User side source-load interaction energy control method and system and medium

Abstract

The invention relates to a user side source-load interaction energy control method and system and a medium, and relates to the technical field of power systems, and the method comprises the steps: obtaining the real-time working state information of user side energy equipment; according to the power consumption habit and historical data of the user, predicting the power consumption demand in a specific time period in the future, and predicting the generating capacity in combination with external environmental factors; calculating an optimal load value at each time point based on the acquired working state information of the energy equipment and the predicted power consumption demand and power generation amount; using the optimal load value to calculate a charging and discharging time table of the energy storage system, an optimal use strategy of renewable energy sources and optimal operation time of energy-consuming electric appliances; and adjusting the working state of each energy device according to the calculation result. According to the method, the economical efficiency and the self-sufficiency rate of energy use are remarkably improved, the load curve of the power grid is effectively smoothed, the stability of the power grid is enhanced, meanwhile, the total power utilization cost of a user is reduced, and the capability of dealing with emergencies is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a method, system and medium for interactive energy control of source and load on the user side. Background Art

[0002] With the wide application of renewable energy (such as solar energy, wind energy) and the development of distributed energy systems, how to effectively manage and optimize energy use has become an important research topic. Traditional power system designs mainly rely on centralized power generation and distribution models, which are not flexible enough when faced with renewable energy sources with strong volatility and obvious intermittency, and are difficult to meet the requirements of modern power grids for stability and efficiency.

[0003] Especially on the user side, since households or small commercial users have more and more distributed energy resources (such as photovoltaic power generation systems, energy storage devices, electric vehicle charging devices, etc.), the effective integration and management of these resources have become particularly important. However, existing technologies often lack a comprehensive energy management system that can comprehensively consider user electricity consumption habits, external environmental factors (such as weather forecasts), power grid operating conditions, and load curve characteristics. In addition, under extreme weather conditions, the accuracy of power generation prediction will significantly decrease, and sudden high-energy-consuming events may cause sudden changes in the load curve, and existing methods and technologies are insufficient in dealing with these problems.

[0004] Based on this, this case is proposed. Summary of the Invention

[0005] One object of the present invention is to provide a method for interactive energy control of source and load on the user side, which is used to improve the overall performance of the distributed energy system and provide more economical and efficient energy services for users.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] A method for interactive energy control of source and load on the user side includes the following steps:

[0008] S10. Obtain the real-time working state information of the user-side energy equipment;

[0009] S20. According to the user's electricity consumption habits and historical data, predict the electricity demand within a specific future time period, and combine external environmental factors to predict the power generation;

[0010] S30. Based on the obtained energy equipment working state information and the predicted electricity demand and power generation, calculate the optimal load value at each time point;

[0011] S40. Using the optimal load value, calculate the charge and discharge schedule of the energy storage system, the optimal usage strategy of renewable energy, and the optimal operation time of energy-consuming appliances;

[0012] S50. According to the calculation results, adjust the working states of each energy device and monitor its execution in real time.

[0013] Furthermore, the real-time working state information of the user-side energy devices includes, but is not limited to, the real-time power generation of the photovoltaic power generation system, the state of the energy storage system, the state of the electric vehicle charging device, the working state of household appliances, the information of the grid connection point, and the environmental sensor data.

[0014] The second object of the present invention is to provide a system based on the above user-side source-load interaction energy control method, including:

[0015] A data acquisition module for obtaining the real-time working state information of the user-side energy devices;

[0016] A prediction module for predicting the electricity demand within a specific future time period based on the user's electricity consumption habits and historical data, and predicting the power generation in combination with external environmental factors;

[0017] A primary optimization calculation module for calculating the optimal load value at each time point through an optimization algorithm according to the obtained information and prediction results;

[0018] A secondary optimization calculation module for using the optimal load value to determine the charge and discharge schedule of the energy storage system, the optimal usage strategy of renewable energy, and the optimal operation time of high-energy-consuming appliances;

[0019] A control module for adjusting the working states of each energy device according to the calculation results and monitoring its execution in real time;

[0020] A dynamic adjustment module for dynamically adjusting the work plans of each device according to the changes in the grid operation status and new user demands during the execution process.

[0021] The third object of the present invention is to provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it can implement the above user-side source-load interaction energy control method.

[0022] The advantages of the present invention are as follows: By comprehensively analyzing the real-time working status information of the user-side energy equipment, combined with the user's electricity consumption habits and external environmental factors, and using an optimization algorithm to predict future electricity consumption demand and power generation, the optimal load value at each time point is calculated. Further, through secondary optimization, the charge and discharge plan of the energy storage system, the optimal use strategy of renewable energy, and the operation time of high-energy-consuming equipment are determined, realizing the efficient management of electric power resources. The results not only significantly improve the economy and self-sufficiency rate of energy use, but also effectively smooth the grid load curve, enhance the grid stability, while reducing the user's total electricity cost and improving the ability to cope with emergencies. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic flow chart of the user-side source-load interactive energy control method in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. in the text are based on the orientation or positional relationships shown in the coordinate system of 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 should not be construed as a limitation to the present invention.

[0025] As Figure 1 shown, the present embodiment proposes a user-side source-load interactive energy control method, including the following steps:

[0026] S10. Obtain the real-time working status information of the user-side energy equipment;

[0027] S20. According to the user's electricity consumption habits and historical data, predict the electricity consumption demand within a specific future time period, and predict the power generation in combination with external environmental factors (such as weather factors);

[0028] S30. Based on the obtained working status information of the energy equipment and the predicted electricity consumption demand and power generation, calculate the optimal load value at each time point;

[0029] S40. Use the optimal load value to calculate the charge and discharge schedule of the energy storage system, the optimal use strategy of renewable energy, and the optimal operation time of energy-consuming electrical appliances;

[0030] S50. According to the calculation results, adjust the working status of each energy equipment and monitor its execution situation in real time.

[0031] In step S10, the user-side energy devices include but are not limited to photovoltaic power generation systems, energy storage systems, electric vehicle charging devices, household appliances, etc. The real-time working state information of the user-side energy devices includes but is not limited to the real-time power generation of the photovoltaic power generation system, the state of the energy storage system, the state of the electric vehicle charging device, the working state of household appliances, the information of the grid connection point, and the environmental sensor data, etc.

[0032] In step S20, the electricity demand can be predicted using time series analysis methods such as autoregressive integrated moving average model (ARIMA), LOESS for seasonal and trend decomposition (STL), etc. The prediction formula in this embodiment is a linear regression model based on historical data, as follows:

[0033]

[0034] In the formula, represents the predicted electricity demand at time t; D t-1 , D t-2 …D t-n respectively represent the actual electricity demands in the past n time periods; a, b1, b2…b n are parameters estimated by minimizing the sum of squared errors; l is the bias compensation term, which is a constant.

[0035] The power generation prediction is for the prediction of photovoltaic power generation. In addition to considering weather forecasts, factors such as solar radiation intensity, temperature, and photovoltaic module efficiency also need to be considered. This embodiment adopts the linear relationship between the output power of the photovoltaic system and the solar radiation intensity, and the formula is as follows:

[0036] P t =A×G t ×(1 - ηT t );

[0037] In the formula, P t represents the predicted power generation at time t; A is the area of the photovoltaic system; G t represents the solar radiation intensity at time t, which can be obtained from weather forecasts or historical data; ηT t is the temperature coefficient of the photovoltaic system, which can be found in the technical manual of the photovoltaic system.

[0038] In step S30, the calculation of the optimal load value at each time point is mainly achieved through an optimization algorithm, aiming to minimize the load peak deviation and the cost of smoothing the load curve. This is called the primary optimization, and the formula is as follows:

[0039]

[0040] In the formula, P peakrepresents the peak load; N is the total number of time periods; λ is a weight coefficient used to balance the relationship between the load curve smoothness and the user's electricity cost; D t represents the actual electricity demand at time t; γ is a weight coefficient used to measure the importance of the difference between the actual electricity demand and the predicted value; C is the total electricity cost of the user, including but not limited to the cost of purchasing electricity from the power grid, the revenue from selling electricity to the power grid, the loss cost of the energy storage system, and the fixed cost.

[0041] The above formula needs to use optimization algorithms (such as Mixed Integer Linear Programming MILP, Particle Swarm Optimization PSO, etc.) to solve, so as to obtain a series of values, representing the ideal load level within each time period.

[0042] Step S40 is a secondary optimization based on the optimal load value to determine the charge and discharge schedule of the energy storage system, the optimal usage strategy of renewable energy (such as photovoltaic), and the optimal operation time of high-energy-consuming electrical appliances. The secondary optimization is achieved by minimizing the deviation between the predicted power generation and the optimal load value, while considering the influence of the electricity purchase cost and the operation frequency of the energy storage system. The calculation model is as follows:

[0043]

[0044] In the formula, ∣P grid,t ∣ represents the absolute value of the electricity quantity purchased or sold from the power grid; ∣P battery,t ∣ represents the absolute value of the net output of the energy storage system; λ1 and λ2 are weight coefficients.

[0045] This formula also relies on appropriate optimization algorithms (such as Mixed Integer Linear Programming MILP, Particle Swarm Optimization PSO, etc.) to find the optimal solution. It can obtain information such as how much electricity the energy storage system should charge or discharge within each time period; when to preferentially use self-generated energy (such as solar energy) and when to store it for future use; when to use high-energy-consuming equipment (such as charging new energy vehicles) to reduce costs.

[0046] This embodiment also proposes a system based on the user-side source-load interaction energy control method described above, including:

[0047] A data acquisition module for obtaining the real-time working state information of the user-side energy equipment;

[0048] A prediction module for predicting the electricity demand within a specific future time period based on the user's electricity consumption habits and historical data, and predicting the power generation in combination with external environmental factors;

[0049] A primary optimization calculation module, which calculates the optimal load value at each time point through an optimization algorithm according to the obtained information and prediction results;

[0050] A secondary optimization calculation module, which uses the optimal load value to determine the charge and discharge schedule of the energy storage system, the optimal usage strategy of renewable energy, and the optimal operation time of high-energy-consuming electrical appliances;

[0051] A control module, which adjusts the working states of each energy device according to the calculation results and monitors their execution conditions in real time;

[0052] A dynamic adjustment module, which dynamically adjusts the work plans of each device according to the changes in the grid operation status and new user demands during the execution process.

[0053] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the user-side source-load interaction energy control method described above can be implemented.

[0054] The above embodiments are only used to explain the concept of the present invention, rather than limiting the protection scope of the rights of the present invention. Any non-substantive modification made to the present invention using this concept shall fall within the protection scope of the present invention.

Claims

1. A user-side source-load interaction energy control method, characterized in that: The following steps are involved: S10. Obtain real-time working status information of energy equipment on the user side; S20. Based on the user's electricity consumption habits and historical data, predict the electricity demand in a specific time period in the future, and predict the power generation in combination with external environmental factors; S30. Calculate the optimal load value at each time point based on the acquired energy equipment working status information and the predicted power demand and power generation; S40. Using the optimal load value, calculate the charging and discharging schedule of the energy storage system, the optimal use strategy of renewable energy, and the optimal operating time of energy-consuming electrical appliances; S50. According to the calculation results, adjust the working status of each energy device and monitor its execution in real time.

2. A user-side source-load interaction energy control method as claimed in claim 1, characterized in that: The power demand prediction formula is a linear regression model based on historical data, as follows: In the formula, represents the predicted electricity demand at time t; D t-1 , D t-2 …D t-n Respectively represent the actual electricity demand in the past n time periods; a, b1, b2…b n is the parameter estimated by minimizing the sum of squared errors; l is the deviation compensation term, which is a constant.

3. A user-side source-load interaction energy control method as claimed in claim 1, characterized in that: The power generation prediction is a prediction of photovoltaic power generation, using the linear relationship between the output power of the photovoltaic system and the solar radiation intensity. The formula is as follows: P t =A×G t ×(1-ηT t ); Where P t represents the predicted power generation at time t; A is the area of ​​the photovoltaic system; G t Represents the solar radiation intensity at time t, which can be obtained from weather forecasts or historical data; ηT t is the temperature coefficient of the photovoltaic system, which can be found in the technical manual of the photovoltaic system.

4. A user-side source-load interaction energy control method as claimed in claim 1, characterized in that: The optimal load value P at each time point t * , which aims to minimize the load peak deviation and the cost of smoothing the load curve, is given by: Where P peak represents the load peak; N is the total number of time periods; λ is a weight coefficient used to balance the relationship between the smoothness of the load curve and the user's electricity cost; D t represents the actual electricity demand at time t; γ is a weight coefficient used to measure the importance of the difference between actual electricity demand and the predicted value; C is the user's total electricity cost, including but not limited to the cost of purchasing electricity from the grid, the income from selling electricity to the grid, the loss cost of the energy storage system and the fixed cost.

5. A user-side source-load interaction energy control method as claimed in claim 1, characterized in that: The charging and discharging schedule of the energy storage system, the optimal use strategy of renewable energy, and the optimal operation time of energy-consuming appliances are determined by minimizing the deviation between the predicted power generation and the optimal load value, while considering the impact of the power purchase cost and the operation frequency of the energy storage system. The calculation model is as follows: In the formula, |P grid,t ∣ represents the absolute value of electricity purchased or sold from the power grid; ∣P battery,t ∣ represents the absolute value of the net output of the energy storage system; λ1 and λ2 are weight coefficients.

6. A user-side source-load interaction energy control method as claimed in claim 1, characterized in that: The real-time working status information of the user-side energy equipment includes but is not limited to the real-time power generation of the photovoltaic power generation system, the status of the energy storage system, the status of the electric vehicle charging device, the working status of household appliances, the information of the grid connection point and the environmental sensor data.

7. A system based on the user-side source-load interactive energy control method according to any one of claims 1 to 6, characterized in that: include: Data acquisition module, used to obtain real-time working status information of energy equipment on the user side; The prediction module predicts the electricity demand in a specific time period in the future based on the user's electricity usage habits and historical data, and predicts the power generation in combination with external environmental factors; The initial optimization calculation module calculates the optimal load value at each time point through the optimization algorithm based on the acquired information and prediction results; A secondary optimization calculation module uses the optimal load value to determine a charging and discharging schedule for the energy storage system, an optimal use strategy for renewable energy, and an optimal operating time for high-energy-consuming electrical appliances; The control module adjusts the working status of each energy device according to the calculation results and monitors its execution in real time; The dynamic adjustment module dynamically adjusts the work plan of each device according to changes in the grid operation status and new user needs during execution.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the user-side source-load interactive energy control method described in any one of claims 1 to 6 can be implemented.

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