5.1. Case Description
A hotel building in China is adopted as the research object in this study, and the DES proposed in this study is used as an energy supply system to match the various load demands of the target building. Meanwhile, the optimization method described in this study is applied to configure the DES internal equipment. The hourly cooling, electrical, and thermal energy consumption of the building during the year is given in
Figure 5a. Due to the alternation of winter and summer in the region, the cooling and thermal loads show obvious seasonal differences, while the electrical energy consumption is less affected by the climate and the load curve is relatively smooth throughout the year.
Figure 5b gives the light and temperature parameters in the region, which mainly affect the PV output power in DES. The major equipment technical parameters in DES are listed in
Table 4.
Table 5 presents the cost per kW of the configured equipment and the energy purchase price. Affected by the policy, the electricity price was divided into two grades: 0.171 and 0.101 USD/kWh in the peak and trough phases.
Table 6 shows the CO
2, SO
2 and NO
x emission factors per unit of natural gas and purchased electricity.
Four algorithms, IZOA, ZOA, SCA, and GWO, are selected to optimize DES, and the optimization results of different algorithms are compared and discussed to test the practicality of the proposed IZOA on the DES configuration problem. The algorithm parameters are set as listed in
Table 2, and the number of populations and iterations are set to 100. The decision variables for the optimal configuration of the DES include the PV, GT, ES, HS, and GB capacities, as well as the heating ratio k
h and cooling ratio k
c of GSHP. The changeable ranges of each variable are given in
Table 7.
5.2. Optimization Results
Figure 6 depicts the changes in the objective function values over 100 iterations when the four algorithms optimize DES. The GWO and SCA sequentially fall into local optimum solutions when solving the DES configuration problem due to their search capability limitations, so the objective function values that eventually converge via the GWO and SCA are higher than the results obtained using the ZOA and IZOA. Since the IZOA introduces a chaotic mapping strategy in the initialization stage, its population has a better initial distribution and the objective function value is closer to the best value at the beginning of the iteration, although the final convergence of the two curves to the function value is approximately equal when comparing the IZOA and ZOA. The IZOA declines significantly faster and converges to the optimal value within the first 10 iterations, while the ZOA gradually finds the optimal solution only after 20 iterations, indicating that the convergence speed and global search ability of the IZOA are obviously better than the ZOA. Overall, the choice of optimization algorithm has an important impact on the solution results of the DES configuration problem, and the IZOA shows a better performance and results regarding this problem.
Table 8 indicates the objective function values and evaluation metrics values of the DES optimized using four different algorithms. The objective functions include the annual cost, fuel consumption, and CO
2, SO
2, and NO
x emissions of the DES, corresponding to the evaluation metrics of ECSR, ESR, and PERR. In terms of economic performance, the DES optimized using GWO performs the worst with a cost of USD 228,368.68, corresponding to a negative ECSR, indicating that the DES economic performance in this case is inferior to the performance of the SP system. The DES optimized using the IZOA achieved the best economic efficiency with an ECSR index of 13.03%, which is an improvement of 13.04%, 0.17%, and 0.26% compared to the DES with the GWO, SCA, and ZOA algorithms. In terms of environmental performance, the configuration scheme obtained using IZOA optimization has the lowest pollution emission of 366,994.87 kg. Compared to the DES system optimized with the GWO, SCA, and ZOA algorithms, the DES system optimized with the IZOA improved by 17.57%, 4.72%, and 0.11% in terms of PERR. In addition, the IZOA can obtain a system configuration scheme with less energy consumption compared to the other three algorithms, and the DES system achieved an ESR of 60.71%. The optimal DES configuration scheme derived via IZOA solving is listed in
Table 9.
Two typical days (winter day and summer day) are selected to analyze the DES electrical and thermal energy distribution over a 24 h period to demonstrate the operating state and energy balance process components under the specified configuration scheme.
Figure 7 gives the DES electric balance diagram on a winter typical day. In the power supply and demand relationship, the output of generation equipment such as PV and GT depend on the electrical load of the building at the current moment and the power consumption of GSHP, E
gshp. This study concludes that 7:00–9:00 and 19:00–22:00 are the peak electricity consumption hours for the selected hotel buildings from the fluctuating trend of E
load in the graph. E
gshp is also at a higher level during the same time period, mainly because of the high heat demand during this time period, which makes GSHP heating require more electrical power consumption. However, the lack of light hours during the late sunrise and early sunset in winter causes the PV to supply power to the DES only from 8:00 to 17:00. Therefore, the power demand during the hours of 1:00–8:00 and 18:00–24:00 is almost entirely supplied by the GT. During the peak power consumption hours of 8:00 and 21:00, the GT reaches its power limit and needs to purchase some power from the utility grid to maintain the DES in a balanced state. During 10:00–16:00, it coincides with the high PV output power and the small E
load, so there is excess electrical power available for ES storage. As the ES gradually reaches its maximum storage capacity, most of the surplus power is sold to the external grid. During the 17:00–18:00 period, ES releases all the stored power.
The operating state of each device within DES on a typical winter day is shown in
Figure 8. Most of the heat energy generated is provided to users via HE to fulfill the district heating demand H
load, and a small portion flows to AC to match the cooling load. GSHP produces heat steadily throughout the day, and the heat provided by it, H
gshp, has a similar trend to H
load over a 24 h period. Since DES works in FEL mode, the heat H
hr recovered by HR depends on the electricity generation of GT, E
gt. During 10:00–18:00, E
gt is 0, GT does not work, and GB becomes the major heat source inside DES. Furthermore, the larger heat load in winter makes DES produce little surplus heat after satisfying the demand of users, which limits the heat absorption and discharge power of the heat storage unit. The winter operation status reflected in
Figure 8 demonstrates the DES system’s operation strategy to cope with the heating and cooling demand during the cold season, as well as the role and energy conversion of each device at different times of the year. This solution helps to guarantee users adequate comfort and energy utilization efficiency in winter.
The operation strategy of the energy system in winter obviously takes full account of the balance between electricity and heat to ensure that the system can operate effectively and remain stable during high-load periods. By rationally distributing the output of different generating equipment at different times of the day, the energy system is able to satisfy the building’s power demand and heat demand, while maximizing the efficiency of energy use. Photovoltaic power generation equipment comes into play during daytime hours, while GT plays a role during periods of low light. GSHP provides a stable heating capacity throughout the time period, synchronizing with the building’s heat load and allowing the system to flexibly cope with the switch between different energy sources. Through the ES’s energy storage function, the DE system is able to store excess power when there is sufficient PV generation and release it when needed, further improving energy utilization efficiency. The interaction with the external grid also gives the system more flexibility, enabling it to buy or sell power when needed and keeping the system running stably. Overall, the DES’s operational status on winter days demonstrates its strengths and capabilities in energy supply and balancing. Through the comprehensive utilization of different energy devices, the rational arrangement of operating hours and the use of energy storage, the DES provides users with a comfortable and efficient energy solution, and makes a positive contribution to the development of sustainable energy.
Figure 9 and
Figure 10 demonstrate the DES supply and demand balance for electricity and heat energy on a typical day in summer. The electrical load curve in summer is similar to the winter load curve due to the low fluctuation of electricity consumption changes throughout the year. The difference is that the increase in sunshine hours and temperature in summer directly affects the PV and GT in DES power generation share, the PV working hours are extended from 8:00–17:00 to 6:00–19:00, and the electricity generation per hour is increased compared with a typical winter day. Correspondingly, GT generates less E
gt in summer, mainly concentrated in the hours of 1:00–6:00 and 20:00–24:00. The thermal power distribution of each component differs significantly from a typical winter day. The hotel building requires very little thermal energy supplied during that season, so the DES heat power output for each time period is clearly less than that in winter. The AC provides less cooling, mainly because the DES is configured with a larger cooling ratio kc, although there is a large cooling demand from users in summer which allows most of the cooling load to be satisfied by the GSHP. Moreover, due to the FEL mode, the GT generates much more heat than the building demand on summer days. In order to use this energy wisely, HS keeps heat storage during 1:00–7:00 and 19:00–24:00 when the GT is operating, while releasing heat energy from 8:00 to 12:00 to reduce the use of GB. In summary, the DES achieves a balance of energy supply and demand during typical days in both winter and summer, with no energy shortage or wasteful conditions, verifying the rationality of the configuration scheme.
The DES demonstrated efficient operation and energy utilization during a typical day of energy supply and demand balancing in the summer. The high temperatures and prolonged sunlight in the summer directly impacted the performance of the PV generation and GT equipment in the system. The working hours of the PV were extended to 6 a.m. to 7 p.m., resulting in an increase in the hourly power generation compared to the winter season. At the same time, GT generation decreased during the summer months, mainly from 1 a.m. to 6 a.m. and 8 p.m. to 12 p.m. During the summer months, the heat demand of the hotel is relatively low, so the thermal power output of the DES at different times of the day decreases accordingly. The reduction in cooling provided using the air conditioning system is mainly due to the fact that the system is configured with a larger cooling ratio, allowing most of the cooling load to be met by the GSHP. In addition, due to the application of the FEL mode, the GT produces much more excess thermal energy than the building needs during the summer months. In order to efficiently utilize this extra energy, the thermal storage system stores thermal energy during the GT operation from 1 a.m. to 7 a.m. and from 7 p.m. to 12 a.m., and then releases the heat from 8 a.m. to 12 p.m., reducing the reliance on the boiler.
Overall, the DES was able to balance energy supply and demand on a typical operating day in both winter and summer, fully utilizing the performance of the various equipment to ensure the system operated efficiently and consistently, both to meet user demand and to ensure the efficient use of energy. These analyses and results verify the rationality of the system configuration scheme. The results reveal the characteristics of the system’s power and heat supply and demand in different seasons, which can help provide guidance for the design of similar projects, including the optimization of equipment configuration, operation strategy and energy management.