Time-of-use electricity price optimization method considering demand response
By establishing a demand price elasticity matrix and a price elasticity matrix that considers the uncertainty of demand response, combining Sigmoid function and whale optimization algorithm, the time-sharing electricity price is optimized, and the problem of user response behavior is ignored is solved, and the grid load pressure is reduced and operation is achieved. Improved efficiency.
Patent Information
- Application Number
- CN202411243655.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-05
- Publication Date
- 2025-02-11
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing time-sharing electricity price formulation method ignores users' response behavior to changes in electricity price and their uncertainty, resulting in poor electricity price adjustment effect.
By establishing a demand price elasticity matrix and a price elasticity matrix that takes into account the uncertainty of demand response, combining Sigmoid function and whale optimization algorithm, time-sharing electricity prices are optimized to guide users to reasonably arrange their electricity consumption time.
Effectively reduce the load pressure during peak periods of power grids, improve overall operating efficiency, and enhance user electricity use experience and trust in the power market.
Smart Images
Figure CN119417496A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of electric power systems, and in particular relates to a time-of-use electricity price optimization method taking demand response into account. Background Art
[0002] With the continuous development and improvement of the power market, power demand side management has become one of the important means to ensure the safe and stable operation of the power grid and improve the efficiency of power resource utilization. As an effective incentive mechanism, time-of-use electricity prices guide users to reasonably arrange their electricity consumption time by adjusting electricity prices in different time periods, thereby achieving the purpose of peak shaving and valley filling. However, traditional time-of-use electricity price formulation methods often ignore the user's response behavior to changes in electricity prices and their uncertainty, resulting in unsatisfactory actual results.
[0003] Problems with existing time-of-use electricity prices: Most time-of-use electricity price strategies set electricity prices based solely on the balance of electricity supply and demand, without fully considering users' actual response to changes in electricity prices, resulting in a significant reduction in the effectiveness of electricity price adjustments. Summary of the invention
[0004] The technical problem to be solved by the present invention is to provide a time-of-use electricity price optimization method taking demand response into account. By guiding users to reasonably arrange their electricity consumption time, the load pressure of the power grid during peak periods can be effectively reduced and the overall operating efficiency can be improved. Allowing users to participate in electricity demand management can not only improve users' electricity consumption experience, but also increase users' trust in the electricity market.
[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A time-of-use electricity price optimization method taking into account demand response, the steps are: Step 1: Establish a demand price elasticity matrix; Step 2: Establish a price elasticity matrix considering the uncertainty of demand response; Step 3: Establish the objective function; Step 4: Set constraints; Step 5: Establish a multi-objective solution; Step 6: Optimize time-of-use electricity prices. Preferably, in step 1, the demand of users in a certain period of time is not only affected by the electricity price of the period of time, but also related to the electricity price of the adjacent period of time. Therefore, the demand price elasticity matrix considering the time-of-use electricity price is expressed as: (1); In the formula, Qj and Qj' respectively represent the demand in each period before and after considering the time-of-use electricity price; CPj and CPj' respectively represent the peak, valley and normal period electricity prices before and after considering the time-of-use electricity price; If i=j represents the self-elastic coefficient, otherwise it represents the mutual elastic coefficient.
[0006] Preferably, in step 2, it is assumed that the electricity price without considering the time-of-use electricity price is C P0 , at this time the user psychological index HI CP0 Always remain unchanged and set as the benchmark value; the electricity price after considering the time-of-use electricity price is C Pm , where m=1,2,3, and the user psychological index is represented by HI CPm ; In order to simulate the psychological behavior response of users when electricity prices change, the uncertain mapping relationship between electricity prices and user response levels is described as: When C Pm = C P0 When the user response level (HI CPm -HI CP0 )=0, the user does not respond to the electricity price; When C Pm >C P0 When the electricity price increases, the user response level (HI CPm -HI CP0 )>0, the higher the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first increasing slowly and then accelerating; When C Pm <C P0 When the electricity price decreases, the user response level (HI CPm -HI CP0 )<0, the lower the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first decreasing slowly and then decreasing rapidly.
[0007] Preferably, in step 2, an improved Sigmoid function is used to accurately fit C Pm and HI CPm The mapping relationship between them is as shown in expression (3), and the user psychological behavior response index HI is associated with the load demand: When HI>HI CP0 When HI is high, users will save electricity, and the higher the HI is, the more electricity users will save; When HI = HI CP0 When , the user's electricity consumption behavior remains unchanged; When HI <HI CP0 When , the user will increase electricity consumption, and the lower HI is, the more the user increases electricity consumption; Consider that pessimistic users will save electricity only when the psychological index is very high, and increase electricity consumption when the psychological index is very low; optimistic users have a lower threshold for participation and response, and will save electricity when the psychological index is slightly higher, and increase electricity consumption when the psychological index is slightly lower; Introducing adjustable variables To characterize the degree of participation of different types of users in response, in both pessimistic and optimistic cases, assuming that there is a positive linear relationship between load demand and HI, the expression is as follows: (2); (3); In the formula, is a measure of user engagement response, a The parameter describes the steepness of the function. a The larger the value, the greater the function C Pm = C P0 nearby HI The greater the change in the indicator; b The parameter describes the degree of deviation of the function on the vertical axis. b The larger the value, the higher the HI The smaller the indicator; Assuming different The values are as follows: Optimistic scenario: =0.8 or 0.9; Pessimistic scenario: =1.1 or 1.2; According to formula (2), the total load size after considering the uncertainty of demand response in the three periods of peak, valley and flat is obtained. Therefore, in order to obtain the hourly load size, the principle of proportional allocation is adopted, and the formula is as follows: (4); In the formula, They represent the load without and with demand response respectively.
[0008] Preferably, in step 3, the model of the present invention adopts two objective functions: 1) Peak load is expressed as f 1; (5); 2) The peak-to-valley load difference in the load curve is expressed as f 2: (6); in: (7); (8); In the formula, CP=(CP1, CP2, CP3) is the decision variable.
[0009] Preferably, in step 4, the constraints include: The user satisfaction and power supplier interest relationship before and after considering the time-of-use electricity price are established respectively, and the constraint formulas are (9)-(10); including: after considering the time-of-use electricity price, the user gets economic benefits, the expenditure cost should not increase, and the power supplier's interest should not decrease; The basic constraints of time-of-use electricity prices are established. The constraint formulas are (11)-(13), including: to prevent load reversal during peak and valley periods, the peak, flat and valley electricity prices are reduced in sequence, and considering the limitation of marginal cost, the valley electricity price should be greater than or equal to the marginal electricity price: (9); (10); (11); (12); (13); In the formula, is the benefit coefficient, and Cm is the marginal cost.
[0010] Preferably, in step 5, according to the importance of the two objective functions in the problem, a weight coefficient w1 and w2 are respectively assigned, and a linear weighting method is used, and a penalty function M(CP) is introduced into the objective function to transform the constrained multi-objective optimization problem into a single-objective optimization problem; therefore, the single-objective optimization problem is: (14).
[0011] Preferably, in step 6, the method for optimizing the time-of-use electricity price is: Create an initial whale population expression: X i ( C P ){ i =1,2,…, N , X i ( C P )=( C P1 , C P2 , C P3 )}; Calculate the fitness value corresponding to each individual whale according to formula (14); in, X* ( C P ) represents the whale individual with the best fitness value; K max Indicates the maximum number of iterations; when K < K max hour; but i =1, otherwise, update a , A , C , l , p； (1) If condition 1 is met: p <0.5; And if condition 2 is met: |A| ≥1; Then execute [1]: select a random individual X rand ( C P ), and based on: Updates the next generation position where: , ; , ; (2) If condition 1 and condition 3 are met: |A| <1; Then execute [2]: According to Update next generation location; (3) End the two commands of condition 2 and condition 3; (4) If condition 4 is met p ≥0.5; Then execute [4] according to Update next generation location; (5) Check whether each whale is beyond the search boundary and correct its position; (6) Recalculate the fitness value of the whale according to formula (14); (7) If there is a better whale position, update X* ( C P ), t = t +1; Returns X*(C P ) to the upper layer to continue iterating; End the whole process; in, X Indicates the location of individual whales; K , Kmax Represent the current and maximum number of iterations respectively; N is the number of individuals in the population; a To control the parameters; A and C is the coefficient vector; D Indicates individual distance X rand Length; b is the logarithmic spiral constant; l is a uniformly distributed random number between [0,1]; p It is called the selection probability, which is a random number between [0,1]; r 1. r 2 represents a uniformly distributed random number between [0,1].
[0012] Preferably, a time-of-use electricity price optimization system taking demand response into account comprises a processing unit and a storage unit, and the program instructions executed by the processing unit are the time-of-use electricity price optimization method taking demand response into account. Preferably, a processing unit executes program instructions for the time-of-use electricity price optimization method taking demand response into account.
[0013] The present invention can achieve the following beneficial effects: Based on the Sigmoid function cloud model, the present invention establishes a price elasticity matrix that takes into account the uncertainty of demand response. Then, considering the user satisfaction and the interests of power suppliers before and after the time-of-use electricity price, a time-of-use electricity price optimization method taking into account demand response is proposed based on the whale optimization algorithm (WOA). By guiding users to reasonably arrange their electricity use time, the load pressure during the peak period of the power grid can be effectively reduced and the overall operation efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a typical demand-price relationship curve diagram of the present invention; Figure 2 A demand-price relationship graph considering demand response uncertainty for the present invention. DETAILED DESCRIPTION
[0015] The preferred solution is Figure 1 to Figure 2 As shown in FIG. 1 , a time-of-use electricity price optimization method taking into account demand response is shown in FIG. 1 . The steps are as follows: 1. Demand price elasticity matrix: Demand price elasticity refers to the ratio of the rate of change of user electricity consumption to the corresponding price change rate over a period of time. According to economic principles, the typical demand-price relationship is as follows: Figure 1 shown.
[0016] Generally speaking, the demand of users in a certain period of time is not only affected by the electricity price of that period of time, but also related to the electricity price of adjacent periods of time. Therefore, the demand price elasticity matrix considering time-of-use electricity prices can be expressed as: (1); Where Qj and Qj' represent the demand in each period before and after the time-of-use electricity price; CPj and CPj' represent the peak, valley and normal electricity prices before and after the time-of-use electricity price; if i=j(i, j=1,2,3), represents the self-elastic coefficient, otherwise it represents the mutual elastic coefficient.
[0017] 2. Price elasticity matrix considering demand response uncertainty: Assume that the electricity price without considering the time-of-use electricity price is C P0 , at this time the user psychological index HI CP0 Always remain unchanged and set as the benchmark value; the electricity price after considering the time-of-use electricity price is C Pm (m=1,2,3), user psychological index is expressed as HI CPm In order to simulate the psychological behavior response degree of general users when the electricity price changes, the present invention can describe the uncertain mapping relationship between the electricity price and the user's response degree as: When C Pm = C P0 When the user response level (HI CPm -HI CP0 )=0, the user does not respond to the electricity price; When C Pm >C P0 When the electricity price increases, the user response level (HI CPm -HI CP0 )>0, the higher the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first increasing slowly and then accelerating; When C Pm <C P0 When the electricity price decreases, the user response level (HI CPm -HI CP0 )<0, the lower the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first decreasing slowly and then decreasing rapidly.
[0018] Therefore, the present invention adopts an improved Sigmoid function to accurately fit C Pm and HI CPm The mapping relationship between them (such as expression (3)) is used to associate the user psychological behavior response index HI with the load demand: when HI>HICP0 When HI= HI, users will save electricity, and the higher HI is, the more electricity users save. CP0 When HI <HI CP0 When the psychological index is high, the user will increase the electricity consumption, and the lower the HI is, the more the user will increase the electricity consumption. However, pessimistic users will save electricity only when the psychological index is very high, and increase electricity consumption when the psychological index is very low; optimistic users have a relatively low threshold for participating in the response, and will save electricity when the psychological index is slightly higher, and will increase electricity consumption when the psychological index is slightly lower. Therefore, the present invention introduces an adjustable variable To characterize the degree of participation of different types of users in response, in both pessimistic and optimistic cases, it is assumed that there is a positive linear relationship between load demand and HI. The specific expression is as follows: (2); (3); In the formula, is a measure of user engagement response, a The parameter describes the steepness of the function. a The larger the value, the greater the function C Pm = C P0 nearby HI The greater the change in the indicator; b The parameter describes the degree of deviation of the function on the vertical axis. b The larger the value, the higher the HI The smaller the index is (it should be noted that the solution method for a and b adopts the traditional particle swarm optimization algorithm). At this time, the new demand price relationship is as follows Figure 2 As shown. We assume different The values are as follows: Optimistic scenario: =0.8 or 0.9; Pessimistic scenario: =1.1 or 1.2; According to formula (2), the total load size after considering the uncertainty of demand response in the three periods of peak, valley and flat is obtained. Therefore, in order to obtain the load size per hour, the present invention adopts the principle of proportional allocation. The specific formula is as follows: (4); 3. Objective function: The model of the present invention adopts two objective functions: 1) The peak load is expressed as f 1; 2) The peak-to-valley load difference in the load curve is expressed as f 2: (5); (6); in: (7); (8); In the formula, CP=(CP1, CP2, CP3) is the decision variable.
[0019] 4. Constraints: The constraints include: Constraints (9)-(10) respectively establish the user satisfaction and power supplier interest relationship before and after considering the time-of-use electricity price. The specific meaning is that after considering the time-of-use electricity price, the user gets economic benefits, the expenditure cost should not increase, and the power supplier's profit should not decrease; Constraints (11)-(13) establish the basic constraints of the time-of-use electricity price. Specifically, in order to prevent the load reversal during the peak and valley periods, the peak, flat and valley electricity prices are reduced in turn, and considering the limitation of marginal cost, the valley electricity price should be greater than or equal to the marginal electricity price: (9); (10); (11); (12); (13); In the formula, is the benefit coefficient, and Cm is the marginal cost.
[0020] 5. Multi-objective solution: The present invention assigns a weight coefficient w1 and w2 respectively according to the importance of the two objective functions in the problem, uses a linear weighting method, and introduces a penalty function M(CP) into the objective function to transform the constrained multi-objective optimization problem into a single-objective optimization problem. Therefore, the single-objective optimization problem of the present invention is: (14); 6. Time-of-use electricity price optimization: The whale optimization algorithm (WOA) is a novel group optimization algorithm inspired by the whale's predation behavior. Compared with traditional algorithms, its advantages are fewer algorithm implementation steps, faster optimization speed and higher optimization accuracy. Therefore, the present invention uses the whale algorithm to solve the above optimization problem.
[0021] In the present invention, some index symbols are first declared: X Indicates the location of individual whales; K , K max Represent the current and maximum number of iterations respectively; Nis the number of individuals in the population; a To control the parameters; A and C is the coefficient vector; D Indicates individual distance X rand Length; b is the logarithmic spiral constant; l is a uniformly distributed random number between [0,1]; p It is called the selection probability, which is a random number between [0,1]; r 1. r 2 represents a uniformly distributed random number between [0,1].
[0022] In step 6, the method for optimizing the time-of-use electricity price is: Create an initial whale population expression: X i ( C P ){ i =1,2,…, N , X i ( C P )=( C P1 , C P2 , C P3 )}; Calculate the fitness value corresponding to each individual whale according to formula (14); in, X* ( C P ) represents the whale individual with the best fitness value; K max Indicates the maximum number of iterations; when K < K max hour; but i =1, otherwise, update a , A , C , l , p； (1) If condition 1 is met: p <0.5; And if condition 2 is met: |A| ≥1; Then execute [1]: select a random individual X rand ( C P), and based on: Updates the next generation position where: , ; , ; (2) If condition 1 and condition 3 are met: |A| <1; Then execute [2]: According to Update next generation location; (3) End the two commands of condition 2 and condition 3; (4) If condition 4 is met p ≥0.5; Then execute [4] according to Update next generation location; (5) Check whether each whale is beyond the search boundary and correct its position; (6) Recalculate the fitness value of the whale according to formula (14); (7) If there is a better whale position, update X* ( C P ), t = t +1; Returns X*(C P ) to the upper layer to continue iterating; End the whole process; in, X Indicates the location of individual whales; K , K max Represent the current and maximum number of iterations respectively; N is the number of individuals in the population; a To control the parameters; A and C is the coefficient vector; D Indicates individual distance X rand Length; b is the logarithmic spiral constant; l is a uniformly distributed random number between [0,1]; p It is called the selection probability, which is a random number between [0,1]; r 1. r 2 represents a uniformly distributed random number between [0,1].
[0023] Preferably, a time-of-use electricity price optimization system taking demand response into account comprises a processing unit and a storage unit, and the program instructions executed by the processing unit are the time-of-use electricity price optimization method taking demand response into account. Preferably, a processing unit executes program instructions for the time-of-use electricity price optimization method taking demand response into account.
[0024] Table 1 gives the pseudo code diagram of WOA to solve the time-of-use electricity price optimization.
[0025] Table 1 Pseudo code
[0026] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.
Claims
1. A time-of-use electricity price optimization method taking into account demand response, characterized in that The following steps are involved: Step 1: Establish a demand price elasticity matrix; Step 2: Establish a price elasticity matrix considering the uncertainty of demand response; Step 3: Establish the objective function; Step 4: Set constraints; Step 5: Establish a multi-objective solution; Step 6: Optimize time-of-use electricity prices.
2. A time-of-use electricity price optimization method taking demand response into account according to claim 1, characterized in that: In step 1, the demand of users in a certain period of time is not only affected by the electricity price of that period of time, but also related to the electricity prices of adjacent periods of time. Therefore, the demand price elasticity matrix considering time-of-use electricity prices is expressed as: (1)； In the formula, Qj and Qj' respectively represent the demand in each period before and after considering the time-of-use electricity price; CPj and CPj' respectively represent the peak, valley and normal period electricity prices before and after considering the time-of-use electricity price; If i=j represents the self-elastic coefficient, otherwise it represents the mutual elastic coefficient.
3. A time-of-use electricity price optimization method taking demand response into account according to claim 1, characterized in that: In step 2, it is assumed that the electricity price without considering the time-of-use electricity price is C P0 , at this time the user psychological index HI CP0 Always remain unchanged and set as the benchmark value; the electricity price after considering the time-of-use electricity price is C Pm , where m=1,2,3, and the user psychological index is represented by HI CPm ; In order to simulate the psychological behavior response of users when electricity prices change, the uncertain mapping relationship between electricity prices and user response levels is described as: When C Pm = C P0 When the user response level (HI CPm -HI CP0 )=0, the user does not respond to the electricity price; When C Pm > C P0 When the electricity price increases, the user response level (HI CPm -HI CP0 )>0, the higher the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first increasing slowly and then accelerating; When C Pm < C P0 When the electricity price decreases, the user response level (HI CPm -HI CP0 )<0, the lower the electricity price, the lower the probability that users will be affected by other non-electricity price factors when making decisions. CPm It shows a trend of first decreasing slowly and then decreasing rapidly.
4. A time-of-use electricity price optimization method taking demand response into account according to claim 3, characterized in that: In step 2, the improved Sigmoid function is used to accurately fit C Pm and HI CPm The mapping relationship between them is as shown in expression (3), and the user psychological behavior response index HI is associated with the load demand: When HI>HI CP0 When HI is high, users will save electricity, and the higher the HI is, the more electricity users will save; When HI = HI CP0 When , the user's electricity consumption behavior remains unchanged; When HI < HI CP0 When , the user will increase electricity consumption, and the lower HI is, the more the user increases electricity consumption; Consider that pessimistic users will save electricity only when the psychological index is very high, and increase electricity consumption when the psychological index is very low; optimistic users have a lower threshold for participation and response, and will save electricity when the psychological index is slightly higher, and increase electricity consumption when the psychological index is slightly lower; Introducing adjustable variables To characterize the degree of participation of different types of users in response, in both pessimistic and optimistic cases, assuming that there is a positive linear relationship between load demand and HI, the expression is as follows: (2)； (3)； In the formula, is a measure of user engagement response, a The parameter describes the steepness of the function. a The larger the value, the greater the function C Pm = C P0 nearby HI The greater the change in the indicator; b The parameter describes the degree of deviation of the function on the vertical axis. b The larger the value, the higher the HI The smaller the indicator; Assuming different The values are as follows: Optimistic scenario: =0.8 or 0.9; Pessimistic scenario: =1.1 or 1.2; According to formula (2), the total load size after considering the uncertainty of demand response in the three periods of peak, valley and flat is obtained. Therefore, in order to obtain the hourly load size, the principle of proportional allocation is adopted, and the formula is as follows: (4)； In the formula, They represent the load without and with demand response respectively.
5. A time-of-use electricity price optimization method taking demand response into account according to claim 1, characterized in that: In step 3, the model of the present invention adopts two objective functions: 1) Peak load is expressed as f 1; (5)； 2) The peak-to-valley load difference in the load curve is expressed as f 2: (6)； in: (7)； (8)； In the formula, CP=(CP1, CP2, CP3) is the decision variable.
6. A time-of-use electricity price optimization method taking demand response into account according to claim 1, characterized in that: In step 4, the constraints include: The user satisfaction and power supplier interest relationship before and after considering the time-of-use electricity price are established respectively, and the constraint formulas are (9)-(10); including: after considering the time-of-use electricity price, the user gets economic benefits, the expenditure cost should not increase, and the power supplier's interest should not decrease; The basic constraints of time-of-use electricity prices are established. The constraint formulas are (11)-(13), including: to prevent load reversal during peak and valley periods, the peak, flat and valley electricity prices are reduced in sequence, and considering the limitation of marginal cost, the valley electricity price should be greater than or equal to the marginal electricity price: (9)； (10)； (11)； (12)； (13)； In the formula, is the benefit coefficient, and Cm is the marginal cost.
7. A time-of-use electricity price optimization method taking demand response into account according to claim 1, characterized in that: In step 5, according to the importance of the two objective functions in the problem, a weight coefficient w1 and w2 are assigned respectively, and the linear weighting method is used. The penalty function M(CP) is introduced into the objective function to transform the constrained multi-objective optimization problem into a single-objective optimization problem; therefore, the single-objective optimization problem is: (14)。 8. A time-of-use electricity price optimization method taking demand response into account according to claim 7, characterized in that: In step 6, the method for optimizing the time-of-use electricity price is: Create an initial whale population expression: X i ( C P ){ i =1,2,…, N , X i ( C P )=( C P1 , C P2 , C P3 )}; Calculate the fitness value corresponding to each individual whale according to formula (14); in, X* ( C P ) represents the whale individual with the best fitness value; K max Indicates the maximum number of iterations; when K < K max hour; but i =1, otherwise, update a , A , C , l , p； (1) If condition 1 is met: p <0.5; And if condition 2 is met: |A| ≥1; Then execute [1]: select a random individual X rand ( C P ), and based on: Updates the next generation position where: , ; , ; (2) If condition 1 and condition 3 are met: |A| <1; Then execute [2]: According to Update next generation location; (3) End the two commands of condition 2 and condition 3; (4) If condition 4 is met p ≥0.5; Then execute [4] according to Update next generation location; (5) Check whether each whale is beyond the search boundary and correct its position; (6) Recalculate the fitness value of the whale according to formula (14); (7) If there is a better whale position, update X* ( C P ), t = t +1; Returns X*(C P ) to the upper layer to continue iterating; End the whole process; in, X Indicates the location of individual whales; K , K max Represent the current and maximum number of iterations respectively; N is the number of individuals in the population; a To control the parameters; A and C is the coefficient vector; D Indicates individual distance X rand Length; b is the logarithmic spiral constant; l is a uniformly distributed random number between [0,1]; p It is called the selection probability, which is a random number between [0,1]; r 1. r 2 represents a uniformly distributed random number between [0,1].
9. A time-of-use electricity price optimization system taking into account demand response, comprising a processing unit and a storage unit, characterized in that: The program instructions executed by the processing unit are a time-of-use electricity price optimization method taking demand response into account according to any one of claims 1-8.
10. A processing unit, characterized in that: The executed program instructions are a time-of-use electricity price optimization method taking demand response into account according to any one of claims 1-8.