Collaborative optimization between passive design measures and active heating systems for building heating in Qinghai-Tibet plateau of China

Renewable Energy 147 (2020) 683e694

Contents lists available at ScienceDirect

Renewable Energy journal homepage: www.elsevier.com/locate/renene

Collaborative optimization between passive design measures and active heating systems for building heating in Qinghai-Tibet plateau of China Xiaoliang Wang a, b, *, Xianmin Mai a, Bo Lei b, Haiquan Bi b, Bing Zhao a, Gang Mao a a b

Architecture and Urban Planning College, Southwest Minzu University, Chengdu, 610041, PR China School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, PR China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 April 2019 Received in revised form 21 July 2019 Accepted 10 September 2019 Available online 12 September 2019

Due to cold winter and cool summer in Qinghai-Tibet plateau, heating energy consumption accounts for a large proportion in the building energy consumption and reducing heating energy consumption is one of the main energy-saving methods for buildings. In the process of building heating, passive design measures and active heating systems (AHS) are always working together. To optimize the heating performance, it is necessary to coordinate the relationship between passive design measures and the AHS. However, it is impossible to obtain the specific quantitative optimized relationship between passive design measures and AHS based on existing research methods. To address this lack of knowledge, a collaborative optimization design method (CODM) is proposed in this paper to optimize the cost and the energy consumption of heating in the life cycle of the building. In CODM, comprehensive effects of passive design measures and the AHS on the total cost and the total energy consumption for building heating are analyzed. A railway passenger station is selected as a case study and results show that compared with initial design, the optimal total heating cost and total heating energy consumption for building heating can reduce 1948 ￥/m2 and 2292 kW h/m2 for the life cycle of the building, respectively. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Collaborative optimization design method (CODM) Passive design measure Active heating system (AHS) Multi-objective optimization Life cycle

1. Introduction In Qinghai-Tibet plateau, it is cold in winter and cool in summer. This climate type results in that building heating is essential in winter for meeting the comfort requirement, while in summer, natural ventilation is widely used for cooling instead of the special refrigeration system. Thus, in Qinghai-Tibet plateau, one of the main focuses is on building heating for saving energy and reducing building investment. In recent years, with the increasing demand for energy efficiency, the optimization of building heating in Qinghai-Tibet plateau of China has attracted more and more attention from designers and researchers. In terms of the building heating, there are two main ways including passive design measures and active heating systems (AHS). Passive design measures aim to improve thermal comfort without energy consumed during heating season through

* Corresponding author. Architecture and Urban Planning College, Southwest Minzu University, Chengdu, 610041, PR China. E-mail address: [email protected] (X. Wang). https://doi.org/10.1016/j.renene.2019.09.031 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

reasonable building orientation, improved thermal performance of the window, increased glazing area, enhanced heat capacity and insulation of building constructions [1,2]. Also, passive solar buildings can be identified as passive design measures due to their improved thermal performance and no energy consumption [3], such as the direct benefit passive room and the Trombe wall, etc. While the AHS relies on the special heating equipment to meet the requirement of building heating, such as the boiler heating system, air source heat pump system and so on. In the process of building heating, continuous energy is input to the AHS to generate hot water or air used for building space heating. In the actual engineering, passive design measures and the AHS work together for meeting the building heating requirement [4]. Generally, the improved thermal performance of the passive building will decrease the rated capacity and operating energy consumption of the AHS, resulting in increased building construction cost and lower AHS cost. On the contrary, the decreased investment of passive building will lead to the increasing operating energy consumption and cost of AHS [5]. Therefore, there may exist optimized design decisions for passive design measures and AHS to minimize the energy consumption and the cost for the building heating.

684

X. Wang et al. / Renewable Energy 147 (2020) 683e694

Nomenclature

fsalv

A Ao Aw C Ca-fa Ca-op Cin Cop Cp Cpm Cpo

Fco g i N P PWF q qh qmax Qload

CT Cw Dwin Ea ET fom

Building heating area Opaque building envelope area Window area Price of opaque building envelop per cubic meter Facility cost of AHS Operating cost of AHS Initial investment of AHS Annual operating cost of AHS Building construction cost per square meter Cost of building materials per square meter Other necessary costs including the expenses from the pile foundation, masonry, earthwork engineering, labor and design, etc. Total heating cost of the passive building with AHS Price of window per square meter The whole heating season Annual energy consumption of AHS Total heating energy consumption in the life cycle Percentage of the operating and maintenance cost of AHS compared to the initial investment of AHS

Over the past years, a large number of previous studies have presented various methods for optimizing the energetic, economic and environmental performance of buildings [6e9]. For example, a multi-objective optimization model was presented by W. M. Wang et al. [10] to help designers find better design alternatives satisfying several conflicting criteria. Tuhus-Dubrow and Krarti [11] developed a simulation environment using genetic algorithm optimization technique to select the best combinations of several building envelope features in order to optimize energy consumption and life cycle costs. F.P. Chantrelle et al. [12] developed a multi-criteria tool named MultiOpt to optimize design measures of building envelopes, heating and cooling loads and control strategies. A sequential search technique was employed to optimize the design of residential buildings in Tunisia in order to minimize their life cycle energy costs while increasing their energy efficiency [13]. Karmellos et al. [14] proposed a methodology to optimize the primary energy consumption and the initial investment cost of the building. To speed up the optimization and reduce convergence time for the building system design optimization, W.L. Xu et al. [15] proposed two improvement strategies including adaptive operator approach and adaptive meta-model approach used for modifying the behaviors of conventional evolutionary algorithms. The typical residential building located in Mediterranean was optimized by S. Jaber and A. Ajib [16] by changing the building orientation, window size and thermal insulation, and the results show that after optimization the life cycle cost and the energy consumption per square meter can be reduced by 11.94% and 25.31%, respectively. A simulation-based multi-objective optimization scheme combining with TRNSYS, GenOpt and the Tchebycheff optimization technique is employed to optimize the retrofit cost, energy savings and thermal comfort of a residential building [17]. M.S. Tokarik and R.C. Richman [18] performed a multi-objective optimization analysis of an existing house to evaluate the economic and energetic performance by coupling a genetic algorithm to EnergyPlus. However, most researchers have not discussed the relationship between passive heating design measures and active heating systems, and the optimal design for the building heating can't be obtained based on existing methods [19]. Meanwhile, due to timeconsuming of the current optimization process, the building

Percentage of the salvage cost of AHS compared to the initial investment of AHS Collaborative coefficient Inflation rate Discount rate Working life of the building Electricity price Present worth factor Transient heating load for building per square meter Rated heating capacity of AHS Maximum transient heating load per square meter Annual accumulated heating load per square meter

Greek symbols Time Thickness of opaque building envelop Percentage of the transportation cost of building materials compared to the purchasing cost saz Percentage of the installation charge of building materials compared to the purchasing cost

t d sys

optimization is still not a common technique used in building design stage. Therefore, it is necessary to study the optimal relationship between passive design measures and active heating systems for buildings in Qinghai-Tibet plateau of China. Thus, in this paper, we analyze the comprehensive optimization relationship between passive design measures and active heating systems in detail. The main aim of this study is to propose a collaborative optimization design method (COMD) for achieving the optimal optimization design between passive design measures and active heating systems for buildings in Qinghai-Tibet Plateau of China. In this paper, the total heating energy consumption and the total heating cost are considered as two objective functions. This method deals with the optimization of the passive building and the active heating system (AHS) separately. First, a multi-objective genetic algorithm named NSGA-II coded in Matlab environment is employed to find out the optimal parameter combinations of various passive design parameters with the aim of minimizing passive design cost and reduce the accumulated heating load simultaneously. Successively, based on the Pareto optimization of passive building design, building simulations are conducted in the TRNSYS environment to obtain the cost and energy performance of the AHS, and the interrelations between the passive building design measures and AHS are established using data fitting method. Finally, comprehensive effects of passive building design measures and the AHS on the total heating cost and the total heating energy consumption are analyzed. With the method established in this paper, reasonable architectural design and heating system selection can be made in the early stage of the whole building design, which is beneficial for saving energy and money for building heating in Qinghai-Tibet plateau. 2. Methodology 2.1. Building heating model The heating model for the passive building integrating with an active heating system is shown in Fig. 1. In this configuration, the passive building is coupled with the active heating system in order

X. Wang et al. / Renewable Energy 147 (2020) 683e694

685

Fig. 2 presents the economic and energetic relations between the passive building and AHS.

2.2.1. Passive building As shown in Fig. 2, three indicators (including the annual accumulated heating load Qload, the maximum transient heating load qmax and the building construction cost Cp) are used to evaluate the economic and energetic performance of the passive building without any AHS. Among them, the annual accumulated heating load Qload is selected to evaluate the energy performance of the passive building and it can be expressed as [20]. Fig. 1. Heating model for passive building integrating with an active heating system.

Qload ¼ to provide the heating demand of the building during the heating season. If the indoor temperature can maintain the set-point temperature for building heating only through the effect of the passive building, the AHS will not work. Otherwise, an AHS will be employed to provide auxiliary heat for satisfying the building heating load. The size of an AHS is selected in a way to meet the maximum transient heating load of the building during the heating season. For the economic and energy performance of building heating, passive building brings the building construction cost, but the energy consumption during the process of building construction is not considered in this paper since the changed passive design parameters have little impact on the energy consumption occurred in the construction process. While the AHS not only consumes energy, but also needs lots of money including the facility cost and the operating cost for building heating.

2.2. Economic and energetic relations between passive building and AHS In order to optimize the design measures of the building with AHS for saving energy and money, conventional multi-objective optimization method is feasible theoretically but needs much time to obtain Pareto solutions due to lots of existing related design parameters. To address this problem, this paper attempts to decouple the economic and energetic relationship between passive building and AHS theoretically and make the optimization process of passive building and AHS conducted separately.

1 A

 ½qðtÞ ,dt

ð

(1)

Dwin

where q (t) is the transient heating load of the building. Dwin represents the whole heating season. A is the building heating area. In the passive building, the maximum transient heating load qmax is employed to determine the rated capacity of AHS. Both of the annual accumulated heating load Qload and the maximum transient heating load qmax are closely related to the investment of the passive building. In order to assess the economic performance of the passive building, the building construction cost Cp is adopted and the corresponding formula can be written as [21].

Cp ¼ Cpm þ Cpo

(2)

where Cpm is the construction cost directly related to the consumption of building materials and it mainly includes the purchase cost, the transportation cost and the installation charge. It can be calculated with the following equation [20]:

0 1 1 6 6 X X   1@X Cpm ¼ Ci di Aoi A þ Cwj Awj A 1 þ sys þ saz A i j

(3)

j

where j represents the type of building envelope including south wall, east wall, north wall, west wall, roof and ground. i is the layer number of the specific opaque building envelop. wj represents the window with different orientation. Ci is the price of the opaque building envelop, ￥/m3. di (m) and Aoi (m2) are the thickness and the area of the opaque building envelop, respectively. Cwj is the price of the window, ￥/m2. Awj is the area of the window. sys is the percentage of the transportation cost of building materials compared to the purchasing cost, %. saz is the percentage of the installation charge of building materials compared to the purchasing cost, %. Where Cpo represents the other necessary costs including the expenses from the pile foundation, masonry, earthwork engineering, labor and design, etc. It is usually determined by the market price and the value of Cpo is between 1000￥/m2 ~2000￥/m2. In order to evaluate the economic and energetic interrelation of passive building without AHS, the relationship between the annual accumulated heating load Qload and the building construction cost Cp should be established, and the function can be expressed as

Qload ¼ gðCP Þ

(4)

Meanwhile, the influence of the building construction cost Cp on the maximum transient heating load qmax is also analyzed and qmax is written as the function of Cp.

qmax ¼ kðCP Þ Fig. 2. Economic and energetic relations between the passive building and AHS.

(5)

686

X. Wang et al. / Renewable Energy 147 (2020) 683e694

2.2.2. AHS For AHS, three indicators (including the initial investment Cin, the annual operating cost Cop and the annual energy consumption of AHS Ea) are used to evaluate the economic and energetic performance of the AHS. For the certain passive building, to absolutely meet the requirement of building heating, AHS is used and the rated heating capacity of AHS qh is determined by the maximum transient heating load qmax. In the design process, qh is usually 1.1 times as much as qmax for considering certain margin. When qmax is determined, qh can be easily calculated and then the initial investment of AHS Cin can be calculated based on qh and the selected product type. Thus, essentially, there exists the interrelation between Cin and qmax and it can be expressed as:

Cin ¼ hðqmax Þ

(7)

After the AHS is determined, the annual energy consumption of AHS Ea can be obtained for maintaining the heating set-point temperature of the passive building. At the same time, the corresponding annual operating cost of AHS Cop could be calculated based on Ea and the electricity price P. The relation is

Cop ¼ Ea  P

(9)

The collaborative coefficient Fco represents that how much the annual total heat demand for building heating can be satisfied when consuming 1 kW h of energy from the AHS in heating season. The value of the Fco equals to the ratio of the annual total heat demand for building heating to the consumed energy by the AHS every year. The higher the value of the Fco is, the more heating demand for the building can be satisfied with 1 kW h of energy from the AHS. Furthermore, although in theory the collaborative coefficient Fco can be calculated, numerical simulation could be employed to find the specific value of Fco and in this research Fco is expressed as the function of Qload.

Fco ¼ f ðQload Þ

(10)

2.2.3. Passive building with AHS For the passive building with AHS, the total heating energy consumption ET and the total heating cost CT (including the building construction cost Cp, the facility cost of AHS Ca-fa and the operating cost of AHS Ca-op) are used to evaluate the economic and energetic performance of the passive building with AHS. For the passive building with AHS, the passive building has no energy consumption, while the AHS need to consume energy throughout the life cycle. Therefore, the total heating energy consumption ET refers to the energy consumption of AHS in the life cycle and it can be expressed as

N ET ¼ ,Ea A

(12)

The facility cost of AHS Ca-fa considers the initial investment, operating and maintenance cost and the salvage cost of the AHS during the life cycle. It equals to Ref. [13]: or

Ca"TT5843c571""ADfa ¼

 !  Cin 1þg N 1 þ fom  PWF  fsalv 1þi A

(13)

where fom is the percentage of the operating and maintenance cost of AHS compared to the initial investment of AHS. fsalv is the percentage of the salvage cost of AHS compared to the initial investment of AHS. i is the discount rate. g is the inflation rate. PWF is the present worth factor and it is defined as [13]. The operating cost of AHS Ca-op refers to the operating cost of the AHS during the life cycle. It can be expressed as [23]:

Ca"TT5843c571""ADop ¼

Ea ,P,PWF A

(14)

(8)

Considering the annual energy consumption of AHS Ea is closely related to the annual accumulated heating load of passive building Qload, a collaborative coefficient Fco is proposed in this paper to build the relationship between them and the formula is expressed as:

Ea ¼ Qload =Fco

CT ¼ Cp þ Ca"TT5843c571""ADfa þ Ca"TT5843c571""ADop

(11)

where N is the working life of the building, normally 15 years [22]. The total heating cost of the passive building with AHS CT can be calculated [10] by

2.3. Collaborative optimization design method (CODM) For the present analysis, in order to obtain the optimized measures of passive design measures and AHS for heating, a collaborative optimization design method (COMD) has been proposed. In this method, optimizations for passive building and the AHS are carried out separately, and the mutual effects between them are established through the collaborative relationship based on numerical simulation. The process COMD for the passive building with AHS is shown in Fig. 3. The process can be summarized as: Step 1: Economic and energetic optimization of passive building In this step, passive building simulation model is first built in the transient simulation software named TRNSYS based on the building configuration and the corresponding weather data. Then a multiobjective optimization method named NSGA-II is used for the optimization of passive building design, and the multi-objective optimization procedure is coded in Matlab to be coupled with the building simulation of TRNSYS. The passive design measures are optimized by multi-objective optimization method for the building without considering any AHS and the Pareto optimization results referring to the optimal combinations of various passive design measures can be obtained. Fig. 4 shows the scheme of MATLAB and TRNSYS coupling algorithm. Step 2: Numerical simulation and optimization of AHS Based on the Pareto optimization results of passive design measures integrated in the studied building, AHS is employed for building space heating, and the energy consumption and cost of AHS can be achieved using numerical simulation conducted in TRNSYS. Certainly, the optimization for AHS is also on the basis of the optimized passive building design measures. Considering this article mainly aims to find the optimal passive building design measures, thus the optimization for AHS is not involved in this paper due to its various forms and complex control strategies. After the optimization of passive buildings and the simulation of the AHS, the economic and energetic interrelation between the passive building and AHS can be established using the data fitting method.

X. Wang et al. / Renewable Energy 147 (2020) 683e694

687

Fig. 3. Schematic structure of collaborative optimization design method.

Fig. 4. Scheme of coupling method of Matlab and TRNSYS.

Step 3: Collaborative optimization design between passive design measures and AHS According to the economic and energetic interrelation between the passive building and AHS, the total heating energy consumption and the total heating cost for the passive building with AHS can be expressed as the function of the passive building construction cost. Meanwhile, the relationship between the total heating energy consumption and the total heating cost can be established for evaluating the energetic and economic performance for building heating.

2.4. Implementation of building simulation and multi-objective optimization 2.4.1. Multi-objective optimization of the passive building Multi-objective optimization is an available approach to deal with real engineering problems with objectives which are conflicting and need to be addressed simultaneously. Unlike singleobjective optimization problem, there is no unique optimal solution to multi-objective optimization problems. Instead, a set of nondominated solutions, known as Pareto optimal solutions, can be obtained in such problems, representing a hierarchy of best possible trade-offs between the considered objective functions

688

X. Wang et al. / Renewable Energy 147 (2020) 683e694

[19]. The set of optimal solutions provides decision-makers with the flexibility to choose a suitable alternative design based on their preferences in a given project. A multi-objective optimization problem can generally be formulated as follows [24]: Find x¼(xi) c i ¼ 1,2, …,Npar Minimize or Maximize Fj(x) c j ¼ 1,2, …,Nobj

gk ðxÞ  0,,,c,k ¼ 1; 2; ::::; m hs ðxÞ ¼ 0,,,,c,s ¼ 1; 2; :::::; n where x is the vector of design parameters, Fj(x) is the jth objective function, Npar and Nobj are the number of design parameters and objective functions, and gk(x) and hs(x) are the inequality and equality constraints, respectively. For this type of optimization problem, the genetic algorithm named NSGA-II have been proven to provide a robust and efficient approach to achieve a set of reliable optimal design measures of passive design measures due to its advantages in computing performance. The advantages of NSGA-II are as follows [12]: (1) a new hierarchical fast non-winning sorting algorithm is introduced to reduce the computational complexity from O(mN3) to O(mN2); (2) using the crowding distance comparison operator to replace the shared parameter fitness method; (3) the introduction of the optimal protection mechanism expands the sampling space, and the offspring compete with the father to produce the next generation, which is conducive to the maintenance of excellent individuals; (4) the high efficiency and good distribution of solution set can be obtained. Matlab is very valuable as a master controller for optimization, while TRNSYS does not include any robust multi-objective optimization routines. As such, TRNSYS is coupled with Matlab in the present study, using its powerful genetic algorithm to perform the optimization of the passive building, thereby determining optimum design measures of the passive design measures. As illustrated in the step 1 of Fig. 3, Matlab first defines the objective functions and a set of design parameters of the passive building through a userdeveloped code. Then the TRNSYS engine is called by Matlab to perform a building simulation based on the defined set of design parameters. After each simulation completes, the results obtained by TRNSYS is imported to the Matlab environment for conducting the energetic and economic analyses and the multi-objective optimization of the passive building. Unless a stopping criterion is reached, the design variables are updated and sent to the TRNSYS environment in order for a new simulation. Through the selection, crossover and mutation operators, the optimal solutions of the passive building are achieved. The tuning parameters of the NSGAII approach employed in this paper are presented in Table 1.

convenient for researchers and designers to set up the relationship among various components. Thus, TRNSYS is employed to establish the energetic and economic relationship between passive building and AHS in this study, and the corresponding design measures of the AHS can be obtained based on various optimal passive building design measures achieved in step 1 of CODM seen in Fig. 3. After obtaining Pareto solutions of passive building design measures, the AHS is employed for the passive building heating and the simulation models are established for every optimal passive building design measures.

3. A case study 3.1. Description of the building model In order to demonstrate the collaborative optimization approach, the developed method is applied to a typical railway passenger station to investigate the effect of passive heating design measures and active heating systems on the total energy consumption and total cost of the building heating at the Tibetan plateau of China. Fig. 5 illustrates the 3D architectural view of the railway passenger station with the dimension of 28.8 m (length)  14.4 m (width)  7.0 m (high). The station facing south is comprised of only one floor, and has the total floor area of 414.72 m2. The glazing fraction for south and north wall is 30% and 15%, respectively. The other walls and roof are built without windows. According to the requirement of the corresponding specification for the railway passenger station of China, the thermal characteristics, internal heat gains, infiltration, ventilation and operation schedules (i.e. heating, occupancy and internal gains) for the thermal zone are determined. The wall materials, floor and roof construction, and windows are chosen to meet the minimum criteria specified in the energy saving design standard for public buildings [25]. The detailed parameters are listed in Table 2. The set-point temperature is 16  C for heating and the building requires heating on every day of the heating season. The number of air changes for the thermal zone is set to 2.0 air changes per hour. The annual accumulated heating load of the station is determined by running the respective TRNSYS [26] model throughout a year. The air source heat pump system is used as an active heating system for the station. The heat capacity of the air source heat pump system is determined based on the maximum heating load

2.4.2. Simulation of energetic and economic performance of AHS The energetic and economic performance of AHS is simulated by using TRNSYS. This software is a very useful tool for modeling of energy and economic behavior of HVAC systems. In the software, the user can specify the components that constitute the system and the manner in which they are connected, and sets the inputs, outputs and design parameters for every component. It is quite

Table 1 Tuning parameters used for NSGA-II method of the passive building. Parameter

Value

Population size Crossover fraction Mutation probability Minimum function tolerance (stopping criterion) Maximum number of generations

100 0.9 0.01 0.0001 200

Fig. 5. Three dimensional view of the studied building.

X. Wang et al. / Renewable Energy 147 (2020) 683e694

689

Table 2 Detailed architectural design parameters. Architectural design parameter Orientation Exterior window

Opaque building construction

Value

Glazing ratio Window characteristic parameter Exterior wall U ¼ 0.6 W/ (m2$K) Roof U ¼ 0.55 W/(m2$K) Ground U ¼ 1.0 W/ (m2$K)

Facing south South wall: 30%; North wall: 15%; East or west wall or roof: 0. Double glazed window: single glass (6 mm)þ air (12 mm)þ single glass (6 mm); Heat transfer coefficient: U ¼ 2.5 W/ (m2$K); Solar heat gain coefficient: SHGC ¼ 0.66. Form outside to inside: cement mortar (20 mm) þ Insulation (30 mm) þBrick (150 mm) þ cement mortar (20 mm); Form outside to inside: cement mortar (20 mm) þ Insulation (35 mm) þ concrete (240 mm) þ cement mortar (20 mm); Form outside to inside: cement mortar (20 mm) þ Insulation (15 mm) þ concrete (200 mm) þ cement mortar (20 mm);

with considering a reasonable safety margin. A coupled simulation is conducted for the building and the air source heat pump to maintain the indoor temperature at the set-point for heating, and the annual energy consumption of the air source heat pump system is obtained. 3.2. Weather and location The investigated building is located in Lhasa, China. Its average elevation is 3650 m, above sea level, latitude 29 360 N, and longitude 91060 E. Time is GMT þ8.0 h. Lhasa stands in the northern part of the Himalayas and its weather is strongly influenced by the sinking airflow form the Himalayas. As a result, Lhasa enjoys a plateau semi-arid monsoon climate, with cold winter and cool summer. The historical maximum air temperature, minimum air temperature and the annual average temperature are 29.6  C,16.5  C and 7.4  C, respectively. In Lhasa, heating in winter is necessary for buildings, but cooling during the summer season is needless. The heating season is from November 1st to March 31st of the next year. Meanwhile, Lhasa enjoys sunny weather all the year round, but with scarce rain. In fact, Lhasa enjoys more than 3000 sunshine hours per year and the solar radiation intensity is rich enough, which can be exploited for building heating to reduce the energy consumption. The weather file used in the simulation is obtained from the typical year weather data of China based on the past 30 years’ climate data [27]. Fig. 6(a) and Fig. 6(b) describe the annual outdoor temperature and the total horizontal solar radiation, respectively.

(a) Outdoor temperature

3.3. Objective functions and decision variables In the present work, the total heating energy consumption ET (Eq. (11)) and the total heating cost CT (Eq. (12)) are selected as two conflicting objective functions to investigate the energy and economic performance of the building with AHS. Considering that the main purpose in this paper is to determine the optimal passive building design measures for minimizing the two objective functions simultaneously, thus passive building design parameters are selected as decision variables, and the selected nine passive building design parameters and their feasible range of variation are listed in Table 3. It should be noted that in the optimization for the passive building design, the annual accumulated heating load (Qload) and the passive building construction cost (CP) are selected to analyze the energy and economic performance of the passive building, respectively. 3.4. Parameter settings related to economic and energetic performance In the collaborative optimization process, some parameters

(b) Total horizontal solar radiation Fig. 6. Some meteorological parameters of Lhasa.

related to economic performance need to be set reasonably in order to accomplish the optimization. These parameters mainly include the lifetime of the studied building N, the inflation rate g, the discount rate i, the energy price P and the percentage of operating,

690

X. Wang et al. / Renewable Energy 147 (2020) 683e694

Table 3 Specifications of the decision variables. Variable name (Passive building design parameter)

Unit

Range of variation

Initial value

Window-wall ratio on south wall Window-wall ratio on north wall Heat transfer coefficient of window Brick layer thickness of exterior wall Concrete thickness of roof Concrete layer thickness of ground Insulation layer thickness of exterior wall Insulation layer thickness of roof Insulation layer thickness of ground

% % W/(m2,K) mm mm mm mm mm mm

Continues[10, 90] Continues[10, 90] Continues[1.5, 4.0] Continues[0, 500] Continues[0, 500] Continues[0, 500] Continues[0, 500] Continues[0, 500] Continues[0, 500]

30 15 2.5 150 240 200 30 35 15

maintenance and salvage cost of AHS compared to the initial investment of AHS (fom, fsalv). The detailed parameters used in this research are listed in Table 4. 4. Results and discussion 4.1. Bi-objective optimization for cost and heating load of passive building The Pareto optimal solution obtained from multi-objective optimization of the studied passive building is shown in Fig. 7. The vertical axis in this figure denotes the annual accumulated heating load, while the horizontal axis represents the building construction cost of the passive building to each design point. As depicted in Fig. 7, the conflicting relation between the two objective functions is evident in these figures; lower accumulated heating load design measures clearly have higher total building construction cost of the passive building. As seen in Fig. 7, the lowest accumulated heating load is achieved at design point B, while the total building construction cost has its highest value at this point. The highest accumulated heating load occurs at design point A, where the total building construction cost stands at its minimum. If the accumulated heating load is considered as the sole objective function, then point B would be represented by the optimal design point of the system. In other words, point B shows an extreme design where the passive design measures are most weighted to contribute to meeting the heating load requirements of the building. Were the building construction cost to be the sole objective in the optimization process, then point A would be preferred as the optimum design. Fig. 7 also show the accumulated heat load and passive building construction cost corresponding to the initial design of passive building. Optimal solutions for passive building have lower accumulated heat load and reduced energy consumption than the initial design of passive building, which indicates that the current design of passive building is worth for optimization to achieve more economy-efficient design choice. Using data fitting, the annual accumulated heating load can be expressed as the function of the passive building construction cost.

 1:09 Qload ¼ 43237:86  Cp  Cpo

(15)

Fig. 7. The set of Pareto optimal solutions obtained from multi-objective optimization for the passive building.

In addition, it should be noted that each solution on the Pareto front in Fig. 7 represents a unique trade-off between the optimal values of objective functions without being dominated by one another. Thus, it can be selected as an optimal design point of the passive building depending on the preferences and criteria of the decision-maker for a given project. Certainly, to comprehensively analyze the energetic and economic performance of the optimal design of the passive building, a final optimal solution should be selected from the Pareto frontier presented in Fig. 7. Since the dimensions of the objective functions considered in this paper are not the same (i.e. Qload in kWh/m2 and CP in ￥/m2), all objectives must be non-dimensionalized prior to performing any decision-making. In this paper, the weighted sum method (WSM) is used to determine the final optimal design point of the passive building. In this method, a non-dimensionalized objective is defined as follows [28]:

Table 4 Parameter setting used in this research. Parameter

Unit

Value

Lifetime of the building N Inflation rate g Discount rate i Electricity price of Lhasa The percentage of operating and maintenance cost fom The percentage of salvage cost fsalv

year % % ￥/kWh ($/kWh) % %

15.0 6.0 4.0 0.85 (0.13) 8.0 10.0

X. Wang et al. / Renewable Energy 147 (2020) 683e694

min , FðxÞ ¼

k X i¼1

Pi

691

!

fi ðxÞ  fi ðxÞmin fi ðxÞmax  fi ðxÞmin

(16)

where k is the number of objectives. x represents the vector of the design variables. F(x) is the comprehensive objective function, FðxÞ ¼ ff1 ðxÞ; f2 ðxÞ; ……; fk ðxÞg. fi ðxÞ denotes the first i objective function. fi ðxÞmax and fi ðxÞmin are the maximum and minimum of the first i objective function, respectively. Pi is the weight coefficient and it is used to reflect the importance of every goal. The higher the weight coefficient is, the more attention will be paid to the corresponding objective. The weight coefficient Pi should satisfy the following relationship [29]. k X

Pi ¼ 1

(17)

i¼1

Assuming equal weight coefficients are assigned to both objective functions, based on the WSM, the optimal design of passive building seen in Fig. 7 can be obtained. It should be noted that the selection of the final optimal point of the passive building merely depends on the significance and value of each objective function to the designer under certain circumstances. The optimal values of design parameters corresponding to the optimal point selected by SWM decision-maker as well as point A and B are presented in Table 5. As shown in this table, the final optimal point selected by SWM has reached an equal trade-off between the energetic and economic facet of the proposed passive building. After the multi-objective optimization for the passive building, numerous optimal design measures named Pareto solution can be achieved. However, in order to configure the active heating system, the maximum transient heating load qmax during the heating season is employed to determine the rated heating capacity of AHS. Based on the Pareto frontier, it can be found that the maximum transient heating load qmax is closely related to the passive building construction cost CP seen in Fig. 8, and lower qmax clearly have higher CP of the passive building. The relationship between qmax and CP can be described by

4.2.1. Energetic collaborative relationship As mentioned in section 2.2, in order to establish the collaborative relationship between the annual accumulated heating load of passive building Qload and the annual energy consumption of ASHP Ea, the collaborative coefficient Fco is proposed in this paper. Based on the optimal passive building, the ASHP is employed to provide heated air for building space during the heating season and the annual energy consumption of ASHP Ea can be achieved through numerical simulation. Afterwards the collaborative coefficient Fco can be calculated by Eq. (9). Fig. 9 displays the relationship between Fco and Qload. With the increase of the accumulated heating load of passive building, the collaborative coefficient increases quickly at first and then has a slow growth. Through data fitting, the collaborative coefficient Fco can be expressed as the function of Qload as follows:

 0:41 qmax ¼ 553:69  Cp  Cpo

Fco ¼ 1:78 þ 0:09 lnðQload þ 66:85Þ

(18)

4.2. Determination of collaborative relationships between passive building and AHS Based on the obtained Pareto frontier of the passive building, an energy-efficient AHS called an air source heat pump (ASHP) is used for building heating. In this part, the collaborative energetic and economic relationships between passive building and ASHP are analyzed.

Fig. 8. The relationship between qmax and CP after optimization of passive building.

(19)

4.2.2. Economic collaborative relationship According to the value of the maximum transient heating load, the rated heating capacity of the AHS can be achieved, which considers about 10% margin. In this paper, the air source heat pump (ASHP) is used as AHS. In order to determine the initial investment of ASHP, the prices of different air source heat pumps with different heating capacity existing in the market are analyzed. Fig. 10 shows the relationship between the initial investment of ASHP and the

Table 5 Optimal design parameters of the passive building at three indicative solutions on the Pareto frontier. Design parameter

Point A (in favor of economic design)

Point B (in favor of energetic design)

Optimal design point selected by SWM

Window-wall ratio on south wall Window-wall ratio on north wall Heat transfer coefficient of window Brick layer thickness of exterior wall Concrete thickness of roof Concrete layer thickness of ground Insulation layer thickness of exterior wall Insulation layer thickness of roof Insulation layer thickness of ground

0.71 0.14 3.86 24 13 12 6 18 15

0.9 0.11 3.4 395 391 398 158 131 208

0.9 0.13 3.5 185 145 134 74 57 76

692

X. Wang et al. / Renewable Energy 147 (2020) 683e694

4.3. Collaborative optimization between passive design measures and active systems

Fig. 9. The relationship between Fco and Qload.

rated heating capacity of ASHP. It can be found that the initial investment of ASHP increases with the increased rated heating capacity of ASHP. Furthermore, through data fitting, the initial investment of ASHP can be expressed as the function of the rated heating capacity of ASHP.

Cin ¼ 0:3643  ðqrated Þ0:7115

(20)

where qrated is determined by 1.1 times the maximum transient heating load qmax . Thus, the relationship between the initial investment of ASHP and the maximum transient heating load can be established as Eq. (21).

Cin ¼ 0:3643  ð1:1  qmax Þ0:7115

Fig. 10. The relationship between Cin and qrated.

Based on the CODM proposed in this paper, the energetic and economic performance of the building with ASHP can be expressed as the function of the passive building construction cost. Fig. 11 presents the relationship between the total heating energy consumption of the building with ASHP and the passive building construction cost in the whole lifetime of the studied case building. It can be found that the total heating energy consumption decreases with the increased passive building construction cost. Specifically, when the passive building construction cost per square meter is lower than 2200 ￥/m2, the total heating energy consumption decreases quickly as the passive building construction cost increases. At this stage, the total heating energy consumption of the building with ASHP can be significantly reduced through reasonable passive building design. While with the passive building construction cost exceeds 2200 ￥/m2, the decline of the total heating energy consumption will not be significantly reduced and the effect of the passive building design on the total heating energy consumption of the building with ASHP is no longer obvious. Fig. 12 displays the relationship between various building heating costs and the passive building construction cost in the whole lifetime of the studied case building. Results show that as the passive heating construction cost increases, the facility cost and the operating cost of the ASHP are decreased. While the total heating cost first decreases and then increases with the increasing passive building construction cost. For the changing curve of the total heating cost, when the passive building construction cost is lower than 2200 ￥/m2, the facility cost and the operating cost of the ASHP will be reduced with the increasing passive building construction cost due to the decrease of the total heating demand. At this stage, the sum of the decreasing rate of the facility cost and the decreasing rate of the operating cost is greater than the increasing rate of the passive building construction cost, resulting in the decrease of the total heating cost. While as the passive building construction cost exceeds 2200 ￥/m2, since the total heating demand will not be significantly reduced with the increasing passive building construction cost, the sum of the decreasing rate of the facility cost and the decreasing rate of the operating cost will be

(21)

Fig. 11. The relationship between ET and CP.

X. Wang et al. / Renewable Energy 147 (2020) 683e694

Fig. 12. The relation between building heating cost and the passive building construction cost.

lower than the increasing rate of the passive building construction cost. As a result, the total heating cost will increase. In order to illustrate the relationship between the economy and energy consumption for the building heating, the total heating cost and the total heating energy consumption are drawn in the same figure (seen in Fig .13). As shown in Fig. 13, as the total heating energy consumption decreases, the total heating cost will first reduce and then increase. In particular, when the total heating energy consumption is higher than is 405 kW h/m2, the total heating cost will decrease with the decreasing total heating energy consumption. When the total heating energy consumption is lower than is 405 kW h/m2, the total heating cost will increase with the decreasing total heating energy consumption. The lowest total heating cost is about 2660 ￥/m2 for the whole lifetime. Fig. 13 also gives the economic and energetic level for the initial design scheme. It can be found that compared with the initial design, the optimal total heating cost and total heating energy consumption for building heating can reduce 1948 ￥/m2 and 2292 kW h/m2 for the life cycle of the building, respectively, which indicates the

Fig. 13. The relationship between the total heating cost and the total heating energy consumption.

693

optimization for the building heating in Qinghai-Tibet plateau has great economic and energy saving potential. Fig. 14 shows the percentage of various costs that constitute the total heating cost in the optimization process of the building heating. It can be found that as the total heating energy consumption increases, the percentage of the passive building construction cost will be reduced from 87% to 50%, and the proportion of the operating cost consumed by the ASHP will increase from 4% to 40%. It should be noted that the percentage of the facility cost for the ASHP compared to the total heating cost stay almost invariant although the facility cost has increased with the increasing total heating energy consumption. Accordingly, it is inferred that using the CODM proposed in this paper may lead to a more desirable and more efficient design for the building located in the Qinghai-Tibet plateau than conventional design methods. From the above discussion about the proposed CODM, it can be concluded that reasonably increasing passive building construction cost can effectively decrease the building heating load demand, and save the energy consumption, facility cost and operating cost for the AHS. From the overall, it is beneficial to saving the energy consumption and money for the building heating. However, although reducing passive construction cost can decrease the building heating load demand, excessive passive construction cost may cause extreme increase total heating cost of the building. Therefore, in practical optimization design, the degree of passive building investment should be reasonably controlled based on the CODM. As an overall result, the combination of passive building parameters has a noticeable influence on the energy consumption and the cost for building heating. At the same time, it should be noted that he combination of the architectural design parameters relies on the climate where the building is located. Therefore, for other different climates, the optimization for the building heating should be made according to the local climate. 5. Conclusions In this work, a collaborative optimization design method (CODM) has been proposed to evaluate the economic and energy performance of the building heating in Qinghai-Tibet plateau. In order to implement the simulation-based optimization, the genetic algorithm named NSGA-II has been adopted and coded in Matlab

Fig. 14. The percentage of various costs with the change of the total heating energy consumption.

694

X. Wang et al. / Renewable Energy 147 (2020) 683e694

environment and coupled with TRNSYS program. The case study for a railway passenger station has indicated that the CODM proposed in this paper is quite helpful for researchers or designers to harmonically optimize passive building measures and the active heating systems for building heating in Qinghai-Tibet plateau of China. Coordination relations between energy consumption and cost for the building heating have been established. The following conclusions are obtained: (1) For the optimization of the building heating, passive building measures and active heating systems should be integrated into account. The case study has shown that reasonably increasing passive building construction cost can effectively decrease the energy consumption and costs for the building heating. (2) The combination of various passive building parameters has a noticeable influence on the energy consumption and the cost for building heating. (3) The CODM proposed in this paper is beneficial for saving energy and money for building heating in Qinghai-Tibet plateau, which could be used for guiding the optimization design of building heating.

[10]

[11]

[12]

[13]

[14]

[15]

[16]

[17]

Acknowledgments

[18]

This research was supported by the National Key R&D Program of China (No. 2018YFC0705000) and the Fundamental Research Funds for the Central Universities,Southwest Minzu University (No. 2018NQN57).

[19] [20]

[21]

References [1] Machairas V, Tsangrassoulis A, Axarli K. Algorithms for optimization of building design: a review. Renew. Sustain. Energy Rev. 2014; 31:101e112. https://doi.org/10.1016/j.rser.2013.11.036. [2] Evins R. A review of computational optimization methods applied to sustainable building design. Renew. Sustain. Energy Rev. 2013; 22:230-245. https://doi.org/10.1016/j.rser.2013.02.004. [3] Stevanovi c S. Optimization of passive solar design strategies: a review. Renew. Sustain. Energy Rev. 2013; 25:177-196. https://doi.org/10.1016/j.rser.2013.04. 028. [4] Aksoy UT, Inalli M. Impacts of some building passive design parameters on heating demand for a cold region. Build. Environ. 2006; 41:1742-1754. https://doi.org/10.1016/j.buildenv.2005.07.011. [5] Gong XZ, Akashi Y, Sumiyoshi D. Optimization of passive design measures for residential buildings in different Chinese areas. Build. Environ. 2012; 58:4657. https://doi.org/10.1016/j.buildenv.2012.06.014. [6] Nguyen AT, Reiter S, Rigo P. A review on simulation-based optimization methods applied to building performance analysis. Appl. Energy 2014; 113: 1043e1058. https://doi.org/10.1016/j.apenergy.2013.08.061. [7] Shirazi A, Taylor RA, Morrison GL, White SD. A comprehensive, multi-objective optimization of solar-powered absorption chiller systems for air-conditioning applications. Energy Convers. Manag. 2017; 132:281-306. https://doi.org/10. 1016/j.enconman.2016.11.039. [8] Yu W, Li BZ, Jia HY, Zhang M, Wang D. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build. 2015; 88:135e143. https://doi.org/10.1016/j.enbuild. 2014.11.063. [9] Eini S, Shahhosseini H, Delgarm N, Lee M, Bahadori A. Multi-objective

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

optimization of a cascade refrigeration system: exergetic, economic, environmental, and inherent safety analysis. Appl. Therm. Eng. 2016; 107: 804e817. https://doi.org/10.1016/j.applthermaleng.2016.07.013. Wang WM, Zmeureanu R, Rivard H. Applying multi-objective genetic algorithms in green building design optimization. Build. Environ. 2005; 40:15121525. https://doi.org/10.1016/j.buildenv.2004.11.017. Tuhus-Durow D, Krarti M. Genetic-algorithm based approach to optimize building envelope design for residential buildings. Build. Environ. 2010; 45: 1574-1584. https://doi.org/10.1016/j.buildenv.2010.01.005. Chantrelle FP, Lahmidi H, Keilholz W, Mankibi ME, Michel P. Development of a multicriteria tool for optimizing the renovation of buildings. Appl. Energy 2011; 88:1386-1394. https://doi.org/10.1016/j.apenergy.2010.10.002. Ihm P, Krarti M. Design optimization of energy efficient residential buildings in Tunisia. Build. Environ. 2012; 58:81-90. https://doi.org/10.1016/j.buildenv. 2012.06.012. Karmellos M, Kiprakis A, Mavrotas G. A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: model, software and case studies. Appl. Energy 2015; 139: 131e150. https://doi.org/10.1016/j. apenergy.2014.11.023. Xu WL, Chong A, Karaguzel OT, Lam KP. Improving evolutionary algorithm performance for integer type multi-objective building system design optimization. Energy Build. 2016; 127:714-729. https://doi.org/10.1016/j.enbuild. 2016.06.043. Jaber S, Ajib S. Optimum, technical and energy efficiency design of residential building in Mediterranean region. Energy Build. 2011; 43:1829-1834. https:// doi.org/10.1016/j.enbuild.2011.03.024. Asadi E, Silva MGD, Antunes CH, Dias L. A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Build. Environ. 2012; 56: 370-378. https://doi.org/10.1016/j.buildenv. 2012.04.005. Tokarik MS, Richman RC. Life cycle cost optimization of passive energy efficiency improvements in a Toronto house. Energy Build. 2016; 118:160-169. https://doi.org/10.1016/j.enbuild.2016.02.015. X.S. Yang, Multi-objective optimization, in: Chapter 14 e Nature-Inspired Optimization Algorithms, Elsevier, Oxford, 2014, pp. 197e211. Wang XL. reportCollabirative Optimization between Passive and Active Heating for Buildings in Cold Area. Doctoral thesis 2017. Southwest Jiaotong University.([In Chinese]). Xu J, Kim JH, Hong HK, Koo J. A systematic approach for energy efficient building design factors optimization. Energy Build. 2015; 89: 87-96. https:// doi.org/10.1016/j.enbuild.2014.12.022. Wang LF, Liu LX, Zhang WQ, Huang ZP, Dai DH. Study on service life of architectural construction materials in high elevation and severe frigid zone in Qinghai-Tibet plateau. J. Qinghai. Univ. (Nat. Sci.) 2005; 23 (1): 11-14. DOI: 10.13901/j.cnki.qhwxxbzk.2005.01.003.([In Chinese]). Wu R, Mavromatidis G, Orehounig K, Carmeliet J. Multiobjective optimisation of energy systems and building envelope retrofit in a residential community. Appl. Energy 2017; 190: 634-649. https://doi.org/10.1016/j.apenergy.2016.12. 161. Delgarm N, Sajadi B, Kowsary P, Delgarm S. Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl. Energy 2016; 170: 293-303. https:// doi.org/10.1016/j.apenergy.2016.02.141. China Academy of Building Research, Design Standard for Energy Efficiency of Public Buildings (GB 50189-2015), China building industry press, Beijing, 2015. TRNSYS 18 Manuals, Component mathematical reference. Available online: http://sel.me.wisc.edu/trnsys/user18-resources/index.html; 2018 [accessed on 1 November 2018]. Meteorological information center of China, Department of Building Technology Science of Tsinghua University. Special Meteorological Data Sets for the Analysis of the Thermal Environment of Chinese Buildings, China building industry press, Beijing, 2005. J.H. Ryu, S. Kim, H. Wan, Pareto front approximation with adaptive weighted sum method in multi-objective simulation optimization, in: Proceedings of the 2009 Winter Simulation Conference, USA, 2009. R.T. Marler, J.S. Arora, Survey of multi-objective optimization methods for engineering, Struct. Multidiscip. Optim. 26 (6) (2004) 369e395.