Learning a Multi-Agent Controller for Shared Energy Storage System

Deployment of shared energy storage systems (SESS) allows users to use the stored energy to meet their own energy demands while saving energy costs without installing private energy storage equipment. In this paper, we consider a group of building users in the community with SESS, and each user can schedule power injection from the grid as well as SESS according to their demand and real-time electricity price to minimize energy cost and meet energy demand simultaneously. SESS is encouraged to charge when the price is low, thus providing as much energy as possible for users while achieving energy savings. However, due to the complex dynamics of buildings and real-time external signals, it is a challenging task to find high-performance power dispatch decisions in real-time. By designing a multi-agent reinforcement learning framework with state-aware reward functions, SESS and users can realize power scheduling to meet the users' energy demand and SESS's charging/discharging balance without additional communication, so as to achieve energy optimization. Compared with the baseline approach without the participation of the SESS, the energy cost is saved by around 2.37% to 21.58%.Comment: Accepted to 2023 IEEE PES General Meetin

Laxity-Aware Scalable Reinforcement Learning for HVAC Control

Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills. Given their highly shiftable load and significant contribution to a building's energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems can provide valuable demand flexibility to the power systems by adjusting their energy consumption in response to electricity price and power system needs. To exploit this flexibility in both operation time and power, it is imperative to accurately model and aggregate the load flexibility of a large population of HVAC systems as well as designing effective control algorithms. In this paper, we tackle the curse of dimensionality issue in modeling and control by utilizing the concept of laxity to quantify the emergency level of each HVAC operation request. We further propose a two-level approach to address energy optimization for a large population of HVAC systems. The lower level involves an aggregator to aggregate HVAC load laxity information and use least-laxity-first (LLF) rule to allocate real-time power for individual HVAC systems based on the controller's total power. Due to the complex and uncertain nature of HVAC systems, we leverage a reinforcement learning (RL)-based controller to schedule the total power based on the aggregated laxity information and electricity price. We evaluate the temperature control and energy cost saving performance of a large-scale group of HVAC systems in both single-zone and multi-zone scenarios, under varying climate and electricity market conditions. The experiment results indicate that proposed approach outperforms the centralized methods in the majority of test scenarios, and performs comparably to model-based method in some scenarios.Comment: In Submissio

Preliminary aerodynamic design methodology for aero engine lean direct injection combustors

The Lean Direct Injection (LDI) combustor is one of the low-emissions combustors with great potential in aero-engine applications, especially those with high overall pressure ratio. A preliminary design tool providing basic combustor sizing information and qualitative assessment of performance and emission characteristics of the LDI combustor within a short period of time will be of great value to designers. In this research, the methodology of preliminary aerodynamic design for a second-generation LDI (LDI-2) combustor was explored. A computer code was developed based on this method covering the design of air distribution, combustor sizing, diffuser, dilution holes and swirlers. The NASA correlations for NOx emissions are also embedded in the program in order to estimate the NOx production of the designed LDI combustor. A case study was carried out through the design of an LDI-2 combustor named as CULDI2015 and the comparison with an existing rich-burn, quick-quench, lean-burn combustor operating at identical conditions. It is discovered that the LDI combustor could potentially achieve a reduction in liner length and NOx emissions by 18% and 67%, respectively. A sensitivity study on parameters such as equivalence ratio, dome and passage velocity and fuel staging is performed to investigate the effect of design uncertainties on both preliminary design results and NOx production. A summary on the variation of design parameters and their impact is presented. The developed tool is proved to be valuable to preliminarily evaluate the LDI combustor performance and NOx emission at the early design stage

Inference-based statistical network analysis uncovers star-like brain functional architectures for internalizing psychopathology in children

To improve the statistical power for imaging biomarker detection, we propose a latent variable-based statistical network analysis (LatentSNA) that combines brain functional connectivity with internalizing psychopathology, implementing network science in a generative statistical process to preserve the neurologically meaningful network topology in the adolescents and children population. The developed inference-focused generative Bayesian framework (1) addresses the lack of power and inflated Type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of biomarkers' influence on behavior variants, (3) quantifies the uncertainty and evaluates the likelihood of the estimated biomarker effects against chance and (4) ultimately improves brain-behavior prediction in novel samples and the clinical utilities of neuroimaging findings. We collectively model multi-state functional networks with multivariate internalizing profiles for 5,000 to 7,000 children in the Adolescent Brain Cognitive Development (ABCD) study with sufficiently accurate prediction of both children internalizing traits and functional connectivity, and substantially improved our ability to explain the individual internalizing differences compared with current approaches. We successfully uncover large, coherent star-like brain functional architectures associated with children's internalizing psychopathology across multiple functional systems and establish them as unique fingerprints for childhood internalization

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities

Review of modern low emissions combustion technologies for aero gas turbine engines

Pollutant emissions from aircraft in the vicinity of airports and at altitude are of great public concern due to their impact on environment and human health. The legislations aimed at limiting aircraft emissions have become more stringent over the past few decades. This has resulted in an urgent need to develop low emissions combustors in order to meet legislative requirements and reduce the impact of civil aviation on the environment. This article provides a comprehensive review of low emissions combustion technologies for modern aero gas turbines. The review considers current high Technologies Readiness Level (TRL) technologies including Rich-Burn Quick-quench Lean-burn (RQL), Double Annular Combustor (DAC), Twin Annular Premixing Swirler combustors (TAPS), Lean Direct Injection (LDI). It further reviews some of the advanced technologies at lower TRL. These include NASA multi-point LDI, Lean Premixed Prevaporised (LPP), Axially Staged Combustors (ASC) and Variable Geometry Combustors (VGC). The focus of the review is placed on working principles, a review of the key technologies (includes the key technology features, methods of realising the technology, associated technology advantages and design challenges, progress in development), technology application and emissions mitigation potential. The article concludes the technology review by providing a technology evaluation matrix based on a number of combustion performance criteria including altitude relight auto-ignition flashback, combustion stability, combustion efficiency, pressure loss, size and weight, liner life and exit temperature distribution

Multi-Fidelity Combustor Design and Experimental Test for a Micro Gas Turbine System

© 2022 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/A multi-fidelity micro combustor design approach is developed for a small-scale combined heat and power CHP system. The approach is characterised by the coupling of the developed preliminary design model using the combined method of 3D high-fidelity modelling and experimental testing. The integrated multi-physics schemes and their underlying interactions are initially provided. During the preliminary design phase, the rapid design exploration is achieved by the coupled reduced-order models, where the details of the combustion chamber layout, flow distributions, and burner geometry are defined as well as basic combustor performance. The high-fidelity modelling approach is then followed to provide insights into detailed flow and emission physics, which explores the effect of design parameters and optimises the design. The combustor is then fabricated and assembled in the MGT test bench. The experimental test is performed and indicates that the designed combustor is successfully implemented in the MGT system. The multi-physics models are then verified and validated against the test data. The details of refinement on lower-order models are given based on the insights acquired by high-fidelity methods. The shortage of conventional fossil fuels and the continued demand for energy supplies have led to the development of a micro-turbine system running renewable fuels. Numerical analysis is then carried out to assess the potential operation of biogas in terms of emission and performance. It produces less NOx emission but presents a flame stabilisation design challenge at lower methane content. The details of the strategy to address the flame stabilisation are also provided.Peer reviewe