Explorations of Integrated Multi-Energy Strategy under Energy Simulation by DeST 3.0: A Case Study of College Dining Hall (2024)

1. Introduction

With the global energy crisis becoming worse and public awareness of environmental protection rising, energy conservation and emission reductions in the construction industry—a major energy consumer and source of carbon emissions—have become even more important [1]. Data provided by the International Energy Agency (IEA) indicate that heating, cooling, electrical equipment, and lighting in buildings account for one-third of the world’s energy consumption. Building operations account for 30% of global final energy consumption and 26% of global energy-related emissions (8% direct emissions from buildings and 18% indirect emissions from electricity and heat production used in buildings) [2]. Developing countries can account for 52 percent of building energy consumption [3].

The “2023 China Building Energy Consumption and Carbon Emission Research Report” shows how big of a problem China’s building energy consumption is. Overall process energy consumption for residential structures in the nation climbed from 1.72 billion tce to 1.91 billion tce (tons of standard coal equivalent) between 2017 and 2021. This represents 36.3% of the nation’s overall energy consumption and grew at an average annual growth rate of 2.6%. The entire national house development process was predicted to use 5.0% more energy in 2020 than it does now. The whole national house construction process’s carbon emissions increased from 3.86 billion t CO₂ to 4.07 billion t CO₂ between 2017 and 2021, growing at an average annual growth rate of 1.3% and rising by around 1.05 times. The national house development process as a whole had 4.3% more carbon emissions in 2021 than it did in 2020. China’s total house development process produced 4.8% more carbon emissions in 2021 than it did in 2020. Due to the notably differing building standards in China, the country’s overall building energy consumption level is still significantly lower than that of industrialized nations [4].

2. Literature Review

The behavior of building users and their consumption patterns play a critical role in determining the overall energy consumption of a building [5]. Universities and colleges, owing to their large building scale and dense population, exhibit significant characteristics. With diverse activities among residents, energy consumption varies significantly over time, seasons, and personal habits [6]. In northern China, where the climate is cold, heating energy consumption remains high in universities. Large public buildings, such as college dining halls, require essential air circulation and heating to ensure a comfortable dining environment for students during summer and winter. This often results in high energy consumption and unique patterns among universities in northern China.

Since university campus buildings observe summer and winter holidays, their energy usage patterns exhibit notable seasonal fluctuations. It is noteworthy to observe that during the periods spanning from mid-to-late January to mid-February and July to August, the campus energy demand remains significantly lower than the average consumption levels [7].

As the global energy crisis intensifies and environmental awareness increases, energy-saving work has become a popular topic in modern society. Buildings, as significant consumers of energy and carbon emissions, can save 30 to 80% of their energy consumption if they can be made more energy-efficient [8]. Among these, college dining hall buildings, with their high energy consumption and dense population, are particularly important for energy saving. Therefore, constructing a reasonable power supply system and reducing energy consumption in college dining hall buildings has become a hot topic and a challenging research issue. In investigating the current trends and challenges in BEMP (Building Energy Management Plan) validation [9], notably, in building energy simulation validation, the three methods—analytical, empirical, and comparative—differ in case selection, favoring mature, tailored, and both types of cases, respectively.

Energy simulation software, as an essential tool in building energy-saving design, plays a crucial role in predicting and evaluating building energy consumption. This provides strong support for energy-saving design. However, different energy simulation software packages vary in terms of their function, accuracy, and operational complexity. Currently, various energy simulation software packages are available in the market, such as EnergyPlus, TRNSYS, and DOE-2 [10]. Each software program has its own characteristics and calculation capacity, as shown in Table 1. For example, EnergyPlus has a strong function but is relatively complex to operate, TRNSYS is highly flexible but may not be sufficiently precise in specific scenarios, and DOE-2 can generate detailed reports but has a higher learning curve. Therefore, when selecting energy simulation software, it is necessary to consider the specific project requirements, user skill level, and software features. Among the numerous energy simulation software packages, the DeST 3.0 software developed by Tsinghua University has gained wide application in the building energy-saving field owing to its high professionalism, accuracy, and user-friendliness. DeST 3.0 software is based on advanced computational models and databases, enabling high-precision building environment simulation analysis, comprehensive evaluation of building energy consumption, and providing targeted optimization suggestions [11]. Compared to other software, DeST 3.0 software is more suitable for the energy simulation of complex building types, such as college dining halls, and can accurately reflect their energy consumption characteristics, providing more precise data support for energy-saving design [11,12,13].

Before predicting energy consumption, it is essential to conduct a thorough analysis of a building’s consumption characteristics [14]. The intricate system of buildings and their environments, with interdependent subsystems, complicates thermal processes, making accurate consumption calculations difficult [15]. There are many factors that affect the thermal performance of buildings, especially window-to-wall ratio, enclosure structure, building orientation, shading coefficient, and energy consumption behavior. Jiang Yang [16] studied public buildings in the Nanchang area and found that an increase in the window-to-wall ratio will significantly lead to an increase in air conditioning load. Zhou Sitong et al. [17] used DeST-C to analyze the impact of building orientation on energy consumption in office buildings. They found that when the building orientation angle increased from 0° south to 157.5° south, the cumulative heat load of the building did not change significantly throughout the year, but when the angle increased from 67.5° to 112.5° towards the south, the cumulative cooling load changed more significantly throughout the year. As mentioned above, most previous studies primarily focused on using DeST 3.0 for various types of analysis. The precise parameter settings enhance the credibility of the results obtained from energy consumption analysis performed using DeST 3.0 software. However, despite the widespread application of DeST 3.0 in building energy consumption simulation, which encompasses detailed modeling of building thermal performance, system controls, and occupant behavior, research on tailored energy supply strategies based on these simulations remains relatively scarce. To bridge this gap, a hybrid energy utilization system is proposed through specific analysis of energy consumption in high-energy-consuming buildings using DeST 3.0 software. This method offers increased accuracy and helps the development of practical solutions to improve energy efficiency and sustainability in such buildings.

With the continuous development of science and technology, renewable energy technologies such as solar energy and wind energy have been widely used. However, due to their intermittency and instability, they have also brought many inconveniences to use [18]. It is apparent that a single renewable energy source is insufficient to support a continuous energy supply system. To overcome the disadvantages described above, the integration of various renewable energy sources has been proposed [19,20]. A hybrid renewable energy system (RES) can make full use of the advantages of different renewable energy sources to realize the efficient use of energy, energy savings, and emission reduction [21]. Settino [22] provides an interesting solution to the stochastic nature of solar energy in the integration of biomass combustion in Organic Rankine and photovoltaic systems for small-scale cogeneration applications, where the addition of biomass combustion makes the system more stable in terms of energy supply and can compensate for the discontinuity of solar energy. The power curve of the wind turbine and wind speed data are typically used to calculate the amount of electricity produced while using wind energy. The power that the turbine can generate at various wind speeds is shown by the turbine power curve. This is typically not a linear connection since the turbine cannot start in conditions where the wind speed is too low, and the power is limited to a maximum value in order to safeguard the turbine in conditions where the wind speed is too high [23]. McGowan et al. developed the models of a hybrid wind–photovoltaics–diesel system using the methods of HYBRID2 and SOMES, respectively. They compared the simulation results of the two models and found that both HYBRID2 and SOMES gave similar results. The main differences were in energy flows due to the different modeling methods and strategies of subcomponents [24]. These practical application studies demonstrate the enormous potential and benefits of hybrid renewable energy systems in different buildings.

These practical application studies demonstrate the enormous potential and benefits of hybrid renewable energy systems in different buildings. Therefore, in the face of the background of the huge proportion of global building energy consumption, this study, entitled “DeST 3.0-based energy consumption simulation and hybrid renewable energy system re-search for university dining halls”, uses DeST 3.0 software to carefully simulate and analyze the cooling and heating loads and overall energy consumption of the dining halls in the Dongshan Campus of Shanxi University. Based on a rigorous and comprehensive evaluation of these simulation results, an innovative energy-saving system that skillfully combines solar, wind, and biomass energy is proposed. The design of this hybrid energy system not only saves energy and reduces emissions in the college dining hall, but it also serves as a model for the sustainable development of other high-energy-consuming buildings by expanding renewable energy options and presenting a novel approach to achieving emission peaks and carbon neutrality.

3. Methodology and Analysis

3.1. Overview of the Simulation

University buildings have grown to serve the diverse demands of students as higher education expands. The newly constructed dining hall at Shanxi University’s Dongshan Campus in Taiyuan, Shanxi Province, China (38° N 112°33′ E), which serves as the primary focus of this study’s simulation analysis, boasts spacious dining rooms and a range of functional spaces, offering greater utility than traditional campus dining options. As seen in Figure 1, this canteen is a four-story glass curtain wall structure with a floor size of 16,634.98 m² and a floor height of 5.4 m.

Due to the region’s harsh winters and intense summers, maintaining a healthy indoor atmosphere necessitates the use of air conditioning in summer and a heating system in winter. Notably, the majority of the kitchen equipment in this dining hall runs on natural gas, with a small portion relying on electricity, ensuring both efficiency and environmental friendliness in meeting the various functional demands of the dining halls, kitchens, offices, and storage spaces, each tailored to provide students with the most comfortable dining environment.

3.2. Principle of Calculation on DeST 3.0 Software

Establishing a dynamic model of the building’s thermal processes, DeST 3.0 uses a modular and phased simulation calculation mode, taking into account all aspects of heat storage and release from the building envelope and its objects, including convection heat transfer of air and radiation heat transfer between the interior surfaces of the room envelope. It also applies the room heat balance method and uses a computer to conduct a time-by-time simulation of the building for a specific amount of time, say, or for the entire year—8760 h. According to the room heat balance model, the room temperature of the building room k is calculated in the following equation [25]:

$t_{k} (τ) = t_{b} (τ) + \sum_{j} Φ_{j, 0, k} t_{j} (τ) + Φ_{h v a c, k} q_{h v a c, k} (τ) - Φ_{h v a c, k} c_{p} ρ G_{o u t, k} (τ) \times [t_{o u t} (τ) - t_{k} (τ)] + \sum_{j} Φ_{h v a c, k} c_{p} ρ G_{j k} (τ) \times [t_{j} (τ) - t_{k} (τ)]$

where Φ_hvac_,k—the influence coefficient of air conditioning heat on room temperature in room k at the current time; Φ_j_,0,k—the influence coefficient of room j on the room temperature of room k at the current moment; t_bzk (τ)—the room temperature of room k when air conditioning, natural ventilation, and adjacent room heat transfer are not affected at the current moment; G_out_,k—the outdoor ventilation quantity at the current moment; G_jk (τ),—the ventilation quantity from the jth adjacent room to room k; t_out (τ)—the outdoor temperature at the current moment; t_k (τ), t_j (τ)—the room temperature of the current time in k and j rooms; q_hvac_,k (τ)—the air conditioning heat quantity (or cooling) that needs to be put into room k at the current moment.

3.3. Model Creation

Each of the canteen’s functioning areas had a different work and rest schedule. Different thermal disturbance coefficients, lighting configurations, maximum room temperatures, and air conditioning start and stop times were established to meet the unique requirements of each space. The canteen’s functional rooms’ work and rest hours, as well as the maximum and lowest air conditioning operating temperatures and humidity levels, are shown in Table 2 and Table 3, respectively. Thermal disturbance is a parameter in the DeST 3.0 software. Thermal disturbance is an important parameter in the simulation of a building’s thermal environment, which covers a variety of external and internal factors that affect the thermal conditions inside the building. These factors include, but are not limited to, outdoor meteorological conditions (e.g., temperature, humidity, solar radiation, etc.), ambient thermal conditions around the building, and indoor heat generation (e.g., heat generated by personnel, equipment, lighting, etc.).

Based on the climate characteristics of China’s cold northern regions, the model heating time period is set to run from 1 November to 31 March of the following year, with a temperature control of 18 °C. The air conditioning season runs from April to October, with a temperature control of 26 °C and intermittent operation.

The primary focus of our modeling process was to accurately input parameters, particularly during winter and summer vacations when campus energy consumption is significantly reduced. To achieve this, we customized operating schedules, heat disturbance coefficients, lighting settings, maximum indoor temperatures, and air conditioning on/off times based on the actual conditions of each functional area. Additionally, we set personnel loads, lighting, and equipment heat disturbance coefficients according to the detailed functional classification of the rooms. We also took into account important factors such as the window-to-wall ratio (0.6), building orientation (facing south with its back to the north), maintenance structure, and energy consumption behavior, which have a greater impact on the simulation results. The output of this process is hourly data on humidity load, heating, and cooling, providing a more realistic representation of the canteen’s energy consumption under various seasonal and operational conditions.

4. Results

4.1. Simulation and Analysis of Cold/Heat Loads

As seen in Figure 2 and Figure 3, the DeST 3.0 program can simulate and analyze the building’s energy consumption as well as the loads associated with indoor cooling and heating on an hourly basis throughout the year. Based on the selected model parameters, the annual heating demand for the student canteen is 238,998.70 kWh, while the annual cooling demand for the student canteen is 911,524.65 kWh.

There are notable seasonal differences in the hot and cold loads. Due to summer vacations, cold loads are at their lowest in August, with the greatest concentration occurring between May and September. During the winter holidays in February, when the average temperature is below 10 °C and a lot of heat is consumed for interior heating, the heat load increases dramatically from November to March. The energy consumption of most cities in northern China, where the air conditioning season typically lasts from 1 April to 1 July and the heating season spans from 15 November to 15 March, is comparable to this pattern of fluctuating cooling and heating demands.

It is important to keep in mind, nevertheless, that the canteen uses a little bit more energy than other public food facilities. This is mostly because college dining halls typically need larger floor areas to accommodate more students, which means that their cooling and heating load requirements are correspondingly higher. On-campus catering buildings are characterized by high patronage and concentrated business hours.

4.2. Electricity Consumption Analysis and Simulation

Since the university’s catering buildings are a crucial part of the campus building complex, energy conservation is very concerned with how much electricity they consume. A canteen building may be small in size, yet it consumes ten to twenty times as much energy as a typical residential home. For this reason, developing a practical energy supply system is essential to reducing emissions and saving energy. The monthly simulation results for the college dining hall’s electrical load are displayed in Figure 4. Air conditioning and lighting systems consume about 81% of the building’s yearly power usage, according to statistics. The highest and lowest monthly building energy consumption are 63.07 × 10³ kWh and 192.50 × 10³ kWh, and the air conditioning system had the highest energy consumption in July. The average monthly building energy consumption is 99.82 × 10³ kWh. The calculated sum of various energy consumption (cooling and heating loads, lifts, lighting, natural gas, etc.) in the student canteen for the whole year is 2.39 × 10⁶ kWh.

Figure 5 shows the cooling and heating loads and electricity consumption on the summer solstice and winter solstice. The winter solstice and summer solstice are the days when the sun hits the Tropic of Capricorn and Tropic of Cancer, which are the shortest and longest sunshine hours of the year.

5. Discussion

Due to the unpredictable nature of natural resources, renewable energy sources like wind [26] and solar pose challenges in power generation, resulting in fluctuating output and difficulty in dispatch, thereby threatening grid stability [27]. Therefore, this paper aims to develop a hybrid renewable energy system for college dining halls, guided by the principles of efficiency, stability, and economy, with the intention of enhancing energy utilization efficiency, ensuring stable power supply, and achieving the utilization of kitchen garbage.

5.1. Biomass Generating Units

As an alternative to traditional waste incineration, biomass gasification has fewer emissions of flue gases, is more cost-effective and environmentally friendly [28], and is the most economical way of generating electricity among all renewable energy sources. The majority of living waste found in colleges and universities is composed of draft paper, food packaging bags, and dining hall kitchen garbage. Relevant surveys show that these institutions produce 1.796 kg of garbage per capita each week [29], with dining hall kitchen garbage and draft paper adding up to a weekly production of up to 2.796 kg. Staff, full-time teachers, and bloggers at Shanxi University make up about 20,000 of the school’s workforce. Based on the calculation of the 43-week period, the total amount of garbage produced by the campus in a year is estimated to be 2404.56 t.

Dry anaerobic fermentation is used in this paper. The materials treated by dry anaerobic fermentation technology are organic wastes with total solid content greater than 20%, and the materials are pre-treated to show a certain fluidity or semi-solid state. Dry anaerobic fermentation technology requires a reasonable and reliable sorting process, with higher requirements for large-size impurities such as plastic bags, rubber, and stones, and lower requirements for small-size sand and soil [30].

Yangqing Li et al. [31] conducted a test case analysis and learned that the biomass anaerobic fermentation system could be operated stably when the pH was maintained at 8.0–8.2, the alkalinity was maintained at 12,000–14,000 mg/L, the volatile fatty acids were 2500–3000 mg/L, and ammonia nitrogen was around 2500 mg/L. After stable operation of the system, the gas production per unit feed volume of the anaerobic fermentation tank was about 140 m³/t, and the methane content was maintained at 55% to 65%.

The yearly combustion heat of methane is calculated as $E_{{C H}_{4}}$ :

$E_{{C H}_{4}} = V_{c} \times q \times η_{g}$

(1)

$V_{c} = m \times η_{i} \times η_{t}$

(2)

where V_c—annual methane production, m³; q—the calorific value of combustion of methane is 55 MJ/m³; η_g—gas boilers can be up to 90%; m—annual garbage production on campus is estimated to be 2404.56 t; η_i—the gas production rate is taken as 140 m³/t; η_t—the methane content is taken as 60%.

Calculations are provided. V_c = 201,983.04 m³; $E_{{C H}_{4}} = 2.8 \times 10^{6} k W h$ .

The amount of electricity generated in a year is E_e:

$E_{b} = V_{c} \times q \times η_{q}$

(3)

where η_q—thermal power generation efficiency is taken as 42%.

Calculations are available: E_b = 4,665,808.224 MJ. Converted to 1.3 × 10⁶ kWh.

5.2. Solar Energy Utilization Program

There are two main ways to generate electricity from solar power: one is solar photovoltaic power generation and the other is solar thermal power generation. Solar photovoltaic power generation is based on the photovoltaic effect through the power electronic device directly converting solar radiation into electricity; the most widely used form is composed of different materials and different structures of the various solar cells [32]. Solar thermal power generation through the solar collector device will convert heat energy into electricity; this power generation device is similar to conventional power generation equipment, and according to the collector of different devices, can be divided into parabolic trough systems, tower systems, and disk systems [33,34].

Photothermal conversion is a basic way of utilizing solar energy, which can be widely used in building heating, hot water supply, and greenhouses [35].

The amount of solar energy used should be determined by the availability of solar energy resources in the area. For example, when the heat source’s temperature is below 100 °C, solar energy is primarily used to heat homes and provide hot water for domestic use. When the heat source’s temperature is between 100 °C and 250 °C, it is typically used for lithium bromide refrigeration and heating. Finally, when the heat source’s temperature is above 250 °C, solar energy is typically used for photovoltaic power generation or metallurgy, among other applications. Solar radiation is a measure of solar energy resources [36]. The distribution of solar energy in different regions is presented in Table 4. Taiyuan City’s annual radiation is 1397 kWh/m², and photothermal and photovoltaic energy are compared in this study.

5.2.1. Photothermal System

The annual solar irradiation in Taiyuan is 1397 kWh/m² and the annual sunshine duration is about 2400 h. The average irradiance G = 582.08 W/m² is calculated. There are four main types of radiation in vacuum tube collectors, which are direct radiation, reflection of direct radiation, scattering radiation, and reflection of scattering radiation. The calculation formula and results are as follows (details of the formula calculations can be found in Supplementary Material S1 or in the book “Solar Thermal Technology [37]”):

The total amount of solar radiation that can be absorbed is the sum of these four categories:

$G_{e f f} = G_{b, T} + G_{b, w} + G_{d, T} + G_{d, w}$

(4)

$G_{b, T} = G_{b, n} \cos i_{t} G (Ω) {(τ α)}_{t}$

(5)

$G_{b, w} = G_{b, n} \cos i_{c} ρ F_{w - t} \frac{W}{D_{2}} {(τ α)}_{60 °}$

(6)

$G_{d, T} = π G_{d, θ} F_{t - s} {(τ α)}_{60 °}$

(7)

5.2.2. Photovoltaic System

If a photovoltaic power generation system is used, 3000 m² of photovoltaic power generation modules can be laid on the same footprint as the solar thermal system (there is still room on the building’s roof).

The formula for calculating photovoltaics power generation is as follows:

$E = Q \times S \times η_{1} \times η_{2}$

(13)

where Q represents the total annual radiation of the tilted surface, i.e., the radiation of the photovoltaic module in the case of tilting, which can be obtained by multiplying the horizontal radiation by the cosine of the tilting angle; S represents the area of the photovoltaics module; η₁ represents the photovoltaic conversion efficiency of the photovoltaics module, which can reach up to 25% of the market photovoltaics module; and η₂ represents the integrated efficiency of the photovoltaics system, which is complicated by the factors and takes a value generally in the range of 75~85%, and we take 80%.

Calculations:

$Q = 1397 \times \cos 38 °; E_{P V} = 660.51 \times 10^{3} k W \cdot h$

5.3. Wind Energy Utilization Program

A power curve model that has been simplified is used to determine the corresponding power for each wind speed data point. The power generation for each time period is then calculated by multiplying the power by the duration of the time period (e.g., the number of hours). The sum of the power generation throughout all time intervals yields the overall amount of wind power produced. The wind turbine, which is the main part of the wind power generation system, generates electrical energy by using the wind energy that is gathered to power a generator that transforms mechanical energy. Figure 6 displays the wind turbine’s power output curve. The curve indicates that the fan is in the shutdown stage when the actual wind speed is less than the cut-in wind speed because the fan’s output power is less than the system loss; the fan’s output power increases to the rated wind speed when the wind speed is greater than the cut-in wind speed, roughly equal to the third power of the wind speed [38]; when the wind speed is greater than the rated wind speed but less than the cut-out wind speed, appropriate steps must be taken to limit the fan’s output power to avoid the wind generator system overloading and suffering; to protect the system, the wind turbine must be turned off when the wind speed exceeds the cut-out wind speed [39].

From the power output curve of the wind turbine, wind power generation can be modeled.

$P_{W G} (v, t) = \{\begin{matrix} 0, v < V_{c} \\ \binom{\frac{1}{2} C_{P} ρ S v^{3}, V_{c} \leq v < V_{r}}{P_{W G R}, V_{r} \leq v < V_{f}} \\ 0, v \geq V_{f} \end{matrix}$

(14)

where v is the wind speed at each moment; CP is the coefficient of performance of the wind turbine, the maximum value of which is 0.593, but in the actual operation of the process, we take the best value of 0.46–0.48; ρ is the density of the air, taken as 1.29 kg/m³; S is the area swept by the fan blades; PWGR is the rated power of the wind turbine.

Evaluating the parameters of wind turbines is crucial for assessing power generation. In this context, a 1.5 MW wind turbine was selected for examination, with relevant details presented in Table 5.

The wind speed was analyzed and recategorized based on the China Meteorological Science Data Centre. Figure 7 illustrates the monthly average wind velocity below or exceeding 3 m/s for the cut-in wind speed with a 1.5 MW wind turbine. In this region, wind speeds oscillate with the months and seasons, with fewer hours of wind speeds greater than 3 m/s than less than 3 m/s, and the total number of hours with wind speeds less than 3 m/s is greater than 300 h per month.

By integrating the power output curves and real wind velocity, the power generation E_w was assessed in Figure 8, revealing an annual total of 0.56 × 10⁶ kWh. The highest power generation is in March, which is springtime in northern China, with frequent sandy and dusty weather and generally higher-than-usual wind speeds, making the wind farm’s capacity the highest of the year, with a specific value of 0.08 × 10⁶ kWh. The next highest power generation is in December, which is winter in China, and although wind speeds are not as high as in the spring when sand and dust are common, the low temperatures increase the density of the air, which is favorable for wind power generation, and therefore the capacity is also considerable, with a value of 0.075 × 10⁶ kWh.

5.4. Hybrid Renewable Energy Systems

A hybrid renewable energy system was created based on energy consumption and climate factors. The system consists of a lithium bromide refrigeration system, biomass, solar, wind, and an energy storage system as a back-up energy source. By design, solar energy is used to collect heat through collectors for cooling and heating the building, or it is used directly for electricity generation using photovoltaic modules and for power supply through the energy storage unit. Wind energy is used to generate electricity, which is stored through an energy storage device and used to supply power to the user. The use of energy storage devices increases the utilization of wind energy for power generation. Due to the instability of wind and solar energy, heating, cooling, and power supply to the users also become unstable. When solar and wind energy are unstable or insufficient, electricity is generated from biomass alone and excess energy is stored in batteries. When solar and wind energy is stable and sufficient, it is used by solar and wind for heating, cooling, and electricity generation [40].

As shown in Figure 9 and Figure 10, two energy supply schemes are designed in this paper. Strategy 1: Energy complementarity through biomass, light, and wind energy. The fermentation of kitchen garbage results in the production of biogas, which is then burned to generate electricity using a steam turbine. This process can produce up to 1.3 × 10⁶ KWh of power annually. Light energy is collected using vacuum tube collectors to provide year-round heating, cooling, and domestic hot water supply. There are 10,620 collectors in 354 groups, and the annual heat collection capacity can reach 0.606 × 10⁶ kWh. Wind energy is collected using 1.5 MW wind turbines, and the annual wind power generation capacity can reach 0.56 × 10⁶ kWh based on survey data. To prevent wind and light resource instability, the system uses energy storage devices to store excess electricity, provides energy on cloudy and rainy days or in windless weather, and uses a solar energy absorption lithium bromide unit for refrigeration. In the summer, the cooling capacity can reach 0.181 × 10⁶ kWh. The first strategy’s operating system is displayed in Figure 9. The process is as follows:

The kitchen garbage generated in the dining hall is fed into a fermentation tank where it undergoes fermentation to produce biogas. This biogas is then purified through a purification device. The purified biogas enters a biogas internal combustion engine, where it is burned and converted into kinetic energy. This energy is subsequently harnessed by a steam turbine to generate electricity. An energy storage device accumulates electricity generated from wind and biomass sources. An inverter is utilized to convert the direct current (DC) electricity into alternating current (AC), which is then supplied to the canteen. Vacuum tube collectors harness solar energy for heating purposes, and they can also supply cooling for the canteen through a lithium bromide refrigeration system. Additionally, the vacuum tube collectors maintain a constant temperature in the fermentation tank.

Strategy 2: Energy complementarity through biomass, light, and wind energy. Under the premise that the use of biomass and wind energy remains unchanged, the use of light energy is changed to photovoltaic power generation, 3000 m² of photovoltaic power generation modules are laid, and the calculation shows that the annual power generation capacity can reach 0.660 × 10⁶ kWh. Air conditioning units are used for cooling and heating, etc. The operation system of Strategy 2 is shown in Figure 10. The process is as follows: The kitchen garbage generated in the dining hall is fed into a fermentation tank where it undergoes fermentation to produce biogas. This biogas is then purified through a purification device. The purified biogas enters a biogas internal combustion engine, where it is burned and converted into kinetic energy. This energy is subsequently harnessed by a steam turbine to generate electricity. An energy storage device accumulates electricity generated from solar, wind, and biomass sources. An inverter is utilized to convert the direct current (DC) electricity into alternating current (AC), which is then supplied to the canteen. The electricity generated by photovoltaic modules is utilized to power heating and cooling systems that maintain a constant temperature within the fermentation tank.

The calculated capacities of the two sets of systems differ, with the sum of the effective capacities of Strategy 1 being 2.375 × 10⁶ kWh, and the combined effective capacities of Strategy 2 being 2.52 × 10⁶ kWh. Figure 11 shows the capacity share of the various energy sources, and it can be seen that the share of photovoltaics is 26% larger than that of solar thermal (22%).

In addition to having a better ratio than Strategy 1, Strategy 2 has a number of other benefits. Firstly, energy consumption is lower for photovoltaic systems than for solar thermal systems. Secondly, compared to thermal energy, the electrical energy produced by a solar system is a more efficient energy source. Furthermore, higher electricity generation efficiency can be attained with a larger solar system installation. This higher capacity is essential to maintaining the student canteen’s dependable and seamless functioning.

Although the design program is in line with the strategic goal of energy saving and emission reduction, there are still some limitations. The program uses too much equipment, which makes the maintenance more cumbersome; the dry anaerobic fermentation of domestic waste is a difficult problem for the treatment of digestate, which is a feasible solution as the school’s green fertilizer; the wind power generation occupies a large area and cannot be located close to the residential area, or it will be easy to disturb the people, and if the site is too far away, it will increase the cost. Economic analysis is also a major limiting factor in the implementation of the program, but considering that the economic analysis requires a lot of investigation and a lot of space, it can be calculated separately in a new article, so I will not go into too much detail here.

6. Conclusions

This study has underscored the significance of personalized and innovative energy solutions through a comprehensive analysis of energy consumption patterns in a college dining hall located in northern China. In this study, the annual energy consumption, cooling, and heating loads of the dining hall have been simulated using DEST 3.0 software, and the results have been analyzed to propose and compare two energy-saving systems. The main conclusions that have been reached are as follows.

(1): The annual energy consumption of the dining hall has been conclusively attributed primarily to its cooling and heating systems, with air conditioning and lighting systems contributing a substantial share to the overall energy usage. This revelation underscores the significant potential for energy savings within these areas.
(2): Innovative energy supply strategies combining solar, wind, and biomass have proven to be the most effective. This strategy capitalizes on the superior energy production efficiency of photovoltaic systems over solar thermal systems, wherein the electrical energy generated by photovoltaics is not only recognized for its higher quality but also its versatility. Furthermore, the expansion of the installation area has facilitated even more efficient energy generation.

The methodology that has been adopted in this study, encompassing a detailed analysis and specialized design approach, has served as a valuable reference for similar high-energy-consuming buildings, particularly restaurants, canteens, and hotels. However, it is still important to acknowledge the limitations that have been identified in this study. Although we have used three complementary energy sources, the instability of nature has still caused fluctuations in the energy supply, and the national grid has still been needed when necessary. The dining hall’s change from a unified power supply from the national grid to a separate power supply using a mixture of energy sources has required its own deployment of power, which may have resulted in failures. The amount of equipment that has been required for the system is large and expensive, and the investment that has been needed is significant, with the potential for too slow a payback. Thus, the insights and practices that have emerged from this research hold significant promise in enhancing global environmental sustainability within the specific context of buildings with similar energy profiles.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16146222/s1: Specific steps for calculations on solar thermal collectors and lithium bromide refrigeration have been placed in the Supplementary Materials and submitted to the system. Supplementary Material S1: Figure S1: Various angles in calculation of solar irradiance; Figure S2: Radiation schematic diagram of vacuum tube collector; Table S1: Correction factors by latitude; Table S2: Solar air collector parameters. Supplementary Material S2: Figure S1: Lithium bromide absorption refrigeration process; Figure S2: Absorption refrigeration cycle h-ξ diagram; Table S1: Parameters for calculation.

Author Contributions

Conceptualization, Y.N. and Y.X.; methodology, Z.W. and Y.X.; software, X.W.; formal analysis, C.G.; investigation, Y.X.; resources, Q.W.; writing—original draft preparation, Y.N.; writing—review and editing, Y.W.; supervision, L.C.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shanxi Province Basic Research Program, China, grant number “202103021223031” and grant number “20210302124126”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data utilized in this paper have been presented in a clear and comprehensive manner within the document. We are committed to transparency in research and, therefore, are willing to share the datasets analyzed or generated during this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Building model.

Figure 2. Annual cooling and heating load of the building.

Figure 3. Hourly humidification of the building throughout the year.

Figure 4. Monthly building energy consumption.

Figure 5. Daily winter solstice heat load and summer solstice cool load.

Figure 6. Wind turbine power curve.

Figure 7. Annual wind resource distribution.

Figure 8. Monthly power generation from wind turbines.

Figure 9. Schematic of the renewable system consisting of photothermal, wind, and biomass energy.

Figure 10. Schematic of the renewable system consisting of photovoltaic, wind, and biomass energy.

Figure 11. Comparison of the share of each energy source and of photovoltaic power generation and solar thermal collectors.

Table 1. Comparison of basic characteristics and calculation capacity.

Fundamental Characteristic	DeST 3.0	EnergyPlus	DOE-2	eQUEST	IBLAST
Outcome simultaneity	√	√			√
Multiple time intervals		√			√
Output interface	√	√
Custom output reports	√	√
Room heat balance calculations	√	√			√
Building heat balance calculations	√
Convection heat transfer on internal surfaces	√	√			√
Long-wave interradiation between internal surfaces	√	√
Heat transfer between neighboring rooms	√
Humidity calculation	√	√			√
Thermal comfort calculations		√			√
Sky background radiation modeling	√	√	√	√
Form calculator	√	√	√	√
Solar transmission distribution		√	√	√

Table 2. Indoor thermal disturbance parameters work and rest schedule.

Sites	Dining Hall		Bathroom	Business Premises
Lighting	6:00–17:00	17:00–22:00	5:00–22:00	6:00–22:00
Thermal disturbance	0.5	1	1	0.5
Appliances	5:00–17:00	17:00–22:00	5:00–22:00	6:00–22:00
Thermal disturbance	1	0.2	0.8	0.5
Officer	7:00–13:00	17:00–22:00	5:00–22:00	6:00–22:00
Thermal disturbance	1	0.8	1	0.5

Table 3. Parameter settings for air conditioning system.

Parameters	Limit Value	Hidden Meaning
Lower limit of room temperature	18 °C	Lower limit of indoor design temperature achieved by air conditioning operation
Upper room temperature	16 °C	The air conditioner starts to run when the room temperature falls below this value
Lower humidity limit	0.65	The air conditioner starts to run when the indoor humidity is higher than this value
Upper humidity limit	0.2	The air conditioner starts to run when the indoor humidity is below this value

Table 4. Distribution of solar energy by type of area.

Area Category	Annual Radiation (kWh/m²Y)	Annual Sunshine Hours (h/Y)	Average Annual Peak Sunshine Hours (h)	Average Daily Peak Sunshine Hours (h)
Category 1 (i.e., class A) area	1855–2333	3200–3300	1854–2300	5.08–6.3
Category II area	1625–1855	3000–3200	1624–1854	4.45–5.08
Group III area	1393–1625	2200–3000	1387–1624	3.8–4.45
Group IV area	1163–1393	1400–2200	1132–1387	3.1–3.8
Group V areas	3344–4190	1000–1400	913–1132	2.5–3.1

Table 5. Wind turbine basic information for 1.5 MW.

Cut-In Wind Speed	Cut-Out Wind Speed	Rated Wind Speed	Impeller Diameter	Blade Length	Swept Area
3 m/s	25 m/s	11 m/s	70 m	34 m	3879 m²

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