Electricity market clearing method considering new energy fluctuation risk and related device

Abstract

The invention discloses a new energy fluctuation risk-considered power market clearing method and a related device, and belongs to the technical field of medium and long term power markets, and the method comprises the steps: determining a loss function of new energy participating in the medium and long term power market; quantifying the risk of the new energy by adopting the conditional value-at-risk, adding the conditional value-at-risk into a clearing objective function of medium-and-long-term time-phased transaction, and determining the new energy risk measurement based on CVaR in a probability scene by combining a loss function of the new energy participating in the medium-and-long-term electricity market; quotation and quantity reporting data and other data of centralized transactions of medium and long term electricity market subjects are collected and processed, new energy risk measurement based on CvaR is added to a target function of a market clearing model, and a medium and long term electricity market clearing model considering new energy CVaR is given; and solving the medium-and-long-term electricity market clearing model considering the new energy CVaR to obtain a market clearing result. According to the method, social welfare and risk minimization are comprehensively considered, and a more perfect medium-and-long-term electricity market clearing model is provided.

Description

Background Art

[0002] In the process of building new power systems, the penetration rate of new energy is increasing day by day, and a large number of new energy generating units represented by wind power and photovoltaics are connected to the grid, which also brings many problems. In the power market with a high proportion of new energy, the intermittent, volatile and random nature of wind power and photovoltaic generating units brings about problems such as increased volatility of electricity prices, higher balancing costs of new energy, and lower marginal electricity prices of the system. Domestic and foreign scholars' research on the participation of new energy in the power market focuses on transaction models, market entry impacts, subject behavior strategy analysis, power market mechanism design that adapts to a high proportion of new energy, and mechanisms and algorithms to promote the consumption of new energy.

[0003] In the existing power market trading mechanism, although there is a market assessment mechanism for new energy deviation, it is essentially an ex post facto behavior and cannot quantify the volatility risk of new energy from an ex ante perspective. Therefore, the clearing results may bring greater trading risks to market entities. Although the robust optimization method guarantees the security of the clearing results, the medium- and long-term power market is essentially a financial contract without physical constraints; therefore, robust optimization is too conservative in the medium- and long-term market clearing calculations and lacks economy and practicality. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and related device for clearing the electricity market taking into account the volatility risk of new energy sources in view of the deficiencies in the above-mentioned prior art, and to consider both the volatility risk of new energy sources and social welfare in the objective function to optimize the market clearing, so as to solve the technical problem that the transaction risk of new energy sources in the medium- and long-term electricity market is difficult to define and quantify.

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

[0006] A method for clearing a power market taking into account the risk of fluctuations in renewable energy sources comprises the following steps:

[0007] S1. Determine the loss function of new energy participating in the medium and long-term electricity market;

[0008] S2. Use conditional risk value to quantify the risk of new energy, add conditional risk value to the clearing objective function of medium- and long-term time-segment transactions, and determine the new energy risk measurement based on CVaR under the probability scenario in combination with the loss function of new energy participating in the medium- and long-term electricity market obtained in step S1; S3. Collect and process the quotation and volume data of concentrated transactions of medium- and long-term electricity market entities and other data, add the new energy risk measurement based on CvaR under the probability scenario obtained in step S2 to the objective function of the market clearing model, and provide a medium- and long-term electricity market clearing model taking into account the CVaR of new energy;

[0009] S4. Solve the medium- and long-term electricity market clearing model taking into account the CVaR of new energy sources and obtain the market clearing results.

[0010] Preferably, the loss function of new energy participating in the medium- and long-term electricity market is:

[0011]

[0012] Among them, z i,t,w P is the power shortage loss of new energy i in the market w scenario during period t; i,t P is the amount of electricity that the new energy source i wins the bid in the market during period t; i,t,w is the output of new energy source i in market scenario w during period t; ΔT is the time granularity; are the penalty prices for the unbalanced electricity volume on both sides of the market during period t.

[0013] Preferably, the new energy risk measurement based on CVaR in the probabilistic scenario is specifically:

[0014] Let p(y) be the probability density function of the uncertainty factor y, x be the new condition, let the loss function of the uncertainty factor y be f(x,y), let β be a quantile value of f(x,y), and determine the cumulative distribution function of the loss function f(x,y) not exceeding β;

[0015] For any confidence level α∈(0,1), when φ(x,β)>α, the VaR value is the smallest value in the β set.

[0016] Introduce auxiliary variable F α (x, β) Solve the expression of CVaR, and only consider the expectation of the part of the loss function exceeding β when the uncertainty factor y is traversed, that is, the tail loss;

[0017] The Monte Carlo method is chosen to perform sampling simulation on the uncertainty factor y and obtain the optimization problem while considering CVaR.

[0018] Preferably, the optimization problem is:

[0019]

[0020] Among them, w is the scene; W is the scene set; π w is the scene probability; φ w is the system operating cost under scenario w, CVaR α is the cost under the worst case scenario of (1-α)×100%; α is the confidence level; β is the risk aversion coefficient.

[0021] Preferably, the cost CVaR in the worst case (1-α)×100% α for:

[0022]

[0023] Among them, θ w and ξ are two introduced auxiliary variables.

[0024] Preferably, the auxiliary variable θ w Specifically:

[0025]

[0026] Preferably, the quotation and quantity data and other data are specifically:

[0027] Thermal power companies quote and report their output, and electricity buyers quote and report their output. In particular, new energy power generation companies need to provide additional historical output data to calculate the fluctuation level and new energy CVaR.

[0028] Preferably, the medium- and long-term electricity market clearing model taking into account the CVaR of new energy is as follows:

[0029]

[0030]

[0031] Among them, f t SW is the social welfare value of the market during period t; f t CVaR N is the CVaR of new energy in the market during period t; B / N TG / N NE = number of electricity purchasers / number of thermal power enterprises / number of new energy enterprises; P j,t / P i,t is the winning bid quantity of electricity purchaser j / generation enterprise i in the market during period t, a decision variable; p j,t / p i,t is the market price quoted by electricity purchaser j / generation enterprise i in period t; t is the risk aversion coefficient of the market during period t; CVaR α,i,t is the CVaR of new energy i under the market confidence level α in period t.

[0032] Preferably, the constraints of the medium- and long-term electricity market clearing model taking into account the CVaR of new energy are as follows:

[0033]

[0034]

[0035] Among them, z i,t,wP is the power shortage loss of new energy i in the market w scenario during period t; i,t,w is the output of new energy i in market scenario w during period t; i,t,w is the probability of new energy i in market scenario w during period t; ΔT is the time granularity; Q is the penalty price for the imbalanced electricity between the power generation and consumption sides of the market during period t; i,t is the upper limit of electricity reported by thermal power plant i in period t; It is the upper limit of the trading electricity volume in the market during period t.

[0036] In a second aspect, an embodiment of the present invention provides a power market clearing system taking into account the risk of fluctuations in new energy sources, including:

[0037] Function module, which determines the loss function of new energy participating in the medium and long-term electricity market;

[0038] The quantitative module uses conditional risk value to quantify the risk of new energy, adds the conditional risk value to the clearing objective function of medium- and long-term time-segment transactions, and combines the loss function of new energy participating in the medium- and long-term electricity market to determine the new energy risk measurement based on CVaR under the probability scenario;

[0039] The data module collects and processes the quotation and volume data of the concentrated transactions of the medium- and long-term power market entities and other data, adds the new energy risk measurement based on CvaR under the probability scenario to the objective function of the market clearing model, and provides a medium- and long-term power market clearing model taking into account the CVaR of new energy;

[0040] The clearing module solves the medium- and long-term electricity market clearing model taking into account the CVaR of new energy and obtains the market clearing results.

[0041] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for clearing the electricity market taking into account the volatility risk of new energy when executing the computer program.

[0042] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for clearing the electricity market taking into account the volatility risk of new energy.

[0043] Compared with the prior art, the present invention has at least the following beneficial effects:

[0044] A method for clearing the electricity market that takes into account the volatility risk of renewable energy defines a loss function for renewable energy participating in the market, uses conditional value at risk theory to quantify the risk of renewable energy participating in the electricity market, and considers renewable energy risk in the clearing objective function of the medium- and long-term electricity market. The risk of renewable energy participating in the market can be controlled through optimization methods.

[0045] Furthermore, the loss function of new energy participating in the medium- and long-term electricity market is defined. The loss function takes into account the power purchase losses in the spot market caused by the power shortage caused by the fluctuation of new energy. No penalty is imposed on the over-generation of new energy, and new energy power generation enterprises are given profit space in the spot market, which can ensure the fairness of the market trading mechanism.

[0046] Furthermore, the new energy risk measurement based on CVaR in the probabilistic scenario considers the potential maximum loss at a given confidence level, and also measures the average loss above this threshold, thus providing more information about extreme losses. CVaR satisfies subadditivity, positive homogeneity, monotonicity, and transitive invariance, and is a consistent risk measurement method, which means that it can more accurately reflect the overall risk of the investment portfolio. Calculating CVaR based on probabilistic scenarios can make the calculation feasible by sampling historical data when the probability of the output distribution of new energy is unknown.

[0047] Furthermore, compared to the general market clearing that collects quotation and volume data, the present invention requires additional historical data on new energy. Trading institutions can use historical data to calculate the volatility level of new energy and accurately measure the risks of new energy participating in market transactions, which helps to ensure the fairness of market transactions.

[0048] Furthermore, the medium- and long-term electricity market clearing model taking into account the CVaR of new energy sources considers the social welfare function and the CVaR function of new energy sources. The social welfare function helps to maximize the temporal social welfare, and the CVaR function helps to minimize the risk of new energy participating in market transactions. The medium- and long-term electricity market clearing model taking into account the CVaR of new energy sources is more perfect by comprehensively considering the two.

[0049] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0050] In summary, the present invention takes into account the volatility risk of new energy, defines the loss function and uses the CVaR risk theory to quantify the risk, which is helpful to control the risk of the new energy market. The method is fair, takes into account the loss of insufficient electricity, and guarantees the profit space of new energy enterprises. The volatility level is calculated by historical data to ensure the fairness of market transactions; comprehensively considers social welfare and risk minimization, and proposes a more complete medium- and long-term electricity market clearing model.

[0051] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The wind power clearing quantity of the power market clearing model of the present invention without considering the CVaR of new energy;

[0053] Figure 2 The photovoltaic clearing quantity of the electricity market clearing model of the present invention without considering the CVaR of new energy;

[0054] Figure 3 The electricity market clearing model of the present invention considers the wind power clearing quantity of new energy CVaR;

[0055] Figure 4 The electricity market clearing model of the present invention considers the photovoltaic clearing quantity of new energy CVaR;

[0056] Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0057] Figure 6 A block diagram of an electronic device provided according to an embodiment of the present invention;

[0058] Figure 7 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. 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.

[0060] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0061] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0062] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0063] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0064] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0065] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0066] The present invention provides a method for clearing electricity market taking into account the volatility risk of renewable energy. The volatility risk of renewable energy participating in electricity market transactions is quantified by using the conditional value at risk (CVaR), and CVaR is added to the objective function of the electricity market clearing model for optimization and solution. The solution result considers both social welfare maximization and the volatility risk of renewable energy output. Finally, a medium- and long-term market transaction example is designed and analyzed, which verifies the effectiveness of the proposed method.

[0067] See also Figure 7 The present invention provides a method for clearing the electricity market taking into account the risk of fluctuations in renewable energy, comprising the following steps:

[0068] S1. Based on the CVaR calculation method under the general loss function, determine the loss function of new energy participating in the medium and long-term electricity market;

[0069] In the medium and long-term electricity market, the market risk brought by the participation of new energy in transactions is defined as the market loss of insufficient electricity. New energy is volatile, so the market-clearing electricity of new energy may not be fully delivered at the time of delivery. The insufficient electricity will cause losses to both the generation and use sides of the market.

[0070] For sellers of new energy, if the electricity is insufficient, they need to purchase electricity in the spot market to make up for the gap, otherwise they will face deviation assessment; for buyers of new energy, if new energy cannot deliver electricity, buyers also need to purchase electricity in the spot market to ensure their own production. The loss function only calculates the loss when the new energy is insufficient. When there is a surplus of new energy, it can participate in the spot market to sell electricity, so the deviation of excess new energy is not considered in the penalty function. Suppose the market loss caused by new energy unit i in scenario w during period t is z i,t,w , then it is expressed as:

[0071]

[0072] Among them, z i,t,w is the power shortage loss of new energy i in the market w scenario during period t, which is a non-negative continuous variable; P i,t is the amount of electricity that new energy source i wins the bid in the market during period t, which is a non-negative continuous variable; P i,t,w is the output of new energy source i in the market scenario w during period t, is a parameter; ΔT is the time granularity; are the penalty prices for unbalanced electricity on both sides of the market during period t, and are the parameters.

[0073] S2. Provide a CVaR calculation method for new energy participating in the medium- and long-term electricity market, and determine the new energy risk measurement based on CVaR under probabilistic scenarios;

[0074] Conditional Value at Risk (CVaR) is a commonly used tool for measuring risk. Compared with VaR, CVaR can better measure the tail loss of risk. In order to better measure the systemic risk caused by the volatility of new energy in the time-sharing market, the present invention uses CVaR to quantify the risk of new energy and adds CVaR to the clearing objective function of medium- and long-term time-sharing transactions.

[0075] Let p(y) be the probability density function of the uncertainty factor y, and x be the new condition. Let the loss function of the uncertainty factor y be f(x,y), and let β be a quantile value of f(x,y). The cumulative distribution function of the loss function f(x,y) not exceeding β is expressed as follows:

[0076] φ(x,β)=∫ f(x,y)≤β p(y)dy (2)

[0077] For any confidence level α∈(0,1), when φ(x,β)>α, the VaR value is the smallest value in the β set, that is:

[0078] VaR:β α (x)=min{β∈R:φ(x,β)≥α}(3)

[0079] Therefore, the expression of CVaR is as follows, which is the tail expectation of loss exceeding β when the confidence level is α.

[0080]

[0081] In general, it is difficult to directly calculate the formula (4), so we introduce the auxiliary variable F α (x, β) is solved as shown in formula (5):

[0082]

[0083] Formula (5) is interpreted as: when the uncertainty factor y is traversed, only the expectation of the part of the loss function exceeding β is considered, that is, the tail loss.

[0084] In actual operation, it is difficult to obtain the probability distribution p(y) expression of the uncertain factor, so we can choose to use the Monte Carlo method to perform sampling simulation on the uncertain factor y, so formula (5) is transformed into formula (6):

[0085]

[0086] Where n is the number of Monte Carlo sampling; [a] + The meaning is max{a,0}.

[0087] When CVaR is not considered, a simple optimization problem is written as:

[0088]

[0089] Among them, w is the scene; W is the scene set; π w is the scene probability; φ w is the system operation cost under scenario w.

[0090] Further considering CVaR, the optimization problem becomes:

[0091]

[0092] Among them, CVaR αis the cost under the worst (1-α)×100% condition, i.e., CVaR risk; parameter β∈[0,∞) is the risk aversion coefficient. When β is 0, it means risk neutrality. At this time, the objective function only minimizes the cost. The larger β is, the higher the risk aversion is. θ w and ξ are two introduced auxiliary variables.

[0093] In the present invention, step S1 gives the definition of the loss function of new energy participating in the market under discrete scenarios, which is replaced by φ in formula (7) w Combining equations (8)-(11) can complete the quantification of new energy risks.

[0094] S3. Collect and process the quotation and volume data of the concentrated transactions of the medium- and long-term power market entities and other necessary data, and provide a medium- and long-term power market clearing model taking into account the CVaR of new energy;

[0095] The objective function is formula (12), social welfare formula (13), new energy CVaR formula (14), new energy loss function formula (15), CVaR calculation formula (17) and relaxation constraints (18)-(19), the upper limit constraint of the unit's winning bid electricity (20), and the upper limit constraint of the total market transaction electricity (21).

[0096]

[0097]

[0098] Among them, f t SW is the social welfare value of the market during period t; f t CVaR N is the CVaR of new energy in the market during period t; B / N TG / N NE / N w P is the number of electricity purchasing users / the number of thermal power enterprises / the number of new energy enterprises / the number of new energy scenarios; j,t / P i,t is the winning bid quantity of electricity purchaser j / generation enterprise i in the market during period t, a decision variable; p j,t / p i,t is the price quoted by electricity purchaser j / generation enterprise i in the market during period t, and is a parameter; β t is the risk aversion coefficient of the market during period t, parameter; CVaR α,i,t is the CVaR of new energy i at the market confidence level α in period t, which is a non-negative continuous variable; i,t,w is the power shortage loss of new energy i in the market w scenario during period t, which is a non-negative continuous variable; P i,t,w is the output of new energy source i in the market w scenario during period t, and is a parameter;i,t,w is the probability of new energy i in market scenario w during period t; ΔT is the time granularity; is the penalty price for the imbalanced electricity consumption between the power generation and consumption sides of the market during period t, and is a parameter; Q i,t is the upper limit of electricity reported by thermal power plant i in period t; It is the upper limit of the trading electricity volume in the market during period t.

[0099] S4. Write a program in Python to call the gurobi solver to solve the model and obtain the market clearing result.

[0100] The effectiveness of the medium- and long-term electricity market clearing model taking into account the CVaR of new energy is verified through examples of medium- and long-term electricity market transactions.

[0101] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0102] In yet another embodiment of the present invention, a system for clearing the electricity market taking into account the risk of fluctuations in renewable energy sources is provided. The system can be used to implement the above-mentioned method for clearing the electricity market taking into account the risk of fluctuations in renewable energy sources. Specifically, the system for clearing the electricity market taking into account the risk of fluctuations in renewable energy sources includes a function module, a quantification module, a data module and a clearing module.

[0103] Among them, the function module determines the loss function of new energy participating in the medium and long-term electricity market;

[0104] The quantitative module uses conditional risk value to quantify the risk of new energy, adds the conditional risk value to the clearing objective function of medium- and long-term time-segment transactions, and combines the loss function of new energy participating in the medium- and long-term electricity market to determine the new energy risk measurement based on CVaR under the probability scenario;

[0105] The data module collects and processes the quotation and volume data of the concentrated transactions of the medium- and long-term power market entities and other data, adds the new energy risk measurement based on CvaR under the probability scenario to the objective function of the market clearing model, and provides a medium- and long-term power market clearing model taking into account the CVaR of new energy;

[0106] The clearing module solves the medium- and long-term electricity market clearing model taking into account the CVaR of new energy and obtains the market clearing results.

[0107] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor 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 gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the power market clearing method taking into account the risk of new energy fluctuations, including:

[0108] Determine the loss function of new energy participating in the medium- and long-term electricity market; use conditional value at risk to quantify the risk of new energy, add the conditional value at risk to the clearing objective function of medium- and long-term time-segment transactions, and determine the new energy risk measurement based on CVaR under the probabilistic scenario in combination with the loss function of new energy participating in the medium- and long-term electricity market; collect and process the quotation and volume data of concentrated transactions of medium- and long-term electricity market entities and other data, add the new energy risk measurement based on CvaR under the probabilistic scenario to the objective function of the market clearing model, and give a medium- and long-term electricity market clearing model that takes into account the CVaR of new energy; solve the medium- and long-term electricity market clearing model that takes into account the CVaR of new energy to obtain the market clearing result.

[0109] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0110] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0111] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0112] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the power market clearing method taking into account the volatility risk of new energy in the above-mentioned embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium:

[0113] Determine the loss function of new energy participating in the medium- and long-term electricity market; use conditional value at risk to quantify the risk of new energy, add the conditional value at risk to the clearing objective function of medium- and long-term time-segment transactions, and determine the new energy risk measurement based on CVaR under the probabilistic scenario in combination with the loss function of new energy participating in the medium- and long-term electricity market; collect and process the quotation and volume data of concentrated transactions of medium- and long-term electricity market entities and other data, add the new energy risk measurement based on CvaR under the probabilistic scenario to the objective function of the market clearing model, and give a medium- and long-term electricity market clearing model that takes into account the CVaR of new energy; solve the medium- and long-term electricity market clearing model that takes into account the CVaR of new energy to obtain the market clearing result.

[0114] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0115] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0116] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics 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, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0117] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0118] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0119] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0120] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0121] See also Figure 6 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0122] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can execute the following steps: Figure 7 Follow the steps shown in .

[0123] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0124] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0125] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0126] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0127] 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, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0128] Example Analysis

[0129] In order to verify the effectiveness of the present invention, an example of a power market is selected for calculation. In this system, there are 58 thermal power entities, 98 load entities, 12 wind power entities, and 11 photovoltaic entities. The installed capacity of renewable energy accounts for 31%. The parameters of the new energy units are shown in Table 1:

[0130] Table 1 New energy generator assembly status

[0131]

[0132]

[0133] Step 1: The trading center organizes the transaction application data, including the quotations and quantities of thermal power companies, the quotations and quantities of electricity buyers, and especially the historical output data provided by new energy power generation companies;

[0134] Step 2: The trading center applies the clearing model proposed in the present invention to perform clearing calculations on the electricity market.

[0135] Table 2 Risks of wind power to the system when the objective function does not consider CVaR

[0136]

[0137]

[0138] Table 3 Risks of photovoltaics to the system when the objective function does not consider CVaR

[0139]

[0140] The results are analyzed as Figures 1 to 4 , as shown in Tables 2 and 3. When the market is cleared, if the market risk brought by the volatility of new energy is not considered, new energy will bid based on its extremely low marginal cost, resulting in all the declared electricity winning the bid, such as Figure 1 and Figure 2 Although CVaR is not calculated in the market clearing function, such clearing results have a great adverse impact on the system in practice. After taking the CVaR of new energy into account in the objective function, the clearing results are as follows: Figure 3 and Figure 4 As shown, even if new energy sources participate in the market with extremely low bids at this time, they cannot clear all of their declared electricity, and high-risk electricity with strong fluctuations will not be cleared, which can effectively reduce the risks brought by the large-scale participation of new energy sources in the market.

[0141] In summary, the present invention provides a method and related device for clearing the electricity market taking into account the volatility risk of new energy, focusing on the management of volatility risk of new energy, using the CVaR theory to quantify and control risks, and ensuring market fairness. Considering the loss function and historical data, the profit space and transaction fairness of new energy enterprises are maintained. Integrating social welfare and risk minimization, a complete medium- and long-term electricity market clearing model is provided to bring a more robust trading environment to market participants.

[0142] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0143] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0144] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0145] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0146] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0147] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0148] If the integrated module / unit 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 processes 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 various 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 capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes 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 generate 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 1A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] 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.

[0151] 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 in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for clearing the electricity market taking into account the risk of fluctuations in renewable energy, characterized in that: The following steps are involved: S1. Determine the loss function of new energy participating in the medium and long-term electricity market; S2. Use conditional risk value to quantify the risk of new energy, add conditional risk value to the clearing objective function of medium- and long-term time-segment transactions, and determine the new energy risk measurement based on CVaR under the probability scenario in combination with the loss function of new energy participating in the medium- and long-term electricity market obtained in step S1; S3. Collect and process the quotation and volume data of concentrated transactions of medium- and long-term electricity market entities and other data, add the new energy risk measurement based on CvaR under the probability scenario obtained in step S2 to the objective function of the market clearing model, and provide a medium- and long-term electricity market clearing model taking into account the CVaR of new energy; S4. Solve the medium- and long-term electricity market clearing model taking into account the CVaR of new energy sources and obtain the market clearing results.

2. The method for clearing the electricity market taking into account the volatility risk of new energy sources according to claim 1, characterized in that: The specific loss function of new energy participating in the medium and long-term electricity market is: Among them, z i,t,w P is the power shortage loss of new energy i in the market w scenario during period t; i,t P is the amount of electricity that the new energy source i wins the bid in the market during period t; i,t,w is the output of new energy source i in market scenario w during period t; ΔT is the time granularity; are the penalty prices for the unbalanced electricity volume on both sides of the market during period t.

3. The method for clearing the electricity market taking into account the risk of fluctuations in renewable energy according to claim 1, characterized in that: The specific risk measurement of new energy based on CVaR in the probability scenario is: Let p(y) be the probability density function of the uncertainty factor y, x be the new condition, let the loss function of the uncertainty factor y be f(x,y), let β be a quantile value of f(x,y), and determine the cumulative distribution function of the loss function f(x,y) not exceeding β; For any confidence level α∈(0,1), when φ(x,β)>α, the VaR value is the smallest value in the β set. Introduce auxiliary variable F α (x, β) Solve the expression of CVaR, and only consider the expectation of the part of the loss function exceeding β when the uncertainty factor y is traversed, that is, the tail loss; The Monte Carlo method is chosen to perform sampling simulation on the uncertainty factor y and obtain the optimization problem while considering CVaR.

4. The method for clearing the electricity market taking into account the volatility risk of new energy sources according to claim 3 is characterized in that: The optimization problem is: Among them, w is the scene; W is the scene set; π w is the scene probability; φ w is the system operating cost under scenario w, CVaR α is the cost under the worst case scenario of (1-α)×100%; α is the confidence level; β is the risk aversion coefficient.

5. The method for clearing the electricity market taking into account the risk of fluctuations in renewable energy according to claim 4, characterized in that: Cost CVaR in the worst case (1-α)×100% α for: Among them, θ w and ξ are two introduced auxiliary variables.

6. The method for clearing the electricity market taking into account the risk of fluctuations in renewable energy according to claim 5, characterized in that: Auxiliary variable θ w Specifically: 。 7. The method for clearing the electricity market taking into account the volatility risk of new energy sources according to claim 1, characterized in that: The quotation and quantity data and other data are as follows: Thermal power companies quote prices and quantities, and electricity buyers quote prices and quantities; new energy power generation companies need to provide additional historical output data to calculate fluctuation levels and new energy CVaR.

8. The method for clearing the electricity market taking into account the volatility risk of new energy sources according to claim 1, characterized in that: The medium- and long-term electricity market clearing model taking into account the CVaR of new energy is as follows: in, is the social welfare value of the market during period t; N is the CVaR of new energy in the market during period t; B / N TG / N NE = number of electricity purchasers / number of thermal power enterprises / number of new energy enterprises; P j,t / P i,t is the winning bid quantity of electricity purchaser j / generation enterprise i in the market during period t, a decision variable; p j,t / p i,t is the market price quoted by electricity purchaser j / generation enterprise i in period t; t is the risk aversion coefficient of the market during period t; CVaR α,i,t is the CVaR of new energy i under the market confidence level α in period t.

9. The method for clearing the electricity market taking into account the volatility risk of new energy sources according to claim 8, characterized in that: The constraints of the medium- and long-term electricity market clearing model taking into account the CVaR of new energy are as follows: Among them, z i,t,w P is the power shortage loss of new energy i in the market w scenario during period t; i,t,w is the output of new energy i in market scenario w during period t; i,t,w is the probability of new energy i in market scenario w during period t; ΔT is the time granularity; Q is the penalty price for the imbalanced electricity between the power generation and consumption sides of the market during period t; i,t is the upper limit of electricity reported by thermal power plant i in period t; It is the upper limit of the trading electricity volume in the market during period t.

10. A power market clearing system taking into account the risk of fluctuations in renewable energy, characterized in that: include: Function module, which determines the loss function of new energy participating in the medium and long-term electricity market; The quantitative module uses conditional risk value to quantify the risk of new energy, adds the conditional risk value to the clearing objective function of medium- and long-term time-segment transactions, and combines the loss function of new energy participating in the medium- and long-term electricity market to determine the new energy risk measurement based on CVaR under the probability scenario; The data module collects and processes the quotation and volume data of the concentrated transactions of the medium- and long-term power market entities and other data, adds the new energy risk measurement based on CvaR under the probability scenario to the objective function of the market clearing model, and provides a medium- and long-term power market clearing model taking into account the CVaR of new energy; The clearing module solves the medium- and long-term electricity market clearing model taking into account the CVaR of new energy and obtains the market clearing results.

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