Green Scheduling of Control Systems

Electricity usage under peak load conditions can cause issues such as reduced power quality and power outages. For this reason, commercial electricity customers are often subject to demand-based pricing, which charges very high prices for peak electricity demand. Consequently, reducing peaks in electricity demand is desirable for both economic and reliability reasons. In this thesis, we investigate the peak demand reduction problem from the perspective of safe scheduling of control systems under resource constraint. To this end, we propose Green Scheduling as an approach to schedule multiple interacting control systems within a constrained peak demand envelope while ensuring that safety and operational conditions are facilitated. The peak demand envelope is formulated as a constraint on the number of binary control inputs that can be activated simultaneously. Using two different approaches, we establish a range of sufficient and necessary schedulability conditions for various classes of affine dynamical systems. The schedulability analysis methods are shown to be scalable for large-scale systems consisting of up to 1000 subsystems. We then develop several scheduling algorithms for the Green Scheduling problem. First, we develop a periodic scheduling synthesis method, which is simple and scalable in computation but does not take into account the influence of disturbances. We then improve the method to be robust to small disturbances while preserving the simplicity and scalability of periodic scheduling. However the improved algorithm usually result in fast switching of the control inputs. Therefore, event-triggered and self-triggered techniques are used to alleviate this issue. Next, using a feedback control approach based on attracting sets and robust control Lyapunov functions, we develop event-triggered and self-triggered scheduling algorithms that can handle large disturbances affecting the system. These algorithms can also exploit prediction of the disturbances to improve their performance. Finally, a scheduling method for discrete-time systems is developed based on backward reachability analysis. The effectiveness of the proposed approach is demonstrated by an application to scheduling of radiant heating and cooling systems in buildings. Green Scheduling is able to significantly reduce the peak electricity demand and the total electricity consumption of the radiant systems, while maintaining thermal comfort for occupants

Data-driven Demand Response Modeling and Control of Buildings with Gaussian Processes

This paper presents an approach to provide demand response services with buildings. Each building receives a normalized signal that tells it to increase or decrease its power demand, and the building is free to implement any suitable strategy to follow the command, most likely by changing some of its setpoints. Due to this freedom, the proposed approach lowers the barrier for any buildings equipped with a reasonably functional building management system to participate in the scheme. The response of the buildings to the control signal is modeled by a Gaussian Process, which can predict the power demand of the buildings and also provide a measure of its confidence in the prediction. A battery is included in the system to compensate for this uncertainty and improve the demand response performance of the system. A model predictive controller is developed to optimally control the buildings and the battery, while ensuring their operational constraints with high probability. Our approach is validated by realistic co-simulations between Matlab and the building energy simulator EnergyPlus

OpenBuildNet Framework for Distributed Co-Simulation of Smart Energy Systems

The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development. We introduce an open-source framework, OpenBuildNet, for distributed co-simulation of large-scale smart energy systems. Using a loose-coupling approach to co-simulate parallel processes, it can leverage and seamlessly integrate specialized simulation and computation tools in a common platform. Users can therefore benefit from the capabilities of state-of-the-art and widely used tools in each domain. OpenBuildNet is scalable and highly flexible as it uses a decentralized architecture, message-based communication, and peer-to-peer data exchange between subsystem nodes. It also provides a set of easy-to-use software tools tailored for researchers and engineers. This paper presents the architecture and tool suite of OpenBuildNet, and demonstrates its usefulness in a case study of controlling multiple buildings for demand response

OpenBuildNet Framework for Distributed Co-Simulation of Smart Energy Systems

The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development. We introduce an open-source framework, OpenBuildNet, for distributed co-simulation of large-scale smart energy systems. Using a loose-coupling approach to co-simulate parallel processes, it can leverage and seamlessly integrate specialized simulation and computation tools in a common platform. Users can therefore benefit from the capabilities of state-of-the-art and widely used tools in each domain. OpenBuildNet is scalable and highly flexible as it uses a decentralized architecture, message-based communication, and peer-to-peer data exchange between subsystem nodes. It also provides a set of easy-to-use software tools tailored for researchers and engineers. This paper presents the architecture and tool suite of OpenBuildNet, and demonstrates its usefulness in a case study of controlling multiple buildings for demand response

Providing ancillary service with commercial buildings: the Swiss perspective

Ancillary services constitute the cornerstone of the power grid. They allow for an efficient system operation, provide resilience to uncertainties and establish safeguards against unprecedented events. Their importance is growing due to the rise of grid decentralisation and integration of intermittent, renewable power sources, which lead to more variability and uncertainty in the system. Today, the vast share of ancillary services is provided by large generating units. An ongoing effort by research and business entities focuses on using variation of loads connected to the power grid in order to increase significantly the provision of such services, hopefully at a reduced cost. We examine here, from an economic perspective, the use of commercial buildings as ancillary service providers based on real prices from the Swiss electricity market. We calculate the effect of retail electrical prices on the economic performance of a building and find that for the rates charged in the least expensive cantons a single building can reduce its overall energy costs, when participating in the ancillary services market. For the high end of prices this gradually becomes prohibitive but can be alleviated for a building that has a need for electricity during nighttime hours, as well as daytime. Finally, we show, the counter-intuitive result that providing ancillary services can increase the comfort levels of a building at a decreased cost

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Chemical characterization, source apportionment, and health risk assessment nexus of PM2.5-bound major heavy metals in Bien Hoa city, southern Vietnam

Bien Hoa, a city in Dong Nai province of Vietnam, is a populous area and a hotspot for industrial establishments. Dioxins, as residue from the U.S. - Vietnam war, are also a major concern to the city. Remedy of contaminated soil is being conducted in the area. With said situation, it raises the question of the overall air quality in Bien Hoa city. Thus, our research group conducted this work to fill the knowledge gap. The methodology involves the collection of 40 samples of PM2.5, at two sites (urban and industrial zone), from both the rainy (Oct. 15, 2021–Oct. 25, 2021) and the dry (Mar. 15, 2022–Mar. 25, 2022) seasons, to assess the concentration of eleven (11) heavy metals (As, Pb, Mn, Fe, Cd, Cr, Zn, Co, Al, Cu, and Ni). The analysis is performed using an ICP-MS iCAP RQ. The result shows that the concentration of PM2.5 in the dry season is higher (at 80 μg/m3), potentially due to increased moisture content and wet scrubbing, in the rainy season (28.3 μg/m3). The trace of Zn is the most abundant in PM2.5-bond from both seasons. It is caused by the contribution of emissions from road-traffic activities, namely vehicular exhaust, tyre abrasion and degradation of lubricates. The contribution of industrial processes, such as combustion, metallurgical production, biomass incineration and waste treatment also plays a role in high Zn concentration. Out of eleven, six heavy metals (including Pb, As, Cr, Cu, Zn và Cd) have enrichment factor values over 100. Health risk assessment shows that the total cancer risk (TCR) values are all within 10−4 ≤ TCR ≤10−3, which indicates a moderate cancer risk from respiratory exposure in the city

Prioritization of Factors Impacting Lecturer Research Productivity Using an Improved Fuzzy Analytic Hierarchy Process Approach

Improving the scientific research productivity of lecturers is an important strategy contributing to improving the reputation of universities, attracting external funding sources, and improving the credibility of both domestic and international students. This study was carried out with the aim of determining the priority of the university’s governance factors that affect lecturers’ scientific research productivity. Six university governance factors were considered, including (i) research objectives and strategies, (ii) decentralization, (iii) leadership, (iv) support for research activities, (v) policy towards lecturers, and (vi) resources for research activities. In this study, an improved analytic hierarchy process method using generalized triangular fuzzy numbers and a centroid index was proposed. The research data were collected via in-depth interviews with experts and administrators at Vietnam National University, Hanoi (VNU). The results indicate that “resources for research activities” constitute the most important factor affecting the research productivity of lecturers at VNU, followed by research objectives and strategies and leadership

Development of a fast immunosorbent assay for site-screening dioxin contamination in Vietnam

Dioxins are a group of chemical compounds that cause environmental pollution and many harmful effects on human health. High-Resolution Gas Chromatography/High-Resolution Mass Spectrometry (HRGC/HRMS) is the standard method for determining dioxin concentrations in soil samples and provides the most accurate results. However, this method is time-consuming, costly, and requires modern equipment. Currently, competitive ELISA is a reliable method used for dioxin detection analysis, offering fast implementation time and low cost. Vietnam is a global hotspot for dioxin contamination, with a high number of dioxin samples for analysis. Therefore, it is essential to optimize this reliable, fast, and low-cost ELISA method for it to be applicable and replace the expensive and complex HRGC/HRMS method currently in use in Vietnam. This study presented optimized conditions for ELISA method using commercial antibodies to detect dioxin. The optimal dilution for the anti-dioxin antibody and the conjugated antibody is 1:2000 and 1:1000, respectively. The reconstitution buffer consists of 50% DMSO/H2 O, with the addition of 0.05% Triton X-100. The incubation time for anti-dioxin antibody incubated with dioxin is 60 min, while the incubation time for Horseradish Peroxidase (HRP) conjugated polyclonal antibody incubated with 3,3’,5,5’-Tetramethylbenzidine (TMB) substrate is 10 min. The quenching time for the enzyme-substrate reaction is 5 min. The half-maximal inhibitory concentration (IC50) of this method is 8500 pg/well and the limit of detection (LOD) is 2.02 pg/well. Although there is a difference between the analytical results of the two methods, the well-correlated results demonstrate the potential of the ELISA method for detecting and screening dioxin contamination before performing confirmatory analysis with HRGC/HRMS. These results serve as the basis for the development of a rapid dioxin detection kit, providing a new and efficient method for detecting and screening dioxin contamination in Vietnam

Interdisciplinary Assessment of Hygiene Practices in Multiple Locations: Implications for COVID-19 Pandemic Preparedness in Vietnam

10.3389/fpubh.2020.589183Frontiers in Public Health858918