Electricity purchase control method in electricity spot transaction market
By constructing a prediction curve model for the change of electricity energy and combining the transmission cost, selecting the optimal power purchase price, the high operating cost problem caused by instability in the electricity spot trading market is solved, and the reduction of electricity purchase cost and decision-making effect is achieved. promote.
Patent Information
- Application Number
- CN202411336751.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-25
- Publication Date
- 2025-01-10
AI Technical Summary
In the electricity spot trading market, due to the instability of the spot price of electricity, electricity buyers often purchase electricity during high-priced periods, resulting in an increase in operating costs.
By obtaining data on the real-time spot market of electricity, a prediction curve model for the price change of electricity is constructed, combining the cost of electricity transmission, selecting the best purchase price, and achieving accurate prediction of electricity prices and optimal purchase decisions.
It effectively reduces the cost of electricity purchasing and improves the accuracy and effectiveness of electricity purchasing decisions.
Smart Images
Figure CN119295117A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power trading, and in particular to a power purchase control method in a power spot trading market. Background Art
[0002] With the reform of the power system and the rapid development of the Internet industry, power trading based on the Internet platform has become a future development trend. Faced with the new situation of power sales development, a large number of diversified power market demand-side entities such as power sales companies and load aggregators have begun to emerge. On the one hand, power sales companies can purchase the required electricity through quotations and bids in the spot market. On the other hand, they can sell the purchased electricity to decentralized end users in the retail market through dynamic pricing mechanisms such as real-time electricity prices. Power sales companies optimize their electricity purchase costs by participating in the spot market, and then provide more competitive power products and services in the retail market. The changes and development of the retail market will in turn affect the operating strategies of power sales companies and the trading situation in the spot market. The three influence and promote each other.
[0003] In the trading market of the power system, power sellers generally purchase electricity at regular intervals based on power consumption forecast data. However, since the spot price of electricity is constantly changing, there will be obvious differences in the spot price of electricity in different periods of time. This method often leads to increased operating costs for power buyers due to purchasing electricity during periods with higher spot prices. For this reason, we provide a power purchase control method in the power spot trading market to solve this problem. Summary of the invention
[0004] In view of the deficiencies of the prior art, the present invention provides a power purchase control method in a power spot trading market to solve this problem.
[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for controlling power purchase in a power spot trading market, comprising the following steps: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market and build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of the same historical period of the previous year, and calculate the real-time electricity price change rates of multiple same historical periods in different years through data, and build an electricity price change prediction curve model; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the forecast electricity prices and final electricity transmission costs of all sales channels; Step 6. The optimal electricity purchase forecast data calculation model combines the predicted electricity price of each sales channel in the same cycle period with the final cost of electricity transmission of each sales channel, and finally compares the final electricity purchase price of each sales channel in the same cycle period, and finally selects the optimal electricity price for purchase.
[0006] Furthermore, the duration of the historical cycle is 15-30 days.
[0007] Furthermore, the historical electricity prices include normal prices and peak and valley prices.
[0008] Furthermore, the electricity price change prediction curve model performs price change curve prediction calculations for normal prices and peak and valley prices.
[0009] Furthermore, the optimal electricity purchase forecast data calculation model predicts the electricity price of each sales channel in the same cycle period based on the normal price of electricity, and combines it with the final cost of electricity transmission, and performs statistical calculation of the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
[0010] Furthermore, the optimal electricity purchase forecast data calculation model predicts the peak and valley prices of electricity for each sales channel in the same cycle period, and combines it with the final cost of electricity transmission, and performs statistical calculations on the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
[0011] Furthermore, the optimal electricity purchase forecast data calculation model is based on statistics of the peak and valley electricity forecast prices of the same cycle period of all sales channels, and is based on the splitting of the peak and valley electricity forecast prices of the same cycle period of each sales channel and reorganization with the peak and valley electricity forecast prices of other sales channels. After calculation and statistical comparison of multiple groups of data, the optimal peak and valley electricity matching prices of different sales channels are obtained, and combined with the final cost of electricity transmission, the final electricity purchase price data is statistically calculated according to the purchase volume in the cheapest electricity price cycle period.
[0012] Furthermore, the optimal electricity purchase forecast data calculation model compares the optimal purchase price of normal electricity prices, the optimal purchase price of peak-valley prices, and the optimal purchase price of peak-valley split and matching prices in the same cycle period, and finally selects the optimal electricity price for purchase.
[0013] Furthermore, the basic parameters for electricity sales include the credibility of the power supply merchant, the scale of the power supply enterprise, and the power supply mode, and the basic data for electricity sales are all historical electricity prices and real-time prices of the power supply merchant.
[0014] Compared with the prior art, the present invention provides a method for controlling power purchase in a power spot trading market, which has the following beneficial effects: The power purchase control method in the power spot trading market compares the historical electricity prices of all sales channels in the last historical period with the historical electricity prices of 15-30 days in the same historical period of the previous year, and calculates the real-time electricity price change rates of multiple same historical periods in different years through data, constructs an electricity price change prediction curve model for normal electricity prices and peak-valley prices, and constructs an optimal electricity purchase prediction data calculation model for electricity prices through the predicted price data of the final electricity transmission cost of each seller and the normal electricity prices of all sales channels. The optimal purchase price of normal electricity prices, the optimal purchase price of peak-valley prices, and the optimal purchase price of peak-valley split and matching prices in the same period are compared through the optimal purchase prediction data calculation model, and finally the optimal electricity price is selected for purchase. After analyzing and calculating the electricity purchase data, the final electricity purchase cost is effectively reduced and the use effect is improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 It is a flow chart of the electricity purchasing control method of the present invention. DETAILED DESCRIPTION
[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0017] Embodiment 1: This embodiment introduces a method for controlling power purchase in a power spot trading market, comprising the following steps: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market, that is, to collect data on the credibility of power suppliers, the scale of power supply enterprises, and the power supply mode, and to collect statistics on all historical electricity prices and real-time price data of power suppliers, so as to facilitate the grasp of the basic situation of power suppliers, so as to purchase good and stable electricity, and to build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of 15 days in the same historical period of the previous year, and calculate the real-time electricity price change rate of multiple 15 days in the same historical period of different years through data calculation, build an electricity price change prediction curve model for the normal electricity price, and perform curve prediction calculation on the normal electricity price change; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the predicted normal electricity prices of all sales channels and the final cost of electricity transmission; Step 6: Use the optimal electricity purchase forecast data calculation model to predict the normal price of electricity for each sales channel in the same cycle period, combine it with the final cost of electricity transmission, and perform statistical calculations on the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
[0018] Embodiment 2: This embodiment introduces a method for controlling power purchase in a power spot trading market, comprising the following steps: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market, that is, to collect data on the credibility of power suppliers, the scale of power supply enterprises, and the power supply mode, and to collect statistics on all historical electricity prices and real-time price data of power suppliers, so as to facilitate the grasp of the basic situation of power suppliers, so as to purchase good and stable electricity, and to build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of the same historical period of 20 days in the previous year, and calculate the real-time electricity price change rate of multiple 20-day periods in the same historical period in different years through data calculation, build an electricity price change prediction curve model for the peak and valley prices of electricity, and perform curve prediction calculation on the changes of its peak and valley prices; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the predicted peak and valley prices of all electricity sales channels and the final cost of electricity transmission; Step 6: Use the optimal electricity purchase forecast data calculation model to predict the peak and valley prices of electricity for each sales channel in the same cycle period, and combine it with the final cost of electricity transmission. Calculate the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
[0019] Embodiment 3: This embodiment introduces a method for controlling power purchase in a power spot trading market, comprising the following steps: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market, that is, to collect data on the credibility of power suppliers, the scale of power supply enterprises, and the power supply mode, and to collect statistics on all historical electricity prices and real-time price data of power suppliers, so as to facilitate the grasp of the basic situation of power suppliers, so as to purchase good and stable electricity, and to build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of the same historical period of 25 days in the previous year, and calculate the real-time electricity price change rate of multiple same historical periods of 25 days in different years through data calculation, build an electricity price change prediction curve model for the peak and valley prices of electricity, and perform curve prediction calculation on the changes of its peak and valley prices; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the predicted peak and valley prices of all electricity sales channels and the final cost of electricity transmission; The sixth step is to use the optimal electricity purchase forecast data calculation model to collect statistics based on the peak and valley electricity forecast prices of the same cycle period of all sales channels, and to split the peak and valley electricity forecast prices of the same cycle period of each sales channel and reorganize them with the peak and valley electricity forecast prices of other sales channels. After statistical comparison of multiple groups of data, the optimal peak and valley electricity matching prices of different sales channels are obtained, and combined with the final cost of electricity transmission, the final electricity purchase price data is statistically calculated according to the purchase volume in the cheapest electricity price cycle period.
[0020] Embodiment 4: This embodiment introduces a method for controlling power purchase in a power spot trading market, comprising the following steps: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market, that is, to collect data on the credibility of power suppliers, the scale of power supply enterprises, and the power supply mode, and to collect statistics on all historical electricity prices and real-time price data of power suppliers, so as to facilitate the grasp of the basic situation of power suppliers, so as to purchase good and stable electricity, and to build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of the same historical period of 30 days in the previous year, and calculate the real-time electricity price change rates of multiple 30-day historical periods in different years through data calculation, build an electricity price change prediction curve model for various electricity prices, and perform curve prediction calculations on the changes of various electricity prices; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the forecast prices of various prices of electricity from all sales channels and the final cost of electricity transmission; Step 6: Use the optimal electricity purchase forecast data calculation model to compare the optimal purchase price of normal electricity prices in the same cycle period, the optimal purchase price of peak-valley prices, and the optimal purchase price of peak-valley split prices, and finally select the optimal electricity price for purchase.
[0021] In summary: the present invention collects data on the credibility of power supply merchants, the scale of power supply enterprises, and the power supply mode, and collects statistics on all historical electricity prices and real-time price data of power supply merchants, so as to facilitate the grasp of the basic situation of power supply merchants, so as to purchase good and stable electricity, and compares the historical electricity prices of all sales channels in the last historical period with the historical electricity prices of 15-30 days in the same historical period of the previous year, and calculates the real-time electricity price change rate of multiple same historical periods of 15-30 days in different years through data, constructs an electricity price change prediction curve model for normal electricity prices and peaks and valleys, and designs multiple groups of electricity prices in the same historical period of 15-30 days. Test data, and finally obtain accurate data from multiple groups of data, effectively improving the accuracy of the electricity price change prediction curve, and then evaluating and calculating the final cost of electricity transmission for each seller, and constructing the optimal electricity purchase forecast data calculation model with the predicted price data of normal electricity prices of all sales channels. The optimal electricity purchase forecast data calculation model is used to compare the optimal purchase price of normal electricity prices in the same period, the optimal purchase price of peak and valley prices, and the optimal purchase price of peak and valley split prices. Finally, the optimal electricity price is selected for purchase. After analyzing and calculating the electricity purchase data, the final electricity purchase cost is effectively reduced and the use effect is improved.
[0022] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for controlling power purchase in a power spot trading market, characterized in that: The following steps are involved: The first step is to obtain the basic parameters and data of electricity sales in the real-time spot market and build an optimization control strategy for electricity purchase; The second step is to collect and organize the historical boundary information of electricity sales of all sales channels in the spot market; The third step is to compare the electricity prices of the previous historical period of all sales channels with the electricity prices of the same historical period of the previous year, and calculate the real-time electricity price change rates of multiple same historical periods in different years through data, and build an electricity price change prediction curve model; Step 4: Collect data on the power transmission costs and transmission losses of all sales channels, and evaluate and calculate the final power transmission costs of each seller; Step 5: Build an optimal electricity purchase forecast data calculation model based on the forecast electricity prices and final electricity transmission costs of all sales channels; Step 6. The optimal electricity purchase forecast data calculation model combines the predicted electricity price of each sales channel in the same cycle period with the final cost of electricity transmission of each sales channel, and finally compares the final electricity purchase price of each sales channel in the same cycle period, and finally selects the optimal electricity price for purchase.
2. The power purchase control method in the power spot trading market according to claim 1 is characterized by: The duration of the historical cycle is 15-30 days.
3. The power purchase control method in the power spot trading market according to claim 1 is characterized by: The historical electricity prices include normal prices and peak and valley prices.
4. The power purchase control method in the power spot trading market according to claim 1 is characterized by: The electric energy price change prediction curve model performs price change curve prediction calculations on normal prices and peak and valley prices.
5. The power purchase control method in the power spot trading market according to claim 4 is characterized by: The optimal electricity purchase forecast data calculation model predicts the electricity price of each sales channel in the same cycle period for the normal price of electricity, and combines it with the final cost of electricity transmission, and performs statistical calculations on the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
6. The power purchase control method in the power spot trading market according to claim 5 is characterized by: The optimal electricity purchase forecast data calculation model also predicts the peak and valley prices of electricity for each sales channel in the same cycle period, and combines it with the final cost of electricity transmission, and performs statistical calculations on the final electricity purchase price data in the cheapest electricity price cycle period based on the purchase volume.
7. The power purchase control method in the power spot trading market according to claim 6, characterized in that: The optimal electricity purchase forecast data calculation model is based on statistics of the peak and valley electricity forecast prices of the same cycle period of all sales channels, and is based on the splitting of the peak and valley electricity forecast prices of the same cycle period of each sales channel and reorganization with the peak and valley electricity forecast prices of other sales channels. After calculation and statistical comparison of multiple groups of data, the optimal peak and valley electricity matching prices of different sales channels are obtained, and combined with the final cost of electricity transmission, the final electricity purchase price data is statistically calculated according to the purchase volume in the cheapest electricity price cycle period.
8. The power purchase control method in the power spot trading market according to claim 7, characterized in that: The optimal electricity purchase forecast data calculation model compares the optimal purchase price of normal electricity prices, the optimal purchase price of peak-valley prices, and the optimal purchase price of peak-valley split and matching prices in the same cycle period, and finally selects the optimal electricity price for purchase.
9. The power purchase control method in the power spot trading market according to claim 1, characterized in that: The basic parameters for electricity sales include the credibility of the power supply merchant, the scale of the power supply enterprise, and the power supply mode. The basic data for electricity sales are all historical electricity prices and real-time prices of the power supply merchant.