Distributed production and marketing user interaction response method and system

Abstract

The invention relates to the technical field of low-voltage power distribution networks, in particular to a distributed production and marketing user interaction response method and system.The distributed production and marketing user interaction response system comprises a master station, regional gateways and a micro-grid, the regional gateways are connected with the master station, and the micro-grid comprises a micro-grid gateway, a micro-grid internal load and a distributed power source. And the micro-grid internal load and the distributed power supply are connected with the regional gateway through the micro-grid gateway. According to the distributed production and marketing user interaction response method, the micro-grid user participates, and local power generation maximum consumption and short-term prediction and day-ahead total amount balance in the whole grid range are realized through a price mechanism. And through the self-commitment of short-term prediction of the micro-grid user, punitive electricity price is given for the day-ahead prediction and the condition that the deviation between the actual electricity load and the self-commitment exceeds a threshold value. According to the method, electricity distribution and sale prices are gradually released, and the method has important significance on management improvement of regional electricity distribution and sale companies and low-voltage power distribution networks.

Description

Technical Field

[0001] The present invention relates to the technical field of low-voltage distribution network, and in particular to a distributed production and marketing user interactive response method and system. Background Art

[0002] Renewable energy sources such as solar energy have been widely used. Users can generate electricity independently by installing equipment such as photovoltaic panels. With the large-scale access of distributed power sources such as photovoltaics to the power grid, the distribution system has changed significantly, and the power grid management model and its relationship with users have also changed accordingly.

[0003] Photovoltaic power generation also has some problems, such as unstable power generation and impact on the power grid. Therefore, how to consume the electricity generated by photovoltaic power generation locally has become an important issue. There are many benefits to local consumption, including reducing the impact on the power grid, reducing transmission costs, and improving energy efficiency. At the same time, local consumption also helps promote the development of renewable energy and promote the adjustment and optimization of the energy structure.

[0004] As an important technical means to guide users to participate in grid operation and regulation and realize "source-load interaction", vigorously promoting the practical application of demand response technology is one of the inevitable choices to solve the problem of regional self-consumption of distributed energy.

[0005] The existing load management and demand response systems are mainly managed by power grid companies, and the user participation method is relatively simple. They cannot form consultations with the power grid as equal subjects and cannot well stimulate the enthusiasm of user participation.

[0006] Therefore, a distributed production and marketing user interactive response method is needed to solve the above problems. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention is proposed.

[0008] Therefore, the problem to be solved by the present invention is that the existing load management and demand response system is mainly managed by the power grid company, the user participation method is relatively single, and it cannot form consultations with the power grid as equal subjects, and cannot well stimulate the enthusiasm of user participation.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In a first aspect, an embodiment of the present invention provides a distributed production and marketing user interactive response method, which includes: constructing a distributed production and marketing user interactive response system according to a smart microgrid, wherein the distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid, wherein the regional gateway is connected to the master station, and the microgrid includes a microgrid gateway, a load in the microgrid, and a distributed power source, wherein the load in the microgrid and the distributed power source are connected to the regional gateway via the microgrid gateway;

[0011] The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway based on the load and distributed power conditions in the microgrid;

[0012] The regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines it with the regional gateway's own T0 time prediction data to derive the regional T0 time load and distributed power output curve and aggregate and report the regional T0 time data to the master station;

[0013] According to the day-ahead load and distributed generation output curve reported by the microgrid gateway, the difference between the day-ahead data and the T0 time data is compared. If the ratio of the difference to the day-ahead data exceeds the first threshold, the electricity price is increased;

[0014] The regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and after consultation within the region, reports the day-ahead deviation after consultation within the region to the master station.

[0015] As a preferred solution of the distributed production and marketing user interactive response method of the present invention, wherein: the T0 time is one week, the microgrid gateway reports the load and distributed power output curve at T0 time to the regional gateway according to the load and distributed power supply conditions in the microgrid, wherein the load and distributed power output curve at T0 time can be expressed as follows:

[0016]

[0017] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, P DG (t) is the output forecast value of distributed power generation in the regional gateway in time period t, is the power generated by distributed generation of microgrid gateway n in time period t, R DG is the weighted coefficient of the aggregated prediction value of the distributed power sources of the microgrid gateway under the jurisdiction of the regional gateway, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, P WP-DG (t) is the power generation of distributed generation in the regional gateway during time period t predicted at time T0;

[0018] P L (t) is the load power forecast value in the microgrid in time period t under the regional gateway, is the load power in microgrid gateway n during time period t, is the bias coefficient of the load power prediction value in microgrid gateway n, R L is the weighting coefficient of the aggregated forecast value of the microgrid gateway load under the jurisdiction of the regional gateway, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

[0019] As a preferred solution of the distributed production and marketing user interactive response method of the present invention, wherein: the T0 time data represents the day-ahead load and the distributed power output curve, is the power generated by distributed generation of microgrid gateway n in time period t, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, is the load power in microgrid gateway n during time period t,

[0020] when When the relative value is larger than the actual value, Less than 1;

[0021] when When the actual value is smaller, Greater than 1;

[0022] when When it matches the actual value,

[0023] when When the relative value is larger than the actual value, Less than 1;

[0024] when When the actual value is smaller, Greater than 1

[0025] when When it matches the actual value,

[0026] As a preferred solution of the distributed production and marketing user interactive response method of the present invention, wherein: the difference between the comparison date data and the T0 time data, wherein the difference can be expressed as follows:

[0027] ΔP(t)=|(P DG (t)-P L (t))-(P WP-DG (t)-P WP-L (t))|

[0028] Among them, P DG (t) is the predicted value of the distributed generation power in period t, P L (t) is the power forecast value of the load in the microgrid during period t, P WP-DG (t) is the power generation of distributed generation in the regional gateway in time period t predicted at time T0, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

[0029] 6. As a preferred solution of the distributed production and marketing user interactive response method of the present invention, wherein: the difference between the regional day-ahead data and the T0 time data is compared, and after the regional consultation, the day-ahead deviation after the consultation is reported to the master station, wherein the negotiated day-ahead deviation can be expressed by the following expression:

[0030]

[0031] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, is the power generated by distributed generation of microgrid gateway n in time period t, is the load power in microgrid gateway n during time period t, The power generation of distributed generation of microgrid gateway n in time period t predicted at time T0, is the load power in microgrid gateway n during time period t predicted at time T0, ΔP d (t) is the deviation after regional gateway aggregation.

[0032] As a preferred solution of the distributed production and marketing user interactive response method described in the present invention, when the microgrid gateway is a household microgrid gateway, the first threshold is 6%, and when the microgrid gateway is an enterprise microgrid gateway, the first threshold is 3%.

[0033] As a preferred solution of the distributed production and marketing user interactive response method of the present invention, when the difference between the load and distributed power output curve of the day before and the load and distributed power output curve at time T0 exceeds a first threshold, the electricity price is increased by 30%;

[0034] When the microgrid is a household microgrid, the loads within the microgrid include air conditioners, electric water heaters, electric heating and charging piles; when the microgrid is an enterprise microgrid, the loads within the microgrid include industrial loads, air conditioners, power lighting and energy storage; the distributed power source is distributed photovoltaic.

[0035] In a second aspect, an embodiment of the present invention provides a distributed production and marketing user interactive response system, which includes:

[0036] A construction module is used to construct a distributed production and marketing user interactive response system according to the intelligent microgrid. The distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid. The regional gateway is connected to the master station. The microgrid includes a microgrid gateway, loads in the microgrid, and distributed power sources. The loads in the microgrid and the distributed power sources are connected to the regional gateway through the microgrid gateway.

[0037] Data reporting module: The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway based on the load and distributed power conditions in the microgrid;

[0038] Data aggregation module: the regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines it with the regional gateway's own T0 time prediction data to obtain the regional T0 time load and distributed power output curve and aggregate and report the regional T0 time data to the master station;

[0039] The adjustment module compares the difference between the day-ahead data and the T0 time data according to the day-ahead load and the distributed generation output curve reported by the microgrid gateway. If the ratio of the difference to the day-ahead data exceeds a first threshold, the electricity price is increased.

[0040] In the processing module, the regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and reports the day-ahead deviation after consultation within the region to the master station.

[0041] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the distributed production and marketing user interactive response method as described in the first aspect of the present invention are implemented.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the distributed production and marketing user interactive response method as described in the first aspect of the present invention are implemented.

[0043] The beneficial effects of the present invention are as follows: the distributed production and marketing user interactive response method of the present invention achieves the maximum consumption of localized power generation and the balance of short-term forecast and day-ahead total volume within the entire network by actively participating in microgrid users and through price mechanisms. Through the self-commitment of short-term weekly forecasts of microgrid users, a punitive electricity price is given if the deviation between the day-ahead forecast, actual power load and self-commitment exceeds the threshold. This method is of great significance to the management and improvement of regional power distribution companies and low-voltage distribution networks as the distribution and sales power prices are gradually liberalized. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flow chart of a distributed production and marketing user interactive response method;

[0046] Figure 2 A diagram of a computer device for a distributed production and marketing user interactive response method;

[0047] Figure 3 A network diagram of a distributed production and marketing user interactive response method and system;

[0048] Figure 4 This is a schematic diagram of the equipment installation and deployment of Example 1. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] Example 1

[0053] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a distributed production and marketing user interactive response method, including:

[0054] S100: constructing a distributed production and marketing user interactive response system according to the smart microgrid, wherein the distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid, wherein the regional gateway is connected to the master station, and the microgrid includes a microgrid gateway, loads in the microgrid, and distributed power sources, and the loads in the microgrid and the distributed power sources are connected to the regional gateway via the microgrid gateway;

[0055] S200: The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway according to the load and distributed power conditions in the microgrid;

[0056] S300: The regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines the T0 time prediction data of the regional gateway itself to obtain the load and distributed power output curve of the regional T0 time and aggregates and reports the regional T0 time data to the master station;

[0057] S301: T0 is one week. The microgrid gateway reports the load and distributed power output curve at T0 to the regional gateway according to the load and distributed power conditions in the microgrid. The load and distributed power output curve at T0 can be expressed as follows:

[0058]

[0059] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, P DG (t) is the output forecast value of distributed power generation in the regional gateway in time period t, is the power generated by distributed generation of microgrid gateway n in time period t, R DG is the weighted coefficient of the aggregated prediction value of the distributed power sources of the microgrid gateway under the jurisdiction of the regional gateway, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, P WP-DG (t) is the power generation of distributed generation in the regional gateway during time period t predicted at time T0;

[0060] P L (t) is the load power forecast value in the microgrid in time period t under the regional gateway, is the load power in microgrid gateway n during time period t, is the bias coefficient of the load power prediction value in microgrid gateway n, R L is the weighting coefficient of the aggregated forecast value of the microgrid gateway load under the jurisdiction of the regional gateway, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

[0061] S302: T0 time data represents the day-ahead load and distributed generation output curve, is the power generated by distributed generation of microgrid gateway n in time period t, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, is the load power in microgrid gateway n during time period t,

[0062] when When the relative value is larger than the actual value, Less than 1;

[0063] when When the actual value is smaller, Greater than 1;

[0064] when When it matches the actual value,

[0065] when When the relative value is larger than the actual value, Less than 1;

[0066] when When the actual value is smaller, Greater than 1

[0067] when When it matches the actual value,

[0068] S400: According to the day-ahead load and distributed generation output curve reported by the microgrid gateway, the difference between the day-ahead data and the T0 time data is compared. If the ratio of the difference to the day-ahead data exceeds a first threshold, the electricity price is increased;

[0069] S401: Compare the difference between the day-ahead data and the T0 time data, where the difference can be expressed as follows:

[0070] ΔP(t)=|(P DG (t)-P L (t))-(P WP-DG (t)-P WP-L (t))|

[0071] Among them, P DG (t) is the predicted value of the distributed generation power in period t, P L (t) is the power forecast value of the load in the microgrid during period t, P WP-DG (t) is the power generation of distributed generation in the regional gateway in time period t predicted at time T0, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

[0072] S402: When the microgrid gateway is a household microgrid gateway, the first threshold is 6%. When the microgrid gateway is an enterprise microgrid gateway, the first threshold is 3%.

[0073] For a home microgrid, it is recommended to set the first threshold value within the range of 5%-8%. In this embodiment, 6% is selected as the threshold value for the following reasons:

[0074] A rate lower than 5% may be too strict and may not be conducive to user participation;

[0075] If it is higher than 8%, it may affect the accuracy of power grid dispatch;

[0076] The 6% threshold strikes a good balance between ensuring prediction accuracy and encouraging user participation.

[0077] For enterprise microgrids, it is recommended to set the first threshold value in the range of 2%-4%. In this embodiment, 3% is selected as the threshold value for the following reasons:

[0078] Enterprises use a large amount of electricity, which has a more significant impact on the power grid and requires stricter control;

[0079] The 3% threshold can ensure the accuracy of the enterprise's forecast without causing excessive pressure on the enterprise.

[0080] S403: When the difference between the load and distributed power output curve of the day before and the load and distributed power output curve of the time T0 exceeds the first threshold, the electricity price is increased by 30%;

[0081] When the microgrid is a household microgrid, the loads within the microgrid include air conditioners, electric water heaters, electric heating and charging piles. When the microgrid is an enterprise microgrid, the loads within the microgrid include industrial loads, air conditioners, power lighting and energy storage; the distributed power source is distributed photovoltaics.

[0082] S500: The regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and reports the day-ahead deviation after consultation within the region to the master station.

[0083] S501: Compare the difference between the regional day-ahead data and the T0 time data, and report the day-ahead deviation after consultation in the region to the master station. The day-ahead deviation after consultation can be expressed as follows:

[0084]

[0085] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, is the power generated by distributed generation of microgrid gateway n in time period t, is the load power in microgrid gateway n during time period t, The power generation of distributed generation of microgrid gateway n in time period t predicted at time T0, is the load power in microgrid gateway n during time period t predicted at time T0, ΔP d (t) is the deviation after regional gateway aggregation.

[0086] T0 time: refers to the time span of one week, which is used for short-term load and distributed power output forecasting. Choosing one week as the forecast period can balance the forecast accuracy and computational complexity.

[0087] Microgrid gateway: An interface device that connects the internal devices of the microgrid with the external network, with data collection, processing and communication functions.

[0088] Bias coefficient: used to correct the deviation between the predicted value and the actual value to improve the prediction accuracy.

[0089] The microgrid gateway reports T0 time data: providing basic data for the regional gateway to achieve bottom-up data aggregation.

[0090] The regional gateway aggregates and reports data: integrating the microgrid information in the region to provide a basis for the main station’s decision-making.

[0091] Compare the day-ahead data with the T0 time data: evaluate the forecast deviation and provide a basis for electricity price adjustment.

[0092] Intra-regional consultation: Promote the optimal allocation of power resources within the region and improve energy utilization efficiency.

[0093] This differentiated threshold setting fully considers the characteristics of different types of users, which is conducive to improving the overall prediction accuracy and system operation efficiency. By introducing these mechanisms, the method of the present invention can effectively motivate users to actively participate in demand response, improve the local consumption rate of distributed energy, realize the benign interaction between the power grid and users, and lay the foundation for building a more efficient and flexible smart grid.

[0094] Furthermore, this embodiment also provides a distributed production and marketing user interactive response system, including:

[0095] A construction module is used to construct a distributed production and marketing user interactive response system according to the intelligent microgrid. The distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid. The regional gateway is connected to the master station. The microgrid includes a microgrid gateway, loads in the microgrid, and distributed power sources. The loads in the microgrid and the distributed power sources are connected to the regional gateway through the microgrid gateway.

[0096] Data reporting module: The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway based on the load and distributed power conditions in the microgrid;

[0097] Data aggregation module: the regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines it with the regional gateway's own T0 time prediction data to obtain the regional T0 time load and distributed power output curve and aggregate and report the regional T0 time data to the master station;

[0098] The adjustment module compares the difference between the day-ahead data and the T0 time data according to the day-ahead load and the distributed generation output curve reported by the microgrid gateway. If the ratio of the difference to the day-ahead data exceeds a first threshold, the electricity price is increased.

[0099] In the processing module, the regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and reports the day-ahead deviation after consultation within the region to the master station.

[0100] This embodiment also provides a computer device, which is suitable for the distributed production and marketing user interactive response method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the distributed production and marketing user interactive response method proposed in the above embodiment.

[0101] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0102] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the distributed production and marketing user interactive response method proposed in the above embodiment is implemented.

[0103] In summary, the distributed production and marketing user interactive response method and system proposed in the present invention realizes effective interaction between microgrid users, regional gateways and master stations by establishing a multi-level data reporting and processing mechanism. The core advantage of this method is that it introduces a short-term prediction mechanism based on T0 time (one week), and combines differentiated threshold settings and electricity price adjustment strategies to effectively motivate users to actively participate in demand response.

[0104] Specifically, this method reports the load and distributed power output curve at T0 time through the microgrid gateway, providing accurate short-term forecast data for the regional gateway and the master station. By comparing the difference between the day-ahead data and the T0 time data and setting a reasonable threshold (6% for household microgrids and 3% for enterprise microgrids), the system can promptly detect significant deviations and take corresponding electricity price adjustment measures. This mechanism not only improves the forecast accuracy, but also provides users with clear economic incentives to encourage them to participate more actively in demand response.

[0105] At the same time, this method also introduces an intra-regional consultation mechanism, allowing the regional gateway to perform internal optimization before reporting data to the master station, which further improves the flexibility and efficiency of the system. Through this multi-level and multi-dimensional interactive response mechanism, the present invention effectively improves the local absorption rate of distributed energy, improves the supply and demand balance of the power grid, and provides strong support for the gradual liberalization of distribution and sales electricity prices and the realization of carbon emission reduction targets.

[0106] In summary, this embodiment not only optimizes the grid operation efficiency, but also promotes user participation, making an important contribution to building a smarter and greener modern power system.

[0107] Example 2

[0108] Reference Figure 1 - Figure 4 , which is the second embodiment of the present invention, and this embodiment provides a distributed production and marketing user interactive response method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0109] In order to verify the actual effect of the distributed production and marketing user interactive response method proposed in this invention, a typical residential area in a provincial capital city was selected as the test object. The residential area includes 500 household users and 10 small commercial users, with a total installed capacity of 2MW distributed photovoltaic system. The test period is from July 1, 2023 to September 30, 2023, a total of 92 days.

[0110] During the test preparation phase, all users in the residential area were first upgraded with smart meters and home energy management systems to ensure that each household had the ability to collect and report real-time electricity data. At the same time, high-performance edge computing devices were installed at the regional level as regional gateways for data aggregation and preliminary processing. The master station was deployed in the data center of the local power company and equipped with big data analysis and artificial intelligence prediction models.

[0111] During the implementation process, the system operates strictly in accordance with the method of the present invention:

[0112] T0 time prediction: Every Sunday at 8 pm, the system automatically triggers the load and distributed power output prediction for T0 time (one week). The prediction model comprehensively considers multiple factors such as historical data, weather forecasts, holiday information, etc., and uses deep learning algorithms to generate high-precision prediction results.

[0113] Data reporting today: At 10 p.m. every day, the smart systems of each household and commercial user report the next day's 24-hour electricity consumption plan and photovoltaic power generation expectations to the regional gateway based on the electricity consumption plan set by the user.

[0114] Data aggregation and analysis: After receiving the day-ahead data from all users, the regional gateway combines the T0 time forecast results to calculate the overall power consumption plan and power generation expectations at the regional level, and reports them to the master station before 11 pm every day.

[0115] Deviation calculation and electricity price adjustment: The system monitors actual electricity consumption and generation in real time and calculates the deviation from the day-ahead plan every hour. For household users, if the deviation exceeds 6%, the tiered electricity price mechanism is activated; for commercial users, the threshold is set at 3%.

[0116] Internal regional optimization: When the deviation is about to exceed the threshold, the regional gateway will first try to transfer and peak load within the region, such as notifying some users to delay the use of high-power appliances, or activating the energy storage system for regulation.

[0117] Demand response incentives: For users who actively participate in demand response, the system will record their contributions and give them electricity bill discounts or green energy points rewards at the end of the month.

[0118] Continuous optimization: The system evaluates and optimizes the prediction model and response strategy once a week to continuously improve prediction accuracy and response efficiency.

[0119] After 92 days of continuous operation, a large amount of valuable data was collected. The following table shows the comparison of the key indicators between the method of the present invention (Scheme A) and the traditional demand response method (Scheme B):

[0120]

[0121] By comparing and analyzing the data in the table, it can be clearly seen that the distributed production and marketing user interactive response method (Scheme A) proposed in the present invention has significant advantages and innovations compared with the traditional demand response method (Scheme B):

[0122] The prediction accuracy is greatly improved: the average error rate of load prediction of Scheme A is 3.2%, which is 57.3% lower than 7.5% of Scheme B; the average error rate of distributed generation prediction is also reduced from 9.2% to 4.1%, an increase of 55.4%. This significant improvement is mainly due to the T0 time (one week) short-term prediction mechanism introduced by the present invention, combined with the deep learning algorithm, which can more accurately capture the short-term changes in electricity consumption and power generation patterns.

[0123] The load curve smoothing effect is obvious: the peak-to-valley difference is reduced from 680kW to 420kW, an improvement of 38.2%. This shows that the method of the present invention can more effectively realize load transfer and peak-to-valley filling, providing strong support for the stable operation of the power grid.

[0124] Improved utilization efficiency of distributed energy: The local consumption rate increased from 62.3% to 78.5%, an increase of 26.0%. This improvement fully reflects the unique advantages of the present invention in promoting the local consumption of distributed energy, which is conducive to reducing power grid transmission losses and improving overall energy utilization efficiency.

[0125] User participation has been significantly improved: the average number of responses per week has increased from 1.8 to 4.3 times, an increase of 138.9%. This shows that the differentiated threshold setting and flexible incentive mechanism of the present invention can effectively mobilize the enthusiasm of user participation and lay a good mass foundation for the in-depth development of demand response.

[0126] Outstanding economic benefits: the system dispatching cost reduction ratio increased from 6.2% to 15.7%, an increase of 153.2%; the average electricity bill savings of users increased from 5.1% to 12.3%, an increase of 141.2%. These data strongly prove that the present invention can not only optimize the operation of the power grid, but also bring considerable economic benefits to power companies and users, achieving a win-win situation for all parties.

[0127] Improved system response speed and flexibility: Although not directly reflected in the table, through the internal optimization mechanism of the region, the method of the present invention can quickly make local adjustments when the deviation is about to exceed the threshold, avoiding the problem of frequent triggering of global responses in traditional methods, and greatly improving the response speed and flexibility of the system.

[0128] In summary, the distributed production and marketing user interactive response method proposed in the present invention has shown significant innovation and advantages in many aspects such as prediction accuracy, load management, distributed energy utilization, user participation and economic benefits. This method based on multi-level data processing, differentiated threshold setting and flexible incentive mechanism not only effectively solves the problems of low prediction accuracy and insufficient user participation in traditional demand response systems, but also provides a new technical path for building a more intelligent, efficient and sustainable modern power system.

[0129] Example 3

[0130] Reference Figure 1 - Figure 4 , which is the second embodiment of the present invention.

[0131] The distributed production and sales user interactive response system of the present invention is used for regional power distribution companies and includes a master station, a regional gateway / coordinator, a home microgrid and an enterprise microgrid. The home microgrid includes a microgrid gateway, household electrical equipment, energy storage, distributed photovoltaic and other facilities, and the equipment that can participate in the supply and demand interaction in the home includes air conditioners, electric water heaters, electric heating, charging piles (electric vehicles), etc. The enterprise microgrid includes a microgrid gateway, industrial loads, air conditioners, power lighting, energy storage, distributed photovoltaic and other facilities.

[0132] The home microgrid gateway is installed at the meter box, and manages the residents' own distributed photovoltaics, electric vehicle charging piles, and indoor electricity. The enterprise microgrid is installed at the enterprise transformer, and manages the enterprise's internal distributed photovoltaics, energy storage, electric vehicle charging piles, and production and office electricity.

[0133] At the 10kV outlet of the distribution point company, a regional gateway / coordinator is installed to coordinate and manage the residential microgrid users, residential users and enterprise microgrid users under its jurisdiction under the 10kV outlet line.

[0134] The present invention allows users to actively participate in the supply and demand interaction and demand response of the power grid, reach a commitment with the power grid based on the short-term forecast of power generation and power consumption of their own microgrid, and then negotiate based on the actual power generation and power consumption situation in the past few days, and comprehensively balance from multiple angles such as the comfort and economy of their own power consumption, and finally give a corresponding degree of participation to achieve a balance between benefits and power comfort.

[0135] The distributed production, marketing and user interactive response system of the present invention includes a master station, a regional gateway / coordinator, a home microgrid and an enterprise microgrid. The home microgrid includes a microgrid gateway, household electrical equipment, energy storage, distributed photovoltaic and other facilities, and the equipment that can participate in the supply and demand interaction in the home includes air conditioners, electric water heaters, electric heating, charging piles (electric vehicles), etc. The enterprise microgrid includes microgrid gateways, industrial loads, air conditioners, power lighting, energy storage, distributed photovoltaic and other facilities.

[0136] The microgrid gateway is used to collect power generation and energy consumption information of renewable energy power generation, energy storage equipment, charging piles, and electrical equipment within the microgrid, and conduct short-term, day-ahead, and real-time forecasts of power generation and energy consumption. Based on the comprehensive balance of its own power consumption from multiple angles such as comfort and economy, combined with the forecast data, the short-term energy consumption curve and power generation curve are fed back to the superior equipment regional gateway / coordinator, and this is taken as a commitment; at the same time, the day-ahead forecast is compared with the short-term forecast to form an energy gap / surplus curve, and the upper and lower adjustment range of the electricity price that the user is willing to bear is given according to the gap situation, which is used for the superior regional gateway to conduct balance and consultation within the jurisdiction, so as to achieve the balance of each residential microgrid and industrial microgrid.

[0137] The regional gateway / coordinator is used to communicate with the upper master station and receive the short-term energy consumption curve and power generation output curve sent by the lower microgrid gateway. It summarizes the energy consumption and power generation output curves of the users under its jurisdiction, makes predictions based on its own node historical data, and makes margin adjustments based on the consultation and adjustment of historical date data to form the energy consumption curve and power generation output curve of the regional gateway. It also coordinates within the region based on the day-ahead data and consultation information sent by each microgrid gateway. If it is found that the problem cannot be solved through balance within the region, it will be reported to the master station for consultation and resolution on a larger scale.

[0138] As the management center of the entire system, the master station realizes data collection, data analysis, electricity consumption and power generation forecasts in all links, provides a portrait of the energy usage habits and preferences of each microgrid user, and classifies each microgrid user from multiple angles such as comfort and economy, ensuring that the local consumption of new energy is maximized within the area under the jurisdiction of the regional gateway / coordinator, and maximizing the balance between short-term forecasts and current total amounts across the entire network.

[0139] See also Figure 1-4 As shown, the distributed production and marketing user interactive response method of the present invention includes the following steps:

[0140] Step 1: The microgrid gateway reports the load and distributed photovoltaic output curves for the past week based on the load and distributed photovoltaic conditions in the microgrid;

[0141] Step 2: The regional gateway / coordinator obtains the load and output curves of the region in the past week based on the load and output curves reported by the microgrid gateways under its jurisdiction and the load and output curves of distributed power sources predicted by the regional gateway, and reports the load and output curves of distributed photovoltaic power sources in the past week to the master station;

[0142] In step 2, the regional gateway / coordinator can make its own load and PV output forecast for the past week based on its own load and output history data, and perform weighted processing based on the data reported by each microgrid to obtain the weighted load and PV output forecast, and report it to the master station based on this.

[0143]

[0144] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, P DG (t) is the predicted value of distributed photovoltaic output in time period t at the regional gateway, is the predicted value of distributed photovoltaic power generation power of microgrid gateway n in time period t, R DG is the weighting coefficient of the aggregated prediction value of distributed photovoltaics of the microgrid gateway under the jurisdiction of the regional gateway, is the bias coefficient of the predicted value of distributed photovoltaic output of microgrid gateway n, P WP-DG (t) is the distributed photovoltaic power generation power in the regional gateway during the weekly forecast period t;

[0145] P L (t) is the load power forecast value in the microgrid in time period t under the regional gateway, is the power forecast value of the load in microgrid gateway n during time period t, is the bias coefficient of the load power prediction value in microgrid gateway n, R L is the weighting coefficient of the aggregated forecast value of the microgrid gateway load under the jurisdiction of the regional gateway, P WP-L The power of the load in the regional gateway in the weekly forecast period t.

[0146] Among them, when When the relative value is larger than the actual value, Less than 1, when When the actual value is smaller, Greater than 1, when When it matches the actual value, when When the relative value is larger than the actual value, Less than 1, when When the actual value is smaller, Greater than 1, when When it matches the actual value,

[0147] Step 3: The master station determines the load and output curves of the jurisdiction in the past week based on the load and output curves reported by the regional gateway;

[0148] Among them, the master station compares the deviation between the load and output curves reported by the regional gateway and the weekly forecast based on the historical forecasts of each regional gateway / coordinator and the price sensitivity coefficient of each time period on the previous day, and comprehensively considers the deviation, local consumption of new energy, and economy, and performs weighted processing to determine the load and output curves for the past week within the jurisdiction.

[0149] Step 4: The microgrid gateway reports the day-ahead load and output curve based on recent energy consumption and photovoltaic output. When the difference with the forecast for the past week exceeds a threshold of 1, the negotiation conditions are given;

[0150] ΔP(t)=|(P DG (t)-P L (t))-(P WP-DG (t)-P WP-L (t))|

[0151] Among them, P DG (t) is the predicted value of distributed photovoltaic power generation power in time period t, P L (t) is the power forecast value of the load in the microgrid during period t, P WP-DG (t) is the distributed photovoltaic power generation power in the weekly forecast period t, P WP-L is the power of the load in the microgrid during the weekly forecast period t.

[0152] Negotiation conditions: Threshold 1 in step 4 is 6% for residential microgrids and 3% for enterprise microgrids, that is, when the weekly forecast value of microgrid users does not exceed the day-ahead forecast (1+threshold 1), no punitive treatment will be carried out. When it exceeds threshold 1, the excess part needs to bear a 30% punitive electricity price increase, and the reduced part still pays electricity fees according to the weekly forecast results. When it exceeds threshold 2, that is, ±15%, a larger punitive electricity price will be borne.

[0153] Step 5: The regional gateway / coordinator first conducts intra-regional consultation based on the day-ahead load and output curve reported by the microgrid gateways under its jurisdiction and the overall deviations of the day-ahead and the past week, and reports the day-ahead deviation after consultation in the region;

[0154]

[0155] Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, is the distributed photovoltaic power generation power of microgrid gateway n in time period t, is the load power in microgrid gateway n during time period t, The distributed photovoltaic power generation power of microgrid gateway n in the weekly forecast period is is the load power in microgrid gateway n during the weekly forecast period t, ΔP d (t) is the deviation after regional gateway aggregation.

[0156] Step 6: The master station compares the total daily average load and output of the area under its jurisdiction with the weekly forecast, and performs intra-regional balance based on the consultation strategy to achieve the consumption of new energy and load balance.

[0157] The distributed production and marketing user interactive response method of the present invention achieves the maximum consumption of localized new energy and the balance of short-term prediction and total amount on the day before in the whole network by allowing microgrid users to actively participate and through the price mechanism.

[0158] Through the self-commitment of microgrid users' short-term weekly forecasts, a certain amount of electricity fee rewards will be given to those with a high degree of compliance between the day-ahead forecast and actual power load and the self-commitment, and a punitive electricity price will be given for deviations exceeding the threshold. Appropriate rewards will be given to distributed photovoltaics that can be fully consumed in the microgrid or fully consumed in the region.

[0159] The method and system are of great significance for improving the management of regional power distribution companies and low-voltage distribution networks as the power distribution prices are gradually liberalized.

[0160] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distributed production and marketing user interactive response method, characterized by: include, According to the intelligent microgrid, a distributed production and marketing user interactive response system is constructed. The distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid. The regional gateway is connected to the master station. The microgrid includes a microgrid gateway, loads in the microgrid, and distributed power sources. The loads in the microgrid and the distributed power sources are connected to the regional gateway through the microgrid gateway. The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway based on the load and distributed power conditions in the microgrid; The regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines it with the regional gateway's own T0 time prediction data to obtain the regional T0 time load and distributed power output curve and aggregate and report the regional T0 time data to the master station; According to the day-ahead load and distributed generation output curve reported by the microgrid gateway, the difference between the day-ahead data and the T0 time data is compared. If the ratio of the difference to the day-ahead data exceeds the first threshold, the electricity price is increased; The regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and after consultation within the region, reports the day-ahead deviation after consultation within the region to the master station.

2. The distributed production and marketing user interactive response method according to claim 1, characterized in that: The T0 time is one week. The microgrid gateway reports the load and distributed power output curve at T0 time to the regional gateway according to the load and distributed power conditions in the microgrid. The load and distributed power output curve at T0 time can be expressed as follows: Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, P DG (t) is the output forecast value of distributed power generation in the regional gateway in time period t, is the power generated by distributed generation of microgrid gateway n in time period t, R DG is the weighted coefficient of the aggregated prediction value of the distributed power sources of the microgrid gateway under the jurisdiction of the regional gateway, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, P WP-DG (t) is the power generation of distributed generation in the regional gateway during time period t predicted at time T0; P L (t) is the load power forecast value in the microgrid in time period t under the regional gateway, is the load power in microgrid gateway n during time period t, is the bias coefficient of the load power prediction value in microgrid gateway n, R L is the weighting coefficient of the aggregated forecast value of the microgrid gateway load under the jurisdiction of the regional gateway, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

3. The distributed production and marketing user interactive response method according to claim 2, characterized in that: The T0 time data represents the day-ahead load and distributed generation output curve. (t) is the power generation of distributed generation of microgrid gateway n in time period t, is the bias coefficient of the output prediction value of the distributed generation of the microgrid gateway n, (t) is the power of the load in microgrid gateway n during period t, when When (t) is larger than the actual value, Less than 1; when (t) is smaller than the actual value. Greater than 1; when When (t) matches the actual value, when When (t) is larger than the actual value, Less than 1; when (t) is smaller than the actual value. Greater than 1 when When (t) matches the actual value, 4. The distributed production and marketing user interactive response method according to claim 3, characterized in that: The difference between the data before the day and the data at T0 is compared, wherein the difference can be expressed as follows: ΔP(t)=|(P DG (t)-P L (t))-(P WP-DG (t)-P WP-L (t))| Among them, P DG (t) is the predicted value of the distributed generation power in period t, P L (t) is the power forecast value of the load in the microgrid during period t, P WP-DG (t) is the power generation of distributed generation in the regional gateway in time period t predicted at time T0, P WP-L The power of the load in the regional gateway in period t predicted at time T0.

5. The distributed production and marketing user interactive response method according to claim 4, characterized in that: The difference between the regional day-ahead data and the T0 time data is compared, and after consultation within the region, the day-ahead deviation after consultation within the region is reported to the master station, wherein the day-ahead deviation after consultation can be expressed as follows: Where M is the number of microgrid gateways under the jurisdiction of the regional gateway, is the power generated by distributed generation of microgrid gateway n in time period t, is the load power in microgrid gateway n during time period t, The power generation of distributed generation of microgrid gateway n in time period t predicted at time T0, is the load power in microgrid gateway n during time period t predicted at time T0, ΔP d (t) is the deviation after regional gateway aggregation.

6. The distributed production and marketing user interactive response method according to claim 5, characterized in that: When the microgrid gateway is a household microgrid gateway, the first threshold is 6%. When the microgrid gateway is an enterprise microgrid gateway, the first threshold is 3%.

7. The distributed production and marketing user interactive response method according to claim 6, characterized in that: When the difference between the load and distributed power output curve on the day before and the load and distributed power output curve at time T0 exceeds the first threshold, the electricity price is increased by 30%; When the microgrid is a household microgrid, the loads within the microgrid include air conditioners, electric water heaters, electric heating and charging piles; when the microgrid is an enterprise microgrid, the loads within the microgrid include industrial loads, air conditioners, power lighting and energy storage; the distributed power source is distributed photovoltaic.

8. A distributed production and marketing user interactive response system, based on the distributed production and marketing user interactive response method according to any one of claims 1 to 7, characterized in that: Also includes, A construction module is used to construct a distributed production and marketing user interactive response system according to the intelligent microgrid. The distributed production and marketing user interactive response system includes a master station, a regional gateway, and a microgrid. The regional gateway is connected to the master station. The microgrid includes a microgrid gateway, loads in the microgrid, and distributed power sources. The loads in the microgrid and the distributed power sources are connected to the regional gateway through the microgrid gateway. Data reporting module: The microgrid gateway reports the load and distributed power output curve at time T0 to the regional gateway based on the load and distributed power conditions in the microgrid; Data aggregation module: the regional gateway aggregates the T0 time data reported by the subordinate microgrid gateways and combines it with the regional gateway's own T0 time prediction data to obtain the regional T0 time load and distributed power output curve and aggregate and report the regional T0 time data to the master station; The adjustment module compares the difference between the day-ahead data and the T0 time data according to the day-ahead load and the distributed generation output curve reported by the microgrid gateway. If the ratio of the difference to the day-ahead data exceeds a first threshold, the electricity price is increased. In the processing module, the regional gateway aggregates the day-ahead data of the subordinate microgrid gateways, and reports the day-ahead load and distributed generation output curve of the region, compares the difference between the regional day-ahead data and the T0 time data, and reports the day-ahead deviation after consultation within the region to the master station.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed production and marketing user interactive response method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed production and marketing user interactive response method described in any one of claims 1 to 7 are implemented.

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