Optimization Method, Device, Equipment and Storage Medium for Rural Heat Storage Electric Heating Operation

Abstract

The present invention discloses an optimization method, device, equipment and storage medium for the operation strategy of rural heat storage electric heating based on chance-constrained, belonging to the technical field of power grid operation and control. The specific steps are as follows: Step 1, obtain the outdoor and indoor temperatures and room heat parameters; Step 2, establish an objective function for minimizing the heating electricity cost and optimizing the comfort of users within 24 hours of the whole day; Step 3, construct a chance-constrained stochastic optimization model for electric heating based on the outdoor and indoor temperatures, room heat parameters obtained in Step 1 and the objective function in Step 2; Step 4, use the quantum genetic algorithm to solve the stochastic optimization model for electric heating established in Step 3; Step 5, output the operation strategy of rural heat storage electric heating based on chance-constrained.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation and control, and specifically to an operation optimization method, device, equipment and storage medium for rural heat storage electric heating. Background Art

[0002] The current mainstream household electric heating systems mainly include heat storage electric heating and air source heat pumps. Compared with air source heat pumps, the heat storage electric heating system has a more flexible operation mode, is convenient for interacting with the power grid, and has broad application prospects.

[0003] Building electric heating facilities with heat storage functions is an important part of the rural lifestyle revolution. However, due to the lack of reasonable and effective operation control means and the lack of scientific coordination between power consumption and heat release, the existing operation mode can neither give full play to the economic advantages of heat storage during valley periods nor achieve stable and comfortable indoor heating temperatures for users. How to achieve the balance and coordination of economy and comfort and obtain an optimized dispatching operation plan with the best benefit index is an urgent problem to be solved after the large-scale access of rural heat storage electric heating at present. Summary of the Invention

[0004] To solve the above problems, the present invention provides an operation strategy optimization method, device, equipment and storage medium for rural heat storage electric heating based on chance constraints, which realizes the optimization of the uncertainty of user heating demand and gives full play to the economic advantages of heat storage during valley periods.

[0005] To achieve the above object, the present invention is realized through the following technical solutions:

[0006] The present invention is an operation optimization method for rural heat storage electric heating, and the specific steps are as follows:

[0007] Step 1, obtain outdoor and indoor temperatures and room heat parameters;

[0008] Step 2, establish an objective function for the lowest heating electricity cost and the optimal comfort of the user within 24 hours of the whole day;

[0009] Step 3, construct a stochastic optimization model for electric heating with chance constraints based on the outdoor and indoor temperatures, room heat parameters obtained in Step 1 and the objective function in Step 2;

[0010] Step 4, use the quantum genetic algorithm to solve the stochastic optimization model for electric heating established in Step 3;

[0011] Step 5, output the operation strategy for rural heat storage electric heating based on chance constraints.

[0012] A further improvement of the present invention lies in that: in the step 1, the room thermal parameters include the equivalent thermal resistance between the indoor air - heat storage body and indoor - outdoor, the indoor air heat capacity, the electric power of the regenerative electric heating, and the electro - thermal conversion efficiency of the direct - heating device of the regenerative electric heating. Among them, the electric power of the regenerative electric heating includes the electric power of the direct - heating device of the regenerative electric heating and the electric power of the heat storage device.

[0013] A further improvement of the present invention lies in that: the specific process of step 2 is as follows:

[0014] Step 2.1, establish the objective function for the optimal heating comfort of users :

[0015] ;

[0016] In the formula: and are the weight distribution coefficients of the sub - objective functions and respectively, and the weight values are taken as 1:1. The sub - objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub - objective function reflects the actual value of the indoor temperature and the amplitude of indoor temperature fluctuation within 24 hours, and its expression is:

[0017] ;

[0018] In the formula: is the actual value of the indoor temperature at the time period; is the actual value of the indoor temperature at the time period; is the expected value of the indoor temperature set by the user;

[0019] Step 2.2, establish the objective function for the lowest heating electricity cost of users :

[0020] ;

[0021] In the formula: is the electric power of the direct - heating device of the regenerative electric heating at the time period; is the electric power of the heat storage device of the regenerative electric heating at the time period; , and respectively represent the sets of the heat storage, heat release, and suspended working time periods of the heat storage device of the regenerative electric heating; is the simulation step size; is the time period electricity price;

[0022] Among them,

[0023] ;

[0024] In the formula: is the starting time period of the peak electricity price; is the ending time period of the peak electricity price; is the starting time period of the valley electricity price; is the ending time period of the valley electricity price; is the peak electricity price; is the valley electricity price.

[0025] A further improvement of the present invention lies in that: The specific process of step 3 is as follows:

[0026] Step 3.1, establish the chance constraint conditions;

[0027] The chance constraint conditions include:

[0028] The operation constraint of the direct-heating equipment for electric heating:

[0029] ;

[0030] In the formula: is the electric power of the direct-heating equipment of the heat storage type electric heating in the time period,

[0031] The operation constraint of the heat storage equipment for electric heating:

[0032] ;

[0033] ;

[0034] ;

[0035] In the formula: is the energy stored by the heat storage equipment of the heat storage type electric heating at time t; is the energy stored by the heat storage equipment of the heat storage type electric heating at time t + 1; is the energy storage efficiency of the heat storage equipment of the heat storage type electric heating; is the energy release efficiency of the heat storage equipment of the heat storage type electric heating; is the energy coefficient of the heat dissipation loss or self-loss of the heat storage equipment of the heat storage type electric heating to the environment by itself; is the electric power of the heat storage equipment of the heat storage type electric heating in the time period, is the simulation step size; and They are respectively the lower and upper limits of the heat load state;

[0036] Thermal energy supply-demand balance constraint:

[0037] ;

[0038] ;

[0039] In the formula: represents time period, , and respectively represent the sets of the heat storage, heat release, and work suspension time periods of the heat storage device of the heat storage type electric heating; is the starting time period of the peak electricity price; is the ending time period of the peak electricity price; is the starting time period of the valley electricity price; is the ending time period of the valley electricity price; is the electro-thermal conversion efficiency of the direct heating device of the heat storage type electric heating; , is the total heat demand of users in the time period; and are respectively the heat demands in the peak electricity price period and the valley electricity price period; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; is the equivalent thermal resistance between indoors and outdoors;

[0040] Electro-thermal coupling constraint:

[0041] ;

[0042] In the formula, is the heat capacity of indoor air, is the actual value of the indoor temperature in the time period, is the actual value of the indoor temperature in the time period;

[0043] Step 3.2, analyze the uncertain factors and construct a mathematical programming model containing random variables;

[0044] The uncertain factor is the temperature prediction value. For the analysis of the problem containing uncertain factors, consider the following mathematical programming model containing random variables:

[0045] ;

[0046] In the formula: is the decision variable; is a random variable; is the objective function of the mathematical programming model; is the th constraint function containing random variables;

[0047] Step 3.3, using the theory of chance-constrained programming to establish uncertainty factors into the constraint conditions of the mathematical programming model, expressed as:

[0048] ;

[0049] In the formula: is the confidence level in the chance constraint; represents the probability operator in the chance constraint; the feasible solution of the mathematical programming model is if and only if holds;

[0050] Step 3.4, simulating and processing the constraint conditions of the mathematical programming model through the sampling average approximation method, and transforming the constraint condition formula containing random variables into a deterministic constraint, specifically:

[0051] Step 3.41, establishing the uncertainty constraint conditions, and the uncertainty constraint conditions are:

[0052] ;

[0053] In the formula: is the heat supply of the heat storage device and the direct heating device during the heat storage period, is the heat supply of the direct heating device during the heat release section and the suspended operation section of the heat storage device, is the total heat demand of the user, is the confidence level of the uncertainty constraint condition; is the electric power of the direct heating device of the heat storage type electric heating during the period; is the electric power of the heat storage device of the heat storage type electric heating during the period; 、 and respectively represent the sets of the heat storage, heat release, and suspended operation periods of the heat storage device of the heat storage type electric heating; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; and are the equivalent thermal resistances between the indoor air-heat storage body and between the indoor and outdoor respectively;

[0054] Step 3.42, obtain a set of daily temperature fluctuation curve samples , where is the serial number of the daily temperature fluctuation curve sample, , and among them: N is the number of users in the control group;

[0055] Step 3.43, calculate the indicator function whose expression is:

[0056] ;

[0057] Step 3.44, transform the uncertainty constraint condition to obtain the deterministic constraint condition:

[0058] ;

[0059] Step 3.5, obtain the stochastic optimization model for electric heating based on chance constraint, and the stochastic optimization model for electric heating is:

[0060] ;

[0061] In the formula: is the objective function for the optimal heating comfort of users, is the objective function for the lowest heating electricity cost of users; and are the weight distribution coefficients of the sub-objective functions and respectively, and the weight values are taken as 1:1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub-objective function reflects the actual value and the fluctuation range of the indoor temperature within 24 hours; is the electric power of the direct heating equipment of the heat storage type electric heating during the period; is the electric power of the heat storage equipment of the heat storage type electric heating during the period; , and respectively represent the sets of the heat storage, heat release, and suspension working periods of the heat storage equipment of the heat storage type electric heating; is the electricity price during the period, is the simulation step size; represents the probability operator in the chance constraint; is the heat supply of the heat storage equipment and the direct heating equipment during the heat storage period; is the heat supply of the direct heating equipment during the heat release section and the suspension working section of the heat storage equipment, is the total heat demand of the user; is the confidence level of the uncertainty constraint condition; is the energy storage efficiency of the energy storage device of the heat storage type electric heating; is the energy release efficiency of the energy storage device of the heat storage type electric heating; is the energy coefficient of the energy dissipation loss or self-loss of the energy storage device of the heat storage type electric heating to the environment by itself; is the upper limit value of the electric power of the energy storage device of the heat storage type electric heating; is the upper limit value of the electric power of the direct heating device of the heat storage type electric heating; is the energy stored by the energy storage device in the t time period; is the energy stored by the energy storage device of the heat storage type electric heating in the t+1 time period; and are the lower limit and the upper limit of the heat load state respectively.

[0062] The rural heat storage type electric heating operation optimization device of the present invention includes a collection module, an objective function module, an electric heating stochastic optimization model module, and an electric heating stochastic optimization model solving module;

[0063] The collection module is used to collect outdoor and indoor temperatures and room heat parameters;

[0064] The objective function module is used to establish an objective function for the lowest heating electricity cost and the optimal comfort of the user within 24 hours of the whole day;

[0065] The electric heating stochastic optimization model module is used to construct an electric heating stochastic optimization model with chance constraints based on the objective function established by the objective function module;

[0066] The electric heating stochastic optimization model solving module is used to solve the established electric heating stochastic optimization model by using the quantum genetic algorithm and output the solution result.

[0067] A further improvement of the present invention lies in that: the room heat parameters include the equivalent thermal resistance between the indoor air and the heat storage body and between the indoor and the outdoor, the indoor air heat capacity, the electric power of the heat storage type electric heating, and the electro-thermal conversion efficiency of the direct heating device of the heat storage type electric heating, wherein the electric power of the heat storage type electric heating includes the electric power of the direct heating device of the heat storage type electric heating and the electric power of the energy storage device.

[0068] A further improvement of the present invention lies in that: the objective function module establishes the following objective function:

[0069] Establish an objective function for the optimal comfort of the user's heating : ;

[0070] In the formula: and are the weight distribution coefficients of the sub-objective functions and respectively. The weight values are taken as 1:1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours. The sub-objective function reflects the actual value and the fluctuation range of the indoor temperature within 24 hours. Its expression is:

[0071] ;

[0072] In the formula: is the actual value of the indoor temperature in the time period; is the actual value of the indoor temperature in the

[0073] Establish the objective function with the lowest user heating electricity cost :

[0074] ;

[0075] In the formula: is the electric power of the direct heating equipment of the heat storage electric heating in the is the electric power of the heat storage equipment of the heat storage electric heating in the , and respectively represent the sets of the heat storage, heat release, and suspension working periods of the heat storage equipment of the heat storage electric heating; is the simulation step size; is the electricity price in the

[0076] ;

[0077] In the formula: is the starting time period of the peak electricity price; is the ending time period of the peak electricity price; is the starting time period of the valley electricity price; is the ending time period of the valley electricity price; is the peak electricity price; is the valley electricity price.

[0078] A further improvement of the present invention lies in: the following operations are performed on the electric heating stochastic optimization model:

[0079] a. Establish the chance constraint conditions, specifically as follows:

[0080] Operating constraints of the direct heating equipment of the electric heating:

[0081] ;

[0082] In the formula: is the upper limit of the electric power of the direct heating device for regenerative electric heating;

[0083] Operating constraints of the heat storage device for regenerative electric heating:

[0084] ;

[0085] ;

[0086] ;

[0087] In the formula: is the energy stored by the heat storage device of regenerative electric heating in the (t + 1) period; is the energy stored by the heat storage device of regenerative electric heating in the t period; is the energy storage efficiency of the heat storage device of regenerative electric heating; is the energy release efficiency of the heat storage device of regenerative electric heating; is the energy coefficient of the heat dissipation loss or self-loss of the heat storage device of regenerative electric heating to the environment; is the upper limit of the electric power of the heat storage device of regenerative electric heating; and are the lower limit and upper limit of the heat load state respectively;

[0088] Thermal energy supply and demand balance constraint:

[0089] ;

[0090] ;

[0091] In the formula: is the electro-thermal conversion efficiency of the direct heating device of regenerative electric heating; , is the total heat demand of users in the period; and are the heat demands in the peak and valley periods of the electricity price respectively; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; is the equivalent thermal resistance between the indoor and outdoor;

[0092] ;

[0093] In the formula, is the heat capacity of indoor air, is the actual value of the indoor temperature during the is the actual value of the indoor temperature during the

[0094] b. Analyze the uncertain factors and construct a mathematical programming model with random variables. Among them, the uncertain factor is the temperature prediction value. For the analysis of problems with uncertain factors, consider the following mathematical programming model with random variables:

[0095] ;

[0096] In the formula: is the decision variable; is the random variable; is the objective function of the mathematical programming model; is the th constraint function with random variables;

[0097] c. Use the chance-constrained programming theory to establish the uncertain factors into the constraint conditions of the mathematical programming model. The specific operation is as follows:

[0098] The chance-constrained programming theory defines the meaning of the mathematical programming model on a certain probability basis, which is characterized as:

[0099] ;

[0100] In the formula: is the confidence level in the chance constraint; represents the probability operator in the chance constraint;

[0101] d. Simulate and process the constraint conditions of the mathematical programming model through the sampling average approximation method, and transform the constraint conditions with random variables into deterministic constraints. Specifically:

[0102] d1. Transform the uncertain constraint conditions. The uncertain constraint conditions are:

[0103] ;

[0104] In the formula: is the heat supply of the heat storage equipment and the direct heating equipment during the heat storage period, is the heat supply of the direct heating equipment in the heat release section and the suspended working section of the heat storage equipment, is the total heat demand of the user, is the confidence level of the uncertain constraint condition; is the electric power of the direct heating equipment of the heat storage type electric heating during the is The electric power of the heat storage device for time-period heat storage electric heating; is the electro-thermal conversion efficiency of the direct heating device for heat storage electric heating; is the energy release efficiency of the heat storage device for heat storage electric heating; , and respectively represent the sets of heat storage, heat release, and work suspension periods of the heat storage device for heat storage electric heating; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user's location obtained through networking; and are the equivalent thermal resistances between the indoor air-heat storage body and between the indoor and outdoor respectively;

[0105] d2, through two-way scenario optimization with Latin hypercube sampling, obtain sets of daily temperature fluctuation curve samples , where: , N is the number of control group users;

[0106] d3, calculate the indicator function , and its expression is:

[0107] ;

[0108] In the formula: is the serial number of the control group user;

[0109] d4, transform the uncertainty constraint condition to obtain the deterministic constraint condition:

[0110] ;

[0111] e, obtain the stochastic optimization model for electric heating based on chance-constrained programming. The stochastic optimization model for electric heating is:

[0112] ;

[0113] In the formula: is the objective function for the optimal heating comfort of the user, is the objective function for the lowest heating electricity cost of the user; and are respectively the weight distribution coefficients of the sub-objective functions and , taking the weight values as 1:1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub-objective function reflects the actual value of the indoor temperature and the indoor temperature fluctuation range within 24 hours; is The electric power of the direct heating device for time-period heat storage electric heating; is The electric power of the heat storage device for time-period heat storage electric heating; , and respectively represent the sets of the heat storage, heat release, and work suspension periods of the heat storage device of the heat storage electric heating; is the time-period electricity price, is the simulation step size; represents the probability operator in the chance constraint; is the heat supply of the heat storage device and the direct heating device during the heat storage period; is the heat supply of the direct heating device during the heat release section and the work suspension section of the heat storage device, is the total heat demand of the user; is the confidence level of the uncertainty constraint condition; is the energy storage efficiency of the heat storage device of the heat storage electric heating; is the energy release efficiency of the heat storage device of the heat storage electric heating; is the energy coefficient of the heat dissipation loss or self-loss of the heat storage device of the heat storage electric heating to the environment by itself; is the upper limit value of the electric power of the heat storage device of the heat storage electric heating; is the upper limit value of the electric power of the direct heating device of the heat storage electric heating; is the energy stored by the heat storage device of the heat storage electric heating at time t; is the energy stored by the heat storage device of the heat storage electric heating at time t + 1; and are respectively the lower and upper limits of the heat load state.

[0114] The electronic device of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method as described above are implemented.

[0115] The computer-readable storage medium of the present invention stores a computer program. When the computer program is executed by a processor, the steps of the method as described above are implemented.

[0116] The beneficial effects of the present invention are as follows: The method of the present invention takes into account the user's own characteristics and external environmental factors, constructs a multi-objective function that comprehensively considers economy and comfort; then considers the uncertainty of the environment, adopts a chance-constrained stochastic optimization algorithm, establishes a stochastic optimization model for electric heating, and uses a quantum genetic algorithm for solution to realize the uncertainty optimization of the user's heating demand, fully exert the economic advantages of heat storage during off-peak periods, and achieve stable and comfortable indoor heating temperature for users. Description of the Drawings

[0117] Figure 1 is the flow chart of the optimized operation strategy of rural heat storage electric heating based on chance-constrained;

[0118] Figure 2 is the bus model of the heat storage electric heating system;

[0119] Figure 3 is the simulation curve of the stochastic optimized operation strategy of rural heat storage electric heating based on chance-constrained;

[0120] Figure 4 is the schematic diagram of the occurrence probabilities of 5 typical scenarios in the embodiments of the present invention;

[0121] Figure 5 is the schematic diagram of 5 typical temperature scenarios in the embodiments of the present invention. Detailed Embodiments

[0122] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further elaborates the present invention in conjunction with the drawings and embodiments. It should be noted that the embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0123] Similarly, it should be understood that the following embodiments are only used to further illustrate the present invention and cannot be construed as limiting the protection scope of the present invention. Those skilled in the art's non-essential improvements and adjustments based on the above content of the present invention all fall within the protection scope of the present invention. The parameter settings in the following specific embodiments only represent a feasible example, and those skilled in the art can make targeted modifications according to specific business scenarios.

[0124] Figure 2 is the schematic diagram of the bus model of the heat storage electric heating system, and its working principle is as follows: The heat storage electric heating system purchases electricity from the power grid. During the low electricity consumption period, that is, the period with low electricity prices, the direct heating equipment generates heat to convert electrical energy into heat energy to heat the users. At the same time, the heat storage equipment generates heat at a preset power and stores it until the heat storage body is full; during the peak electricity consumption period and power supply interruption, the heat storage body releases heat to heat the users and retains the original buffering function. If the remaining heat of the heat storage body cannot maintain the room temperature demand, the direct heating equipment is turned on to assist in heating.

[0125] As Figure 1 shown, the present invention is an optimized operation method for rural heat storage electric heating based on chance-constrained. Considering the user's own characteristics and external environmental factors, an objective function that comprehensively considers economy and comfort is constructed; then, considering the uncertainty of the environment, a chance-constrained stochastic optimization algorithm is adopted to establish a stochastic optimization model for electric heating, and a quantum genetic algorithm is used for solution. The specific steps are as follows:

[0126] Step 1: Obtain the outdoor temperature, indoor temperature, and room thermal parameters.

[0127] Step 2: Establish an objective function for the lowest heating electricity cost and optimal comfort for the user within 24 hours of the whole day.

[0128] Step 3: Based on the outdoor temperature, indoor temperature, and room thermal parameters obtained in Step 1 and the objective function in Step 2, construct a stochastic optimization model for electric heating with chance constraints.

[0129] Step 4: Use the quantum genetic algorithm to solve the established stochastic optimization model for electric heating.

[0130] Step 5: Output the operation strategy of rural heat storage electric heating based on chance constraints.

[0131] The information obtained in Step 1 includes outdoor temperature, indoor temperature, the equivalent thermal resistance between indoor air - heat storage body and indoor - outdoor, indoor air heat capacity, the electric power of heat storage electric heating, and the electro - thermal conversion efficiency of the direct - heating equipment of heat storage electric heating. Among them, the electric power of heat storage electric heating includes the electric power of the direct - heating equipment of heat storage electric heating and the electric power of the heat storage equipment.

[0132] The following objective function is established in Step 2:

[0133] Step 2.1: Establish an objective function for the optimal comfort of user heating :

[0134] ;

[0135] In the formula: and are the weight distribution coefficients of the sub - objective functions and respectively, with the weight values taken as 1∶1. The sub - objective function and the sub - objective function respectively reflect the deviation between the actual value and the expected value of the indoor temperature within 24 hours and the amplitude of indoor temperature fluctuation. Their expressions are:

[0136] ;

[0137] In the formula: is the actual value of the indoor temperature at the time period; is the actual value of the indoor temperature at the time period; is the expected value of the indoor temperature set by the user;

[0138] Step 2.2: Establish an objective function for the lowest heating electricity cost of the user :

[0139] ;

[0140] In the formula: is the electric power of the direct heating device of the time-period heat storage type electric heating; is the electric power of the heat storage device of the time-period heat storage type electric heating; , and respectively represent the sets of the heat storage, heat release and work suspension periods of the heat storage device of the heat storage type electric heating; is the simulation step size; is the time-period electricity price;

[0141] Among them,

[0142] ;

[0143] In the formula: is the start time period of the peak electricity price; is the end time period of the peak electricity price; is the start time period of the valley electricity price; is the end time period of the valley electricity price; is the peak electricity price; is the valley electricity price.

[0144] The specific content of step 3 is:

[0145] Step 3.1, establish the chance constraint condition;

[0146] Operating constraint of the direct heating device of the electric heating:

[0147] ;

[0148] In the formula: is the upper limit value of the electric power of the direct heating device of the heat storage type electric heating;

[0149] Operating constraint of the heat storage device of the electric heating:

[0150] ;

[0151] ;

[0152] ;

[0153] In the formula: is the energy stored by the heat storage device of the heat storage type electric heating at time t; is the energy stored by the heat storage device of the heat storage type electric heating at time t + 1; is the energy storage efficiency of the heat storage device of the heat storage type electric heating; is the energy release efficiency of the heat storage device for regenerative electric heating; is the energy coefficient of the heat storage device for regenerative electric heating for heat dissipation loss or self-loss to the environment; is the upper limit of the electric power of the heat storage device for regenerative electric heating; and are the lower and upper limits of the heat load state respectively;

[0154] Thermal energy supply-demand balance constraint:

[0155] ;

[0156] ;

[0157] In the formula: is the electro-thermal conversion efficiency of the direct heating device for regenerative electric heating; , is the total heat demand of users in the and are the heat demands in the peak electricity price period and the valley electricity price period respectively; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking;

[0158] Electro-thermal coupling constraint:

[0159] ;

[0160] Step 3.2, analyze the uncertain factors and construct a mathematical programming model containing random variables; the uncertain factor is the temperature prediction value,

[0161] Regarding the analysis of the problem with uncertain factors, consider the following mathematical programming model containing random variables:

[0162] ;

[0163] In the formula: is the decision variable; is the random variable; is the objective function of the mathematical programming model; is the th constraint function containing random variables. However, due to the existence of the random variable , the meaning of the constraint conditions and the minimization objective of this model is not clear.

[0164] Step 3.3, using the chance-constrained programming theory, establish the uncertain factors into the constraint conditions of the model; the chance-constrained programming theory means that the decisions made are allowed to not satisfy the constraint conditions to a certain extent, but the probability that the decisions made make the constraint conditions hold should be greater than or equal to a certain confidence level. In the present invention, the chance-constrained programming theory defines the meaning of the mathematical programming model on a certain probability basis, which can be characterized as follows:

[0165] ;

[0166] In the formula: is the confidence level in the chance constraint; represents the probability operator in the chance constraint.

[0167] Step 3.4, simulate and process the constraint conditions of the mathematical programming model through the sampling average approximation method, and transform the constraint condition formula containing random variables into a deterministic constraint;

[0168] The sampling average approximation method simulation technology refers to generating and reducing technologies by means of scenario sampling, generating through scenario optimization of random variables, and approximately replacing the expected value function with the average function of samples, thereby transforming the model constraint conditions containing random variables into deterministic constraint conditions.

[0169] Transforming the constraint condition formula containing random variables into a deterministic constraint means:

[0170] (1) Uncertainty constraint condition: ;

[0171] In the formula, is the electric power of the direct heating equipment of the time-of-use heat storage electric heating; is the electric power of the heat storage equipment of the time-of-use heat storage electric heating; is the electro-thermal conversion efficiency of the direct heating equipment of the heat storage electric heating; is the energy release efficiency of the heat storage equipment of the heat storage electric heating; , and respectively represent the sets of the heat storage, heat release and work suspension periods of the heat storage equipment of the heat storage electric heating; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user's local area obtained through networking; and are the equivalent thermal resistances between the indoor air-heat storage body and between the indoor and outdoor respectively;

[0172] (2) By means of two-way scenario optimization of Latin hypercube sampling, obtain M sets of daily temperature fluctuation curve samples , where:

[0173] ;

[0174] (3) Calculate the indicator function , and its expression is:

[0175] ;

[0176] In the formula, is the serial number of the users in the control group;

[0177] (4) Transform the uncertainty constraint conditions to obtain the deterministic constraint conditions:

[0178] ;

[0179] Step 3.5, obtain the stochastic optimization model for electric heating based on chance constraints. The stochastic optimization model for electric heating is:

[0180] ;

[0181] In the formula: is the objective function for the optimal heating comfort of users, is the objective function for the lowest heating electricity cost of users; and are the weight distribution coefficients of the sub-objective functions and respectively, and the weight values are taken as 1:1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub-objective function reflects the actual value of the indoor temperature and the indoor temperature fluctuation range within 24 hours; is the electric power of the direct heating equipment of the heat storage type electric heating during the period; is the electric power of the heat storage equipment of the heat storage type electric heating during the and respectively represent the sets of the heat storage, heat release, and suspension working periods of the heat storage equipment of the heat storage type electric heating; is the electricity price during the period, represents the probability operator in the chance constraint; is the heat supply of the heat storage equipment and the direct heating equipment of the heat storage type electric heating during the heat storage period, is the heat supply of the direct heating equipment of the heat storage type electric heating during the heat release section and the suspension working section of the heat storage equipment of the heat storage type electric heating, is the total heat demand of the users; is the confidence level for the uncertainty constraint condition; is the energy storage efficiency of the energy storage device for regenerative electric heating; is the energy release efficiency of the energy storage device for regenerative electric heating; is the energy coefficient of the energy storage device for regenerative electric heating for the energy dissipated to the environment or self-consumed by itself; is the upper limit value of the electric power of the energy storage device for regenerative electric heating; is the upper limit value of the electric power of the direct heating device for regenerative electric heating; is the energy stored by the energy storage device for regenerative electric heating in the t time period; and are respectively the lower limit and the upper limit of the heat load state.

[0182] In step 4, a quantum genetic algorithm based on qubits and the superposition characteristics of quantum states is used to solve the stochastic optimization model of electric heating. Figure 3 is the simulation curve of rural regenerative electric heating based on chance constraints: construct the application scenario of regenerative electric heating. The building area of this user is , considering the heat load demand and the energy storage can generally meet the heat load demand throughout the day, select the rated power of the energy storage part of the heating equipment to be the rated power of the direct heating part is 3kW , and other main parameter settings are shown in Table 1.

[0183] Table 1 Main parameter settings

[0184]

[0185] In this embodiment, the historical samples (scenarios) of the ambient effective temperature are from the measured values of a meteorological station from November 1 to February 28 from 2019 to 2021, the sampling interval is 1 hour, and 24 sampling points in 1 day are used as a temperature sequence, and a total of 360 actual temperature sequences are obtained. After scenario generation and reduction, 5 typical scenarios are generated, and the occurrence probabilities of each scenario are as Figure 4 shown, and the temperature fluctuation conditions under each scenario are as Figure 5 shown.

[0186] (1) The traditional operation strategy of regenerative electric heating mainly focuses on meeting the economic needs of user heating, and preferentially energizes for energy storage during the valley period. Compare the optimized operation strategy proposed in the present invention with 2 traditional operation strategies to verify its effectiveness. Since the occurrence probability of scenario 3 is relatively large, and its average temperature is relatively large and the fluctuation range is relatively small among all scenarios, scenario 3 is taken as an example. Strategy 1 starts to energize for energy storage at the rated power until the total energy storage during the valley period; Strategy 2 evenly distributes the heating demand throughout the day during the valley period.

[0187] (2)Total heat demand in Scenario 3 is , where and are respectively and . The user - set temperature is set at 22 °C, and the confidence level is taken as 0.95. Strategy 1 operates at the rated power - on power for 3.57 h during the valley period. The room - temperature fluctuates significantly throughout the day, with the highest and lowest room - temperatures being 29.4 °C and 9.6 °C respectively. The user comfort index is 3.12, and the all - day heating cost is 11.42 yuan. Strategy 2 operates at a heat - storage power of 6.25 kw and a direct - heating power of for 8 h during the valley period. The room - temperature remains above 21 °C from 05:00 to 14:00. The user comfort index is 2.23, and the all - day heating cost is 16.74 yuan. When the optimized operation strategy of the present invention is adopted, the highest and lowest room - temperatures throughout the day are 24.5 °C and 19.3 °C respectively. The user comfort index is 1.74, and the all - day heating cost is 18.92 yuan. This strategy significantly reduces the user comfort index without significantly increasing the heating economy.

[0188] When the outdoor air temperature is relatively low, only heat - storage during the valley period cannot meet the all - day heat - load demand. Both Strategy 1 and Strategy 2 will continuously store heat at the rated power during the valley period. After the valley period ends, Strategy 1 will continue to power on at the rated power until the total heat - storage amount is ; Strategy 2 will evenly distribute the remaining heating demand during the peak period. Analyzing under different confidence levels, considering the differences in the user - set comfortable temperatures, adopting the operation strategy proposed in the present invention, by adjusting the start - stop working state of the heat - storage device and the operation time of the direct - heating device, the heat - load change is tracked. Since each typical temperature curve has a similar change pattern, that is, the time periods of the peaks and valleys are the same, only the numerical values are different, that is, the process of solving the fitness function using the quantum genetic algorithm has the same property as the temperature change. Therefore, to reduce the length of the article, the multi - objective optimization solution sets under different temperature - set values are solved to obtain the corresponding Pareto curves.

[0189] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products of the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general - purpose computers, special - purpose computers, embedded processors, or other programmable data - processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data - processing devices generate for implementing in the process Figure 1 one process or multiple processes and / or blocks Figure 1Apparatus for functions specified in one or more boxes.

[0190] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured article including an instruction apparatus, and the instruction apparatus implements the process Figure 1 one or more processes and / or boxes Figure 1 functions specified in one or more boxes.

[0191] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide for implementing the process Figure 1 one or more processes and / or boxes Figure 1 steps for functions specified in one or more boxes.

[0192] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0193] Obviously, those skilled in the art can make various changes and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these modifications and variations.

Claims

1. An operation optimization method for rural heat storage electric heating, characterized in that: It includes the following steps: Step 1: Obtain the outdoor temperature, indoor temperature, and room thermal parameters; Step 2: Establish an objective function for the lowest heating electricity cost and optimal comfort for the user within 24 hours of the whole day; Step 3: Based on the outdoor temperature, indoor temperature, and room thermal parameters obtained in Step 1 and the objective function in Step 2, construct a stochastic optimization model for electric heating with chance constraints; Step 4: Use the quantum genetic algorithm to solve the stochastic optimization model for electric heating established in Step 3; Step 5: Output the operation strategy of rural heat storage electric heating based on chance constraints; The specific process of Step 3 is as follows: Step 3.1: Establish chance constraint conditions; The chance constraint conditions include: Operation constraints of the direct heating equipment of heat storage electric heating: ； In the formula: is the electric power of the direct heating equipment for time-period heat storage electric heating, the upper limit value of the electric power of the direct heating equipment for heat storage electric heating; Operation constraints of the heat storage equipment of heat storage electric heating: ； ； ； In the formula: is the energy stored by the heat storage device of the regenerative electric heating in the t period; is the energy stored by the heat storage device of the regenerative electric heating in the t+1 period; is the energy storage efficiency of the heat storage device of the regenerative electric heating; is the energy release efficiency of the heat storage device of the regenerative electric heating; is the energy coefficient of the heat storage device of the regenerative electric heating for heat dissipation loss or self-loss to the environment; is the electric power of the heat storage device of the regenerative electric heating in the period; is the upper limit value of the electric power of the heat storage device of the regenerative electric heating; is the simulation step size; and are the lower limit and upper limit of the heat load state respectively; Thermal energy supply-demand balance constraint: ； ； Wherein: represents time period, , and respectively represent the sets of heat storage, heat release and stop working time periods of the heat storage device of the regenerative electric heating; is the starting time period of the peak electricity price; is the ending time period of the peak electricity price; is the starting time period of the valley electricity price; is the ending time period of the valley electricity price; is the electro-thermal conversion efficiency of the direct heating device of the regenerative electric heating; , is the total heat demand of the user during the time period; and are respectively the heat demands during the peak electricity price period and the valley electricity price period; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; is the equivalent thermal resistance between indoor and outdoor; Electro-thermal coupling constraint: ； In the formula, is the heat capacity of indoor air, is the actual value of the indoor temperature in the period, and is the actual value of the indoor temperature in the Step 3.2: Analyze the uncertain factors and construct a mathematical programming model containing random variables; The uncertain factor is the temperature prediction value, and the constructed mathematical programming model containing random variables is: ； In the formula: is a decision variable; is a random variable; is the objective function of the mathematical programming model; is the th constraint function containing random variables; Step 3.3: Use the chance constraint programming theory to establish the uncertain factors into the constraint conditions of the mathematical programming model, expressed as: ； where: is the confidence level in the chance constraint; represents the probability operator in the chance constraint; Step 3.4: Process the constraint conditions of the mathematical programming model through the sampling average approximation method, and transform the constraint conditions containing random variables into deterministic constraints. Specifically: Step 3.41: Establish the uncertain constraint conditions, and the uncertain constraint conditions are: ； In the formula: is the heat supply of the heat storage device and the direct heating device during the heat storage period, is the heat supply of the direct heating device in the heat release section and the suspended operation section of the heat storage device, is the total heat demand of the user, is the confidence level of the uncertainty constraint condition; is the electric power of the direct heating device of the time-of-use heat storage electric heating; is the electric power of the heat storage device of the time-of-use heat storage electric heating; is the electro-thermal conversion efficiency of the direct heating device of the heat storage electric heating; is the energy release efficiency of the heat storage device of the heat storage electric heating; 、 and respectively represent the sets of the heat storage, heat release and suspended operation periods of the heat storage device of the heat storage electric heating; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; and are the equivalent thermal resistances between the indoor air-heat storage body and between the indoor and outdoor respectively; Step 3.42, obtain groups of daily temperature fluctuation curve samples , where is the serial number of the daily temperature fluctuation curve sample, , and: , where N is the number of users in the control group; Step 3.43, calculate the indicator function , and its expression is: ； In the formula, is the serial number of the user in the control group; Step 3.44: Transform the uncertain constraint conditions to obtain the deterministic constraint conditions: ； Step 3.5: Obtain a stochastic optimization model for electric heating based on chance constraints, and the stochastic optimization model for electric heating is: ； In the formula: is the objective function for the optimal heating comfort of users, is the objective function for the lowest heating electricity cost of users; and are the weight distribution coefficients of the sub-objective functions and respectively, with the weight values taken as 1:

1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub-objective function reflects the actual value of the indoor temperature and the indoor temperature fluctuation range within 24 hours; is the electric power of the direct heating equipment of the time-of-use heat storage electric heating; is the electric power of the heat storage equipment of the time-of-use heat storage electric heating; , and represent the sets of the heat storage, heat release, and work suspension periods of the heat storage equipment of the heat storage electric heating respectively; is the time-of-use electricity price, is the simulation step size; represents the probability operator in the chance constraint; is the heat supply of the heat storage equipment and the direct heating equipment during the heat storage period; is the heat supply of the direct heating equipment during the heat release section and the work suspension section of the heat storage equipment, is the total heat demand of users; is the confidence level of the uncertainty constraint condition; is the energy storage efficiency of the heat storage equipment of the heat storage electric heating; is the energy release efficiency of the heat storage equipment of the heat storage electric heating; is the energy coefficient of the heat storage equipment of the heat storage electric heating for the energy dissipated to the environment or self-consumed by itself; is the upper limit value of the electric power of the heat storage equipment of the heat storage electric heating; is the upper limit value of the electric power of the direct heating equipment of the heat storage electric heating; is the energy stored by the heat storage equipment at time t; is the energy stored by the heat storage equipment of the heat storage electric heating at time t + 1; and are the lower and upper limits of the heat load state, respectively.

2. The operation optimization method of rural heat storage electric heating according to claim 1, characterized in that: The room thermal parameters in Step 1 include the equivalent thermal resistance between the indoor air and the heat storage body and between the indoor and outdoor, the indoor air heat capacity, the electric power of the heat storage electric heating, and the electro-thermal conversion efficiency of the direct heating equipment of the heat storage electric heating. Among them, the electric power of the heat storage electric heating includes the electric power of the direct heating equipment of the heat storage electric heating and the electric power of the heat storage equipment.

3. The operation optimization method of rural heat storage electric heating according to claim 1, characterized in that: The specific process of Step 2 is as follows: Step 2.1, establish the objective function for the optimal heating comfort of users : ； Wherein: and are the weight distribution coefficients of the sub-objective functions and respectively. The weight values are taken as 1:

1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours. The sub-objective function reflects the actual value and the fluctuation range of the indoor temperature within 24 hours, and its expression is: ； Wherein: is the actual value of the indoor temperature during the period; is the actual value of the indoor temperature during the period; is the expected value of the indoor temperature set by the user; Step 2.2, establish the objective function of minimizing the user's heating electricity cost : ； Wherein: is the electric power of the direct heating device of the time-period heat storage type electric heating; is the electric power of the heat storage device of the time-period heat storage type electric heating; 、 and respectively represent the sets of the heat storage, heat release and suspension working periods of the heat storage device of the heat storage type electric heating; is the simulation step size; is the time-period electricity price; Among them, ； Wherein: is the start time period of peak electricity price; is the end time period of peak electricity price; is the start time period of valley electricity price; is the end time period of valley electricity price; is the peak electricity price; is the valley electricity price.

4. Rural heat storage electric heating operation optimization device, including a collection module and an objective function module, characterized in that: It also includes a stochastic optimization model module for electric heating and a solving module for the stochastic optimization model of electric heating; The acquisition module is used to collect the outdoor temperature, indoor temperature, and room thermal parameters; The objective function module is used to establish an objective function for the lowest heating electricity cost and optimal comfort for the user within 24 hours of the whole day; The stochastic optimization model module for electric heating is used to construct a stochastic optimization model for electric heating with chance constraints based on the objective function established by the objective function module; The solving module for the stochastic optimization model of electric heating is used to solve the established stochastic optimization model of electric heating by using the quantum genetic algorithm and output the solution result; The stochastic optimization model of electric heating performs the following operations: a. Establish chance constraint conditions, specifically as follows: Operation constraints of the direct heating equipment of heat storage electric heating: ； In the formula: is the upper limit of the electric power of the direct heating device for regenerative electric heating; Operation constraints of the heat storage equipment of heat storage electric heating: ； ； ； Where: is the energy stored in the heat storage device of the regenerative electric heating during the time period t + 1; is the energy stored in the heat storage device of the regenerative electric heating during the time period t; is the energy storage efficiency of the heat storage device of the regenerative electric heating; is the energy release efficiency of the heat storage device of the regenerative electric heating; is the energy coefficient of the heat dissipation loss or self-loss of the heat storage device of the regenerative electric heating to the environment; is the upper limit value of the electric power of the heat storage device of the regenerative electric heating; and are the lower limit and the upper limit of the heat load state, respectively; Thermal energy supply-demand balance constraint: ； ； Wherein: is the electro-thermal conversion efficiency of the direct heating device for regenerative electric heating; , is the total heat demand of the user during a certain period; and are the heat demands during the peak and valley periods of the electricity price respectively; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; is the equivalent thermal resistance between the indoor and outdoor; Electro-thermal coupling constraint: ； In the formula, is the heat capacity of indoor air, is the actual value of the indoor temperature during is the actual value of the indoor temperature during b. Analyze the uncertain factors and construct a mathematical programming model containing random variables. Among them, the uncertain factor is the temperature prediction value. For the analysis of the problem with uncertain factors, consider the following mathematical programming model containing random variables: ； In the formula: is a decision variable; is a random variable; is the objective function of the mathematical programming model; is the constraint function containing random variables; c. Using the chance-constrained programming theory, the uncertain factors are incorporated into the constraint conditions of the mathematical programming model. The specific operations are as follows: The chance-constrained programming theory defines the meaning of the mathematical programming model on a certain probability basis, which is characterized as: ； Wherein: is the confidence level in the chance constraint; represents the probability operator in the chance constraint; d. By using the sampling average approximation method to simulate and process the constraint conditions of the mathematical programming model, the constraint conditions containing random variables are transformed into deterministic constraints. Specifically: d1. Establish the uncertain constraint conditions, which are: ； Wherein: is the heat supply of the heat storage device and the direct heating device during the heat storage period, is the heat supply of the direct heating device in the heat release section and the suspended operation section of the heat storage device, is the total heat demand of the user, is the confidence level of the uncertainty constraint condition; is the electric power of the direct heating device of the time-sharing heat storage type electric heating; is the electric power of the heat storage device of the time-sharing heat storage type electric heating; is the electro-thermal conversion efficiency of the direct heating device of the heat storage type electric heating; is the energy release efficiency of the heat storage device of the heat storage type electric heating; 、 and respectively represent the sets of the heat storage, heat release and suspended operation periods of the heat storage device of the heat storage type electric heating; is the expected value of the indoor temperature set by the user; is the 24-hour outdoor temperature forecast data of the user obtained through networking; and are the equivalent thermal resistances between the indoor air and the heat storage body and between the indoor and the outdoor respectively; d2, obtain the group of daily temperature fluctuation curve samples , , where: , N is the number of users in the control group, and m is the serial number of the daily temperature fluctuation curve sample group; d3, calculate the indicator function , and its expression is: ； In the formula, is the serial number of the user in the control group; d4. Transform the uncertain constraint conditions to obtain the deterministic constraint conditions: ； e. Obtain the stochastic optimization model of electric heating based on chance constraints. The stochastic optimization model of electric heating is: ； In the formula: is the objective function for the optimal heating comfort of users, is the objective function for the lowest heating electricity cost of users; and are the weight distribution coefficients of the sub-objective functions and respectively, with the weight values taken as 1:

1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours, and the sub-objective function reflects the actual value of the indoor temperature and the indoor temperature fluctuation amplitude within 24 hours; is the electric power of the direct heating equipment of the time-of-use heat storage electric heating during the is the electric power of the heat storage equipment of the time-of-use heat storage electric heating during the , and respectively represent the sets of the heat storage, heat release, and work suspension periods of the heat storage equipment of the heat storage electric heating; is the time-of-use electricity price, is the simulation step size; represents the probability operator in the chance constraint; is the heat supply of the heat storage equipment and the direct heating equipment during the heat storage period; is the heat supply of the direct heating equipment during the heat release section and the work suspension section of the heat storage equipment, is the total heat demand of users; is the confidence level of the uncertainty constraint condition; is the energy storage efficiency of the heat storage equipment of the heat storage electric heating; is the energy release efficiency of the heat storage equipment of the heat storage electric heating; is the energy coefficient of the heat storage equipment of the heat storage electric heating for the energy dissipation loss or self-loss to the environment; is the upper limit value of the electric power of the heat storage equipment of the heat storage electric heating; is the upper limit value of the electric power of the direct heating equipment of the heat storage electric heating; is the energy stored by the heat storage equipment of the heat storage electric heating at time t; is the energy stored by the heat storage equipment of the heat storage electric heating at time t+1; and are the lower and upper limits of the heat load state respectively.

5. The rural heat storage type electric heating operation optimization device according to claim 4, wherein: The room thermal parameters include the equivalent thermal resistance between the indoor air-heat storage body and indoor-outdoor, the indoor air heat capacity, the electric power of the heat storage type electric heating, and the electro-thermal conversion efficiency of the direct heating device of the heat storage type electric heating. Among them, the electric power of the heat storage type electric heating includes the electric power of the direct heating device of the heat storage type electric heating and the electric power of the heat storage device.

6. The rural heat storage electric heating operation optimization device according to claim 4, characterized in that: The objective function module establishes the following objective function: Establish the objective function for the optimal heating comfort of users : ; Wherein: and are the weight distribution coefficients of the sub-objective functions and respectively, and the weight values are taken as 1:

1. The sub-objective function reflects the deviation between the actual value and the expected value of the indoor temperature within 24 hours. The sub-objective function reflects the actual value of the indoor temperature and the amplitude of the indoor temperature fluctuation within 24 hours, and its expression is: ； In the formula: is the actual value of the indoor temperature during the period; is the actual value of the indoor temperature during the period; is the expected value of the indoor temperature set by the user; Establish the objective function for the lowest heating electricity cost of users : ; Wherein: is the electric power of the direct heating device of the time-period heat storage electric heating; is the electric power of the heat storage device of the time-period heat storage electric heating; , and respectively represent the sets of the heat storage, heat release and work suspension periods of the heat storage device of the heat storage electric heating; is the simulation step size; is the time-period electricity price, wherein: ； Wherein: is the start time period of peak electricity price; is the end time period of peak electricity price; is the start time period of valley electricity price; is the end time period of valley electricity price; is the peak electricity price; is the valley electricity price.

7. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it realizes the steps of the method according to any one of claims 1 to 3.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, it realizes the steps of the method according to any one of claims 1 to 3.

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