Electric power market uncertainty operation scene analysis method and system

Abstract

The invention relates to a power market uncertainty operation scene analysis method and system, and relates to the technical field of power systems. Executing Monte Carlo simulation; calculating a net demand to evaluate a supply and demand balance state of the system under specific conditions, and identifying potential excess power generation or additional traditional energy demand conditions; based on the simulation result, generating an uncertain operation scene; evaluation and strategy making: evaluating each generated uncertain operation scene, and making a risk management strategy based on an evaluation result; the method has the advantages that a series of representative uncertain operation scenes can be effectively generated, and on this basis, comprehensive decision support is provided for electricity market participants through preliminary and deep calculation and evaluation of three dimensions of economic benefit, reliability and environmental influence, and economic benefit maximization is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for analyzing the uncertain operation scenarios of the power market. Background Art

[0002] With the global energy structure transformation towards renewable energy, the complexity and uncertainty of the power market are increasing day by day. Traditional power systems mainly rely on fossil fuel power generation, and their output is relatively stable and controllable. However, the introduction of renewable energy such as wind energy and solar energy, due to its intermittency and volatility, makes the power supply and demand balance of the power system more difficult to predict and manage. In addition, factors such as extreme weather events and policy changes further increase the uncertainty of the power market.

[0003] Existing power market analysis methods often focus on deterministic models, which assume that all input parameters are known and fixed, ignoring the uncertainty factors widely existing in actual operations. Although some studies have begun to explore how to use probability methods to evaluate the operation of the power system, most existing methods are still insufficient in dealing with multi-dimensional uncertainties, especially in comprehensively considering economic benefits, reliability, and environmental impacts.

[0004] Based on this, this case is proposed. Summary of the Invention

[0005] One of the purposes of the present invention is to provide a method for analyzing the uncertain operation scenarios of the power market, which can effectively generate a series of representative uncertain operation scenarios and a complete evaluation system, providing comprehensive decision-making support for power market participants.

[0006] In order to achieve the above purpose, the technical solution of the present invention is as follows:

[0007] A method for analyzing the uncertain operation scenarios of the power market includes the following steps:

[0008] S10. Data collection and processing: Obtain a multi-dimensional data set related to the power market through multiple data sources. The data at least includes historical load demand D, renewable energy power generation G re , market price, weather forecast, and policy change information, and determine the statistical parameters of the total load demand D and renewable energy power generation G re based on these data. The statistical parameters include the average load demand μD, the standard deviation σD of the total load demand, the average power generation μG re , and the standard deviation σG of the renewable energy power generation re ;

[0009] S20. Perform Monte Carlo simulation: For each simulation, sample from the normal distribution N(μD,σD 2Extract a sample d representing the total load demand i , from the normal distribution N(μG re , σG re 2 ) extract a sample g representing the renewable energy power generation re,i ;

[0010] S30. Calculate the net demand: For each simulation, calculate the net demand N i = d i - g re,i , to evaluate the supply-demand balance state of the system under specific conditions and identify potential situations of excess power generation or additional conventional energy demand;

[0011] S40. Based on the simulation results, generate uncertainty operation scenarios;

[0012] S50. Evaluation and strategy formulation: Evaluate each generated uncertainty operation scenario and formulate risk management strategies based on the evaluation results.

[0013] Furthermore, the step S40 includes the following process:

[0014] S41. After performing the Monte Carlo simulation, obtain no less than 10,000 simulation data;

[0015] S42. Conduct statistical analysis on the simulation results to understand the probability distribution characteristics of different variables;

[0016] S43. Use the clustering algorithm to group the simulation results, with each group representing a typical scenario, that is, find the respective cluster centers;

[0017] S44. Calculate the comprehensive score of the cluster centers and select the scenarios with higher scores as the final uncertainty operation scenarios.

[0018] Furthermore, the comprehensive score S1 of the cluster centers = A1*PE + A2*PR + A3*ES;

[0019] In the formula, A1, A2, and A3 respectively represent the weights of the three dimensions of preliminary economic benefits PE, preliminary reliability PR, and preliminary environmental impact ES, and A1 + A2 + A3 = 1;

[0020] Among them, the preliminary economic benefit PE = 1 / (N i + k), N i is the net demand in the current simulation result, k is a constant to avoid the denominator from becoming zero; the preliminary reliability PR = 1 - LOLP, where LOLP is the loss of load probability in the current simulation result; the preliminary environmental impact ES = G re / G total , G reRenewable energy power generation, G total is the total power generation.

[0021] The second object of the present invention is to provide a system based on the above power market uncertainty operation scenario analysis method, including:

[0022] A data acquisition unit for automatically acquiring and integrating power market-related information from multiple data sources;

[0023] A data processing unit for performing Monte Carlo simulation and calculating the net demand;

[0024] A scenario generation unit for generating uncertainty operation scenarios;

[0025] A comprehensive evaluation unit for evaluating each generated uncertainty operation scenario;

[0026] A strategy optimization unit for proposing targeted risk mitigation strategies based on the evaluation results.

[0027] The advantages of the present invention are as follows: By combining Monte Carlo simulation and clustering algorithms, a series of representative uncertainty operation scenarios can be effectively generated. On this basis, the present invention also provides a complete evaluation system, including preliminary and in-depth calculations and evaluations of economic benefits, reliability, and environmental impacts, thereby providing comprehensive decision-making support for power market participants, effectively improving the accuracy of prediction and the reliability of the system, providing a scientific basis for resource allocation and investment decisions, and contributing to maximizing economic benefits. Description of the Drawings

[0028] Figure 1 It is a framework schematic diagram of the power market uncertainty operation scenario analysis method in the embodiment. Specific Embodiments

[0029] The following further describes the present invention in detail with reference to the embodiments. It should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. in the text are based on the orientation or positional relationships shown in the coordinate system of the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0030] As Figure 1 shown, this embodiment proposes a power market uncertainty operation scenario analysis method, including the following steps:

[0031] S10. Data collection and processing;

[0032] S20. Perform Monte Carlo simulation;

[0033] S30. Calculate the net demand to evaluate the supply - demand balance state of the system under specific conditions and identify potential excess power generation or additional traditional energy demand situations;

[0034] S40. Generate uncertainty operation scenarios based on the simulation results;

[0035] S50. Evaluation and strategy formulation: Evaluate each generated uncertainty operation scenario and formulate risk management strategies based on the evaluation results.

[0036] In step S10, a multi - dimensional data set related to the power market is obtained through multiple data sources. The data includes at least historical load demand D, renewable energy power generation G re , market price, weather forecast, and policy change information. Based on these data, the total load demand D and renewable energy power generation G re are determined, and the statistical parameters of the total load demand D and renewable energy power generation G re include the average load demand μD, the standard deviation σD of the total load demand, the average power generation μG re .

[0037] Monte Carlo simulation is a widely used method for evaluating the impact of uncertainty on system behavior. It simulates the potential future states of the system by generating a large number of possible scenarios. In the power market, it can be used to simulate the fluctuations of variables such as power generation, load demand, or market price.

[0038] In step S20, for each simulation, a sample d representing the total load demand is drawn from the normal distribution N(μD,σD 2 ), and a sample g representing the renewable energy power generation is drawn from the normal distribution N(μG i re ,σG re 2 ). Then in step S30, for each simulation, the net demand N re,i i = d i - g re,i is calculated.

[0039] In step S40, it includes the following:

[0040] S41. After performing Monte Carlo simulation, obtain no less than 10,000 times of simulation data;

[0041] S42. Conduct statistical analysis on the simulation results to understand the probability distribution characteristics of different variables;

[0042] ​​S43. Group the simulation results using a clustering algorithm, with each group representing a typical scenario, i.e., find the respective cluster centers;

[0043] S44. Calculate the comprehensive score of the cluster centers and select the scenario with a higher score as the final uncertainty operation scenario;

[0044] Among them, the comprehensive score S1 of the cluster center = A1 * PE + A2 * PR + A3 * ES;

[0045] In the formula, A1, A2, and A3 respectively represent the weights of the three dimensions of preliminary economic benefit PE, preliminary reliability PR, and preliminary environmental impact ES, and A1 + A2 + A3 = 1;

[0046] Among them, the preliminary economic benefit PE = 1 / (N i + k), where N i is the net demand in the current simulation result, and k is a constant to avoid the denominator from becoming zero; the preliminary reliability PR = 1 - LOLP, where LOLP is the loss of load probability in the current simulation result; the preliminary environmental impact ES = G re / G total , where G re is the renewable energy power generation, and G total is the total power generation.

[0047] During the process of generating the uncertainty operation scenario, a preliminary simple score is used to screen the most representative scenarios. This score is mainly used for quick identification and classification, rather than for detailed evaluation. This not only improves efficiency but also provides an accurate scenario basis for subsequent detailed evaluation.

[0048] In step S50, it includes in-depth economic benefit evaluation, in-depth reliability evaluation, and in-depth environmental impact evaluation to help decision-makers formulate corresponding management strategies based on the evaluation results.

[0049] Among them, the calculation formula for the in-depth economic benefit is as follows:

[0050] NS = (NPV - NPV min ) / (NPV max - NPV min );

[0051] Among them, NS represents the in-depth economic benefit evaluation value, NPV is the net present value in the current scenario, and NPV max and NPV min are respectively the minimum and maximum NPV values among all evaluation scenarios.

[0052] The calculation formula for the in-depth reliability is as follows:

[0053] RS = 1 - (LOLP - LOLP min) / (LOLP max -LOLP min );

[0054] Wherein, RS represents the deep reliability evaluation value, LOLP is the loss of load probability in the current simulation result, and LOLP min and LOLP max are the minimum and maximum LOLP values in all evaluation scenarios respectively.

[0055] The calculation formula for the deep environmental impact is as follows:

[0056]

[0057] Wherein, CO 2,total is the total carbon dioxide emission in the current scenario, and CO 2,max and CO 2,min are the minimum and maximum carbon dioxide emissions in all evaluation scenarios respectively.

[0058] Meanwhile, this application also proposes a system based on the above-mentioned power market uncertainty operation scenario analysis method, including:

[0059] A data acquisition unit for automatically obtaining and integrating power market-related information from multiple data sources;

[0060] A data processing unit for performing Monte Carlo simulation and calculating the net demand;

[0061] A scenario generation unit for generating uncertainty operation scenarios;

[0062] A comprehensive evaluation unit for evaluating each generated uncertainty operation scenario;

[0063] A strategy optimization unit for proposing targeted risk mitigation strategies based on the evaluation results.

[0064] The above embodiments are only used to explain the concept of the present invention, rather than limiting the protection scope of the rights of the present invention. Any non-substantive modification made to the present invention using this concept shall fall within the protection scope of the present invention.

Claims

1. A method for analyzing uncertain operation scenarios in a power market, characterized in that: The following steps are involved: S10. Data collection and processing; S20. Performing a Monte Carlo simulation: for each simulation, drawing a sample representing the total load demand from a normal distribution and drawing a sample representing the renewable energy generation from a normal distribution; S30. Calculate net demand: For each simulation, calculate the net demand based on the samples drawn to evaluate the supply and demand balance of the system under specific conditions and identify potential excess generation or additional traditional energy demand; S40. Generate uncertain operation scenarios based on simulation results; S50. Evaluation and strategy formulation: Evaluate each uncertainty operating scenario generated and formulate a risk management strategy based on the evaluation results.

2. A method for analyzing power market uncertainty operation scenarios according to claim 1, characterized in that: In step S10, a multidimensional data set related to the power market is obtained through various data sources, and the data at least includes historical load demand D, renewable energy power generation G re , market prices, weather forecasts, and policy change information, and based on these data, determine the total load demand D and renewable energy generation G re The statistical parameters include the average load demand μD, the standard deviation of the total load demand σD, the average power generation μG re , standard deviation of renewable energy generation σG re .

3. A method for analyzing power market uncertainty operation scenarios according to claim 2, characterized in that: In step S30, the net demand N i =d i -g re，i , where d i is from the normal distribution N(μD,σD 2 ) is a sample representing the total load demand, g re,i is from the normal distribution N(μG re , σG re 2 ) is a sample representing renewable energy generation.

4. A method for analyzing power market uncertainty operation scenarios according to claim 3, characterized in that: The step S40 includes the following process: S41. after executing the Monte Carlo simulation, obtaining simulation data of not less than 10,000 times; S42. Perform statistical analysis on the simulation results to understand the probability distribution characteristics of different variables; S43. Use a clustering algorithm to group the simulation results, each group represents a typical scenario, that is, find each cluster center; S44. Calculate the comprehensive score of the cluster center and select the scenario with a higher score as the final uncertainty operation scenario.

5. A method for analyzing power market uncertainty operation scenarios according to claim 4, characterized in that: The comprehensive score of the cluster center S1 = A1*PE+A2*PR+A3*ES; In the formula, A1, A2, and A3 represent the weights of the three dimensions of preliminary economic benefits PE, preliminary reliability PR, and preliminary environmental impact ES, respectively, A1+A2+A3=1; The initial economic benefit PE = 1 / (N i +k), N i is the net demand in the current simulation results, k is a constant to avoid the denominator returning to zero; preliminary reliability PR = 1-LOLP, LOLP is the load loss probability in the current simulation results; preliminary environmental impact ES = G re / G total , G re Renewable energy generation, G total is the total power generation.

6. The method for analyzing power market uncertainty operation scenarios according to claim 1, characterized in that: The evaluation includes a deep economic benefit evaluation, and the calculation formula for deep economic benefit is as follows: NS=(NPV-NPV min ) / (NPV max -NPV min ); Among them, NS represents the deep economic benefit assessment value, NPV is the net present value under the current scenario, and NPV max and NPV min are the minimum and maximum NPV values ​​among all evaluated scenarios, respectively.

7. The method for analyzing power market uncertainty operation scenarios according to claim 1, characterized in that: The evaluation includes a deep reliability evaluation, and the calculation formula of deep reliability is as follows: RS=1-(LOLP-LOLP min ) / (LOLP max -LOLP min ); Among them, RS represents the deep reliability assessment value, LOLP is the load loss probability in the current simulation result, and LOLP min and LOLP max are the minimum and maximum LOLP values ​​in all evaluated scenarios, respectively.

8. The method for analyzing power market uncertainty operation scenarios according to claim 1, characterized in that: The assessment includes an in-depth environmental impact assessment, the calculation formula for the in-depth environmental impact is as follows: Among them, CO 2,total is the total carbon dioxide emissions in the current scenario, CO 2,max and CO 2,min are the minimum and maximum CO2 emissions in all evaluated scenarios, respectively.

9. A system based on the power market uncertainty operation scenario analysis method according to any one of claims 1 to 8, characterized in that: include: A data acquisition unit, used to automatically acquire and integrate power market related information from multiple data sources; a data processing unit for performing Monte Carlo simulations and calculating net demand; A scenario generation unit, used to generate uncertain operation scenarios; A comprehensive evaluation unit, used to evaluate each generated uncertainty operation scenario; The strategy optimization unit proposes targeted risk mitigation strategies based on the evaluation results.

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