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Life Cycle Analysis of Distributed Energy System Projects’Energy Consumption and GHG Emission – A Case of BeerBrewery Auxiliary Power Supply in China

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Life Cycle Analysis of Distributed Energy System Projects’Energy Consumption and GHG Emission – A Case of BeerBrewery Auxiliary Power Supply in China

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PURPOSE AND SCOPE

The case study begins by informing that through the method of life cycle analysis of a product (LCA) the energy consumption and greenhouse gas emissions (GHG) of the Chinese project of distributed energy systems are evaluated, It was found that this system saves 8% of energy and reduces 38% of GHG emissions compared to traditional systems and renewable energy systems (solar and wind) in the region where the case is located study.
For this reason, distributed energy systems based on natural gas can be a medium-term solution for a profitable and efficient generation of a product as important as energy, supply the demand of the large Chinese population and minimize the environmental impact on the matter. of air and public health

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Biomass energy can save life cycle energy consumption by 31% compared to the case of All Coal (coal only), but the emission reduction rate is not as significant as in the base case. Wind energy also has good emission. Reducing effect Wind energy significantly reduces energy consumption and GHG emissions (35% and 35% respectively) compared to the All Coal case. 5.2. Sensitivity analysis As mentioned above, we rely on the literature data to derive the results. To examine whether our results are solid, we conducted sensitivity analyzes focusing on COP refrigeration and boiler efficiency. As shown in Fig. (2), COP does not make a difference in the performance of Distributed Energy System projects. However, when the efficiency of the boiler is set at 60%, a more modest number, the performance of natural gas Projects based as the base case are even more prominent.

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As this case of the product system is compared with traditional systems and renewable energies it is necessary to mention both the inputs and outputs of the system (energy consumption and GHG emissions)
Entries (elementary flows):
Natural gas
Winds of the region
Local solar light
Carbon
Wine or beer lees
Departures (elementary flows):
Electricity
Hot
Refrigeration
Energy consumption

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The data collection was carried out using previous studies of other projects belonging to the Chinese distributed electric power system to compare with the case study of the Snow brewery.

The following steps were used to calculate the data:
1) Convert the output to energy input, like Eq. (1) sample.
Ejk denotes the energy input jth (when converted to electricity or heat) of the system into technology k (case k).
Yijk is the type I output while Cijk is its conversion rate that is based on factors such as the energy efficiency of the machine.

Ejk = ΣiYijk / Cijk (1)

2) Use the energy input to calculate the energy consumption of the life cycle and the GHG emissions based on ancient literature, as shown in Eq. (two).
LCk denotes the energy consumption of the life cycle of the technology k, and aj is the relationship between the energy consumption of the life cycle and the energy input j. As sample ec. (3), GHGk denotes life cycle
GHG emission from technology k, and bj is the relationship between the GHG emission of the life cycle and the energy input j. GHG emission is measured as CO2 equivalent, as described in the equation. (3).

LCk = Σj
Ejk * aj (2)
GHGk = EMCO2 + 23EMCH4 + 296EMN2O = Ejk * bj (3)

3) Calculate the energy saving and the emission reduction rate by comparing the case k with the case k 'in the previous results, as described in Eq. (4-6). REkk ', RLCkk', RGHGkk 'are the energy saving rate of the cycle and reduction of the GHG emissions of the life cycle of the case k compared to the case k

REkk '= 1-Σj Ejk / ΣjEjk' (4)
RLCkk '= 1-LCk / LCk' (5)
RGHGkk '= 1-GHGk / GHGk' (6

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We designed 6 cases to better capture the potential development of the Distributed Energy System projects, as shown in Table 1. Case 1 (GN only) is the base case, source China Snow Breweries Distributed Energy System project operated in this model. We compared the All NG case (GN only in Spanish) with traditional coal projects with electricity purchased on the grid or generated by private power plants. We also investigate the efficiency of renewable technology by comparing several cases with renewable energies and the case with electricity purchased from the grid. Considering the location of the project where the wind and solar energy is relatively scarce and the wine is relatively easy to obtain, we designed cases 4-6

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There are reasons behind the configuration of our case. We establish these cases where coal is burned to generate heat. Because coal is still the dominant energy used by central heating systems in China. According to China in the energy flow diagram of 2012, coal represents 85% of the energy used in heating

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Energy models can benefit the climate. Maximum local solar / biomass utilization is economically reasonable, and can reduce energy loss in transportation and other processes. The location of Sichuan province is unique, with little solar resource but even less wind energy. We can assume local solar energy.
Energy provides part of the demand for energy, leaving the rest satisfied by the electricity grid. But for the wind to be able, this story will not be maintained According to OSGeo, a GIS laboratory, Sichuan has an average annual wind speed of 1-2 m / s. The wind resource is insignificant and the amount can fall below the economy threshold, so the electricity from the outside wind from other provinces can be a good substitute. To differentiate the distant wind power from the local wind energy, we assume a line loss rate of 10%.

We use data from the IPCC to calculate the calorific value of natural gas (8900 Kcal / M3), and other data used in the previous stage in the input conversion is presented in Table 2.

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Biomass energy can save life cycle energy consumption by 31% compared to the case of All Coal (coal only), but the emission reduction rate is not as significant as in the base case. Wind energy also has good emission. Reducing effect Wind energy significantly reduces energy consumption and GHG emissions (35% and 35% respectively) compared to the All Coal case. 5.2. Sensitivity analysis As mentioned above, we rely on the literature data to derive the results. To examine whether our results are solid, we conducted sensitivity analyzes focusing on COP refrigeration and boiler efficiency. As shown in Fig. (2), COP does not make a difference in the performance of Distributed Energy System projects. However, when the efficiency of the boiler is set at 60%, a more modest number, the performance of natural gas Projects based as the base case are even more prominent.

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The project we are investigating has an outstanding performance in energy saving and emission reduction.
Comparing the case using coal and electricity from the grid, the Gas-based Distributed Energy System project saves 8% of energy and reduces GHG emissions by 38% in a Life Cycle Analysis view. The renewable energy technology establishes alternatives to reduce GHG emissions. Due to the environment of the project site, solar energy can hardly improve performance. However, it is possible to use biomass (byproduct of beer production) or wind energy in the distance to improve the system. Thus, it shows renewable energy. Technology, especially the integrated use of biomass and wind energy, has great potential in the
Development of projects of the Distributed Energy System.
More research must be done to provide useful information for decision-making of companies in search for resources, use of technology and fundraising. Policy makers should also support distributed energy.