Distributed electric energy transaction method and system based on virtual power plant

Abstract

The application provides a distributed electric energy transaction method and system based on a virtual power plant, and belongs to the technical field of energy; the method first performs scheduling and transaction among power plants; in the scheduling process among the power plants, an electric energy price and a deployment time are used as comprehensive indexes; a lowest scheduling scheme of the comprehensive indexes is obtained through calculation, and the electric energy scheduling work of the virtual power plant in a future period of time is completed through the lowest scheduling scheme of the comprehensive indexes; subsequently, transaction between the power plants and users is performed; a unit price of power supply of each power plant to a corresponding user is obtained through actual conditions of the power plants in the virtual power plant, and a total transaction price between the virtual power plant and the users is obtained in combination with total consumed electric energy of the corresponding users; the method divides the electric energy transaction of the virtual power plant into scheduling and transaction among the power plants and transaction between the power plants and the users; the process of scheduling and transaction is clearer and simpler, the scheduling algorithm is simplified, and the cost and safety of technical implementation are increased.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy, in particular to a distributed power transaction method and system based on virtual power plant. BACKGROUND

[0002] In the past few decades, traditional power systems have mainly relied on large centralized power plants, which usually use coal and natural gas as the main energy source, and transmit power to end users through long-distance power transmission networks. However, this traditional energy production and distribution model has some obvious challenges, including energy supply instability, environmental pollution, low energy efficiency, and slow response to local market demand changes.

[0003] With the advancement of technology and the promotion of policies, distributed energy systems have gradually emerged. Distributed energy refers to small-scale power generation or energy storage near consumers or directly at the consumer end, mainly including solar, wind, water, and energy storage batteries, etc. Renewable energy equipment. The advantage of these methods is that they can provide more flexible, low-carbon, and efficient power supply, reducing dependence on traditional centralized power networks. However, the uneven distribution, volatility, and unpredictability of distributed energy have brought management difficulties. In order to optimize the use of these distributed energy resources and ensure the reliability and economy of power supply, virtual power plant (VPP) technology has emerged.

[0004] Virtual power plant technology integrates multiple distributed energy resources into a unified virtual power supplier through information communication technology (ICT), and has the scheduling and control functions of centralized power plants. Virtual power plants not only optimize the power output of each distributed power unit, but also realize intelligent scheduling of energy storage devices, balance management of loads, and flexible participation in the electricity market. The core of virtual power plant is its flexible scheduling and market response capability for distributed resources, enabling these resources to participate in electricity market transactions like traditional power plants, and adjusting in real time according to demand and price changes, thereby improving the stability of the power grid, enhancing the intelligent level of the power system, and reducing the overall system cost.

[0005] In terms of electricity trading, with the development of smart grid technology and electricity market mechanisms, virtual power plants play an increasingly important role in electricity trading. Traditional electricity trading usually relies on long-term power contracts and grid scheduling between large power plants, but with the emergence of distributed energy and virtual power plants, electricity trading has become more complex and dynamic. The distributed electricity trading method of virtual power plants uses highly automated control systems to enable these distributed energy sources to flexibly buy and sell in the electricity market through real-time data transmission, market demand response, and optimized scheduling, further improving the efficiency of power resources and promoting the integration and consumption of renewable energy.

[0006] As the proportion of renewable energy increases, the power system will rely more on flexible, intelligent and efficient electricity trading systems. The distributed electricity trading method based on virtual power plants not only complements and innovates traditional power systems, but also provides important technical support for building a low-carbon, efficient and intelligent energy internet. Its promotion and application will help achieve a wider energy transition and promote the popularization of green and low-carbon technologies and the realization of sustainable development goals.

[0007] However, there are still many problems in the scheduling and trading of virtual power plants, such as overly complex scheduling algorithms, increased technical implementation costs and safety issues. Therefore, the main research direction of virtual power plant technology is to adopt technologies that can improve and solve these problems. SUMMARY

[0008] The purpose of the present application is to overcome the above problems existing in the prior art and greatly improve its technical effect on the basis of the prior art. The present application provides a distributed electricity trading method based on virtual power plants, which comprises:

[0009] S1, scheduling and trading between each power plant: the each power plant refers to a plurality of distributed energy resources distributed in different locations that make up a virtual power plant; the steps of scheduling and trading between each power plant are: S11, predicting the power generation of each power plant and the required power of the corresponding power plant user in a future period of time through a neural network algorithm, calculating the difference between the power generation of each power plant and the required power of the corresponding power plant user, and determining whether the power generation of the corresponding power plant is surplus or insufficient; the future period of time refers to a time set according to the number and state of the power plants in the virtual power plant; S12, supplying power to the power plants with insufficient capacity through scheduling of the power plants with surplus capacity obtained in S11, the scheduling taking the electricity price and the deployment time as comprehensive indicators; S13, obtaining the lowest scheduling scheme of the comprehensive indicators according to S12, and completing the electricity scheduling of the virtual power plant in the future period of time through the lowest scheduling scheme of the comprehensive indicators;

[0010] S2, transaction between the power plant and the user: the unit price of each power plant supplying power to the corresponding user is obtained through the actual situation of each power plant in the virtual power plant, and the total transaction price between the virtual power plant and the user is obtained in combination with the total power consumption of the corresponding user;

[0011] The lowest scheduling scheme of the comprehensive index comprises: assuming that there are m power generation plants with excess capacity and n power generation plants with insufficient capacity, a formula of the lowest scheduling scheme of the comprehensive index is:

[0012]

[0013] The formula is: The formula is:

[0014] In addition, i represents the i th power generation plant with excess capacity, j represents the j th power generation plant with insufficient capacity, p(i, j) represents the price of the i th power generation plant with excess capacity scheduling the j th power generation plant with insufficient capacity, t(i, j) represents the time required for the i th power generation plant with excess capacity scheduling the j th power generation plant with insufficient capacity, and a and β are weights, and x(i, j) represents the power quantity scheduled from the i th power generation plant with excess capacity to the j th power generation plant with insufficient capacity. i Y j represents the total insufficient power quantity of the j th power generation plant with insufficient capacity.

[0015] The total transaction price between the virtual power plant and the user is obtained in combination with the total power consumption of the corresponding user, and a formula of the total transaction price between the virtual power plant and the user is:

[0016]

[0017] The formula is: 总 Q a represents the unit price of the a th power plant supplying power to the user, and W a represents the total power quantity of the a th power plant supplying power to the user.

[0018] Specifically, the plurality of distributed energy resources distributed at different locations comprises: a plurality of distributed power plants distributed at different locations, and the power plants generate power by different energy resources, and the different energy resources comprise: wind energy, water energy and solar energy.

[0019] Specifically, the neural network algorithm predicts the power generation of each power plant and the electricity demand of the corresponding power plant users in the future by: extracting historical power generation data of each power plant and historical electricity demand data of the corresponding power plant users; training the historical power generation data of each power plant and the historical electricity demand data of the corresponding power plant users using the Long Short-Term Memory (LSTM) neural network algorithm to obtain LSTM models of each power plant and LSTM models of the electricity demand data of the corresponding power plant users; and finally predicting the power generation of each power plant and the electricity demand data of the corresponding power plant users in the future using the LSTM models of each power plant and the LSTM models of the electricity demand data of the corresponding power plant users.

[0020] Specifically, the calculation of the difference between the power generation of each power plant and the electricity required by the corresponding power plant users includes: determining whether the corresponding power plant has excess capacity by subtracting the electricity required by the corresponding power plant users from the power generation of the power plant; if the value of the difference between the power generation of the power plant and the electricity required by the corresponding power plant users is positive, it proves that the corresponding power plant has excess capacity; if the value of the difference between the power generation of the power plant and the electricity required by the corresponding power plant users is negative, it proves that the corresponding power plant has insufficient capacity.

[0021] Specifically, the future period refers to the time set according to the number and status of power plants in the virtual power plant, including: the more power plants in the virtual power plant, the longer the time set; the better the status of each power plant, the shorter the time set accordingly.

[0022] Specifically, the minimum dispatch scheme for obtaining comprehensive indicators includes: the minimum dispatch scheme corresponds to a scheme in which power plants with excess capacity dispatch power to power plants with insufficient capacity, and the power of virtual power plants is dispatched in accordance with the minimum dispatch scheme for a period of time in the future.

[0023] Specifically, the transactions between the power plant and the user include: each power plant in the virtual power plant has a corresponding user group, and the corresponding user group needs to provide the power plant with the fees for the electricity consumed to complete the transaction between the power plant and the user.

[0024] Furthermore, this invention also provides a distributed power trading system based on virtual power plants. This system is used to implement the aforementioned distributed power trading method based on virtual power plants. The system includes: power plant modules; a data acquisition module; a data analysis module; and a power dispatch and trading module. Each power plant module is used to convert various energy resources into electricity, including wind power, hydropower, and solar power. The data acquisition module is used to collect data from each power plant and user power consumption data. The power plant data includes historical power generation data and real-time power generation data. The user power consumption data includes historical user usage data. The data analysis module includes: capacity forecasts for each power plant in the virtual power plant and corresponding user electricity consumption forecasts; by analyzing the capacity forecasts for each power plant and the corresponding user electricity consumption forecasts, it determines whether the capacity of each power plant is excessive or insufficient; the power dispatch and trading module includes: based on the capacity excess or insufficient situation of each power plant obtained from the data analysis module, it performs dispatch and trading between the power plants in the virtual power plant using electricity price and dispatch time as comprehensive indicators; based on the unit price of electricity provided by each power plant to users and the corresponding user's electricity consumption, it calculates the total transaction price between the entire virtual power plant and users.

[0025] The beneficial effects of this invention are:

[0026] This invention presents a method and system for distributed power trading based on virtual power plants; it has the following advantages:

[0027] 1. This method divides the electricity trading of virtual power plants into: scheduling and trading between power plants, and trading between power plants and users; making the scheduling and trading process clearer and simpler, simplifying the scheduling algorithm, and increasing the cost and security issues of technical implementation.

[0028] 2. This method provides a way to determine whether the power generation of a corresponding power plant is in excess or insufficient capacity when scheduling and trading between power plants; and obtains the minimum scheduling scheme by scheduling power plants with excess capacity to power plants with insufficient capacity, taking electricity price and dispatch time as comprehensive indicators, and provides a calculation formula for obtaining the minimum scheduling scheme: The minimum scheduling scheme based on comprehensive indicators is used to complete the power dispatching work of the virtual power plant for a future period of time, so that the dispatching work can be carried out automatically and efficiently. Attached Figure Description

[0029] Figure 1 This is a flowchart of the distributed power trading method based on a virtual power plant according to the present invention.

[0030] Figure 2 This is a schematic diagram of the distributed power trading system based on a virtual power plant according to the present invention. Detailed Implementation

[0031] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the specific embodiments given herein are only for illustration and explanation of the present invention and cannot be used to limit the present invention.

[0032] It should be noted that many specific details are set forth in the following description in order to provide a full understanding of the present invention. However, the present invention may have other embodiments and variations thereof. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0033] like Figure 1 The flowchart shown is a process for a distributed energy trading method based on a virtual power plant according to an embodiment of the present invention. The flowchart includes: Step S1, scheduling and trading between power plants: Each power plant refers to multiple distributed energy resources distributed in different locations that constitute the virtual power plant; the steps for scheduling and trading between power plants are: S11, using a neural network algorithm to predict the power generation of each power plant and the electricity demand of the corresponding power plant users in the future, calculating the difference between the power generation of each power plant and the electricity demand of the corresponding power plant users, and determining whether the power generation of the corresponding power plant is in excess or insufficient; the future period refers to... The time is set according to the number and status of power plants in the virtual power plant; S12, power plants with excess capacity obtained from S11 are used to supply power to power plants with insufficient capacity, the scheduling is based on a comprehensive indicator of electricity price and dispatch time; S13, the minimum scheduling scheme of the comprehensive indicators is obtained according to S12, and the power dispatch work of the virtual power plant for a period of time is completed through the minimum scheduling scheme of the comprehensive indicators; Step S2, transaction between power plants and users: the unit price of each power plant supplying power to the corresponding user is obtained through the actual situation of each power plant in the virtual power plant, and the total transaction price between the virtual power plant and the user is obtained by combining the total electricity consumed by the corresponding user.

[0034] In the above embodiments, specifically, the method for obtaining the minimum scheduling scheme of the comprehensive index is as follows: assuming there are m power plants with excess capacity and n power plants with insufficient capacity, the formula for obtaining the minimum scheduling scheme of the comprehensive index is:

[0035]

[0036] Among them, formula formula

[0037] Additionally, i refers to the i-th power plant with excess capacity, j refers to the j-th power plant with insufficient capacity, p(i,j) refers to the price at which the i-th power plant with excess capacity dispatches electricity to the j-th power plant with insufficient capacity, t(i,j) refers to the time required for the i-th power plant with excess capacity to dispatch electricity to the j-th power plant with insufficient capacity, α and β are weights, and x(i,j) refers to the amount of electricity dispatched from the i-th power plant with excess capacity to the j-th power plant with insufficient capacity; Y i Let y represent the total surplus electricity of the i-th power plant with overcapacity. j This represents the total shortfall in electricity from the j-th power plant with insufficient capacity.

[0038] In the above embodiments, specifically, the method for obtaining the total transaction price between the virtual power plant and the user by combining the total electricity consumption of the corresponding user is as follows: The formula for the total transaction price between the virtual power plant and the user is obtained by combining the unit price of electricity supplied by each power plant to the corresponding user with the total electricity consumption of the corresponding user:

[0039]

[0040] Q 总 The total transaction price between the virtual power plant and the user is represented by 'a', where 'a' represents the a-th power plant in the virtual power plant, 'R' represents the total number of power plants in the virtual power plant, and 'K' represents the total transaction price between the virtual power plant and the user. a W represents the unit price of electricity supplied by the a-th power plant to the user. a This represents the total amount of electricity supplied to users by the a-th power plant.

[0041] In step S1, the following is also included: multiple distributed energy resources located in different locations refer to multiple distributed power plants located in different locations, wherein the power plants generate electricity using different energy resources, including wind power, hydropower and solar power.

[0042] In the above embodiments, specifically, the neural network algorithm predicts the power generation of each power plant and the electricity demand of the corresponding power plant users in the future as follows: by extracting historical power generation data of each power plant and historical electricity demand data of the corresponding power plant users, the LSTM neural network algorithm is used to train the historical power generation data of each power plant and the historical electricity demand data of the corresponding power plant users to obtain LSTM models of each power plant and LSTM models of the electricity demand data of the corresponding power plant users; finally, the power generation of each power plant and the electricity demand data of the corresponding power plant users are predicted in the future using the LSTM models of each power plant and the LSTM models of the electricity demand data of the corresponding power plant users.

[0043] In the above embodiments, specifically, calculating the difference between the power generation of each power plant and the electricity required by the corresponding power plant users means: judging whether the corresponding power plant has excess capacity by subtracting the electricity required by the corresponding power plant users from the power generation of the power plant; if the value of the power generation of the power plant minus the electricity required by the corresponding power plant users is positive, it proves that the corresponding power plant has excess capacity; if the value of the power generation of the power plant minus the electricity required by the corresponding power plant users is negative, it proves that the corresponding power plant has insufficient capacity.

[0044] In the above embodiments, specifically, the future period of time refers to the time set according to the number and status of power plants in the virtual power plant; the more power plants there are in the virtual power plant, the longer the time is set; the better the status of each power plant, the shorter the time is set accordingly.

[0045] In the above embodiments, specifically, the minimum dispatch scheme for obtaining comprehensive indicators refers to a scheme in which power plants with excess capacity dispatch power to power plants with insufficient capacity, and the power of the virtual power plant is dispatched in accordance with the minimum dispatch scheme for a period of time in the future.

[0046] In the above embodiments, specifically, the transaction between the power plant and the user refers to the following: each power plant in the virtual power plant has a corresponding user group, and the corresponding user group needs to provide the power plant with the fees consumed by the electricity to complete the transaction between the power plant and the user.

[0047] like Figure 2 The diagram shown is a schematic of a distributed power trading system based on a virtual power plant according to the present invention. The schematic includes: power plant modules S100; a data acquisition module S200; a data analysis module S300; and a power dispatch and trading module S400. Each power plant module S100 is used to convert various energy resources into electricity, including wind power, hydropower, and solar power. The data acquisition module S200 is used to collect data from each power plant and data on user power consumption. The power plant data includes historical power generation data and real-time power generation data. The user power consumption data includes historical power consumption data. The data analysis module S300 includes: capacity forecasts for each power plant in the virtual power plant and corresponding user electricity consumption forecasts; by analyzing the capacity forecasts for each power plant and the corresponding user electricity consumption forecasts, it determines whether the capacity of each power plant is excessive or insufficient; the power dispatch and trading module S400 includes: based on the capacity excess or insufficient situation of each power plant obtained from the data analysis module, it performs dispatch and trading between each power plant in the virtual power plant using electricity price and dispatch time as comprehensive indicators; based on the unit price of electricity provided by each power plant to users and the corresponding user's electricity consumption, it calculates the total transaction price between the entire virtual power plant and users.

Claims

1. A distributed power trading method based on virtual power plants, characterized in that, The method includes: S1, Scheduling and Trading between Power Plants: Each power plant refers to multiple distributed energy resources located in different places that make up the virtual power plant. The steps for scheduling and trading between power plants are as follows: S11, using a neural network algorithm to predict the power generation of each power plant and the electricity demand of the corresponding power plant users in the future, calculating the difference between the power generation of each power plant and the electricity demand of the corresponding power plant users, and determining whether the power generation of the corresponding power plant is in excess or insufficient; the future period refers to the time set according to the number and status of the power plants in the virtual power plant; S12, by scheduling the power plants with excess capacity obtained in S11 to supply power to the power plants with insufficient capacity, the scheduling uses electricity price and dispatch time as comprehensive indicators; S13, obtaining the minimum scheduling scheme based on the comprehensive indicators in S12, and completing the power scheduling work of the virtual power plant in the future period using the minimum scheduling scheme based on the comprehensive indicators. S2, Transactions between power plants and users: The unit price of electricity supplied by each power plant to the corresponding user is obtained through the actual situation of each power plant in the virtual power plant, and the total transaction price between the virtual power plant and the user is obtained by combining the total electricity consumed by the corresponding user. The minimum scheduling scheme for obtaining the comprehensive index includes: assuming there are m power plants with excess capacity and n power plants with insufficient capacity, the formula for obtaining the minimum scheduling scheme for obtaining the comprehensive index is: Among them, formula formula Additionally, i refers to the i-th power plant with excess capacity, j refers to the j-th power plant with insufficient capacity, p(i,j) refers to the price at which the i-th power plant with excess capacity dispatches electricity to the j-th power plant with insufficient capacity, t(i,j) refers to the time required for the i-th power plant with excess capacity to dispatch electricity to the j-th power plant with insufficient capacity, α and β are weights, and x(i,j) refers to the amount of electricity dispatched from the i-th power plant with excess capacity to the j-th power plant with insufficient capacity; Y i Let y represent the total surplus electricity of the i-th power plant with overcapacity. j This represents the total shortfall in electricity volume for the j-th power plant with insufficient capacity. The step of obtaining the total transaction price between the virtual power plant and the user by combining the total electricity consumption of the corresponding user includes: combining the unit price of electricity supplied by each power plant to the corresponding user with the total electricity consumption of the corresponding user, and obtaining the formula for the total transaction price between the virtual power plant and the user is as follows: Q 总 The total transaction price between the virtual power plant and the user is represented by 'a', where 'a' represents the a-th power plant in the virtual power plant, 'R' represents the total number of power plants in the virtual power plant, and 'K' represents the total transaction price between the virtual power plant and the user. a W represents the unit price of electricity supplied by the a-th power plant to the user. a This represents the total amount of electricity supplied to users by the a-th power plant.

2. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The multiple distributed energy resources located in different locations include multiple distributed power plants located in different locations, which generate electricity using different energy resources, including wind power, hydropower, and solar power.

3. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The neural network algorithm for predicting the power generation of each power plant and the electricity demand of the corresponding power plant users in the future includes: extracting historical power generation data of each power plant and historical electricity demand data of the corresponding power plant users, training the historical power generation data of each power plant and the historical electricity demand data of the corresponding power plant users using the Long Short-Term Memory (LSTM) neural network algorithm to obtain LSTM models of each power plant and LSTM models of the electricity demand data of the corresponding power plant users; and finally predicting the power generation of each power plant and the electricity demand data of the corresponding power plant users in the future using the LSTM models of each power plant and the LSTM models of the electricity demand data of the corresponding power plant users.

4. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The calculation of the difference between the power generation of each power plant and the power required by the corresponding power plant users includes: determining whether the corresponding power plant has excess capacity by subtracting the power required by the corresponding power plant users from the power generation of the power plant; if the value of the difference between the power generation of the power plant and the power required by the corresponding power plant users is positive, it proves that the corresponding power plant has excess capacity; if the value of the difference between the power generation of the power plant and the power required by the corresponding power plant users is negative, it proves that the corresponding power plant has insufficient capacity.

5. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The "future period" refers to the time set according to the number and status of power plants in the virtual power plant. The more power plants there are in the virtual power plant, the longer the time will be set; the better the status of each power plant, the shorter the time will be set accordingly.

6. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The minimum dispatch scheme for obtaining comprehensive indicators includes: the minimum dispatch scheme corresponds to a scheme in which power plants with excess capacity dispatch power to power plants with insufficient capacity, and the power of virtual power plants is dispatched in accordance with the minimum dispatch scheme for a period of time in the future.

7. The distributed power trading method based on virtual power plants according to claim 1, characterized in that, The transactions between the power plant and the user include: each power plant in the virtual power plant has a corresponding user group, and the corresponding user group needs to provide the power plant with the fees consumed by the electricity to complete the transaction between the power plant and the user.

8. A distributed power trading system based on virtual power plants, characterized in that: The system is used to implement the distributed power trading method based on a virtual power plant as described in claim 1. The system includes: power plant modules; a data acquisition module; a data analysis module; and a power dispatch and trading module. Each power plant module is used to convert various energy resources into electricity, including wind power, hydropower, and solar power. The data acquisition module is used to collect data from each power plant and data on user power consumption. The power plant data includes historical power generation data and real-time power generation data. The user power consumption data includes historical user power consumption data. The data analysis module includes: capacity prediction for each power plant in the virtual power plant and corresponding user power consumption prediction; by analyzing the capacity prediction for each power plant and the corresponding user power consumption prediction, it determines whether the capacity of each power plant is excessive or insufficient. The power dispatch and trading module includes: based on the excess or insufficient capacity of each power plant obtained from the data analysis module, it performs dispatch and trading between the power plants in the virtual power plant using electricity price and dispatch time as comprehensive indicators; based on the unit price of electricity provided by each power plant to users and the corresponding user power consumption, it calculates the total transaction price between the entire virtual power plant and users.

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