AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
In many chiller plants, high coefficient of performance (COP) is only achieved at a few favorable part load ratios (PLRs), while the COP is low at many other non-favorable PLRs. To address this issue, this study proposes a generic load regulation strategy that aims to maintain chiller plants operating at high COP, particularly under non-favorable PLRs. This is achieved by incorporating thermal energy storage (TES) units and timely optimizing the charging and discharging power of the integrated TES units. The optimal charging and discharging power is determined by solving a dynamic optimization problem, taking into account the performance constraints of the TES units and the chiller plants. To provide an overview of the energy-saving potential of the proposed strategy, a comprehensive analysis was conducted, considering factors such as building load profiles, COP/PLR curves of chillers, and attributes of the TES units. The analysis revealed that the proposed load regulation strategy has the potential to achieve energy savings ranging from 5.7% to 10.8% for chiller plants with poor COPs under unfavorable PLRs, particularly in buildings with significant load variations.
Alam M, Devapriya S, Sanjayan J (2022). Experimental investigation of the impact of design and control parameters of water-based active phase change materials system on thermal energy storage. Energy and Buildings, 268: 112226.
Alizadeh M, Sadrameli SM (2016). Development of free cooling based ventilation technology for buildings: thermal energy storage (TES) unit, performance enhancement techniques and design considerations—A review. Renewable and Sustainable Energy Reviews, 58: 619–645.
Arena S, Casti E, Gasia J, et al. (2018). Numerical analysis of a latent heat thermal energy storage system under partial load operating conditions. Renewable Energy, 128: 350–361.
Campos G, Liu Y, Schmidt D, et al. (2021). Optimal real-time dispatching of chillers and thermal storage tank in a university campus central plant. Applied Energy, 300: 117389.
Candanedo JA, Dehkordi VR, Stylianou M (2013). Model-based predictive control of an ice storage device in a building cooling system. Applied Energy, 111: 1032–1045.
Chang Y-C, Lin J-K, Chuang M-H (2005). Optimal chiller loading by genetic algorithm for reducing energy consumption. Energy and Buildings, 37: 147–155.
Chiu JNW, Gravoille P, Martin V (2013). Active free cooling optimization with thermal energy storage in Stockholm. Applied Energy, 109: 523–529.
Cox SJ, Kim D, Cho H, et al. (2019). Real time optimal control of district cooling system with thermal energy storage using neural networks. Applied Energy, 238: 466–480.
Crawley DB, Lawrie LK, Winkelmann FC, et al. (2001). EnergyPlus: creating a new-generation building energy simulation program. Energy and Buildings, 33: 319–331.
Garimella S, Lockyear K, Pharis D, et al. (2022). Realistic pathways to decarbonization of building energy systems. Joule, 6: 956–971.
Hou J, Luo X, Huang G, et al. (2020). Development of event-driven optimal control for central air-conditioning systems. Journal of Building Performance Simulation, 13: 378–390.
Huang S, Zuo W, Sohn MD (2016). Amelioration of the cooling load based chiller sequencing control. Applied Energy, 168: 204–215.
Huang P, Huang G, Augenbroe G, et al. (2018). Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types. Energy and Buildings, 158: 684–697.
Hydeman M, Gillespie KL Jr (2002). Tools and techniques to calibrate electric chiller component models. ASHRAE Transactions, 108(1): 733–741.
Kang Y, Jiang Y, Zhang Y (2003). Modeling and experimental study on an innovative passive cooling system—NVP system. Energy and Buildings, 35: 417–425.
Kang X, Wang X, An J, et al. (2022). A novel approach of day-ahead cooling load prediction and optimal control for ice-based thermal energy storage (TES) system in commercial buildings. Energy and Buildings, 275: 112478.
Kaspar K, Ouf M, Eicker U (2022). A critical review of control schemes for demand-side energy management of building clusters. Energy and Buildings, 257: 111731.
Li W, Gong G, Fan H, et al. (2021). A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems. Applied Energy, 282: 116223.
Liu Z, Tan H, Luo D, et al. (2017). Optimal chiller sequencing control in an office building considering the variation of chiller maximum cooling capacity. Energy and Buildings, 140: 430–442.
Liu Y, Ming H, Luo X, et al. (2023). Timetabling optimization of classrooms and self-study rooms in university teaching buildings based on the building controls virtual test bed platform considering energy efficiency. Building Simulation, 16: 263–277.
Lu J, Tian X, Feng C, et al. (2023). Clustering compression-based computation-efficient calibration method for digital twin modeling of HVAC system. Building Simulation, 16: 997–1012.
Ma Z, Wang S, Xu X, et al. (2008). A supervisory control strategy for building cooling water systems for practical and real time applications. Energy Conversion and Management, 49: 2324–2336.
Miretti F, Misul D, Spessa E (2021). DynaProg: Deterministic Dynamic Programming solver for finite horizon multi-stage decision problems. SoftwareX, 14: 100690.
Powell KM, Cole WJ, Ekarika UF, et al. (2013). Optimal chiller loading in a district cooling system with thermal energy storage. Energy, 50: 445–453.
Sehar F, Rahman S, Pipattanasomporn M (2012). Impacts of ice storage on electrical energy consumptions in office buildings. Energy and Buildings, 51: 255–262.
Seo BM, Lee KH (2016). Detailed analysis on part load ratio characteristics and cooling energy saving of chiller staging in an office building. Energy and Buildings, 119: 309–322.
Sun Y, Wang S, Huang G (2009). Chiller sequencing control with enhanced robustness for energy efficient operation. Energy and Buildings, 41: 1246–1255.
Tibshirani R, Walther G, Hastie T (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society Series B: Statistical Methodology, 63: 411–423.
Wan H, Xu X, Xu T, et al. (2022). Development of a quasi-2D variable resistance–capacitance model for tube-encapsulated phase change material storage tanks. Applied Thermal Engineering, 214: 118868.
Woods J, Mahvi A, Goyal A, et al. (2021). Rate capability and Ragone plots for phase change thermal energy storage. Nature Energy, 6: 295–302.
Wu R, Ren Y, Tan M, et al. (2024). Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network. Building Simulation, 17: 371–386.
Xiong Q, Alshehri HM, Monfaredi R, et al. (2022). Application of phase change material in improving trombe wall efficiency: An up-to-date and comprehensive overview. Energy and Buildings, 258: 111824.
Yang S, Gao HO, You F (2022). Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage. Applied Energy, 327: 120054.
Zhang S, Niu J (2016). Two performance indices of TES apparatus: Comparison of MPCM slurry vs. stratified water storage tank. Energy and Buildings, 127: 512–520.
Zhang Y, Akkurt N, Yuan J, et al. (2020). Study on model uncertainty of water source heat pump and impact on decision making. Energy and Buildings, 216: 109950.
Zou W, Sun Y, Gao D, et al. (2023). Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation. Energy, 262: 125469.
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
京公网安备11010802044758号