Spot market risk management method, device and equipment considering renewable energy

Abstract

The present invention discloses a spot market risk management method, device and equipment considering renewable energy, the method comprising: pre-clearing before real-time market clearing, obtaining pre-clearing node marginal electricity price and pre-clearing probability marginal electricity price considering wind power uncertainty; when the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price exceeds a set threshold, sending it to each demand response subject; receiving load response applications from each demand response subject, generating a decision result; the decision result is returned to each demand response subject, and each demand response subject acts according to the returned decision result when the real-time market is cleared. The present invention solves the problems of increased importance of the real-time market and price risk caused by increased penetration of renewable energy.

Description

Technical Field

[0001] The present invention belongs to the field of power system automation, and in particular relates to a spot market risk management method, device, equipment and medium taking renewable energy into consideration. Background Art

[0002] With the gradual deepening of the construction of the power spot market, various pilot areas have entered the trial operation stage of the power market. As the focus of power market reform, the spot market is the key to the market design of each province. At present, the provinces have initially formed the rules of the power spot market. Most provincial power spot markets have chosen a centralized market model and adopted a pricing model based on node marginal electricity prices. This pricing model is based on the concept of marginal cost in economics, but the particularity of the real-time balance of power commodities also leads to the drawbacks of this pricing model. For example, when congestion occurs, this pricing model may generate high node electricity prices, which brings short-term power purchase cost risks to market members.

[0003] On the other hand, the power generation and proportion of renewable energy are increasing; in order to promote the development of renewable energy, various places have formulated a mechanism to give priority to the consumption of renewable energy in the spot market rules. Specifically, the consumption of renewable energy can be guaranteed by taking the forecast value of renewable energy as the spot market margin and participating in market clearing. When large-scale renewable energy penetrates the power system, the scale of the real-time market will increase, and the market boundary of real-time clearing will have greater uncertainty. The node electricity price calculated according to the clearing model is also uncertain due to the boundary conditions. This uncertainty is caused by the forecast error of renewable energy. Under unfavorable conditions, the real-time electricity price of some nodes may double, bringing short-term electricity purchase cost risks to market members. Summary of the invention

[0004] The purpose of the present invention is to provide a spot market risk management method, device, equipment and medium taking into account renewable energy, so as to solve the problems of increased real-time market importance and price risk caused by increased penetration of renewable energy.

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

[0006] Consider a spot market risk management approach for renewable energy, including the following steps:

[0007] Pre-clearing is performed before the real-time market clears to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0008] When the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price exceeds a set threshold, the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price are sent to each demand response entity;

[0009] Receive load response applications from various demand response entities and generate decision results;

[0010] The decision results are returned to each demand response entity.

[0011] The present invention is further improved in that: the pre-clearing node marginal electricity price LMP k Determine as follows:

[0012] LMP k =LMP 能量 +LMP 阻塞 +LMP 网损 (1)

[0013] in:

[0014] LMP 能量 is the energy component, LMP 网损 is the network loss component, LMP 阻塞 is the blocking component.

[0015] The present invention is further improved in that: LMP 能量 Determined by the dual multiplier of the economic dispatch power balance; LMP 网损 Determined by the network loss power and energy price; LMP 阻塞 Determined by the line constraint dual multiplier and the power transfer factor.

[0016] A further improvement of the present invention is that: the pre-clearing probability marginal electricity price π k,p Determine as follows:

[0017]

[0018] In the formula, π k,p represents the probability marginal electricity price of node k; E(·) represents the expectation of a certain function; π i The uncertain variables may lead to different electricity price levels; n π is the total number of discretely distributed electricity price levels; Pr(π k =π i ) represents the node marginal electricity price π k Equal to π i probability.

[0019] A further improvement of the present invention is that the step of performing pre-clearing before the real-time market clearing to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power specifically includes:

[0020] A time period is set before the real-time market clearing, and pre-clearing is performed based on market information, renewable energy forecast information, and load forecast information to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power.

[0021] A further improvement of the present invention is that the step of receiving the load response application of each demand response subject and generating a decision result specifically includes:

[0022] According to the load response application of each demand response subject, a greedy algorithm is used to generate decision results.

[0023] A further improvement of the present invention is that the step of using a greedy algorithm to generate a decision result specifically includes:

[0024] Obtain the load response of N nodes;

[0025] Arrange the obtained N node load responses from large to small;

[0026] Select nodes for load response in descending order; after each selection, determine whether the first condition is met; if the first condition is met, continue to determine whether the second condition is met; if the second condition is met, continue to determine whether the third condition is met; if the second condition is not met, the greedy algorithm ends and outputs the nodes that need to respond to the load; if the first condition is not met, continue to determine whether the third condition is met;

[0027] If the third condition is met, k=k+1, and the process continues to return to determine whether the first condition is met; if the third condition is not met, the greedy algorithm ends the output of the node that needs to respond to the load;

[0028] The first condition is: R k ≤mR0; the second condition is R k-1 -R k ≥αR0; The third condition is k<N, 0≤D k ≤D max ; R k is the system electricity price risk after selecting k nodes for load response according to the greedy algorithm; D k is the total load response of k load response nodes; D max It represents the maximum load response allowed; R0 is the system electricity price risk before response; m is the risk limit coefficient; α is the system risk contribution coefficient.

[0029] A further improvement of the present invention is that each demand response entity is a power sales company or a large user.

[0030] Consider spot market risk management devices for renewable energy, including:

[0031] The pre-clearing module is used to perform pre-clearing before the real-time market clearing, and obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0032] A judgment module, configured to send the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price to each demand response subject when the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price exceeds a set threshold;

[0033] A decision result generation module is used to receive the load response application of each demand response subject and generate a decision result;

[0034] The distribution module is used to return the decision results to each demand response entity.

[0035] An electronic device comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the spot market risk management method considering renewable energy.

[0036] A computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the spot market risk management method considering renewable energy is implemented.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The present invention provides a spot market risk management method, device, equipment and medium considering renewable energy. In order to achieve the purpose of managing price risks and forming effective feedback to power sales companies, a pre-clearing mechanism is set before the traditional real-time clearing market to form a two-stage real-time market of pre-clearing and actual clearing; in a certain period before the real-time market clearing, ISO performs pre-clearing based on market information to obtain pre-clearing node marginal electricity price and pre-clearing probability marginal electricity price considering wind power uncertainty, wherein the pre-clearing node marginal electricity price represents the node electricity price corresponding to the predicted mean of renewable energy, and the pre-clearing probability marginal electricity price is the electricity price information calculated according to the definition of probability marginal electricity price, and the two together can reflect the degree to which the electricity price deviates from the predicted level due to the uncertainty of renewable energy; when there is an electricity price risk, ISO publishes the electricity price information to each power sales company in the form of private information and waits for the feedback of the power sales company. After each power sales company decides the load response power according to the electricity price information, its own resource cost and management strategy, it submits the load response application to ISO. ISO runs the decision-making program according to the power application of each node, and returns the decision result to each power sales company. When the real-time market is cleared, each power sales company manages electricity consumption according to the returned results, and the ISO performs real-time clearing according to the power after load response and other latest market information. In order to ensure the long-term effectiveness of the mechanism, information such as the approved power application and risk management effect can also be announced to provide sufficient feedback to the power sales company.

[0039] The present invention solves the problem of real-time congestion caused by renewable energy in the power market, and makes up for the problem that although the original real-time market has high electricity price incentives, the real-time time is too short for the load side to act in time, and the load response process based on the electricity price signal is chaotic. Although the effect of this mechanism is not only to solve the electricity price risk caused by real-time congestion but also to reduce the call of high-cost units, considering that serious congestion in the power market is the main reason for high electricity prices, the present invention can better reflect the nature of incentive compatibility when congestion leads to high electricity price risks, and can also better form incentives for users who cause congestion, so that they adjust their electricity usage methods to achieve the unity of market entity needs and market management needs.

[0040] The present invention taps into the demand response potential on the user side through an incentive-compatible mechanism design, which saves costs and reduces price risks from the perspective of electricity sellers, and reduces system congestion risks and improves safety from the perspective of system operators. The technical solution of the present invention adapts to the requirements of ensuring renewable energy consumption and exploring demand-side potential in the construction of the power market, and is consistent with the market model actually adopted by the power market, and can ensure the stable operation of the system at the economic and safety levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0042] Figure 1 is a schematic diagram of probabilistic marginal electricity price;

[0043] Figure 2 This is the ISO greedy algorithm decision flow chart;

[0044] Figure 3 A flow chart of a spot market risk management method for renewable energy considered for the present invention;

[0045] Figure 4 A structural block diagram of a spot market risk management device for renewable energy considered in the present invention;

[0046] Figure 5 This is a structural block diagram of an electronic device. DETAILED DESCRIPTION

[0047] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0048] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0049] (1) Probabilistic marginal electricity price based on real-time market basic pricing model

[0050] The general process of the real-time market is to form a preliminary start-up and shutdown plan in the day before, and to conduct real-time economic dispatch in real time according to the ultra-short-term load forecast and renewable energy forecast, to form a real-time node marginal electricity price for clearing and forming a real-time dispatch plan. The node electricity price is generally determined according to the following formula:

[0051] LMP k =LMP 能量 +LMP 阻塞 +LMP 网损 (1)

[0052] in:

[0053] LMP 能量 is the energy component, determined by the dual multiplier of the economic dispatch power balance;

[0054] LMP 网损 is the grid loss component, which is determined by the grid loss power and energy price;

[0055] LMP 阻塞 is the blocking component, which is determined by the line constraint dual multiplier and the power transfer factor.

[0056] The impact of network loss on electricity prices is small and can be ignored in some markets. The blocking component and energy component become the main factors determining the real-time node electricity price, which is easily affected by renewable energy and generates real-time electricity price risks.

[0057] The real-time output of renewable energy is unpredictable, but the uncertainty of renewable energy output can be modeled according to probability distribution. Renewable energy with different output levels may cause different electricity prices at nodes. Each node presents different real-time electricity price levels and corresponding probabilities due to the probability of renewable energy. In order to express the real-time electricity price level and its probability of each node with concise information, the concept of probability margin is defined and used as reference information to reflect the uncertainty of electricity prices:

[0058]

[0059] Probabilistic marginal electricity price π k,p It is the estimate of the real-time electricity price based on the forecast information at a certain moment before the real time.k,p represents the probability marginal electricity price of node k; E(·) represents the expectation of a certain function; π i The uncertain variables may lead to different electricity price levels; n π is the total number of discretely distributed electricity price levels; Pr(π k =π i ) represents the node marginal electricity price π k Equal to π i probability.

[0060] (2) Pre-clearing settlement method

[0061] The electricity price risk caused by the uncertainty of renewable energy can be obtained from the forecast information at a certain moment before the real time. This information can provide effective information for the load side for the electricity sellers or large users who master the regional load to respond to the load. Therefore, the centralized price risk management method based on private information and public information is designed as follows:

[0062] In order to achieve the purpose of managing price risks and forming effective feedback to power sales companies, a pre-clearing mechanism is set up before the traditional real-time clearing market, forming a two-stage real-time market of pre-clearing and actual clearing. In a certain period before the real-time market clears, ISO pre-clears based on market information, and obtains the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power. The pre-clearing node marginal electricity price represents the node electricity price corresponding to the predicted mean of renewable energy, and the pre-clearing probability marginal electricity price is the electricity price information calculated according to the definition of probability marginal electricity price. Together, the two can reflect the degree to which the electricity price deviates from the predicted level due to the uncertainty of renewable energy. When there is an electricity price risk, ISO will release the electricity price information to each power sales company in the form of private information and wait for the feedback of the power sales company. After each power sales company decides the load response power based on the electricity price information, its own resource cost and management strategy, it submits the load response application to ISO. ISO runs the decision-making program based on the power application of each node and returns the decision results to each power sales company. When the real-time market is cleared, each power sales company manages electricity consumption according to the returned results, and the ISO performs real-time clearing according to the power after load response and other latest market information. In order to ensure the long-term effectiveness of the mechanism, information such as the approved power application and risk management effect can also be announced to provide sufficient feedback to the power sales company.

[0063] (3) ISO management method

[0064] After receiving the response application from the power sales company, ISO completes the screening of the application and returns it in a short time. Since the wind speed uncertainty information in the forecast environment is complex and changeable and the load itself also has uncertainty factors, in order to meet the purpose of the mechanism and the speed of decision-making, considering the incentives of different power sales companies when facing electricity price information, the greedy algorithm is selected as the selection strategy of ISO decision-making response load nodes;

[0065] See also Figure 2 As shown, the specific steps of the greedy algorithm include:

[0066] S11. Obtain the load response of N nodes, k=0; N is the total number of load nodes (when no power sales company participates in the load response of the node, the corresponding response is 0);

[0067] S12, arranging the obtained N node load response quantities from large to small;

[0068] S13, select nodes in order from large to small for load response according to the order of S12; after each selection, determine whether R k ≤mR0, if satisfied, proceed to step S14, otherwise proceed to step S15;

[0069] S14. Determine whether R is satisfied k-1 -R k ≥αR0, if satisfied, proceed to step S15, otherwise the greedy algorithm ends and proceeds to step S16;

[0070] S15. Determine whether k<N, 0≤D k ≤D max If yes, k=k+1 and go to step S13; otherwise, the greedy algorithm ends and goes to step S16;

[0071] S16. Output the nodes that need to respond to the load.

[0072] Where: R k is the system electricity price risk after selecting k nodes for load response according to the greedy algorithm; D k is the total load response of k load response nodes; D max represents the maximum load response allowed (defined as a value related to the magnitude of wind power fluctuation, as shown in equations (4) and (5)); R0 is the system electricity price risk before response; m is the risk limit coefficient (i.e., threshold), which represents the proportion of the expected system electricity price risk that should be reduced at least after the mechanism is operated; α is the system risk contribution coefficient, the purpose of which is to stop the iteration when the load response node contributes less than a certain value to the overall system risk or increases the system electricity price risk, so as to eliminate the load nodes that contribute less to electricity price risk management.

[0073]

[0074] Pr(P wmin ≤P w ≤P wmax )=1-ε (4)

[0075] D max =max(P wmax-P w ,P w -P wmin ) (5)

[0076] In formula (3), k is the number of selected load response nodes; N is the total number of nodes; π p,i-k is the pre-clearing probability marginal price of node i after selecting k node load responses; π l,i-k is the pre-clearing node marginal electricity price of node i after selecting k node load responses. In formula (4): P w is the predicted wind power; P wmin and P wmax They are the lower and upper confidence limits of the wind power confidence interval with a confidence level of 1-ε. Formula (5) considers the causes of risk formation and limits the total response to values ​​related to wind power errors.

[0077] (4) Analysis of the incentive effect of e-commerce retailers

[0078] Although the decision of load response power by each power sales company after receiving the electricity price information is the subject's own behavior, due to the existence of multiple independent power sales companies in the system, the decision of each power sales company on the response power is not only determined by its own risk, but also affected by the response strategy of other power sales companies. When there are multiple high-risk nodes, the power sales companies that are more affected by the electricity price risk hope to maximize their own interests and gamble on the level of response power applied. In a rational market environment, the high-price risk node will make load response decisions based on the game equilibrium of the Cournot model by choosing conservative or aggressive strategies, forming an incomplete information static game model between power sales companies. This game process is similar to the process of oligopoly distributors deciding on quotation strategies to maximize their own interests in a market with elastic electricity demand.

[0079] Example 1

[0080] See also Figure 3 As shown, the present invention provides a spot market risk management method considering renewable energy, which can be embedded in the spot market clearing process of the power market as a practical clearing method. Specifically, it includes the following steps:

[0081] Demand response entities are determined. In the provincial market, market entities that participate in power market clearing and are affected by spot market prices are allowed to participate in the pre-clearing load response mechanism, such as power sales companies and large users.

[0082] S1: ISO pre-clearing mechanism is running

[0083] In a certain period before the real-time market clearing, ISO conducts pre-clearing based on market information, renewable energy forecast information, and load forecast information to obtain the pre-clearing node marginal electricity price and the pre-clearing probabilistic marginal electricity price that takes into account the uncertainty of wind power.

[0084] S2: When the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering wind power uncertainty exceeds the set threshold, ISO determines that there is an electricity price risk, and publishes the pre-clearing node marginal electricity price of each node and the pre-clearing probability marginal electricity price considering wind power uncertainty to each power sales company in the form of private information, and waits for feedback from each power sales company.

[0085] S3: After receiving the private information sent by ISO, each power sales company decides on the load response quantity based on the electricity price information, its own resource cost and management strategy, and submits the load response application to ISO.

[0086] S4: ISO runs the greedy algorithm decision-making procedure based on the power requested by each node to decide the allowed load response node;

[0087] S5: ISO returns the decision result of the application to each load node, and each node acts according to the returned decision result;

[0088] S6: When the real-time market is cleared, each power sales company manages electricity consumption according to the returned results, and the ISO performs real-time clearing according to the power after load response and other latest market information.

[0089] Afterwards, ISO publishes relevant pre-clearing management results. To ensure the long-term effectiveness of the mechanism, information such as the amount of electricity applied for and the effect of risk management must be published on a public platform as public information to guide the demand side to declare the response electricity more reasonably.

[0090] Example 2

[0091] See also Figure 4 As shown, the present invention also provides a spot market clearing device considering renewable energy, comprising:

[0092] The pre-clearing module is used to perform pre-clearing before the real-time market clearing, and obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0093] A judgment module is used to send the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering wind power uncertainty to each demand response subject when the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering wind power uncertainty exceeds a set threshold;

[0094] The load response decision module is used for each demand response subject to decide the load response quantity and submit a load response application based on the received pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0095] A decision result generation module is used to generate a decision result according to the load response application of each demand response subject;

[0096] The execution module is used to return the decision results to each demand response entity. When the real-time market is cleared, each demand response entity acts according to the returned decision results.

[0097] Example 3

[0098] See also Figure 5 As shown, the present invention also provides an electronic device 100 for a spot market risk management method taking into account renewable energy; 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.

[0099] The memory 101 can be used to store the computer program 103. The processor 102 implements various functions of the electronic device 100 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.

[0100] 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.

[0101] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a spot market clearing method considering fluctuations in renewable energy. The processor 102 may execute the plurality of instructions to implement:

[0102] Pre-clearing is performed before the real-time market clears to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0103] When the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering wind power uncertainty exceeds a set threshold, the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering wind power uncertainty are sent to each demand response entity;

[0104] Each demand response entity decides on the load response quantity and submits a load response application based on the received pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power;

[0105] According to the load response application of each demand response subject, a decision result is generated and returned to each demand response subject. When the real-time market is cleared, each demand response subject acts according to the returned decision result.

[0106] Specifically, the specific implementation method of the processor 102 for the above instructions can refer to Figure 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0107] Example 4

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 The steps for the functions specified in one or more boxes.

[0113] 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 spot market risk management method considering renewable energy, characterized in that: The following steps are involved: Pre-clearing is performed before the real-time market clears to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power; When the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price exceeds a set threshold, the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price are sent to each demand response entity; Receive load response applications from various demand response entities and generate decision results; The decision results are returned to each demand response subject; Pre-clearing node marginal electricity price LMP k Determine as follows: LMP k =LMP 能量 +LMP 阻塞 +LMP 网损 (1) in: LMP 能量 is the energy component, LMP 网损 is the network loss component, LMP 阻塞 is the blocking component; LMP 能量 Determined by the dual multiplier of the economic dispatch power balance; LMP 网损 Determined by the network loss power and energy price; LMP 阻塞 Determined by the line constraint dual multiplier and the power transfer factor; Pre-clearing probability marginal electricity price π k,p Determine as follows: In the formula, π k,p represents the probability marginal electricity price of node k; E(·) represents the expectation of a certain function; π i The uncertain variables may lead to different electricity price levels; n π is the total number of discretely distributed electricity price levels; Pr(π k =π i ) represents the node marginal electricity price π k Equal to π i probability; The step of receiving the load response application of each demand response subject and generating a decision result specifically includes: According to the load response application of each demand response subject, a greedy algorithm is used to generate decision results; The step of using the greedy algorithm to generate a decision result specifically includes: Obtain the load response of N nodes; Arrange the obtained N node load responses from large to small; Select nodes for load response in descending order; after each selection, determine whether the first condition is met; if the first condition is met, continue to determine whether the second condition is met; if the second condition is met, continue to determine whether the third condition is met; if the second condition is not met, the greedy algorithm ends and outputs the nodes that need to respond to the load; if the first condition is not met, continue to determine whether the third condition is met; If the third condition is met, k=k+1, and the process continues to return to determine whether the first condition is met; if the third condition is not met, the greedy algorithm ends the output of the node that needs to respond to the load; The first condition is: R k ≤mR0; the second condition is R k-1 -R k ≥αR0; The third condition is k<N, 0≤D k ≤D max ; R k is the system electricity price risk after selecting k nodes for load response according to the greedy algorithm; D k is the total load response of k load response nodes; D max It represents the maximum load response allowed; R0 is the system electricity price risk before response; m is the risk limit coefficient; α is the system risk contribution coefficient.

2. The spot market risk management method considering renewable energy according to claim 1, characterized in that: The step of performing pre-clearing before the real-time market clearing to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power specifically includes: A time period is set before the real-time market clearing, and pre-clearing is performed based on market information, renewable energy forecast information, and load forecast information to obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power.

3. A spot market risk management device considering renewable energy, characterized in that: include: The pre-clearing module is used to perform pre-clearing before the real-time market clearing, and obtain the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price considering the uncertainty of wind power; A judgment module, configured to send the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price to each demand response subject when the difference between the pre-clearing node marginal electricity price and the pre-clearing probability marginal electricity price exceeds a set threshold; A decision result generation module is used to receive the load response application of each demand response subject and generate a decision result; An execution module is used to return the decision results to each demand response subject; Pre-clearing node marginal electricity price LMP k Determine as follows: LMP k =LMP 能量 +LMP 阻塞 +LMP 网损 (1) in: LMP 能量 is the energy component, LMP 网损 is the network loss component, LMP 阻塞 is the blocking component; LMP 能量 Determined by the dual multiplier of the economic dispatch power balance; LMP 网损 Determined by the network loss power and energy price; LMP 阻塞 Determined by the line constraint dual multiplier and the power transfer factor; Pre-clearing probability marginal electricity price π k,p Determine as follows: In the formula, π k,p represents the probability marginal electricity price of node k; E(·) represents the expectation of a certain function; π i The uncertain variables may lead to different electricity price levels; n π is the total number of discretely distributed electricity price levels; Pr(π k =π i ) represents the node marginal electricity price π k Equal to π i probability; The step of receiving the load response application of each demand response subject and generating a decision result specifically includes: According to the load response application of each demand response subject, a greedy algorithm is used to generate decision results; The step of using the greedy algorithm to generate a decision result specifically includes: Obtain the load response of N nodes; Arrange the obtained N node load responses from large to small; Select nodes for load response in descending order; after each selection, determine whether the first condition is met; if the first condition is met, continue to determine whether the second condition is met; if the second condition is met, continue to determine whether the third condition is met; if the second condition is not met, the greedy algorithm ends and outputs the nodes that need to respond to the load; if the first condition is not met, continue to determine whether the third condition is met; If the third condition is met, k=k+1, and the process continues to return to determine whether the first condition is met; if the third condition is not met, the greedy algorithm ends the output of the node that needs to respond to the load; The first condition is: R k ≤mR0; the second condition is R k-1 -R k ≥αR0; The third condition is k<N, 0≤D k ≤D max ; R k is the system electricity price risk after selecting k nodes for load response according to the greedy algorithm; D k is the total load response of k load response nodes; D max It represents the maximum load response allowed; R0 is the system electricity price risk before response; m is the risk limit coefficient; α is the system risk contribution coefficient.

4. An electronic device, characterized in that: The electronic device includes a processor and a memory, and the processor is used to execute a computer program stored in the memory to implement the spot market risk management method considering renewable energy as described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the spot market risk management method considering renewable energy as described in any one of claims 1 to 2 is implemented.

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