Cost-effective Scheduling of Load and Microgrid in Wastewater Treatment Plant

ABSTRACT COST-EFFECTIVE SCHEDULING OF LOAD AND MICROGRID IN WASTEWATER TREATMENT PLANT By Wentao Zou The University of Wisconsin-Milwaukee,2018 Under the Supervision of Dr. Lingfeng Wang As a big consumer of energy, water and wastewater treatment used about 75000 to 100000 GWh electricity, which accounts for nearly 3% of U.S. annual energy [1]. Not only being energy-intensive, wastewater treatment plant (WWTP) also consumes a lot of electricity during peak hours, which makes WWTP a good candidate of DR (demand response). The main purpose of demand response is to improve the stability of the electric grid and reduce the use of electricity during peak period to lower the total system costs. Two kinds of strategies can be utilized to reduce electrical loads during peak periods, which are load shifting and load shedding. Load shedding strategy is to reduce the total electrical load during demand response event and load shifting is to reschedule the time of some electrical load to partial-peak or off-peak hours. In this work, both of them are used to reach a better financial benefit. The process and energy consumption of WWTP have been analyzed. It is found that the aeration in secondary treatment and pumps for wastewater pumping and sludge pumping are two main processes which consume the majority of total electric power. Based on shifting loads of aerations and pumps, a load shifting model is formulated to shift load from on-peak hours to off-peak hours. Several constraints have been taken into consideration such the storage capacity, maximum holding time of wastewater when it stays in storage tanks, maximum treatment capacity of WWTP, etc. This model can effectively reduce the annual electricity cost while the quality of effluent and the reliability of WWTP are not compromised. In the case study analysis, 22% cost reduction is achieved by using the load shifting model. A software tool has also been developed to help users calculate the amount of cost they can save when the load shifting model is applied. The software tool is user friendly and easy to use. The influent data and electricity price data need to be loaded by users, and some kinds of parameters need to be typed in depending on different situations. For instance, the size of the WWTP and the capacity of storage tank need to be loaded. In addition to demand response, WWTP can save more money with the help of a microgrid. A microgrid is a smaller version of traditional power grid which can provide backup power to WWTP so that the power generated by a microgrid can be used during on-peak hours or sold back to the main grid if possible. A microgrid can also increase the reliability of WWTP. As a discrete energy system with distributed energy sources, a microgrid can operate in parallel with or independently from the main power grid. This feature of the microgrid makes sure WWTP can still receive reliable energy when no electricity can be provided by the main grid. A microgrid model is developed. A battery bank is also involved in the formulation. Constraints including microgrid capacity, charge and discharge efficiency of battery bank, and battery capacity have been considered. The method used to solve this formulation is particle swarm optimization (PSO). A detailed description of the problem-solving process has been displayed step by step. The case study shows the microgrid model can increase the cost reduction further to 29% of total energy expense based on the load shifting model

Spontaneous electric-polarization topology in confined ferroelectric nematics

Topological spin and polar textures have fascinated people in different areas of physics and technologies. However, the observations are limited in magnetic and solid-state ferroelectric systems. Ferroelectric nematic is the first liquid-state ferroelectric that would carry many possibilities of spatially distributed polarization fields. Contrary to traditional magnetic or crystalline systems, anisotropic liquid crystal interactions can compete with the polarization counterparts, thereby setting a challenge in understating their interplays and the resultant topologies. Here, we discover chiral polarization meron-like structures during the emergence and growth of quasi-2D ferroelectric nematic domains, which are visualized by fluorescence confocal polarizing microscopy and second harmonic generation microscopies. Such micrometre-scale polarization textures are the modified electric variants of the magnetic merons. Unlike the conventional liquid crystal textures driven solely by the elasticity, the polarization field puts additional topological constraints, e.g., head-to-tail asymmetry, to the systems and results in a variety of previously unidentified polar topological patterns. The chirality can emerge spontaneously in polar textures and can be additionally biased by introducing chiral dopants. An extended mean-field modelling for the ferroelectric nematics reveals that the polarization strength of systems plays a dedicated role in determining polarization topology, providing a guide for exploring diverse polar textures in strongly-polarized liquid crystals

Adapter Learning in Pretrained Feature Extractor for Continual Learning of Diseases

Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic forgetting issue, a novel adapter-based strategy is proposed to help effectively learn a set of new diseases at each round (or task) of continual learning, without changing the shared feature extractor. The learnable lightweight task-specific adapter(s) can be flexibly designed (e.g., two convolutional layers) and then added to the pretrained and fixed feature extractor. Together with a specially designed task-specific head which absorbs all previously learned old diseases as a single 'out-of-distribution' category, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are comparable and consequently the appropriate classifier head can be more accurately selected during model inference. Extensive empirical evaluations on three image datasets demonstrate the superior performance of the proposed method in continual learning of new diseases. The source code will be released publicly.Comment: 10 page

Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction

Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target ad. In particular, we explore auxiliary ads from two viewpoints: one is from the spatial domain, where we consider the contextual ads shown above the target ad on the same page; the other is from the temporal domain, where we consider historically clicked and unclicked ads of the user. The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent. In order to effectively utilize these auxiliary data, we propose the Deep Spatio-Temporal neural Networks (DSTNs) for CTR prediction. Our model is able to learn the interactions between each type of auxiliary data and the target ad, to emphasize more important hidden information, and to fuse heterogeneous data in a unified framework. Offline experiments on one public dataset and two industrial datasets show that DSTNs outperform several state-of-the-art methods for CTR prediction. We have deployed the best-performing DSTN in Shenma Search, which is the second largest search engine in China. The A/B test results show that the online CTR is also significantly improved compared to our last serving model.Comment: Accepted by KDD 201

Adipose tissues of MPC1± mice display altered lipid metabolism-related enzyme expression levels

Mitochondrial pyruvate carrier 1 (MPC1) is a component of the MPC1/MPC2 heterodimer that facilitates the transport of pyruvate into mitochondria. Pyruvate plays a central role in carbohydrate, fatty, and amino acid catabolism. The present study examined epididymal white adipose tissue (eWAT) and intrascapular brown adipose tissue (iBAT) from MPC1± mice following 24 weeks of feeding, which indicated low energy accumulation as evidenced by low body and eWAT weight and adipocyte volume. To characterize molecular changes in energy metabolism, we analyzed the transcriptomes of the adipose tissues using RNA-Sequencing (RNA-Seq). The results showed that the fatty acid oxidation pathway was activated and several genes involved in this pathway were upregulated. Furthermore, qPCR and western blotting indicated that numerous genes and proteins that participate in lipolysis were also upregulated. Based on these findings, we propose that the energy deficiency caused by reduced MPC1 activity can be alleviated by activating the lipolytic pathway

A safety check method to maximize the effective reserve by optimizing the power of the tie-line in the power market

To ensure the stability of the electricity spot market and the safety of the provincial and regional power systems, a safety check method is proposed to maximize the effective reserve resources in the power system by optimizing the power of each tie-line. This safety check method accurately models the tie-line equipment and the effective reserve resources and is coupled with each constraint of the electricity spot market clearing model to form a safety check algorithm to optimize the power of tie-line power. The model involved in this paper is a linear model, which has a clear implementation method in practical dispatching applications. Through this method, the power configuration scheme of each tie-line to meet the electricity spot market constraints can be obtained, and the safety check results have the executability of the power market. The rationality and feasibility of the safety check algorithm results are verified by simulating the provincial-scale electricity spot market. According to the simulation results, this method can release effective reserve resources and provide more guarantees for the safe operation of the power grid. In addition, this method can save up to 4.9% of the total operation cost of the power system and improve the dispatching economy of the power system. This method is of great significance to ensure the safe operation of the power system and the day-ahead market and real-time market scheduling in the actual power spot system. In addition, this method also has great guiding significance for the analysis of the actual reserve situation of the power market after the event