Capacity market demand curve evaluation method and system based on Monte Carlo simulation

Abstract

The invention discloses a capacity market demand curve evaluation method and system based on Monte Carlo simulation. Uncertain influence factors are fully considered, and the historical annual return rate of the reference unit is calculated more accurately by using a peak load prediction model and a return rate function model. And by utilizing a return rate function model, comprehensively considering a delivery year prediction peak load, a system reserve margin and other related factors, and accurately calculating the delivery year return rate of the reference unit. A risk utility function is constructed based on a historical year reference unit return rate and a reference unit delivery annual return rate, and risk characteristics reflected by historical data and uncertainty of future prediction data are fully utilized. Through analysis of historical data, the risk bearing capacity and the income expectation of a power generation subject under different market conditions are known, and a risk utility function which better conforms to the actual situation is constructed in combination with prediction data. And finally, calculating an evaluation index of the demand curve to realize adjustment of operating parameters of the power system.

Description

Technical Field

[0001] The present invention belongs to the field of power market and capacity market, and relates to a capacity market demand curve evaluation method and system based on Monte Carlo simulation. Background Art

[0002] Under the background of the new power system, the proportion of new energy units continues to increase, the spot market electricity price tends to the operating cost, and the fixed cost recovery of traditional energy units is difficult. In order to ensure the sufficient capacity of the power system, it is necessary to study and establish a capacity market mechanism. The capacity demand curve in the capacity market is a key factor in the operation of the market. The intersection of the declaration curve of the capacity market subject and the capacity demand curve is the capacity price. Therefore, the design of the capacity curve directly affects the clearing result of the capacity market.

[0003] The capacity market is organized in advance and has a long time period. The capacity demand curve is designed based on the predicted peak load of the capacity delivery year, the system capacity reserve margin and the reference unit net cost. However, there are uncertainties in the predicted peak load of the capacity delivery year, cost calculation, and investment behavior of power generation entities. These uncertainties will lead to uncontrollable changes in the capacity market results. Therefore, the current capacity demand curve evaluation method cannot fully consider the impact of uncertain factors, making the quotation curve relatively single, and the evaluation results lack comprehensiveness. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the capacity demand curve evaluation in the prior art does not take into account the influence of uncertain factors, the quotation curve is relatively single, and therefore the evaluation result lacks comprehensiveness, and to provide a capacity market demand curve evaluation method and system based on Monte Carlo simulation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention proposes a capacity market demand curve evaluation method based on Monte Carlo simulation, comprising:

[0007] Obtain historical annual peak load values, predicted peak load for the delivery year, system reserve margin and capacity demand curve;

[0008] Determine the historical year reference unit rate of return based on historical annual peak load values ​​and reference unit power market earnings; obtain the reference unit delivery year rate of return based on the predicted peak load in the delivery year and the system reserve margin combined with the rate of return function model;

[0009] The risk-utility function of the power generation entity is constructed based on the reference unit yields in historical years and the reference unit delivery annual yields. The capacity quotation curve is constructed according to the risk-utility function, and the generation side quotation curve and the capacity demand curve are used to calculate the evaluation indicators of the demand curve to achieve the adjustment of the power system operating parameters.

[0010] Preferably, the obtaining of the historical annual peak load value is specifically as follows:

[0011] L f,y =L peak,y-n ·(1+r e ) n (1+r w )

[0012] Among them, L f,y is the historical annual peak load value; L peak,y-n is the historical weather normalized peak load in year yn; e is the economic growth rate; r w is the load error caused by weather changes; peak is the annual peak load period.

[0013] Preferably, the historical year reference unit rate of return is determined based on the historical annual peak load value and the reference unit power market earnings, specifically as follows:

[0014] r=f(L peak ,IRM)

[0015] Among them, r is the reference unit rate of return in historical years; L peak is the historical peak load; IRM is the historical system reserve margin; f() is the functional relationship between the historical reference unit yield and the historical peak load and the historical system reserve margin.

[0016] Preferably, the risk utility function of the power generation entity is constructed based on the historical reference unit yield rate and the reference unit delivery annual yield rate, specifically:

[0017]

[0018] Among them, U is the risk utility function of the power generation entity; E(r) is the expected rate of return; σ 2 is the rate of return variance; A is the risk aversion coefficient. If the risk aversion coefficient A is a positive value, it means that the investor is risk-averse. If the risk aversion coefficient A is a negative value, it means that the investor is risk-seeking. If the risk aversion coefficient A is 0, it means that the investor is risk-neutral.

[0019] Preferably, the capacity quotation curve is constructed according to the risk utility function, specifically:

[0020] Based on the risk-utility function, the annual profit rate is converted into the maximum capacity that the power generator is willing to invest in.

[0021] The capacity quotation curve is constructed based on the existing capacity on the power generation side and the maximum capacity that power generators are willing to invest in.

[0022] Preferably, the generation side quotation curve and the capacity demand curve are used to calculate the evaluation index of the demand curve, specifically:

[0023] Using the power generation side quotation curve and the capacity demand curve, simulate the capacity market clearing pricing to obtain the clearing capacity clearing results and the capacity electricity price clearing results; based on the clearing capacity clearing results and the capacity electricity price clearing results, calculate the evaluation index of the demand curve;

[0024] Among them, the evaluation indicators of the demand curve include resource abundance, power plant profits and consumer costs; the resource abundance evaluation indicators include the installed capacity reserve margin and the proportion of simulation times when the system reserve margin exceeds the target; the profit evaluation indicators on the power generation side include the reference unit profit, capacity market price, average income and profit, and standard deviation of income and profit.

[0025] Preferably, the calculation formula of the installed reserve margin IRM is as follows:

[0026]

[0027] The proportion of simulation times that the system reserve margin exceeds the target γ IRM The calculation formula is as follows:

[0028]

[0029] The Capacity Market Reference Unit Profit Pro unit The calculation formula is as follows:

[0030] Pro unit =I sum -C sum

[0031] The consumer cost C cus The calculation formula is as follows:

[0032] C cus =Cap sum ·p c

[0033] Among them, ICAP is the installed capacity of the unit; EFORd is the equivalent outage rate of the unit; L peak is the system peak load; is the number of simulations where the system reserve margin exceeds the target; n s is the total number of simulations; I sumThe total revenue of the reference unit, including the revenue from the electricity energy market, capacity market and ancillary service market; C sum is the total cost of the reference unit; Cap sum Total capacity purchased for consumers; p c is the capacity market price.

[0034] The present invention proposes a capacity market demand curve evaluation system based on Monte Carlo simulation, comprising:

[0035] A first acquisition module, the first acquisition module is used to obtain the historical annual peak load value, the delivery year predicted peak load, the system reserve margin and the capacity demand curve;

[0036] A second acquisition module, the second acquisition module is used to determine the historical year reference unit rate of return based on the historical annual peak load value and the reference unit power market income; obtain the reference unit delivery year rate of return according to the delivery year forecast peak load and system reserve margin combined with the function model of the rate of return;

[0037] A parameter processing module is used to construct a risk-utility function of a power generation entity based on the reference unit yield rate in historical years and the reference unit delivery annual yield rate; construct a capacity quotation curve according to the risk-utility function, use the power generation side quotation curve and the capacity demand curve, calculate the evaluation index of the demand curve, and realize the adjustment of the power system operation parameters.

[0038] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a capacity market demand curve evaluation method based on Monte Carlo simulation are implemented.

[0039] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a capacity market demand curve evaluation method based on Monte Carlo simulation.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention proposes a capacity market demand curve evaluation method based on Monte Carlo simulation, which fully considers weather changes, economic development and other uncertain factors, and uses the peak load forecasting model and the yield function model to more accurately calculate the historical annual yield of the reference unit. The yield function model is used to comprehensively consider the predicted peak load of the delivery year, the system reserve margin and other related factors to more accurately calculate the annual yield of the reference unit. A risk utility function is constructed based on the historical year reference unit yield and the reference unit delivery annual yield, and the risk characteristics reflected by the historical data and the uncertainty of the future forecast data are fully utilized. Through the analysis of historical data, the risk tolerance and profit expectations of the power generation subject under different market conditions are understood, and combined with the future forecast data, a risk utility function that is more in line with the actual situation is constructed. A capacity quotation curve is constructed according to the risk utility function, so that the power generation subject can make a reasonable quotation according to its own risk tolerance and profit expectation. The power generation side quotation curve and the capacity demand curve are used to calculate the evaluation index of the demand curve, and the adjustment of the power system operation parameters is realized. Therefore, the present invention comprehensively considers the operation of the power generation subject, and the evaluation index calculated can more comprehensively reflect the supply and demand balance and system reliability of the capacity market.

[0042] The present invention proposes a capacity market demand curve evaluation system based on Monte Carlo simulation, which obtains the evaluation index of the demand curve and realizes the adjustment of the power system operation parameters by dividing the system into a first acquisition module, a second acquisition module and a parameter processing module. The modularization concept is adopted to make each module independent of each other, which is convenient for unified management of each module. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 This is a flow chart of the capacity market demand curve evaluation method based on Monte Carlo simulation of the present invention.

[0045] Figure 2 It is the capacity demand curve evaluation index diagram of the present invention.

[0046] Figure 3 This is a flow chart of the capacity demand curve evaluation method of the present invention.

[0047] Figure 4 This is a diagram of the capacity market demand curve evaluation system based on Monte Carlo simulation of the present invention.

[0048] Figure 5 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0050] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0052] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0053] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0054] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0056] Example 1

[0057] The present invention proposes a capacity market demand curve evaluation method based on Monte Carlo simulation, such as Figure 1 As shown, including:

[0058] S1. Obtain the historical annual peak load value, the predicted peak load of the delivery year, the system reserve margin and the capacity demand curve;

[0059] The acquisition of the historical annual peak load value is specifically as follows:

[0060] L f,y =L peak,y-n ·(1+r e ) n (1+r w )

[0061] Among them, L f,y is the historical annual peak load value; L peak,y-n is the historical weather normalized peak load in year yn; e is the economic growth rate; r w is the load error caused by weather changes; peak is the annual peak load period.

[0062] S2. Determine the rate of return of the reference unit in the historical year based on the historical annual peak load value and the power market income of the reference unit; obtain the rate of return of the reference unit in the delivery year based on the function model of the predicted peak load and system reserve margin combined with the rate of return;

[0063] The historical year reference unit rate of return is determined based on the historical annual peak load value and the reference unit power market revenue situation, specifically:

[0064] r=f(L peak ,IRM)

[0065] Among them, r is the reference unit rate of return in historical years; L peakis the historical peak load; IRM is the historical system reserve margin; f() is the functional relationship between the historical reference unit yield and the historical peak load and the historical system reserve margin.

[0066] S3. Construct the risk-utility function of the power generation entity based on the reference unit yields in historical years and the reference unit delivery annual yields; construct the capacity quotation curve based on the risk-utility function, use the power generation side quotation curve and the capacity demand curve, calculate the evaluation index of the demand curve, and realize the adjustment of the power system operating parameters.

[0067] The risk utility function of the power generation entity is constructed based on the historical reference unit rate of return and the reference unit delivery annual rate of return, specifically:

[0068]

[0069] Among them, U is the risk utility function of the power generation entity; E(r) is the expected rate of return; σ 2 is the rate of return variance; A is the risk aversion coefficient. If the risk aversion coefficient A is a positive value, it means that the investor is risk-averse. If the risk aversion coefficient A is a negative value, it means that the investor is risk-seeking. If the risk aversion coefficient A is 0, it means that the investor is risk-neutral.

[0070] The capacity quotation curve is constructed according to the risk utility function, specifically:

[0071] Based on the risk-utility function, the annual profit rate is converted into the maximum capacity that the power generator is willing to invest in.

[0072] The capacity quotation curve is constructed based on the existing capacity on the power generation side and the maximum capacity that power generators are willing to invest in.

[0073] The evaluation index of the demand curve is calculated by using the power generation side quotation curve and the capacity demand curve, which is specifically:

[0074] Using the power generation side quotation curve and the capacity demand curve, simulate the capacity market clearing pricing to obtain the clearing capacity clearing results and the capacity electricity price clearing results; based on the clearing capacity clearing results and the capacity electricity price clearing results, calculate the evaluation index of the demand curve;

[0075] Among them, the evaluation indicators of the demand curve include resource abundance, power plant profits and consumer costs; the resource abundance evaluation indicators include the installed capacity reserve margin and the proportion of simulation times when the system reserve margin exceeds the target; the profit evaluation indicators on the power generation side include the reference unit profit, capacity market price, average income and profit, and standard deviation of income and profit.

[0076] The calculation formula of the installed reserve margin IRM is as follows:

[0077]

[0078] The proportion of simulation times that the system reserve margin exceeds the target γ IRM The calculation formula is as follows:

[0079]

[0080] The Capacity Market Reference Unit Profit Pro unit The calculation formula is as follows:

[0081] Pro unit =I sum -C sum

[0082] The consumer cost C cus The calculation formula is as follows:

[0083] C cus =Cap sum ·p c

[0084] Among them, ICAP is the installed capacity of the unit; EFORd is the equivalent outage rate of the unit; L peak is the system peak load; is the number of simulations where the system reserve margin exceeds the target; n s is the total number of simulations; I sum The total revenue of the reference unit, including the revenue from the electricity energy market, capacity market and ancillary service market; C sum is the total cost of the reference unit; Cap sum Total capacity purchased for consumers; p c is the capacity market price.

[0085] Combination Figure 2 and Figure 3 The capacity market demand curve evaluation method based on Monte Carlo simulation proposed in the present invention is described in detail:

[0086] The capacity market supply curve is the driving factor of market results, affecting prices, capacity bids and system reliability distribution. The capacity market supply curve is constructed by the price-quantity pair supply quotations of market participants. However, when the capacity demand curve is designed and evaluated, the capacity market auction has not yet been carried out. Therefore, it is necessary to consider uncertain factors such as load growth, investment behavior of market players and bidding behavior of market players, construct the market supply curve for the delivery year, generate multiple supply curves and simulate the results of capacity market clearing, and evaluate the capacity demand curve based on multiple indicators.

[0087] Assumptions are as follows:

[0088] The capacity supply curve is mainly based on the cost-benefit analysis of market players and the simulation of investment behavior. However, the investment behavior of market players is short-sighted and pays more attention to the market benefits and market price forecasts in the short term. In addition, the capacity resource construction behavior has a certain lag. Therefore, the following assumptions are made when simulating the supply curve:

[0089] 1) The investment and construction of capacity entities are affected by factors such as entity income, geographical location, and regulatory policies. However, factors such as geographical location and regulatory policies are time-sensitive and limited. Therefore, this invention only considers the impact of the income of power generation entities. It is assumed that the higher the income of market entities, the higher the investment enthusiasm of power generation entities.

[0090] 2) The investment risk of capacity market players also needs to be considered when investing in construction. Under the condition of the same returns, the higher the investment risk, the less the power generation entity will invest. That is, the greater the variance of the market entity's returns, the lower the enthusiasm of the power generation entity for investment and construction.

[0091] 3) Capacity market auctions are generally conducted three to four years in advance, and the demand curve review needs to be conducted even earlier, so the revenue of power generation entities is predicted based on the historical revenue of market entities.

[0092] 4) Capacity market auctions and capacity demand curve design are both based on load demand during peak periods. Therefore, when simulating and evaluating supply curves and demand curves, the focus is on the capacity of power generation entities during peak load periods.

[0093] 5) It is assumed that all power generation entities in the capacity market participate in full according to their maximum capacity, and that electricity users purchase their capacity requirements in full from the capacity market.

[0094] Evaluation indicators: The capacity demand curve evaluation is to simulate the possible capacity market structure by combining historical actual data and future forecasts. Therefore, the capacity demand curve evaluation indicators are proposed from three dimensions: resource abundance, power plant profits and consumer costs.

[0095] 1) Resource adequacy

[0096] Resource adequacy indicators include installed reserve margin and the proportion of simulation times when the system reserve margin exceeds the target. The installed reserve margin is the proportional relationship between the possible installed capacity and peak load in the assessment year. The installed capacity needs to take into account the existing units of the system and the new capacity that may be added in the simulation. The calculation formula of the installed reserve margin IRM is as follows:

[0097]

[0098] Among them, ICAP is the installed capacity of the unit; EFORd is the equivalent outage rate of the unit; L peak is the system peak load, and peak is the annual peak load period.

[0099] For the capacity demand curve, the ratio of simulation times when the system reserve margin exceeds the target can illustrate the ability of the demand curve design to meet the system capacity adequacy. It is the ratio of the number of simulation times when the reserve margin exceeds the target requirement in the simulation results of the demand curve to the total number of simulations. The ratio of simulation times when the system reserve margin exceeds the target is γ IRM The calculation formula is as follows:

[0100]

[0101] in, is the number of simulations where the system reserve margin exceeds the target; n s is the total number of simulations.

[0102] 2) Power plant profits

[0103] The profit evaluation indicators of the power generation side include the profit of the reference unit, the capacity market price, the average value of income and profit, and the standard deviation of income and profit. The profit of the reference unit is based on the income of the power market and the auxiliary service market, and the capacity market income is analyzed by comprehensively considering the simulated capacity market evaluation results, and then the profit of the reference unit is evaluated. The calculation formula is as follows:

[0104] Pro unit =I sum -C sum

[0105] Among them, Pro unit is the profit of the reference unit in the capacity market; sum The total revenue of the reference unit, including the revenue from the electricity energy market, capacity market and ancillary service market; C sum The total cost of the reference unit includes fixed costs, variable costs and other costs.

[0106] The capacity market price is determined by the supply curve and the capacity demand curve clearing pricing model.

[0107] The revenue and profit mean values ​​and the revenue and profit standard deviations are the revenue and profit mean values ​​and standard deviations of multiple simulation results for a certain capacity demand curve.

[0108] 3) Consumer costs

[0109] Consumer cost mainly refers to the capacity fee paid by users, which is the product of the total capacity purchased by the user and the capacity price. The calculation formula is as follows.

[0110] C cus =Cap sum ·p c

[0111] Among them, Ccus is the total cost to consumers; Cap sum Total capacity purchased for consumers; p c is the capacity market price.

[0112] The detailed evaluation process is as follows:

[0113] For the capacity demand curve designed for the capacity market, this section proposes an evaluation method based on the Monte Carlo simulation method, simulates the capacity supply curve of power generation resources, performs capacity market clearing calculations, calculates demand curve evaluation index analysis, and establishes a clear method for evaluating the effectiveness of the capacity demand curve.

[0114] 1) Forecast actual annual peak load

[0115] Based on historical load data, the weather-normalized peak load is calculated, and considering economic uncertainty, the economic growth rate is randomly selected using the Monte Carlo method to calculate the weather-normalized peak load forecast value. On the basis of the weather-normalized peak load, the random impact of weather is considered, the sampling weather changes are simulated to calculate the peak load weather error and make adjustments to obtain the peak load forecast value for the final delivery year.

[0116] The actual delivery year peak load forecast value is calculated as follows:

[0117] L f,y =L peak,y-n ·(1+r e ) n (1+r w )

[0118] Among them, the yth year is the capacity delivery year; L peak,y-n is the historical weather normalized peak load in year yn; e is the economic growth rate; r w Load error caused by weather changes.

[0119] 2) Forecast reference unit delivery annual yield

[0120] According to the historical operation results of the power market, the fixed annual cost, electricity market income, ancillary service market income, capacity market income and other data are statistically analyzed to calculate the historical annual yield of the reference unit. At the same time, combined with the actual peak load of the capacity market in the historical operation years and the system installed capacity reserve margin, a functional model between the yield of the reference unit and the peak load and system reserve margin is constructed.

[0121] r=f(L peak ,IRM)

[0122] Among them, r is the historical annual rate of return of the reference unit; L peakis the historical peak load; IRM is the historical system reserve margin; f() is the functional relationship between the historical reference unit yield and the historical peak load and the historical system reserve margin.

[0123] According to the predicted peak load and system reserve margin in the delivery year, combined with the function model of the rate of return, the annual rate of return of the reference unit in the capacity delivery year is predicted. In addition, considering the impact of the historical year rate of return and the predicted annual rate of return, different weight coefficients are assigned to the rate of return. If the years are similar, a larger weight coefficient is assigned to correct the annual rate of return in the capacity delivery year.

[0124] 3) Constructing the risk utility function of power generation entities

[0125] Combining historical investment data and survey data of power generators, we analyze the considerations of historical power generation resource investment and construction for capacity price risk and establish a risk-utility function. The risk-utility function quantifies the size of the risk and investment preferences of power generators for different return situations. The annual profit rate is converted into the maximum capacity that power generators are willing to invest in.

[0126]

[0127] Among them, U is the utility value; E(r) is the expected rate of return; σ 2 is the rate of return variance; A is the risk aversion coefficient, which indicates the investor's attitude towards risk. If A is a positive value, it means that the investor is risk-averse. The larger the A value, the higher the degree of risk aversion. If A is a negative value, it means that the investor is risk-seeking and is willing to take greater risks for higher expected returns. If A is 0, it means that the investor is risk-neutral and only considers expected returns without considering risks when making decisions.

[0128] 4) Construct the power generation side quotation curve and simulate the clearing pricing

[0129] Based on the existing capacity and potential new capacity on the power generation side, combined with the cost-benefit analysis of power generation entities, quotations are made and a capacity quotation curve is constructed. The power generation side quotation curve and the capacity demand curve are used to simulate the capacity market clearing pricing and obtain clearing results such as clearing capacity and capacity electricity price.

[0130] 5) Calculate the capacity demand curve evaluation index

[0131] According to the capacity market clearing results, the evaluation indicators of the demand curve are analyzed and calculated. Randomly select economic growth parameters and weather combination samples, calculate the average and standard deviation of the evaluation indicators after multiple simulations, and evaluate the design effect of the demand curve.

[0132] Example 2

[0133] The present invention proposes a capacity market demand curve evaluation system based on Monte Carlo simulation, such as Figure 4 As shown, it includes a first acquisition module, a second acquisition module and a parameter processing module;

[0134] The first acquisition module is used to obtain the historical annual peak load value, the delivery year predicted peak load, the system reserve margin and the capacity demand curve;

[0135] The acquisition of the historical annual peak load value is specifically as follows:

[0136] L f,y =L peak,y-n ·(1+r e ) n (1+r w )

[0137] Among them, L f,y is the historical annual peak load value; L peak,y-n is the historical weather normalized peak load in year yn; e is the economic growth rate; r w is the load error caused by weather changes; peak is the annual peak load period.

[0138] The second acquisition module is used to determine the historical year reference unit rate of return based on the historical annual peak load value and the reference unit power market revenue; obtain the reference unit delivery year rate of return according to the delivery year forecast peak load and system reserve margin combined with the function model of the rate of return;

[0139] The historical year reference unit rate of return is determined based on the historical annual peak load value and the reference unit power market revenue situation, specifically:

[0140] r=f(L peak ,IRM)

[0141] Among them, r is the reference unit rate of return in historical years; L peak is the historical peak load; IRM is the historical system reserve margin; f() is the functional relationship between the historical reference unit yield and the historical peak load and the historical system reserve margin.

[0142] The parameter processing module is used to construct a risk-utility function of the power generation entity based on the reference unit yield rate in historical years and the reference unit delivery annual yield rate; construct a capacity quotation curve according to the risk-utility function, use the power generation side quotation curve and the capacity demand curve, calculate the evaluation index of the demand curve, and realize the adjustment of the power system operation parameters.

[0143] The risk utility function of the power generation entity is constructed based on the historical reference unit rate of return and the reference unit delivery annual rate of return, specifically:

[0144]

[0145] Among them, U is the risk utility function of the power generation entity; E(r) is the expected rate of return; σ 2 is the rate of return variance; A is the risk aversion coefficient. If the risk aversion coefficient A is a positive value, it means that the investor is risk-averse. If the risk aversion coefficient A is a negative value, it means that the investor is risk-seeking. If the risk aversion coefficient A is 0, it means that the investor is risk-neutral.

[0146] The capacity quotation curve is constructed according to the risk utility function, specifically:

[0147] Based on the risk-utility function, the annual profit rate is converted into the maximum capacity that the power generator is willing to invest in.

[0148] The capacity quotation curve is constructed based on the existing capacity on the power generation side and the maximum capacity that power generators are willing to invest in.

[0149] The evaluation index of the demand curve is calculated by using the power generation side quotation curve and the capacity demand curve, which is specifically:

[0150] Using the power generation side quotation curve and the capacity demand curve, simulate the capacity market clearing pricing to obtain the clearing capacity clearing results and the capacity electricity price clearing results; based on the clearing capacity clearing results and the capacity electricity price clearing results, calculate the evaluation index of the demand curve;

[0151] Among them, the evaluation indicators of the demand curve include resource abundance, power plant profits and consumer costs; the resource abundance evaluation indicators include the installed capacity reserve margin and the proportion of simulation times when the system reserve margin exceeds the target; the profit evaluation indicators on the power generation side include the reference unit profit, capacity market price, average income and profit, and standard deviation of income and profit.

[0152] The calculation formula of the installed reserve margin IRM is as follows:

[0153]

[0154] The proportion of simulation times that the system reserve margin exceeds the target γ IRM The calculation formula is as follows:

[0155]

[0156] The Capacity Market Reference Unit Profit Pro unit The calculation formula is as follows:

[0157] Pro unit =I sum -C sum

[0158] The consumer cost C cus The calculation formula is as follows:

[0159] C cus =Cap sum ·p c

[0160] Among them, ICAP is the installed capacity of the unit; EFORd is the equivalent outage rate of the unit; L peak is the system peak load; is the number of simulations where the system reserve margin exceeds the target; n s is the total number of simulations; I sum The total revenue of the reference unit, including the revenue from the electricity energy market, capacity market and ancillary service market; C sum is the total cost of the reference unit; Cap sum Total capacity purchased for consumers; p c is the capacity market price.

[0161] Example 3

[0162] See also Figure 5 As shown, the present invention also provides an electronic device 100 for a capacity market demand curve evaluation method based on Monte Carlo simulation; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0163] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the capacity market demand curve evaluation method based on Monte Carlo simulation described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0164] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0165] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a capacity market demand curve evaluation method based on Monte Carlo simulation, and the processor 102 can execute the plurality of instructions to implement:

[0166] Obtain historical annual peak load values, predicted peak load for the delivery year, system reserve margin and capacity demand curve;

[0167] Determine the historical year reference unit rate of return based on historical annual peak load values ​​and reference unit power market earnings; obtain the reference unit delivery year rate of return based on the predicted peak load in the delivery year and the system reserve margin combined with the rate of return function model;

[0168] The risk-utility function of the power generation entity is constructed based on the reference unit yields in historical years and the reference unit delivery annual yields. The capacity quotation curve is constructed according to the risk-utility function, and the generation side quotation curve and the capacity demand curve are used to calculate the evaluation indicators of the demand curve to achieve the adjustment of the power system operating parameters.

[0169] Example 4

[0170] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0171] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0173] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0174] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A capacity market demand curve evaluation method based on Monte Carlo simulation, characterized in that: include: Obtain historical annual peak load values, predicted peak load for the delivery year, system reserve margin and capacity demand curve; Determine the historical year reference unit rate of return based on historical annual peak load values ​​and reference unit power market earnings; obtain the reference unit delivery year rate of return based on the predicted peak load in the delivery year and the system reserve margin combined with the rate of return function model; The risk-utility function of the power generation entity is constructed based on the reference unit yields in historical years and the reference unit delivery annual yields. The capacity quotation curve is constructed according to the risk-utility function, and the generation side quotation curve and the capacity demand curve are used to calculate the evaluation indicators of the demand curve to achieve the adjustment of the power system operating parameters.

2. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 1 is characterized in that: The acquisition of the historical annual peak load value is specifically as follows: L f,y =L peak,y-n ·(1+r e ) n (1+r w ) Among them, L f,y is the historical annual peak load value; L peak,y-n is the historical weather normalized peak load in year yn; e is the economic growth rate; r w is the load error caused by weather changes; k The peak load period of the year.

3. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 1 is characterized in that: The historical year reference unit rate of return is determined based on the historical annual peak load value and the reference unit power market revenue situation, specifically: r=f(L peak ,IRM) Among them, r is the reference unit rate of return in historical years; L peak is the historical peak load; IRM is the historical system reserve margin; f() is the functional relationship between the historical reference unit yield and the historical peak load and the historical system reserve margin.

4. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 1 is characterized in that: The risk utility function of the power generation entity is constructed based on the historical reference unit rate of return and the reference unit delivery annual rate of return, specifically: Among them, U is the risk utility function of the power generation entity; E(r) is the expected rate of return; σ 2 is the rate of return variance; A is the risk aversion coefficient. If the risk aversion coefficient A is a positive value, it means that the investor is risk-averse. If the risk aversion coefficient A is a negative value, it means that the investor is risk-seeking. If the risk aversion coefficient A is 0, it means that the investor is risk-neutral.

5. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 1 is characterized in that: The capacity quotation curve is constructed according to the risk utility function, specifically: Based on the risk-utility function, the annual profit rate is converted into the maximum capacity that the power generator is willing to invest in. The capacity quotation curve is constructed based on the existing capacity on the power generation side and the maximum capacity that power generators are willing to invest in.

6. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 1 is characterized in that: The evaluation index of the demand curve is calculated by using the power generation side quotation curve and the capacity demand curve, which is specifically: Using the power generation side quotation curve and the capacity demand curve, simulate the capacity market clearing pricing to obtain the clearing capacity clearing results and the capacity electricity price clearing results; based on the clearing capacity clearing results and the capacity electricity price clearing results, calculate the evaluation index of the demand curve; Among them, the evaluation indicators of the demand curve include resource abundance, power plant profits and consumer costs; the resource abundance evaluation indicators include the installed capacity reserve margin and the proportion of simulation times when the system reserve margin exceeds the target; the profit evaluation indicators on the power generation side include the reference unit profit, capacity market price, average income and profit, and standard deviation of income and profit.

7. The capacity market demand curve evaluation method based on Monte Carlo simulation according to claim 6 is characterized in that: The calculation formula of the installed reserve margin IRM is as follows: The proportion of simulation times that the system reserve margin exceeds the target γ IRM The calculation formula is as follows: The Capacity Market Reference Unit Profit Pro unit The calculation formula is as follows: Pro unit =I sum -C sum The consumer cost C cus The calculation formula is as follows: C cus =Cap sum ·p c Among them, ICAP is the installed capacity of the unit; EFORd is the equivalent outage rate of the unit; L peak is the system peak load; is the number of simulations where the system reserve margin exceeds the target; n s is the total number of simulations; I sum The total revenue of the reference unit includes the revenue from the electricity energy market, capacity market and ancillary service market; C sum is the total cost of the reference unit; Cap sum Total capacity purchased for consumers; p c is the capacity market price.

8. A capacity market demand curve evaluation system based on Monte Carlo simulation, characterized in that: include: A first acquisition module, the first acquisition module is used to obtain the historical annual peak load value, the delivery year predicted peak load, the system reserve margin and the capacity demand curve; A second acquisition module, the second acquisition module is used to determine the historical year reference unit rate of return based on the historical annual peak load value and the reference unit power market income; obtain the reference unit delivery year rate of return according to the delivery year forecast peak load and system reserve margin combined with the function model of the rate of return; A parameter processing module is used to construct a risk-utility function of a power generation entity based on the reference unit yield rate in historical years and the reference unit delivery annual yield rate; construct a capacity quotation curve according to the risk-utility function, use the power generation side quotation curve and the capacity demand curve, calculate the evaluation index of the demand curve, and realize the adjustment of the power system operation parameters.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the capacity market demand curve evaluation method based on Monte Carlo simulation as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the capacity market demand curve evaluation method based on Monte Carlo simulation as claimed in any one of claims 1 to 7 are implemented.

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