A time-of-use electricity price calculation method based on supply and demand scenarios and system cost

Abstract

The application provides a kind of time-of-use electricity price calculation method based on supply and demand situation and system cost, and relates to the technical field of electric power price.The method first collects the capacity cost data of the generator set of system, considers that capacity cost is allocated differently in different load duration, adopts the method of peak load responsibility to allocate system capacity cost, and calculates the system time-sharing capacity cost allocated by each hour of electricity quantity;Then, according to system load data and power supply unit output level, the supply and demand situation of the electricity market is judged, and the supply and demand situation includes system supply and demand balance, system supply cannot meet demand, system oversupply, and system extreme;Then, according to the supply and demand situation of the electricity market, the corresponding time-of-use electricity price calculation method is selected, and the system unit price at the moment is calculated;Finally, the calculation result is counted, the typical load curve is extracted and the time period clustering is carried out.Compared with the traditional single electricity price mode, the application scientifically and perfectly constructs the time-of-use transaction pricing reference standard, and is more reliable and effective.

Description

Technical Field

[0001] This invention relates to the field of electricity pricing technology, and in particular to a time-of-use pricing method based on supply and demand scenarios and system costs. Background Technology

[0002] my country has proposed implementing time-of-use (TOU) trading in the medium- and long-term electricity market, shifting from a single-volume pricing model to a time-of-use pricing model with price curves. This represents a significant transformation in the trading model, moving from volume-based trading to electricity trading. TOU trading effectively addresses the issue that a single volume price cannot reflect the value of electricity at different times. However, a scientifically sound method for calculating the trading price for each time period is currently lacking. At present, provinces and cities in my country mainly implement TOU trading by referring to government-mandated prices, which is essentially still a form of regulated pricing. Therefore, the industry urgently needs to develop a method for calculating trading prices in the medium- and long-term electricity market to provide a more scientific and reliable basis for market participants to sign medium- and long-term TOU trading contracts with price curves. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a reliable and effective method for calculating time-of-use electricity prices based on supply and demand scenarios and system costs.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical method: a time-of-use electricity pricing method based on supply and demand scenarios and system costs, comprising:

[0005] Step S1: Calculate the time-of-use capacity cost F per unit of electricity under different annual load levels of the power system based on the peak load responsibility method. i ;

[0006] Step S2: Predict the output level of each power unit in the power system, including the output level of clean energy units and thermal power units. The clean energy units include hydropower units, wind power units, and photovoltaic units.

[0007] Step S3: Calculate the time-of-use net load of the power system and compare it with the output constraints of thermal power units to determine the supply and demand scenario of the power market; the net load is the difference between the total load of the power system and the output of clean energy units; the supply and demand scenario includes four scenarios: system supply and demand balance, system supply shortage, system oversupply, and system extreme oversupply.

[0008] Scenario 1, System supply and demand equilibrium: The system net load is greater than the minimum output of the thermal power units and less than the maximum output of the thermal power units;

[0009] Scenario 2, System supply falls short of demand: The system net load exceeds the maximum output of the thermal power unit;

[0010] Scenario 3, System oversupply: The system net load is less than the minimum output of the thermal power unit and is not zero;

[0011] Scenario 4, extreme oversupply in the system: the net load of the system is less than the minimum output of the thermal power unit;

[0012] Step S4: Select the appropriate time-of-use pricing method based on the supply and demand scenario of the power system, as follows:

[0013] If it is scenario 1: then based on the system's average cost pricing, first calculate the system's time-of-use electricity cost V. i Then, the time-sharing capacity cost F i With time-of-use electricity cost V i The unit electricity price P at time i is obtained by superposition. 1,i ;

[0014] If it is scenario 2: then pricing is based on the user's offload value, and the production function evaluation method is used to calculate the user's offload value to obtain the unit electricity price P at time i. 1,i ;

[0015] If it is scenario 3: then the system's time-of-use electricity cost V is calculated based on the system's marginal cost pricing. i Let it be the unit electricity price P at time i. 1,i ;

[0016] If it is scenario 4: then the pricing is based on the off-load value of the power generation enterprise, and the off-load value of the power generation enterprise is measured by the unit start-up and shutdown costs, thus obtaining the unit electricity price P at time i. 1,i ;

[0017] Step S5: Calculate the unit electricity price P at each point in time throughout the year in the statistical system. 1,i The calculation results are used to extract the curves of typical load days in typical months based on the SOM algorithm, and then fuzzy c-means clustering is performed on the extracted curves of typical load days in typical months to obtain the time-of-use clustering results of electricity prices in the medium and long term electricity market.

[0018] Furthermore, in step S1, the time-of-use capacity cost F per unit of electricity under different load levels of the power system is calculated based on the peak load responsibility method. i The process includes the following steps:

[0019] S101. Collect system power generation capacity cost data and calculate the total system capacity cost;

[0020] Collect data on fixed asset depreciation, material costs, repair costs, employee wages and benefits of the system's generator sets, and calculate the total system capacity cost using the following formula;

[0021] C fx =∑C dep +∑C mat +∑C rep +∑C sal(1)

[0022] In the formula: C fx C represents the total system capacity cost. dep C is the depreciation expense of the generator set as a fixed asset; mat C is the material cost of the generator set; rep C. Repair costs for the generator set; sal For employee salaries and benefits expenses related to the generator set;

[0023] S102. Convert the system's annual time-series load curve into a continuous load curve by arranging the loads in ascending order, and determine the duration corresponding to each load level.

[0024] S103. On the continuous load curve, the total electricity consumption is divided horizontally, and the capacity cost ΔD per unit of electricity consumption under the same load level is calculated using the following formula. i ;

[0025]

[0026] ΔP i =P i -P i-1 (4)

[0027]

[0028] ΔQ i =ΔP i t i (5)

[0029]

[0030] In the formula: P i Represents the load at time i; ΔQ i Indicates load level P i To P i-1 The amount of electricity between them; ΔC i Indicates the amount of electricity ΔQ i Capacity cost; ΔP i Indicates load level P i To P i-1 The load difference between them; P max Q represents the maximum load of the system; i The load level is represented by P. i The total energy is determined by the vertical energy block Q. i,j Composition, 1≤j≤i; t i This represents the i-th hour of the year, where 1 ≤ i ≤ 8760h;

[0031] S104. Calculate the capacity cost to be allocated to the divided power blocks according to the following formula;

[0032] Among them, the power block Q i,i Capacity cost C i,i for:

[0033]

[0034] Additionally, the battery block Q i,j On the capacity cost C i,j for:

[0035]

[0036] S105. Calculate the P under different loads after the accumulation of power blocks according to the following formula. i The capacity cost to be allocated F i ;

[0037]

[0038]

[0039] In the formula: C i For a load level of P i The sum of the capacity costs allocated to the electricity consumption.

[0040] Furthermore, step S2, when predicting the output level of each power unit in the power system, includes:

[0041] S201. Predict the power output level of the hydropower unit, as shown in the following formula:

[0042] P HY,i =gηa i μ i (11)

[0043] In the formula: P HY,i Represents the output of the hydropower unit at time i; g represents the acceleration due to gravity; η represents the power generation efficiency of the hydropower unit; α i μ represents the net head of water used for power generation at time i; i This represents the water flow rate at time i;

[0044] Based on the seasonality of hydropower output and the limitations of reservoir capacity, the hydropower output constraints are determined as follows:

[0045] P HY,min ≤P HY,i ≤P HY,max (12)

[0046] In the formula: P HY-min P represents the minimum output of the hydroelectric generator unit. HY-max This indicates the maximum output of the hydroelectric generator unit;

[0047] S202. Predict the output level of the wind turbine, as shown in the following formula:

[0048]

[0049] In the formula: P WT P represents the output of the wind turbine at time i; r Indicates the rated power of the wind turbine; V i V represents the wind speed at time i; r V ci V cu These represent the rated wind speed, cut-in wind speed, and cut-out wind speed, respectively; the wind power output is represented by a Weibull distribution, as follows:

[0050] V i =c(-lnβ) 1 / k (14)

[0051]

[0052]

[0053] In the formula: c represents the scale parameter; k represents the shape parameter; β represents a random number that follows a uniform distribution on the interval 0 to 1; E WT σ represents the average wind speed. WT The standard deviation of wind speed is represented by Γ(), which is the gamma function.

[0054] S203. Predict the output level of the photovoltaic unit, as shown in the following formula:

[0055] P PV,i =λ PV γ PV,i S PV (17)

[0056] In the formula: P PV λ represents the output of the photovoltaic unit at time i; PV Indicates the rated photoelectric conversion efficiency; S PV Indicates the total area of ​​the photovoltaic module; γ PV,i Let represent the radiation intensity of the photovoltaic module at time i. The radiation intensity follows a Beta distribution, and its probability density function is as follows:

[0057]

[0058] In the formula: γ max This represents the maximum value of the radiation intensity of the photovoltaic module; a and b represent the shape parameters of the Beta distribution, respectively.

[0059] S204. The system prioritizes the consumption of clean energy. The power generation of thermal power units is determined by the difference between the total system load and the power generation of clean energy, and the following constraints are met:

[0060] P TH-min ≤P TH,i ≤P TH-max (19)

[0061] In the formula: P TH,i P represents the output of the thermal power unit at time i; TH-min P represents the minimum output of a thermal power unit. TH-max This indicates the maximum output of the thermal power unit.

[0062] Furthermore, in step S3: calculate the time-sharing net load of the power system and compare it with the output constraints of thermal power units to determine the supply and demand scenario of the power market; the net load is the difference between the total load of the power system and the output of clean energy units, as shown in equation (20); the supply and demand scenario includes four scenarios: system supply and demand balance, system supply shortage, system oversupply, and system extreme oversupply, as shown in equations (21)-(24).

[0063] P CL,i =P SY,i -P HY,i -P WT,i -P PV,i (20)

[0064] In the formula: P CL,i P represents the net load of the system at time i; SY,i P represents the total load of the system at time i; HY,i P represents the output of the hydroelectric generator at time i; WT,i P represents the output of the wind turbine at time i; PV,i This represents the output of the photovoltaic unit at time i;

[0065] Scenario 1, System Supply and Demand Balance: The system net load is greater than the minimum output of the thermal power units but less than the maximum output of the thermal power units, that is:

[0066] P TH-min ≤P CL,i ≤P TH-max (twenty one)

[0067] Scenario 2, System Supply Falls Short of Demand: The system's net load exceeds the maximum output of the thermal power units, i.e.:

[0068] P CL,i >P TH-max (twenty two)

[0069] Scenario 3, System oversupply: The system net load is less than the minimum output of the thermal power unit, and is not zero, i.e.:

[0070] 0 < P CL,i <P TH-min (twenty three)

[0071] Scenario 4, Extreme Oversupply in the System: The net load of the system is less than the minimum output of the thermal power units, i.e.:

[0072] P CL,i =0 (24)

[0073] In step S4, the corresponding time-of-use pricing method is selected based on the supply and demand scenario of the power system, as follows:

[0074] If it is scenario 1: then based on the system's average cost pricing, first calculate the system's time-of-use electricity cost V. i Then, the time-sharing capacity cost F i With time-of-use electricity cost V i The unit electricity price P at time i is obtained by superposition. 1,i ,Right now:

[0075]

[0076] In the formula: η represents the standard coal consumption rate of thermal power; α represents the standard coal price at time i;

[0077] If it is scenario 2: then pricing is based on the user's offload value, and the production function evaluation method is used to calculate the user's offload value to obtain the unit electricity price P at time i. 1,i ,Right now:

[0078] P 2,i =GVA / G (26)

[0079] In the formula: GVA represents the total added value of the industry, and G represents the electricity consumption of the industry;

[0080] If it is scenario 3: then the system's time-of-use electricity cost V is calculated based on the system's marginal cost pricing. i Let it be the unit electricity price P at time i. 1,i ,Right now:

[0081]

[0082] If it is scenario 4: then the pricing is based on the off-load value of the power generation enterprise, and the off-load value of the power generation enterprise is measured by the unit start-up and shutdown costs, thus obtaining the unit electricity price P at time i. 1,i ,Right now:

[0083] P 4,i =H / D (28)

[0084] In the formula: H represents the start-up and shutdown cost of the unit, and D represents the power generation lost due to unit shutdown.

[0085] Based on the deepening of my country's market-oriented electricity price reform and the increasingly serious problem of peak power shortage in the system, this invention proposes a time-of-use (TOU) electricity price calculation method based on supply and demand scenarios and system costs. This method comprehensively considers the user utility of changes in production costs and electricity market supply and demand scenarios, and divides electricity prices into four typical time-of-use scenarios and corresponding pricing. Compared with the traditional single-electricity price model, this method scientifically and comprehensively constructs a time-of-use transaction pricing reference standard, which is more reliable and effective. It can provide a strong basis and reference for market participants' quotations in medium- and long-term time-of-use transactions, for provinces (municipalities, and autonomous regions) to formulate medium- and long-term time-of-use transaction rules, and for government market price supervision. Attached Figure Description

[0086] Figure 1 This is a schematic diagram illustrating the time-of-use capacity cost allocation in the time-of-use electricity pricing method based on supply and demand scenarios and system costs involved in this invention.

[0087] Figure 2 This is a flowchart of the time-of-use electricity pricing calculation method based on supply and demand scenarios and system costs involved in this invention;

[0088] Figure 3 This is a diagram showing the relationship between monthly power supply capacity and electricity demand in Province H in an embodiment of the present invention;

[0089] Figure 4 This is a graph showing the calculation results of the time-of-use electricity price for 8760 hours per year in Province H in an embodiment of the present invention;

[0090] Figure 5 This is a monthly electricity price curve for Province H in an embodiment of the present invention;

[0091] Figure 6 This is a graph showing the average daily electricity price in Province H in August 2019, as described in this invention.

[0092] Figure 7 This is a graph showing the average daily electricity price in Province H in May 2019, as described in this embodiment of the invention.

[0093] Figure 8 This is a graph showing the average daily electricity price in Province H in January 2019, as described in this embodiment of the invention.

[0094] Figure 9 This is a time-averaged electricity price curve for the day of maximum load in Province H during the summer of 2019, as described in an embodiment of the present invention.

[0095] Figure 10 This is a time-average electricity price curve for the lowest load day in summer 2019 in Province H, as shown in this embodiment of the invention. Detailed Implementation

[0096] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to embodiments and accompanying drawings. The content mentioned in the embodiments is not intended to limit the present invention.

[0097] like Figure 2 As shown, a time-of-use electricity pricing method based on supply and demand scenarios and system costs mainly includes five steps, as detailed below.

[0098] Step S1: Calculate the time-of-use capacity cost F per unit of electricity under different annual load levels of the power system based on the peak load responsibility method. i .

[0099] S101. The system time-sharing capacity cost consists of the fixed asset depreciation cost, material cost, repair cost, and employee wages and welfare expenditure data of the generator set. Collect this data and calculate the total system capacity cost according to the following formula.

[0100] C fx =∑C dep +∑C mat +∑C rep +∑C sal (1)

[0101] In the formula: C fx C represents the total system capacity cost. dep C is the depreciation expense of the generator set as a fixed asset; mat C is the material cost of the generator set; rep C. Repair costs for the generator set; sal This covers the employee salaries and benefits expenses for the generator set.

[0102] S102. Calculating the time-of-use capacity cost per unit of electricity under different annual load levels in a power system based on the peak-load responsibility method requires considering both demand level and load duration; that is, the price is the same for the same load duration. For example... Figure 1 As shown, the system's annual time-series load curve is transformed into a continuous load curve by arranging the loads in ascending order, and the duration corresponding to each load level is determined.

[0103] S103, such as Figure 1 As shown, the total electricity consumption is horizontally divided on the continuous load curve, and the capacity cost ΔD per unit of electricity consumption at the same load level is calculated using the following formula. i .

[0104]

[0105] ΔP i =P i -P i-1 (4)

[0106]

[0107] ΔQ i =ΔP i t i (5)

[0108]

[0109] In the formula: P i Represents the load at time i; ΔQ i Indicates load level P i To P i-1 The amount of electricity between them; ΔC i Indicates the amount of electricity ΔQ i Capacity cost; ΔP i Indicates load level P i To P i-1 The load difference between them; P max Q represents the maximum load of the system; i The load level is represented by P. i The total energy is determined by the vertical energy block Q. i,j Composition, 1≤j≤i; t i Let represent the i-th hour of the year, where 1 ≤ i ≤ 8760h.

[0110] S104. Calculate the capacity cost to be allocated to the divided power blocks according to the following formula.

[0111] Among them, the power block Q i,i Capacity cost C i,i for:

[0112]

[0113] Additionally, the battery block Q i,j On the capacity cost C i,j for:

[0114]

[0115] S105. Calculate the P under different loads after the accumulation of power blocks according to the following formula. i The capacity cost to be allocated F i .

[0116]

[0117]

[0118] In the formula: C i For a load level of P i The sum of the capacity costs allocated to the electricity consumption.

[0119] Step S2: Predict the output level of each power unit in the power system.

[0120] The power system contains various power generation units, including hydropower, thermal power, wind power, and solar power. Among them, hydropower units, wind power units, and photovoltaic units are clean energy units. This invention prioritizes the consumption of clean energy in the system, with thermal power units serving as system peak-shaving backup units. The predicted output of each hydropower, wind power, and photovoltaic unit, and the output constraints of the thermal power unit are as follows. S201, Predicting the output level of the hydropower unit, as shown in the following formula:

[0121] P HY,i =gηa i μ i (11)

[0122] In the formula: P HY,i Represents the output of the hydropower unit at time i; g represents the acceleration due to gravity; η represents the power generation efficiency of the hydropower unit; α i μ represents the net head of water used for power generation at time i, and is taken as a constant. i Let i represent the water flow rate at time i.

[0123] Based on the seasonality of hydropower output and the limitations of reservoir capacity, the hydropower output constraints are determined as follows:

[0124] P HY,min ≤P HY,i ≤P HY,max (12)

[0125] In the formula: P HY-min P represents the minimum output of the hydroelectric generator unit. HY-max This indicates the maximum output of the hydroelectric generator unit.

[0126] S202. Predict the output level of the wind turbine, as shown in the following formula:

[0127]

[0128] In the formula: P WT P represents the output of the wind turbine at time i; r Indicates the rated power of the wind turbine; V i V represents the wind speed at time i; r V ci V cu These represent the rated wind speed, cut-in wind speed, and cut-out wind speed, respectively.

[0129] Wind power output is mainly affected by wind speed. In this invention, the wind speed is represented by a Weibull distribution, as follows:

[0130] V i =c(-lnβ) 1 / k (14)

[0131]

[0132]

[0133] In the formula: c represents the scale parameter; k represents the shape parameter; β represents a random number that follows a uniform distribution on the interval 0 to 1; E WT σ represents the average wind speed. WT Γ represents the standard deviation of wind speed; Γ() is the gamma function.

[0134] S203. Predict the output level of the photovoltaic unit, as shown in the following formula:

[0135] P PV,i =λ PV γ PV,i S PV (17)

[0136] In the formula: P PV λ represents the output of the photovoltaic unit at time i; PV Indicates the rated photoelectric conversion efficiency; S PV Indicates the total area of ​​the photovoltaic module; γ PV,i Let represent the radiation intensity of the photovoltaic module at time i. The radiation intensity follows a Beta distribution, and its probability density function is as follows:

[0137]

[0138] In the formula: γ max denoted by , where represents the maximum value of the photovoltaic module's radiation intensity; a and b represent the shape parameters of the Beta distribution, respectively.

[0139] S204. In this invention, the system prioritizes the consumption of clean energy. The power generation of the thermal power unit is determined by the difference between the total system load and the power generation of clean energy, and the following constraints are met:

[0140] P TH-min ≤P TH,i ≤P TH-max (19)

[0141] In the formula: P TH,i P represents the output of the thermal power unit at time i; TH-min P represents the minimum output of a thermal power unit. TH-max This indicates the maximum output of the thermal power unit.

[0142] Step S3: Calculate the time-of-use net load of the power system and compare it with the output constraints of thermal power units to determine the supply and demand scenario of the power market.

[0143] The aforementioned net load is the difference between the total load of the power system and the output of clean energy units, as shown in the following formula:

[0144] P CL,i =P SY,i -P HY,i -P WT,i -P PV,i (20)

[0145] In the formula: P CL,i P represents the net load of the system at time i; SY,i P represents the total load of the system at time i; HY,i P represents the output of the hydroelectric generator at time i; WT,i P represents the output of the wind turbine at time i; PV,i This represents the output of the photovoltaic unit at time i.

[0146] This invention obtains the following four scenarios by comparing the net load of the system with the output constraints of the thermal power unit.

[0147] Scenario 1, System Supply and Demand Balance: The system net load is greater than the minimum output of the thermal power units but less than the maximum output of the thermal power units, that is:

[0148] P TH-min ≤P CL,i ≤P TH-max (twenty one)

[0149] Scenario 2, System Supply Falls Short of Demand: The system's net load exceeds the maximum output of the thermal power units, i.e.:

[0150] P CL,i >P TH-max (twenty two)

[0151] Scenario 3, System oversupply: The system net load is less than the minimum output of the thermal power unit, and is not zero, i.e.:

[0152] 0 < P CL,i <P TH-min (twenty three)

[0153] Scenario 4, Extreme Oversupply in the System: The net load of the system is less than the minimum output of the thermal power units, i.e.:

[0154] P CL,i =0 (24)

[0155] Step S4: Select the appropriate time-of-use pricing method based on the supply and demand scenario of the power system, as follows:

[0156] If it is Scenario 1: the market is in supply and demand equilibrium, then pricing is based on the system's average cost. First, calculate the system's time-of-use electricity cost V. i Then, the time-sharing capacity cost F i With time-of-use electricity cost V i The unit electricity price P at time i is obtained by superposition.1,i ,Right now:

[0157]

[0158] In the formula: η represents the standard coal consumption rate of thermal power; α represents the standard coal price at time i.

[0159] In scenario 2, where the market is in a state of supply shortage, pricing is based on the user's off-load value. The production function evaluation method is used to calculate the user's off-load value, resulting in the unit electricity price P at time i. 1,i ,Right now:

[0160] P 2,i =GVA / G (26)

[0161] In the formula: GVA represents the total added value of the industry, and G represents the electricity consumption of the industry.

[0162] In scenario 3, where the market experiences oversupply, the system's time-of-use electricity cost V is calculated based on the system's marginal cost. i Let it be the unit electricity price P at time i. 1,i ,Right now:

[0163]

[0164] In scenario 4, where the market is in a state of extreme oversupply, pricing is based on the off-load value of power generation companies. The off-load value of power generation companies is measured using the unit start-up and shutdown costs, resulting in the unit electricity price P at time i. 1,i ,Right now:

[0165] P 4,i =H / D (28)

[0166] In the formula: H represents the start-up and shutdown cost of the unit, and D represents the power generation lost due to unit shutdown.

[0167] Step S5: Calculate the unit electricity price for 8760 hours of the system in a year. Extract the curves of typical load days in typical months based on the SOM algorithm. Then, perform fuzzy c-means clustering on the extracted curves of typical load days in typical months to obtain the time-of-use clustering results of the electricity market's time-of-use trading price in the medium and long term.

[0168] To further illustrate this invention, the following calculations are based on 8760 hours of load data from H Province from November 2018 to October 2019, power generation enterprise cost data, monthly changes in coal prices throughout the year, power supply structure, and typical daily hydropower output during the wet and dry seasons, to simulate the annual medium- and long-term market time-of-use electricity price.

[0169] H Province's power structure is mainly based on hydropower and thermal power. In 2019, the province's approved total system capacity cost was 17.509 billion yuan. The standard coal consumption coefficient for thermal power units was 0.311 kg / kWh, and the price of electricity purchased from outside the province was 0.296 yuan / kWh. The value of power shortage and load loss was taken as H Province's 2019 industrial GDP increase of 7.823 yuan / kWh. The installed capacity of wind power and photovoltaic power was 4071 MW and 2649 MW, respectively. The marginal variable cost of hydropower, wind power, and photovoltaic power was set at 0 yuan / kWh. The cut-in, rated, and cut-out wind speeds of wind turbines were 3 m / s, 12 m / s, and 22 m / s, respectively. The relationship between H Province's monthly power supply capacity and electricity demand is as follows: Figure 3 As shown.

[0170] According to the relevant provisions of the "Basic Rules for Medium and Long-Term Electricity Transactions" in H Province regarding the types of transactions, the time-of-use electricity prices for the year, month, and typical day are calculated separately. The calculation process is based on the Maltab 2018a platform.

[0171] I. Annual Time-of-Use Electricity Price

[0172] Figure 4 The calculation results for the time-of-use electricity price for H Province over an annual period of 8760 hours show the following characteristics of the electricity price curve in H Province:

[0173] (1) The overall electricity price fluctuates greatly. The average annual electricity price is 0.217 yuan / kWh, the maximum electricity price is 7.8231 yuan / kWh, and the minimum electricity price is 0.066 yuan / kWh, which is about 120 times.

[0174] (2) There are significant differences in electricity prices between quarters, with prices in winter and summer being significantly higher than those in spring and autumn. Electricity prices in summer are higher than those in winter.

[0175] (3) Electricity prices change relatively little compared to annual and quarterly changes, but peak electricity prices will form on specific dates.

[0176] (4) There are an average of 5 hours of power shortage throughout the year, of which 3 hours occur on the summer peak load day, indicating that the power shortage is a short-term rather than a long-term situation, and there is no extreme oversupply.

[0177] Table 1. Annual Time Period Division and Time-of-Use Electricity Price Calculation Results

[0178]

[0179] II. Monthly Time-of-Use Electricity Pricing

[0180] Monthly electricity prices in H province, as follows: Figure 5 As shown. Figure 5The calculation results of monthly average electricity price, monthly maximum electricity price, monthly minimum electricity price, and annual average electricity price were compared. The results show that the monthly maximum electricity price fluctuates significantly, the minimum electricity price fluctuates relatively steadily, and the monthly average electricity price fluctuates significantly less than the monthly maximum electricity price.

[0181] Based on seasonal differences, H Province's electricity prices are divided into peak months (July-August, November-December), average months (January-February, September-October), and low months (March-May). Typical months are then used for time-period clustering, as follows:

[0182] (1) Calculation results of typical peak months

[0183] Table 2. Time Period Division and Time-of-Use Electricity Price Calculation Results for August

[0184]

[0185] The average daily electricity price in H Province in August 2019 is as follows: Figure 6 As shown, the average monthly electricity price in August was 0.388 yuan / kWh, with the highest daily average price at 1.385 yuan / kWh, representing a 256.96% increase from the monthly average price. The lowest daily average price was 0.216 yuan / kWh, representing a 44.33% decrease from the monthly average price. The highest daily average price was 6.41 times the lowest daily average price. This is because the abundant hydropower generation during the high-water season reduced the space for thermal power generation, thus increasing peak-hour costs.

[0186] (2) Calculation results of typical low-valley months

[0187] Table 4. Time Period Division and Time-of-Use Electricity Price Calculation Results for May

[0188]

[0189] The average daily electricity price in H Province in May 2019 was as follows: Figure 7 As shown, the average monthly electricity price in May was 0.160 yuan / kWh, with the highest daily average price at 0.177 yuan / kWh, representing a 10.63% increase from the average monthly price. The lowest daily average price was 0.132 yuan / kWh, representing a 17.5% decrease from the average monthly price. The highest daily average price was 1.34 times the lowest daily average price.

[0190] (3) Calculation results of typical flat monthly segments

[0191] Table 5. Time Period Division and Time-of-Use Electricity Price Calculation Results for January

[0192]

[0193] III. Time-of-use electricity pricing on a typical day

[0194] The average daily electricity price in H Province in January 2019 is as follows: Figure 8 As shown, the average monthly electricity price in January was 0.278 yuan / kWh, with the highest daily average price at 0.389 yuan / kWh, representing a 39.93% increase from the average monthly price. The lowest daily average price was 0.214 yuan / kWh, representing a 23.02% decrease from the average monthly price. This was due to a significant reduction in hydropower generation during the dry season, leading to increased utilization of thermal power units.

[0195] like Figure 9 As shown, the average daily electricity price in Province H during the summer of 2019, when the load was at its peak, was RMB 1.066 / kWh. The average price at the peak was RMB 7.832 / kWh (21:00 and 22:00 were under supply shortage scenarios, and user load loss value pricing was adopted), which was 634.7% higher than the average daily price. The average price at the minimum was RMB 0.255 / kWh, which was 76.07% lower than the average daily price. The average price at the peak was 30.71 times that at the minimum.

[0196] like Figure 10 As shown, the average daily electricity price in Province H during the winter of 2019 was 0.137 yuan / kWh on the day with the lowest load, and the average electricity price at the highest time was 0.217 yuan / kWh, representing an increase of 58.39% based on the average daily electricity price. The average electricity price at the lowest time was 0.067 yuan / kWh (from 3 to 9 am, it was a scenario of oversupply, and the system marginal cost pricing was adopted), representing a decrease of 51.09% based on the average daily electricity price. The average electricity price at the highest time was 3.24 times that at the lowest time.

[0197] The above embodiments are preferred implementations of the present invention. In addition, the present invention can be implemented in other ways. Any obvious substitutions without departing from the concept of the present technical solution are within the protection scope of the present invention.

[0198] To facilitate understanding by those skilled in the art of the improvements of this invention over the prior art, some of the accompanying drawings and descriptions have been simplified, and for clarity, some other elements have been omitted from this application. Those skilled in the art should realize that these omitted elements may also constitute the content of this invention.

Claims

1. A method for calculating time-of-use electricity prices based on supply and demand scenarios and system costs, characterized in that, include: Step S1: Calculate the time-of-use capacity cost per unit of electricity under different annual load levels of the power system based on the peak load responsibility method. F i ; Step S2: Predict the output level of each power unit in the power system, including the output level of clean energy units and thermal power units. The clean energy units include hydropower units, wind power units, and photovoltaic units. Step S3: Calculate the time-of-use net load of the power system and compare it with the output constraints of thermal power units to determine the supply and demand scenario of the power market; the net load is the difference between the total load of the power system and the output of clean energy units; the supply and demand scenario includes four scenarios: system supply and demand balance, system supply shortage, system oversupply, and system extreme oversupply. Scenario 1, System supply and demand equilibrium: The system net load is greater than the minimum output of the thermal power units and less than the maximum output of the thermal power units; Scenario 2, System supply falls short of demand: The system net load exceeds the maximum output of the thermal power unit; Scenario 3, System oversupply: The system net load is less than the minimum output of the thermal power unit and is not zero; Scenario 4, extreme oversupply in the system: the net load of the system is less than the minimum output of the thermal power unit; Step S4: Select the appropriate time-of-use pricing method based on the supply and demand scenario of the power system, as follows: If it is Scenario 1: Then, based on the system's average cost pricing, first calculate the system's time-of-use electricity cost. V i Then, the time-sharing capacity cost F i With time-of-use electricity cost V i Superimposed i Electricity price per unit time P 1,i ; If it is scenario 2: then the user's unloaded value is priced according to the user's unloaded value, and the production function evaluation method is used to calculate the user's unloaded value. i Electricity price per unit time P 2,i ; If it is scenario 3: then price based on the system's marginal cost and calculate the system's time-of-use electricity cost. V i take it as i Electricity price per unit time P 3,i ; If it is scenario 4: then the pricing is based on the power generation company's load loss value, and the unit start-up and shutdown costs are used to measure the power generation company's load loss value, resulting in... i Electricity price per unit time P 4,i ; Step S5: Calculate the unit electricity price at each point in time throughout the year using the statistical system. P 1,i The calculation results are used to extract the curves of typical load days in typical months based on the SOM algorithm, and then fuzzy c-means clustering is performed on the extracted curves of typical load days in typical months to obtain the time-of-use clustering results of electricity prices in the medium and long term electricity market.

2. The time-of-use electricity pricing calculation method based on supply and demand scenarios and system costs according to claim 1, characterized in that: In step S1, the time-of-use capacity cost per unit of electricity under different load levels of the power system is calculated based on the peak load responsibility method. F i The process includes the following steps: S101. Collect system power generation capacity cost data and calculate the total system capacity cost; Collect data on fixed asset depreciation, material costs, repair costs, employee wages and benefits of the system's generator sets, and calculate the total system capacity cost using the following formula; （1） In the formula: C fx Total system capacity cost; C dep Depreciation expense for generator sets; C mat For the material cost of the generator set; C rep Repair costs for the generator set; C sal For employee salaries and benefits expenses related to the generator set; S102. Convert the system's annual time-series load curve into a continuous load curve by arranging the loads in ascending order, and determine the duration corresponding to each load level. S103. On the continuous load curve, the total electricity consumption is divided horizontally, and the capacity cost Δ per unit of electricity consumption under the same load level is calculated using the following formula. D i ; （2） （4） （3） （5） （6） In the formula: P i express i Load at time; Δ Q i Indicates load level P i to P i-1 The amount of electricity between them; Δ C i Indicates the amount of electricity Δ Q i Capacity cost; Δ P i Indicates load level P i to P i-1 The load difference between them; P max Represents the maximum load of the system; Q i Indicates the load level as P i The total power is composed of vertical power blocks. Q i,j Composition, 1≤j≤ i ; t i Indicates the first of the year i Hours, 1≤ i ≤8760h; S104. Calculate the capacity cost to be allocated to the divided power blocks according to the following formula; Among them, power block Q i,i Capacity cost C i,i for: （7） In addition, the power block Q i,j On-capacity cost C i,j for: （8） S105. Calculate the accumulated power blocks under different loads according to the following formula. P i Amortized capacity costs F i ; （9） （10） In the formula: C i For load level P i The sum of the capacity costs allocated to the electricity consumption, Δ P j Indicates load level P j to P j-1 The load difference between them; t j Indicates the first of the year j hours, 1≤j≤ i ,1≤ i ≤8760h.

3. The time-of-use electricity pricing calculation method based on supply and demand scenarios and system costs according to claim 2, characterized in that: Step S2, predicting the output level of each power unit in the power system, includes: S201. Predict the power output level of the hydropower unit, as shown in the following formula: （11） In the formula: P HY,i express i The output of the hydroelectric generator at any given moment; g represents the acceleration due to gravity; These represent the power generation efficiency of the hydroelectric generator unit; This represents the net head of water generated at time i; express i Water flow rate at any given time; Based on the seasonality of hydropower output and the limitations of reservoir capacity, the hydropower output constraints are determined as follows: （12） In the formula: P HY-min This indicates the minimum output of the hydroelectric generator unit; P HY-max This indicates the maximum output of the hydroelectric generator unit; S202. Predict the output level of the wind turbine, as shown in the following formula: （13） In the formula: P WT，i express i The output of the wind turbine at all times; P r This indicates the rated power of the wind turbine generator set; V i express i Wind speed at any given moment; V r , V ci , V cu These represent the rated wind speed, cut-in wind speed, and cut-out wind speed, respectively; the wind power output is represented by a Weibull distribution, as follows: （14） （15） （16） In the formula: c Indicates the scale parameter; k Indicates shape parameters; β Represents a random number that follows a uniform distribution in the range 0 to 1; E WT This represents the average wind speed. The standard deviation of wind speed; It is a gamma function; S203. Predict the output level of the photovoltaic unit, as shown in the following formula: （17） In the formula: P PV，i express i The output of the photovoltaic unit at all times; Indicates the rated photoelectric conversion efficiency; S PV This indicates the total area of ​​the photovoltaic module; express i The radiation intensity of the photovoltaic module at any given time follows a Beta distribution, and its probability density function is as follows: （18） In the formula: This represents the maximum radiation intensity of the photovoltaic module; a、b These represent the shape parameters of the Beta distribution; S204. The system prioritizes the consumption of clean energy. The power generation of thermal power units is determined by the difference between the total system load and the power generation of clean energy, and the following constraints are met: （19） In the formula: P TH,i express i The output of thermal power units at all times; P TH-min This indicates the minimum output of the thermal power unit; P TH-max This indicates the maximum output of the thermal power unit.

4. The time-of-use electricity pricing calculation method based on supply and demand scenarios and system costs according to claim 3, characterized in that: In step S3: calculate the time-sharing net load of the power system and compare it with the output constraints of thermal power units to determine the supply and demand scenario of the power market; the net load is the difference between the total load of the power system and the output of clean energy units, as shown in equation (20); the supply and demand scenario includes four scenarios: system supply and demand balance, system supply shortage, system oversupply, and system extreme oversupply, as shown in equations (21)-(24). （20） In the formula: P CL,i express i Net load of the time-of-use system; P SY,i express i Total load of the time-based system; P HY,i express i The output of the hydroelectric generator unit at all times; P WT,i express i The output of the wind turbine at all times; P PV,i express i The output of the photovoltaic unit at all times; Scenario 1, System Supply and Demand Balance: The system net load is greater than the minimum output of the thermal power units but less than the maximum output of the thermal power units, that is: （21） Scenario 2, System Supply Falls Short of Demand: The system's net load exceeds the maximum output of the thermal power units, i.e.: （22） Scenario 3, System oversupply: The system net load is less than the minimum output of the thermal power unit, and is not zero, i.e.: （23） Scenario 4, Extreme Oversupply in the System: The net load of the system is less than the minimum output of the thermal power units, i.e.: （24） In step S4, the corresponding time-of-use pricing method is selected based on the supply and demand scenario of the power system, as follows: If it is Scenario 1: Then, based on the system's average cost pricing, first calculate the system's time-of-use electricity cost. V i Then, the time-sharing capacity cost F i With time-of-use electricity cost V i Superimposed i Electricity price per unit time P 1,i ,Right now: （25） In the formula: This indicates the standard coal consumption rate for thermal power plants; express i The price per unit of standard coal at that moment; If it is scenario 2: then the user's unloaded value is priced according to the user's unloaded value, and the production function evaluation method is used to calculate the user's unloaded value. i Electricity price per unit time P 2,i ,Right now: （26） In the formula: GVA This represents the total added value of the industry. G This indicates the industry's electricity consumption; If it is scenario 3: then price based on the system's marginal cost and calculate the system's time-of-use electricity cost. V i take it as i Electricity price per unit time P 3,i ,Right now: （27） If it is scenario 4: then the pricing is based on the power generation company's load loss value, and the unit start-up and shutdown costs are used to measure the power generation company's load loss value, resulting in... i Electricity price per unit time P 4,i ,Right now: （28） In the formula: H This indicates the start-up and shutdown costs of the generating unit. D This indicates the amount of electricity lost due to unit shutdown.

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