abstract

A microgrid (MG) is expected to supply its local loads independently; however, due to intermittency of wind and solar-based energy resources as well as the load uncertainty, it is probable that the MG experiences power deficiency (overloading). This problem can be mitigated by coupling the overloaded MG to another neighboring MG that has surplus power. Considering a distribution network composed of several islanded MGs, defining the suitable MGs (alternative) to be coupled with the overloaded MG is a challenge. An MG overload management technique is developed in this paper, which first identifies the overloaded MG(s) and then selects the most suitable alternative. The alternative selection is based on different criteria, such as available surplus power, reliability, supply security, power loss, electricity cost, and CO 2 emissions in the alternative MGs. Moreover, the frequency and voltage deviation in the system of coupled MGs are considered in the selection. A dynamic multicriteria decision-making algorithm is developed for this purpose. To contemplate the uncertainties in the considered distribution network, a cloud theory-based probabilistic analysis is deployed as the research framework and the performance of the developed technique is evaluated in MATLAB.

Multi-level supervisory emergency control for operation of remote area microgrid clusters

“…Reference [27] has proposed a transformative architecture for coupling the nearby MGs to improve their resiliency during faults. References [28] and [29] identify the MG cluster with self-healing capabilities while the management of MG restoration after faults is explained in [30]. A decision-making-based approach is proposed in [31] to determine the most suitable HMGs to be coupled with a PMG, considering various criteria, such as the available surplus power, electricity cost, reliability and the distance between the neighboring MGs.…”

Power transaction management amongst coupled microgrids in remote areas

“…As the internal structure of the MGs is not important in the developed strategy, for simplicity, all MGs are assumed to have the same topology in the numerical analyses of this paper. The impedance data for all of MGs buses is taken from [9]. The pre-defined input data assumed for all participating MGs and the cost data are listed in Table 1 and 2 while Table 3 shows the distance of each considered MG from a central node at which they are coupled.…”

“…A transformative architecture is proposed for coupling the nearby MGs in [8] to improve the system resiliency during faults. A decision-making-based approach is proposed in [9] to determine the most suitable HMG(s) to be coupled with an overloaded PMG, which considers different criteria such as available surplus power, electricity cost, reliability and the distance of the neighboring MGs as well as the voltage/frequency deviation in the CMG. Ref.…”

Trilateral Planning Model for Integrated Community Energy Systems and PV-Based Prosumers—A Bilevel Stochastic Programming Approach

“…In [22], the interactions and energy trading decisions of geographically distributed resources were studied using a game theory-based framework. In [23], coupling neighboring microgrids were proposed for overload management while addressing several R issues such as reliability, supply security, power losses, electricity costs, and CO2 emission. An optimization model for a load aggregator with ESS to determine its net imported power in electricity markets was presented in [24].…”