Day-ahead dynamic time-sharing pricing method for high-energy-consumption industry

Abstract

The invention relates to a day-ahead dynamic time-sharing pricing method for a high-energy-consumption industry, and the method comprises the steps: carrying out the classification and statistics of the load data of a power grid side of a high-energy-consumption enterprise, and carrying out the investigation and analysis of a client side, thereby mastering the flexibility of the enterprise for the power consumption time period and the sensitivity of the electricity price under different day types; then, constructing a time-sharing load-time-sharing electricity price mapping model of the high-energy-consumption enterprise in different day types; then, based on the mapping model, according to the cost-benefit characteristics of the new energy and the high-energy-consumption enterprises, making a clearing mechanism for day-ahead collaborative optimization of the high-energy-consumption enterprises, and performing quotation and clearing from the enterprises based on the real cost-benefit; then, price is adopted as a decision variable, a county power grid new energy and high-energy-consumption industry enterprise collaborative safety constraint economic dispatching marginal electricity price clearing model is constructed, and finally, a mature stable branch and bound method and other solving algorithms are adopted, and a high-energy-consumption industry enterprise day-ahead dynamic time-sharing pricing model is constructed; the method has the advantages of high efficiency, reasonability and good use effect.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power grids, and in particular relates to a day-ahead dynamic time-of-use pricing method for high-energy-consuming industries. Background Art

[0002] For a long time, in terms of power supply, although a lot of technical research and application have been carried out on both the power supply side and the grid side, with the continuous growth of power load and the continuous improvement of the penetration rate of new energy, the regional power grid has gradually shown new characteristics such as dual uncertainty of source and load, difficulty in balancing supply and demand during different periods of time, and increased peak-to-valley differences. The power supply and demand has gradually shifted from overall balance to overall tension, facing the overall situation of "tight balance of the entire network, local gaps, and difficult supply guarantee". If extreme weather or external electricity is less than expected, the power supply gap will further increase, and the power supply is facing multiple challenges; especially for industrial county power grids dominated by high-energy-consuming enterprises, they face the problems of source-load time-based supply and demand balance and power The problem of ensuring supply is more prominent, and load resources are urgently needed to participate in coordinated operation. At present, a large amount of research and application on demand response, orderly power consumption, interruptible load and other aspects have been carried out in terms of load-side resource participation in coordinated operation. However, load-side resources still have the problems of great difficulty in implementation and low user enthusiasm, especially the phenomenon of seasonal power supply gap is becoming more and more prominent. Therefore, in order to correctly guide high-energy-consuming industries and enterprises to optimize peak and valley power consumption, solve the mismatch between the peak and valley periods of time-of-use electricity prices and the peak and valley periods of high-energy-consuming industries and enterprises, and dynamically adapt to the problem of ensuring power supply under the dual uncertainty of source and load, it is necessary to develop an efficient, reasonable and effective day-ahead dynamic time-of-use pricing method for high-energy-consuming industries. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a method for day-ahead dynamic time-of-use pricing in high-energy-consuming industries that is efficient, reasonable and has good use effects.

[0004] The object of the present invention is achieved as follows: a day-ahead dynamic time-sharing pricing method for high energy consumption industries, comprising the following steps: Step 1: Categorize and compile grid-side load data for high-energy-consuming enterprises to understand their flexibility in electricity consumption at different time periods and on different day types. Then, conduct customer-side research and analysis on high-energy-consuming enterprises to determine their sensitivity to electricity prices across different day types and electricity consumption time periods. Step 2: Based on the data statistics and research analysis results in step 1, a method combining mechanism modeling and statistical modeling is used to construct a mapping model between the load of high-energy-consuming enterprises in different time periods of different days and the time-of-use electricity price; Step 3: Based on the time-of-use load-to-use electricity price mapping model in Step 2, analyze the price elasticity characteristics of high-energy-consuming enterprises. Then, based on the incentive-compatible mechanism and the cost-effectiveness characteristics of new energy and high-energy-consuming enterprises, develop a clearing mechanism suitable for the collaborative optimization of new energy and high-energy-consuming enterprises. This will guide new energy and high-energy-consuming enterprises to quote and clear prices based on real cost-effectiveness, thereby maximizing the overall benefits of all parties. Step 4: Based on the bids and clearing of high-energy-consuming enterprises based on real cost-effectiveness, and using a mechanism that encourages compatibility and price as a decision variable, a safety-constrained economic dispatch marginal price clearing model for the county power grid, which coordinates new energy and high-energy-consuming industry enterprises, is constructed according to the safety-constrained economic dispatch model. Step 5: According to the electricity price clearing model constructed in step 4, adopt mature and stable branch and bound method and other solving algorithms to build a day-ahead dynamic time-of-use pricing model for high-energy-consuming industry enterprises, and realize the calculation of day-ahead dynamic time-of-use pricing for high-energy-consuming industry enterprises.

[0005] Furthermore, in step 1, the grid-side load data is collected through the original data collection terminal, and after the data collection is completed, it is filtered by the filtering module, analyzed and classified by the statistical module, and an electronic statistical table is generated.

[0006] Furthermore, the security-constrained economic dispatch model in step 4 is established in accordance with linear programming and genetic algorithms, and based on data such as power generation costs, load demands and transmission losses.

[0007] Furthermore, in step 3, a load electricity price elasticity model for enterprises in high-energy-consuming industries may be constructed based on the electricity price elasticity characteristics of high-energy-consuming enterprises.

[0008] Beneficial effects of the present invention: The present invention classifies and counts the load data on the grid side of high-energy-consuming enterprises and conducts research and analysis on the customer side, thereby understanding the flexibility of high-energy-consuming enterprises to electricity consumption periods and sensitivity to electricity prices under different day types, and then adopts a method that combines mechanism modeling and statistical modeling to construct a mapping model of time-sharing load and time-sharing electricity price for high-energy-consuming enterprises on different day types; then, based on the time-sharing load and time-sharing electricity price mapping model, in accordance with the incentive-compatible mechanism and according to the cost-effectiveness characteristics of new energy and high-energy-consuming enterprises, a clearing mechanism suitable for the collaborative optimization of new energy and high-energy-consuming enterprises on the day before is produced, thereby Guide new energy and high-energy-consuming enterprises to quote and clear based on real cost-effectiveness; then, according to the quotes and clearing based on real cost-effectiveness of high-energy-consuming enterprises, use price as the decision variable, and according to the safety-constrained economic dispatch model, construct a safety-constrained economic dispatch marginal electricity price clearing model for the collaboration between new energy and high-energy-consuming industry enterprises in the county power grid; finally, according to the constructed electricity price clearing model, adopt mature and stable branch and bound method and other solving algorithms to construct a day-ahead dynamic time-of-use pricing model for high-energy-consuming industry enterprises, and realize the calculation of day-ahead dynamic time-of-use pricing for high-energy-consuming industry enterprises; in general, the present invention has the advantages of high efficiency, rationality and good use effect. DETAILED DESCRIPTION

[0009] The present invention will be further described below.

[0010] Example: A method for day-ahead dynamic time-of-use pricing in high-energy-consuming industries, comprising the following steps: Step 1: Classify and compile grid-side load data of high-energy-consuming enterprises to understand their flexibility in electricity consumption time periods under different day types. Subsequently, conduct customer-side research and analysis on high-energy-consuming enterprises to determine their sensitivity to electricity prices under different day types and electricity consumption time periods. Grid-side load data is collected through the original data acquisition terminal. After data collection is completed, it is filtered by the filtering module, analyzed and classified by the statistical module, and an electronic statistical table is generated. Step 2: Based on the data statistics and research analysis results in step 1, a method combining mechanism modeling and statistical modeling is used to construct a mapping model between the load of high-energy-consuming enterprises in different time periods of different days and the time-of-use electricity price; Step 3: Based on the time-of-use load-time-of-use electricity price mapping model in Step 2, analyze the price elasticity characteristics of high-energy-consuming enterprises. Based on the price elasticity characteristics of high-energy-consuming enterprises, construct a load price elasticity model for high-energy-consuming industries. Then, according to the incentive-compatible mechanism and the cost-effectiveness characteristics of new energy and high-energy-consuming enterprises, develop a clearing mechanism suitable for the collaborative optimization of new energy and high-energy-consuming enterprises on the day before. This will guide new energy and high-energy-consuming enterprises to quote and clear prices based on real cost-effectiveness, thereby maximizing the overall benefits of all parties. Step 4: Based on the bids and clearing of high-energy-consuming enterprises based on real cost-effectiveness, and using a mechanism that encourages compatibility and price as the decision variable, a safety-constrained economic dispatch marginal price clearing model for the county power grid, which collaborates with new energy and high-energy-consuming enterprises, is constructed according to the safety-constrained economic dispatch model. The safety-constrained economic dispatch model is based on linear programming and genetic algorithms, and is built based on data such as generation costs, load demand, and transmission losses. Step 5: According to the electricity price clearing model constructed in step 4, adopt mature and stable branch and bound method and other solving algorithms to build a day-ahead dynamic time-of-use pricing model for high-energy-consuming industry enterprises, and realize the calculation of day-ahead dynamic time-of-use pricing for high-energy-consuming industry enterprises.

[0011] When the present invention is used, first, the grid-side load data is collected through the original data collection terminal, and after the data collection is completed, the data is screened and filtered by the filtering module, and then analyzed and classified by the statistical module, and an electronic statistical table is generated, so as to grasp the flexibility of high-energy-consuming enterprises in electricity consumption time periods under different day types, and then conduct customer-side research and analysis on high-energy-consuming enterprises, so as to grasp the sensitivity of high-energy-consuming enterprises to electricity prices in different day types and electricity consumption time periods; then, based on the data statistics and research and analysis results, a method combining mechanism modeling and statistical modeling is adopted to construct a mapping model of time-sharing load-time-sharing electricity price of high-energy-consuming enterprises in different day types, and then, based on the time-sharing load-time-sharing electricity price mapping model, the electricity price elasticity characteristics of high-energy-consuming enterprises are analyzed, and based on the electricity price elasticity characteristics of high-energy-consuming enterprises, a load electricity price elasticity model of high-energy-consuming industry enterprises is constructed, and then , according to the incentive-compatible mechanism and the cost-effectiveness characteristics of new energy and high-energy-consuming enterprises, a clearing mechanism suitable for the collaborative optimization of new energy and high-energy-consuming enterprises on the day before is produced, thereby guiding new energy and high-energy-consuming enterprises to quote and clear based on real cost-effectiveness, and realizing the maximization of the overall benefits of multiple parties; finally, according to the quotation and clearing based on real cost-effectiveness by high-energy-consuming enterprises, based on the incentive-compatible mechanism, and using price as the decision variable, according to the safety-constrained economic dispatch model, a safety-constrained economic dispatch marginal electricity price clearing model for the collaboration between new energy and high-energy-consuming industry enterprises in the county power grid is constructed; after completing the above operations, according to the constructed electricity price clearing model, a mature and stable branch and bound method and other solving algorithms are adopted to construct a dynamic time-of-use pricing model for high-energy-consuming industry enterprises on the day before, and realize the calculation of dynamic time-of-use pricing for high-energy-consuming industry enterprises on the day before; in general, the present invention has the advantages of high efficiency, rationality and good use effect.

[0012] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A day-ahead dynamic time-of-use pricing method for high energy consumption industries, characterized by: The following steps are involved: Step 1: Classify and compile grid-side load data for high-energy-consuming enterprises to understand their flexibility in electricity consumption at different time periods on different day types. Then, conduct customer-side research and analysis on high-energy-consuming enterprises to determine their sensitivity to electricity prices at different time periods and day types. Step 2: Based on the data statistics and research analysis results in step 1, a method combining mechanism modeling and statistical modeling is used to construct a mapping model between the load of high-energy-consuming enterprises in different time periods of different days and the time-of-use electricity price; Step 3: Based on the time-of-use load-to-use electricity price mapping model in Step 2, analyze the price elasticity characteristics of high-energy-consuming enterprises. Then, based on the incentive-compatible mechanism and the cost-effectiveness characteristics of new energy and high-energy-consuming enterprises, develop a clearing mechanism suitable for the collaborative optimization of new energy and high-energy-consuming enterprises. This will guide new energy and high-energy-consuming enterprises to quote and clear prices based on real cost-effectiveness, thereby maximizing the overall benefits of all parties. Step 4: Based on the bids and clearing of high-energy-consuming enterprises based on real cost-effectiveness, and using a mechanism that encourages compatibility and price as a decision variable, a safety-constrained economic dispatch marginal price clearing model for the county power grid, which coordinates new energy and high-energy-consuming industry enterprises, is constructed according to the safety-constrained economic dispatch model. Step 5: According to the electricity price clearing model constructed in step 4, adopt mature and stable branch and bound method and other solving algorithms to build a day-ahead dynamic time-of-use pricing model for high-energy-consuming industry enterprises, and realize the calculation of day-ahead dynamic time-of-use pricing for high-energy-consuming industry enterprises.

2. The method for day-ahead dynamic time-sharing pricing for high-energy-consuming industries according to claim 1, characterized in that: In step 1, the grid-side load data is collected through the original data collection terminal, and after the data collection is completed, it is filtered by the filtering module, analyzed and classified by the statistical module, and an electronic statistical table is generated.

3. The method for day-ahead dynamic time-sharing pricing for high-energy-consuming industries according to claim 1, characterized in that: The security-constrained economic dispatch model in step 4 is established in accordance with linear programming and genetic algorithms and based on data such as power generation costs, load demands and transmission losses.

4. The method for day-ahead dynamic time-sharing pricing for high-energy-consuming industries according to claim 1, characterized in that: In step 3, a load electricity price elasticity model for enterprises in high-energy-consuming industries may be constructed based on the electricity price elasticity characteristics of high-energy-consuming enterprises.