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Huang, Pei
Publications (10 of 25) Show all publications
Huang, P. & Sun, Y. (2019). A clustering based grouping method of nearly zero energy buildings for performance improvements. Applied Energy, 235, 43-55
Open this publication in new window or tab >>A clustering based grouping method of nearly zero energy buildings for performance improvements
2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 235, p. 43-55Article in journal (Refereed) Published
Abstract [en]

Collaborations among nearly zero energy buildings (nZEBs) (e.g. renewable energy sharing) can improve nZEBs’ performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this paper proposes a clustering based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results demonstrate that the proposed method can effectively improve nZEBs’ performances at the community level. The propose method can provide the decision makers a means to group nZEBs, which maximize the collaboration benefits and thus assists the planning of nZEB communities.

Keywords
Nearly zero energy building, Community, Clustering, Grouping, Collaborations
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30841 (URN)10.1016/j.apenergy.2018.10.116 (DOI)
Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Huang, P. & Sun, Y. (2019). A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level. Energy, 174, 911-921
Open this publication in new window or tab >>A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level
2019 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 174, p. 911-921Article in journal (Refereed) Published
Abstract [en]

Collaborations (e.g. renewable energy sharing) among nearly zero energy buildings can improve performances at cluster level. Demand response control is helpful to enable such collaborations. Existing studies have developed some dynamic pricing demand response control methods to reduce the nearly zero energy building cluster’ electricity bills and eliminate the power grid's undesirable peaks. However, in these controls the collaborations among buildings are not allowed/enabled, since each building interacts with the grid and there is no direct interaction among buildings. Meanwhile, for performance optimizations at building cluster level, the computation costs of these non-collaborative controls are excessively high especially as a number of buildings considered. Therefore, this study proposes a collaborative demand response of nearly zero energy buildings in response to dynamic pricing for cluster-level performance improvements. Considering the building cluster as one ‘lumped’ building, in which the renewable generations, energy demands and battery capacities of individual buildings are aggregated, the collaborative control first identifies the optimal performance at cluster level in response to the dynamic pricing. Then, based on the identified optimal performance, the proposed control coordinates individual buildings' operations using non-linear programming, thereby realizing the collaborations. For validation, the proposed collaborative demand response control is compared with a game-theory based non-collaborative demand response control. The developed control effectively reduces the cluster-level peak energy exchanges and electricity bills by 18% and 45.2%, respectively, with significant computational load reduction. This study will provide the decision makers a computation-efficient demand response control of nearly zero energy buildings which enables full collaborations and thus helps improve the performances.

Keywords
Nearly zero energy building, Collaborations, Demand response, Dynamic pricing, Cluster level performance
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30844 (URN)10.1016/j.energy.2019.02.192 (DOI)
Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Huang, P., Xu, T. & Sun, Y. (2019). A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance. Energy and Buildings, 199, 275-286
Open this publication in new window or tab >>A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance
2019 (English)In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 199, p. 275-286Article in journal (Refereed) Published
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-30587 (URN)10.1016/j.enbuild.2019.07.003 (DOI)000482245700023 ()2-s2.0-85068486421 (Scopus ID)
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-09-17Bibliographically approved
Huang, P., Fan, C., Zhang, X. & Wang, J. (2019). A hierarchical coordinated demand response control for buildings with improved performances at building group. Applied Energy, 242, 684-694
Open this publication in new window or tab >>A hierarchical coordinated demand response control for buildings with improved performances at building group
2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 242, p. 684-694Article in journal (Refereed) Published
Abstract [en]

Demand response control is one of the common means used for building peak demand limiting. Most of the existing demand response controls focused on single building’s performance optimization, and thus may cause new undesirable peak demands at building group, imposing stress on the grid power balance and limiting the economic savings. A few latest studies have demonstrated the potential benefits of demand response coordination, but the proposed methods cannot be applied in large scales. The main reason is that, for demand response coordination of multiple buildings, associated computational load and coordination complexity, increasing exponentially with building number, are challenges to be solved. This study, therefore, proposes a hierarchical demand response control to optimize operations of a large scale of buildings for group-level peak demand reduction. The hierarchical control first considers the building group as a ‘virtual’ building and searches the optimal performance that can be achieved at building group using genetic algorithm. To realize such optimal performance, it then coordinates each single building’s operation using non-linear programming. For validations, the proposed method has been applied on a case building group, and the study results show that the hierarchical control can overcome the challenges of excessive computational load and complexity. Moreover, in comparison with conventional independent control, it can achieve better performances in aspects of peak demand reduction and economic savings. This study provides a coordinated control for application in large scales, which can improve the effectiveness and efficiency in relieving the grid stress, and reduce the end-users’ electricity bills.

Keywords
Peak demand limiting, Demand response, Building group coordination, Economic cost, Grid interaction
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-29729 (URN)10.1016/j.apenergy.2019.03.148 (DOI)000470045800054 ()2-s2.0-85063025035 (Scopus ID)
Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2020-01-02Bibliographically approved
Huang, P., Copertaro, B., Zhang, X., Shen, J., Löfgren, I., Rönnelid, M., . . . Svanfeldt, M. (2019). A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating. Applied Energy
Open this publication in new window or tab >>A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118Article in journal (Refereed) In press
Abstract [en]

As large energy prosumers in district energy systems, on the one hand, data centers consume a large amount of electricity to ensure the Information Technologies (IT) facilities, ancillary power supply and cooling systems work properly; on the other hand, data centers produce a large quantity of waste heat due to the high heat dissipation rates of the IT facilities. To date, a systematic review of data centers from the perspective of energy prosumers, which considers both integration of the upstream green energy supply and downstream waste heat reuse, is still lacking. As a result, the potentials for improving data centers’ performances are limited due to a lack of global optimization of the upstream renewable energy integration and downstream waste heat utilization. This study is intended to fill in this gap and provides such a review. In this regard, the advancements in different cooling techniques, integration of renewable energy and advanced controls, waste heat utilization and connections for district heating, real projects, performance metrics and economic, energy and environmental analyses are reviewed. Based on the enormous amount of research on data centers in district energy systems, it has been found that: (1) global controls, which can manage the upstream renewable production, data centers’ operation and waste heat generation and downstream waste heat utilization are still lacking; (2) regional climate studies represent an effective way to find the optimal integration of renewable energy and waste heat recovery technologies for improving the data centers’ energy efficiency; (3) the development of global energy metrics will help to appropriately quantify the data center performances.

Keywords
Data center, District energy system, Renewable energy, Waste heat recovery, Energy efficiency
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-31116 (URN)10.1016/j.apenergy.2019.114109 (DOI)2-s2.0-85075714407 (Scopus ID)
Available from: 2019-11-20 Created: 2019-11-20 Last updated: 2019-12-09Bibliographically approved
Huang, P. & Sun, Y. (2019). A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty. Renewable energy, 134, 215-227
Open this publication in new window or tab >>A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty
2019 (English)In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 134, p. 215-227Article in journal (Refereed) Published
Abstract [en]

Collaborations among nZEBs (e.g. renewable energy sharing and battery sharing) can improve the nZEBs' performance at the cluster level. To enable such collaborations, existing studies have developed many demand response control methods to control the operation of nZEB systems. Unfortunately, due to lack of consideration of demand prediction uncertainty, most of the demand response control methods fail to achieve the desired performance. A few methods have considered the impacts of uncertainty, but they merely perform simple and limited collaborations among nZEBs, and thus they cannot achieve the optimal performance at the cluster level. This paper, therefore, proposes a nZEB control method that enables full collaborations among nZEBs and takes account of the demand prediction uncertainty. The proposed robust control method first analyzes the demand prediction uncertainty, next optimizes the nZEB cluster operation under uncertainty, and then coordinates single nZEB's operation using the cluster operational parameters. The performance of the robust control has been studied and compared with a deterministic control. Case studies show that the robust control can effectively increase the cluster load matching and reduce the grid interaction with the demand prediction uncertainty existed. The proposed method can achieve robust performance improvements for the nZEB cluster in practice particularly as uncertainty exists.

Keywords
Near-zero energy building cluster, Robust control, Uncertainty, Economic cost, Load matching, Grid interaction
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30840 (URN)10.1016/j.renene.2018.11.024 (DOI)
Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Huang, P. & Huang, G. (2019). Building Automation for Energy Efficiency (1ed.). In: Umberto Desideri, Francesco Asdrubali (Ed.), Handbook of Energy Efficiency in Buildings: A Life Cycle Approach (pp. 627-649). United Kingdom: Butterworth-Heinemann
Open this publication in new window or tab >>Building Automation for Energy Efficiency
2019 (English)In: Handbook of Energy Efficiency in Buildings: A Life Cycle Approach / [ed] Umberto Desideri, Francesco Asdrubali, United Kingdom: Butterworth-Heinemann, 2019, 1, p. 627-649Chapter in book (Refereed)
Abstract [en]

In this chapter, the authors introduce current building energy management system (BEMS) from its development, current structure and main components, communications and standards, main functions and benefits, as well as future development trends. The information in this chapter can guide the readers in the direction of understanding, operation, and design of BEMS

Place, publisher, year, edition, pages
United Kingdom: Butterworth-Heinemann, 2019 Edition: 1
Keywords
Building Automation, HVAC system
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30847 (URN)10.1016/B978-0-12-812817-6.00008-5 (DOI)9780128128176 (ISBN)
Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
Chai, J., Huang, P. & Sun, Y. (2019). Climate change impact on energy balance of net-zero energy buildings in typical climate regions of China. E3S Web of Conferences, 111, Article ID 04004.
Open this publication in new window or tab >>Climate change impact on energy balance of net-zero energy buildings in typical climate regions of China
2019 (English)In: E3S Web of Conferences, E-ISSN 2267-1242, Vol. 111, article id 04004Article in journal (Refereed) Published
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30857 (URN)10.1051/e3sconf/201911104004 (DOI)
Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-07Bibliographically approved
Huang, P., Ma, Z., Xiao, L. & Sun, Y. (2019). Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities. Applied Energy, 255, Article ID 113855.
Open this publication in new window or tab >>Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities
2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 255, article id 113855Article in journal (Refereed) Published
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-30819 (URN)10.1016/j.apenergy.2019.113855 (DOI)000497978100093 ()2-s2.0-85072244758 (Scopus ID)
Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2020-01-02Bibliographically approved
Huang, P., Augenbroe, G., Huang, G. & Sun, Y. (2019). Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method. Journal of Building Performance Simulation, Taylor & Francis, 12(2), 117-132
Open this publication in new window or tab >>Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method
2019 (English)In: Journal of Building Performance Simulation, Taylor & Francis, ISSN 1940-1493, E-ISSN 1940-1507, Vol. 12, no 2, p. 117-132Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Taylor & Francis, 2019
National Category
Building Technologies
Identifiers
urn:nbn:se:du-30835 (URN)10.1080/19401493.2018.1487998 (DOI)
Available from: 2019-09-30 Created: 2019-09-30 Last updated: 2019-10-01Bibliographically approved
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