Power capacity market clearing method and device considering flexibility

Abstract

The invention discloses an electric power capacity market clearing method and device considering flexibility. The method comprises the following steps: constructing an electric power capacity market clearing model considering flexible adjustment requirements of a system; performing dual change and linearization processing on the clearing model, and solving the model; carrying out capacity market pricing and settlement based on a solving result; wherein the step of constructing the power capacity market clearing model considering the flexible adjustment demand of the system comprises the following steps: determining a capacity demand curve which can adopt a variable resource demand curve; constructing a target function with maximization of social welfare as a target; determining capacity market clearing constraint conditions considering flexibility; according to the invention, the capacity value of the new energy can be priced and metered, and the new energy operation cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of power market clearing methods, and in particular, relates to a power capacity market clearing method and device taking flexibility into consideration. Background Art

[0002] The practice of power market pricing involves market design, market operation and market supervision. In the context of power market pricing, the power capacity market, as an important part of the power system operation, is related to the efficiency of resource allocation, cost sharing and market fairness. The clearing, pricing and settlement mechanism of the power capacity market is a key link to ensure fair, just and efficient market operation. In recent years, with the large-scale centralized grid connection of renewable energy power generation and the increasing complexity of the power system form and operation characteristics, the clearing, pricing and settlement mechanism of the power capacity market faces new challenges and opportunities. On the one hand, the intermittent characteristics of renewable energy power generation put forward higher requirements on the flexibility and balance ability of the power system; on the other hand, the continuous development and improvement of the power market has also promoted the innovation and optimization of the power capacity market mechanism. In the new power system dominated by low variable marginal cost power generation technology, it is becoming more and more important to improve the system power generation capacity margin and realize the mining of the capacity value of various types of power generation resources through market mechanism guidance and reasonable dispatch and control of power electronic equipment.

[0003] The above-mentioned disclosed technical solutions have at least the following technical problems: In the previous power capacity market clearing, the cost issues brought about by the output volatility of new energy and load demand were not considered, and only the capacity value provided by traditional energy was often focused on. Under the construction of new power systems, the configuration ratio of new energy units such as wind power and photovoltaics has increased, and new energy units have shown certain capacity value. At the same time, the power system has certain flexibility adjustment requirements. The traditional capacity market trading system cannot comprehensively evaluate the capacity value of power generation resources and carry out power capacity market clearing that takes into account the system flexibility requirements. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the purpose of the present invention is to provide

[0005] The present invention adopts the following technical solutions:

[0006] A method for clearing an electricity capacity market taking flexibility into account, the method comprising the following steps:

[0007] Construct a power capacity market clearing model that takes into account the system's flexible adjustment needs;

[0008] Performing dual transformation and linearization processing on the clearing model, and solving the model;

[0009] Carry out capacity market pricing and settlement based on the solution results;

[0010] The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes:

[0011] Determine the capacity demand curve, which can adopt the variable resource demand curve;

[0012] Construct an objective function to maximize social welfare;

[0013] Determine capacity market clearing constraints that take flexibility into account.

[0014] Furthermore, the objective function for maximizing social welfare is constructed, including:

[0015] The objective function is to maximize social welfare (i.e., minimize the negative of social welfare CNSW):

[0016]

[0017] Where: P i g , They are the winning bid capacities of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; are the declared prices of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; P l d is the winning capacity of the first segment of the capacity demand curve; d l is the price of the first segment of the capacity demand curve.

[0018] Furthermore, the determination of capacity market clearing constraints taking into account flexibility includes:

[0019] 1) The capacity supply and demand balance constraint to ensure system margin during load peak period is:

[0020]

[0021] Where: Ω n is the set of power generation resources participating in the capacity market; ρ Cap is the dual variable of the above formula;

[0022] 2) The capacity supply and demand balance constraint that meets the system's flexible adjustment needs. The flexible adjustment needs of the power system come from the random fluctuations of load, wind power, and photovoltaic power. The flexible ramping demand is used to describe the system's flexible adjustment needs. The fluctuation of load, wind power, and photovoltaic output is expressed as the sum of the predicted value and the uncertainty deviation value. Among them, the uncertainty deviation value is described by a box deviation interval.

[0023]

[0024] Where: and The output fluctuations of load n, wind farm h, and photovoltaic power station k are respectively; and are the predicted values ​​of the output fluctuations of load n, wind farm h, and photovoltaic power station k, respectively; and are the load, wind power, and photovoltaic fluctuations corresponding to the system flexible adjustment demand predicted by the market operation agency based on historical data; and are the uncertainty deviation values ​​of the output fluctuation of load n, wind farm h, and photovoltaic power station k respectively; and They are the maximum positive deviation and the minimum negative deviation of the load fluctuation uncertainty deviation value respectively; and They are the maximum positive deviation and minimum negative deviation of the uncertainty deviation value of the wind farm h output fluctuation respectively; and They are the maximum positive deviation and minimum reverse deviation of the uncertainty deviation value of the output fluctuation of the photovoltaic power station k;

[0025] Therefore, the flexible adjustment demand F R As shown below:

[0026]

[0027] Since the flexible adjustment demand is divided into two directions, upward and downward, the capacity supply and demand balance constraint that meets the flexible adjustment demand is as follows: the minimum capacity provided by thermal power units and energy storage power stations to meet the flexible adjustment demand can meet the flexible adjustment demand under the worst fluctuation conditions of load, wind power, and photovoltaic power. The default winning flexibility resources (thermal power units and energy storage power stations) provide the same size of upward and downward capacity to meet the flexible adjustment demand.

[0028]

[0029] Where: F i g , They are the capacity of thermal power unit i and energy storage power station m that meet the flexible adjustment needs of the system; is the dual variable;

[0030] 3) The capacity constraints for the winning bid of thermal power units are:

[0031]

[0032]

[0033] Where: Pi g，max is the maximum installed capacity of thermal power unit i; is the ramp rate of thermal power unit i; and is the dual variable of the corresponding constraint;

[0034] The proposed capacity market mechanism can simultaneously ensure the abundance of system capacity during load peak periods and the abundance of system capacity for flexible adjustment of demand, but the actual method of calling the winning resources is determined by the actual dispatching strategy or spot market clearing.

[0035] 4) The capacity constraints for wind farm bids are:

[0036]

[0037] Where: is the maximum installed capacity of wind farm h; is the available capacity of wind farm h participating in the capacity market, where: It is the credible capacity factor for converting the installed capacity when the wind farm h participates in the capacity market. The effective load carrying capacity (ELCC) or the de-rating factor can be used as the credible capacity factor; is the dual variable;

[0038] 5) The capacity constraints for the winning bid of a photovoltaic power station are:

[0039]

[0040] Where: is the maximum installed capacity of photovoltaic power station k; is the credible capacity factor for converting the installed capacity when the photovoltaic power station k participates in the capacity market; is the dual variable;

[0041] 6) The capacity constraints for energy storage power station bids are:

[0042]

[0043] Where: is the maximum installed capacity of the energy storage station m; It is the credible capacity factor used to convert the installed capacity when the energy storage power station m participates in the capacity market; is the ramp rate of the energy storage power station m; is the dual variable;

[0044] 7) The capacity demand bidding constraints are:

[0045]

[0046] Where: P l d,max is the maximum capacity of the lth segment of the capacity demand curve; is the dual variable;

[0047] Furthermore, the dual transformation and linearization processing of the clearing model includes:

[0048] Since there are nonlinear constraints in the capacity supply and demand balance constraints that meet the system's flexible adjustment needs, the capacity market clearing model cannot be solved directly. This type of robust optimization model with embedded optimization can be solved through dual transformation and linearization.

[0049] The general form of the robust optimization model with embedded optimization can be written as:

[0050]

[0051] Where: x is the decision variable vector; c is the objective function coefficient vector; b n is the right-hand constant term vector corresponding to constraint n; a n is the deterministic component vector of the coefficient corresponding to constraint n; M n ξ is the uncertainty component vector, where M n is the coefficient matrix corresponding to the uncertainty component, ξ is the variable vector characterizing the uncertainty; Z is the value set of ξ, that is, it must satisfy the constraint Dξ+q≥0, where D is the coefficient vector of the constraint and q is the constant term vector of the constraint.

[0052] When the value of x is fixed, each embedded optimization constraint decision variable is an uncertainty component ξ, which can be expressed as follows.

[0053]

[0054] Where: u n is the dual variable of the constraint Dξ+q≥0. From the strong duality theorem formula, we can get:

[0055] (M n ξ) T xq T u n =0

[0056] According to the KKT (Karush-Kuhn-Tucker) condition, we can get

[0057]

[0058] u n ≥0

[0059] Therefore, the robust optimization model can be transformed into the following form:

[0060]

[0061] In the capacity market robust optimization clearing model established by this method, the capacity constraints provided by thermal power units and energy storage power stations to meet the flexible adjustment needs upward and downward can be replaced by:

[0062]

[0063] Where: is the deterministic component vector of the constraint corresponding coefficient; is the corresponding right-hand constant term vector in the constraint; is the coefficient matrix corresponding to the uncertainty components.

[0064] Furthermore, the capacity market pricing and settlement based on the solution results include:

[0065] Capacity market pricing; capacity market settlement.

[0066] Furthermore, the capacity market pricing includes:

[0067] The capacity market clearing price considering flexible adjustment demand can be divided into the capacity price to ensure the system margin during the load peak period and the capacity price to meet the system's flexible adjustment demand according to the system demand type. Among them, the capacity price to meet the system's flexible adjustment demand is composed of the flexible adjustment forecast demand price and the flexible adjustment uncertainty deviation demand price.

[0068] 1) Capacity price to ensure system redundancy during peak load periods

[0069] As in the current capacity market, for the capacity price that guarantees the system margin during the peak load period, the dual variable ρ of the capacity supply and demand balance constraint that represents the system margin during the peak load period in the clearing model is taken out: Cap .

[0070] 2) Capacity price that meets the system's flexible adjustment needs

[0071] Since the flexible adjustment demand is expressed as a combination of a predicted value and an uncertainty deviation value, the capacity price that meets the system's flexible adjustment demand is also composed of two parts: the corresponding flexible adjustment predicted demand price and the flexible adjustment uncertainty deviation demand price.

[0072] Flexibly adjust forecast demand prices It is related to the predicted values ​​of load, wind power and photovoltaic fluctuations, and is obtained by taking the partial derivative of the extended Lagrangian function L with respect to the predicted value of the system's flexible adjustment demand.

[0073] The price of the flexible adjustment uncertainty deviation demand is related to the load, wind power, and photovoltaic fluctuation uncertainty deviation value, so that the capacity cost of balancing this part of the fluctuation is allocated to the load, wind power, and photovoltaic that cause the uncertainty flexible adjustment demand. The price of the flexible adjustment uncertainty deviation demand is divided into and in, represents the price that flexibly adjusts upward deviations in demand, Represents a price that flexibly adjusts for downward deviations in demand.

[0074] Furthermore, the capacity market settlement includes:

[0075] 1) Settlement rules between thermal power units and energy storage power stations

[0076] Flexible resources such as thermal power units and energy storage power stations can not only provide capacity to ensure system redundancy during load peak periods, but also provide capacity to meet the system's flexible adjustment needs to cope with load and new energy fluctuations.

[0077] Thermal power units Energy storage power station The benefits are as follows, including the capacity benefits of ensuring the system margin during the peak load period and the capacity benefits of meeting the flexible adjustment needs. The capacity benefits of ensuring the system margin during the peak load period are the settlement price multiplied by the winning capacity corresponding to the margin during the peak load period, and the capacity benefits of meeting the flexible adjustment needs are the settlement price. Multiply by the winning bid capacity that meets the flexible adjustment needs.

[0078]

[0079] Where: For the income of thermal power units; Profits for energy storage power stations.

[0080] 2) Settlement rules for wind farms and photovoltaic power stations

[0081] The revenue of wind farms and photovoltaic power stations is divided into two parts: the revenue from providing capacity to ensure system surplus during peak load periods and the cost of flexible adjustment demand caused by their own output fluctuations. Among them, the capacity revenue from ensuring system surplus during peak load periods is the settlement price. Multiplying by the winning bid capacity corresponding to the margin of the load peak period, the shared flexible adjustment demand cost is divided into the flexible adjustment forecast demand price Settlement and flexible adjustment of uncertainty deviation demand price The two parts of the settlement are settled by multiplying the corresponding price by the corresponding flexible adjustment demand forecast value and uncertainty deviation value.

[0082]

[0083] Where: are the revenues of wind farm h and photovoltaic power station k in the capacity market respectively;

[0084] 3) Load settlement rules

[0085] The load needs to pay the capacity market to ensure the system margin during the peak load period and meet the system's flexible adjustment needs, so its income is negative. Among them, the capacity fee for ensuring the system margin during the peak load period is the settlement price. Multiplying the capacity requirement of the system margin corresponding to the peak load period, the cost of meeting the system's flexible adjustment needs is divided into the flexible adjustment forecast demand price Settlement and flexible adjustment of uncertainty deviation demand price The two parts of the settlement are settled by multiplying the corresponding price by the corresponding flexible adjustment demand forecast value and uncertainty deviation value.

[0086]

[0087] Where: The revenue of the load in the capacity market.

[0088] A power capacity market clearing device considering flexibility, comprising:

[0089] The power capacity market clearing model construction module is used to construct a power capacity market clearing model that takes into account the system's flexible adjustment needs;

[0090] A model solving module, used for performing dual transformation and linearization processing on the clearing model and solving the model;

[0091] Capacity market pricing and settlement module, used to carry out capacity market pricing and settlement based on the solution results;

[0092] The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes:

[0093] Determine the capacity demand curve, which can adopt the variable resource demand curve;

[0094] Construct an objective function to maximize social welfare;

[0095] Determine capacity market clearing constraints that take flexibility into account.

[0096] A non-transitory computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements a method for clearing a power capacity market taking flexibility into consideration as described above.

[0097] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for clearing a power capacity market taking flexibility into consideration as described above is implemented.

[0098] The beneficial technical effects of the present invention are:

[0099] 1. The present invention takes into account the flexibility supply and demand factors of the system, and can take into account the flexibility supply value and flexibility demand cost of various types of power generation resources in the clearing of the power capacity market, so as to meet the market capacity demand while ensuring that the flexibility demand of the system in the period of high proportion of new energy and high load can be met;

[0100] 2. The present invention takes into account the capacity value of new energy in the design of the capacity market mechanism. Through the unified power capacity market clearing, the capacity value of new energy can be priced and measured, thereby reducing the operating cost of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a flow chart of a method for clearing a power capacity market taking flexibility into consideration in an embodiment of the present invention;

[0102] Figure 2 is a capacity demand curve in an embodiment of the present invention;

[0103] Figure 3 This is the capacity market clearing using the VRR curve in the embodiment of the present invention. DETAILED DESCRIPTION

[0104] The following is a further clear and complete description of a method and device for clearing a power capacity market taking into account flexibility provided by the present invention in conjunction with the accompanying drawings:

[0105] A method for clearing an electricity capacity market taking flexibility into account, the method comprising the following steps:

[0106] S1. Constructing a power capacity market clearing model that takes into account the system's flexible adjustment needs; when constructing the power capacity market clearing model, it is necessary to obtain the operating parameters and quotation data of various entities before market clearing;

[0107] The operating parameters and quotation data of various entities are as follows:

[0108] Table 1 Thermal power unit operating parameters

[0109]

[0110]

[0111] Table 2 Wind power, photovoltaic and load fluctuation parameters

[0112] serial number type Predicted value / MW Maximum positive deviation / MW Minimum negative deviation / MW G3~G9 Wind Farm 1 0.5 -0.5 G10~G14 Photovoltaic power station 1 0.5 -0.5 load 50 0.5 -0.5

[0113] The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes:

[0114] 1) Determine the capacity demand curve. The capacity demand curve can adopt the variable resource requirement (VRR) curve, which has been widely used in the MISO power market in the United States and the New York Independent System Operator (NYISO) power market. The market operator determines the VRR curve based on parameters such as the system load forecast peak, installed capacity reserve margin, and the cost of new units. Figure 3 The figure shows the capacity market clearing using the VRR curve. The red curve is the VRR curve, and the blue curve is the capacity supply curve of power generation resources. The intersection of the two is the clearing price of the capacity market.

[0115] 2) constructing an objective function with the goal of maximizing social welfare; the constructing an objective function with the goal of maximizing social welfare includes:

[0116] The objective function is to maximize social welfare (i.e., minimize the negative of social welfare CNSW):

[0117]

[0118] Where: P i g , They are the winning bid capacities of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; are the declared prices of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; P l d is the winning capacity of the first segment of the capacity demand curve; d l is the price of the first segment of the capacity demand curve.

[0119] 3) Determining the capacity market clearing constraints that take into account flexibility; the determination of the capacity market clearing constraints that take into account flexibility includes:

[0120] 1) The capacity supply and demand balance constraint to ensure system margin during load peak period is:

[0121]

[0122] Where: Ω nis the set of power generation resources participating in the capacity market; ρ Cap is a dual variable, representing the settlement price of the system margin capacity during the peak load period;

[0123] 2) The capacity supply and demand balance constraint that meets the system's flexible adjustment needs. The flexible adjustment needs of the power system come from the random fluctuations of load, wind power, and photovoltaic power. The flexible ramping demand is used to describe the system's flexible adjustment needs. The fluctuation of load, wind power, and photovoltaic output is expressed as the sum of the predicted value and the uncertainty deviation value. Among them, the uncertainty deviation value is described by a box deviation interval.

[0124]

[0125] Where: and The output fluctuations of load n, wind farm h, and photovoltaic power station k are respectively; and are the predicted values ​​of the output fluctuations of load n, wind farm h, and photovoltaic power station k, respectively; and are the load, wind power, and photovoltaic fluctuations corresponding to the system flexible adjustment demand predicted by the market operation agency based on historical data; and are the uncertainty deviation values ​​of the output fluctuation of load n, wind farm h, and photovoltaic power station k respectively; and They are the maximum positive deviation and the minimum negative deviation of the load fluctuation uncertainty deviation value respectively; and They are the maximum positive deviation and minimum negative deviation of the uncertainty deviation value of the wind farm h output fluctuation respectively; and They are the maximum positive deviation and minimum reverse deviation of the uncertainty deviation value of the output fluctuation of the photovoltaic power station k;

[0126] Therefore, the flexible adjustment demand F R As shown below:

[0127]

[0128] Since the flexible adjustment demand is divided into two directions, upward and downward, the capacity supply and demand balance constraint that meets the flexible adjustment demand is as follows: the minimum capacity provided by thermal power units and energy storage power stations to meet the flexible adjustment demand can meet the flexible adjustment demand under the worst fluctuation conditions of load, wind power, and photovoltaic power. The default winning flexibility resources (thermal power units and energy storage power stations) provide the same size of upward and downward capacity to meet the flexible adjustment demand.

[0129]

[0130] Where: F i g , They are the capacity of thermal power unit i and energy storage power station m that meet the flexible adjustment needs of the system; is the dual variable;

[0131] 3) The capacity constraints for the winning bid of thermal power units are:

[0132]

[0133] Where: P i g，max is the maximum installed capacity of thermal power unit i; is the ramp rate of thermal power unit i; and is the dual variable of the corresponding constraint;

[0134] The proposed capacity market mechanism can simultaneously ensure the abundance of system capacity during load peak periods and the abundance of system capacity for flexible adjustment of demand, but the actual method of calling the winning resources is determined by the actual dispatching strategy or spot market clearing.

[0135] 4) The capacity constraints for wind farm bids are:

[0136]

[0137] Where: is the maximum installed capacity of wind farm h; is the available capacity of wind farm h participating in the capacity market, where: It is the credible capacity factor for converting the installed capacity when the wind farm h participates in the capacity market. The effective load carrying capacity (ELCC) or the de-rating factor can be used as the credible capacity factor; is the dual variable;

[0138] 5) The capacity constraints for the winning bid of a photovoltaic power station are:

[0139]

[0140] Where: is the maximum installed capacity of photovoltaic power station k; is the credible capacity factor for converting the installed capacity when the photovoltaic power station k participates in the capacity market; is the dual variable;

[0141] 6) The capacity constraints for energy storage power station bids are:

[0142]

[0143] Where: is the maximum installed capacity of the energy storage power station m; It is the credible capacity factor used to convert the installed capacity when the energy storage power station m participates in the capacity market; is the ramp rate of the energy storage power station m; is the dual variable;

[0144] 7) The capacity demand bidding constraints are:

[0145]

[0146] Where: P l d,max is the maximum capacity of the lth segment of the capacity demand curve; is the dual variable;

[0147] S2, performing dual transformation and linearization processing on the clearing model, and solving the model;

[0148] Specifically, the dual transformation and linearization processing of the clearing model includes:

[0149] Since there are nonlinear constraints in the capacity supply and demand balance constraints that meet the system's flexible adjustment needs, the capacity market clearing model cannot be solved directly. This type of robust optimization model with embedded optimization can be solved through dual transformation and linearization.

[0150] The general form of the robust optimization model with embedded optimization can be written as:

[0151]

[0152] Where: x is the decision variable vector; c is the objective function coefficient vector; b n is the right-hand constant term vector corresponding to constraint n; a n is the deterministic component vector of the coefficient corresponding to constraint n; M n ξ is the uncertainty component vector, where M n is the coefficient matrix corresponding to the uncertainty component, ξ is the variable vector characterizing the uncertainty; Z is the value set of ξ, that is, it must satisfy the constraint Dξ+q≥0, where D is the coefficient vector of the constraint and q is the constant term vector of the constraint.

[0153] When the value of x is fixed, each embedded optimization constraint decision variable is an uncertainty component ξ, which can be expressed as follows.

[0154]

[0155] Where: u nis the dual variable of the constraint Dξ+q≥0. From the strong duality theorem formula, we can get:

[0156] (M n ξ) T xq T u n =0

[0157] According to the KKT (Karush-Kuhn-Tucker) condition, we can get

[0158]

[0159] u n ≥0

[0160] Therefore, the robust optimization model can be transformed into the following form:

[0161]

[0162] The corresponding constraint in the capacity market robust optimization clearing model established by this method can be replaced by:

[0163]

[0164] Where: is the deterministic component vector of the constraint corresponding coefficient; is the corresponding right-hand constant term vector in the constraint; is the coefficient matrix corresponding to the uncertainty components.

[0165] S3. Carry out capacity market pricing and settlement based on the solution results;

[0166] The capacity market pricing and settlement based on the solution results include: capacity market pricing and capacity market settlement.

[0167] The capacity market pricing includes:

[0168] The capacity market clearing price considering flexible adjustment demand can be divided into the capacity price to ensure the system margin during the load peak period and the capacity price to meet the system's flexible adjustment demand according to the system demand type. Among them, the capacity price to meet the system's flexible adjustment demand is composed of the flexible adjustment forecast demand price and the flexible adjustment uncertainty deviation demand price.

[0169] 1) Capacity price to ensure system redundancy during peak load periods

[0170] As in the current capacity market, for the capacity price that guarantees the system margin during the peak load period, the dual variable ρ of the capacity supply and demand balance constraint that represents the system margin during the peak load period in the clearing model is taken out: Cap .

[0171] 2) Capacity price that meets the system's flexible adjustment needs

[0172] Since the flexible adjustment demand is expressed as a combination of a predicted value and an uncertainty deviation value, the capacity price that meets the system's flexible adjustment demand is also composed of two parts: the corresponding flexible adjustment predicted demand price and the flexible adjustment uncertainty deviation demand price.

[0173] Flexibly adjust forecast demand prices It is related to the predicted values ​​of load, wind power and photovoltaic fluctuations, and is obtained by taking the partial derivative of the extended Lagrangian function L with respect to the predicted value of the system's flexible adjustment demand.

[0174] The price of the flexible adjustment uncertainty deviation demand is related to the load, wind power, and photovoltaic fluctuation uncertainty deviation value, so that the capacity cost of balancing this part of the fluctuation is allocated to the load, wind power, and photovoltaic that cause the uncertainty flexible adjustment demand. The price of the flexible adjustment uncertainty deviation demand is divided into and in, represents the price that flexibly adjusts upward deviations in demand, Represents a price that flexibly adjusts for downward deviations in demand.

[0175] The system flexibly adjusts the upward deviation price of demand Equivalent to flexible adjustment of forecast demand price Flexible adjustment of demand downward deviation price The main reason for the value being zero is that the flexible adjustment demand is greater than zero, which means that the capacity needed to meet the flexible adjustment demand is increased.

[0176] Table 3 Market Clearing Prices

[0177]

[0178] The capacity market settlement includes:

[0179] 1) Settlement rules between thermal power units and energy storage power stations

[0180] Flexible resources such as thermal power units and energy storage power stations can not only provide capacity to ensure system redundancy during load peak periods, but also provide capacity to meet the system's flexible adjustment needs to cope with load and new energy fluctuations.

[0181] Thermal power units Energy storage power station The benefits are as follows, including the capacity benefits of ensuring the system margin during the peak load period and the capacity benefits of meeting the flexible adjustment needs. The capacity benefits of ensuring the system margin during the peak load period are the settlement price multiplied by the winning capacity corresponding to the margin during the peak load period, and the capacity benefits of meeting the flexible adjustment needs are the settlement price. Multiply by the winning bid capacity that meets the flexible adjustment needs.

[0182]

[0183] Where: For the income of thermal power units; Profits for energy storage power stations.

[0184] 2) Settlement rules for wind farms and photovoltaic power stations

[0185] The revenue of wind farms and photovoltaic power stations is divided into two parts: the revenue from providing capacity to ensure system surplus during peak load periods and the cost of flexible adjustment demand caused by their own output fluctuations. Among them, the capacity revenue from ensuring system surplus during peak load periods is the settlement price. Multiplying by the winning bid capacity corresponding to the margin of the load peak period, the shared flexible adjustment demand cost is divided into the flexible adjustment forecast demand price Settlement and flexible adjustment of uncertainty deviation demand price The two parts of the settlement are settled by multiplying the corresponding price by the corresponding flexible adjustment demand forecast value and uncertainty deviation value.

[0186]

[0187] Where: are the revenues of wind farm h and photovoltaic power station k in the capacity market respectively.

[0188] 3) Load settlement rules

[0189] The load needs to pay the capacity market to ensure the system margin during the peak load period and meet the system's flexible adjustment needs, so its income is negative. Among them, the capacity fee for ensuring the system margin during the peak load period is the settlement price. Multiplying the capacity requirement of the system margin corresponding to the peak load period, the cost of meeting the system's flexible adjustment needs is divided into the flexible adjustment forecast demand price Settlement and flexible adjustment of uncertainty deviation demand price The two parts of the settlement are settled by multiplying the corresponding price by the corresponding flexible adjustment demand forecast value and uncertainty deviation value.

[0190]

[0191] Where: The revenue of the load in the capacity market.

[0192] The revenue and expenditure in the capacity market are analyzed as shown in Tables 4 and 5 below. Revenue includes the fees paid by the load to cause system demand during peak hours and the fees paid by the load, wind power, and photovoltaics to cause flexible adjustment needs; expenditure includes the fees paid to thermal power, energy storage, wind power, and photovoltaics to ensure the system margin during load peak hours, and the fees paid to thermal power and energy storage to meet the system's flexible adjustment needs. It can be seen that the overall revenue and expenditure of the system are balanced, and the proposed capacity market mechanism can effectively distinguish the contributions of different types of resources to ensuring system margin during load peak hours and meeting flexible adjustment needs, and give corresponding rewards. Under the same conditions, the better the flexible adjustment ability of the resources and the larger the available capacity coefficient, the more revenue can be obtained.

[0193] Table 4 Market Returns

[0194]

[0195] Table 5 Market expenditure

[0196]

[0197]

[0198] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0199] Example 2

[0200] This embodiment provides a power capacity market clearing device taking flexibility into consideration, including:

[0201] The power capacity market clearing model construction module is used to construct a power capacity market clearing model that takes into account the system's flexible adjustment needs;

[0202] A model solving module, used for performing dual transformation and linearization processing on the clearing model and solving the model;

[0203] Capacity market pricing and settlement module, used to carry out capacity market pricing and settlement based on the solution results;

[0204] The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes:

[0205] Determine the capacity demand curve, which can adopt the variable resource demand curve;

[0206] Construct an objective function to maximize social welfare;

[0207] Determine capacity market clearing constraints that take flexibility into account.

[0208] A non-transitory computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements a method for clearing a power capacity market taking flexibility into consideration as described above.

[0209] Furthermore, the present invention adopts the following technical solutions:

[0210] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an electricity capacity market clearing method taking flexibility into consideration as described above is implemented.

[0211] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that the facilities of the present invention can be implemented by means of software plus the necessary general hardware platform. The embodiments of the present invention can be implemented using an existing processor, or by a dedicated processor used for this purpose or other purposes for an appropriate system, or by a hard-wired system. The embodiments of the present invention also include a non-transitory computer-readable storage medium, which includes a machine-readable medium for carrying or having a machine-executable instruction or data structure stored thereon; such a machine-readable medium can be any available medium that can be accessed by a general or special-purpose computer or other machine with a processor. For example, such a machine-readable medium can include RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the required program code in the form of machine-executable instructions or data structures, and can be accessed by a general or special-purpose computer or other machine with a processor. When information is transmitted or provided to a machine via a network or other communication connection (hard wiring, or wireless, or a combination of hard wiring and wireless), the connection is also considered a machine-readable medium.

[0212] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A method for clearing the power capacity market taking into account flexibility, characterized in that: The method comprises the following steps: Construct a power capacity market clearing model that takes into account the system's flexible adjustment needs; Performing dual transformation and linearization processing on the clearing model, and solving the model; Carry out capacity market pricing and settlement based on the solution results; The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes: Determine the capacity demand curve, which can adopt the variable resource demand curve; Construct an objective function to maximize social welfare; Determine capacity market clearing constraints that take flexibility into account.

2. A method for clearing the power capacity market taking into account flexibility according to claim 1, characterized in that: The objective function for maximizing social welfare is constructed, including: The objective function is to maximize social welfare (i.e., minimize the negative of social welfare CNSW): Where: P i g , They are the winning bid capacities of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; are the declared prices of thermal power unit i, wind farm h, photovoltaic power station k, and energy storage power station m in the capacity market; P l d is the winning capacity of the first segment of the capacity demand curve; d l is the price of the first segment of the capacity demand curve.

3. A method for clearing the power capacity market taking into account flexibility according to claim 2, characterized in that: The determination of capacity market clearing constraints taking into account flexibility includes: 1) The capacity supply and demand balance constraint to ensure system margin during load peak period is: Where: Ω n is the set of power generation resources participating in the capacity market; ρ Cap is a dual variable, representing the settlement price of the system margin capacity during the peak load period; 2) Capacity supply and demand balance constraints that meet the system's flexible adjustment needs: Where: and The output fluctuations of load n, wind farm h, and photovoltaic power station k are respectively; and are the predicted values ​​of output fluctuation of load n, wind farm h, and photovoltaic power station k respectively; and are the uncertainty deviation values ​​of the output fluctuation of load n, wind farm h, and photovoltaic power station k respectively; and They are the maximum positive deviation and the minimum negative deviation of the load fluctuation uncertainty deviation value respectively; and They are the maximum positive deviation and minimum negative deviation of the uncertainty deviation value of the wind farm h output fluctuation respectively; and They are the maximum positive deviation and minimum reverse deviation of the uncertainty deviation value of the output fluctuation of the photovoltaic power station k; Therefore, the flexible adjustment demand F R As shown below: By default, the winning thermal power units and energy storage power stations provide the same size of upward and downward capacity to meet the flexible adjustment needs: Where: F i g , They are the capacity of thermal power unit i and energy storage power station m that meet the flexible adjustment needs of the system; is the dual variable; 3) The capacity constraints for the winning bid of thermal power units are: Where: P i g，max is the maximum installed capacity of thermal power unit i; is the ramp rate of thermal power unit i; and is the dual variable of the corresponding constraint; 4) The capacity constraints for wind farm bids are: Where: is the maximum installed capacity of wind farm h; is the available capacity of wind farm h participating in the capacity market, where: is the credible capacity factor used to convert the installed capacity when wind farm h participates in the capacity market; is the dual variable; 5) The capacity constraints for the winning bid of a photovoltaic power station are: Where: is the maximum installed capacity of photovoltaic power station k; is the credible capacity factor for converting the installed capacity when the photovoltaic power station k participates in the capacity market; is the dual variable; 6) The capacity constraints for energy storage power station bids are: Where: is the maximum installed capacity of the energy storage power station m; It is the credible capacity factor used to convert the installed capacity when the energy storage power station m participates in the capacity market; is the ramp rate of the energy storage power station m; is the dual variable; 7) The capacity demand bidding constraints are: Where: P l d,max is the maximum capacity of the lth segment of the capacity demand curve; is the dual variable.

4. A method for clearing the power capacity market taking into account flexibility according to claim 3, characterized in that: The clearing model is subjected to dual transformation and linearization processing. In the established capacity market robust optimization clearing model, the capacity constraints provided by thermal power units and energy storage power stations to meet the flexible regulation needs upward and downward can be replaced by: Where: is the deterministic component vector of the constraint corresponding coefficient; x is the decision variable vector; is the corresponding right-hand constant term vector in the constraint; is the coefficient matrix corresponding to the uncertainty component; D T is the coefficient vector of the corresponding constraint, q is the constant term vector of the corresponding constraint; is the dual variable.

5. A method for clearing a power capacity market taking flexibility into account according to claim 1, characterized in that: The capacity market pricing and settlement based on the solution results include: Capacity market pricing; capacity market settlement.

6. A method for clearing the power capacity market taking into account flexibility according to claim 5, characterized in that: The capacity market pricing includes: The capacity market clearing price considering flexible adjustment demand can be divided into the capacity price that guarantees the system margin during the load peak period and the capacity price that meets the system flexible adjustment demand according to the system demand type; 1) Capacity price to ensure system redundancy during peak load periods As in the current capacity market, for the capacity price that guarantees the system margin during the peak load period, the dual variable ρ that represents the capacity supply and demand balance constraint of the system margin during the peak load period in the clearing model is taken out: Cap . 2) Capacity price that meets the system's flexible adjustment needs Since the flexible adjustment demand is expressed as a combination of a predicted value and an uncertainty deviation value, the capacity price that meets the system's flexible adjustment demand is also composed of two parts: the corresponding flexible adjustment predicted demand price and the flexible adjustment uncertainty deviation demand price. Flexibly adjust forecast demand prices It is related to the predicted values ​​of load, wind power and photovoltaic fluctuations, and is obtained by taking the partial derivative of the extended Lagrangian function L with respect to the predicted value of the system's flexible adjustment demand. The price of the flexible adjustment uncertainty deviation demand is related to the uncertainty deviation value of the load, wind power, and photovoltaic fluctuations, so that the capacity cost of balancing this part of the fluctuation is allocated to the load, wind power, and photovoltaic that cause the uncertainty flexible adjustment demand; the price of the flexible adjustment uncertainty deviation demand is divided into and in, represents the price that flexibly adjusts upward deviations in demand, Represents a price that flexibly adjusts for downward deviations in demand.

7. A method for clearing the power capacity market taking into account flexibility according to claim 5, characterized in that the capacity market settlement comprises: 1) Settlement rules between thermal power units and energy storage power stations Flexible resources such as thermal power units and energy storage power stations can not only provide capacity to ensure system redundancy during peak load periods, but also provide capacity to meet the system's flexible adjustment needs to cope with load and new energy fluctuations; Thermal power units Energy storage power station The benefits are as follows, including the capacity benefits of ensuring the system margin during the peak load period and the capacity benefits of meeting the flexible adjustment needs. The capacity benefits of ensuring the system margin during the peak load period are the settlement price multiplied by the winning capacity corresponding to the margin during the peak load period, and the capacity benefits of meeting the flexible adjustment needs are the settlement price. Multiply by the winning bid capacity that meets the flexible adjustment requirements: Where: For the income of thermal power units; Profits for energy storage power stations. 2) Settlement rules for wind farms and photovoltaic power stations The revenue of wind farms and photovoltaic power stations is divided into two parts: the revenue from providing capacity to ensure system redundancy during peak load periods and the cost of flexible adjustment demand caused by their own output fluctuations: Where: are the revenues of wind farm h and photovoltaic power station k in the capacity market respectively; 3) Load settlement rules The load needs to pay the capacity market to ensure the system margin during the load peak period and to meet the system's flexible adjustment needs, so its income is negative: Where: The revenue of the load in the capacity market.

8. A power capacity market clearing device taking into account flexibility, characterized in that: include: The power capacity market clearing model construction module is used to construct a power capacity market clearing model that takes into account the system's flexible adjustment needs; A model solving module, used for performing dual transformation and linearization processing on the clearing model and solving the model; Capacity market pricing and settlement module, used to carry out capacity market pricing and settlement based on the solution results; The construction of a power capacity market clearing model that takes into account the system's flexible adjustment needs includes: Determine the capacity demand curve, which can adopt the variable resource demand curve; Construct an objective function to maximize social welfare; Determine capacity market clearing constraints that take flexibility into account.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for clearing a power capacity market taking flexibility into consideration is implemented as claimed in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, an electricity capacity market clearing method taking flexibility into consideration is implemented as described in any one of claims 1 to 7.

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