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The monitoring of the indoor air quality against the presence of contaminant sources is of paramount importance since it directly influences the occupants’ comfort, productivity, health and safety. Various approaches exist in the literature for monitoring and controlling the building environment. However, there is an identified lack of automated tools for evaluating, testing and comparing such approaches. This paper presents COMOB (COntaminat MOnitoring in Buildings), a developed MATLAB toolbox for sensor placement and contaminant monitoring in buildings. Specifically, the COMOB toolbox realizes an offline sensor placement procedure for positioning the contaminant sensors inside the building, such that the harmful effects due to a possible airborne contaminant event are minimized. In addition, an online contaminant monitoring approach is also implemented for detecting and isolating contaminant events in the building interior. Besides the aforementioned algorithms, the modularity of the COMOB toolbox facilitates the implementationof various other algorithms related to air-quality monitoring in buildings. In particular, the COMOB toolbox is able to generate multiple airflow and contaminant concentration data-sets by varying the building parameters; thus enabling the comprehensive and systematic evaluation and testing of the developed algorithms. Finally, the functionalities of the COMOB toolbox are demonstrated through a case-study corresponding to a real building model. COMOB is released under an open-source license, and is available at https://github.com/KIOS-Research/COMOB-Toolbox.

Grid-scale Battery Energy Storage Systems(BESSs) are gaining increasing consideration over the last fewyears since their connection has been shown to improve thedynamic behavior of the power grid. However, existingIntentional Controlled Islanding (ICI) approaches do notconsider the changes in structure and operation that modernpower systems are experiencing due to the increasing integrationof grid-scale BESSs. In this paper a previously proposed ICIalgorithm (for minimal power-flow disruption) is modified toconsider the impact of BESSs on the power system. Morespecifically, the proposed modified ICI algorithm aims todetermine the optimal islanding solution that minimizes thepower imbalance between the generation (i.e., synchronousgenerators and the BESSs) and the consumption (i.e., loads andBESSs) within each island. This is achieved through determiningthe island that each BESS should be, and the optimal activepower set-points for the BESSs that minimize the powerimbalance in the formed islands.

Indoor Air Quality monitoring is an essentialingredient of intelligent buildings. The release of variousairborne contaminants into the buildings, compromises thehealth and safety of occupants. Therefore, early contaminantdetection is of paramount importance for the timely activationof proper contingency plans in order to minimize the impact ofcontaminants on occupants health. The objective of this workis to enhance the performance of a distributed contaminantdetection methodology, in terms of the minimum detectablecontaminant release rates, by considering the joint problemof partitioning selection and observer gain design. Towardsthis direction, a detectability analysis is performed to deriveappropriate conditions for the minimum guaranteed detectablecontaminant release rate for specific partitioning configurationand observer gains.The derived detectability conditions arethen exploited to formulate and solve an optimization problemfor jointly selecting the partitioning configuration and observergains that yield the best contaminant detection performance.

Intentional Controlled Islanding (ICI) has been proposed as a corrective measure of last resort to split the power system into several sustainable islands and prevent cascading outages. This paper proposes a novel ICI algorithm based on a Linear Programming (LP) formulation that directly determines an islanding solution with minimal power-flow disruption for any given number of islands, while ensuring that each island contains only coherent generators. In addition, the proposed algorithm enables operators to constrain any transmission line to be excluded from the solution, allows the control of the size of islands and ensures their connectivity. The basis of the proposed LP formulation is an exact Mixed Integer Linear Programming (MILP) Formulation. A Search Space Reduction (SSR) procedure that generates additional constraints for reducing the search space of the MILP is also proposed. In most cases, these additional constraints are enough for the relaxed MILP formulation (LP formulation) to generate optimal solutions. Nonetheless, the proposed LP formulation is executed as a part of a recursive linearization procedure which ensures that optimal solutions are always obtained. Multiple simulation results demonstrate the ability of the proposed LP ICI algorithm to meet the requirement of real-time controlled islanding in large-scale power systems., A. Kyriacou, P. Demetriou, C. Panayiotou and E. Kyriakides, "Controlled Islanding Solution for Large-Scale Power Systems," in IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 1591 - 1602. doi: 10.1109/TPWRS.2017.2738326 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The automatic preservation of the indoor air quality (IAQ) is an important task of the intelligent building design in order to ensure the health and safety of the occupants. The IAQ, however, is often compromised by various airborne contaminants that penetrate the indoor environment as a result of accidents or planned attacks. In this paper, we provide the detailed analysis, implementation, and evaluation of a distributed methodology for detecting and isolating multiple contaminant events in large-scale buildings. Specifically, we consider the building as a collection of interconnected subsystems, and we design a contaminant event monitoring software agent for each subsystem. Each monitoring agent aims to detect the contaminant and isolate the zone where the contaminant source is located, while it is allowed to exchange information with its neighboring agents. For configuring the subsystems, we implement both exact and heuristic partitioning solutions. A main contribution of this paper is the investigation of the impact of the partitioning solution on the performance of the distributed contaminant detection and isolation (CDI) scheme with respect to the detectability and isolability of the contaminant sources. The performance of the proposed distributed CDI methodology is demonstrated using the models of real building case studies created on CONTAM.11CONTAM is a multizone simulation program developed by the U.S. National Institute of Standards and Technology. 26 6 1925 1941