^{1}

^{2}

^{*}

^{3}

A combined system of heating, power and biogas (CHPB) system has been developed and tested in a single building in MinQin County, Gansu Province, China. The proposed system satisfies the user’s demand of power, heat, and gas. The CHPB system can effectively overcome seasonal, climate and many other factors which affect the production of the renewable energy. For this purpose, experiments were conducted extensively during the winter period from November 2014 to March 2015. Compared with conventional energy supply systems meets the test household indoor temperature level, the system can reduce the consumption of standard coal 5819.30 kg/year, and save energy costs 11,046.20 yuan/year, the system’s payback period of 4.37 years, also can save 27.03 tons of carbon dioxide emissions. As a result, the CHPB system have been successfully tested for single building, use solar energy and biomass as input and produce power, heat, and gas steadily. These results contributed to the construction of energy supply systems.

PV (photovoltaic) systems are becoming an important part of the electricity system all around the globe, especially in most developed countries. A vigorous growth of the global PV market is still expected due to the strong PV technology price decreases and rise of electricity prices produced by conventional sources together with the clear advantages of green and renewable energy sources as PV on delivering safe and clean energy [

For the inhabitants of rural China’s northwest, the domestic hot water, cooking gas and electricity are the most important energy needs. Currently, China has become the world’s largest producer and user of solar water heaters [

Hosseinalizadeh et al. [

The vigorous growth of renewable energy in rural areas leads to huge development of rural economy and the protection of the environment, which is of great significance, further it related to the national energy security issues. However, the current irrational energy structure, energy supply and energy use methods are relatively backward problems are still widespread in China’s vast rural areas [

The solar water heaters are used to control the temperature required for biogas fermentation. Solar thermal, solar photovoltaic, anaerobic fermentation biogas system, three subsystem are ultimately realize the benefits of heat, power, and gas [

There are two groups of solar hot water, groupⅠprovide a nearly unwavering temperature condition to thermostatic pool for anaerobic fermentation, the biogas produced by the thermostat digesters is stored in the storage bag and partly used as cooking gas, and group II cooperatively provide heating and domestic hot water. Photovoltaic battery power used to drive the circulating water pump for conveying hot water in vacuum tube solar water heater group to thermostatic pool for anaerobic fermentation the other part is used to drive the heating hot water, and the rest is used for living electricity. The working principle of the system as shown in the

According to “GB50495-2009 solar heating engineering technical specifications” [

The ecological farmhouse for the north and south, brick and concrete structure, the external dimensions of the building are 12.02 m × 9.74 m × 3.30 m and the area is 117.07 m^{2}; the internal structure of the building is divided into three bedrooms, a living room, a kitchen, a bathroom and a hall. The northeast corner bedroom size is 3.00 m × 3.36 m, the area is 10.08 m^{2}, southwest corner bedroom size 3.00 m × 3.60 m, and the area is 10.80 m^{2}; southeast corner of the kitchen size

is 3.00 m × 3.60 m, and the area is 10.80 m^{2}; living room size is 3.00 m× 3.30 m, and the area is 9.90 m^{2}; 4.80 m × 3.60 m, and the area is 17.28 m^{2}; bathroom size of 1.56 m × 2.11 m, and the area is 3.29 m^{2}; hall size of 3.23 m × 4.54 m, and the area is 14.67 m^{2}.

The north-east corner of the building bedroom and northwest corner of the north wall bedroom has a window in the middle of the southwest corner bedroom, living room walls have a window on the south wall, windows are three glass windows (double insulating glass plus single-layer ordinary glass), the kitchen windows are double glazed windows with dimensions of 1.70 m × 1.43 m, the living room and the entrance hall of the partition wall on both sides of a window, the window is a single glass windows, the size of 1.02 m × 1.43 m, the door outside the left and right sides of a window, windows for the single glass windows; three bedrooms, kitchen and bathroom door height are 2.04 m, in addition to the bathroom door for the glass door, the three bedroom and kitchen doors are wooden doors, aluminum doors for the living room door glass doors, a height of 2.31 m, the door is a common security doors, a height of 2.75 m. Indoor radiator for the aluminum composite radiator, north-east corner bedroom and northwest corner of the bedroom north wall of a group of radiator, southwest corner bedroom south wall of a group of radiator, living room south wall has two sets of radiator, a single radiator size of 2.30 m × 0.08 m × 0.63 m, bathroom west wall has a group of radiators, the size of 0.80 m × 0.08 m × 0.63 m. An architectural diagram of the house is shown in

1) According to “solar heating engineering technical specifications” and “cold areas of residential building energy efficiency design standards” to calculate the building total heat consumption as shown in Equation (1).

Q H = Q w h + Q l f s + Q l f q (1)

Q_{H} is the total heat consumption of buildings (W); Q_{wh} is the basic heat consumption retaining structure (W); Q_{lfs} is cold air penetration heat consumption (W); Q_{lfq} is cold air intrusion heat consumption (W).

The Q_{wh} stands for the basic heat consumption by retaining structure as shown in Equation (2), (3) and (4):

Q w h = ∑ Q ′ i (2)

Q ′ i = Q i ⋅ ε (3)

Q i = ( t i − t e ) K i F i (4)

Q ′ i is revised the basic heat transfer of the structure(W); Q_{i} is basic heat transfer of the enclosure structure (W); ε is total correction (the total correction rate, the risk attaching rate and the height addition rate); t_{i} is the indoor air calculates the temperature (˚C); t_{e} [_{i} is the heat transfer coefficient of each envelope (W/(m^{2}・K)), F_{i} is the area of the envelope (m^{2}).

Calculated, the detail of the building envelope is presented in

Cold air penetration heat consumption Q_{lfs} [

Q l f s = 0.278 V N C P ρ ( t i − t e ) (5)

V is the interior volume of the room (m^{3}); N [_{p} is cold air constant pressure specific heat capacity (kJ/(kg・˚C)); ρ is air density.

Calculated, cold air penetration heat consumption is Q l f s = 1581.64 W ;

Cold air intrusion heat consumption Q_{lfq} as shown in Equation (6):

Q l f q = 0.65 Q d o o r (6)

Calculated, cold air intrusion heat consumption is Q l f q = 129.82 W

Building envelope | Structure | Heat transfer coefficient K_{i} [W/(m^{2}・K)] | Heat transfer area F_{i} [m^{2}] | Correction coefficient ε_{i} |
---|---|---|---|---|

Roof | From top to bottom: decorative surface layer for 50 mm, stone concrete cover for 40 mm, waterproof layer for 400 mm, slope layer for 20 mm, polystyrene board insulation layer for 80 mm, cement mortar for 1:3, reinforced concrete for roof slab | 0.394 | 117.07 | 0.94 |

Exterior wall | From the inside out: mixed mortar for 20 mm, solid brick for 240 mm, adhesive and cement mortar leveling layer for 30 mm, polystyrene board insulation layer for 80 mm, anti-crack mortar and alkali-resistant fiberglass mesh for 5 mm, decorative surface layer for 15 mm | 0.393 | South 25.23 | South 0.82 |

North 22.56 | North 0.95 | |||

East 26.17 | East 0.91 | |||

West 26.17 | West 0.90 | |||

Window | Double glazed windows, glass thickness 4 mm, air layer thickness 12 mm | 2.92 | South 2 windows: 2.43 | South 0.43 |

Nouth 2 windows: 2.43 | North 0.75 | |||

Door | 2 | Double open security doors 4.68 | 1.1 | |

Ground | concrete base for 100 mm, cement mortar leveling layer for 20 mm, reflective layer of aluminum foil anti―radiation layer for 0.05 mm, extrusion polystyrene board insulation for 60 mm, concrete filling for 50 mm, cement mortar leveling layer for 20 mm, floor tile ground floor for 8 mm | 0.35 | 117.07 | 1 |

Consequently, building total heat consumption Q H = Q w h + Q l f s + Q l f q = 6026.12 W . Per square meter heat consumption is 51.47 W/m^{2}.

2) According to “GB50495-2009 solar heating engineering technical specifications” [

Q w = m w q r c w ρ w ( t r − t f ) 86400 (7)

Q_{w} is average heat consumption of domestic hot water (W); mw is the number of water users (people); q_{r} [_{w} is specific heat capacity of water(J/(kg・˚C)); ρ_{w} is hot water density(kg/L); t_{r} is design the hot water temperature(˚C); t_{f} is design the cold water temperature(˚C).

Calculated, average heat consumption of domestic hot water is Q W = 24.70 MJ / d .

3) Calculation of heat consumption of digester

① The amount of heat required to heat the feedstock [

Q 1 = c L m L ( T D − T S ) (8)

Q_{1} [_{L} is specific heat capacity of liquid (kJ/(kg・˚C)); m_{L} [_{D} is temperature of liquid in biogas fermentation (˚C); T_{S} [

Calculated, Q 1 = 2.91 MJ / d .

② Heat loss of heat transfer pipeline of biogas digester [

Q 2 = 2 π ⋅ T D − T A 1 λ 1 ln d o d i + 1 λ 2 ln 4 a d o ⋅ L ⋅ ( 1 + β ) (9)

Q_{2} is heat loss of biogas digesters’ heat transfer pipelines (W); T_{A} is the temperature of the media outside the tank (˚C); λ_{1} is the coefficient of the polystyrene’s thermal conductivity (W/(m^{2}・K)); λ_{2} is the coefficient of the soil’s thermal conductivity (W/(m^{2}・K)); d_{0} is the outer diameter of the heat transfer pipe(m); d_{i} is the internal diameter of the heat transfer pipe (m); a is buried depth (m); L is the length of the root pipe(m); β is local heat loss coefficient.

Calculated, Q 2 = 56.41 MJ / d .

③ Heat consumption of biogas digester’s exterior-protected construction as shown in Equation (10)

Q 3 = F ⋅ 1 1 h 1 + δ 3 λ 3 + δ 4 λ 4 + 1 h 2 ⋅ ( T D − T A ) (10)

Q_{3} is heat consumption of biogas digester’s exterior-protected construction(W); F is the external surface area of biogas digesters (m^{2}); h_{1} is internal convection heat transfer coefficient of digesters (W/(m^{2}・K)); δ_{3} is the thickness of concrete slab (m); δ_{4} is the thickness of biogas insulation layer(m); λ_{3} is thermal conductivity of concrete slab (W/(m・K)); λ_{4} is thermal conductivity of polystyrene board (W/(m・K)); h_{2} is heat transfer coefficient between the outer surface of insulation layer and environment (W/(m^{2}・K)).

Calculated, Q 3 = 17.24 MJ / d

④ Anaerobic reactions produce biological heat

When anaerobic fermentation reaction, 3% of the effective energy of the fermentation liquid is released in the form of heat, which is the heat generated by the reaction when the Human and livestock manure converted to methane [_{4}(W), Q 4 = 0.0195 MJ / d .

The total heat requirement of biogas digesters is Q_{m}(W), Q m = Q 1 + Q 2 + Q 3 − Q 4 = 76.56 MJ / d .

From the above calculation, the building energy output is Q_{0}(W), Q 0 = Q H + Q W + Q m = 621.92 MJ / d .

Total electricity consumption equal to Electricity for digester circulating pump [

The system power requirements are provided by the amount of electricity generated by the solar PV modules.

The experiment was conducted in Minqin County, Gansu Province, China. Specific geographical position is in the longitude between 101˚49'41'' - 104˚12'10'', latitude between 38˚3'45'' - 39˚27'37''. 206 km long from east to west, north-south width of 156 km, with a total area of 15,900 square kilometers. The county’s lowest elevation of 1298 meters, the highest elevation of 1936 meters, with an average elevation of 1400 meters, there is composed of the desert, low hills and plains three topography composition. The user home throughout the winter heating period for experimenting, a total of 101 days of experiment time, the total power generation 260.7 kWh, photoelectric conversion efficiency of 8.2%, the average daily generating capacity of 2.6 kWh, the system itself, the cumulative power consumption of 127.3 kWh. Thus it can be seen, during the experiment period, the surplus power generation was 133.4 kWh, which fully met the users’ energy demand.

average power generation efficiency = P V array power generation the solar energy projected onto the P V array = ∑ U I t ∑ I θ t A = ∑ U I ∑ I θ A

1) Natural environmental factors: solar radiation intensity

Irradiation intensity is the decisive factor affecting the power of PV arrays. The reasonable estimation of solar radiation is very important for the design and research of photovoltaic power generation system. The solar radiation to the unit area of radiation power called radiosity or irradiance, unit for W/m^{2}, the amount of solar energy projected onto a unit area over a period of time (Day, month, and year) is called the amount of radiation.

When the radiation intensity changes, the PV array short-circuit current, maximum power and other parameters will change. Short-circuit current affected by radiation intensity, radiation weakened, and the output current decreases. While the output voltage is affected by the radiation intensity is relatively small. ^{2} and gradually increases, at 13:02 increase to the peak of 1017.8 W/m^{2}, after that the solar irradiance began to decline, 18:01 down to 120 W/m^{2}. And hence the amount of solar radiation is 158.5 MJ, photovoltaic power generation system 3.7 kWh, which is much higher than the system power consumption.

can make the photovoltaic unit of the combined thermal and electric system can provide the users with uninterrupted electric energy, to meet the user’s energy needs.

2) PV modules

① Conversion efficiency of PV modules

The power generation efficiency of PV systems is closely related to the performance of PV components. Characterization of photovoltaic components performance parameters are open circuit voltage, short circuit current, and maximum operating point current, Therefore, in order to improve the intensity of the radiation received by PV modules, some institutions focus on the development and integration of high-power PV power generation technology [

The system aim to a lower the cost, reduce the area and to ensure that users suffer uninterrupted supply of electricity demand, as far as possible. To improve the efficiency of photovoltaic power generation, select 10 monocrystalline silicon photovoltaic panels, size 1200 mm * 537 mm * 35 mm, with a power 100 Wp, so a result the component conversion efficiency is relatively high, without a greater impact.

② Shadows shade, the PV module is out of alignment

The system uses ten photovoltaic panels, each two photoelectric cells connected in series to form one group, and by coupling five groups together the designated output is achieved.

Many factors affect the performance of the PV system in actual operation such as: accumulation of dust, excretion of birds, leaves, snow and other just to mansion a few of them. The parameters of each photovoltaic module in the PV array are different from each other. These factors often cause the PV array to be mismatched and the overall power of the array will be lower than the expected value.

③ Inverters

The magnitude of the DC power input to the inverter is determined from the current-voltage curve of the PV array, at the maximum peak of the solar PV array power. According to different parameters, voltage, current and capacitance, etc., there are many methods of operation adapted to inverters of the maximum power peak tracking. But each method has some limitations; making the maximum power inverter tracking less than the required efficiency.

Based on the experimental data, the multivariate regression equation method was adopted; analyze the influence of ambient temperature, daily average heat collection and solar power generation efficiency on daily generation as shown in

R Square | Standard deviation | Significance F | η | Q s - d | T |
---|---|---|---|---|---|

0.889 | 0.213 | 2.211E−42 | 29.602 | 0.021 | 0.002 |

The relationship between relative power generation efficiency, ambient temperature and daily average heat accumulation:

Y = − 2.305 + 29.602 η + 0.021 Q s − d + 0.002 T (11)

where in, η ―power generation efficiency;

Q s − d ―daily average heat accumulation, MJ;

T―ambient temperature, ˚C.

From the table, Significance F < 0.05, indicating that the linear model of relative power generation efficiency, ambient temperature and daily average heat collection is significant. The results from the formula (11) can be explained in the factors affecting power generation, of which 88.9% by the ambient temperature, power generation efficiency and daily average heat between the linear relationship to explain, indicating a higher degree of regression of the regression equation. The prediction error is 0.213.

Partial regression coefficient η = 29.602, the daily average heat and ambient temperature conditions are maintained unchanged, the power generation efficiency for each additional 1% relative to the average daily power generation increased 0.296 kWh; partial regression coefficient Q_{s-d} = 0.021, the power generation efficiency and ambient temperature conditions are maintained unchanged, the daily average heat for each additional 1 MJ, relative to the daily average increase of power generation 0.021 kWh; partial regression coefficient T = 0.002 that the power generation efficiency and daily average heat collection conditions are maintained unchanged, the ambient temperature for each additional 1˚C, relative to the average daily power generation increased by 0.002 kWh.

The main factors influencing the anaerobic fermentation process of solar constant temperature biogas digesters are solar hot water temperature, digester temperature, ambient temperature, etc. According to the experimental data analysis found that the solar hot water temperature, biogas digesters temperature and ambient temperature on the biogas production gas coupling effects as shown in

Y = 1.032 + 0.055 T 1 + 0.163 T 2 + 0.071 T 3 (12)

where in, T_{1}―solar hot water temperature, ˚C;

T_{2}―temperature of biogas digesters, ˚C;

T_{3}―ambient temperature, ˚C.

R Square | Standard deviation | Significance F | T_{1} | T_{2} | T_{3} |
---|---|---|---|---|---|

0. 938 | 0.025 | 0.015 | 0.055 | 0.163 | 0.071 |

affecting biogas production, of which 93.8% by the solar hot water temperature, ambient temperature and temperature of biogas digesters between the linear relationship to explain, indicating a higher degree of regression of the regression equation. The prediction error is only 0.025.

Partial regression coefficient T_{1} = 0.055, said in the temperature of biogas digesters and ambient temperature are maintained unchanged conditions, the solar hot water temperature for each additional 1˚C relative to the average biogas production increased 0.055 m^{3}; partial regression coefficient T_{2} = 0.163, said in the solar hot water temperature and ambient temperature are maintained unchanged conditions, the temperature of biogas digesters for each additional 1˚C, relative to the average biogas production increased 0.163 m^{3}; partial regression coefficient T_{3} = 0.071 that the solar hot water temperature and temperature of biogas digesters are maintained unchanged conditions, the ambient temperature for each additional 1˚C, relative to the average biogas production increased 0.071 m^{3}.

During the winter heating experiment, the maximum outdoor ambient temperature is 32.36˚C and the lowest is −18.16˚C.

The main factors influencing daily average heat collection efficiency are daily average heat accumulation and ambient temperature, etc. According to the experimental data analysis found that the daily average heat accumulation and ambient temperature on the daily average heat collection efficiency as shown in

y = 0.962 + 0.007 T 3 + 0.003 Q s − d (13)

where in, Q s − d ―daily average heat accumulation, MJ;

T_{3}―ambient temperature, ˚C.

From the analysis of variance table can be seen, Significance F < 0.05, indicating that the linear model of daily average heat accumulation and ambient temperature is significant. The results from the formula can be explained in the factors affecting daily average heat collection efficiency, of which 95.7% by the daily average heat accumulation and ambient temperature between the linear relationship to explain, indicating a higher degree of regression of the regression equation. The prediction error is only 0.024.

Partial regression coefficient Q_{s−d} = 0.003, said the ambient temperature is maintained unchanged conditions, the daily average heat accumulation for each additional 1 MJ relative to the daily average heat collection efficiency increased 0.003; partial regression coefficient T_{3} = 0.007 that the daily average heat accumulation is maintained unchanged conditions, the ambient temperature for

R Square | Standard deviation | Significance F | T_{3} | Q s - d |
---|---|---|---|---|

0.957 | 0.024 | 0.018 | 0.007 | 0.003 |

each additional 1˚C, relative to the daily average heat collection efficiency increased 0.007 m^{3}.

Assuming that the fermenting raw materials and the output of the biogas slurry sludge benefit is quite, can be ignored.

Solar photovoltaic power generation system, three-year battery warranty, solar components warranty for ten years, inverter, controller and other parts warranty for one year. In the course of not violating the operating system can be used normally for 15 years, during storage, the natural attenuation of consumables such as inverters require maintenance.

The management fee for the biogas fermentation system includes the annual out-of-the-art labor costs and the biennial maintenance costs, maintenance costs, including maintenance of materials in the system, spare parts costs.

Solar energy is clean energy inexhaustible, biomass energy as the fermentation raw material is waste recycling for the user. Water within the system can be recycled, closed pipe water consumption is minimal, basically the same.

The experimental results show that when the system is running stably, 1.50 m^{3} per day biogas production. In the system, the burning efficiency of coal-fired stoves is 60%, the burning efficiency of biogas stoves is 75%, the average calorific value of biogas is 22.94 MJ/m^{3}, the calorific value of standard coal is 29.30 MJ/kg [

The value of biogas can calculate as below in Equation (14).

Device | Specifications Model | Cost | |
---|---|---|---|

Solar photovoltaic modules | 1000 W | 16,000 | |

Vacuum tube solar collector components | Vacuum collector tube φ58*1800 Circulating pump CRS25-8 | 16,200 | |

Solar constant temperature biogas tank components | Vacuum collector tube φ58*1800 | 9200 | |

total | 41,400 |

System to increase the cost of fees | Solar constant temperature biogas digester fermentation raw materials costs | Maintenance, management and operating costs | Total Investment Costs |
---|---|---|---|

System installation fee | |||

41,400.00 yuan | And biogas fertilizer income offset | 100.00 yuan | 41,500.00 yuan |

T 1 = C 1 × E 1 C 2 × E 2 × T 2 (14)

where in, C_{1}―Calorific value of biogas;

E_{1}―Combustion efficiency of biogas;

C_{2}―Calorific value of the alternative fuel;

E_{2}―Alternative fuel combustion efficiency;

T_{1}―Value of biogas;

T_{2}―Price of alternative fuels.

Calculated the value of biogas is 1.50 yuan/m^{3}.

The experimental study shows that the system is stable throughout the year to provide households with continuous hot water, the average consumption of hot water is Q_{w} = 33.18 kJ/d, the calorific value of standard coal is 29.30 MJ/kg, by considering the traditional way using coal to provide hot water of calculation of the annual consumption of hot water of standard coal 413.33 kg.

According to the heat transfer formula, the heating energy consumption of traditional rural buildings in winter is 279.77 MJ/d, during the experiment period; 680.70 kg of standard coal was consumed. According to the investigation of the building area, traditional farm houses, including winter heating, the average annual use of about 6500.00 kg of standard coal. Compared with the traditional way of supply, the system is equivalent to annual savings of 5819.30 kg of standard coal heating.

During the experiment period, the total generating capacity was 231.37 kWh, the average daily generating capacity was 2.49 kWh and the total electricity consumption was 124.27 kWh. Thus, the system produces more energy by 107.10 kWh, providing electricity more than the user demands. Farmers consume about 3.00 kWh of electricity a day, a total of about 1277.50 kWh a year; in accordance with the power of the standard coal conversion coefficient 0.41 kg/kWh calculation, compared with the traditional use of electricity in the family, the annual savings of 385.00 kg of standard coal.

The system can be stable during the winter season for households to provide continuous hot water, biogas, and to meet heating needs.

The cumulative cost is 42,503.76 yuan, the cumulative benefits value is 110,877.13 yuan, the benefit cost ratio is 2.61, the investment recovery period is 4.37 years, the net present value is 68,373.37 yuan, and the internal rate of return is 25.50%. Compared with the traditional rural energy supply system, the rural green building annual savings of coal is 7364.13 kg per kilogram of standard coal emissions of carbon dioxide 3.67 kg, 27.03 tons of carbon dioxide emissions.

Daily life cooking is no longer subject to smoke, reduce labor intensity, the liberation of women labor, manure into the pool fermentation, reduce disease transmission rate, improve the rural health level; the same time, efficient use of clean energy to help farmers From the traditional way of life to a healthy, civilized, healthy lifestyle change, the construction of new rural areas had a positive impact.

To improve the rural living environment and ecological environment, and promote the pace of urbanization in rural areas to ease the contradiction between rural living energy and industrial energy, while achieving energy saving.

I would like to express my gratitude to all those who helped me during the writing of this paper. I gratefully acknowledge the help of my supervisor, Mr. Li Jinping, who has offered me valuable suggestions in the academic studies. In the preparation of the paper, he has spent much time reading through each draft and provided me with inspiring advice. Without his patient instruction, insightful criticism and expert guidance, the completion of this paper would not have been possible. I should finally like to express my gratitude to my beloved parents who have always been helping me out of difficulties and supporting without a word of complaint. This work was supported by the national high technology research and development program of China (2014AA052801).

Yang, J.-Y., Li, J.-P. and Feng, R. (2018) Techno-Economic Study on an Energy System in Northwest China. Open Journal of Energy Efficiency, 7, 1-18. https://doi.org/10.4236/ojee.2018.71001