Presell electricity price-based capacity allocation method for fused salt system of combined heat and power generation unit

Abstract

The invention discloses a pre-selling electricity price-based fused salt system capacity allocation method for a combined heat and power generation generator set, and solves the problem of how to fully consider an electricity selling price factor when the fused salt energy storage and release system capacity is allocated to the generator set. The method comprises the following steps: determining the lowest electricity selling price and the highest electricity selling price according to a clearing electricity price occurring in the previous year, calculating the energy storage and discharge hours of the fused salt energy storage and discharge system, and then, carrying out energy storage capacity configuration according to the load demand per unit hour when the energy storage and discharge system discharges energy. Meanwhile, energy storage capacity configuration is carried out according to the deep adjustment load consumed by the thermal power generating unit, and the capacity of fused salt energy storage configuration is determined.

Description

Technical Field

[0001] This invention relates to a molten salt energy storage and release system for a combined heat and power (CHP) thermal power plant, and particularly to a method for allocating the capacity of a molten salt energy storage and release system for a CHP thermal power generating unit based on a pre-sale electricity price. Background Technology

[0002] Within the State Grid, with the increasing grid connection of new energy power generation, 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.

[0004] The existing molten salt energy storage and release systems of cogeneration thermal power generating units are all equipped and operated based on the generator capacity, heating load and the peak shaving and valley filling requirements of the power grid, without considering the revenue from electricity sales to the grid of the cogeneration units. How to combine the grid sales of the units with the equipment of molten salt energy storage and release systems has become another new challenge that cogeneration power plants need to face.

[0005] Existing cogeneration thermal power generating units are equipped with molten salt energy storage systems based on the grid's primary and secondary frequency regulation and AGC (Automatic Generation Control) scheduling capabilities, as well as the unit's own requirements for thermal-electric decoupling. However, after the molten salt energy storage system is installed, it has been found that the system's economic performance is not ideal, failing to achieve optimal economic results. How to equip generating units with molten salt energy storage systems while fully considering electricity sales prices and achieving economical operation of both the thermal power generating units and the molten salt energy storage system has become a new problem in the current allocation of molten salt energy storage system capacity for cogeneration units. Summary of the Invention

[0006] This invention provides a method for allocating the capacity of molten salt system in cogeneration generator sets based on pre-sale electricity prices, solving the technical problem of how to fully consider electricity sales price factors when equipping generator sets with molten salt energy storage and release system capacity.

[0007] The present invention solves the above technical problems through the following technical solutions: A method for allocating capacity for a molten salt energy storage and release system in a combined heat and power (CHP) generator unit based on pre-sale electricity prices is disclosed. The system includes a CHP thermal power generator unit, a power grid, and a molten salt energy storage and release system. The CHP thermal power plant has established a pre-sale electricity price system, and the most probable electricity price in this system is D. When the electricity price is low, this invention reduces grid-connected power transmission and increases molten salt energy storage, converting electrical energy into thermal energy in the molten salt for storage. When the grid-connected electricity price is high, the stored molten salt energy is converted back into electrical energy (or thermal energy), thereby increasing the capacity allocation of the grid-connected power generation system. The grid-supplied electricity increases the revenue from electricity sales of combined heat and power plants. The molten salt energy storage and release system is configured using the following method. The specific overall concept of this invention is as follows: Based on the clearing price of the previous year, the minimum and maximum electricity sales prices are determined, and the number of hours for energy storage and release of the molten salt energy storage and release system is calculated accordingly. Then, the energy storage capacity is configured based on the unit hourly load demand when the energy storage and release system releases energy, and the energy storage capacity is also configured based on the absorption of deep-shortage loads by thermal power units, thus determining the capacity of the molten salt energy storage configuration.

[0008] A method for allocating capacity of molten salt system for cogeneration generator sets based on pre-sale electricity prices, 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 retrieve all the cleared electricity prices realized by the cogeneration power plant in the previous year (unit: yuan / megawatt-hour). (If the number of days of electricity sales in the previous year was d days and the number of electricity sales charges per day was h, then there are a total of d×h cleared electricity prices). 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 clearing price D corresponding to the highest probability in the curve. Step 9: When the on-grid electricity price of the cogeneration thermal power generating unit is less than or equal to 30% of the maximum probable electricity price D, design the molten salt energy storage and release system for the thermal power generating unit to store energy, converting the electrical energy of the generating unit into the thermal energy of the molten salt system, and determine the number of working hours h1 in the year for energy release; when the on-grid electricity price of the cogeneration thermal power generating unit is less than 130% of the maximum probable electricity price D, but greater than 30% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a stopped state, and determine the number of working hours h2 in the stopped state throughout the year; when the on-grid electricity price of the cogeneration thermal power generating unit is greater than 130% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a energy release state, and determine the number of working hours h3 in the energy release state throughout the year. Alternatively, when the electricity price generated by the cogeneration thermal power generating unit is less than or equal to 20%-40% of the maximum probability electricity price D, the molten salt energy storage and release system of the thermal power generating unit can be designed to store energy, converting the electrical energy of the generating unit into the thermal energy of the molten salt system, and determining the number of working hours h1 in the whole year for energy release; similarly, when the electricity price generated by the cogeneration thermal power generating unit is less than 120%-140% of the maximum probability electricity price D, but greater than 20%-40% of the maximum probability electricity price D, the molten salt energy storage and release system can be designed to be in a stopped state, and determining the number of hours h2 in the stopped state throughout the year; Step 10: Add the total number of working hours in the year that are in the energy release phase (h1) to the total number of working hours in the year that are in the energy release phase (h3), divide the sum by the actual number of days the unit operates (T), and then divide by the designed number of energy storage and release cycles per day (n) to obtain the actual number of single cycle energy storage and release hours (h) of the pre-designed molten salt energy storage and release system in a day. Step 11: Based on the load P1 demand of the single cycle energy storage and release hours h, configure the capacity of the molten salt energy storage and release system; that is, multiply the load P1 per unit hour by the actual single cycle energy storage and release hours h of the pre-designed molten salt energy storage and release system in a day to obtain the first energy storage capacity configuration W1 of the molten salt energy storage and release system.

[0009] Step 12: Configure the deep-cycle load P2 of the cogeneration generator set. That is, multiply the deep-cycle load P2 by the number of hours h of the actual single cycle energy storage and release of the pre-designed molten salt energy storage and release system in a day to obtain the second energy storage capacity configuration W2 of the molten salt energy storage and release system. Step 13: Compare the first energy storage capacity configuration W1 with the second energy storage capacity configuration W2, and select the larger value as the capacity of the molten salt system of the cogeneration generator unit.

[0010] In the context of today's spot market electricity trading and grid connection, this invention provides a new method for allocating the capacity of molten salt storage systems for combined heat and power (CHP) thermal power generating units. This method provides a universal electricity sales reference curve based on the formulation of pre-sale electricity prices, which increases the probability of winning the bid, maximizes the electricity sales price, and also equips the system with an economically appropriate capacity for molten salt storage. Attached Figure Description

[0011] 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 a one-time adjustment of electricity sales prices; and Curve C represents the probability curve after a one-time adjustment of electricity sales prices. Detailed Implementation

[0012] The present invention will now be described in detail with reference to the accompanying drawings: A method for allocating capacity of molten salt system for cogeneration generator sets based on pre-sale electricity prices, 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 retrieve all the cleared electricity prices realized by the cogeneration power plant in the previous year (unit: yuan / megawatt-hour). (If the number of days of electricity sales in the previous year was d days and the number of electricity sales charges per day was h, then there are a total of d×h cleared electricity prices). 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 clearing price D corresponding to the highest probability in the curve. Step 9: When the on-grid electricity price of the cogeneration thermal power generating unit is less than or equal to 30% of the maximum probable electricity price D, design the molten salt energy storage and release system for the thermal power generating unit to store energy, converting the electrical energy of the generating unit into the thermal energy of the molten salt system, and determine the number of working hours h1 in the year for energy release; when the on-grid electricity price of the cogeneration thermal power generating unit is less than 130% of the maximum probable electricity price D, but greater than 30% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a stopped state, and determine the number of working hours h2 in the stopped state throughout the year; when the on-grid electricity price of the cogeneration thermal power generating unit is greater than 130% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a energy release state, and determine the number of working hours h3 in the energy release state throughout the year. Step 10: Add the total number of working hours in the year that are in the energy release phase (h1) to the total number of working hours in the year that are in the energy release phase (h3), divide the sum by the actual number of days the unit operates (T), and then divide by the designed number of energy storage and release cycles per day (n) to obtain the actual number of single cycle energy storage and release hours (h) of the pre-designed molten salt energy storage and release system in a day. Step 11: Based on the load P1 demand of the single cycle energy storage and release hours h, configure the capacity of the molten salt energy storage and release system; that is, multiply the load P1 per unit hour by the actual single cycle energy storage and release hours h of the pre-designed molten salt energy storage and release system in a day to obtain the first energy storage capacity configuration W1 of the molten salt energy storage and release system.

[0013] Step 12: Configure the deep-cycle load P2 of the cogeneration generator set. That is, multiply the deep-cycle load P2 by the number of hours h of the actual single cycle energy storage and release of the pre-designed molten salt energy storage and release system in a day to obtain the second energy storage capacity configuration W2 of the molten salt energy storage and release system. Step 13: Compare the first energy storage capacity configuration W1 with the second energy storage capacity configuration W2, and select the larger value as the capacity of the molten salt system of the cogeneration generator unit.

Claims

1. A method for allocating capacity of molten salt system for cogeneration generator sets based on pre-sale electricity prices, 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 retrieve all the cleared electricity prices realized by the cogeneration power plant in the previous year (unit: yuan / megawatt-hour). (If the number of days of electricity sales in the previous year was d days and the number of electricity sales charges per day was h, then there are a total of d×h cleared electricity prices). 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 clearing price D corresponding to the highest probability in the curve. Step 9: When the on-grid electricity price of the cogeneration thermal power generating unit is less than or equal to 30% of the maximum probable electricity price D, design the molten salt energy storage and release system for the thermal power generating unit to store energy, converting the electrical energy of the generating unit into the thermal energy of the molten salt system, and determine the number of working hours h1 in the year for energy release; when the on-grid electricity price of the cogeneration thermal power generating unit is less than 130% of the maximum probable electricity price D, but greater than 30% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a stopped state, and determine the number of working hours h2 in the stopped state throughout the year; when the on-grid electricity price of the cogeneration thermal power generating unit is greater than 130% of the maximum probable electricity price D, design the molten salt energy storage and release system to be in a energy release state, and determine the number of working hours h3 in the energy release state throughout the year. Step 10: Add the total number of working hours in the year that are in the energy release phase (h1) to the total number of working hours in the year that are in the energy release phase (h3), divide the sum by the actual number of days the unit operates (T), and then divide by the designed number of energy storage and release cycles per day (n) to obtain the actual number of single cycle energy storage and release hours (h) of the pre-designed molten salt energy storage and release system in a day. Step 11: Based on the load P1 demand of the single cycle energy storage and release hours h, configure the capacity of the molten salt energy storage and release system; that is, multiply the load P1 per unit hour by the actual single cycle energy storage and release hours h of the pre-designed molten salt energy storage and release system in a day to obtain the first energy storage capacity configuration W1 of the molten salt energy storage and release system.

2. The method for allocating capacity of a molten salt system for a combined heat and power (CHP) generator unit based on a pre-sale electricity price, as described in claim 1, is characterized in that... Step 12: Configure the deep-cycle load P2 of the cogeneration generator set. That is, multiply the deep-cycle load P2 by the number of hours h of the actual single cycle energy storage and release of the pre-designed molten salt energy storage and release system in a day to obtain the second energy storage capacity configuration W2 of the molten salt energy storage and release system. Step 13: Compare the first energy storage capacity configuration W1 with the second energy storage capacity configuration W2, and select the larger value as the capacity of the molten salt system of the cogeneration generator unit.

Images