A time-of-use electricity price optimization method and terminal for an incremental distribution electricity operator

Abstract

The application discloses a kind of incremental distribution power operator's time-of-use electricity price optimization method and terminal, constructs incremental distribution power operator profit maximization's time-of-use electricity price optimization upper model, and introduces conditional risk value in objective function to measure risk, it is convenient to propose more economic and reasonable time-of-use electricity price optimization strategy;Build user power consumption cost minimization and power consumption satisfaction benefit maximization lower model;On the basis of upper and lower model, incremental distribution power operator two-stage distribution robust optimization scheduling model is constructed, and column and constraint generation algorithm (C&CG) is used to solve model, can effectively reduce the operation risk of distribution power operator and reduce user expenditure cost.

Description

Technical Field

[0001] This invention relates to the technical field of time-of-use pricing optimization, and in particular to a time-of-use pricing optimization method and terminal for incremental power distribution operators. Background Technology

[0002] With the accelerated construction of new power systems and the deepening reform of the power system, the power grid structure and power system are undergoing profound changes. This transformation has led to the emergence of numerous new players, such as electric vehicles, distributed power sources, integrated power generation, grid-load-storage systems, and energy storage technologies, further enriching the types of participants in the power market. Currently, electricity retailers, acting as a link between the diversified power market and users, rely on their core profit model of purchasing electricity from the upstream market and reselling it to end users. To maximize operating profits, incremental distribution and retail operators must make optimized decisions in both the electricity purchase and sales stages. In the current highly competitive electricity retail market, incremental distribution and retail operators urgently need to design reasonable pricing strategies to improve user experience and attract consumers. When making energy choices, consumers consider cost and living comfort as two main factors. Today, consumers are increasingly aware of the importance of managing energy consumption patterns optimally. Time-of-use pricing is widely used in retail packages between electricity retailers and users. It not only helps respond to the demand-side management of grid operators but also incentivizes users to optimize their electricity consumption behavior based on different prices during peak and off-peak hours. This pricing model forms a new paradigm of supply and demand interaction, helping multiple parties in the energy supply chain achieve a win-win situation. Therefore, how to scientifically and rationally formulate time-of-use pricing strategies has become a key issue facing incremental power distribution operators and the power market. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a time-of-use pricing optimization method and terminal for incremental power distribution operators, which can effectively reduce the operational risks of power sales operators and reduce user expenditure costs.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] A method for optimizing time-of-use pricing for incremental power distribution operators includes the following steps:

[0006] We construct a higher-level model for optimizing time-of-use electricity pricing to maximize the profits of incremental power distribution operators by combining risk metrics.

[0007] Construct a lower-level model that minimizes user electricity costs and maximizes electricity satisfaction benefits;

[0008] A two-stage optimization scheduling model for incremental power distribution operators is constructed by combining the upper-level model and the lower-level model. The two-stage optimization scheduling model is solved by using a column sum constraint generation algorithm to obtain the time-of-use pricing optimization strategy.

[0009] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0010] A time-of-use pricing optimization terminal for an incremental power distribution operator 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, it implements the various steps of the aforementioned time-of-use pricing optimization method for an incremental power distribution operator.

[0011] The beneficial effects of this invention are as follows: It constructs an upper-level model for optimizing time-of-use pricing to maximize the profits of incremental power distribution operators, and introduces conditional value of risk into the objective function to measure risk, facilitating the development of more economical and reasonable time-of-use pricing optimization strategies; it constructs a lower-level model to minimize user electricity costs and maximize user satisfaction benefits; based on the upper and lower-level models, it constructs a two-stage distributed bar optimization scheduling model for incremental power distribution operators, and uses the column sum and constraint generation algorithm (C&CG) to solve the model, which can effectively reduce the operational risks of power retailers and reduce user expenditure costs. Attached Figure Description

[0012] Figure 1 This is a flowchart of a time-of-use pricing optimization method for incremental power distribution operators according to an embodiment of the present invention;

[0013] Figure 2 This is a schematic diagram of a time-of-use pricing optimization terminal for an incremental power distribution operator according to an embodiment of the present invention;

[0014] Figure 3 This is a schematic diagram of a two-stage optimized scheduling model according to an embodiment of the present invention.

[0015] Label Explanation:

[0016] 1. A time-of-use pricing optimization terminal for incremental power distribution operators; 2. Memory; 3. Processor. Detailed Implementation

[0017] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0018] Please refer to Figure 1 This invention provides a method for optimizing time-of-use electricity pricing for incremental power distribution operators, comprising the following steps:

[0019] We construct a higher-level model for optimizing time-of-use electricity pricing to maximize the profits of incremental power distribution operators by combining risk metrics.

[0020] Construct a lower-level model that minimizes user electricity costs and maximizes electricity satisfaction benefits;

[0021] A two-stage optimization scheduling model for incremental power distribution operators is constructed by combining the upper-level model and the lower-level model. The two-stage optimization scheduling model is solved by using a column sum constraint generation algorithm to obtain the time-of-use pricing optimization strategy.

[0022] As can be seen from the above description, the beneficial effects of this invention are as follows: It constructs an upper-level model for optimizing time-of-use electricity pricing to maximize the profits of incremental power distribution operators, and introduces conditional value of risk into the objective function to measure risk, facilitating the formulation of more economical and reasonable time-of-use electricity pricing optimization strategies; it constructs a lower-level model to minimize user electricity costs and maximize user satisfaction benefits; based on the upper and lower-level models, it constructs a two-stage distributed bar optimization scheduling model for incremental power distribution operators, and uses the column sum and constraint generation algorithm (C&CG) to solve the model, which can effectively reduce the operational risks of power retailers and reduce user expenditure costs.

[0023] Furthermore, a higher-level model for optimizing time-of-use electricity pricing to maximize the profits of incremental power distribution operators is constructed by combining risk metrics, including:

[0024] The objective function for constructing the upper-level model of time-of-use pricing optimization is:

[0025] max F=γF EXP -(1-γ)C CVaR

[0026] In the formula, F EXP C represents the expected operating revenue of incremental power distribution operators. CVaR The system operating condition risk value is represented by (1-γ), the risk preference coefficient is represented by γ, the proportion of expected cost in the system operating cost is represented by F, and the incremental power distribution operator's profit is represented by F.

[0027] Where, max F EXP =M-C1-C2-C3;

[0028] In the formula, M represents the electricity sales revenue of incremental power distribution operators, C1 represents the electricity purchase cost in the medium- and long-term market and the spot market, C2 represents the cost of distributed power transmission in the incremental distribution area, and C3 represents the daily cost of energy storage system configuration and operation.

[0029] Power balance constraints, renewable energy output constraints, energy storage system operation constraints, and electricity price constraints are set for the upper-level model of the time-of-use electricity price optimization.

[0030] As described above, the main risks for incremental power distribution operators come from the uncertainty of upstream market prices, load instability, and the volatility of power output from sources such as wind and solar power. Therefore, using CVaR (Conditional Value at Risk) to characterize risk losses can lead to more economical and reasonable time-of-use pricing optimization strategies.

[0031] Furthermore, a lower-level model for optimizing time-of-use electricity pricing is constructed to minimize user electricity costs and maximize benefits from electricity satisfaction, including:

[0032] Construct the objective function for the lower-level model:

[0033] minf = C L +C H +C bess +C pv -M CP

[0034] In the formula, C L C represents the cost of electricity used by the load. H C represents the cost of comfort. bess C pv M represents the cost expenditure of energy storage and user photovoltaic systems. CP Indicates the benefits of electricity user satisfaction;

[0035]

[0036] In the formula, denoted as the load adjustment amount after the user responds to the time-of-use electricity price at time t, where α and β both represent the load adjustment satisfaction coefficients.

[0037]

[0038] In the formula, a cut b cut This represents the interruptible load compensation factor. This represents the amount of load interruption at time t; This represents the load reduction state variable at time t, when the load is reduced. otherwise

[0039] As described above, the electricity satisfaction benefit is calculated by considering the load reduction status, load interruption amount, and load compensation coefficient. Therefore, based on user satisfaction, a lower-level model is established with the goal of minimizing user electricity costs, which further improves the rationality of subsequent time-of-use pricing optimization strategies.

[0040] Furthermore, a two-stage optimized scheduling model for incremental power distribution operators is constructed by combining the upper-level model and the lower-level model, including:

[0041] The time-of-use electricity price output by the upper-level model is input into the lower-level model, and the electricity consumption plan output by the lower-level model is input into the upper-level model to construct a two-stage optimized scheduling model for incremental power distribution operators.

[0042] Furthermore, a two-stage optimal scheduling model for incremental power distribution operators is constructed, including:

[0043]

[0044] In the formula, x represents the time period, level, and energy storage operation plan of the electricity price in the first stage of optimization; y represents all continuous decision variables in the second stage of optimization, including the charging and discharging power of the energy storage battery and the amount of load interruption or transfer at each time. , respectively, are the coefficient vectors of x and y; U represents the set of random variables representing prediction errors in the model;

[0045] F EXP C represents the expected operating revenue of incremental power distribution operators. CVaR Let C represent the system operating condition risk value, (1-γ) represent the risk preference coefficient, γ represent the proportion of expected cost in the system operating cost, and C represent the risk value under operating conditions. L C represents the cost of electricity used by the load. H C represents the cost of comfort. bess C pv M represents the cost expenditure of energy storage and user photovoltaic systems. CP This indicates the benefits of electricity user satisfaction.

[0046] As described above, users follow the time-of-use pricing implemented by incremental power distribution operators and adjust their own electricity consumption plans through demand response and other means. Considering the impact of uncertain factors, the two-level optimization design of the incremental power distribution operator's time-of-use pricing can be modeled as a two-stage robust optimization problem: The first-stage optimization model is responsible for solving the operator's operation strategy problem, mainly providing the corresponding operational efficiency maximization strategy under the worst-case scenario of uncertain factors, and its decision variables are the time period and level of electricity price; The second-stage optimization model is responsible for solving the user's electricity consumption plan problem, mainly determining the situation that can maximize operational efficiency by changing the value of the load demand random variable, and its decision variables are the charging and discharging power of the energy storage battery at each time, the amount of load interruption or transfer.

[0047] Please refer to Figure 2 Another embodiment of the present invention provides a time-of-use pricing optimization terminal for an incremental power distribution operator, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the above-described time-of-use pricing optimization method for an incremental power distribution operator.

[0048] The above-described method and terminal for optimizing time-of-use electricity pricing for incremental power distribution operators is applicable to effectively reducing the operational risks of power retailers and decreasing user expenses. The following detailed implementation methods illustrate this:

[0049] Example 1

[0050] Please refer to Figure 1 and Figure 3A method for optimizing time-of-use pricing for incremental power distribution operators, comprising the following steps:

[0051] S1. Construct an upper-level model for optimizing time-of-use electricity pricing to maximize the profits of incremental power distribution operators by combining risk metrics.

[0052] Specifically, in this embodiment, the incremental power distribution operator refers to an operator that integrates wind and solar turbines and energy storage devices within a region, interacts with the main power grid to formulate power sales strategies, and meets the electricity demand of users within the region. The economic dispatch model of the incremental power distribution operator simultaneously considers the uncertainties of market prices, power output, and load, and the model characterizes the risk loss cost:

[0053] max F=γF EXP -(1-γ)C CVaR

[0054] In the formula, F EXP C represents the expected operating revenue of incremental power distribution operators. CVaR The system operating condition risk value is represented by (1-γ), the risk preference coefficient is represented by γ, the proportion of expected cost in the system operating cost is represented by F, and the incremental power distribution operator's profit is represented by F.

[0055] Where, max F EXP =M-C1-C2-C3;

[0056] In the formula, M represents the electricity sales revenue of incremental power distribution operators, C1 represents the electricity purchase cost in the medium- and long-term market and the spot market, C2 represents the cost of distributed power transmission from the incremental distribution area, and C3 represents the daily cost of energy storage system configuration and operation.

[0057]

[0058] In the formula, x t P represents the time-of-use electricity price set by incremental power distribution operators for different time periods. t This represents the total incremental electricity demand in the distribution and sales area during time period t.

[0059]

[0060] Incremental electricity distribution operators participate in medium- and long-term market transactions based on the projected load curves of users in their respective regions, through bilateral negotiations or centralized bidding, to form the transaction price ρ. b and transaction volume Q b t k is a binary variable, taking values ​​of ±1. When the value is 1, it indicates that the electricity retailer needs to purchase electricity from the spot market, and the transaction price is the electricity price ρ. s and transaction volume Q s tWhen the value is -1, it indicates that electricity is sold to the spot market.

[0061]

[0062] ρ pv ρ wt These are the feed-in tariffs for photovoltaic and wind power in the incremental distribution areas, respectively. Forecasted power output for photovoltaic and wind turbines respectively.

[0063]

[0064] C3 primarily considers the charging and discharging conversion costs and daily operation and maintenance costs of energy storage devices for electricity retailers. C represents the charging power and discharging power of the energy storage device, respectively. BESS The operation and maintenance costs of energy storage devices; Indicates the operation and maintenance costs of energy storage charging state transitions. These represent the energy storage device's transition to charging and discharging, respectively.

[0065] The constraints include the following:

[0066] (1) Power balance constraint

[0067]

[0068] Incremental power distribution areas must maintain a balance between the real-time power they interact with users and the real-time power they interact with the upper-level power grid and distributed power sources.

[0069] (2) Constraints on new energy output

[0070]

[0071] In the formula, These represent the upper limits of photovoltaic and wind power output, respectively.

[0072] (3) Energy storage system operation constraints

[0073]

[0074] In the formula, represent the charging capacity constraints, upper and lower limits of charging power constraints, state of charge / discharge constraints, and state of charge constraints of the energy storage system, respectively. These are the charging / discharging power of the battery, respectively. These are the maximum charging / discharging power of the battery, respectively. These represent the battery's charge / discharge efficiency; Cap represents the battery's rated capacity. Indicates the charge / discharge quantity and state of charge (SOC) of the energy storage system. min SOCmax These represent the maximum and minimum states of charge of an energy storage system.

[0075] (4) Electricity price constraints

[0076]

[0077] In the formula, T f T p T g x represents the peak period, the normal period, and the valley period, respectively. f x p x g These represent the electricity prices for peak, normal, and off-peak periods, respectively; x min x max These represent the upper and lower limits of the peak-valley electricity price ratio, respectively, to avoid the situation where peak and valley prices are reversed.

[0078] Furthermore, the risks for incremental power distribution operators mainly stem from the uncertainty of upstream market prices, load instability, and the volatility of power output from wind and solar power sources. This embodiment employs Conditional Value at Risk (CVaR) to characterize risk losses, thereby proposing a more economical and reasonable time-of-use pricing strategy. Compared to VaR, CVaR better characterizes tail risk and improves the level of risk assessment. The loss of incremental power distribution operators can be expressed as the cost of electricity purchase in a certain scenario minus the expected value of the cost of electricity purchase. It is assumed that, under a confidence level of σ, the operator's loss from electricity purchase does not exceed a certain maximum loss value C. VaRσ If the loss exceeds C VaRσ Expected loss C CVaR It can be represented as:

[0079]

[0080] In the formula, C VaRσ That is, C represents the maximum possible loss for incremental power distribution operators at the σ confidence level. CVaR For losses exceeding C VaRσ The conditional mean. k is the number of scenarios, p(k) is the probability of scenario k occurring; C EXP This represents the expected cost of electricity purchase.

[0081] Introducing variables ξ and ν(k), the objective function is transformed into:

[0082]

[0083] S2. Construct a lower-level model that minimizes user electricity costs and maximizes electricity satisfaction benefits.

[0084] Specifically, the lower-level model uses the minimum total user cost as its objective function. Users are integrated producers and consumers, including power sources such as energy storage and residential photovoltaic systems. The load consists of fixed loads, transferable loads, and interruptible loads. Users reduce their electricity costs by adjusting their load at different times. The objective function for constructing the lower-level model is:

[0085] minf = C L +C H +C bess +C pv -M CP

[0086] In the formula, C L C represents the cost of electricity used by the load. H C represents the cost of comfort. bess C pv M represents the cost expenditure of energy storage and user photovoltaic systems. CP Indicates the benefits of electricity user satisfaction;

[0087]

[0088] In the formula, P L Electricity consumption during period t after a user participates in demand response;

[0089]

[0090] In the formula, denoted as the load adjustment amount after the user responds to the time-of-use electricity price at time t, where α and β both represent the load adjustment satisfaction coefficients.

[0091]

[0092] In the formula, a cut b cut This represents the interruptible load compensation factor. This represents the amount of load interruption at time t; This represents the load reduction state variable at time t, when the load is reduced. otherwise

[0093] S3. Combine the upper-level model and the lower-level model to construct a two-stage optimization scheduling model for incremental power distribution operators, and use the column sum constraint generation algorithm to solve the two-stage optimization scheduling model to obtain the time-of-use pricing optimization strategy.

[0094] The time-of-use electricity price output by the upper-level model is input into the lower-level model, and the electricity consumption plan output by the lower-level model is input into the upper-level model to construct a two-stage optimized scheduling model for incremental power distribution operators.

[0095] Specifically, users follow the time-of-use pricing implemented by incremental power distribution operators and adjust their electricity consumption plans through demand response and other means. Considering the impact of uncertainties, the two-level optimization design of the incremental power distribution operator's time-of-use pricing can be modeled as a two-stage robust optimization problem: The first-stage optimization model solves the operator's operational strategy problem, mainly providing the corresponding strategy to maximize operational efficiency under the worst-case scenario of uncertainties, with the decision variables being the time period and level of the electricity price; the second-stage optimization model solves the user's electricity consumption plan problem, mainly determining the situation that maximizes operational efficiency by changing the value of the load demand random variable, with the decision variables being the charging and discharging power of the energy storage battery at each time point, and the amount of load interruption or transfer. Two-stage robust optimization model. As shown in the following formula:

[0096]

[0097]

[0098] In the formula, x represents the time period, level, and energy storage operation plan of the electricity price in the first stage of optimization; y represents all continuous decision variables in the second stage of optimization, including the charging and discharging power of the energy storage battery and the amount of load interruption or transfer at each time. , respectively, are the coefficient vectors of x and y; U represents the set of random variables representing prediction errors in the model.

[0099] The second-stage inner-layer optimization problem is transformed into a single-layer linear programming problem through dual transformation, and then solved. We obtain:

[0100]

[0101] In the formula: Let Lagrange multiplier vectors be the vectors corresponding to the inequality constraints. b2 represents the coefficient vector and intercept of x, respectively.

[0102] Robust optimization model This can be transformed into solving the following equation:

[0103]

[0104] Optimizing time-of-use electricity pricing is a complex problem involving multiple variables, such as the time of day, price level, and electricity consumption. It is not only a non-convex optimization problem with complementary constraints but also one with numerous variables. To address this, we employ the C&CG (Column and Constraint Generation) partitioning approach to construct the overall algorithm framework. Our method first uses a master-slave iterative approach to decompose the complex problem into a master problem and subproblems, solving these two problems alternately. Each iteration of the algorithm includes optimization of both the master and subproblems. The optimization of the subproblems primarily aims to minimize electricity costs. The optimized objective information is fed back into the master problem in the form of C&CG cuts, generating a new round of problems for further solving. In this way, we can gradually approach the optimal solution, effectively solving this non-convex optimization problem with multiple variables and complementary constraints.

[0105] Example 2

[0106] Please refer to Figure 2 A time-of-use pricing optimization terminal 1 for an incremental power distribution operator includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements the various steps of the time-of-use pricing optimization method for an incremental power distribution operator according to Embodiment 1.

[0107] In summary, this invention provides a time-of-use pricing optimization method and terminal for incremental power distribution operators. It constructs an upper-level model for time-of-use pricing optimization that maximizes the profits of incremental power distribution operators, and introduces conditional value at risk (VAT) into the objective function to measure risk, facilitating the development of more economical and reasonable time-of-use pricing optimization strategies. A lower-level model is constructed to minimize user electricity costs and maximize user satisfaction. Based on the upper and lower-level models, a two-stage distributed bar optimization scheduling model for incremental power distribution operators is built, and the column sum and constraint generation algorithm (C&CG) is used to solve the model. This effectively reduces the operational risks of power retailers and lowers user expenditure costs.

[0108] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for optimizing time-of-use rates of an incremental distribution electric utility operator, characterized by, The method comprises the steps of: building an upper model of time-of-use electricity price optimization of the incremental power supply and sale operator for maximizing profits in combination with risk measurement; building a lower model of time-of-use electricity price optimization for minimizing electricity cost and maximizing satisfaction of users; building a two-stage optimization scheduling model of the incremental power supply and sale operator in combination with the upper model and the lower model, and solving the two-stage optimization scheduling model by using a column and constraint generation algorithm to obtain a time-of-use electricity price optimization strategy; The building of the upper model of time-of-use electricity price optimization of the incremental power supply and sale operator for maximizing profits in combination with risk measurement comprises: building an objective function of the upper model of time-of-use electricity price optimization: max F = building power balance constraints, new energy output constraints, energy storage system operation constraints and electricity price constraints for the upper model of time-of-use electricity price optimization; EXP − (1− The building of the lower model of time-of-use electricity price optimization for minimizing electricity cost and maximizing satisfaction of users comprises: ) C CVaR wherein F EXP represents the expected operating revenue of the incremental power distribution operator, C CVaR represents the risk value of the system operating condition, (1- building an objective function of the lower model: represents the risk preference coefficient, and γ represents the proportion of the expected cost in the system operating cost, F represents the profit of the incremental power distribution operator; where max F EXP = M - C 1- C 2- C 3; In the formula, M represents the incremental distribution of electricity retailers' electricity sales, C 1 represents the purchase cost of medium and long-term market and spot market, C 2 represents the return cost of distributed power supply in incremental distribution area, C 3 represents the configuration and operation cost of energy storage system The building of the two-stage optimization scheduling model of the incremental power supply and sale operator in combination with the upper model and the lower model comprises: inputting the time-of-use electricity price output by the upper model into the lower model, and inputting the electricity consumption plan output by the lower model into the upper model to build a two-stage optimization scheduling model of the incremental power supply and sale operator; The building of the two-stage optimization scheduling model of the incremental power supply and sale operator comprises: min f = C L + C H + C bess + C pv - M CP wherein, C L represents the cost of electricity for the load, C H represents the cost of comfort, C bess , C pv represents the cost of energy storage, user photovoltaic cost outlay, M CP represents the satisfaction of electricity use; In the formula, Indicates t The user response to time-of-use electricity price at the moment, α , β Both represent the load adjustment satisfaction coefficient; In the formula, ɑ cut , b cut This represents the interruptible load compensation factor. express t The amount of load interruption at any given time; express t The load reduction state variable is defined when the load is reduced. =1, otherwise =0; The processor realizes the following steps when executing the computer program: building an upper model of time-of-use electricity price optimization of the incremental power supply and sale operator for maximizing profits in combination with risk measurement; building a lower model of time-of-use electricity price optimization for minimizing electricity cost and maximizing satisfaction of users; wherein, x denotes the time period, level of electricity price and the energy storage operation plan in the first stage optimization; y denotes all continuous decision variables in the second stage optimization, including the energy storage battery charging and discharging power at each time, the load interruption or transfer amount; , are the coefficient vectors of x , y ; U represents the collection of prediction error random variables in the model.

2. A time-of-use price optimization terminal for an incremental distribution electric power operator, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, building a two-stage optimization scheduling model of the incremental power supply and sale operator in combination with the upper model and the lower model, and solving the two-stage optimization scheduling model by using a column and constraint generation algorithm to obtain a time-of-use electricity price optimization strategy; The building of the upper model of time-of-use electricity price optimization of the incremental power supply and sale operator for maximizing profits in combination with risk measurement comprises: building an objective function of the upper model of time-of-use electricity price optimization: building power balance constraints, new energy output constraints, energy storage system operation constraints and electricity price constraints for the upper model of time-of-use electricity price optimization; The building of the lower model of time-of-use electricity price optimization for minimizing electricity cost and maximizing satisfaction of users comprises: building an objective function of the lower model: max F = The building of the two-stage optimization scheduling model of the incremental power supply and sale operator in combination with the upper model and the lower model comprises: EXP -(1- inputting the time-of-use electricity price output by the upper model into the lower model, and inputting the electricity consumption plan output by the lower model into the upper model to build a two-stage optimization scheduling model of the incremental power supply and sale operator; ) C CVaR wherein, F EXP represents the expected operating revenue of the incremental power distribution operator, C CVaR represents the risk value of the system operating condition, (1- The building of the two-stage optimization scheduling model of the incremental power supply and sale operator comprises: represents the risk preference coefficient, and γ represents the proportion of the expected cost in the system operating cost, F represents the profit of the incremental power distribution operator; max F EXP M C 1- C 2- C 3;​​ In the formula, M represents the incremental distribution of electricity retailers' electricity sales, C 1 represents the purchase cost of medium and long-term market and spot market, C 2 represents the return cost of distributed power supply in incremental distribution area, C 3 represents the configuration and operation cost of energy storage system ​ ​ ​ min f = C L + C H + C bess + C pv - M CP wherein, C L represents the cost of electricity for the load, C H represents the cost of comfort, C bess , C pv represents the cost of energy storage, user photovoltaic cost outlay, M CP represents the satisfaction of electricity use; In the formula, represents t The load adjustment satisfaction coefficient of the user response to the time-of-use electricity price at the moment, α , β represents the load adjustment satisfaction coefficient. In the formula, ɑ cut , b cut This represents the interruptible load compensation factor. express t The amount of load interruption at any given time; express t The load reduction state variable is defined when the load is reduced. =1, otherwise =0; ​ ​ ​ wherein, x denotes the time period, level of electricity price and the energy storage operation plan in the first stage optimization; y denotes all continuous decision variables in the second stage optimization, including the energy storage battery charging and discharging power at each time, the load interruption or transfer amount; , are the coefficient vectors of x , y respectively; U represents the collection of prediction error random variables in the model.

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