Integrity Protection of the DC Microgrid

The direct current (DC) microgrid has attracted great attention in the recent years due to its significant advantages over the alternating current (AC) microgrid. These advantages include elimination of unnecessary AC/DC power converters, lower investment cost, lower losses, higher reliability, and resilience to utility-side disturbances. A practical DC microgrid requires an effective control strategy to regulate the DC bus voltages, enable power sharing between the distributed energy resources (DERs), and provide acceptable dynamic response to disturbances. Furthermore, when the power demand of the loads is higher than the power generation of the DERs in the DC microgrid, the power balance cannot be maintained by control actions and the DERs fail to regulate the DC bus voltages. Under such conditions, it is necessary to shed some of the non-critical loads in order to protect the integrity of the DC microgrid. Thus, the DC microgrid also requires an effective load shedding scheme. This thesis is focused on developing advanced control and load shedding strategies for integrity protection of the DC microgrid. The studies reported in this thesis include developing (i) a versatile DC bus signaling control strategy to achieve coordinated decentralized control of the DERs and loads in the DC microgrid without utilizing costly high-bandwidth communication systems, (ii) an improved mode-adaptive droop control strategy to enable desirable and reliable control mode switching by the DERs under various operating conditions, and (iii) adaptive non-communication based load shedding schemes to enable the DC microgrid to ride through the disturbances that cause large power deficit and voltage sags. The performances of the proposed integrity protection schemes are investigated under various generation and load disturbances in both grid-connected and islanded operation modes of the DC microgrid. Comprehensive time-domain simulation studies are conducted on a detailed DC microgrid study system using the PSCAD/EMTDC software. The study results indicate that the proposed control strategies: (i) improve power sharing between the DERs, (ii) effectively regulate the DC bus voltages under various operating conditions, (iii) improve the DC microgrid stability and its dynamic response to large disturbances, (iv) do not require an excessively large grid-tie converter or energy storage systems, and (v) enhance the DC microgrid reliability, flexibility, modularity, and expandability. The study results also indicate that the proposed adaptive load shedding schemes (i) effectively maintain the power balance in the DC microgrid through fast and coordinated shedding of non-critical loads, (ii) prevent the bus voltages in the microgrid from falling below predetermined lower limits, (iii) ensure that the critical loads do not experience excessive steady-state voltage deviations, (iv) minimize the magnitudes and durations of temporary voltage sags caused by sudden disturbances, and (v) increase the reliability of the power supplied to the loads, by preventing over-shedding

Unveiling and unraveling aggregation and dispersion fallacies in group MCDM

Priorities in multi-criteria decision-making (MCDM) convey the relevance preference of one criterion over another, which is usually reflected by imposing the non-negativity and unit-sum constraints. The processing of such priorities is different than other unconstrained data, but this point is often neglected by researchers, which results in fallacious statistical analysis. This article studies three prevalent fallacies in group MCDM along with solutions based on compositional data analysis to avoid misusing statistical operations. First, we use a compositional approach to aggregate the priorities of a group of DMs and show that the outcome of the compositional analysis is identical to the normalized geometric mean, meaning that the arithmetic mean should be avoided. Furthermore, a new aggregation method is developed, which is a robust surrogate for the geometric mean. We also discuss the errors in computing measures of dispersion, including standard deviation and distance functions. Discussing the fallacies in computing the standard deviation, we provide a probabilistic criteria ranking by developing proper Bayesian tests, where we calculate the extent to which a criterion is more important than another. Finally, we explain the errors in computing the distance between priorities, and a clustering algorithm is specially tailored based on proper distance metrics