اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدDistributed Control of Multi-zone HVAC Systems Considering Indoor Air Quality
107
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
This paper studies a scalable control method for multi-zone heating, ventilation and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort and indoor air quality (IAQ) (represented by CO2) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with a upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone thermal comfort with minimal energy cost, and then the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy saving performance of upper level. As both the upper and lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost while the latter fails. Moreover, we demonstrate our method outperforms the demand controlled ventilation strategies (DCVs) for IAQ management with about 8-10% energy cost reduction.
قيم البحث
اقرأ أيضاً
An autonomous adaptive MPC architecture is presented for control of heating, ventilation and air condition (HVAC) systems to maintain indoor temperature while reducing energy use. Although equipment use and occupant changes with time, existing MPC me
thods are not capable of automatically relearning models and computing control decisions reliably for extended periods without intervention from a human expert. We seek to address this weakness. Two major features are embedded in the proposed architecture to enable autonomy: (i) a system identification algorithm from our prior work that periodically re-learns building dynamics and unmeasured internal heat loads from data without requiring re-tuning by experts. The estimated model is guaranteed to be stable and has desirable physical properties irrespective of the data; (ii) an MPC planner with a convex approximation of the original nonconvex problem. The planner uses a descent and convergent method, with the underlying optimization problem being feasible and convex. A year long simulation with a realistic plant shows that both of the features of the proposed architecture - periodic model and disturbance update and convexification of the planning problem - are essential to get the performance improvement over a commonly used baseline controller. Without these features, though MPC can outperform the baseline controller in certain situations, the benefits may not be substantial enough to warrant the investment in MPC.
In this paper, we propose a distributed output-feedback controller design for a linear time-invariant plant interacting with networked agents, where interaction and communication of each agent are limited to its associated input-output channel and it
s neighboring agents, respectively. The design scheme has a decentralized structure so that each agent can self-organize its own controller using the locally accessible information only. Furthermore, under mild conditions, the proposed controller is capable of maintaining stability even when agents join/leave the network during the operation without requiring any manipulation on other agents. This plug-and-play feature leads to efficiency for controller maintenance as well as resilience against changes in interconnections. The key idea enabling these features is the use of Bass algorithm, which allows the distributed computation of stabilizing gains by solving a Lyapunov equation in a distributed manner.
In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed. We propose a distributed learning approach to predict the re
sidual dynamics for each agent. The stability of the consensus protocol using the data-driven model of the dynamics is shown via Lyapunov analysis. The followers ultimately synchronize to the leader with guaranteed error bounds by applying the proposed control law with a high probability. The effectiveness and the applicability of the developed protocol are demonstrated by simulation examples.
Effective traffic optimization strategies can improve the performance of transportation networks significantly. Most exiting works develop traffic optimization strategies depending on the local traffic states of congested road segments, where the con
gestion propagation is neglected. This paper proposes a novel distributed traffic optimization method for urban freeways considering the potential congested road segments, which are called potential-homogeneous-area. The proposed approach is based on the intuition that the evolution of congestion may affect the neighbor segments due to the mobility of traffic flow. We identify potential-homogeneous-area by applying our proposed temporal-spatial lambda-connectedness method using historical traffic data. Further, global dynamic capacity constraint of this area is integrated with cell transmission model (CTM) in the traffic optimization problem. To reduce computational complexity and improve scalability, we propose a fully distributed algorithm to solve the problem, which is based on the partial augmented Lagrangian and dual-consensus alternating direction method of multipliers (ADMM). By this means, distributed coordination of ramp metering and variable speed limit control is achieved. We prove that the proposed algorithm converges to the optimal solution so long as the traffic optimization objective is convex. The performance of the proposed method is evaluated by macroscopic simulation using real data of Shanghai, China.
Security is one of the biggest concern in power system operation. Recently, the emerging cyber security threats to operational functions of power systems arouse high public attention, and cybersecurity vulnerability thus become an emerging topic to e
valuate compromised operational performance under cyber attack. In this paper, vulnerability of cyber security of load frequency control (LFC) system, which is the key component in energy manage system (EMS), is assessed by exploiting the system response to attacks on LFC variables/parameters. Two types of attacks: 1) injection attack and 2) scale attack are considered for evaluation. Two evaluation criteria reflecting the damage on system stability and power generation are used to quantify system loss under cyber attacks. Through a sensitivity-based method and attack tree models, the vulnerability of different LFC components is ranked. In addition, a post-intrusion cyber attack detection scheme is proposed. Classification-based schemes using typical classification algorithms are studied and compared to identify different attack scenarios.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
