6.2. Analysis of Examples
(1) Sunny day in winter
The model and algorithm established in this work we used for optimization calculation. On a sunny winter day, the loads curve before and after the optimization of the photovoltaic greenhouse facility agricultural micro energy network is shown in
Figure 5. After optimization, the stored energy curve of each form of energy storage is shown in
Figure 6. The comparison of the time-shiftable load and energy storage working time before and after optimization is shown in
Table 2.
As can be seen from
Figure 5 and
Figure 6 and
Table 2, the load curve of the micro energy network system before the optimization in the winter sunny day is very different from the PV output curve. During the daytime from 8:00 to 15:00, PV output is much greater than the load. There is no photovoltaic output during the night, but the demand for electricity at night is greater due to the need to heat the greenhouse through far-infrared heating. After optimization, the load curve is close to the PV output curve. In the 8:00–15:00 time period, during which the PV output is relatively high during the day, the reservoir, biogas digester, and block wall with phase-change thermal storage are used to store the electric energy as other forms of energy, thereby increasing the electric load during PV peak output period. On the other hand, the working time of time-shiftable loads, such as sound wave encouragement, Light Emitting Diode (LED) growth lighting, ventilators, etc., are also adjusted to this time period as a supplementary adjustment of the electric load. The two together maximize the consumption of photovoltaic power during daytime hours. In the absence of photovoltaic output during the night, the biogas digester, and block wall with phase-change thermal storage are used together to increase the temperature of the greenhouse to replace the far-infrared heating as the electric load in order to reduce the electric load at night.
Before the optimization, the micro energy network purchased 623.71 kWh from the distribution network and consumed 491.30 kWh of photovoltaic power. After optimization, the micro energy network purchased 26.45 kWh from the distribution network and consumed 1077.71 kWh of photovoltaic power, increasing the amount of photovoltaic energy consumption by 586.41 kWh.
It can be seen that on a sunny winter day, the local optimization method of photovoltaic power supply provided by the facility agriculture micro energy network proposed in this work can fully utilize the energy transfer function of multiform energy storage and the time-shiftable characteristics of time-shiftable agricultural load to achieve the optimal local consumption of photovoltaic power.
(2) Rainy day in winter
The model and algorithm established in this paper were used for the optimization calculation. The loads curve before and after the optimization of the photovoltaic greenhouse facility agricultural micro energy network on a rainy winter day is shown in
Figure 7. The stored energy curve of each form of energy storage after optimization is shown in
Figure 8. The comparison of working time before and after the time-shiftable load optimization is shown in
Table 3.
From
Figure 7 and
Figure 8 and
Table 3, it can be found that on a cloudy rainy day in winter, the load curve of the micro energy network system before the optimization is different from the PV output curve: during the period of 7:00 to 14:00, the photovoltaic output fluctuates to a certain degree, but the load using energy is very small and there is no time-shiftable load; in the night, there is no PV output, but due to the need to increase the temperature of the greenhouse through far-infrared heating, the demand for electricity is increased. After optimization, the load curve is close to the PV output curve: in the 7:00–14:00 time period, the storage reservoir, biogas digester, and block wall with phase-change thermal storage are used to store the electric energy as other forms of energy, thereby increasing the electric load during PV peak output period. In the absence of photovoltaic output during the night, the biogas digester and block wall with phase-change thermal storage are used together to increase the temperature of the greenhouse to replace the far-infrared heating in order to reduce the electrical load at night.
Before the optimization, the micro energy network purchased 483.92 kWh from the distribution network and consumed 72.08 kWh of photovoltaic power. After optimization, the micro energy network purchased 29.86 kWh from the distribution network and consumed 277.14 kWh of photovoltaic power, increasing the amount of photovoltaic energy consumption by 205.06 kWh.
It can be seen that on a rainy winter day, the local optimization method of photovoltaic power supply provided by the facility agriculture micro energy network proposed in this work can fully utilize the energy transfer function of multiform energy storage to achieve the optimal local consumption of photovoltaic power.
(3) Cloudy day in winter
In order to analyze the influence of the final state of each form of energy storage on the next dispatching period in the previous dispatching cycle, the scenario simulation under the cloudy winter day is set to two conditions: (a) perform the simulation calculation of the cloudy day after the above-described ‘(1) Sunny day in winter’ dispatch, when the initial state of the energy storage of each form on this cloudy day is the final state of energy storage after the optimal dispatching of the sunny winter day; (b) perform the simulation calculation of the cloudy day after the above-described ‘(2) Rainy day in winter’ dispatch, when the initial state of the energy storage of each form on this cloudy day is the final state of energy storage after the optimal dispatching of the rainy winter day.
(a) Cloudy day in winter after sunny day
On a cloudy winter day after sunny day dispatching, the loads curve before and after the optimization of the photovoltaic greenhouse facility agricultural micro energy network is shown in
Figure 9. After optimization, the stored energy curve of each form of energy storage is shown in
Figure 10. The comparison of the time-shiftable load and energy storage working time before and after optimization is shown in
Table 4.
As can be seen from
Figure 9 and
Figure 10, and
Table 4, the load curve of the micro energy network system before the optimization on the winter cloudy day is very different from the PV output curve. During the daytime from 7:00 to 16:00, PV output is much greater than the load. There is no photovoltaic output during the night, but the demand for electricity at night is greater due to the need to heat the greenhouse through far-infrared heating. After optimization, the load curve is close to the PV output curve. In the 7:00–16:00 time period, during which the PV output is relatively high during the day, the reservoir, biogas digester, and block wall with phase-change thermal storage are used to store the electric energy as other forms of energy, thereby increasing the electrical load during PV peak output period. On the other hand, the working time of the ventilator is also adjusted to this time period as a supplementary adjustment of the electric load. The two together maximize the consumption of photovoltaic power during daytime hours. In the absence of photovoltaic output during the night, the biogas digester and block wall with phase-change thermal storage are used together to increase the temperature of the greenhouse to replace the far-infrared heating in order to reduce the electric load at night.
Before the optimization, the micro energy network purchased 495.91 kWh from the distribution network and consumed 57.10 kWh of photovoltaic power. After optimization, the micro energy network purchased 13.25 kWh from the distribution network and consumed 551.80 kWh of photovoltaic power, increasing the amount of photovoltaic energy consumption by 494.70 kWh.
(b) Cloudy day in winter after rainy day
The loads curve before and after the optimization of the photovoltaic greenhouse facility agricultural micro energy network on a cloudy winter day after rainy day dispatching is shown in
Figure 11. The stored energy curve of each form of energy storage after optimization is shown in
Figure 12. The comparison of the time-shiftable load and energy storage working time before and after optimization is shown in
Table 5.
After optimization, the micro energy network purchased 214.9 kWh from the distribution network and consumed 582.40 kWh of photovoltaic power, increasing the amount of photovoltaic energy consumption by 525.40 kWh.
Similarly, it can be seen from
Figure 11 and
Figure 12 and
Table 5 that the goal of maximizing the PV output can be achieved after being optimized under the cloudy day after the rainy day. In addition, comparing the optimal results of the cloudy day scenario (a) after a sunny day and (b) after the rainy day, it can be found that, due to the fact that in scenario (a), all types of energy stores have stored more energy at the end of the previous dispatching period (sunny day), the energy storages of biogas digester and block wall with phase-change thermal storage release heat for warming up the greenhouses between 0:00 and 7:00. In scenario (b), at the end of the previous dispatching period (rainy day), all types of stored energy have less energy and cannot meet the heating demand between 0:00 and 7:00. The temperature of the greenhouse can only be increased by means of far-infrared lighting, so the electricity purchased from the distribution network under scenario (b) (214.9 kWh) greatly increases compared to that under scenario (a) (13.25 kWh). Since the photovoltaic power supply basically has no output during this period, it has little effect on the amount of photovoltaic consumption, making little difference in the total amount of photovoltaic consumption between the two scenarios. From the analysis of the above two scenarios, it can be seen that the last state of the energy stores from the last dispatching period has an effect reflected mostly in the initial periods of the next dispatching period. However, in both scenarios, the optimization method proposed in this paper can be used to guide the dispatching system to make full use of the energy transfer function of multiform energy stores and the time-shiftable characteristics of agricultural loads, so as to maximize the local consumption of photovoltaic power.
(4) Analysis of photovoltaic power consumption
As shown in
Table 6, after optimization, the photovoltaic power consumption on a sunny day reached 96.1%, an increase of 52.4%. On rainy days and cloudy days under two conditions, the percentage of photovoltaic power consumption reached 86.0%, 90.7%, and 71.5%. Compared with a rainy day, the optimized PV power consumption and consumption ratio are higher than on sunny days and cloudy days because more time-shiftable loads allow greater optimization space.