Demand response method of comprehensive zero-carbon power plant

Abstract

The invention discloses a demand response method for a comprehensive zero-carbon power plant, and relates to the field of power distribution networks, and the method comprises the following steps: S1, an independent system operator (ISO) predicts a load curve, sets a load reduction target of an IZP in a peak load period, and sends the load reduction target to the IZP; s2, after the IZP receives the load reduction signal, an optimal operation plan is formulated by considering the green value of renewable energy sources; s3, a Stackelberg game is carried out between the ISO and the IZP, and the IZP determines the load reduction amount of the IZP according to a load reduction target set by the ISO; s4, the ISO executes an iterative algorithm based on the dichotomy principle, and an equilibrium solution of the optimal demand response strategy is obtained through calculation. The demand response mechanism is applied to the interaction between the comprehensive zero-carbon power plant and the independent system operator. The mathematical model considering the green value of renewable energy optimizes the operation of the IZP, and the algorithm based on the dichotomy effectively reduces the calculation complexity. Through the cooperation of ISO and IZP, the peak load of the power system is effectively reduced, and the operation efficiency of the system is improved.

Description

Technical Field

[0001] This invention relates to a demand response method, specifically a demand response method for integrated zero-carbon power plants. Background Technology

[0002] An integrated zero-carbon power plant (IZP) effectively integrates uncontrollable green electricity, adjustable loads, and energy storage resources into a grid-friendly entity.

[0003] The existing technology has the following main drawbacks: 1) Model level: Existing research, when considering the participation of IZP in demand response, ignores the green value of renewable energy in power plants, fails to fully explore the potential of ZIP in demand response, and fails to fully reflect the comprehensive benefits of renewable energy to the environment and society. As a result, the constructed models cannot accurately reflect the actual situation, which affects the optimization of demand response strategies.

[0004] 2) Computational Level: When calculating the Stackelberg game equilibrium between ISO and IZP, since ISO typically cannot obtain information about IZP, iterative algorithms are mostly used. Existing iterative algorithms, such as those based on traversal methods, usually require a large number of trials to obtain an exact solution, resulting in high computational complexity and low efficiency. In environments with limited information, it is difficult to quickly and accurately find the optimal solution to the game equilibrium, increasing computational and time costs. Summary of the Invention

[0005] The purpose of this invention is to provide a demand response method for integrated zero-carbon power plants to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A demand response method for integrated zero-carbon power plants (IZP) includes the following steps: S1. The Independent System Operator (ISO) forecasts the load curve and sets load reduction targets for the IZP during peak load periods, and sends these targets to the IZP. After receiving the load reduction signal, S2.IZP formulates the optimal operation plan by taking into account the green value of renewable energy. S3. A Stackelberg game is played between ISO and IZP, with IZP determining its own load reduction amount based on the load reduction target set by ISO. S4.ISO uses an iterative algorithm based on the bisection principle to calculate the equilibrium solution of the optimal demand response strategy; Step S1 specifically involves: ISO will reduce IZP load on the power system during peak load forecasting, thereby improving the operating efficiency of the power grid.

[0007] Assume the predicted peak load of the power system the day before the demand response plan is The peak load of the power system one day after the implementation of the demand response plan is The load reduction during peak load periods is: .

[0008] The relationship between the three variables above is as follows: (1) The relationship between the load reduction of each IZP and the overall system load reduction target is as follows: (2) in, m For IZP tags; M Total IZP; IZP load reduction m .

[0009] The effectiveness of ISO in reducing peak loads can be expressed as a quadratic form.

[0010] (3) in To reduce peak loads for the benefit of ISO; , The utility function coefficient is used to reduce the load.

[0011] Step S2 specifically involves: Integrated zero-carbon power plants typically include a control center that oversees numerous generators, electricity consumers, and energy storage devices to ensure efficient operation of the entire power plant while minimizing operating costs.

[0012] 1) Energy storage device model The relationship between charging (discharging) power and energy levels within the energy storage device at different time periods is given as follows: (4) in, For energy levels in energy storage devices; The charging power for energy storage; The discharge power of the stored energy; The length of the period.

[0013] Energy storage devices k exist t The inequality constraints on charging and discharging power and the energy storage limit constraints during a given time period can be expressed as: (5) in, This represents the upper limit of the energy storage device. A binary variable indicating the charging and discharging state; This represents the maximum charging and discharging power of the energy storage.

[0014] Note that a value of 1 indicates that the energy storage device is in t The period is in charging state, and a value of 0 indicates that the energy storage device is in charging state. t The period is in a discharge state.

[0015] 2) Green Energy Producer Model Renewable energy generator sets i The production cost of green energy is expressed in linear form: (6) in, Cost of green energy production; The production cost per unit of green energy; This refers to the amount of electricity generated.

[0016] Renewable energy generator sets i The profit from selling renewable energy to the grid is: (7) in, For the profit of renewable energy generator sets; The energy value generated from renewable energy sources sold to the grid; The green value for renewable energy; Electricity sold to the power grid.

[0017] Based on the Cournot model, time periods can be... t The green value of renewable energy can be written in flexible form: (8) in, This represents the upper limit of the green value. The green value elasticity coefficient; I This represents the total number of green energy generators.

[0018] The power generation balance constraint in IZP is: (9) in, For IZP I A collection of renewable energy generators; Electricity consumed by electricity consumers themselves; For IZP K A collection of energy storage devices.

[0019] 3) Energy User Model Energy users j The energy consumption utility function is a quadratic form: (10) in, It is an energy consumption utility function; , These are the coefficients in the energy consumption utility function.

[0020] The cost of purchasing electricity from the grid can be expressed in linear form: (11) in, The cost of purchasing electricity from the grid; Energy purchased from the power grid Electricity users j The upper and lower limits for green electricity consumption are as follows: (12) in, This is the lower limit of power consumption; This is the maximum power consumption limit.

[0021] The power balance constraint of IZP is: (13) in, For IZP J A collection of renewable energy users; 4) IZP scheduling model Typically, the primary objective of IZP operators is to maximize overall social welfare or minimize the social costs associated with the entire IZP system.

[0022] (14) in, This represents the total social cost.

[0023] The equality constraints are as follows: (15) in, Step S3 specifically involves: The interaction between ISO and IZP can be described as a typical Stackelberg game. In this game, ISO initiates the game by issuing a load reduction signal, and then IZP determines the amount of load reduction.

[0024] 1) IZP's follower problem ISO determines the load reduction targets for IZPs and sends this load reduction plan to each IZP. mThe constraints in an optimization problem should include the following equality constraints: (16) in, Electricity purchased from the grid after demand response during peak load periods; Electricity sold to the grid after demand response during peak load periods; Electricity purchased from the grid before demand response during peak load periods; Electricity sold to the grid before demand response during peak load periods Note that additional constraints will narrow the feasible region of the IZP optimization problem and lead to increased costs. The increase in IZP costs (or decrease in IZP profits) before and after implementing the demand response scheme is as follows: (17) in, This is due to the increased cost of IZP; The cost of IZP after demand response; IZP cost prior to demand response.

[0025] 2) Leadership issues at ISO The objective function of ISO is to determine the optimal load reduction target to balance the load reduction utility and the increase in IZP cost: (18) in, Total ISO utility; The weighting factor for ISO is used to balance the load reduction utility and the profit loss of IZP.

[0026] Please note, larger This indicates that the utility of load reduction is more important than the profit loss of IZP, and is relatively small. This indicates that the profit loss from IZP is more important than the utility of reduced load. Step S4 specifically involves: The iterative algorithm used to compute the equilibrium of the Stackelberg game relies on the principle of bisection. Bisection is a method that systematically divides the search region into smaller steps; it is a common technique in computer science used to search for items in large datasets.

[0027] The algorithm implemented by ISO for IZP consists of 7 steps: Step S41: ISO has defined the IZP in the demand response plan. m Scope of load reduction targets [ L m,reduce,min , L m,reduce,max ].in, Lm,reduce,min IZP m The minimum load reduction; L m,reduce,max IZP m The maximum load reduction.

[0028] Step S42: ISO initializes the iteration time at the start of the iterative algorithm. n = 1.

[0029] Step S43: ISO split action selection range, in other words, the range of load reduction targets.

[0030] Sub-range 1 of the load reduction target [ L m,reduce,min , ( L m,reduce,max + L m,reduce,min ) / 2] Sub-range 2 of the load reduction target [( L m,reduce,max + L m,reduce,min ) / 2, L m,reduce,max ] Step S44: ISO selects the midpoint between the two intervals of the load reduction target: Midpoint 1 T 1 = ( L m,reduce,max +3 L m,reduce,min ) / 2 Midpoint 2 T 2 = (3 L m,reduce,max + L m,reduce,min ) / 2 Step S45: ISO comparison U ISO,total (T 1) and U ISO,total (T 2) The reward values ​​for these two points: if U ISO,total (T 1)> U ISO,total(T 2), Since the probability of obtaining the optimal value on the left is greater than that on the right, the search area is narrowed down to the left. Therefore, the upper limit of the action selection range will be updated to ( L m,reduce,max + L m,reduce,min ) / 2, while the lower limit of the action selection range remains unchanged.

[0031] if U ISO,total (T 1) U ISO,total (T 2), Since the probability of obtaining the optimal value on the right side is greater than that on the left side, the search area is narrowed to the right. Therefore, the lower bound of the action selection range will be updated to ( L m,reduce,max + L m,reduce,min ) / 2, while the upper limit of the action selection range remains unchanged.

[0032] Step S46: ISO update and iteration time n = n +1.

[0033] Step S47: ISO comparison n and N The value of .

[0034] if n = N End the algorithm if n < N Proceed to step S43 The iterative algorithm described above for calculating the equilibrium of the Stackelberg game is based on the principle of binary search, which reduces computational complexity exponentially.

[0035] Compared with the prior art, the beneficial effects of the present invention are: 1) At the model level, a mathematical model for IZP operation was constructed. This model takes into account the variable green value of renewable energy output, reflects the actual operation of power plants more comprehensively, and helps IZP to formulate more reasonable operation plans. While realizing its own economic benefits, it can give full play to the green value of renewable energy and promote the sustainable development of the power system.

[0036] 2) At the computational level, an iterative algorithm based on the bisection principle is proposed to calculate the optimal demand response strategy. This algorithm is applied to calculate the Stackelberg game equilibrium, which can exponentially reduce the computational complexity and significantly improve the computational efficiency. Attached Figure Description

[0037] Figure 1 This is a diagram of the ISO and IZP models in the demand response of this invention.

[0038] Figure 2 This is a Stackelberg game diagram between ISO and IZP in this invention.

[0039] Figure 3 The diagram shows the ISO iterative algorithm for solving the Stackelberg game equilibrium in this invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] In this embodiment of the invention, a demand response method for integrated zero-carbon power plants is provided, wherein the ISO and IZP models in the demand response are as follows: Figure 1 As shown, the entire mechanism mainly includes the following steps: S1. The Independent System Operator (ISO) forecasts the load curve and sets load reduction targets for the IZP during peak load periods, and sends these targets to the IZP. After receiving the load reduction signal, S2.IZP formulates the optimal operation plan by taking into account the green value of renewable energy. S3. A Stackelberg game is played between ISO and IZP, with IZP determining its own load reduction amount based on the load reduction target set by ISO. S4.ISO uses an iterative algorithm based on the bisection principle to calculate the equilibrium solution of the optimal demand response strategy; Step S1 specifically involves: ISO will reduce IZP load on the power system during peak load forecasting, thereby improving the operating efficiency of the power grid.

[0042] Assume the predicted peak load of the power system the day before the demand response plan is The peak load of the power system one day after the implementation of the demand response plan is The load reduction during peak load periods is: .

[0043] The relationship between the three variables above is as follows: (1) The relationship between the load reduction of each IZP and the overall system load reduction target is as follows: (2) in, m For IZP tags; M Total IZP; IZP load reduction m .

[0044] The effectiveness of ISO in reducing peak loads can be expressed as a quadratic form.

[0045] (3) in To reduce peak loads for the benefit of ISO; , The utility function coefficient is used to reduce the load.

[0046] Step S2 specifically involves: Integrated zero-carbon power plants typically include a control center that oversees numerous generators, electricity consumers, and energy storage devices to ensure efficient operation of the entire power plant while minimizing operating costs.

[0047] 1) Energy storage device model The relationship between charging (discharging) power and energy levels within the energy storage device at different time periods is given as follows: (4) in, For energy levels in energy storage devices; The charging power for energy storage; The discharge power of the stored energy; The length of the period.

[0048] Energy storage devices k exist t The inequality constraints on charging and discharging power and the energy storage limit constraints during a given time period can be expressed as: (5) in, This represents the upper limit of the energy storage device. A binary variable indicating the charging and discharging state; This represents the maximum charging and discharging power of the energy storage.

[0049] Note that a value of 1 indicates that the energy storage device is in t The period is in charging state, and a value of 0 indicates that the energy storage device is in charging state. tThe period is in a discharge state.

[0050] 2) Green Energy Producer Model Renewable energy generator sets i The production cost of green energy is expressed in linear form: (6) in, Cost of green energy production; The production cost per unit of green energy; This refers to the amount of electricity generated.

[0051] Renewable energy generator sets i The profit from selling renewable energy to the grid is: (7) in, For the profit of renewable energy generator sets; The energy value generated from renewable energy sources sold to the grid; The green value for renewable energy; Electricity sold to the power grid.

[0052] Based on the Cournot model, time periods can be... t The green value of renewable energy can be written in flexible form: (8) in, This represents the upper limit of the green value. The green value elasticity coefficient; I This represents the total number of green energy generators.

[0053] The power generation balance constraint in IZP is: (9) in, For IZP I A collection of renewable energy generators; Electricity consumed by electricity consumers themselves; For IZP K A collection of energy storage devices.

[0054] 3) Energy User Model Energy users j The energy consumption utility function is a quadratic form: (10) in, It is an energy consumption utility function; , These are the coefficients in the energy consumption utility function.

[0055] The cost of purchasing electricity from the grid can be expressed in linear form: (11) in, The cost of purchasing electricity from the grid; Energy purchased from the power grid Electricity users j The upper and lower limits for green electricity consumption are as follows: (12) in, This is the lower limit of power consumption; This is the maximum power consumption limit.

[0056] The power balance constraint of IZP is: (13) in, For IZP J A collection of renewable energy users; 4) IZP scheduling model Typically, the primary objective of IZP operators is to maximize overall social welfare or minimize the social costs associated with the entire IZP system.

[0057] (14) in, This represents the total social cost.

[0058] The equality constraints are as follows: (15) in, Step S3 specifically involves: The interaction between ISO and IZP can be described as a typical Stackelberg game, such as Figure 2 As shown. In this game, ISO initiates the process by issuing a load reduction signal, after which IZP determines the amount of load reduction.

[0059] 1) IZP's follower problem ISO determines the load reduction targets for IZPs and sends this load reduction plan to each IZP. m The constraints in an optimization problem should include the following equality constraints: (16) in, Electricity purchased from the grid after demand response during peak load periods; Electricity sold to the grid after demand response during peak load periods; Electricity purchased from the grid before demand response during peak load periods; Electricity sold to the grid before demand response during peak load periods Note that additional constraints will narrow the feasible region of the IZP optimization problem and lead to increased costs. The increase in IZP costs (or decrease in IZP profits) before and after implementing the demand response scheme is as follows: (17) in, This is due to the increased cost of IZP; The cost of IZP after demand response; IZP cost prior to demand response.

[0060] 2) Leadership issues at ISO The objective function of ISO is to determine the optimal load reduction target to balance the load reduction utility and the increase in IZP cost: (18) in, Total ISO utility; The weighting factor for ISO is used to balance the load reduction utility and the profit loss of IZP.

[0061] Please note, larger This indicates that the utility of load reduction is more important than the profit loss of IZP, and is relatively small. This indicates that the profit loss from IZP is more important than the utility of reduced load. Step S4 specifically involves: The iterative algorithm used to compute the equilibrium of the Stackelberg game relies on the principle of bisection. Bisection is a method that systematically divides the search region into smaller steps; it is a common technique in computer science used to search for items in large datasets.

[0062] The algorithm implemented by ISO for IZP includes seven steps, such as... Figure 3 As shown: Step S41: ISO has defined the IZP in the demand response plan. m Scope of load reduction targets [ L m,reduce,min , L m,reduce,max ].in, L m,reduce,min IZP m The minimum load reduction; L m,reduce,max IZP m The maximum load reduction.

[0063] Step S42: ISO initializes the iteration time at the start of the iterative algorithm. n = 1.

[0064] Step S43: ISO split action selection range, in other words, the range of load reduction targets.

[0065] Sub-range 1 of the load reduction target [ L m,reduce,min , ( L m,reduce,max + L m,reduce,min ) / 2] Sub-range 2 of the load reduction target [( L m,reduce,max + L m,reduce,min ) / 2, L m,reduce,max ] Step S44: ISO selects the midpoint between the two intervals of the load reduction target: Midpoint 1 T 1 = ( L m,reduce,max +3 L m,reduce,min ) / 2 Midpoint 2 T 2 = (3 L m,reduce,max + L m,reduce,min ) / 2 Step S45: ISO comparison U ISO,total (T 1) and U ISO,total (T 2) The reward values ​​for these two points: if U ISO,total (T 1)> U ISO,total (T 2), Since the probability of obtaining the optimal value on the left is greater than that on the right, the search area is narrowed down to the left. Therefore, the upper limit of the action selection range will be updated to ( L m,reduce,max + Lm,reduce,min ) / 2, while the lower limit of the action selection range remains unchanged.

[0066] if U ISO,total (T 1) U ISO,total (T 2), Since the probability of obtaining the optimal value on the right side is greater than that on the left side, the search area is narrowed to the right. Therefore, the lower bound of the action selection range will be updated to ( L m,reduce,max + L m,reduce,min ) / 2, while the upper limit of the action selection range remains unchanged.

[0067] Step S46: ISO update and iteration time n = n +1.

[0068] Step S47: ISO comparison n and N The value of .

[0069] if n = N End the algorithm; if n < N Proceed to step 3; The iterative algorithm described above for calculating the equilibrium of the Stackelberg game is based on the principle of binary search, which reduces computational complexity exponentially.

[0070] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0071] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A demand response method for integrated zero-carbon power plants, characterized in that, Includes the following steps: S1. Independent System Operator (ISO) forecasts the load curve and sets load reduction targets for Integrated Zero Carbon Power Plant (IZP) during peak load periods, and sends these targets to the IZP. S2. After receiving load reduction signals, the Integrated Zero-Carbon Power Plant (IZP) formulates an optimal operation plan taking into account the green value of renewable energy. S3. A Stackelberg game is played between the Independent System Operator (ISO) and the Integrated Zero Carbon Power Plant (IZP). The IZP determines its own load reduction amount based on the load reduction target set by the ISO. S4. Independent System Operator (ISO) uses an iterative algorithm based on the bisection principle to calculate the equilibrium solution of the optimal demand response strategy.

2. The demand response method for an integrated zero-carbon power plant according to claim 1, characterized in that, Step S1 specifically involves: Assume the predicted peak load of the power system the day before the demand response plan is The peak load of the power system one day after the implementation of the demand response plan is The load reduction during peak load periods is: ; The relationship between the three variables above is as follows: （1） The relationship between the load reduction of each integrated zero-carbon power plant (IZP) and the overall system load reduction target is as follows: （2） in, m For IZP tags; M Total IZP; To reduce the load on integrated zero-carbon power plants (IZP) m ; The utility of an Independent System Operator (ISO) in reducing peak load can be expressed as a quadratic form: （3） in For the benefit of independent system operators (ISOs), to reduce peak load; , The utility function coefficient is used to reduce the load.

3. The demand response method for an integrated zero-carbon power plant according to claim 2, characterized in that, Step S2 specifically involves: 1) Energy storage device model The relationship between charging / discharging power and energy levels within the energy storage device at different time periods is as follows: （4） in, For energy levels in energy storage devices; The charging power for energy storage; The discharge power of the stored energy; The length of the period; Energy storage devices k exist t The inequality constraints on charging and discharging power and the energy storage limit constraints during the time period are expressed as follows: （5） in, This represents the upper limit of the energy storage device. A binary variable indicating the charging and discharging state; This represents the maximum charging and discharging power of the energy storage. A value of 1 indicates that the energy storage device is in t The period is in charging state, and a value of 0 indicates that the energy storage device is in charging state. t The period is in a discharge state; 2) Green Energy Producer Model Renewable energy generator sets i The production cost of green energy is expressed in linear form: （6） in, Cost of green energy production; The production cost per unit of green energy; For electricity generation; Renewable energy generator sets i The profit from selling renewable energy to the grid is: （7） in, For the profit of renewable energy generator sets; The energy value generated from renewable energy sources sold to the grid; The green value for renewable energy; The amount of electricity sold to the power grid; Based on the Cournot model, time periods t The green value of renewable energy can be written in flexible form: （8） in, This represents the upper limit of the green value. The green value elasticity coefficient; I The total number of green energy generators; The generation balance constraints in the Integrated Zero-Carbon Power Plant (IZP) are: （9） in, For IZP I A collection of renewable energy generators; Electricity consumed by electricity consumers themselves; For IZP K A collection of energy storage devices; 3) Energy User Model Energy users j The energy consumption utility function is a quadratic form: （10） in, It is an energy consumption utility function; , These are the coefficients in the energy consumption utility function; The cost of purchasing electricity from the grid can be expressed in linear form: （11） in, The cost of purchasing electricity from the grid; Energy purchased from the power grid; Electricity users j The upper and lower limits for green electricity consumption are as follows: （12） in, This is the lower limit of power consumption; This is the maximum power consumption limit; The power balance constraint for integrated zero-carbon power plants (IZP) is as follows: （13） in, For IZP J A collection of renewable energy users; 4) Integrated Zero-Carbon Power Plant (IZP) Dispatch Model The goal of an Integrated Zero-Carbon Power Plant (IZP) operator is to maximize overall social welfare or minimize the social costs associated with the entire IZP system. (14) in, Total social cost; The equality constraints are as follows: （15）。 4. The demand response method for an integrated zero-carbon power plant according to claim 3, characterized in that, Step S3 specifically involves: The interaction between Independent System Operator (ISO) and Integrated Zero Carbon Power Plant (IZP) can be described as a typical Stackelberg game, in which ISO initiates load shedding by issuing a load shedding signal, and then IZP determines the amount of load shedding. 1) The follower problem of integrated zero-carbon power plant (IZP) Independent System Operator (ISO) determines the load reduction targets for Integrated Zero-Carbon Power Plants (IZPs) and sends this load reduction plan to each IZP. m Constraints in optimization problems include the following equality constraints: （16） in, Electricity purchased from the grid after demand response during peak load periods; Electricity sold to the grid after demand response during peak load periods; Electricity purchased from the grid before demand response during peak load periods; Electricity sold to the grid before demand response during peak load periods; The increase in integrated zero-carbon power plant (IZP) costs or the decrease in integrated zero-carbon power plant (IZP) profits before and after the implementation of the demand response scheme are as follows: （17） in, To account for the increased cost of IZP (Integrated Zero-Carbon Power Plant); The integrated zero-carbon power plant (IZP) cost after demand response; The integrated zero-carbon power plant IZP cost prior to demand response; 2) Leadership issues of Independent Systems Operators (ISOs) The objective function of the Independent System Operator (ISO) is to determine the optimal load reduction target to balance the load reduction utility with the increase in IZP costs: （18） in, Total utility of ISO for independent system operators; The weighting factor for Independent System Operators (ISOs) is used to balance the load reduction utility and the profit loss of Integrated Zero-Carbon Power Plants (IZPs).

5. The demand response method for an integrated zero-carbon power plant according to claim 4, characterized in that, Step S4 specifically involves: The iterative algorithm used to compute the equilibrium of the Stackelberg game relies on the principle of bisection. The algorithm implemented by Independent System Operator (ISO) for Integrated Zero Carbon Power Plant (IZP) comprises seven steps: Step S41: Independent System Operator (ISO) has defined the Integrated Zero-Carbon Power Plant (IZP) in its demand response solutions. m Scope of load reduction targets [ L m,reduce,min , L m,reduce,max ]; in, L m,reduce,min For Integrated Zero Carbon Power Plant (IZP) m The minimum load reduction; L m,reduce,max For Integrated Zero Carbon Power Plant (IZP) m The maximum load reduction; Step S42: Independent System Operator (ISO) initializes the iteration time at the start of the iterative algorithm. n = 1; Step S43: Independent system operator ISO split action selection range; Sub-range 1 of the load reduction target [ L m,reduce,min , ( L m,reduce,max + L m,reduce,min ) / 2] Sub-range 2 of the load reduction target [( L m,reduce,max + L m,reduce,min ) / 2, L m,reduce,max ] Step S44: Independent System Operator (ISO) selects the midpoint between two intervals for the load reduction target: Midpoint 1 T 1 = ( L m,reduce,max + 3 L m,reduce,min ) / 2 Midpoint 2 T 2 = (3 L m,reduce,max + L m,reduce,min ) / 2 Step S45: Independent System Operator ISO Comparison U ISO,total (T 1) and U ISO,total (T 2) The reward values ​​for these two points: if U ISO,total (T 1) > U ISO,total (T 2) Since the probability of obtaining the optimal value on the left is greater than that on the right, the search area is narrowed to the left, and the upper limit of the action selection range is updated to ( L m,reduce,max + L m,reduce,min The lower limit of the action selection range remains unchanged; if U ISO,total (T 1) < U ISO,total (T 2) Since the probability of obtaining the optimal value on the right side is greater than that on the left side, the search area is narrowed to the right side, and the lower limit of the action selection range is updated to ( L m,reduce,max + L m,reduce,min The value is 2, while the upper limit of the action selection range remains unchanged; Step S46: Independent System Operator ISO Update Time n = n +1; Step S47: Independent System Operator ISO Comparison n and N The value; if n = N End the algorithm; if n < N Proceed to step S43.

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