Electric power spot market risk evaluation method and system based on risk identification

Abstract

The invention discloses an electric power spot market risk evaluation method and system based on risk identification. The method comprises the steps of collecting a grid structure, generator set parameters and system operation prediction data of an analyzed electric power system; using a Monte Carlo method to simulate six basic risk events, and generating a market risk operation scene; a single-period clearing model with cost minimization as a target is constructed, and market clearing calculation in a risk scene is carried out; identifying the risk of each scene through a scene analysis method, and establishing a three-level risk evaluation index system of the spot market; adopting an analytic hierarchy process to distribute index weights; and setting five evaluation grades as {extremely low, low, medium, high and extremely high}, calculating the overall risk score of the market by using a fuzzy comprehensive evaluation method, and determining the risk evaluation grade. According to the invention, the operation risk of the electric power spot market can be assessed comprehensively and specifically, accurate risk rating is provided, and a sufficient basis is provided for risk management and control and decision making. And a more efficient risk management tool is provided for operators.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power spot market, and in particular to an electric power spot market risk assessment method and system based on risk identification. Background Art

[0002] The electricity spot market has the characteristics of complex clearing mechanism and high transaction frequency. However, with the rapid advancement of the electricity spot market and the increase in the penetration rate of new energy, a series of market operation risks such as electricity price fluctuations, abuse of market power, transmission congestion and other problems have become increasingly prominent. These risks may not only cause economic losses to market participants, but also threaten the stable operation of the electricity market. Therefore, accurate risk assessment and management of the electricity spot market is of great significance to ensure the healthy development of the electricity market.

[0003] Traditional risk assessment methods for the electricity spot market, such as the CVaR model and fuzzy comprehensive evaluation, either focus on a single risk indicator and fail to evaluate the overall risk, or adopt a general evaluation system that is difficult to fully reflect the uniqueness and differences of electricity markets in different regions. Electricity markets in different regions may have different market structures, energy structures, and operating characteristics. These differences are often ignored in traditional risk assessment methods, making it difficult to accurately capture and evaluate the specific risks of electricity markets in different regions, which affects the comprehensiveness and accuracy of risk analysis.

[0004] In order to overcome these shortcomings, a more advanced and flexible risk assessment method is needed that can comprehensively consider various risk factors and make targeted adjustments based on the actual conditions of the power markets in different regions to improve the accuracy and efficiency of risk analysis. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a method and system for risk assessment of the electricity spot market based on risk identification. The method for risk assessment of the electricity spot market based on risk identification can identify potential risks according to the actual situation of the electricity spot market in different regions, comprehensively and targetedly consider various possible risk factors, so as to more accurately assess the risk level of the electricity spot market.

[0006] To achieve the above objectives, the present invention provides a method and system for evaluating the operation risk of a power spot market based on Monte Carlo simulation risk identification, which specifically includes:

[0007] In a first aspect, the present invention provides a method for risk assessment of a power spot market based on risk identification, comprising the following steps:

[0008] Collect the grid structure, thermal power and renewable energy unit parameters and system operation forecast data of the power system in the analyzed area;

[0009] Based on the typical load distribution and renewable energy output in the system operation forecast data, the Monte Carlo method is used to simulate six basic risk events to generate market risk operation scenarios;

[0010] Based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, a single-period clearing model with the goal of minimizing costs is constructed, including unit combination and economic dispatch models, to perform market clearing calculations under risk scenarios;

[0011] Based on the market clearing calculation results under risk scenarios, the scenario analysis method is used to identify the risks of each scenario and establish a three-level risk assessment indicator system for the spot market;

[0012] The analytic hierarchy process is used to allocate the weights of risk assessment indicators at all levels;

[0013] Based on the weights of risk assessment indicators at all levels, the evaluation level is set, and the fuzzy comprehensive evaluation method is used to calculate the overall market risk score to obtain the comprehensive risk assessment level of the electricity spot market.

[0014] As a further improvement of the present invention, the collection of the grid structure, thermal power and new energy unit parameters and system operation prediction data of the power system in the analyzed area specifically includes:

[0015] The grid structure data of the power system includes the number of nodes n, the number of transmission lines m, the network topology, the nodes where the new energy power stations and thermal power plants are located, line parameters, etc.; the parameters of thermal power and new energy units, including the upper and lower limits of unit output, quotation curves, the number of thermal power units nf, the number of new energy units nr, the ramp rate and other economic and technical parameters; the system operation prediction data includes the 24-hour typical load distribution curves and new energy output scenarios of each season.

[0016] As a further improvement of the present invention, the six basic risk events are unplanned shutdown of units, unplanned shutdown of lines, physical retention of units, load forecast error, new energy forecast error, and fluctuation in thermal power quotations, among which unplanned shutdown refers to Class II, III, and IV shutdowns in which facilities are not shut down immediately.

[0017] As a further improvement of the present invention, based on the typical load distribution and new energy output in the system operation forecast data, the Monte Carlo method is used to simulate six basic risk events to generate market risk operation scenarios, specifically including:

[0018] Unplanned unit shutdown is used to simulate the situation where the generator unit stops running unexpectedly or fails to generate electricity according to the scheduled plan due to sudden failure or other reasons; Assume that the i1th generator unit has Q i1 (0≤Q i1≤1) and the probability of unplanned outage occurs. This parameter can be adjusted according to the risk level to be simulated. i1=1,…,nf+nr,s i1 Indicates the running status of the unit, the expression is:

[0019]

[0020] u i1 ~U(0,1)

[0021] Unplanned outage of the line is used to simulate the situation where the line becomes unavailable due to failures, natural disasters, etc.; let the probability of unplanned outage of the i2th line be Q li2 (0≤Q li2 ≤1), this parameter can be adjusted according to the risk level to be simulated, i2=1,…,m,s li2 Indicates the line operation status, and its expression is:

[0022]

[0023] u li2 ~U(0,1)

[0024] The physical retention of the unit is used to simulate the behavior of thermal power generating units to reduce the reported available power generation capacity to affect the market clearing price and power generation sequence, thereby obtaining higher profits; let the probability of physical retention of the i3th thermal power unit be Q gi3 (0≤Q gi3 ≤1), this parameter can be adjusted according to the risk level to be simulated, i3=1,…,nf, the retention percentage is 50%, s gi3 Indicates the unit retention state, and its expression is:

[0025]

[0026] u gi3 ~U(0,1)

[0027] Load forecast error is used to simulate the error between the predicted value and the actual value when predicting the power load at a certain moment; let the relative error of load forecast of node i4 δ di4 Subordinate to the normal distribution N(0,σ di4 2 ), i4=1,…,n;

[0028] The prediction error of new energy is used to simulate the deviation between the prediction result and the actual value when predicting the output of new energy. Suppose the prediction relative error δ of the i5th new energy unit wi5 Subordinate to the normal distribution N(0,σ wi5 2 ), i5=1,…,nr;

[0029] Thermal power price fluctuations are used to simulate the fluctuations of thermal power prices within a certain period of time due to changes in production costs and other reasons; c i6_0 is the quadratic cost function of the i6th unit, i6=1,…,nf,c i6 is the quotation curve submitted to the trading center, where P i6 is the active power output of unit i6, δ ci6 is the bidding strategy coefficient, which represents the multiple between the unit's bid and the typical bid, δ ci6 The relevant formula and distribution are as follows:

[0030]

[0031] δ ci6 =|δ i6 |,δ i6 ~N(1,σ ci6 2 )

[0032] The typical power market operation scenario selects typical conditions in each season, all parts of the system maintain normal operation, and the load and renewable energy output are seasonal typical output curves;

[0033] Monte Carlo simulation method is used to sample six basic risk events, that is, i1 、u li2 、u gi3 ,δ di4 ,δ wi5 ,δ ci6 Sampling is carried out to consider the occurrence of a single basic risk event and the simultaneous occurrence of multiple risk events, thus forming a large number of risk operation scenarios for the region's electricity spot market.

[0034] As a further improvement of the present invention, based on the grid structure of the regional power system, thermal power and new energy unit parameters, a single-period clearing model with the goal of minimizing costs is constructed, including unit combination and economic dispatch models, to perform market clearing calculations under risk scenarios; specifically, it includes:

[0035] The objective function of the single-period unit commitment model is to minimize the operating cost:

[0036]

[0037] In the formula, C i,t (P i,t ) represents the operating cost of unit i in period t, i = 1, ..., nf + nr, and is a quadratic curve or multi-segment linear function related to the unit's declared output and energy price; P i,t is the output of unit i in period t;

[0038] The constraints include:

[0039] System load balancing constraints

[0040]

[0041] Where D t is the system load during time period t;

[0042] System positive spare capacity constraint

[0043] It is necessary to ensure that the total startup capacity at each moment is greater than the sum of the load and the positive reserve capacity. The system positive reserve capacity constraint can be expressed as:

[0044]

[0045] In the formula, α i,t represents the start and stop status of unit i in period t, α i,t =0 means the unit is shut down, α i,t =1 means the unit is on; is the maximum output of unit i in period t; is the system positive reserve capacity requirement for time period t;

[0046] Unit output upper and lower limit constraints

[0047]

[0048] Line power flow constraints

[0049]

[0050] In the formula, is the power transmission limit of line j; j = 1,…,m, G j-i is the generator output power transfer distribution factor of the node where unit i is located to line j; n is the number of nodes in the system; G j-k D is the generator output power transfer distribution factor of node k to line j; k,t is the bus load value of node k in period t;

[0051] The SCED model of unit commitment and economic dispatch is as follows:

[0052]

[0053] The above formulas represent the objective function, system load balance constraint, positive reserve capacity constraint, unit output upper and lower limit constraints, and line power flow constraint respectively;

[0054] Spot market clearing calculations are performed based on the given data of unplanned unit outages, unplanned line outages, unit physical retention, thermal power price fluctuations, load forecasts, and renewable energy power generation forecasts in each risk scenario;

[0055] Use the unit output and the winning positive reserve capacity to replace the upper limit of the thermal power unit output, consider the new energy forecast error to modify the new energy output upper limit, consider the load forecast error to modify the load distribution, and substitute it into the SCED model for calculation to determine whether the risk scenario can be cleared. If it cannot be cleared, it indicates that there is a risk of imbalance between supply and demand in the electricity market.

[0056] As a further improvement of the present invention, based on the market clearing calculation results under risk scenarios, the risks of each scenario are identified through scenario analysis, and a three-level risk assessment indicator system for the spot market is established, specifically including:

[0057] Analyze the clearing results of each scenario, judge the market price, market power, market efficiency, market structure, and grid operation safety risks; summarize the risks, select evaluation indicators in a targeted manner, and build a three-level risk assessment indicator system for the spot market.

[0058] As a further improvement of the present invention, the three-level risk assessment indicator system of the spot market includes:

[0059]

[0060]

[0061]

[0062] As a further improvement of the present invention, the weights of risk assessment indicators at each level are allocated using the analytic hierarchy process; specifically, the following steps are performed:

[0063] The analytic hierarchy process is used to determine the weight of the first-level indicators in the comprehensive risk rating of the spot market, the weight of the second-level indicators in the first-level indicators, and the weight of the third-level indicators in the second-level indicators. The specific steps are as follows:

[0064] Determine the evaluation objectives and corresponding indicators, and establish a hierarchical model;

[0065] Construct a judgment matrix; Assume that there are N evaluation indicators in total, and ask experts to compare the importance of the indicators in pairs, and get the judgment matrix X = (x ij ) N×N , where the element x ij (i,j=1,2,...,N) is the comparison result of the importance of evaluation index i and evaluation index j, and there is x ji =1 / x ij ;

[0066] Find the maximum eigenvalue λ and its eigenvector W of the judgment matrix X;

[0067] Conduct consistency checks;

[0068]

[0069] CR=CI / RI

[0070] In the formula, CI is the consistency index, the smaller its value is, the greater the consistency is; CR is the test coefficient; RI is the random consistency index, which is related to the order of the judgment matrix;

[0071] When CR < 0.1, the judgment matrix passes the consistency test;

[0072] Normalize the feature vector W to obtain the weight assignment of each indicator;

[0073] And so on, empowerment is carried out step by step from the highest level downwards.

[0074] As a further improvement of the present invention, based on the weights of risk assessment indicators at all levels, the assessment level is set, and the overall market risk score is calculated using the fuzzy comprehensive evaluation method to obtain the comprehensive risk assessment level of the power spot market; specifically, it includes:

[0075] Determine the indicator set and evaluation set;

[0076] Assume that there are N selected evaluation indicators, which are denoted as e1, e2, …e N , the set of evaluation indicators is E = [e1, e2, …, e N ]; Divide the comments into five levels to form a comment vector V:

[0077] V=[ν1,ν2,ν3,ν4,ν5]=[very low, low, medium, high, very high]

[0078] Determine each ordered pair (e i ,v j )∈E×V ij ; r ij Represents the factor e in E i For V medium level v j The membership degree of each higher level indicator is R = (r ij ) q×5 , q is the number of sub-indicators under the advanced indicator;

[0079] The weight of each index is determined by AHP: A=[a1,a2,…,a q ], where 0≤a1,a2,…,a q ≤1,

[0080] Determine the fuzzy comprehensive evaluation vector;

[0081] The fuzzy comprehensive evaluation vector performs synthetic calculation on the fuzzy relationship matrix based on the evaluation index weights to obtain the comprehensive membership of the evaluation object at each level. The formula is as follows:

[0082]

[0083] For the multi-level evaluation system, the comprehensive membership vector B of the low-level indicators is constructed as the fuzzy relationship matrix R of the high-level indicators, and the same method is used to calculate until the comprehensive membership vector B of the overall market risk is obtained;

[0084] Construct a score table for five review levels, and take the review score vector as C = [0.9, 0.7, 0.5, 0.3, 0.1] T , calculate the comprehensive score of electricity spot market risk as F=B×C, and obtain the final rating by comparing the score table.

[0085] In a second aspect, the present invention provides a power spot market risk assessment system based on risk identification, comprising:

[0086] The data collection module is used to collect the grid structure data, thermal power and new energy unit data, and system operation prediction data of the power system in the analyzed area;

[0087] The risk operation scenario generation module is used to simulate six basic risk events using the Monte Carlo method based on the typical load distribution and renewable energy output in the system operation forecast data to generate market risk operation scenarios;

[0088] The spot market clearing module is used to build a single-period clearing model with the goal of minimizing costs based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, including unit combination and economic dispatch models, and perform market clearing calculations under risk scenarios;

[0089] The risk assessment indicator system construction module is used to identify the risks of each scenario through scenario analysis based on the market clearing calculation results under risk scenarios, and establish a three-level risk assessment indicator system for the spot market;

[0090] The weight generation module is used to allocate the weights of risk assessment indicators at all levels using the analytic hierarchy process;

[0091] The comprehensive risk rating module is used to set the evaluation level based on the weights of risk assessment indicators at all levels, calculate the overall market risk score using the fuzzy comprehensive evaluation method, and obtain the comprehensive risk assessment level of the electricity spot market.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] The present invention integrates data collection, risk scenario simulation, clearing model construction, scenario analysis, risk assessment index system design, analytic hierarchy process weight allocation and fuzzy comprehensive evaluation to comprehensively assess the risk level. For the typical operation scenarios of the electricity spot market, Monte Carlo simulation is used to extensively sample six basic risk events to generate multiple risk scenarios to ensure that the simulation is both comprehensive and flexible, and fully covers the extreme and conventional risks of the market. Through the constructed clearing model to process each risk scenario, the scenario analysis method is used to accurately identify risks, and then a multi-level risk assessment index system is constructed, which realizes an in-depth analysis from the overall market to specific scenarios, and the risk assessment is more comprehensive and profound. Finally, the analytic hierarchy process and fuzzy comprehensive evaluation method are integrated to determine the assessment level of the operation risk of the electricity spot market, providing a solid basis for risk decision-making. The core of the present invention lies in the comprehensive application of clearing models, scenario analysis and fuzzy comprehensive evaluation, which provides a more accurate risk rating for the electricity spot market and can significantly improve the comprehensive assessment and decision-making support capabilities of market risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] Figure 1 It is a flow chart of the risk assessment method of the electricity spot market based on risk identification according to the present invention;

[0095] Figure 2 A schematic diagram of the actual output and prediction error of new energy sources that cause system imbalance under different new energy predictions in the embodiment;

[0096] Figure 3 It is a load-side weighted electricity price distribution diagram of 776 sampling clearings when multiple risk events occur simultaneously in the embodiment;

[0097] Figure 4 A schematic diagram of a risk assessment index system for the electricity spot market constructed in the embodiment;

[0098] Figure 5 A schematic diagram of a power spot market risk assessment system based on risk identification provided by the present invention. DETAILED DESCRIPTION

[0099] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the examples of the present invention. Obviously, the described embodiments are only 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 should fall within the scope of protection of the present invention.

[0100] As attached Figure 1As shown, the present invention provides a method for risk assessment of the electricity spot market based on risk identification, comprising the following steps:

[0101] S1, collect the grid structure, thermal power and new energy unit parameters and system operation forecast data of the power system in the analyzed area;

[0102] S2, based on the typical load distribution and renewable energy output in the system operation forecast data, uses the Monte Carlo method to simulate six basic risk events and generate market risk operation scenarios;

[0103] S3, based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, build a single-period clearing model with the goal of minimizing costs, including unit combination and economic dispatch models, and perform market clearing calculations under risk scenarios;

[0104] S4, based on the market clearing calculation results under risk scenarios, identify the risks of each scenario through scenario analysis and establish a three-level risk assessment indicator system for the spot market;

[0105] S5, using the analytic hierarchy process to allocate the weights of risk assessment indicators at all levels;

[0106] S6, based on the weights of risk assessment indicators at all levels, set the five-level assessment level as {very low, low, medium, high, very high}, use the fuzzy comprehensive evaluation method to calculate the overall market risk score, and obtain the comprehensive risk assessment level of the power spot market, providing decision support for risk management and control.

[0107] The core of the risk assessment method for the power spot market based on risk identification provided by the present invention is to comprehensively assess the risk level of the power spot market. The following is an analysis of the principle of this method:

[0108] Step S1: Data collection: Collect necessary data to establish the basis for modeling and analysis. This includes the grid structure of the power system (describing the physical connection and transmission capacity of the power grid), thermal power and renewable energy unit parameters (such as capacity, efficiency, cost, etc.), and system operation forecast data (such as load forecast, renewable energy output forecast, etc.). The accuracy and completeness of the data directly affect the accuracy of subsequent analysis and evaluation.

[0109] Step S2: Risk event simulation, using the Monte Carlo method to simulate six basic risk events. Generate market risk operation scenarios to cover possible market fluctuations and uncertainties. The Monte Carlo method simulates the behavior of complex systems through a large number of random samplings and is suitable for dealing with complex problems with uncertainty.

[0110] Step S3: Market clearing calculation, construct a single-period clearing model with the goal of minimizing costs, including unit combination and economic dispatch model. Calculate the market clearing results under the risk scenario, that is, determine the power generation and electricity price of each unit. Solve the model through optimization algorithms (such as linear programming, nonlinear programming, etc.) to obtain the optimal power generation plan that meets the load demand and system constraints.

[0111] Step S4: Risk identification and establishment of indicator system. Based on the market clearing calculation results, the scenario analysis method is used to identify the risks of each scenario. A three-level risk assessment indicator system for the spot market is established to fully reflect market risks. The scenario analysis method identifies possible risk points and influencing factors by considering different market conditions and variable combinations, and then constructs a risk assessment indicator system.

[0112] Step S5: Weight allocation, use the analytic hierarchy process to allocate the weights of risk assessment indicators at each level. Determine the importance of each risk assessment indicator in the overall risk assessment. The analytic hierarchy process decomposes complex problems into multiple sub-problems by building a hierarchical model, and determines the relative importance of each sub-problem through comparison and judgment.

[0113] Step S6: Comprehensive evaluation and grading, using the fuzzy comprehensive evaluation method to calculate the overall market risk score and set a five-level evaluation level. The comprehensive risk evaluation level of the power spot market is obtained to provide decision support for risk management. The fuzzy comprehensive evaluation method uses fuzzy mathematics to deal with the fuzziness and uncertainty in the evaluation, converting qualitative evaluation into quantitative evaluation, thereby obtaining more accurate and objective evaluation results.

[0114] This method achieves a comprehensive, systematic and scientific evaluation of the risks in the electricity spot market by comprehensively using a variety of technologies and methods such as data collection, Monte Carlo simulation, optimization model, scenario analysis, hierarchical analysis method and fuzzy comprehensive evaluation method. This method not only takes into account the complexity and uncertainty of the market, but also achieves refined management and control of risks by building a multi-level risk assessment indicator system. Therefore, this method is of great significance for improving the stability and security of the electricity spot market.

[0115] The following will be combined with the attached Figure 1 The present invention is further described. However, the content of the present invention is not limited to this.

[0116] The first step is to collect the grid structure data of the power system in the analyzed area, such as the number of nodes n, the number of transmission lines m, the network topology, transmission line parameters, the distribution nodes of renewable energy power stations and thermal power plants, etc.; collect the economic and technical parameters of thermal power and renewable energy units, including the upper and lower limits of unit output, quotation curves, the number of thermal power units nf, the number of renewable energy units nr, the unit ramp rate, etc.; collect the forecast data of system operation, including the 24-hour typical load distribution curves and renewable energy output scenarios in each season.

[0117] In the second step, the Monte Carlo method is used to simulate the occurrence of six basic risk events under various typical load distributions and renewable energy output conditions to generate a large number of risk operation scenarios.

[0118] The six basic risk events are unplanned outage of units, unplanned outage of lines, physical retention of units, load forecast error, new energy forecast error, and thermal power price fluctuations. Unplanned outages refer to the second, third, and fourth types of outages where facilities are not shut down immediately. The specific settings of each risk event are as follows:

[0119] 1) Unplanned shutdown of the unit is used to simulate the situation where the generator unit stops running unexpectedly or fails to generate electricity according to the scheduled plan due to sudden failure or other reasons. Assume that the i1th (i1=1,…,nf+nr)th generator unit has Q i1 = 1% probability of unplanned outage, s i1 Indicates the operating status of the unit. Assume that the i1th generator unit has Q i1 The probability of unplanned outage occurs, 0≤Q i1 ≤1. This parameter can be adjusted according to the risk level to be simulated. Its expression is:

[0120]

[0121] u i1 ~U(0,1)(2)

[0122] 2) Unplanned outage of the line, used to simulate the situation where the line becomes unavailable due to faults, natural disasters, etc. Assume that the i1th generator has Q i1 The probability of unplanned outage occurs, 0≤Q li2 ≤1, this parameter can be adjusted according to the risk level to be simulated. Let the probability of unplanned outage of the i2th line (i2=1,…,m) be Q li2 =1%,

[0123] s li2 Indicates the line operation status, and its expression is:

[0124]

[0125] uli2 ~U(0,1)(4)

[0126] 3) Unit physical retention is used to simulate the behavior of thermal power generating units to reduce the reported available generating capacity to affect the market clearing price and generation sequence, thereby obtaining higher profits. Let the probability of physical retention of the i3th thermal power unit be Q gi3 , 0≤Q gi3 ≤1. This parameter can be adjusted according to the risk level to be simulated. Let the probability of physical retention of the i3th unit (i3=1,…,nf) be Q gi3 =5%, retention percentage is 50%, s gi3 Indicates the unit retention state, and its expression is:

[0127]

[0128] u gi3 ~U(0,1)(6)

[0129] 4) Load forecast error, which is used to simulate the error between the predicted value and the actual value when predicting the power load at a certain moment. Suppose the relative error of load forecast of node i4 (i4 = 1, ..., n) is di4 Subordinate to the normal distribution N(0,σ di4 2 ),Pick

[0130] σ di4 =0.02.

[0131] 5) New energy prediction error, which is used to simulate the deviation between the prediction result and the actual value when predicting the output of new energy. Suppose the prediction relative error δ of the i5th (i5=1,…,nr) new energy unit wi5 Subordinate to the normal distribution N(0,σ wi5 2 ),Pick

[0132] σ wi5 =0.15.

[0133] 6) Thermal power quotation fluctuations are used to simulate the situation where thermal power quotation fluctuates within a certain period of time due to changes in production costs and other reasons. The quadratic cost function of the i6th (i6 = 1, ..., nf) unit is as shown in formula (7), and the quotation curve submitted to the trading center is as shown in formula (8), where P i6 is the active output of unit i6, δ ci6 is the bidding strategy coefficient, which represents the multiple between the unit's bid and the typical bid, δ ci6 The distribution is shown in formula (9), taking σ ci6 =0.02.

[0134]

[0135] δ ci6 =|δ i6 |,δ i6 ~N(1,σ ci6 2 ) (9) In the typical power market operation scenarios of each season, all parts of the system maintain normal operation, and the load and renewable energy output are typical output curves. The Monte Carlo simulation method is used to sample six basic risk events, that is, the variable u i1 、u li2 、u gi3 , δ di4 , δ wi5 , δ ci6 Sampling is carried out to consider the occurrence of a single basic risk event and the simultaneous occurrence of multiple risk events, thus forming a large number of risk operation scenarios for the region's electricity spot market.

[0136] The third step is to build a single-period clearing model with the goal of minimizing costs based on the grid structure of the regional power system, parameters of thermal power and renewable energy units, including unit combination and economic dispatch models, and perform market clearing calculations under risk scenarios.

[0137] The single period unit commitment (SCUC) model is as follows:

[0138] 1) Objective function

[0139] The objective function aims to minimize the operating cost.

[0140]

[0141] In the formula, C i,t (P i,t ) represents the operating cost of unit i in period t, i = 1, ..., nf + nr, and is a quadratic curve or multi-segment linear function related to the unit's declared output and energy price; P i,t is the output of unit i in period t.

[0142] 2) System load balancing constraints

[0143]

[0144] Where D t is the system load during period t.

[0145] 3) System positive reserve capacity constraints

[0146] It is necessary to ensure that the total startup capacity at each moment is greater than the sum of the load and the positive reserve capacity. The system positive reserve capacity constraint can be expressed as:

[0147]

[0148] In the formula, α i,t represents the start and stop status of unit i in period t, α i,t =0 means the unit is shut down, α i,t =1 means the unit is on; is the maximum output of unit i in period t; is the system positive spare capacity requirement for period t.

[0149] 4) Upper and lower limits of unit output

[0150]

[0151] 5) Line flow constraints

[0152]

[0153] In the formula, is the power transmission limit of line j; j = 1,…,m, G j-i is the generator output power transfer distribution factor of the node where unit i is located to line j; n is the number of nodes in the system; G j-k D is the generator output power transfer distribution factor of node k to line j; k,t is the bus load value of node k in period t;

[0154] The single-period economic dispatch (SCED) model is as follows:

[0155]

[0156] Equations (16) to (20) represent the objective function, system load balance constraint, positive reserve capacity constraint, unit output upper and lower limit constraints, and line flow constraint, respectively.

[0157] The spot market clearing calculation is performed based on the given data of unit unplanned outage, line unplanned outage, unit physical retention, thermal power quotation fluctuation, load forecast, and new energy power generation forecast in each risk scenario. Due to the existence of load forecast error and new energy forecast error, the unit output and the winning positive reserve capacity are used to replace the upper limit of the thermal power unit output, the new energy forecast error is considered to modify the upper limit of the new energy output, and the load forecast error is considered to modify the load distribution. The SCED model is substituted for calculation to determine whether the risk scenario can be cleared. If it cannot be cleared, it indicates that there is a risk of imbalance between supply and demand in the power market.

[0158] Step 4: Based on the market clearing calculation results under risk scenarios, identify the risks of each scenario through scenario analysis and establish a three-level risk assessment indicator system for the spot market.

[0159] First, analyze the clearing results of each scenario to determine the risks of market price, market power, market efficiency, market structure, and grid operation safety. Summarize the risks, select evaluation indicators in a targeted manner, and build a three-level risk evaluation indicator system for the spot market. The first-level indicators include market internal operation risk indicators, grid operation risk indicators, and external environment risk indicators. According to the identification situation and the characteristics of the power market, the final system can be improved from the basic indicator system in Table 1.

[0160] Table 1 Basic risk assessment indicator system for the spot market

[0161]

[0162]

[0163] Step 5: Use the analytic hierarchy process to assign weights to risk assessment indicators at each level.

[0164] The analytic hierarchy process (AHP) is used to determine the weight of the first-level indicators in the comprehensive risk rating of the spot market, the weight of the second-level indicators in the first-level indicators, and the weight of the third-level indicators in the second-level indicators. The specific steps are as follows:

[0165] 1) Determine the evaluation objectives and corresponding indicators, and establish a hierarchical model.

[0166] 2) Construct a judgment matrix. Assuming there are N evaluation indicators, ask experts to compare the importance of the indicators in pairs, and obtain the judgment matrix X = (x ij ) N×N , where the element x ij (i,j=1,2,...,N) is the comparison result of the importance of evaluation index i and evaluation index j, and there is x ji =1 / x ij The element scaling method used is shown in Table 2.

[0167] Table 2 AHP scale table

[0168]

[0169] 3) Obtain the maximum eigenvalue λ and its eigenvector W of the judgment matrix X.

[0170] 4) Perform consistency check.

[0171]

[0172] CR=CI / RI(22)

[0173] In the formula, CI is the consistency index, the smaller its value is, the greater the consistency is; CR is the test coefficient; RI is the random consistency index, which is related to the order of the judgment matrix, and its value can be obtained by looking up the table.

[0174] When CR < 0.1, the judgment matrix passes the consistency test. When the judgment matrix constructed by experts fails to pass the consistency test, experts are consulted to make reasonable adjustments to the matrix so that it has satisfactory consistency.

[0175] 5) Normalize the feature vector W to obtain the weight assignment of each indicator.

[0176] 6) And so on, empowering the people step by step from the highest level downwards.

[0177] Step 6: Set the five-level evaluation level as {very low, low, medium, high, very high}, use the fuzzy comprehensive evaluation method to calculate the overall market risk score, and clarify the risk evaluation level. The steps are as follows:

[0178] 1) Determine the indicator set and evaluation set

[0179] Assume that there are N selected evaluation indicators, which are denoted as e1, e2, …e N , the set of evaluation indicators is E = [e1, e2, …, e N ]; Divide the comments into five levels to form a comment vector V:

[0180] V=[ν1,ν2,ν3,ν4,ν5]=[very low, low, medium, high, very high](23)

[0181] 2) Determine each ordered pair (e i ,v j )∈E×V ij ; r ij Represents the factor e in E i For V medium level v j The membership degree of each higher level indicator is R = (r ij ) q×5 , q is the number of sub-indicators under the advanced indicator.

[0182] 3) Determine the weight of each indicator through AHP: A=[a1,a2,…,a q ], where 0≤a1,a2,…,a q ≤1,

[0183] 4) Determine the fuzzy comprehensive evaluation vector.

[0184] The fuzzy comprehensive evaluation vector performs synthetic calculation on the fuzzy relationship matrix based on the evaluation index weights to obtain the comprehensive membership of the evaluation object at each level. The formula is as follows:

[0185]

[0186] 5) For the multi-level evaluation system, the comprehensive membership vector B of the low-level indicators is constructed as the fuzzy relationship matrix R of the high-level indicators, and the same method is used to calculate until the comprehensive membership vector B of the overall market risk is obtained.

[0187] 6) Construct a score table for five review levels as shown in Table 3, and take the review score vector as C = [0.9, 0.7, 0.5, 0.3, 0.1] T , calculate the comprehensive score of electricity spot market risk as F=B×C, and obtain the final rating by comparing the score table.

[0188] Table 3 Comment score table

[0189]

[0190] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments. It is obvious that the described embodiments are only part of the embodiments of the present invention.

[0191] The test system analyzed in this embodiment has a total of 129 nodes, including 89 thermal power generating units, 206 wind power generating units, and 202 photovoltaic generating units.

[0192] The first step is to collect necessary data, including network topology, unit distribution, line parameters, unit parameters, typical load distribution curves in four seasons and typical renewable energy output scenarios.

[0193] In the second step, Monte Carlo is used to sample the distribution of six risk events to generate a large number of risk operation scenarios.

[0194] Step 3: Based on the simulation of unplanned outages of units, unplanned outages of lines, physical retention of units, and fluctuations in thermal power bids, modify marginal conditions, use a single-period clearing model to clear risk scenarios, and identify risks in the clearing results. Consider the simulation of new energy forecast errors and load forecast errors, replace the thermal power output cap with the sum of the cleared output and the positive and standby winning bids, modify the load distribution, and perform economic dispatch clearing again to determine whether there is a risk of supply and demand imbalance.

[0195] Step 4: Identify the risks arising from the clearing results and build a risk assessment indicator system based on this.

[0196] When an unplanned shutdown event occurs alone, the sample clearing data during the summer load peak and spring load trough periods show that the load-side weighted electricity price in spring is between 342 and 245 yuan / MWh, while the summer electricity price is between 418 and 668 yuan / MWh. The electricity price fluctuates greatly, and there is an electricity price risk. In addition, nodal electricity prices are observed in the summer, indicating that there is a risk of transmission congestion in the region.

[0197] In order to assess the risk of system imbalance caused by new energy forecast errors, the forecast error threshold required for the imbalance of supply and demand in the power system under maximum spinning reserve is calculated. The sampling is selected during the peak load period of 12:00 in summer. Since the maximum spinning reserve capacity of the system is 8.43% of the total load, the reserve capacity is set to 8.43%. The sampling results are shown in the attached Figure 2 As shown in the figure, when the predicted output of renewable energy is greater than 25% of the total capacity of renewable energy, the prediction error reaches a certain value and the system becomes unbalanced, so the system is at risk of imbalance; at the same time, when the output of renewable energy is the largest, wind and solar power abandonment is observed.

[0198] The physical retention of the units was sampled and cleared 200 times. It was observed that at the peak load in summer, if 50% of the physical retention occurred in 13 specific units, the increase in income of the thermal power units would be 7.34×10 6 Yuan, far greater than the loss of 7.6×10 5 Yuan, so there is market power risk in the market.

[0199] The same method can be used to observe electricity price fluctuations, transmission congestion, and system imbalance risks caused by unplanned line outages, load forecast errors, and fluctuations in thermal power quotations.

[0200] When the four types of risky unpredictable error events occurred simultaneously, 1,000 simulations were performed at the peak load at 12:00 in summer. The results showed that 776 clearings were successful and 514 transmission congestions occurred. The distribution of electricity prices is detailed in the attached Figure 3 , among which the lowest and highest weighted electricity prices were 406.81 yuan / MW and 867.17 yuan / MW, with large fluctuations. The 776 results were sampled three times for two types of forecast error events. Among the 2328 results, economic dispatch clearing failed 22 times, transmission congestion occurred 1369 times, and wind and solar power were abandoned 554 times. The lowest and highest weighted average electricity prices were 367.89 yuan / MW and 2172.87 yuan / MW, respectively, and the electricity price fluctuations were more severe.

[0201] Finally, based on the risks identified by scenario analysis and comprehensive consideration of the risks that may be caused by the external environment, a three-level risk assessment indicator system for the spot market was specifically constructed. Figure 4 shown.

[0202] In step 5, the analytic hierarchy process was used to solicit opinions from 25 experts and practitioners in the power market industry to determine the weight of each level of indicators in the upper level indicators.

[0203] Step 6: Set the indicator comments to five evaluation levels: {very low, low, medium, high, very high}. 25 experts and practitioners vote on the three-level indicators based on these levels to determine the membership relationship of the three-level indicators. Based on these membership relationships, the fuzzy evaluation matrix of the secondary indicators is calculated. Then, the fuzzy synthesis operator is used to combine the weights A of the indicators at each level and the fuzzy evaluation matrix R of the secondary indicators. The fuzzy comprehensive evaluation vector B of the secondary index is obtained by the formula B = A·R. Then, the fuzzy evaluation matrix R of the primary index is constructed, and by analogy, the fuzzy comprehensive evaluation vector B = [0.645, 0.189, 0.103, 0.050, 0.013] of the overall risk of the spot market is finally calculated. Finally, the overall market risk score is calculated to be 0.7805, and the risk level is judged to be "low".

[0204] This method can systematically and specifically identify and evaluate the operational risks of the analyzed electricity spot market, provide decision makers with accurate comprehensive risk level judgment and management strategy basis, and enhance market stability and reliability.

[0205] Second, as attached Figure 5 As shown, the present invention provides a power spot market risk assessment system based on risk identification, which mainly includes:

[0206] The data collection module is used to collect the grid structure, thermal power and new energy unit parameters and system operation forecast data of the power system in the analyzed area;

[0207] The risk operation scenario generation module is used to simulate six basic risk events using the Monte Carlo method based on the typical load distribution and renewable energy output in the system operation forecast data to generate market risk operation scenarios;

[0208] The spot market clearing module is used to build a single-period clearing model with the goal of minimizing costs based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, including unit combination and economic dispatch models, and perform market clearing calculations under risk scenarios;

[0209] The risk assessment indicator system construction module is used to identify the risks of each scenario through scenario analysis based on the market clearing calculation results under risk scenarios, and establish a three-level risk assessment indicator system for the spot market;

[0210] The weight generation module uses the analytic hierarchy process to allocate the weights of risk assessment indicators at all levels;

[0211] The comprehensive risk rating module is used to set the evaluation level based on the weights of risk assessment indicators at all levels, calculate the overall market risk score using the fuzzy comprehensive evaluation method, and obtain the comprehensive risk assessment level of the electricity spot market.

[0212] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a risk assessment method for a spot electricity market based on risk identification when executing the computer program.

[0213] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a risk assessment method for a spot electricity market based on risk identification.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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 method for risk assessment of the electricity spot market based on risk identification, characterized in that: include: Collect the grid structure, thermal power and renewable energy unit parameters and system operation forecast data of the power system in the analyzed area; Based on the typical load distribution and renewable energy output in the system operation forecast data, the Monte Carlo method is used to simulate six basic risk events to generate market risk operation scenarios; Based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, a single-period clearing model with the goal of minimizing costs is constructed, including unit combination and economic dispatch models, to perform market clearing calculations under risk scenarios; Based on the market clearing calculation results under risk scenarios, the scenario analysis method is used to identify the risks of each scenario and establish a three-level risk assessment indicator system for the spot market; The analytic hierarchy process is used to allocate the weights of risk assessment indicators at all levels; Based on the weights of risk assessment indicators at all levels, the evaluation level is set, and the fuzzy comprehensive evaluation method is used to calculate the overall market risk score to obtain the comprehensive risk assessment level of the electricity spot market.

2. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: The collection of the grid structure, thermal power and new energy unit parameters and system operation forecast data of the power system in the analyzed area specifically includes: The grid structure data of the power system includes the number of nodes n, the number of transmission lines m, the network topology, the nodes where the new energy power stations and thermal power plants are located, line parameters, etc.; the parameters of thermal power and new energy units, including the upper and lower limits of unit output, quotation curves, the number of thermal power units nf, the number of new energy units nr, the ramp rate and other economic and technical parameters; the system operation prediction data includes the 24-hour typical load distribution curves and new energy output scenarios of each season.

3. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: The six basic risk events are unplanned unit shutdown, unplanned line shutdown, unit physical retention, load forecast error, new energy forecast error, and thermal power quotation fluctuation. Among them, unplanned shutdown refers to Class II, III, and IV shutdowns where the facilities are not shut down immediately.

4. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: Based on the typical load distribution and renewable energy output in the system operation forecast data, the Monte Carlo method is used to simulate six basic risk events to generate market risk operation scenarios, including: Unplanned unit shutdown is used to simulate the situation where the generator unit stops running unexpectedly or fails to generate electricity according to the scheduled plan due to sudden failure or other reasons; Assume that the i1th generator unit has Q i1 The probability of unplanned outage occurs, i1=1,…,nf+nr, s i1 Indicates the running status of the unit, the expression is: in i1 ~U(0,1) Unplanned outage of the line is used to simulate the situation where the line becomes unavailable due to failures, natural disasters, etc.; let the probability of unplanned outage of the i2th line be Q li2 i2=1,…,m,s li2 Indicates the line operation status, and its expression is: in li2 ~U(0,1) The physical retention of the unit is used to simulate the behavior of thermal power generating units to reduce the reported available power generation capacity to affect the market clearing price and power generation sequence, thereby obtaining higher profits; let the probability of physical retention of the i3th thermal power unit be Q gi3 , i3=1,…,nf, the retention percentage is 50%, s gi3 Indicates the unit retention state, and its expression is: in gi3 ~U(0,1) Load forecast error is used to simulate the error between the predicted value and the actual value when predicting the power load at a certain moment; let the relative error of load forecast of node i4 δ di4 Subordinate to the normal distribution N(0,σ di4 2 ), i4=1,…,n; The prediction error of new energy is used to simulate the deviation between the prediction result and the actual value when predicting the output of new energy. Suppose the prediction relative error δ of the i5th new energy unit wi5 Subordinate to the normal distribution N(0,σ wi5 2 ), i5=1,…,nr; Thermal power price fluctuations are used to simulate the fluctuations of thermal power prices within a certain period of time due to changes in production costs and other reasons; c i6_0 is the quadratic cost function of the i6th unit, i6=1,…,nf,c i6 is the quotation curve submitted to the trading center, where P i6 is the active power output of unit i6, δ ci6 is the bidding strategy coefficient, which represents the multiple between the unit's bid and the typical bid, δ ci6 The relevant formula and distribution are as follows: d ci6 =|δ i6 |,d i6 ~N(1,σ ci6 2 ) The typical power market operation scenario selects typical conditions in each season, all parts of the system maintain normal operation, and the load and renewable energy output are seasonal typical output curves; Monte Carlo simulation method is used to sample six basic risk events, that is, i1 、u li2 、u gi3 , δ di4 , δ wi5 , δ ci6 Sampling is carried out to consider the occurrence of a single basic risk event and the simultaneous occurrence of multiple risk events, thus forming a large number of risk operation scenarios for the region's electricity spot market.

5. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: Based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, a single-period clearing model with the goal of minimizing costs is constructed, including unit combination and economic dispatch models, to perform market clearing calculations under risk scenarios; specifically, it includes: The objective function of the single-period unit commitment model is to minimize the operating cost: In the formula, C i,t (P i,t ) represents the operating cost of unit i in period t, i = 1, ..., nf + nr, and is a quadratic curve or multi-segment linear function related to the unit's declared output and energy price; P i,t is the output of unit i in period t; The constraints include: The system load balancing constraints meet: Where D t is the system load during time period t; The system positive reserve capacity constraint is satisfied: it is necessary to ensure that the total startup capacity at each moment is greater than the sum of the load and the positive reserve capacity. The system positive reserve capacity constraint can be expressed as: In the formula, α i,t represents the start and stop status of unit i in period t, α i,t =0 means the unit is shut down, α i,t =1 means the unit is on; is the maximum output of unit i in period t; is the system positive reserve capacity requirement for time period t; The upper and lower limits of the unit output are satisfied: The line flow constraints meet: In the formula, is the power transmission limit of line j; j = 1,…,m, G j-i is the generator output power transfer distribution factor of the node where unit i is located to line j; n is the number of nodes in the system; G j-k is the generator output power transfer distribution factor of node k to line j; D k,t is the bus load value of node k in period t; The SCED model of unit commitment and economic dispatch is as follows: The above formulas represent the objective function, system load balance constraint, positive reserve capacity constraint, unit output upper and lower limit constraints, and line power flow constraint respectively; Spot market clearing calculations are performed based on the given data of unplanned unit outages, unplanned line outages, unit physical retention, thermal power price fluctuations, load forecasts, and renewable energy power generation forecasts in each risk scenario; Use the unit output and the winning positive reserve capacity to replace the upper limit of the thermal power unit output, consider the new energy forecast error to modify the new energy output upper limit, consider the load forecast error to modify the load distribution, and substitute it into the SCED model for calculation to determine whether the risk scenario can be cleared. If it cannot be cleared, it indicates that there is a risk of imbalance between supply and demand in the electricity market.

6. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: Based on the market clearing calculation results under risk scenarios, the scenario analysis method is used to identify the risks of each scenario and establish a three-level risk assessment indicator system for the spot market, including: Analyze the clearing results of each scenario, judge the market price, market power, market efficiency, market structure, and grid operation safety risks; summarize the risks, select evaluation indicators in a targeted manner, and build a three-level risk assessment indicator system for the spot market.

7. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: The three-level risk assessment indicator system of the spot market includes:

8. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: The analytic hierarchy process is used to allocate the weights of risk assessment indicators at all levels; specifically, it includes: The analytic hierarchy process is used to determine the weight of the first-level indicators in the comprehensive risk rating of the spot market, the weight of the second-level indicators in the first-level indicators, and the weight of the third-level indicators in the second-level indicators. The specific steps are as follows: Determine the evaluation objectives and corresponding indicators, and establish a hierarchical model; Construct a judgment matrix; Assume that there are N evaluation indicators in total, and ask experts to compare the importance of the indicators in pairs, and get the judgment matrix X = (x ij ) N×N , i, j = 1, 2, ..., N, where element x ij is the comparison result of the importance of evaluation index i and evaluation index j, and there is x ji =1 / x ij ; Find the maximum eigenvalue λ and its eigenvector W of the judgment matrix X; Conduct consistency checks; CR=CI / RI In the formula, CI is the consistency index, the smaller its value is, the greater the consistency is; CR is the test coefficient; RI is the random consistency index, which is related to the order of the judgment matrix; When CR < 0.1, the judgment matrix passes the consistency test; Normalize the feature vector W to obtain the weight assignment of each indicator; And so on, empowerment is carried out step by step from the highest level downwards.

9. The method for risk assessment of the electricity spot market based on risk identification according to claim 1, characterized in that: Based on the weights of risk assessment indicators at all levels, the assessment level is set, and the overall market risk score is calculated using the fuzzy comprehensive evaluation method to obtain the comprehensive risk assessment level of the electricity spot market; Specifically include: Determine the indicator set and evaluation set; Assume that there are N selected evaluation indicators, which are denoted as e1, e2, …e N , the set of evaluation indicators is E = [e1, e2, …, e N ]; Divide the comments into five levels to form a comment vector V: V=[ν1,ν2,ν3,ν4,ν5]=[very low, low, medium, high, very high] Determine each ordered pair (e i ,v j )∈E×V ij ; r ij Represents the factor e in E i For V medium level v j The membership degree is i = 1, 2, ..., N, j = 1, 2, ..., 5, and the fuzzy relationship matrix of each higher-level indicator is R = (r ij ) q×5 , q is the number of sub-indicators under the advanced indicator; The weight of each index is determined by AHP: A=[a1,a2,…,a q ], where 0≤a1,a2,…,a q ≤1, Determine the fuzzy comprehensive evaluation vector; The fuzzy comprehensive evaluation vector performs synthetic calculation on the fuzzy relationship matrix based on the evaluation index weights to obtain the comprehensive membership of the evaluation object at each level. The formula is as follows: For the multi-level evaluation system, the comprehensive membership vector B of the low-level indicators is constructed as the fuzzy relationship matrix R of the high-level indicators, and the same method is used to calculate until the comprehensive membership vector B of the overall market risk is obtained; Construct a score table for five review levels, and take the review score vector as C = [0.9, 0.7, 0.5, 0.3, 0.1] T , calculate the comprehensive score of electricity spot market risk as F=B×C, and obtain the final rating by comparing the score table.

10. A risk assessment system for the electricity spot market based on risk identification, characterized in that: include: The data collection module is used to collect the grid structure data, thermal power and new energy unit data, and system operation prediction data of the power system in the analyzed area; The risk operation scenario generation module is used to simulate six basic risk events using the Monte Carlo method based on the typical load distribution and renewable energy output in the system operation forecast data to generate market risk operation scenarios; The spot market clearing module is used to build a single-period clearing model with the goal of minimizing costs based on the grid structure of the regional power system, thermal power and renewable energy unit parameters, including unit combination and economic dispatch models, and perform market clearing calculations under risk scenarios; The risk assessment indicator system construction module is used to identify the risks of each scenario through scenario analysis based on the market clearing calculation results under risk scenarios, and establish a three-level risk assessment indicator system for the spot market; The weight generation module is used to allocate the weights of risk assessment indicators at all levels using the analytic hierarchy process; The comprehensive risk rating module is used to set the evaluation level based on the weights of risk assessment indicators at all levels, calculate the overall market risk score using the fuzzy comprehensive evaluation method, and obtain the comprehensive risk assessment level of the electricity spot market.

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