Composite energy coupling system and control method

Abstract

The invention relates to the technical field of electric power energy distribution, and discloses a composite energy coupling system, which comprises an energy monitoring module, a data acquisition module, a data analysis module, a scheduling control module, a demand prediction module and an optimization scheduling module, the energy monitoring module is used for continuously monitoring the energy supply states of the main power supply energy, the auxiliary power supply energy and the energy storage system through a sensor and an intelligent electric meter, and carrying out real-time monitoring and data collection on the energy supply states of wind power generation, photovoltaic power generation and hydroelectric generation in the auxiliary power supply energy; and the data acquisition module is used for performing real-time acquisition on electricity demand information of first-stage power supply, second-stage power supply and third-stage power supply. Whether the energy storage system needs to be called to supply power to the main power grid or not is determined by predicting the influence of future weather on power supply from photovoltaic power generation to the energy storage system and the influence of future wind speed on power supply from wind power generation to the energy storage system through the demand prediction module, so that subsequent energy is scheduled in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy distribution, and in particular to a composite energy coupling system and a control method. Background Art

[0002] The composite energy coupling system is a system that integrates multiple energy forms. By real-time monitoring of energy supply and demand, it flexibly adjusts the proportion of different energy sources to achieve optimal energy utilization efficiency.

[0003] The existing patent discloses a new energy consumption optimization scheduling method and device (CN115936367A) that considers the sharing of carbon emission responsibilities among multiple subjects. The method includes: sharing the responsibility of carbon emissions among users according to the amount of clean electricity used by users; considering factors such as output power fluctuation and prediction accuracy to evaluate the reliability of new energy output; building models with different optimization objectives, respectively aiming to achieve safe and stable distribution network layer, regional carbon emission reduction and improve the benefits of each participant. In the technology disclosed in this patent, there is a lack of consideration of additional factors caused by photovoltaic power generation and wind power generation due to weather, sunshine factors and wind speed. Summary of the invention

[0004] The main technical problem solved by the present invention is to provide a composite energy coupling system and a control method, which solves the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, according to one aspect of the present invention, more specifically, a composite energy coupling system includes an energy monitoring module, a data acquisition module, a data analysis module, a scheduling control module, a demand forecasting module, and an optimization scheduling module;

[0006] The energy monitoring module is used to continuously monitor the energy supply status of the main power supply energy, auxiliary power supply energy and energy storage system through sensors and smart meters, and to conduct real-time monitoring and data collection on the energy supply status of wind power generation, photovoltaic power generation and hydropower generation in the auxiliary power supply energy;

[0007] The data acquisition module is used to collect the power demand information of the primary power supply, the secondary power supply and the tertiary power supply in real time;

[0008] The data analysis module is used to analyze the collected energy supply status information, the discharge amount and storage amount of the energy storage system, and the status information of the power required for the primary power supply, the secondary power supply, and the tertiary power supply;

[0009] The dispatching control module adjusts the power supply of the primary power supply, the secondary power supply, and the tertiary power supply, and adjusts the respective power supply ratios of the main power supply energy and the auxiliary power supply energy according to the analysis results of the data analysis module and the optimization results of the optimization dispatching module.

[0010] Furthermore, the demand prediction module is used to estimate the power distribution required for the primary power supply, secondary power supply and tertiary power supply based on the status information of the power required for the primary power supply, secondary power supply and tertiary power supply collected by the data acquisition module.

[0011] Furthermore, the optimization scheduling module is used to automatically optimize the usage ratio of different energy sources based on the prediction results and the current supply and demand situation and the application control and scheduling system.

[0012] Furthermore, the demand forecasting module determines whether it is necessary to call the energy storage system to supplement the power supply to the main grid based on the impact of the weather in the next day on the power supply from photovoltaic power generation to the energy storage system, and the impact of the wind speed in the next day on the power supply from wind power generation to the energy storage system, as follows:

[0013]

[0014] In the formula, C represents the probability coefficient that the energy storage system needs to supplement power supply to the power supply grid in the next day, a represents the influence coefficient of the weather on the probability of photovoltaic power generation supplying power to the energy storage system in the next day, and m represents the influence coefficient of the wind speed on the probability of wind power generation supplying power to the energy storage system in the next day.

[0015] Furthermore, the demand forecasting module determines the probability coefficient of photovoltaic power generation supplying power to the energy storage system based on the impact of the weather on photovoltaic power generation in the next day and the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, which is:

[0016]

[0017] In the formula, a represents the influence coefficient of the weather in the next day on the probability of photovoltaic power generation supplying power to the energy storage system, s represents the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, and d represents the probability of sunny weather in the next day.

[0018] Furthermore, the demand forecasting module determines the probability coefficient of wind power generation supplying power to the energy storage system based on the impact of wind speed on wind power generation in the next day and the historical average proportion of seasonal wind power generation supplying power to the energy storage system, which is:

[0019]

[0020] Where m represents the influence coefficient of wind speed in the next day on the probability of wind power supplying power to the energy storage system, u represents the historical average proportion of seasonal wind power supplying power to the energy storage system, and h represents the average wind speed in the next day.

[0021] A control method for a composite energy coupling system comprises the following steps:

[0022] S1. The energy monitoring module monitors the maximum power supply power and real-time load of the main power supply energy and the auxiliary power supply energy, and monitors the storage energy and discharge amount of the energy storage system in real time;

[0023] S2, real-time load data collection of the primary power supply, secondary power supply and tertiary power supply lines is performed through the data acquisition module;

[0024] S3. Periodically estimate the load of the primary power supply, the secondary power supply, and the tertiary power supply based on historical cycles and power consumption policies;

[0025] S4. According to the energy monitoring module's collection of energy-side information and the data collection module's reception of power consumption information from the power-consuming end, combined with the demand forecasting module's prediction of the power consumption cycle of the power-consuming end, the power supply ratio among wind power supply, photovoltaic power supply, hydropower supply, and energy storage power supply in the auxiliary power supply energy is analyzed;

[0026] S5. Control the power supply of the third-level power supply module to the second-level power supply module and the power supply of the second-level power supply module to the first-level power supply module through the scheduling control module.

[0027] Furthermore, in step S5, when the power demand at the secondary power supply end increases, the tertiary power supply module provides shunt power to the secondary power supply module; when the power demand at the primary power supply end increases, the tertiary power supply module and the secondary power supply module provide shunt power to the primary power supply module.

[0028] Beneficial effects:

[0029] 1. The present invention allows wind power, photovoltaic power and hydropower generation to supply power to the power grid simultaneously, and when the total power supply of wind power, photovoltaic power and hydropower generation exceeds the predetermined power supply, the excess power supply is charged to the energy storage system; and when the total power supply of wind power, photovoltaic power and hydropower generation is less than the predetermined power supply, the energy storage system is used to supplement the power supply, thereby ensuring the stable power supply of the power supply system.

[0030] 2. When the total power supply of wind power, photovoltaic power and hydropower generation exceeds the predetermined power supply, the excess power supply needs to be charged to the energy storage system, and the main sources of power supply and charging to the energy storage system are wind power generation and photovoltaic power generation. This can also solve the problem of unstable wind power generation and photovoltaic power generation.

[0031] 3. The present invention uses a demand forecasting module to predict the impact of future weather on the power supply from photovoltaic power generation to the energy storage system, as well as the impact of future wind speed on the power supply from wind power generation to the energy storage system, to determine whether it is necessary to call on the energy storage system to supplement the power supply to the main power grid, so as to facilitate early scheduling of subsequent energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the structure of the present invention;

[0033] Figure 2 It is a flow chart of the coupling system in the present invention. DETAILED DESCRIPTION

[0034] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0035] Example 1

[0036] like Figure 1 According to one aspect of the present invention, a control method for a composite energy coupling system is provided, comprising the following steps:

[0037] Step 1: The energy monitoring module monitors the maximum power supply power and real-time load of the main power supply energy and the auxiliary power supply energy, and monitors the storage energy and discharge amount of the energy storage system in real time;

[0038] Step 2: collect real-time load data of the primary power supply, secondary power supply and tertiary power supply lines through the data acquisition module;

[0039] Step 3: Periodically estimate the load of the primary power supply, the secondary power supply, and the tertiary power supply according to historical cycles and power consumption policies;

[0040] Step 4: Based on the collection of energy-side information by the energy monitoring module and the reception of power consumption information by the data collection module, and combined with the prediction of the power consumption cycle of the power consumption end by the demand prediction module, the power supply ratio between wind power supply, photovoltaic power supply, hydropower supply, and energy storage power supply in the auxiliary power supply energy is analyzed;

[0041] Step 5: Control the power supply from the third-level power supply module to the second-level power supply module and the power supply from the second-level power supply module to the first-level power supply module through the scheduling control module.

[0042] In step five of this embodiment, when the power demand at the secondary power supply end increases, its tertiary power supply module provides shunt power to the secondary power supply module; when the power demand at the primary power supply end increases, its tertiary power supply module and the secondary power supply module provide shunt power to the primary power supply module.

[0043] Example 2

[0044] like Figure 2 As shown, a composite energy coupling system includes an energy monitoring module, a data acquisition module, a data analysis module, a scheduling control module, a demand forecasting module and an optimization scheduling module;

[0045] The energy monitoring module is used to continuously monitor the energy supply status of the main power supply energy, auxiliary power supply energy and energy storage system through sensors and smart meters, and to monitor and collect data on the energy supply status of wind power generation, photovoltaic power generation and hydropower generation in the auxiliary power supply energy in real time; the data acquisition module is used to collect the power demand information of the primary power supply, secondary power supply and tertiary power supply in real time; the data analysis module is used to analyze the collected energy power supply status information, the discharge and storage capacity of the energy storage system, and the status information of the power required for the primary power supply, secondary power supply and tertiary power supply; the dispatching control module adjusts the power supply of the primary power supply, secondary power supply and tertiary power supply according to the analysis results of the data analysis module and the optimization results of the optimization dispatching module, and adjusts the respective power supply ratios of the main power supply energy and the auxiliary power supply energy.

[0046] In this embodiment, the demand prediction module is used to estimate the power distribution required for the primary power supply, the secondary power supply, and the tertiary power supply according to the state information of the power required for the primary power supply, the secondary power supply, and the tertiary power supply collected by the data acquisition module. The optimization scheduling module is used to automatically optimize the usage ratio of different energy sources by applying the control and scheduling system according to the prediction results and the current supply and demand situation. By allowing wind power, photovoltaic power, and hydropower generation to supply power to the power grid at the same time, and when the total power supply of wind power, photovoltaic power, and hydropower generation exceeds the predetermined power supply, the excess power supply is charged to the energy storage system; and when the total power supply of wind power, photovoltaic power, and hydropower generation is less than the predetermined power supply, the energy storage system is used to supplement the power supply, so as to ensure the stable power supply of the power supply system.

[0047] When the total power supply of wind, photovoltaic and hydropower exceeds the predetermined power supply, the excess power supply needs to be charged to the energy storage system. The main sources of power supply and charging to the energy storage system are wind power generation and photovoltaic power generation. This can also solve the problem of unstable wind and photovoltaic power generation.

[0048] Example 3

[0049] In this embodiment, the demand forecasting module determines the probability coefficient of photovoltaic power generation supplying power to the energy storage system based on the impact of the weather on photovoltaic power generation in the next day and the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, which is:

[0050]

[0051] In the formula, a represents the influence coefficient of the weather in the next day on the probability of photovoltaic power generation supplying power to the energy storage system, s represents the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, and d represents the probability of sunny weather in the next day.

[0052] When the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day is s = 65% (power supply proportion = power supply to the energy storage system ÷ total power supply), the probability of sunny weather in the next day is d = 78%, then:

[0053]

[0054] From the above calculation, it can be known that the influence coefficient of the weather in the next day on the probability of photovoltaic power generation supplying power to the energy storage system is a=39.6%.

[0055] The demand forecasting module determines the probability coefficient of wind power supplying and charging the energy storage system based on the impact of wind speed on wind power generation in the next day and the historical average proportion of seasonal wind power supplying power to the energy storage system. The probability coefficients are:

[0056]

[0057] Where m represents the influence coefficient of wind speed in the next day on the probability of wind power supplying power to the energy storage system, u represents the historical average proportion of seasonal wind power supplying power to the energy storage system, and h represents the average wind speed in the next day.

[0058] When the historical average proportion of seasonal wind power generation supplying power to the energy storage system is u = 35%, and the average wind speed in the next day is h = 5.8 (m / s), then:

[0059]

[0060] From the above calculation, it can be known that the influence coefficient of the wind speed on the probability of wind power generation supplying power to the energy storage system in the next day is m=61%.

[0061] The demand forecasting module determines whether the energy storage system needs to be called to supplement the power supply to the main grid based on the impact of the weather in the next day on the power supply from photovoltaic power generation to the energy storage system, and the impact of the wind speed in the next day on the power supply from wind power generation to the energy storage system.

[0062]

[0063] In the formula, C represents the probability coefficient that the energy storage system needs to supplement power supply to the power supply grid in the next day, a represents the influence coefficient of the weather on the probability of photovoltaic power generation supplying power to the energy storage system in the next day, and m represents the influence coefficient of the wind speed on the probability of wind power generation supplying power to the energy storage system in the next day.

[0064] When the influence coefficient of wind speed on the probability of wind power generation supplying power to the energy storage system in the next day is m=61%, the influence coefficient of weather on the probability of photovoltaic power generation supplying power to the energy storage system in the next day is a=39.6%. Then:

[0065]

[0066] From the above calculation, it can be known that the probability that the energy storage system needs to supplement power supply to the power supply grid in the next day is C=40%.

[0067] The demand forecasting module predicts the impact of future weather on the power supply from photovoltaic power generation to the energy storage system, as well as the impact of future wind speed on the power supply from wind power generation to the energy storage system, to determine whether the energy storage system needs to be called upon to supplement the power supply to the main grid, so as to facilitate the early scheduling of subsequent energy.

[0068] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A composite energy coupling system, characterized in that: It includes energy monitoring module, data acquisition module, data analysis module, dispatching control module, demand forecasting module and optimized dispatching module; The energy monitoring module is used to continuously monitor the energy supply status of the main power supply energy, auxiliary power supply energy and energy storage system through sensors and smart meters, and to conduct real-time monitoring and data collection on the energy supply status of wind power generation, photovoltaic power generation and hydropower generation in the auxiliary power supply energy; The data acquisition module is used to collect the power demand information of the primary power supply, the secondary power supply and the tertiary power supply in real time; The data analysis module is used to analyze the collected energy supply status information, the discharge amount and storage amount of the energy storage system, and the status information of the power required for the primary power supply, the secondary power supply, and the tertiary power supply; The dispatching control module adjusts the power supply of the primary power supply, the secondary power supply, and the tertiary power supply, and adjusts the respective power supply ratios of the main power supply energy and the auxiliary power supply energy according to the analysis results of the data analysis module and the optimization results of the optimization dispatching module.

2. The composite energy coupling system according to claim 1, characterized in that: The demand prediction module is used to estimate the power distribution required for the primary power supply, the secondary power supply, and the tertiary power supply based on the status information of the power required for the primary power supply, the secondary power supply, and the tertiary power supply collected by the data collection module.

3. The composite energy coupling system according to claim 1, characterized in that: The optimization scheduling module is used to automatically optimize the usage ratio of different energy sources based on the prediction results and the current supply and demand situation and the application control and scheduling system.

4. The composite energy coupling system according to claim 1, characterized in that: The demand prediction module determines whether it is necessary to call the energy storage system to supplement the power supply to the main grid based on the impact of the weather in the next day on the power supply from photovoltaic power generation to the energy storage system, and the impact of the wind speed in the next day on the power supply from wind power generation to the energy storage system. In the formula, C represents the probability coefficient that the energy storage system needs to supplement power supply to the power supply grid in the next day, a represents the influence coefficient of the weather on the probability of photovoltaic power generation supplying power to the energy storage system in the next day, and m represents the influence coefficient of the wind speed on the probability of wind power generation supplying power to the energy storage system in the next day.

5. The composite energy coupling system according to claim 4, characterized in that: The demand forecasting module determines the probability coefficient of photovoltaic power generation supplying power to the energy storage system based on the impact of the weather on photovoltaic power generation in the next day and the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, which is: In the formula, a represents the influence coefficient of the weather in the next day on the probability of photovoltaic power generation supplying power to the energy storage system, s represents the historical average proportion of photovoltaic power generation supplying power to the energy storage system during the day, and d represents the probability of sunny weather in the next day.

6. The composite energy coupling system according to claim 5, characterized in that: The demand forecasting module determines the probability coefficient of wind power generation supplying power to the energy storage system based on the impact of wind speed on wind power generation in the next day and the historical average proportion of seasonal wind power generation supplying power to the energy storage system, which is: Where m represents the influence coefficient of wind speed in the next day on the probability of wind power supplying power to the energy storage system, u represents the historical average proportion of seasonal wind power supplying power to the energy storage system, and h represents the average wind speed in the next day.

7. A control method for a composite energy coupling system according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1. The energy monitoring module monitors the maximum power supply power and real-time load of the main power supply energy and the auxiliary power supply energy, and monitors the storage energy and discharge amount of the energy storage system in real time; S2, real-time load data collection of the primary power supply, secondary power supply and tertiary power supply lines is performed through the data acquisition module; S3. Periodically estimate the load of the primary power supply, the secondary power supply, and the tertiary power supply based on historical cycles and power consumption policies; S4. According to the energy monitoring module's collection of energy-side information and the data collection module's reception of power consumption information from the power-consuming end, combined with the demand forecasting module's prediction of the power consumption cycle of the power-consuming end, the power supply ratio among wind power supply, photovoltaic power supply, hydropower supply, and energy storage power supply in the auxiliary power supply energy is analyzed; S5. Control the power supply of the third-level power supply module to the second-level power supply module and the power supply of the second-level power supply module to the first-level power supply module through the scheduling control module.

8. The control method of the composite energy coupling system according to claim 7, characterized in that: In step S5, when the power demand at the secondary power supply end increases, the tertiary power supply module provides shunt power to the secondary power supply module; when the power demand at the primary power supply end increases, the tertiary power supply module and the secondary power supply module provide shunt power to the primary power supply module.

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