144 research outputs found
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
Large Foundation Models for Power Systems
Foundation models, such as Large Language Models (LLMs), can respond to a
wide range of format-free queries without any task-specific data collection or
model training, creating various research and application opportunities for the
modeling and operation of large-scale power systems. In this paper, we outline
how such large foundation model such as GPT-4 are developed, and discuss how
they can be leveraged in challenging power and energy system tasks. We first
investigate the potential of existing foundation models by validating their
performance on four representative tasks across power system domains, including
the optimal power flow (OPF), electric vehicle (EV) scheduling, knowledge
retrieval for power engineering technical reports, and situation awareness. Our
results indicate strong capabilities of such foundation models on boosting the
efficiency and reliability of power system operational pipelines. We also
provide suggestions and projections on future deployment of foundation models
in power system applications.Comment: Code available at https://github.com/chennnnnyize/LLM_PowerSystem
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
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