Distributed energy area cooperative control method suitable for scenic area

Abstract

The invention discloses a distributed energy regional cooperative control method suitable for a scenic spot, which is used for allocating energy in a regional power distribution system containing distributed new energy power generation and energy storage, and comprises the following steps: predicting regional visitor flow change, and predicting a load output curve of a region through data fitting; according to the load output curve, charging configuration is carried out on energy storage equipment in an energy heavy load area in advance, and a mobile energy storage device is adjusted to match a load and new energy output relation so as to realize maximized local consumption; and according to the load output curve, the charging and discharging economic policy of the new energy vehicle is adjusted, the charging price of the energy low-load area is reduced, and the reverse charging price of the full-power vehicle accessing the power grid in the power utilization peak period in the energy heavy-load area is increased. According to the method, scenic spot people flow changes are analyzed and predicted by combining news and network current affairs with big data, a regional load output curve is predicted by combining traditional data fitting to guide energy storage energy configuration, and maximum local energy consumption can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated control of electric energy storage, and in particular to a distributed energy regional coordinated control method suitable for scenic spots. Background Art

[0002] In recent years, with the advancement of science and technology, the continuous breakthroughs in power electronics technology, new energy power generation technology, and DC grid construction and power distribution technology, the application of distributed new energy, mainly wind power, photovoltaics, and energy storage, has become more and more extensive, and the power generation and power consumption of photovoltaic storage loads are mostly DC. Therefore, in order to reduce the power conversion and relief, improve the system operation efficiency, and maximize the local consumption, the research on DC buildings containing distributed new energy has become a hot topic. On the other hand, electric vehicles are developing rapidly, and the proportion of electric vehicles in vehicles is increasing; at the same time, with the emergence of big data and short video platforms, Internet celebrity cities and tourist attractions can attract a large number of people and vehicles in a short time, which will cause the load of scenic spots to change dramatically and show certain rules. How to use network information and big data, consider the flow of people brought about by the time characteristics of scenic spots and network factors, and construct the relationship between the flow of people and the load of electricity through traditional data and machine learning methods, and configure energy storage in advance based on prediction and use the mobile energy storage characteristics of electric vehicle traffic, which is of great significance for the new energy scheduling and regional autonomy of scenic spot buildings. Summary of the invention

[0003] In order to solve the defects of the above-mentioned technology, the present invention provides a distributed energy regional collaborative control method suitable for scenic spots.

[0004] The technical solution adopted by the present invention to achieve the above technical effects is:

[0005] A distributed energy regional collaborative control method applicable to scenic spots, used for energy allocation in a regional power distribution system containing distributed new energy generation and energy storage, comprising the following steps:

[0006] S1. Predict the change of regional passenger flow and fit the load output curve of the predicted area through data fitting;

[0007] S2. According to the load output curve, charge and configure the energy storage equipment in the energy heavy load area in advance, and mobilize mobile energy storage devices to match the load and new energy output relationship to achieve maximum local consumption;

[0008] S3. Adjust the economic policy of charging and discharging of new energy vehicles according to the load output curve, reduce the charging price in low energy load areas, and increase the reverse charging price for fully charged vehicles in high energy load areas when connected to the power grid during peak electricity consumption periods.

[0009] Preferably, in the above-mentioned distributed energy regional coordinated control method applicable to scenic spots, in step S1, the predicted change of regional passenger flow includes:

[0010] Based on news hotspots, the impact of online current events, traditional holiday passenger flow data, and ticket and accommodation reservation data information, analyze and predict changes in scenic spot passenger flow.

[0011] Preferably, in the above-mentioned distributed energy regional coordinated control method applicable to scenic spots, in step S1, the load output curve refers to the ratio between the predicted demand power and the energy storage power of the area in a certain period of time.

[0012] Preferably, in the above-mentioned distributed energy regional coordinated control method applicable to scenic spots, the predicted demand power includes:

[0013] Compare the power consumption of the regional distribution system during the same period, and calculate the power demand in the corresponding period based on the predicted passenger flow.

[0014] Preferably, in the above-mentioned distributed energy regional coordinated control method applicable to scenic spots, the regional power distribution system includes a regional distribution network, a power router or a mobile energy storage device arranged in the area.

[0015] Preferably, in the above-mentioned distributed energy regional collaborative control method applicable to scenic spots, in energy-heavy load areas, the time that fully-charged vehicles are connected to the power grid during peak power consumption periods is increased.

[0016] Preferably, in the above-mentioned distributed energy regional coordinated control method applicable to scenic spots, the mobile energy storage device includes an energy storage vehicle and a new energy electric vehicle with a grid reverse charging function.

[0017] The beneficial effects of the present invention are as follows: the present invention fits the load output curve of the predicted area through the regional passenger flow change data, configures the charging of energy storage equipment in the energy heavy load area according to the load output curve, and mobilizes mobile energy storage devices to match the load and new energy output relationship to achieve maximum local consumption. At the same time, by adjusting the economic policy of new energy vehicle charging and discharging, car owners who need to charge can be guided to charge in areas with sufficient new energy, which makes it easier for electric vehicles to act as mobile energy storage devices, balance regional load output, and assist in suppressing regional load processing peaks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the energy layout of a scenic spot;

[0019] Figure 2 The figure is a schematic diagram of energy layout of the present invention applied to the above-mentioned scenic spot. DETAILED DESCRIPTION

[0020] In order to further understand the present invention, the present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0021] In the description of the present invention, it should be noted that the terms "vertical", "upper", "lower", "horizontal", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, "first", "second", "third", and "fourth" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0022] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection, it can be a mechanical connection, it can be an electrical connection, it can be a direct connection, it can be connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] The embodiment of the present invention proposes a distributed energy regional coordinated control method applicable to a scenic area, which is used for the allocation of energy in a regional power distribution system containing distributed new energy generation and energy storage, and comprises the following steps:

[0024] S1. Predict the change of regional passenger flow and fit the load output curve of the predicted area through data fitting;

[0025] Among them, in step S1, the predicted changes in regional passenger flow include: analyzing and predicting changes in scenic area passenger flow based on news hotspots, the impact of online current events, and traditional holiday passenger flow data and ticket and accommodation reservation data information.

[0026] In step S1, the load output curve refers to the ratio between the predicted power demand and the energy storage power in the area during a certain period of time. The predicted power demand includes: comparing the power consumption of the regional power distribution system during the same period, and calculating the power demand in the corresponding period in combination with the predicted flow of people. The regional power distribution system includes a regional distribution network, a power router or a mobile energy storage device arranged in the area.

[0027] S2. According to the load output curve, charge and configure the energy storage equipment in the energy heavy load area in advance, and mobilize mobile energy storage devices to match the load and new energy output relationship to achieve maximum local consumption;

[0028] S3. Adjust the economic policy of charging and discharging of new energy vehicles according to the load output curve, reduce the charging price in low energy load areas, and increase the reverse charging price for fully charged vehicles in high energy load areas when connected to the power grid during peak electricity consumption periods.

[0029] Among them, in energy-heavy load areas, the time that fully charged vehicles can be connected to the power grid during peak power consumption periods can also be increased. By increasing the time that fully charged vehicles can be connected to the power grid during peak power consumption periods, more reserve power can be provided for regional energy storage facilities. In an embodiment of the present invention, the mobile energy storage device includes an energy storage vehicle and a new energy electric vehicle with a grid reverse charging function.

[0030] The present invention is applicable to the research field of distributed new energy DC building energy control in scenic spots, hot cities, etc., where the flow of people and vehicles changes greatly. Specifically, it is to fit and predict the load change based on news hot spots, factors of Internet celebrity cities, and empirical data of the flow of people in scenic spots, and then construct a load prediction curve taking into account the influence of vehicle flow and human flow based on intelligent algorithms such as machine learning, and reasonably adjust the policy to use mobile electric vehicles as mobile energy storage. Based on the effective guidance of different resource endowments in scenic spots, the load and energy storage ratio is adjusted, and the mobile energy storage is flexibly used to give the optimal ratio scheme for configuring energy storage to meet the load output demand, and assisted by the use of economic policies to guide electric vehicles to discharge to DC buildings with heavy loads as mobile energy storage to relieve load pressure. For areas with light load pressure or surplus new energy power generation, the flow of people and charging vehicles are guided to achieve the maximum local energy consumption.

[0031] The method of the present invention needs to consider time factors / spatial factors in the field of new energy, such as the season and daily illumination intensity of photovoltaics. Such as the energy storage and new energy distribution and load imbalance established in the region. In addition, attention should be paid to weather factors, such as the impact of typhoons, cloudy days, and rainy days on wind turbine photovoltaics, so as to rationally plan the discharge of energy storage. When predicting the flow of people data, it is also necessary to consider the focus of news media, the changes in the off-season and peak season of scenic spots, the holding of major conferences, holidays, golden weeks, etc., and predict the changes in flow of people and traffic through big data methods, combined with controllable data such as hotel reservation data, high-speed rail, and airplanes. On this basis, the load curve fitting of the flow of people over the years is combined to predict future load changes, and then the allocation of energy storage and new energy is optimized in combination with factors such as weather and season. At the same time, the flow of people and load are dispersed in an economic policy-guided manner. At the same time, the charging and discharging habits of some electric vehicle consumers can also be changed through policy guidance, and full-charge parking is guided through economic rewards. Car owners go to nearby areas where new energy is scarce or the load is heavy to feed power to the grid during peak hours. By guiding car owners who need to charge their cars to charge in areas with more abundant new energy, electric vehicles can act as mobile energy storage devices, balance regional load output, assist in suppressing regional load processing peaks, and realize local or nearby new energy consumption.

[0032] For ease of understanding, the principle of the present invention is described below with reference to specific examples. Figure 1 As shown in the figure, the peak season of the scenic spot is used as an example to illustrate. Figure 1 It can be seen from the traditional control and dispatching methods that the passenger flow is large near the scenic area, and the load output demand is large. However, due to the geographical location, the number of photovoltaic panels installed in areas A and B close to the mountainous areas is limited due to the shading effect and mountain factors. In order to meet the source-load matching problem, it is necessary to coordinate and control new energy and energy storage resources from area C through power line connection to achieve nearby consumption. However, new energy electric vehicles are also charged nearby and do not respond to load demand, and cannot provide auxiliary support.

[0033] According to the control strategy proposed by the present invention, Figure 2 As shown in the figure, the changes in passenger and vehicle flows can be predicted through news, online hot spots, reservation information, high-speed rail and airplane ticket purchase information, and intelligent algorithms such as big data analysis and machine learning. On this basis, the corresponding output curve can be predicted based on traditional data fitting. Then, based on the fitting results, the economic policy of electric vehicle charging and discharging is adjusted to correspond to areas with relatively rich new energy represented by Area C, encouraging low-cost charging and low-cost occupancy, guiding passenger and vehicle flows, balancing the load caused by passenger flow, and reducing the pressure on heavy-load areas in Areas A and B.

[0034] Correspondingly, for traditional heavy-load areas such as A and B, the charging and discharging economic policies are used to attract some fully charged vehicles to pass through the stagnation time, feed back energy to the grid during the peak period of electricity consumption, and charge at night to suppress the load output peak. At the same time, according to the predicted output demand, the energy storage equipment in areas A and B is charged and configured in advance, and mobile energy storage devices are mobilized to match the load and new energy output relationship to achieve maximum local consumption; if the load demand is still unsatisfied, it will be met through mutual assistance between regions to achieve local consumption.

[0035] The present invention can well realize the energy dispatching and autonomous operation of distributed new energy in scenic spots and realize the on-site consumption of new energy.

[0036] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions only describe the principles of the present invention. The present invention may have various changes and improvements without departing from the spirit and scope of the present invention, and these changes and improvements fall within the scope of the present invention to be protected, and the scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A distributed energy regional collaborative control method suitable for scenic spots, characterized in that: The method is used for the allocation of energy in a regional power distribution system containing distributed renewable energy generation and energy storage, comprising the following steps: S1. Predict the change of regional passenger flow and fit the load output curve of the predicted area through data fitting; S2. According to the load output curve, charge and configure the energy storage equipment in the energy heavy load area in advance, and mobilize mobile energy storage devices to match the load and new energy output relationship to achieve maximum local consumption; S3. Adjust the economic policy of charging and discharging of new energy vehicles according to the load output curve, reduce the charging price in low energy load areas, and increase the reverse charging price for fully charged vehicles in high energy load areas when connected to the power grid during peak electricity consumption periods.

2. The distributed energy regional collaborative control method applicable to scenic spots according to claim 1 is characterized in that: In step S1, the predicted change of the flow of people in the area includes: Based on news hotspots, the impact of online current events, traditional holiday passenger flow data, and ticket and accommodation reservation data information, analyze and predict changes in scenic spot passenger flow.

3. The distributed energy regional collaborative control method applicable to scenic spots according to claim 1 is characterized in that: In step S1, the load output curve refers to the ratio between the predicted demand power and the stored power in the area during a certain period of time.

4. The distributed energy regional collaborative control method applicable to scenic spots according to claim 3 is characterized in that: The predicted power demand includes: Compare the power consumption of the regional distribution system during the same period, and calculate the power demand in the corresponding period based on the predicted passenger flow.

5. The distributed energy regional collaborative control method applicable to scenic spots according to claim 1 is characterized in that: The regional power distribution system includes a regional distribution network, a power router or a mobile energy storage device arranged in the area.

6. The distributed energy regional collaborative control method applicable to scenic spots according to claim 1 is characterized in that: In energy-heavy areas, increase the time fully-charged vehicles are connected to the grid during peak hours.

7. The distributed energy regional collaborative control method applicable to scenic spots according to claim 5 is characterized in that: The mobile energy storage device includes an energy storage vehicle and a new energy electric vehicle with a grid reverse charging function.

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