Optimizing environmental insulationthickness of buildings with CHP-based district heating system based on amount of energy and energy grade

Abstract

The increase of insulation thickness (IT) results in the decrease of the heat demand and heat medium temperature. A mathematical model on the optimum environmental insulation thickness (OEIT) for minimizing the annual total environmental impact was established based on the amount of energy and energy grade reduction. Besides, a case study was conducted based on a residential community with a combined heat and power (CHP)-based district heating system (DHS) in Tianjin, China. Moreover, the effect of IT on heat demand, heat medium temperature, exhaust heat, extracted heat, coal consumption, carbon dioxide (CO₂) emissions and sulfur dioxide (SO₂) emissions as well as the effect of three types of insulation materials (i.e., expanded polystyrene, rock wool and glass wool) on the OEIT and minimum annual total environmental impact were studied. The results reveal that the optimization model can be used to determine the OEIT. When the OEIT of expanded polystyrene, rock wool and glass wool is used, the annual total environmental impact can be reduced by 84.563%, 83.211%, and 86.104%, respectively. It can be found that glass wool is more beneficial to the environment compared with expanded polystyrene and rock wool.

Graphical abstract

Keywords

optimum environmental insulation thickness / heat medium temperature / energy grade / extracted heat / exhaust heat

1 Introduction

The transition to new energy systems with less pollutant emissions requires new district heating technologies. The energy consumption and pollutant emissions of the 4th generation district heating system (DHS) can be greatly reduced by using the heat pump technique and the integration of renewable energy technologies [1]. On the other hand, combined heat and power (CHP) can raise the overall energy efficiency compared to individually electricity and heat production in conventional systems [2]. Therefore, the application of CHP-based DHS is considered to be the key measure for sustainable development [3].

Currently, there are about 80000 DHS in the world [4], the majority of which are located in the northern hemisphere [5], especially in Europe [1]. Due to the exploitation of national geothermal resources, DHS have a market share of nearly 90% in Iceland [5]. In Finland, DHS are widely used in about 200 cities [6]. In Sweden, DHS are used to provide approximately 91% of space heating and domestic hot water [2]. In China, DHS have experienced the largest growth rate since the 1990s, with the largest annual growth rate of approximately 15% [6]. In 2017, the area of existing buildings in northern China was 14 billion m², 84.5% of which was attributed to buildings connected with DHS [7]. In China, not only are DHS booming, but also CHP plants are growing rapidly. CHP plants account for approximately 48% of district heating supplies [7].

Despite the widespread use and rapid development of DHS, there still exist some problems. High-energy buildings with high heat demand account for a large proportion of building stocks [8]. About 40% of existing buildings in Europe (e.g., UK, France, Sweden, etc.) were built before the 1960s [9–11]. In addition, 75.7% of the existing residential buildings in northern China are energy inefficient [12]. Therefore, a lot of heat loss in winter is caused due to poor insulation performance, which leads to high primary energy consumption.

Moreover, plenty of pollutant emissions are caused by space heating of buildings. Conventional pollutants in atmosphere include SO₂, nitrogen oxides, carbon monoxide, particulate matter, etc [13]. Space heating of buildings is considered to be one of the main reasons for global warming which leads to rainfall redistribution, melting of the iceberg, sea level fluctuations, etc. In 2017, the CO₂ emissions associated with fossil fuels combustion during the operation of buildings in China were about 2.13 billion tons, 22% of which were caused by space heating in northern heating areas [7]. Lots of SO₂ emissions are also attributed to space heating of buildings. Reducing SO₂ emissions can effectively prevent the formation of acid rain. In addition, space heating of buildings leads to an increase in haze weather in winter. The average concentration of particulate matter with an aerodynamic diameter of less than or equal to 2.5 mm in the 2017–2018 heating season in Beijing was about 15 mg/m³ larger than that in the non-heating season [7].

The building envelope improvement with insulation is considered to be a common method for reducing primary energy consumption and environmental pollutant emissions. Some studies focused on the reduction of primary energy consumption through building envelope improvement with insulation [14–18]. Lee et al. [14] studied the effect of external thermal insulation and internal thermal density on energy consumption in different seasons. In Ref. [15], a mathematical model was developed to forecast energy savings of buildings by using vacuum insulation panels. The results showed that the natural gas consumption for space heating of retrofitted buildings was reduced by 12.5% per year. Fang et al. [16] studied the influences of external thermal insulation on building energy consumption by using two types of experimental chambers. To reduce heat loss and energy consumption, Yang et al. [17] proposed a method to predict and optimize the radiative thermal properties of ultrafine fibrous insulations. In Ref. [18], the embodied energy impact of insulation materials was assessed. Moreover, the energy and carbon payback of the energy efficiency measures were evaluated.

Some studies analyzed building envelope improvement with insulation from an economic perspective [19–23]. Dlimi et al. [19] determined the optimum insulation thickness (OIT) of hemp wool based on the life cycle cost analysis, taking into account of the dynamic thermal conditions in Meknes, Morocco. Evin and Ucar [20] proposed a procedure for optimizing the economic IT by using the heating-cooling energy demand. In Ref. [21], the optimum economic IT of aerogel-based thermal insulation materials was determined considering the climate in Nottingham, UK. Jie et al. [22] optimized the economic IT of buildings with CHP-based DHS. In Ref. [23], the OIT of buildings considering economic analysis for different thermal insulation materials was determined under dynamic thermal conditions.

There are some studies on building envelope improvement with insulation based on environmental analysis [24,25]. Dylewski and Adamczyk [24] proposed a method for evaluating the environmental impact of insulation of buildings. Küçüktopcu and Cemek [25] studied the environmental impact of the IT of poultry building exterior walls considering different types of insulation materials and fuels.

Moreover, some studies were conducted by combining economy with the environment to study building envelope improvement with insulation [26–30]. In Ref. [26], the OIT of walls through energetic life cycle cost analysis and exergetic environmental analysis was optimized, respectively. Açıkkalp et al. [27] proposed a method to optimize the IT of building envelope, combining economy with the environment. In Ref. [28], the economic and environmental performance of thermal insulation materials was studied by using life cycle analysis, respectively. Dylewski et al. [29] evaluated the economic and environmental benefits of insulation materials by using net present value and life cycle analysis, respectively. In Ref. [30], the OIT of building walls was determined by using life cycle cost analysis and environmental analysis based on the entransy method, respectively.

In addition, some researches were performed by combining energy, economy, with the environment to study building envelope improvement with insulation [31–33]. In Ref. [31], the OIT of exterior walls was determined considering the energetic, economic and environmental criteria. The results showed that mineral wool with the IT of 11 cm had the best performance based on the energetic, economic and environmental analysis. Jie et al. [32] determined the OIT by considering three criteria (i.e., primary energy consumption, global cost, and pollutant emissions). In Ref. [33], the optimum economic thickness of new aerogel super-insulation materials and four commonly used insulation materials (i.e., expanded polystyrene, extruded polystyrene, polyurethane, and glass fibers) were compared. The energy-saving rate, economic benefits, and greenhouse-gas emissions were further evaluated.

Related references on building envelope improvement with insulation described above are summarized in Table 1. These results are very practical and valuable. However, there exist some problems in above studies. For example, many studies determined the OIT of buildings from the perspective of reducing the amount of energy. However, few articles considered the energy grade reduction of heat sources due to the increase of IT. Besides, although attention was paid to the relationship between the IT and energy grade based on economic analysis in Ref. [22], the OIT was not determined from other perspectives. Moreover, little attention was paid to the impact of the IT on heat medium temperature of DHS. Furthermore, few studies combined energy efficiency retrofit of building envelope with that of CHP plants to study the impact of IT on the environment.

Compared with the separate production of heat and power, CHP can reduce energy consumption and pollutant emissions [34]. At CHP plants, a large amount of exhaust heat is released into external environment through cooling towers. If the exhaust heat can be recycled, the thermal efficiency of CHP plants will be undoubtedly improved. On the other hand, the heat medium temperature of DHS is reduced by retrofitting building envelope, which creates conditions for the recycle of low-grade exhaust heat in CHP plants [35,36]. Therefore, energy efficiency retrofit of building envelope can be combined with that of CHP plants to further reduce fuel consumption and pollutant emissions.

This paper is innovative because the energy efficiency retrofit of building envelope is combined with that of CHP plants to optimize the IT of buildings based on environmental analysis. Besides, the relationship between exhaust and extracted heat in CHP plants and the IT is studied. In addition, the environmental impact of heat supply and insulation materials is studied, and the optimum environmental insulation thickness (OEIT) of buildings is determined by minimizing the annual total environmental impact based on the amount of energy and energy grade reduction. Furthermore, the impact of IT on the heat medium temperature of DHS is investigated.

The purpose of this paper is to optimize the IT of buildings based on environmental analysis, taking into account the reduction in the amount of energy and energy grade. The heat demand of buildings is decreased by increasing the IT. The coal consumption that meets the heat demand of buildings is reduced, which, in turn, reduces the environmental impact of heat supply. On the contrary, the environmental impact of insulation materials gradually increases as the IT increases. Therefore, the OEIT of buildings is used to balance the environmental impacts of heat supply and insulation materials. An optimization model on the OEIT and minimum annual total environmental impact is established, and the impact of three types of insulation materials on the OEIT and minimum annual total environmental impact is studied.

2 Methodology

Figure 1 shows the flow of CHP-based DHS. The return water in the primary heating network (HN) is heated by the exhaust steam and extraction steam at the CHP plant. First, if the return temperature of the primary HN is low enough, exhaust heat can be recycled through the water-water heat exchanger (WWHE). Then, the high-pressure steam from the turbine flows into the generator of absorption heat pump (AHP) as a forced heat source. The extracted heat is transferred to the return water in the primary HN through the AHP. Driven by extraction steam, the exhaust heat is released in the evaporator of the AHP and it is indirectly transferred to the return water in the primary HN. Next, the extraction steam is utilized to heat the return water in the primary HN through the steam-water heat exchanger (SWHE) in order to guarantee the desired supply temperature of the primary HN. After that, the heat of the supply water in the primary HN is transferred to the return water in the secondary HN at substations. Finally, the supply water in the secondary HN flows through radiators to release heat into indoor air.

2.1 Heat medium temperature

Two important parameters, i.e., design heat load and relative heat load ratio, are introduced before introducing the calculation of the heat medium temperature of DHS.

Heat load of buildings can be reduced by increasing the IT of walls. The relationship between insulated and non-insulated design heat load is expressed in Eq. (1) [22].

(1){\Phi }_{\mathrm{d}}^{\mathrm{i}\mathrm{n}\mathrm{s}}=\theta {\Phi }_{\mathrm{d}}^{\mathrm{n}\mathrm{o}\mathrm{n}-\mathrm{i}\mathrm{n}\mathrm{s}},

where {\Phi }_{\mathrm{d}}^{\mathrm{i}\mathrm{n}\mathrm{s}} and \theta {\Phi }_{\mathrm{d}}^{\mathrm{n}\mathrm{o}\mathrm{n}-\mathrm{i}\mathrm{n}\mathrm{s}} are the insulated and non-insulated design heat load (kW), respectively.

The operating heat load of buildings is assumed to change linearly with the difference between indoor and outdoor temperature [22,37]. Then, the relative heat load ratio can be obtained by using Eq. (2) [22].

(2)R={\displaystyle \frac{\theta (T_{\mathrm{n}}-T_{\mathrm{w}})}{T_{\mathrm{n}}-T_{\mathrm{d}\mathrm{w}}}},

where T_w and T_dw are the outdoor temperature and outdoor design temperature for heating (°C), respectively.

It is assumed that there is no heat loss during heat distribution. The operating mass flow rate in the primary and secondary HN is assumed to be constant (i.e., design value). Only the supply and return temperature of the primary and secondary HN is adjusted to meet the heat demand of buildings [22]. The supply and return temperature of the secondary HN can be calculated by using Eqs. (3) and (4), respectively [22,38].

(3)T_{\mathrm{s}}^{\mathrm{s}}={\displaystyle \frac{T_{\mathrm{n}}-[(T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{s}})R+T_{\mathrm{n}}]\exp \left[\left(\ln {\displaystyle \frac{T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{n}}}{T_{\mathrm{d}\mathrm{r}}^s-T_{\mathrm{n}}}}\right)R^{\left(\mathrm{1}-\frac{\mathrm{1}}{\mu }\right)}\right]}{\mathrm{1}-\exp \left[\left(\ln {\displaystyle \frac{T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{n}}}{T_{\mathrm{d}\mathrm{r}}^s-T_{\mathrm{n}}}}\right)R^{\left(\mathrm{1}-\frac{\mathrm{1}}{\mu }\right)}\right]}},

(4)T_{\mathrm{r}}^{\mathrm{s}}={\displaystyle \frac{T_{\mathrm{n}}-[(T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{s}})R+T_{\mathrm{n}}]\exp \left[\left(\ln {\displaystyle \frac{T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{n}}}{T_{\mathrm{d}\mathrm{r}}^s-T_{\mathrm{n}}}}\right)R^{\left(\mathrm{1}-\frac{\mathrm{1}}{\mu }\right)}\right]}{\mathrm{1}-\exp \left[\left(\ln {\displaystyle \frac{T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{n}}}{T_{\mathrm{d}\mathrm{r}}^{\mathrm{s}}-T_{\mathrm{n}}}}\right)R^{\left(\mathrm{1}-\frac{\mathrm{1}}{\mu }\right)}\right]}}-R(T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{s}}),

where m is the radiator exponent.

The heat from the supply water in the primary HN is released to the return water in the secondary HN through plate heat exchangers at substations. The supply and return temperature of the primary HN can be calculated by using Eqs. (5) and (6), respectively [22,39].

(5)T_{\mathrm{s}}^{\mathrm{p}}={\displaystyle \frac{T_{\mathrm{s}}^{\mathrm{s}}-[R(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})+T_{\mathrm{r}}^{\mathrm{s}}]\exp \left[{\displaystyle \frac{K(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})}{\mathrm{\Delta }T}}-{\displaystyle \frac{K(T_{\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{r}}^{\mathrm{s}})}{R\mathrm{\Delta }T}}\right]}{\mathrm{1}-\exp \left[{\displaystyle \frac{K(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})}{\mathrm{\Delta }T}}-{\displaystyle \frac{K(T_{\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{r}}^{\mathrm{s}})}{R\mathrm{\Delta }T}}\right]}},

(6)T_{\mathrm{r}}^{\mathrm{p}}={\displaystyle \frac{T_{\mathrm{s}}^{\mathrm{s}}-[R(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})+T_{\mathrm{r}}^{\mathrm{s}}]\exp \left[{\displaystyle \frac{K(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})}{\mathrm{\Delta }T}}-{\displaystyle \frac{K(T_{\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{r}}^{\mathrm{s}})}{R\mathrm{\Delta }T}}\right]}{\mathrm{1}-\exp \left[{\displaystyle \frac{K(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}})}{\mathrm{\Delta }T}}-{\displaystyle \frac{K(T_{\mathrm{s}}^{\mathrm{s}}-T_{\mathrm{r}}^{\mathrm{s}})}{R\mathrm{\Delta }T}}\right]}}-R(T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}-T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}}),

where K is the relative heat transfer coefficient and DT is the design logarithmic mean temperature difference of plate heat exchanger (°C).

2.2 Exhaust and extracted heat

First, the return water in the primary HN is heated by the exhaust steam in the WWHE. The exhaust heat used in the WWHE is expressed in Eq. (7) [22,36].

(7)

Then, the return water in the primary HN flows into the AHP. It is assumed that the heat transferred in generator equals that transferred in absorber, and that the heat transferred in condenser equals that transferred in evaporator [22,36].

The coefficient of the performance of the AHP is expressed in Eq. (8) [22,36,40].

(8)

The grade lift coefficient of the AHP is expressed in Eq. (9) [22,36,40,41].

(9)\phi ={\displaystyle \frac{(T_{\mathrm{a}\mathrm{b}\mathrm{s}}+\sigma )-(T_{\mathrm{s},\mathrm{e}\mathrm{x}\mathrm{h}}-\sigma )}{(T_{\mathrm{s},\mathrm{e}\mathrm{x}\mathrm{t}}-\sigma )-(T_{\mathrm{c}\mathrm{o}\mathrm{n}}+\sigma )}}=\psi {\displaystyle \frac{(T_{\mathrm{a}\mathrm{b}\mathrm{s}}+\mathrm{273.15}+\sigma )(T_{\mathrm{s},\mathrm{e}\mathrm{x}\mathrm{h}}+\mathrm{273.15}-\sigma )}{(T_{\mathrm{s},\mathrm{e}\mathrm{x}\mathrm{t}}+\mathrm{273.15}-\sigma )(T_{\mathrm{c}\mathrm{o}\mathrm{n}}+\mathrm{273.15}+\sigma )}},

where \phi is the grade lift coefficient of the AHP and \psi is the modified coefficient of grade lift coefficient.

The heat medium temperature from the condenser can be obtained by using Eqs. (8) and (9).

If the return temperature of the primary HN is below the saturation temperature of exhaust steam at exhaust pressure (i.e., ), the extracted heat used in the AHP can be calculated by using Eq. (10) [22,36].

(10)

If the return temperature of the primary HN is not below the saturation temperature of exhaust steam at exhaust pressure (i.e.,

T_r^p\ge T_{\mathrm{s,exh}}-\sigma

), the extracted heat used in the AHP can be calculated by using Eq. (11) [22,36].

(11)

The exhaust heat used in the AHP can be calculated by using Eq. (12) [22,36].

(12)Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{a}}=Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{a}}\mathrm{C}\mathrm{O}\mathrm{P}\mathrm{.}

Next, the return water in the primary HN flows into the SWHE and is heated by extraction steam. The extracted heat used in the SWHE can be calculated by using Eq. (13) [22,36].

(13)

The total exhaust and extracted heat can be obtained by using Eqs. (14) and (15), respectively [22].

(14)Q_{\mathrm{e}\mathrm{x}\mathrm{h},\mathrm{t}}=Q_{\mathrm{e}\mathrm{x}\mathrm{h},\mathrm{w}}+Q_{\mathrm{e}\mathrm{x}\mathrm{h},\mathrm{a}},

(15)Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{t}}=Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{a}}+Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{s}}\mathrm{.}

2.3 Annual environmental impact

The fuel consumption and pollutant emissions caused by heat supply can be reduced by increasing the IT of walls. Therefore, the total environmental impact mainly includes the environmental impact of heat supply and insulation materials. For ease of calculation, the combustion process is assumed to be complete [13,30]. The combustion equation of coal is expressed in Eq. (16) [13].

(16){\mathrm{C}}_{\alpha }{\mathrm{H}}_{\beta }{\mathrm{O}}_{\gamma }{\mathrm{N}}_{\chi }{\mathrm{S}}_{\epsilon }+\xi X({\mathrm{O}}_{\mathrm{2}}+\mathrm{3.76}{\mathrm{N}}_2)\to \alpha {\mathrm{C}\mathrm{O}}_2+{\displaystyle \frac{\beta }{\mathrm{2}}}{\mathrm{H}}_{\mathrm{2}}\mathrm{O}+\mathrm{Y}{\mathrm{O}}_{\mathrm{2}}+\epsilon {\mathrm{S}\mathrm{O}}_{\mathrm{2}}+\mathrm{Z}{\mathrm{N}}_{\mathrm{2}},

where a, b, \gamma, \chi, and e are the coefficients of elements and \xi is the excess air coefficient. X, Y and Z can be determined by the oxygen balance equations, as expressed in Eqs. (17), (18), and (19) [13].

(17)X=\alpha +{\displaystyle \frac{\beta }{\mathrm{4}}}-{\displaystyle \frac{\gamma }{\mathrm{2}}}+\epsilon ,

(18)Y=(\xi -\mathrm{1})\left(\alpha +{\displaystyle \frac{\beta }{\mathrm{4}}}-{\displaystyle \frac{\gamma }{\mathrm{2}}}+\epsilon \right),

(19)Z=\mathrm{3.76}\xi \left(\alpha +{\displaystyle \frac{\beta }{\mathrm{4}}}-{\displaystyle \frac{\gamma }{\mathrm{2}}}+\epsilon \right)+{\displaystyle \frac{\chi }{\mathrm{2}}},

The mass flow rate of extraction steam is expressed in Eq. (20) [22].

(20)G={\displaystyle \frac{Q_{\mathrm{e}\mathrm{x}\mathrm{t},\mathrm{t}}}{h_{\mathrm{e}\mathrm{x}\mathrm{t}}-h_{\mathrm{s},\mathrm{w}\mathrm{a}\mathrm{t}}}},

where G is the mass flow rate of extraction steam (kg/s), h_ext is the enthalpy of extraction steam at extraction pressure (kJ/kg), and h_s,wat is the enthalpy of saturated water at extraction pressure (kJ/kg).

At CHP plants, the use of extraction steam for heating leads to the reduction of power supply. The annual coal consumption for such power supply during a heating season can be calculated by using Eq. (21) [22].

(21)m_{coa}=\underset{i=1}{\overset{\omega }{\mathrm{\Sigma }}}{G}_{\left(i\right)}(h_{\mathrm{e}\mathrm{x}\mathrm{t}(i)}-h_{\mathrm{e}\mathrm{x}\mathrm{h}(i)})\frac{f}{\mathrm{L}\mathrm{H}\mathrm{V}},

where i is the index for operating hours, w is the total operating hours in the whole heating season (h), G_{(i)} is the mass flow rate of extraction steam at time i (kg/s), h_{\mathrm{e}\mathrm{x}\mathrm{t}(i)} is the enthalpy of extraction steam at extraction pressure at time i (kJ/kg), h_{\mathrm{e}\mathrm{x}\mathrm{h}(i)} is the enthalpy of exhaust steam at exhaust pressure at time i (kJ/kg), f is unit power supply coal consumption (MJ/kWh), and LHV is the lower heating value of coal used in this paper (MJ/kg).

The annual pollutant emissions related to the coal combustion can be obtained by using Eqs. (22) and (23), respectively [13].

(22)m_{{\mathrm{C}\mathrm{O}}_2}={\displaystyle \frac{\mathrm{44}\alpha }{M}}{m}_{\mathrm{c}\mathrm{o}\mathrm{a}},

(23)m_{{\mathrm{S}\mathrm{O}}_2}={\displaystyle \frac{\mathrm{64}\epsilon }{M}}{m}_{\mathrm{c}\mathrm{o}\mathrm{a}},

where M is the molar mass of coal (g/mol) which can be calculated by using Eq. (24) [13].

(24)M=\mathrm{12}\alpha +\beta +\mathrm{16}\gamma +\mathrm{14}\chi +\mathrm{32}\epsilon \mathrm{.}

The annual environmental impact of heat supply and insulation materials can be calculated by using Eqs. (25) and (26), respectively [27,42].

(25)B_{\mathrm{h}\mathrm{s}}=\mathrm{L}\mathrm{H}\mathrm{V}{b}_{\mathrm{c}\mathrm{o}\mathrm{a}}{m}_{\mathrm{c}\mathrm{o}\mathrm{a}}+b_{{\mathrm{C}\mathrm{O}}_2}{m}_{{\mathrm{C}\mathrm{O}}_2}+b_{{\mathrm{S}\mathrm{O}}_2}{m}_{{\mathrm{S}\mathrm{O}}_2},

(26)B_{\mathrm{i}\mathrm{n}\mathrm{s}}={\displaystyle \frac{b_{\mathrm{i}\mathrm{n}\mathrm{s}}\rho \delta A_{\mathrm{i}\mathrm{n}\mathrm{s}}}{n{A}_{\mathrm{b}\mathrm{u}\mathrm{i}}}},

where B_hs is the annual environmental impact of heat supply (mPts/(m²·a)), b_coa is the environmental impact point of coal (mPts/MJ), b_{{\mathrm{CO}}_2} is the environmental impact point of CO₂ (mPts/kg), b_{{\mathrm{SO}}_2} is the environmental impact point of SO₂ (mPts/kg), B_ins is the annual environmental impact of insulation materials (mPts/(m²·a)), b_ins is the environmental impact point of insulation material (mPts/kg), r is the density of insulation material (kg/m³), n is the lifetime of insulation material (a), A_ins is the wall insulation area (m²), and A_bui is the total net floor area of building (m²).

2.4 Optimization model

The OEIT of buildings can be obtained by minimizing the annual total environmental impact, which is expressed in Eq. (27) [27].

(27)\min B_{\mathrm{t}}=B_{\mathrm{h}\mathrm{s}}+B_{\mathrm{i}\mathrm{n}\mathrm{s}}

where B_t is the annual total environmental impact (mPts/(m²·a)).

The optimization model on the OEIT of buildings is established in which the decision variable is the IT of buildings while the annual total environmental impact can be regarded as the objective function. The optimization model can be solved by using MATLAB. In addition, the genetic algorithm is applied to the program. Figure 2 shows the flow for solving the optimization model.

3 Case study

An existing residential community with a CHP-based DHS in Tianjin, China, is selected as this case study. Tianjin is located at 38°34′–40°15′N and 116°43′–118°04′E, having a temperate monsoon climate with four distinct seasons. It is hot and wet in summer while cold and dry in winter. The heating season in Tianjin lasts for 2904 h [43]. The outdoor design temperature for heating in Tianjin is - 7.0°C, while the indoor air temperature cannot be lower than 20°C [43]. The heat load duration curve per square meter is described in Fig. 3. The annual heat demand is 278.44 MJ/m². The coal consumption, CO₂ emissions, SO₂ emissions, and total environmental impact during a heating season are 7.062 kg/(m²·a), 25.792 kg/(m²·a), 0.071 kg/(m²·a), and 791.964 mPts/(m²·a), respectively.

The chemical formula of the coal used in this paper is C_7.078H_4.503O_0.71N_0.073S_0.016. Expanded polystyrene, rock wool and glass wool are selected as insulation materials. Based on the market survey, the properties of insulation materials are listed in Table 2 while the values of some related parameters used in the calculations are listed in Table 3.

4 Results

Since the results for different types of insulation materials are similar, only those for expanded polystyrene are shown in this paper. The fitting coefficient of the relationship between insulated and non-insulated design heat load is expressed in Eq. (28).

(28)

The average relative error caused by Eq. (28) is 2.420%, indicating the accuracy of the equation.

4.1 Heat medium temperature

The variations of annual heat demand versus the IT are demonstrated in Fig. 4, from which it can be found that the annual heat demand decreases as the IT increases. The design supply and return temperature of the primary and secondary HN (T_{\mathrm{d}\mathrm{s}}^{\mathrm{p}}/T_{\mathrm{d}\mathrm{r}}^{\mathrm{p}} and T_{\mathrm{d}\mathrm{s}}^{\mathrm{s}}/T_{\mathrm{d}\mathrm{r}}^{\mathrm{s}}) is assumed to be 130/70°C and 85/60°C, respectively [50]. The operating heat medium temperature of DHS should be adjusted according to the actual heat demand of buildings [50]. The variations of heat medium temperature versus the IT and outdoor temperature are depicted in Fig. 5, from which it can be found that as the IT or outdoor temperature increases, the supply and return temperature of the primary and secondary HN gradually decreases. The reason for this is that the heat demand gradually decreases as the IT or outdoor temperature increases. The variations of heat medium temperature from AHP versus the IT and outdoor temperature are exhibited in Fig. 6, from which it can be found that as the IT or outdoor temperature increases, the heat medium temperature from the AHP gradually increases.

4.2 Exhaust and extracted heat

Conditions for the recycle of exhaust heat can be created by reducing the heat medium temperature. The variations of exhaust or extracted heat versus the IT are displayed in Fig. 7, from which it can be seen that no exhaust heat is directly recycled in the WWHE. The return temperature of the primary HN is not below the saturation temperature of the exhaust steam at exhaust pressure. Therefore, the exhaust heat cannot be recycled in the WWHE. The return water in the primary HN flows into the AHP. It can also be found from Fig. 7 that the extracted heat used in the AHP increases to the maximum value and then decreases as the IT increases. The increase in the extracted heat used in the AHP is caused by the fact that the heat medium temperature from the AHP gradually increases as the IT increases. On the contrary, the reduction of the extracted heat used in the AHP results from the fact that the heat demand gradually decreases as the IT increases. It can also be found from Fig. 7 that with the increase in the IT, the extracted heat used in the SWHE decreases and eventually drops to zero. The reason for this is that the heat demand and desired supply temperature of the primary HN decrease as the IT increases. From Fig. 7, it can still be observed that as the IT increases, the total extracted heat gradually decreases while the total exhaust heat increases to the maximum value and then decreases. Here, it should be noted that the variations of heat medium temperature, exhaust heat and extracted heat versus the IT is consistent with that described in Ref. [22].

4.3 Results of environmental analysis

The reduction in the total extracted heat results in the reduction in coal consumption. The variations of coal consumption versus the IT are depicted in Fig. 8, from which it can be found that the coal consumption decreases as the IT increases. Moreover, the variations of CO₂ and SO₂ emissions versus the IT are presented in Fig. 9, from which it can be observed that the CO₂ and SO₂ emissions decrease with the increase in the IT. The reason for this is that coal consumption decreases as the IT increases. It can also be seen from Fig. 9 that the emissions of CO₂ is much larger than that of SO₂, i.e., the latter accounts for approximately 0.329% of the former.

Figure 10 illustrates the effect of the IT on the annual environmental impact. From Fig. 10, it can be seen that when the IT increases, the annual environmental impact of heat supply decreases while the annual environmental impact of insulation materials increases. Therefore, the annual total environmental impact decreases to the minimum value, and then it starts to increase beyond this value.

The optimization model in Section 2 is solved by using the MATLAB software. The genetic algorithm in the program is applied to minimize the annual total environmental impact of buildings, as can be seen in Fig. 2. The OEIT is determined by minimizing the annual total environmental impact of buildings. The optimization results for three types of insulation materials are listed in Table 4, from which the OEIT of glass wool is found to be the largest, followed by the OEIT of expanded polystyrene, and then the OEIT of rock wool. From Table 4, it can also be observed that the minimum annual total environmental impact of rock wool is the largest of that of the three types of insulation materials. The reason for this is that the highest density of rock wool leads to the greatest annual environmental impact of insulation materials (see Table 2). From Table 4, the minimum annual total environmental impact of glass wool is found to be the smallest of that of the three types of insulation materials. This is mainly caused by two reasons. First, glass wool has the lowest heat conductivity coefficient of that of the three types of insulation materials, resulting in the best insulation performance (see Table 2). Therefore, the annual environmental impact of heat supply is the smallest. Next, the environmental impact point of glass wool is the smallest of that of the three types of insulation materials (see Table 3). Consequently, the annual environmental impact of glass wool is the smallest. Therefore, the application of glass wool yields better environmental performance.

From Table 4, it is observed that the annual recycled exhaust heat for rock wool is the largest, followed by the annual recycled exhaust heat for expanded polystyrene, and then the annual recycled exhaust heat for glass wool. The reason for this is that the annual recycled exhaust heat is related to the OEIT of three types of insulation materials. The OEIT of rock wool, expanded polystyrene, and glass wool increases in turn. Therefore, the annual recycled exhaust heat decreases in turn (see Fig. 7). In addition, as can be seen in Table 4, when the OEIT of expanded polystyrene, rock wool, and glass wool is used, the annual total environmental impact can be reduced by 84.563%, 83.211%, and 86.104%, respectively. Therefore, when the OEIT is used in the retrofit, the annual coal consumption and annual pollutant emissions can be greatly reduced, resulting in the great reduction of the annual total environmental impact.

5 Discussion

The annual total energy consumption of the retrofitted buildings mainly includes the annual operating energy consumption of buildings and annual embodied energy of insulation materials. The increase in IT leads to the increase in the annual embodied energy of insulation materials and the decrease in the annual operating energy consumption of buildings. Therefore, there exists an optimum energetic IT for minimizing the annual total energy consumption of the retrofitted buildings. The optimum energetic IT of expanded polystyrene, rock wool, and glass wool is 0.130 m, 0.068 m, and 0.142 m, respectively. Similarly, there also exists an optimum economic IT for minimizing the annual total cost of retrofitted buildings [22]. The optimum economic IT of expanded polystyrene, rock wool, and glass wool is 0.034 m, 0.033 m, and 0.032 m, respectively. It can be found that the OEIT is the largest, followed by the optimum energetic IT, and then the optimum economic IT. This is mainly because the proportion of the equivalent annual insulation material cost to the annual total cost is the largest, followed by the proportion of the annual embodied energy of insulation materials to the annual total energy consumption, and then the proportion of the annual environmental impact of insulation materials to the annual total environmental impact. The above proportion related to economic, energetic, and environmental perspectives are 34.79%, 16.23%, and 13.41%, respectively.

For buildings connected with conventional non-retrofitted CHP-based DHS, the OEIT of expanded polystyrene and minimum annual total environmental impact are 2.090 m and 200.210 mPts/(m²·a), respectively. In addition, it can be seen that 74.702 mPts per square meter can be further reduced in every heating season if CHP plants and building envelope are simultaneously retrofitted. This is because the energy grade of heat sources can be reduced by the energy efficiency retrofit of building envelope, creating conditions for better recycling the exhaust heat in CHP plants.

6 Conclusions

In this paper, the OEIT of buildings was investigated based on the amount of energy and energy grade reduction. A mathematical model to determine the OEIT of buildings with CHP-based DHS was established. An existing residential community with CHP-based DHS in Tianjin, China, was used as a case study. Three types of insulation materials (i.e., expanded polystyrene, rock wool, and glass wool) were considered. The results show that the OEIT and minimum annual total environmental impact can be determined by using the optimization model, based on which the following conclusions can be reached:

Conditions for the recycle of exhaust heat can be created by reducing the heat medium temperature of CHP-based DHS.

The exhaust and extracted heat used in the AHP increases to a maximum value and then decreases as the IT increases. The extracted heat used in the SWHE decreases and eventually drops to zero as the IT increases. No exhaust heat can be recycled by the WWHE.

The annual environmental impact of heat supply decreases as the IT increases. On the contrary, the annual environmental impact of insulation materials increases as the IT increases. Therefore, the annual total environmental impact decreases to the minimum value and then increases as the IT increases.

The OEIT determined by the mathematical model for expanded polystyrene, rock wool, and glass wool is 1.229 m, 0.411 m, and 2.239 m, respectively. Besides, the corresponding annual total environmental impact is 125.508 mPts/(m²·a), 136.604 mPts/(m²·a), and 113.033 mPts/(m²·a), respectively.

When the OEIT of expanded polystyrene, rock wool, and glass wool is used, respectively, approximately 27.106 MJ, 30.256 MJ, and 24.942 MJ of exhaust heat can be recycled per square meter in a heating season.

Glass wool is more beneficial to the environment compared with expanded polystyrene and rock wool.

The OIT of expanded polystyrene determined based on environmental analysis is greater than that based on energetic and economic analysis.

Combining the energy efficiency retrofit of building envelope with that of CHP plants can recycle low-grade exhaust heat, which not only improves the energy efficiency, but also reduces greenhouse gas emissions. Environmental analysis is in line with the global sustainable development strategy. Based on the environmental analysis, this paper can provide reference for taking energy efficiency retrofit measures. In the future, it is necessary to determine the OIT of buildings connected with CHP-based DHS from a more comprehensive perspective.

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