Time-of-use electricity price calculation method and system considering uncertainty of load and renewable energy

Abstract

A time-of-use electricity price calculation method considering load and renewable energy source uncertainty comprises the following steps: S1, constructing a time-of-use electricity price optimization model of an electric vehicle charging station based on the minimum load variance of a user demand curve, the highest user electricity utilization satisfaction degree and the minimum renewable energy source abandoning rate; and S2, solving the time-of-use electricity price optimization model by using a multi-objective genetic algorithm to obtain a time-of-use electricity price making strategy. According to the design, the electric energy generated by the distributed energy sources is fully utilized, and the multiple energy sources are more beneficial to most consumers.

Description

Technical Field

[0001] The present invention relates to a time-of-use electricity price calculation method and system taking into account the uncertainty of load and renewable energy, and is specifically applicable to improving the energy utilization rate of distributed power sources through time-of-use electricity prices. Background Art

[0002] The terminal energy in the transportation sector is becoming more and more electrified, and the charging load of electric vehicles will become one of the largest loads in the future power grid. By 2025, the sales of new energy vehicles will reach about 20% of the total new car sales.

[0003] Net-zero microgrids are able to improve energy efficiency and reduce carbon emissions by effectively utilizing renewable energy sources such as photovoltaic power, wind power, pumped storage, etc. However, due to the operating characteristics of renewable energy, the balance of load and power generation during system operation poses significant problems. The deployment of electric vehicle charging stations can improve the economic dispatch problem by guiding users to change their electricity consumption behavior to match the output power of renewable energy. Based on the deployment of electric vehicle charging stations, time-of-use electricity prices play an important role in reducing peak-valley differences and renewable energy abandonment rates. Summary of the invention

[0004] The purpose of the present invention is to overcome the problem of mismatch between time-of-use electricity price and distributed power generation in the prior art, and to provide a time-of-use electricity price calculation method and system that utilizes unit price to guide charging behavior and considers the uncertainty of load and renewable energy.

[0005] To achieve the above objectives, the technical solution of the present invention is:

[0006] In a first aspect, the present invention provides a method for calculating time-of-use electricity prices taking into account the uncertainty of load and renewable energy, comprising the following steps:

[0007] S1. Based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate, a time-of-use electricity price optimization model is constructed;

[0008] S2. Use a multi-objective genetic algorithm to solve the time-of-use electricity price optimization model and obtain a time-of-use electricity price formulation strategy.

[0009] The time-of-use electricity price optimization model in S1 is:

[0010]

[0011] Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q var is the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q incThe convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESS,char (t) is the amount of electricity charged by the user during period t; Q ESS,dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

[0012] In S1, users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand:

[0013]

[0014] Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t;

[0015] Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is:

[0016]

[0017] Among them, a and b are the parameters of the linearized price elasticity model;

[0018] By modifying the price, the power demand Q1(t) of the user in period t is defined as:

[0019]

[0020] Among them, Q1(t) is the power demand of the user in period t.

[0021] In S1, a user electricity satisfaction model is constructed:

[0022] Define the convenience rate Q of user electricity consumption inc :

[0023]

[0024] Among them, T is the user's charging time;

[0025] Define the inconvenience rate C of users in paying electricity bills inc :

[0026]

[0027] Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t;

[0028] User demand curve load variance Q var :

[0029]

[0030] Among them, Q 1,mean is the average power consumption value of the user demand curve;

[0031] Renewable energy abandonment rateA rec :

[0032]

[0033] in, is the average power demand of users in period t, Q ESS,char (t) is the user’s charging power during period t, Q ESS,dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

[0034] In a second aspect, the present invention provides a time-of-use electricity price calculation system that takes into account the uncertainty of load and renewable energy, including: an electricity price optimization model construction module, an electricity price optimization model solution module;

[0035] Electricity price optimization model construction module: used to build a time-of-use electricity price optimization model based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate;

[0036] Electricity price optimization model solving module: used to solve the time-of-use electricity price optimization model using a multi-objective genetic algorithm and obtain a time-of-use electricity price formulation strategy.

[0037] The time-of-use electricity price optimization model in the electricity price optimization model construction module is:

[0038]

[0039] Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q var is the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q inc The convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESS,char(t) is the amount of electricity charged by the user during period t; Q ESS,dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

[0040] In the electricity price optimization model construction module, users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand:

[0041]

[0042] Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t;

[0043] Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is:

[0044]

[0045] Among them, a and b are the parameters of the linearized price elasticity model;

[0046] By modifying the price, the power demand Q1(t) of the user in period t is defined as:

[0047]

[0048] Among them, Q1(t) is the power demand of the user in period t.

[0049] In the electricity price optimization model construction module, a user electricity satisfaction model is constructed:

[0050] Define the convenience rate Q of user electricity consumption inc :

[0051]

[0052] Among them, T is the user's charging time;

[0053] Define the inconvenience rate C of users in paying electricity bills inc :

[0054]

[0055] Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t;

[0056] User demand curve load variance Q var :

[0057]

[0058] Among them, Q 1,mean is the average power consumption value of the user demand curve;

[0059] Renewable energy abandonment rateA rec :

[0060]

[0061] in, is the average power demand of users in period t, Q ESS,char (t) is the user’s charging power during period t, Q ESS,dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

[0062] In a third aspect, the present invention provides a time-of-use electricity price calculation device that takes into account the uncertainty of load and renewable energy, including a memory and a processor, wherein the memory is used to store a computer program code and transmit the computer program code to the processor;

[0063] The processor is used to execute the aforementioned time-of-use electricity price calculation method considering the uncertainty of load and renewable energy according to the instructions in the computer program code.

[0064] In a fourth aspect, the present invention provides a computer program product, including a computer program, which is executed by a processor to implement the aforementioned method for calculating time-of-use electricity prices that takes into account the uncertainty of load and renewable energy.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. The present invention provides a time-of-use electricity price calculation method that considers the uncertainty of load and renewable energy. The time-of-use electricity price strategy that considers the uncertainty of residential load, wind power and photovoltaic power generation is used to guide users to change their charging strategies. The multi-objectives of this strategy mainly include minimizing load variance, maximizing user satisfaction and minimizing the proportion of users abandoning renewable energy. The time-of-use electricity price is optimized using a linear function of elastic electricity price to user demand. The time-of-use electricity price is solved using a multi-objective genetic algorithm. This time-of-use electricity price strategy is beneficial to both energy consumers and renewable energy, and ultimately achieves sustainable net zero power generation.

[0067] 2. A time-of-use electricity price calculation system considering the uncertainty of load and renewable energy in the present invention includes an electricity price optimization model construction module: used to construct a time-of-use electricity price optimization model based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate; an electricity price optimization model solution module: used to solve the time-of-use electricity price optimization model using a multi-objective genetic algorithm to obtain a time-of-use electricity price formulation strategy; the system is used to implement the steps of the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy as provided in any of the above technical solutions. Therefore, the system also includes all the beneficial effects of the time-of-use electricity price calculation method based on considering the uncertainty of load and renewable energy as provided in any of the above technical solutions, which will not be repeated here.

[0068] 3. A time-of-use electricity price calculation device considering the uncertainty of load and renewable energy of the present invention comprises a processor and a memory, the memory is used to store computer program code, and transmit the computer program code to the processor, and the processor is used to execute the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy provided in any of the above technical solutions according to the instructions in the computer program code. Therefore, the device also includes all the beneficial effects of the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy provided in any of the above technical solutions, which will not be repeated here.

[0069] 4. A computer program product of the present invention, when executed by a processor, implements the steps of the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy as provided in any of the above technical solutions. Therefore, the computer program product also includes all the beneficial effects of the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy as provided in any of the above technical solutions, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of the steps of the present invention.

[0071] Figure 2 It is a system diagram of the present invention.

[0072] Figure 3 It is a diagram of the equipment of the present invention. DETAILED DESCRIPTION

[0073] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0074] Embodiment 1:

[0075] See also Figure 1 , a time-of-use electricity price calculation method considering the uncertainty of load and renewable energy, comprising the following steps:

[0076] S1. Based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate, a time-of-use electricity price optimization model is constructed;

[0077] The optimization model of time-of-use electricity price is:

[0078]

[0079] Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q var is the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q inc The convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESS,char (t) is the amount of electricity charged by the user during period t; Q ESS,dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

[0080] The user demand curve load variance is an important indicator for measuring the stability of the demand curve. The smaller the user demand curve load variance is, the more stable the user demand curve is.

[0081] The smaller the renewable energy abandonment rate is, the smaller the abandonment of distributed power generation power generation is.

[0082] Users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand:

[0083]

[0084] Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t;

[0085] Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is:

[0086]

[0087] Among them, a and b are the parameters of the linearized price elasticity model;

[0088] By modifying the price, the power demand Q1(t) of the user in period t is defined as:

[0089]

[0090] Among them, Q1(t) is the power demand of the user in period t.

[0091] Constructing a user electricity satisfaction model:

[0092] Define the convenience rate Q of user electricity consumption inc :

[0093]

[0094] Among them, T is the user's charging time;

[0095] The deviation between the electricity demand after the TOU electricity price change and the previous electricity demand will bring inconvenience to the user, which is the inconvenience rate of the user's electricity consumption. 1 minus the inconvenience rate is the convenience rate Q. inc .

[0096] Define the inconvenience rate C of users in paying electricity bills inc :

[0097]

[0098] Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t;

[0099] C inc The deviation between the user electricity charges after the time-of-use electricity price change and the previous user electricity charges will cause inconvenience to the users.

[0100] The algorithm for the user to pay the electricity fee originally is the original electricity price of the t period multiplied by the original power consumption of the t period C0(t)=P0(t)·Q0(t); the algorithm for the user to pay the electricity fee after the price adjustment is the adjusted electricity price of the t period multiplied by the adjusted power consumption of the t period C1(t)=(P0(t)+ΔP(t))·Q1(t);

[0101] User demand curve load variance Q var :

[0102]

[0103] Among them, Q 1,mean is the average power consumption value of the user demand curve;

[0104] Renewable energy abandonment rateA rec :

[0105]

[0106] in, is the average power demand of users in period t, QESS,char (t) is the user’s charging power during period t, Q ESS,dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

[0107] S2. Use a multi-objective genetic algorithm to solve the time-of-use electricity price optimization model and obtain a time-of-use electricity price formulation strategy.

[0108] The operation data of a certain power grid, the operation data of the charging station and the electricity price data are collected, and the electricity price is optimized by using the time-of-use electricity price calculation method considering the uncertainty of load and renewable energy of the present invention:

[0109] Under the time-of-use electricity price framework, a day is usually divided into three periods: peak period, off-peak period and off-peak period.

[0110] Time-of-use electricity prices in the past three periods

[0111] period Time (h) Electricity price (yuan / KWh) Peak 7-9，14-19，22 0.9 Off-peak 10-13，20-21 0.6 Low period 1-6，23-24 0.3

[0112] Set the parameters of the linearized price elasticity model to a=500, b=-150;

[0113]

[0114] The optimal time-of-use electricity price setting result is achieved with the minimum load variance of the demand curve and the minimum renewable energy abandonment rate under the condition of high user electricity satisfaction.

[0115] Embodiment 2:

[0116] See also Figure 2 , a time-of-use electricity price calculation system considering the uncertainty of load and renewable energy, the system is used to execute the aforementioned time-of-use electricity price calculation method considering the uncertainty of load and renewable energy, specifically including: an electricity price optimization model construction module, an electricity price optimization model solution module;

[0117] Electricity price optimization model construction module: used to build a time-of-use electricity price optimization model based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate;

[0118] The time-of-use electricity price optimization model in the electricity price optimization model construction module is:

[0119]

[0120] Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q varis the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q inc The convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESS,char (t) is the amount of electricity charged by the user during period t; Q ESS,dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

[0121] In the electricity price optimization model construction module, users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand:

[0122]

[0123] Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t;

[0124] Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is:

[0125]

[0126] Among them, a and b are the parameters of the linearized price elasticity model;

[0127] By modifying the price, the power demand Q1(t) of the user in period t is defined as:

[0128]

[0129] Among them, Q1(t) is the power demand of the user in period t.

[0130] In the electricity price optimization model construction module, a user electricity satisfaction model is constructed:

[0131] Define the convenience rate Q of user electricity consumption inc :

[0132]

[0133] Among them, T is the user's charging time;

[0134] Define the inconvenience rate C of users in paying electricity bills inc :

[0135]

[0136] Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t;

[0137] User demand curve load variance Q var :

[0138]

[0139] Among them, Q 1,mean is the average power consumption value of the user demand curve;

[0140] Renewable energy abandonment rateA rec :

[0141]

[0142] in, is the average power demand of users in period t, Q ESS,char (t) is the user’s charging power during period t, Q ESS,dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

[0143] Electricity price optimization model solving module: used to solve the time-of-use electricity price optimization model using a multi-objective genetic algorithm and obtain a time-of-use electricity price formulation strategy.

[0144] Embodiment 3:

[0145] See also Figure 3 A time-of-use electricity price calculation device that takes into account the uncertainty of load and renewable energy includes a memory and a processor, wherein the memory is used to store computer program code and transmit the computer program code to the processor; the processor is used to execute the aforementioned time-of-use electricity price calculation method that takes into account the uncertainty of load and renewable energy according to the instructions in the computer program code.

[0146] Embodiment 4:

[0147] A computer program product includes a computer program, wherein a processor executes the aforementioned time-of-use electricity price calculation method considering the uncertainty of load and renewable energy.

Claims

1. A method for calculating time-of-use electricity prices taking into account the uncertainty of load and renewable energy, characterized in that: The steps include: S1. Based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate, a time-of-use electricity price optimization model is constructed; S2. Use a multi-objective genetic algorithm to solve the time-of-use electricity price optimization model and obtain a time-of-use electricity price formulation strategy.

2. A time-of-use electricity price calculation method considering the uncertainty of load and renewable energy according to claim 1, characterized in that: The time-of-use electricity price optimization model in S1 is: Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q var is the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q inc The convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESS，char (t) is the amount of electricity charged by the user during period t; Q ESS，dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

3. A time-of-use electricity price calculation method considering the uncertainty of load and renewable energy according to claim 2, characterized in that: In S1, users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand: Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t; Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is: Among them, a and b are the parameters of the linearized price elasticity model; By modifying the price, the power demand Q1(t) of the user in period t is defined as: Among them, Q1(t) is the power demand of the user in period t.

4. A time-of-use electricity price calculation method considering the uncertainty of load and renewable energy according to claim 3, characterized in that: In S1, a user electricity satisfaction model is constructed: Define the convenience rate Q of user electricity consumption inc : Among them, T is the user's charging time; Define the inconvenience rate C of users in paying electricity bills inc : Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t; User demand curve load variance Q var : Among them, Q 1，mean is the average power consumption value of the user demand curve; Renewable energy abandonment rateA rec : in, is the average power demand of users in period t, Q ESS，char (t) is the user’s charging power during period t, Q ESS，dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

5. A time-of-use electricity price calculation system considering the uncertainty of load and renewable energy, characterized in that: Specifically include : Electricity price optimization model construction module, electricity price optimization model solution module; Electricity price optimization model construction module: used to build a time-of-use electricity price optimization model based on the minimum load variance of the user demand curve, the highest user electricity satisfaction and the minimum renewable energy abandonment rate; Electricity price optimization model solving module: used to solve the time-of-use electricity price optimization model using a multi-objective genetic algorithm and obtain a time-of-use electricity price formulation strategy.

6. A time-of-use electricity price calculation system considering the uncertainty of load and renewable energy according to claim 5, characterized in that: The time-of-use electricity price optimization model in the electricity price optimization model construction module is: Among them, F1 is the first objective function, which represents the minimum load variance of the user demand curve; Q var is the load variance of the user demand curve; F2 is the second objective function, which represents the highest user satisfaction with electricity consumption; α is the weight parameter; Q inc The convenience rate of electricity consumption for users; C inc is the inconvenience rate of users in paying electricity bills; F3 is the third objective function, which represents the minimum abandonment rate of renewable energy; A rec is the renewable energy abandonment rate; Q ESs，char (t) is the amount of electricity charged by the user during period t; Q ESS，dis (t) is the amount of electricity discharged by the user during period t, Q PV (t) is the photovoltaic power supply during period t, Q wind (t) is the wind power supply during period t.

7. A time-of-use electricity price calculation system considering the uncertainty of load and renewable energy according to claim 6, characterized in that: In the electricity price optimization model construction module, users adjust their electricity consumption behavior according to the electricity price, and define the price elasticity of user electricity demand: Among them, P0(t) is the original electricity price in period t, Q0(t) is the original electricity consumption in period t, ΔP(t) is the change of time-of-use electricity price in period t, and ΔQ(t) is the change of time-of-use electricity consumption in period t; Assuming that the power consumption is linearly related to the time-of-use electricity price, the price elasticity of the user's electricity demand θ(t) is: Among them, a and b are the parameters of the linearized price elasticity model; By modifying the price, the power demand Q1(t) of the user in period t is defined as: Among them, Q1(t) is the power demand of the user in period t.

8. A time-of-use electricity price calculation system considering the uncertainty of load and renewable energy according to claim 7, characterized in that: In the electricity price optimization model construction module, a user electricity satisfaction model is constructed: Define the convenience rate Q of user electricity consumption inc : Among them, T is the user's charging time; Define the inconvenience rate C of users in paying electricity bills inc : Among them, C0(t) is the electricity fee originally paid by the user in period t, and C1(t) is the electricity fee paid by the user after the price adjustment in period t; User demand curve load variance Q var : Among them, Q 1，mean is the average power consumption value of the user demand curve; Renewable energy abandonment rateA rec : in, is the average power demand of users in period t, Q ESS，char (t) is the user’s charging power during period t, Q ESS，dis (t) is the amount of electricity discharged by the user during period t, is the average photovoltaic power supply during period t, is the average wind power supply in period t.

9. A time-of-use electricity price calculation device considering the uncertainty of load and renewable energy, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store computer program code and transmit the computer program code to the processor; The processor is used to execute the time-of-use electricity price calculation method considering load and renewable energy uncertainty as described in any one of claims 1 to 4 according to the instructions in the computer program code.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor according to the method for calculating time-of-use electricity prices taking into account the uncertainty of load and renewable energy as described in any one of claims 1 to 4.

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