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  • 1. Chai, Jiale
    et al.
    Huang, Pei
    City University of Hong Kong.
    Sun, Yongjun
    Climate change impact on energy balance of net-zero energy buildings in typical climate regions of China2019Inngår i: E3S Web of Conferences, E-ISSN 2267-1242, Vol. 111, artikkel-id 04004Artikkel i tidsskrift (Fagfellevurdert)
  • 2. Chai, Jiale
    et al.
    Huang, Pei
    City University of Hong Kong.
    Sun, Yongjun
    Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions2019Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 185, s. 176-189Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Net-zero energy building (NZEB) is widely considered as a promising solution to the current energy problem. The existing NZEBs are designed using the historical weather data (e.g. typical meteorological year-TMY). Nevertheless, due to climate change, the actual weather data during a NZEB’s lifecycle may differ considerably from the historical weather data. Consequently, the designed NZEBs using the historical weather data may not achieve the desired performances in their lifecycles. Therefore, this study investigates the climate change impacts on NZEB lifecycle performance (i.e., energy balance, thermal comfort and grid interaction) in different climate regions, and also evaluates different measures' effectiveness in mitigating the associated impacts of climate change. In the study, the multi-year future weather data in different Chinese climate regions are firstly generated using the morphing method. Then, using the generated future weather data, the lifecycle performances of the NZEBs, designed using the TMY data, are assessed. Next, to mitigate the climate change impacts, different measures are adopted and their effectiveness is evaluated. The study results can improve understanding of the climate change impacts on NZEB lifecycle performance in different climate regions. They can also help select proper measures to mitigate the climate change impacts in the associated climate regions.

  • 3. Chai, Jiale
    et al.
    Huang, Pei
    City University of Hong Kong.
    Sun, Yongjun
    Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements2019Inngår i: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 158, s. 2762-2767Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Sizing the nZEB systems properly is crucial for nZEBs to achieve the desired performances. The energy demand prediction uncertainties and the components’ degradation are two major factors affecting the nZEB systems sizing. The energy demand prediction has been studied by many researchers, but the impacts of degradation are still neglected in most studies. Neglecting degradation may lead to a system design that can perform as expected only in the beginning several years. This paper, therefore, proposes an uncertainty-based life-cycle performance analysis (LCPA) method to study the impacts of degradation on the nZEBs longitudinal performance. Based on the LCPA method, this study also proposes a two-stage method to enhance the nZEB system sizing. The study can enhance the designers’ understanding of the components’ degradation impacts. Case studies show that an nZEB might not achieve zero energy targets after years due to degradation. The proposed two-stage design method can effectively mitigate this problem.

  • 4.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Augenbroe, Godfried
    Huang, Gongsheng
    Sun, Yongjun
    Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method2019Inngår i: Journal of Building Performance Simulation, Taylor & Francis, ISSN 1940-1493, E-ISSN 1940-1507, Vol. 12, nr 2, s. 117-132Artikkel i tidsskrift (Fagfellevurdert)
  • 5.
    Huang, Pei
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Copertaro, Benedetta
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Shen, Jingchun
    Högskolan Dalarna, Akademin Industri och samhälle, Byggteknik.
    Löfgren, Isabelle
    Rönnelid, Mats
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Fahlen, Jan
    Andersson, Dan
    Svanfeldt, Mikael
    A review of data centers as prosumers in district energy systems: Renewable energy integration and waste heat reuse for district heating2019Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 6.
    Huang, Pei
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik. City University of Hong Kong.
    Fan, Cheng
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Wang, Jiayuan
    A hierarchical coordinated demand response control for buildings with improved performances at building group2019Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 242, s. 684-694Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 7.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    City University of Hong Kong.
    Building Automation for Energy Efficiency2019Inngår i: Handbook of Energy Efficiency in Buildings: A Life Cycle Approach / [ed] Umberto Desideri, Francesco Asdrubali, United Kingdom: Butterworth-Heinemann, 2019, 1, s. 627-649Kapittel i bok, del av antologi (Fagfellevurdert)
    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

  • 8.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Investigation of maximum cooling loss uncertainty in piping network using Bayesian Markov Chain Monte Carlo method2017Inngår i: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 143, s. 258-263Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Heating, Ventilation, and air-conditioning (HVAC) systems have been widely equipped in modern buildings to provide thermal comfort and acceptable indoor air quality, and always represent the largest primary energy end-use. As reported by many researchers, the cooling loss is prevalent in HVAC systems during cooling transmission from cooling sources (chillers) to cooling end-users (conditioning zones), and in some cases, it may even account for as high as 55% of the system total heat flow. At the design stage of an HVAC system, incomplete understanding of the cooling loss may lead to improper sizing of the HVAC system, which may result in additional energy consumption/economic cost (if oversized) or cause insufficient thermal comfort problems (if undersized). Therefore, the cooling loss in a typical HVAC system is significant, and it should be considered in the HVAC system sizing. For HVAC system sizing or retrofit, although there are many studies in the uncertainty in predicting the building peak cooling load, the uncertainty associated with the maximum cooling loss of the HVAC systems are still neglected. Therefore, this study proposes a study to investigate the uncertainty associated with the key parameters in predicting the maximum cooling loss in the HVAC systems using the Bayesian Markov Chain Monte Carlo method. The prior information of the uncertainty together with the available in-situ data is integrated to infer more informative posterior description of the uncertainty. The studied uncertain parameters can either be used for retrofit analysis or be used for prediction of the HVAC system performance. Details of the proposed methodology are illustrated by applying it to a real HVAC system.

  • 9.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Augenbroe, Godfried
    Sizing heating, ventilating, and air-conditioning systems under uncertainty in both load-demand and capacity-supply side from a life-cycle aspect2017Inngår i: Science and Technology for the Built Environment, ISSN 2374-4731, E-ISSN 2374-474X, Vol. 23, nr 2, s. 367-381Artikkel i tidsskrift (Fagfellevurdert)
  • 10.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Augenbroe, Godfried
    Li, Shan
    Optimal configuration of multiple-chiller plants under cooling load uncertainty for different climate effects and building types2018Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 158, s. 684-697Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Configuring the number and size of chillers in a multiple-chiller plant properly is an efficient way to improve the plant energy efficiency. At the design stage, the optimal configuration can be achieved through matching the capacity to load as closely as possible across the full-load profile. However, in spite of the fact that current literature offers practical recommendations, a systematic method to optimize the configuration of multiple-chiller plants is lacking. Due to the lack of accurate information at the design stage and only limited knowledge of the eventual realization it is hard to predict the building’s cooling load. Moreover, there is no operational data to predict the system performance. Both explain the existence of uncertainty in the HVAC plant design process. This paper, therefore, proposes a strategy to optimize the configuration of multiple-chiller plants, which takes account of the load side uncertainty as well as the COP uncertainty and selects the optimal configuration through a life-cycle analysis. Both the load side uncertainty and the COP uncertainty are quantified using statistical distributions. To facilitate applications, the distributions of the cooling load profile of different types of buildings under different weather conditions are investigated and are classified into four categories, and the optimal configuration schemes under each type of cooling load distribution are analyzed and summarized in a tabulated form.

  • 11.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Sun, Yongjun
    A robust design of nearly zero energy building systems considering performance degradation and maintenance2018Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 163, s. 905-919Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Nearly zero energy buildings (nZEBs) are considered as a promising solution to mitigate the energy and environmental problems. A proper sizing of the nZEB systems (e.g. HVAC systems, PV panels, wind turbines and batteries) is essential for achieving the desirable level of thermal comfort, energy balance and grid dependence. Parameter uncertainty, component degradation and maintenance are three crucial factors affecting the nZEB system performances and should be systematically considered in system sizing. Until now, there are some uncertainty-based design methods been developed, but most of the existing studies neglect component degradation and maintenance. Due to the complex impacts of degradation and maintenance, proper sizing of nZEB systems considering multiple criteria (i.e. thermal comfort, energy balance and grid dependence) is still a great challenge. This paper, therefore, proposes a robust design method of nZEB systems using genetic algorithm (GA) which takes into account the parameter uncertainty, component degradation and maintenance. The nZEB life-cycle cost is used as the fitness function, and the user’ performance requirements on thermal comfort, energy balance and grid dependence are defined as three constraints. This study can help improve the designers’ understanding of the impacts of uncertainty, degradation, and maintenance on the nZEB life-cycle performances. The proposed method is effective in minimizing the nZEB life-cycle cost through designing the robust optimal nZEB systems sizes and planning the optimal maintenance scheme, meanwhile satisfying the user specified constraints on thermal comfort, energy balance, and grid dependence during the whole service life.

  • 12.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Sun, Yongjun
    Uncertainty-based life-cycle analysis of near-zero energy buildings for performance improvements2018Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 213, s. 486-498Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Near-zero energy buildings (nZEBs) are considered as an effective solution to mitigating CO2 emissions and reducing the energy usage in the building sector. A proper sizing of the nZEB systems (e.g. HVAC systems, energy supply systems, energy storage systems, etc.) is essential for achieving the desired annual energy balance, thermal comfort, and grid independence. Two significant factors affecting the sizing of nZEB systems are the uncertainties confronted by the building usage condition and weather condition, and the degradation effects in nZEB system components. The former factor has been studied by many researchers; however, the impact of degradation is still neglected in most studies. Degradation is prevalent in energy components of nZEB and inevitably leads to the deterioration of nZEB life-cycle performance. As a result, neglecting the degradation effects may lead to a system design which can only achieve the desired performance at the beginning several years. This paper, therefore, proposes a life-cycle performance analysis (LCPA) method for investigating the impact of degradation on the longitudinal performance of the nZEBs. The method not only integrates the uncertainties in predicting building thermal load and weather condition, but also considers the degradation in the nZEB systems. Based on the proposed LCPA method, a two-stage method is proposed to improve the sizing of the nZEB systems. The study can improve the designers’ understanding of the components’ degradation impacts and the proposed method is effective in the life-cycle performance analysis and improvements of nZEBs. It is the first time that the impacts of degradation and uncertainties on nZEB LCP are analysed. Case studies show that an nZEB might not fulfil its definition at all after some years due to component degradation, while the proposed two-stage design method can effectively alleviate this problem.

  • 13.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Huang, Gongsheng
    Wang, Yu
    HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique2015Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 91, s. 26-36Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Heating, ventilation and air-conditioning (HVAC) systems are widely equipped in modern buildings to provide indoor thermal comfort and guarantee indoor air quality. In a conventional design, the components of an HVAC system are sized according to a deterministic peak load, predicted according to typical weather condition, building physics and internal load. It has been shown by many studies that this prediction is associated with uncertainties since building physical parameters cannot be accurately set and the weather and the internal load used in the design may be different from the real situation after use. Therefore, uncertainty cannot be neglected in order to properly size a HVAC system. In this paper, a prototype of HVAC system design under uncertainty is proposed, which is able to take uncertainty directly in the design, and most importantly it can assess the performance of a design at the design stage in term of multiple performance indices and the customers’ requirements and preferences, i.e. the new design method falls in the framework of multiple criteria decision making. Case studies are used to illustrate the design procedure, and the result is compared with that of a conventional design method.

  • 14.
    Huang, Pei
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Lovati, Marco
    EURAC Research, Bolzano, Italy; University of Trento, Trento, Italy.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Bales, Chris
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Hallbeck, Sven
    NIBE Climate Solutions, Sweden.
    Becker, Anders
    Ferroamp Elektronik AB, Spånga, Sweden.
    Bergqvist, Henrik
    LudvikaHem AB Bobutiken, Ludvika, Sweden.
    Hedberg, Jan
    LudvikaHem AB Bobutiken, Ludvika, Sweden.
    Maturi, Laura
    EURAC Research, Bolzano, Italy.
    Transforming a residential building cluster into electricity prosumers in Sweden: Optimal design of a coupled PV-heat pump-thermal storage-electric vehicle system2019Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 255, artikkel-id 113864Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Smart grid is triggering the transformation of traditional electricity consumers into electricity prosumers. This paper reports a case study of transforming an existing residential cluster in Sweden into electricity prosumers. The main energy concepts include (1) click-and-go photovoltaics (PV) panels for building integration, (2) centralized exhaust air heat pump, (3) thermal energy storage for storing excess PV electricity by using heat pump, and (4) PV electricity sharing within the building cluster for thermal/electrical demand (including electric vehicles load) on a direct-current micro grid. For the coupled PV-heat pump-thermal storage-electric vehicle system, a fitness function based on genetic algorithm is established to optimize the capacity and positions of PV modules at cluster level, with the purpose of maximizing the self-consumed electricity under a non-negative net present value during the economic lifetime. Different techno-economic key performance indicators, including the optimal PV capacity, self-sufficiency, self-consumption and levelized cost of electricity, are analysed under impacts of thermal storage integration, electric vehicle penetration and electricity sharing possibility. Results indicate that the coupled system can effectively improve the district-level PV electricity self-consumption rate to about 77% in the baseline case. The research results reveal how electric vehicle penetrations, thermal storage, and energy sharing affect PV system sizing/positions and the performance indicators, and thus help promote the PV deployment. This study also demonstrates the feasibility for transferring the existing Swedish building clusters into smart electricity prosumers with higher self-consumption and energy efficiency and more intelligence, which benefits achieving the ‘32% share of renewable energy source’ target in EU by 2030.

  • 15.
    Huang, Pei
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik. City University of Hong Kong.
    Ma, Z.
    Xiao, L.
    Sun, Y.
    Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities2019Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 255, artikkel-id 113855Artikkel i tidsskrift (Fagfellevurdert)
  • 16.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Sun, Yongjun
    A clustering based grouping method of nearly zero energy buildings for performance improvements2019Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 235, s. 43-55Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 17.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Sun, Yongjun
    A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level2019Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 174, s. 911-921Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 18.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Sun, Yongjun
    A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty2019Inngår i: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 134, s. 215-227Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 19.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Wang, Yu
    Huang, Gongsheng
    Augenbroe, Godfried
    Investigation of the ageing effect on chiller plant maximum cooling capacity using Bayesian Markov Chain Monte Carlo method2016Inngår i: Journal of Building Performance Simulation, Taylor & Francis, ISSN 1940-1493, E-ISSN 1940-1507, Vol. 9, nr 5, s. 529-541Artikkel i tidsskrift (Fagfellevurdert)
  • 20.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Wang, Yu
    Sun, Yongjun
    Huang, Gongshend
    Review of uncertainty-based design methods of central air-conditioning systems and future research trends2019Inngår i: Science and Technology for the Built Environment, ISSN 2374-4731, E-ISSN 2374-474X, Vol. 25, nr 7, s. 819-835Artikkel i tidsskrift (Fagfellevurdert)
  • 21.
    Huang, Pei
    et al.
    City University of Hong Kong.
    Wu, Hunjun
    Huang, Gongsheng
    Sun, Yongjun
    A top-down control method of nZEBs for performance optimization at nZEB-cluster-level2018Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 159, s. 891-904Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Nearly zero energy buildings (NZEBs) are considered as a promising solution to the mitigation of the energy problems. A proper control of the energy system operation of the nZEB cluster is essential for improving load matching, reducing grid interaction and reducing energy bills. Existing studies have developed many demand response control methods to adjust the operation of energy systems to improve performances. Most of these studies focus on optimizing performances at individual-nZEB-level while neglecting collaborations (e.g. energy sharing and battery sharing) between nZEBs. Only a few studies consider the collaborations and optimize the system operation at nZEB-cluster-level, yet they cannot take full advantage of nZEB collaborations as optimization is conducted in a bottom-up manner lacking global coordination. This paper, therefore, proposes a top-down control method of nZEBs for optimizing performances at the cluster level. The top-down control method first considers the nZEB cluster as ‘one’ and optimizes its energy system operation using the genetic algorithm (GA), and then it coordinates the operation of every single nZEB inside the cluster using non-linear programming (NLP). The top-down control enables collaborations among nZEBs by coordinating single nZEB's operations. Such collaborations can bring significant performance improvements in different aspects. For instance, in aspect of economic cost, the collaborations can reduce the high-priced energy imports from the grid by sharing the surplus renewable energy with nZEBs which have insufficient energy generations. The proposed top-down control has been compared with a traditional non-collaborative control. The study results show that the top-down control is effective in improving performances at cluster level.

  • 22.
    Huang, Pei
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik. City University of Hong Kong.
    Xu, T.
    Guangzhou University, Guangzhou, PR China.
    Sun, Y.
    City University of Hong Kong, Kowloon, Hong Kong; City University of Hong Kong Shenzhen Research Institute, Shenzhen, PR China.
    A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance2019Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 199, s. 275-286Artikkel i tidsskrift (Fagfellevurdert)
  • 23. Sun, Yongjun
    et al.
    Huang, Pei
    City University of Hong Kong.
    Huang, Gongsheng
    A multi-criteria system design optimization for net zero energy buildings under uncertainties2015Inngår i: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 97, s. 196-204Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Net zero energy buildings (NZEBs) have been widely considered to be an effective solution to the increasing energy and environmental problems. Most conventional design methods for NZEB systems are based on deterministic data/information and have not systematically considered the significant uncertainty impacts. Consequently, the conventional design methods lead to popular oversized problems in practice. Meanwhile, NZEB system design methods need to consider customers’ actual performance preferences but few existing methods can take account of them. Therefore, this study proposes a multi-criteria system design optimization for NZEBs under uncertainties. In the study, three performance criteria are used to evaluate the overall NZEB system performance based on user-defined weighted factors. Case studies are conducted to demonstrate the effectiveness of the proposed method.

  • 24. Zhang, Sheng
    et al.
    Huang, Pei
    City University of Hong Kong.
    Sun, Yongjun
    A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties2016Inngår i: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 94, s. 654-665Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Net zero energy buildings (NZEBs) are promising to mitigate the increasing energy and environmental problems. For NZEBs, annual energy balance between renewable energy generation and building energy consumption is an essential and fundamental requirement. Conventional RES (renewable energy system) design methods for NZEBs have not systematically considered uncertainties associated with building energy generation and consumption. As a result, either the annual energy balance cannot be achieved or the initial investment of RES is unnecessarily large. Meanwhile, the uncertainties also have significant impacts on NZEB power mismatch which can cause severe grid stress. In order to overcome the above challenges, this study proposes a multi-criterion RES design optimization method for NZEBs under uncertainties. Under the uncertainties, Monte Carlo simulations have been employed to estimate the annual energy balance and the grid stress caused by power mismatch. Three criteria, namely the annual energy balance reliability, the grid stress and the initial investment, are used to evaluate the overall RES design performance based on user-defined weighted factors. A case study has demonstrated the effectiveness of the proposed method in optimizing the size of RES under uncertainties.

  • 25. Zhang, Sheng
    et al.
    Sun, Yongjun
    Cheng, Yong
    Huang, Pei
    City University of Hong Kong.
    Oladokun, Majeed Olaide
    Lin, Zhang
    Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty2018Inngår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 228, s. 1020-1031Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty.

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