Method for determining online electricity selling quotation of cogeneration thermal power plant

Abstract

The invention discloses a combined heat and power generation thermal power plant online electricity selling quotation determination method, and provides a scientific reference for reporting the highest price and the lowest price of pre-selling electricity which can win the bid and can obtain high economic benefits. According to a probability statistics principle of'bidding Internet surfing and preferential selection 'of provincial power grid dispatching, a bid-winning electricity selling probability curve is obtained through own closest electricity selling historical data and a fitting correction method of a bid-winning electricity price probability curve, and a reference with guiding significance is provided for formulating a weekly pre-electricity selling strategy of a power plant; the highest quotation and the lowest quotation of electricity selling are specifically measured and calculated, so that the feasibility of quotation is improved; and the economic value of power selling of the power plant is improved.

Description

Technical Field

[0001] This invention relates to a pricing strategy for electricity sold to the grid by a combined heat and power (CHP) thermal power plant, and particularly to a method for determining the highest and lowest prices for electricity sold to the grid by a CHP thermal power plant. Background Technology

[0002] Within the State Grid, with the increasing grid connection of new energy sources, the actual output of combined heat and power (CHP) power plants is far lower than their designed output. Currently, CHP power plants sell electricity through competitive bidding in the spot market, resulting in price differences based on time of day and segment. Achieving better economic benefits from electricity sales for CHP power plants has become an urgent goal for these plants.

[0003] The existing pre-sale electricity strategies of various cogeneration power plants are generally based on the bidding strategies of cogeneration power spot trading. Currently, the existing bidding strategies for cogeneration power spot trading can be broadly categorized into four types: 1) cost analysis-based methods; 2) market price prediction methods; 3) estimation of bidding behavior by other power generation companies; and 4) game theory-based methods. Among these, game theory-based methods are widely used. The game theory-based bidding strategy is designed for the current grid dispatching system's one-time clearing price settlement system for electricity sales. The specific operational steps of the weekly bidding strategy are as follows: each cogeneration power plant submits its on-grid electricity price for each time period of the following week at the corresponding time point one week prior to the pre-sale electricity date. After receiving the submitted on-grid pre-sale electricity prices for each time period of the following week, the provincial grid dispatching center determines the price based on the principle of "competitive bidding and selection of the best." The specific cogeneration power plants that won the bids; the on-grid electricity prices submitted by each power plant for the next week's supply at various times are merely based on cost accounting and low-price competition principles. Because the grid dispatch selects the winning bidders not based on the lowest price principle, but rather on a comprehensive consideration of grid power resource allocation and each power plant's generation capacity, safety, stability, and continuity, the winning power plants end up selling electricity at prices far below the grid-side benchmark price given by the provincial dispatch center. This results in the phenomenon of selling electricity at a low price, impacting the economic benefits of the power plants. How to scientifically and rationally formulate a power plant's electricity pricing strategy, especially the highest and lowest reference prices, in the week leading up to the pre-sale date to improve the company's economic efficiency has become a pressing issue for cogeneration power plants in their pre-sale pricing. Summary of the Invention

[0004] This invention provides a method for determining the on-grid electricity sales price of cogeneration thermal power plants, providing a more scientific reference for submitting the highest and lowest pre-sale electricity prices that can both win the bid and obtain higher economic benefits.

[0005] The present invention solves the above technical problems through the following technical solutions: A method for determining the on-grid electricity sales price of a combined heat and power (CHP) thermal power plant, characterized by the following steps: The first step is to retrieve the benchmark on-grid electricity price Kjz (unit: yuan / megawatt-hour) published by the provincial dispatch authority for the province where the cogeneration thermal power plant is located in the previous year; The second step is to use the time point one week before the pre-sale time of the cogeneration power plant as the base time point, and retrieve all realized one-time clearing prices of electricity sales for the cogeneration power plant in the year prior to the base time point (unit: yuan / megawatt-hour). (If the number of electricity sales days in the year prior to the base time point is d days and the number of electricity sales charges per day is h, then there are a total of d×h one-time clearing prices of electricity sales). The third step is to perform probability statistics on the d×h electricity sales clearing prices obtained in the second step to obtain the distribution curve of the electricity sales clearing price probability Ki, with the horizontal axis being the electricity sales price and the vertical axis being the probability of the electricity sales price occurring. Step 4: Based on the distribution curve of the electricity sales price probability Ki obtained in Step 3, calculate the average value Kμ of all realized one-time clearing price probabilities Ki. The calculation formula is as follows: Kμ=(K1+k2+••••••+Kd×h) / (d×h); Step 5: Calculate the root mean square error σ2 of the electricity sales clearing price probability Ki using the following formula: σ2=∑(Ki-Kμ)2 / (d×h); Step 6: Take the square root of the mean square deviation σ2 from step 5 to obtain the standard deviation σ of the electricity sales clearing price probability Ki. Step 7: Use the standard deviation σ obtained in Step 6 to fit and correct the distribution curve of the power sales one-time clearing price probability Ki obtained in Step 3, and obtain the power sales price reference for cogeneration thermal power plants after one-time correction probability curve. Step 8: Using a six sigma table of probability statistics, perform a second fitting correction on the probability curve after the first correction of the electricity sales price to obtain the probability curve of the cogeneration thermal power plant after the second fitting correction, and obtain the electricity sales price corresponding to the highest probability in the curve. Step 9: Divide the highest probability electricity sales price obtained in Step 8 by 0.618 to obtain the highest reference price for pre-sale electricity. If the highest reference price for pre-sale electricity is lower than the on-grid benchmark price Kjz obtained in the first step, then the highest reference price for pre-sale electricity will be determined as the highest price for pre-sale electricity in the coming week. If the highest reference price for pre-sale electricity is greater than the on-grid benchmark price Kjz obtained in the first step, then the on-grid benchmark price Kjz will be determined as the highest price for pre-sale electricity in the coming week. Step 10: Obtain the cost price of electricity from the combined heat and power (CHP) thermal power plant. Multiply the on-grid benchmark price Kjz obtained in Step 1 by 0.618 to obtain the minimum reference price for pre-sale electricity. If the lowest reference price for pre-sale electricity is higher than the cost price, then the lowest reference price for pre-sale electricity will be determined as the lowest price for pre-sale electricity in the coming week. If the lowest reference price for pre-sale electricity is lower than the cost price, then the cost price will be determined as the lowest price for pre-sale electricity in the coming week. The aforementioned 0.618 is a suggested correction factor. This correction factor can be objectively adjusted according to the actual operating conditions of each province and power plant, and is generally controlled between 0.5 and 0.8.

[0006] The daily electricity sales billing quantity is 24, 48, or 96 units.

[0007] In the context of today's spot market electricity trading and grid connection, this invention provides a universal reference curve for cogeneration power plants to formulate weekly pre-sale electricity prices. This increases the probability of winning the bid and maximizes the electricity price, bringing the power plant's realized electricity price close to the grid benchmark price and improving the economic value of the power plant's electricity sales. Furthermore, it determines the highest and lowest bids for power plant electricity sales using the golden ratio principle. Attached Figure Description

[0008] Figure 1 This is the electricity sales probability fitting correction curve of the present invention; Curve A represents the probability of winning the bid for all realized electricity sales prices in the year prior to the benchmark date for combined heat and power (CHP) thermal power plants; Curve B represents the probability curve after the first adjustment of electricity sales prices; and Curve C represents the probability curve after the second adjustment of electricity sales prices. Detailed Implementation

[0009] The present invention will now be described in detail with reference to the accompanying drawings: Existing weekly pre-sale bids for combined heat and power (CHP) thermal power plants are all based on historical discrete data, and generally, the bids are far lower than the actual grid benchmark price subsequently given by the provincial dispatch center, resulting in a widespread phenomenon of power plants selling electricity at rock-bottom prices. This invention, based on the probabilistic statistical principle of "competitive bidding for grid connection and selection of the best" of the provincial grid dispatch center, uses its closest historical electricity sales data and a fitting and correction method for the probability curve of the winning bid to derive the probability curve of winning the bid for electricity sales. This provides a guiding reference for the formulation of weekly pre-sale electricity strategies for power plants, and specifically calculates the highest and lowest bids for selling electricity, improving the feasibility of the bids. The steps are as follows: A method for determining the grid connection electricity sales bid of CHP thermal power plants, characterized by the following steps: The first step is to retrieve the benchmark on-grid electricity price Kjz (unit: yuan / megawatt-hour) published by the provincial dispatch authority for the province where the cogeneration thermal power plant is located in the previous year; The second step is to use the time point one week before the pre-sale time of the cogeneration power plant as the base time point, and retrieve all realized one-time clearing prices of electricity sales for the cogeneration power plant in the year prior to the base time point (unit: yuan / megawatt-hour). (If the number of electricity sales days in the year prior to the base time point is d days and the number of electricity sales charges per day is h, then there are a total of d×h one-time clearing prices of electricity sales). The third step is to perform probability statistics on the d×h electricity sales clearing prices obtained in the second step to obtain the distribution curve of the electricity sales clearing price probability Ki, with the horizontal axis being the electricity sales price and the vertical axis being the probability of the electricity sales price occurring. Step 4: Based on the distribution curve of the electricity sales price probability Ki obtained in Step 3, calculate the average value Kμ of all realized one-time clearing price probabilities Ki. The calculation formula is as follows: Kμ=(K1+k2+••••••+Kd×h) / (d×h); Step 5: Calculate the root mean square error σ2 of the electricity sales clearing price probability Ki using the following formula: σ2=∑(Ki-Kμ)2 / (d×h); Step 6: Take the square root of the mean square deviation σ2 from step 5 to obtain the standard deviation σ of the electricity sales clearing price probability Ki. Step 7: Use the standard deviation σ obtained in Step 6 to fit and correct the distribution curve of the power sales one-time clearing price probability Ki obtained in Step 3, and obtain the power sales price reference for cogeneration thermal power plants after one-time correction probability curve. Step 8: Using a six sigma table of probability statistics, perform a second fitting correction on the probability curve after the first correction of the electricity sales price to obtain the probability curve of the cogeneration thermal power plant after the second fitting correction, and obtain the electricity sales price corresponding to the highest probability in the curve. Step 9: Divide the highest probability electricity sales price obtained in Step 8 by 0.618 to obtain the highest reference price for pre-sale electricity. If the highest reference price for pre-sale electricity is lower than the on-grid benchmark price Kjz obtained in the first step, then the highest reference price for pre-sale electricity will be determined as the highest price for pre-sale electricity in the coming week. If the highest reference price for pre-sale electricity is greater than the on-grid benchmark price Kjz obtained in the first step, then the on-grid benchmark price Kjz will be determined as the highest price for pre-sale electricity in the coming week. Step 10: Obtain the cost price of electricity from the combined heat and power (CHP) thermal power plant. Multiply the on-grid benchmark price Kjz obtained in Step 1 by 0.618 to obtain the minimum reference price for pre-sale electricity. If the lowest reference price for pre-sale electricity is higher than the cost price, then the lowest reference price for pre-sale electricity will be determined as the lowest price for pre-sale electricity in the coming week. If the lowest reference price for pre-sale electricity is lower than the cost price, then the cost price will be determined as the lowest price for pre-sale electricity in the coming week.

[0010] The daily electricity sales quota is 24, 48, or 96 units; corresponding to hourly, 30-minute, and 15-minute electricity sales respectively.

Claims

1. A method for determining the on-grid electricity sales price of a combined heat and power (CHP) thermal power plant, characterized by the following steps: The first step is to retrieve the on-grid benchmark electricity price K published by the provincial dispatch center for the province where the cogeneration power plant is located in the previous year. jz (Unit: Yuan / MWh); The second step is to use the time point one week before the pre-sale time of the cogeneration power plant as the base time point, and retrieve all realized one-time clearing prices of electricity sales for the cogeneration power plant in the year prior to the base time point (unit: yuan / megawatt-hour). (If the number of electricity sales days in the year prior to the base time point is d days and the number of electricity sales charges per day is h, then there are a total of d×h one-time clearing prices of electricity sales). The third step is to perform probability statistics on the d×h electricity sales clearing prices obtained in the second step to obtain the distribution curve of the electricity sales clearing price probability Ki, with the horizontal axis being the electricity sales price and the vertical axis being the probability of the electricity sales price occurring. Step 4: Based on the distribution curve of the electricity sales price probability Ki obtained in Step 3, calculate the average value Kμ of all realized one-time clearing price probabilities Ki. The calculation formula is as follows: Kμ=(K1+k2+••••••+Kd×h) / (d×h); Step 5: Calculate the root mean square error σ2 of the electricity sales clearing price probability Ki using the following formula: σ2 = ∑(Ki - Kμ) 2 / (d×h); Step 6: Take the square root of the mean square deviation σ2 from step 5 to obtain the standard deviation σ of the electricity sales clearing price probability Ki. Step 7: Use the standard deviation σ obtained in Step 6 to fit and correct the distribution curve of the power sales one-time clearing price probability Ki obtained in Step 3, and obtain the power sales price reference for cogeneration thermal power plants after one-time correction probability curve. Step 8: Using a six sigma table of probability statistics, perform a second fitting correction on the probability curve after the first correction of the electricity sales price to obtain the probability curve of the cogeneration thermal power plant after the second fitting correction, and obtain the electricity sales price corresponding to the highest probability in the curve. Step 9: Divide the highest probability electricity sales price obtained in Step 8 by 0.618 to obtain the highest reference price for pre-sale electricity. If the highest reference price for pre-sale electricity is lower than the on-grid benchmark price Kjz obtained in the first step, then the highest reference price for pre-sale electricity will be determined as the highest price for pre-sale electricity in the coming week. If the highest reference price for pre-sale electricity is greater than the on-grid benchmark price Kjz obtained in the first step, then the on-grid benchmark price Kjz will be determined as the highest price for pre-sale electricity in the coming week. Step 10: Obtain the cost price of electricity from the combined heat and power (CHP) thermal power plant. Multiply the on-grid benchmark price Kjz obtained in Step 1 by 0.618 to obtain the minimum reference price for pre-sale electricity. If the lowest reference price for pre-sale electricity is higher than the cost price, then the lowest reference price for pre-sale electricity will be determined as the lowest price for pre-sale electricity in the coming week. If the lowest reference price for pre-sale electricity is lower than the cost price, then the cost price will be determined as the lowest price for pre-sale electricity in the coming week.

2. The method for determining the on-grid electricity sales price of a combined heat and power (CHP) thermal power plant according to claim 1, characterized in that, The daily electricity sales billing quantity is 24, 48, or 96 units.

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