CN110942243A - Method for evaluating operation risk of medium-term and long-term power market in large-scale new energy grid connection - Google Patents
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Abstract
The invention discloses a method for evaluating the operation risk of a medium-term and long-term power market considering large-scale new energy grid connection, which comprises the steps of firstly establishing an index system for evaluating the operation risk of the medium-term and long-term power market considering large-scale new energy grid connection, wherein the index system comprises a power price fluctuation risk, a power supply shortage risk, a credit risk and a system safety risk; then, determining an index order relation by an expert and scoring; then screening and integrating the weights; then determining an evaluation grade domain; then establishing a fuzzy relation matrix and carrying out fuzzy synthesis; and finally determining the medium and long term electric power market operation risk level considering large-scale new energy grid connection. The method can realize scientific and effective evaluation of the medium and long-term electric power market operation risk level considering large-scale new energy grid connection and identification of main risk sources, further adopt corresponding risk suppression measures to reduce market risks, and improve the safety and reliability of medium and long-term electric power market operation.
Description
Technical Field
The invention belongs to the technical field of power market risk assessment, and particularly relates to a method for assessing the operation risk of a large-scale new energy grid-connected medium-and-long-term power market.
Background
In order to deal with environmental pollution and energy shortage, governments of various countries make incentives to promote the development of new energy sources such as wind power and photovoltaic. With the progress of new energy power generation technology and the reduction of investment cost, the possibility of entering the electric power market gradually becomes larger and larger. The permeability of new energy in an electric power system is gradually improved, which not only brings different degrees of influence to other electric power market participants such as traditional power generation enterprises, electricity selling companies, power grid companies and large users, but also influences the operation risk of the whole electric power market. The electric power market risk refers to the possibility and the possible loss degree of market participants in a certain time under the combined action of various uncertain factors existing in the processes of electric power production, transportation, transaction, consumption and the like. Due to the special attributes of electricity as a commodity, the operational risk of the electricity market has distinct characteristics, such as high uncertainty, loss-gain duality, and statistics. Since the electric power system reform began in 2015, various measures are gradually taken in China to promote the electric power system reform, for example, a competition mechanism of the electric power selling side reform is gradually established, a spot trade trial plan is started, and the like, but sufficient attention is not given to the electric power market operation risk considering large-scale new energy grid connection, and research on electric power market operation risk assessment is lacked.
The electric power market operation risk generally includes indexes such as price fluctuation risk, information risk, power supply risk and system risk, and power market participants such as power generation enterprises, power grid enterprises, large power purchasing users and power selling companies face different market risks. After the medium-and-long-term electric power market operation risk assessment index system considering large-scale new energy grid connection is established, a medium-and-long-term electric power market operation risk comprehensive assessment model is established based on an improved analytic hierarchy process and a fuzzy evaluation method, the operation risk level of the medium-and-long-term electric power market can be effectively assessed, the risk source can be determined, and the method has very important practical significance on development and construction of the electric power market under the condition that new energy in China participates in the future.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for evaluating the operation risk of the power market in a medium-term and long-term grid-connected large-scale new energy resource, which is suitable for evaluating the operation risk of the power market.
The invention adopts the following technical scheme:
the method for evaluating the operation risk of the medium-term and long-term power market in the large-scale new energy grid connection is characterized by comprising the following steps of:
s1, establishing an index system including a target layer, a criterion layer and a scheme layer and taking account of the large-scale new energy grid-connected medium and long term electric power market operation risk assessment, wherein the index system includes an electricity price fluctuation risk, an electric power supply shortage risk, a credit risk and a system safety risk;
s2, determining the order relation of the indexes according to the relative importance degree of each layer of indexes, representing the relative importance scale of the indexes based on a 1-9 scale method, and determining the primary weight result of the scoring;
s3, scoring the index set by each expert, eliminating the score of the expert with the maximum scoring deviation, and taking the arithmetic mean value of the weights given by the remaining experts to obtain the final index weight;
s4, dividing the evaluation grade domain of the medium and long term electric power market operation risk index system considering large-scale new energy grid connection into high risk, danger, comparative danger, common and comparative safety;
s5, establishing a fuzzy relation matrix and carrying out fuzzy synthesis to obtain a final fuzzy evaluation vector;
and S6, determining the final medium and long term electric power market operation risk assessment result.
Specifically, in step S1, the criterion layer includes a risk of fluctuation of electricity price including new energy B1; risk of shortage of electric power supply containing new energy B2; credit risk B3 and new energy-containing system security risk B4;
the scheme layer corresponding to the electricity price fluctuation risk B1 containing the new energy comprises an electric power market force risk C11; risk of market imbalance C12; the risk of rising power generation cost C13 and the risk of fluctuation of electricity price caused by new energy C14;
the scheme layer corresponding to the power supply shortage risk B2 containing new energy comprises a power supply and demand contradiction risk C21; generator retention risk C22; an output resistor plug risk C23 and a new energy supply fluctuation risk C24;
the scheme layer corresponding to the credit risk B3 comprises an information asymmetry risk C31; rule, risk of legal immaturity C32 and risk of economic decline C33;
the scheme layer corresponding to the system safety risk B4 containing the new energy comprises an operation mode risk C41; coordination risk between power plant and grid dispatch C42 and safety risk of new energy grid connection C43.
Further, the electric market risk C11 is:
wherein p is the actual electricity price of the market; cMMarginal production cost for power plants;
the market supply and demand unbalance risk C12 is the deviation degree between the actual supply electric quantity and the actual load;
the power generation cost fluctuation risk C13 is the fluctuation degree of the coal purchase cost of the power generation enterprises;
the risk of fluctuation in electricity prices C14 caused by the new energy is measured by the difference in electricity prices at the peak-valley period of load in the case where the new energy participates in the electricity market.
Further, the risk of contradiction between power supply and demand C21 is the degree of deviation between the supply power and the actual load during peak load period;
the generator retention risk C22 is:
wherein c is the generating capacity of the power plant, and q is the actual declared electric quantity supplied by the power plant in the electric power market;
the output resistance blocking risk C33 is the blocking rate of a key power transmission segment, and specifically includes:
wherein, TcThe total blocking time of key power transmission segments in a period of time; t ismThe electric power market operation time in the period of time;
the risk of fluctuation of new energy supply C24 is expressed as a standard deviation of the predicted output of new energy.
Further, the information asymmetry risk C31 is the information difference between the two parties of the transaction, and specifically includes:
C31=|I1-I2|
wherein, I1Information indicating the subject of the transaction 1, I2Information indicating the transaction body 2;
rule, law insecure risk C32 is measured by default cost;
the economic decline risk C33 is expressed in terms of GDP rate of change.
Further, the risk of the operation mode is as follows:
wherein, TdRepresenting the sum of the time of occurrence of an operation mode threatening the safe and stable operation of the system in a period of time;
the coordination risk C42 between the power plant and the grid dispatch is:
wherein, ToRepresenting the sum of the time during which the power plant does not listen to the grid dispatching command for a period of time;
the safety risk C43 of new energy grid connection is represented by the reduction degree of the power quality.
Specifically, in step S3, R is usediAnd RjIndicating that experts i and j are index sets U of a certain layer in an index system1,U2,…,UmThe assigned weight, N, of the weight assigned by experts i and jijComprises the following steps:
the similarity degree of the scores of the expert i and other experts is as follows:
maximum removal of wiThe experts with corresponding values are scored, and the arithmetic mean value of the weights given by the rest experts is the final index weight R ═ R (R)1,R2,…,Rm)。
Specifically, in step S5, the final blur evaluation vector B is:
wherein, S is a fuzzy relation matrix, and R is a final index weight vector.
Specifically, in step S6, the final intermediate and long term power market operation risk assessment result is determined to be level r according to the maximum membership rule:
wherein S isE,jThe jth component of the blur evaluation vector B.
Further, the risk levels of the power rate fluctuation risk, the power supply shortage risk, the credit risk and the system safety risk are determined according to the maximum membership degree principle.
Compared with the prior art, the invention has at least the following beneficial effects:
the invention provides a medium-and-long-term power market operation risk comprehensive assessment method considering large-scale new energy grid connection, which fully considers various risks in the medium-and-long-term power market operation process, such as power price fluctuation risks, power supply shortage risks, credit risks, system safety risks and the like, establishes the medium-and-long-term power market operation risk comprehensive assessment method considering large-scale new energy grid connection based on an improved analytic hierarchy process and a fuzzy evaluation method, can effectively assess medium-and-long-term power market operation risk levels, can obtain main risk sources, and can further adopt corresponding risk suppression measures to reduce risks according to the risk sources. The invention improves the safety and reliability of medium-and long-term electric power market operation, and can reform assistance for a new round of electric power system in China.
Furthermore, the electric power price fluctuation risk considers the electric power market force risk, the market supply and demand unbalance risk, the power generation cost rising risk and the electric power price fluctuation risk caused by new energy, and the electric power market force risk mainly considers that the electric power market force raises the electric power price by a power generation business; the market supply and demand unbalance risk mainly considers that the fluctuation of power load is large, the electricity price mechanism of China is not perfect, and the electricity price fluctuates correspondingly; the risk of rising power generation costs mainly takes into account the rising cost of coal purchase and the rising cost of waste discharge of traditional power generators; the risk of electricity price fluctuation caused by new energy mainly considers that the electricity price difference in the load peak-valley period is enhanced by the reverse peak regulation characteristic of wind power. The four angles can more comprehensively identify the fluctuation risk of the electricity price.
Furthermore, the risk of power supply shortage considers the risk of contradiction between power supply and demand, the risk of retention of a power generator, the risk of transmission blockage and the risk of fluctuation of new energy supply, and the risk of contradiction between power supply and demand mainly considers that the power demand can rapidly rise to aggravate the contradiction between power supply and demand during the peak load period; the retention risk of the power generator mainly considers that the power generator raises the price of electricity to profit from the electricity through economic retention or physical retention; the risk of the output resistor plug mainly considers that the frequency and the position of the output resistor plug are uncontrollable due to frequent changes of power generation quotations; the risk of new energy supply fluctuation mainly takes the defect that the new energy cannot continuously function into consideration. These four angles allow a more comprehensive identification of the risk of power supply shortages.
Furthermore, the credit risk considers the information asymmetric risk, rules, the law insecurity risk and the economic decline risk, and the information asymmetric risk mainly considers that the trading party only masters the information of the trading party and the actual information of the trading party is rarely known; the rule and law infirm risk mainly considers that under the infirm electric power market rules and laws, lower default cost can prompt the default of a trading party to bring loss to the other party; the economic decline risk mainly considers that declined national economy reduces the repayment capacity of users and increases the default probability. These three angles allow a more comprehensive identification of credit risk.
Furthermore, the system safety risk considers the operation mode risk, the coordination risk between a power plant and power grid dispatching and the safety risk of new energy grid connection, and the operation mode risk mainly considers that a plurality of operation modes which threaten the safety and stability of the power system can appear after the electric power market transaction becomes the dispatching foundation; the coordination risk between the power plant and the power grid scheduling mainly considers that the power plant can not be adjusted according to the scheduling instruction of a power grid company after the power plant and the power grid are separated, and the safety of the power grid can be threatened; the safety risk of new energy grid connection mainly considers that voltage fluctuation, harmonic waves, flicker and the like are brought after new energy grid connection, the electric energy quality is reduced, and because the new energy is far away from a load center, long-distance power transmission is often needed, and the risk of direct current conversion failure and the like exists. The three angles can more comprehensively identify the system security risk.
Furthermore, the defects of large calculation amount of building a judgment matrix and performing consistency check by a basic analytic hierarchy process are considered, order relation is introduced and clustering analysis is improved, the order relation is introduced, the relative importance assignment times of experts can be reduced, the steps of building the judgment matrix and performing consistency check are omitted, and the calculation amount is greatly reduced; the cluster analysis is introduced, so that the adverse effect caused by too strong subjectivity of experts can be reduced by eliminating the experts with the largest scoring deviation.
Furthermore, a medium-and-long-term power market operation risk comprehensive evaluation model considering large-scale energy grid connection is established based on an improved analytic hierarchy process and a fuzzy comprehensive evaluation method, and the model can evaluate the medium-and-long-term power market operation risk considering large-scale new energy grid connection under the conditions of small calculated amount and high accuracy, determine the risk level of the medium-and-long-term power market operation risk, identify the main risk source of the medium-and-long-term power market operation risk, and further adopt corresponding risk suppression measures to reduce the risk.
Furthermore, the operation risk level of the medium-and-long-term power market considering the large-scale energy grid connection is determined according to the maximum membership principle, and the operation risk level of the medium-and-long-term power market considering the large-scale energy grid connection can be simply and intuitively determined according to the maximum membership principle.
In summary, for the medium-and-long-term power market operation risk considering the large-scale energy grid connection, the medium-and-long-term power market operation risk comprehensive assessment method considering the large-scale new energy grid connection, which is provided by the invention, can scientifically and effectively assess the medium-and-long-term power market operation risk level and determine a main risk source so as to take relevant risk suppression measures to reduce risks.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention provides a method for evaluating the operation risk of a medium-and-long-term power market in consideration of large-scale new energy grid connection, which fully considers various risks in the operation process of the medium-and-long-term power market, such as power price fluctuation risk, power supply shortage risk, credit risk, system safety risk and the like, establishes a comprehensive evaluation method for the operation risk of the medium-and-long-term power market in consideration of large-scale new energy grid connection based on an improved analytic hierarchy process and a fuzzy evaluation method, can scientifically and effectively evaluate the operation risk level of the medium-and-long-term power market, can clearly know main risk sources, and can further reduce risks by taking corresponding risk inhibition measures according to the risk sources. The invention improves the safety and reliability of medium-and long-term electric power market operation, and can reform assistance for a new round of electric power system in China.
Referring to fig. 1, the method for evaluating the operation risk of the long-term power market in the large-scale new energy grid connection process includes the following steps:
s1, establishing an index system including a target layer, a criterion layer and a scheme layer and taking account of the large-scale new energy grid-connected medium and long term electric power market operation risk assessment, wherein the index system includes an electricity price fluctuation risk, an electric power supply shortage risk, a credit risk, a system safety risk and the like;
based on the principles of scientificity, comprehensiveness, systematicness and the like, the invention establishes a medium-and-long-term power market operation risk comprehensive assessment index system considering large-scale new energy grid connection, and the system comprises three layers as shown in table 1.
Table 1 shows medium and long term power market operation risk evaluation index system for large-scale new energy grid connection
The indexes of the electricity price fluctuation risk B1 containing new energy are specifically as follows:
since the electricity cannot be efficiently stored in a large scale, the price of electricity has strong fluctuation, and each participant in the market faces a great risk. The risk factors affecting the fluctuation of electricity prices are identified as follows:
c11: electric power market force risk
The generator exercising market force will result in electricity prices above the short term marginal production costs. Market forces can be expressed as the extent to which the actual electricity prices of the market are above the marginal production costs of the power plant, i.e.:
wherein p is the actual electricity price of the market; cMIs a marginal production cost for the power plant.
C12: risk of market imbalance
In the case of unbalance of power supply and demand, the price of electricity fluctuates, and C12 can be expressed as the deviation degree between the actual supply power and the actual load.
C13: risk of power generation cost fluctuation
Due to the influence of coal price and emission charge, the power generation cost of traditional power generation enterprises fluctuates to influence the electricity price. C13 may be expressed as the fluctuation in coal purchase costs for power generation enterprises.
C14: risk of fluctuation of electricity price caused by new energy
The marginal production cost of new energy power generation is lower than that of conventional power generation, so that the expansion of the power generation scale can reduce the price of the system. On the other hand, the inverse peak regulation characteristic of the wind power improves the electricity price difference in the load peak-valley period, and is beneficial to power generation businesses to make market force, and the fluctuation of the electricity price is enhanced due to the participation of large-scale new energy. C14 may be measured by the difference in electricity prices at peak and valley periods of load with new sources participating in the electricity market.
The index of the risk of power supply shortage B2 containing new energy is specifically as follows:
the economic society of China develops rapidly, the electricity demand of the society increases year by year, and if insufficient reserve capacity is available, the shortage of power supply is easy to occur. For example, in the case of a new energy province, the risk factors affecting the shortage of power supply are identified as follows:
c21: risk of contradiction between power supply and demand
During the peak load period, the power demand sharply rises to aggravate the power supply and demand contradiction; c21 expresses the degree of deviation between the amount of electricity supplied during peak load periods and the actual load.
C22: generator retention risk
The generator retention risk can be expressed by the retention ratio, i.e.:
in the formula: c is the generating capacity of the power plant and q is the actual declared power supplied by the power plant in the power market.
C23: risk of electrical obstruction in the output
The risk of transmission blockage is exacerbated by frequent changes in the electricity generation quotes in the electricity market, C33 being expressed in terms of the blockage rate of the key transmission segment, namely:
in the formula: t iscThe total blocking time of key power transmission segments in a period of time; t ismRefers to the electricity market operating time in this period.
C24: risk of new energy supply fluctuation
The new energy output is influenced by weather and the like, and has the characteristics of obvious randomness, volatility, intermittence, low utilization hours and the like, and the mature wind power introduced by the current commerce often has a reverse peak shaving characteristic, namely the wind power has the characteristics of large wind at night and small wind at day, the wind power output variation trend is basically opposite to the load variation trend of a power system, so that a large amount of wind has to be abandoned at the night low-valley load period. The power supply shortage risk is aggravated by the defect that the new energy cannot be continuously supplied; c24 is expressed as the standard deviation of the predicted output of the new energy source.
The index credit risk B3 is specifically:
the electric power market credit risk refers to the possibility that one party in the electric power market credit transaction will default in a certain time and the degree of possible loss to the other party under the action of various uncertain factors; risk factors affecting credit risk are identified as follows:
c31: risk of information asymmetry
The electric power market information resources mastered by two parties of electric power market credit transaction are different, one party of the transaction has clear understanding on the true situation of the party, and the other party of the transaction is difficult to obtain the true information on the aspect. The asymmetry of the information of the two parties to the transaction brings credit risk, and C31 can be expressed by the difference of the information of the two parties to the transaction, namely:
C31=|I1-I2|
in the formula: i is1Information indicating the subject of the transaction 1, I2Indicating information grasped by the transaction body 2.
C32 risk of insecurity of rules and law
If no sound power market rules and laws exist, the default cost is very low; once the expected revenue of a breach is higher than the cost of the breach, the transaction member is likely to opt for the breach, posing a credit risk to the party on guard. C32 may be measured by cost of default.
C33: risk of economic decline
When the economy of the country is declined, the repayment capacity of the user is reduced, the default probability is improved, and the credit risk is increased; c33 may be expressed in terms of the rate of change of GDP.
The indexes of the system safety risk B4 containing new energy are specifically as follows:
the premise of stable operation of the power market is that a power system operates safely. The electric power system accident can not only endanger the life and property safety of people, but also influence the stability of the society if serious. Risk factors affecting system security risk are identified as follows:
c41: risk of mode of operation
The electric power market transaction becomes the basis of dispatching operation, the operation mode of an electric power system is more complex, and many unpredictable operation modes threaten the safety and stability of the electric power grid, cause the potential safety hazard of the electric power grid and influence the safe operation of the electric power system; c41 is expressed as:
wherein, TdOperation side for representing safe and stable operation of threat system in a period of timeSum of the times of the formulae.
C42: coordination risk between power plant and grid dispatch
After the power grid is separated, the power plant can be adjusted without following the instructions of the power grid company, so that the safe operation of the power grid faces certain risks; c42 is expressed as:
wherein, ToRepresenting the sum of the times over which the power plant does not hear the grid dispatching instructions.
C43: safety risk of new energy grid connection
After the large-scale new energy power generation power is injected into a power grid, various problems such as voltage fluctuation, harmonic waves, flicker and the like can be caused, the quality of electric energy is reduced, the frequency stability and the transient stability of a power system can be influenced, and the safety of the power grid is endangered. In addition, as the new energy is far away from the load center, long-distance power transmission is often performed by using a trans-regional and trans-provincial power grid, the reliability of the long-distance power transmission is low, and the risk of direct current conversion failure exists, so that the safe operation of a power system is influenced; c43 is expressed in terms of the degree of degradation in power quality.
S2, determining the order relation of the indexes according to the relative importance degree of each layer of indexes, representing the relative importance scale of the indexes by a 1-9 scale method, and determining the primary weight result of the scoring;
the experts determine the order relation of the indexes according to the relative importance degree among the indexes of each layer, represent the relative importance scale of the indexes based on a 1-9 scale method shown in the table 2, and further determine the preliminary weight result scored by each expert.
TABLE 21-9 Scale method
S3, scoring the index set by each expert, eliminating the score of the expert with the maximum scoring deviation, and taking the arithmetic mean value of the weights given by the remaining experts to obtain the final index weight;
with RiAnd RjRespectively representing experts i and j as index sets (U) of a certain layer in an index system1,U2,…,Um) The similarity of the assigned weights of two experts is NijIt can be expressed as:
if n experts are scored for the index set, the similarity of the scores of the expert i and other experts is as follows:
maximum wiThe expert corresponding to the value is the expert with the largest scoring deviation, so the scoring of the expert is eliminated to reduce the influence of too strong subjectivity of the expert, and the arithmetic average value of the weighted weights of the rest experts is the final index weight R ═ (R ═ R-1,R2,…,Rm)。
S4, dividing the evaluation grade domain of the medium and long term electric power market operation risk index system considering large-scale new energy grid connection into high risk, danger, comparative danger, common and comparative safety;
when an evaluation grade domain is determined for a medium-long term power market operation risk index system considering large-scale new energy grid connection, 5 evaluation grades are selected, and the evaluation grade domain is V ═ V (V ═ V)1,V2,V3,V4,V5) Respectively, high risk, dangerous, comparatively dangerous, general, comparatively safe.
S5, establishing a fuzzy relation matrix and carrying out fuzzy synthesis to obtain a final fuzzy evaluation vector;
based on fuzzy statistical method, that is, the expert votes each index under each risk level, then calculates the frequency of the index at each risk level to construct a fuzzy relation matrix S, and then the fuzzy relation matrix S is combined with the final index weight vector R (R)1,R2,…,Rm) Fuzzy synthesis is performed according to the following formula to obtain a final fuzzy evaluation vector B ═ SE,1,SE,2,SE,3,SE,4,SE,5)。
And S6, determining the final medium and long term electric power market operation risk assessment result.
Determining the final evaluation result according to the maximum membership rule, namely if:
the operational risk rating of the medium and long term electricity market is then rating r.
In addition, the risk level of each risk index such as the power price fluctuation risk, the power supply shortage risk, the credit risk, the system safety risk and the like can also be determined by the maximum membership degree principle.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The comprehensive evaluation method for the operation risk of the medium-and-long-term power market, which is provided by the invention and takes the large-scale new energy grid connection into consideration, is applied to the operation risk evaluation of the medium-and-long-term power market, has a good evaluation effect, can effectively determine the operation risk level and the main risk source of the medium-and-long-term power market, and correspondingly provides risk inhibition measures.
Referring to table 3, taking an electric power market in a certain test electric power system as an example, the risk level of the electric power market is comprehensively evaluated by using the comprehensive evaluation method for the operation risk of the medium-long term electric power market considering the large-scale new energy grid connection provided by the present invention. The comprehensive evaluation table (expert vote/frequency) of the long-term operation risks in the test power market shown in table 3 is obtained by expert scoring.
Table 3 test electric power market medium and long term operation risk comprehensive evaluation table
From table 3, the fuzzy relation matrix of the second layer index can be obtained:
and the weights of the second layer index system are determined by expert scoring:
R1=(0.4966 0.3363 0.0616 0.1054)
R2=(0.5529 0.0922 0.1302 0.2247)
R3=(0.4066 0.4053 0.1881)
R4=(0.3290 0.1594 0.5116)
the weights of the first layer index system are:
R=(0.3946 0.2044 0.2354 0.1656)
the fuzzy evaluation vector of the second layer index obtained by fuzzy synthesis is as follows:
B1=(0.4322 0.1345 0.3373 0.0686 0.0272)
B2=(0.1236 0.2200 0.2247 0.3775 0.0542)
B3=(0.0812 0.3593 0.1592 0.3032 0.0971)
B4=(0 0.0841 0.2512 0.5146 0.1501)
the fuzzy evaluation vector of the operation risk of the medium-long term power market is as follows:
A=(0.2149 0.1966 0.2581 0.2608 0.0695)
and testing the operation risk level grade of the electric power market to be general according to the maximum membership principle, wherein the risk of fluctuation of electricity price is high, the risk of credit, the risk of shortage of electric power supply and the risk of system safety are general. It can be seen from the above that the main sources of the operation risk of the electric power market are the power price fluctuation risk caused by the electric power market force, the asymmetric and unfirm information rule, and the credit risk caused by law, so that the operation risk of the electric power market can be correspondingly restrained and tested by means of restraining the electric power market force, controlling the credit risk and the like.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (10)
1. The method for evaluating the operation risk of the medium-term and long-term power market in the large-scale new energy grid connection is characterized by comprising the following steps of:
s1, establishing an index system including a target layer, a criterion layer and a scheme layer and taking account of the large-scale new energy grid-connected medium and long term electric power market operation risk assessment, wherein the index system includes an electricity price fluctuation risk, an electric power supply shortage risk, a credit risk and a system safety risk;
s2, determining the order relation of the indexes according to the relative importance degree of each layer of indexes, representing the relative importance scale of the indexes based on a 1-9 scale method, and determining the primary weight result of the scoring;
s3, scoring the index set by each expert, eliminating the score of the expert with the maximum scoring deviation, and taking the arithmetic mean value of the weights given by the remaining experts to obtain the final index weight;
s4, dividing the evaluation grade domain of the medium and long term electric power market operation risk index system considering large-scale new energy grid connection into high risk, danger, comparative danger, common and comparative safety;
s5, establishing a fuzzy relation matrix and carrying out fuzzy synthesis to obtain a final fuzzy evaluation vector;
and S6, determining the final medium and long term electric power market operation risk assessment result.
2. The method according to claim 1, wherein in step S1, the criterion layer comprises a risk of fluctuation of electricity prices including new energy B1; risk of shortage of electric power supply containing new energy B2; credit risk B3 and new energy-containing system security risk B4;
the scheme layer corresponding to the electricity price fluctuation risk B1 containing the new energy comprises an electric power market force risk C11; risk of market imbalance C12; the risk of rising power generation cost C13 and the risk of fluctuation of electricity price caused by new energy C14;
the scheme layer corresponding to the power supply shortage risk B2 containing new energy comprises a power supply and demand contradiction risk C21; generator retention risk C22; an output resistor plug risk C23 and a new energy supply fluctuation risk C24;
the scheme layer corresponding to the credit risk B3 comprises an information asymmetry risk C31; rule, risk of legal immaturity C32 and risk of economic decline C33;
the scheme layer corresponding to the system safety risk B4 containing the new energy comprises an operation mode risk C41; coordination risk between power plant and grid dispatch C42 and safety risk of new energy grid connection C43.
3. The method of claim 2, wherein the electric market risk C11 is:
wherein p is the actual electricity price of the market; cMMarginal production cost for power plants;
the market supply and demand unbalance risk C12 is the deviation degree between the actual supply electric quantity and the actual load;
the power generation cost fluctuation risk C13 is the fluctuation degree of the coal purchase cost of the power generation enterprises;
the risk of fluctuation in electricity prices C14 caused by the new energy is measured by the difference in electricity prices at the peak-valley period of load in the case where the new energy participates in the electricity market.
4. The method according to claim 2, wherein the risk of contradiction between power supply and demand C21 is the degree of deviation between the amount of supply power and the actual load during peak load period;
the generator retention risk C22 is:
wherein c is the generating capacity of the power plant, and q is the actual declared electric quantity supplied by the power plant in the electric power market;
the output resistance blocking risk C33 is the blocking rate of a key power transmission segment, and specifically includes:
wherein, TcThe total blocking time of key power transmission segments in a period of time; t ismThe electric power market operation time in the period of time;
the risk of fluctuation of new energy supply C24 is expressed as a standard deviation of the predicted output of new energy.
5. The method according to claim 2, wherein the risk of information asymmetry C31 is the information difference between the two parties to the transaction, and specifically comprises:
C31=|I1-I2|
wherein, I1Information indicating the subject of the transaction 1, I2Information indicating the transaction body 2;
rule, law insecure risk C32 is measured by default cost;
the economic decline risk C33 is expressed in terms of GDP rate of change.
6. The method of claim 2, wherein the operational mode risk is:
wherein, TdRepresenting the sum of the time of occurrence of an operation mode threatening the safe and stable operation of the system in a period of time;
the coordination risk C42 between the power plant and the grid dispatch is:
wherein, ToRepresenting the sum of the time during which the power plant does not listen to the grid dispatching command for a period of time;
the safety risk C43 of new energy grid connection is represented by the reduction degree of the power quality.
7. The method of claim 1, wherein in step S3, R is usediAnd RjIndicating that experts i and j are index sets U of a certain layer in an index system1,U2,…,UmThe assigned weight, N, of the weight assigned by experts i and jijComprises the following steps:
the similarity degree of the scores of the expert i and other experts is as follows:
maximum removal of wiThe experts with corresponding values are scored, and the arithmetic mean value of the weights given by the rest experts is the final index weight R ═ R (R)1,R2,…,Rm)。
10. The method according to claim 9, wherein the risk levels of risk of fluctuation of electricity prices, risk of shortage of electricity supply, risk of credit and risk of system safety are determined according to the principle of maximum membership.
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Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111882159A (en) * | 2020-06-24 | 2020-11-03 | 北京工业大学 | PPP urban rail transit project risk evaluation method based on SVM |
CN113505983A (en) * | 2021-07-07 | 2021-10-15 | 广东电网有限责任公司 | Energy industry chain monitoring and early warning system |
CN116894164A (en) * | 2023-09-11 | 2023-10-17 | 湖北华中电力科技开发有限责任公司 | Intelligent management method for data of power platform |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150127425A1 (en) * | 2013-11-07 | 2015-05-07 | Palo Alto Research Center Incorporated | Strategic modeling for economic optimization of grid-tied energy assets |
CN105719062A (en) * | 2016-01-15 | 2016-06-29 | 西安交通大学 | Method for assessing risks and weak links of power grid, with double fault probability characteristics considered |
CN107732970A (en) * | 2017-11-10 | 2018-02-23 | 国网甘肃省电力公司经济技术研究院 | A kind of static security probability evaluation method of failure of new-energy grid-connected power system |
CN107895241A (en) * | 2017-12-18 | 2018-04-10 | 华北电力大学 | A kind of sale of electricity company purchase sale of electricity operational decision making and methods of risk assessment |
CN108805423A (en) * | 2018-05-25 | 2018-11-13 | 国网山东省电力公司聊城供电公司 | A kind of power distribution network methods of risk assessment based on distributed new access |
CN109118121A (en) * | 2018-09-13 | 2019-01-01 | 中国电力科学研究院有限公司 | Go out clear mode evaluation method based on the electricity market for improving PageRank |
CN109559012A (en) * | 2018-10-18 | 2019-04-02 | 国网山东省电力公司经济技术研究院 | Power network planning scheme integrated evaluating method under the conditions of a kind of wind-electricity integration |
CN110111024A (en) * | 2019-05-21 | 2019-08-09 | 南方电网科学研究院有限责任公司 | Scientific and technological achievement market value evaluation method based on AHP fuzzy comprehensive evaluation model |
-
2019
- 2019-11-22 CN CN201911159304.1A patent/CN110942243A/en active Pending
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150127425A1 (en) * | 2013-11-07 | 2015-05-07 | Palo Alto Research Center Incorporated | Strategic modeling for economic optimization of grid-tied energy assets |
CN105719062A (en) * | 2016-01-15 | 2016-06-29 | 西安交通大学 | Method for assessing risks and weak links of power grid, with double fault probability characteristics considered |
CN107732970A (en) * | 2017-11-10 | 2018-02-23 | 国网甘肃省电力公司经济技术研究院 | A kind of static security probability evaluation method of failure of new-energy grid-connected power system |
CN107895241A (en) * | 2017-12-18 | 2018-04-10 | 华北电力大学 | A kind of sale of electricity company purchase sale of electricity operational decision making and methods of risk assessment |
CN108805423A (en) * | 2018-05-25 | 2018-11-13 | 国网山东省电力公司聊城供电公司 | A kind of power distribution network methods of risk assessment based on distributed new access |
CN109118121A (en) * | 2018-09-13 | 2019-01-01 | 中国电力科学研究院有限公司 | Go out clear mode evaluation method based on the electricity market for improving PageRank |
CN109559012A (en) * | 2018-10-18 | 2019-04-02 | 国网山东省电力公司经济技术研究院 | Power network planning scheme integrated evaluating method under the conditions of a kind of wind-electricity integration |
CN110111024A (en) * | 2019-05-21 | 2019-08-09 | 南方电网科学研究院有限责任公司 | Scientific and technological achievement market value evaluation method based on AHP fuzzy comprehensive evaluation model |
Non-Patent Citations (1)
Title |
---|
王钦等: "基于模糊集理论和层次分析法的电力市场综合评价", 《电力系统自动化》 * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111882159A (en) * | 2020-06-24 | 2020-11-03 | 北京工业大学 | PPP urban rail transit project risk evaluation method based on SVM |
CN113505983A (en) * | 2021-07-07 | 2021-10-15 | 广东电网有限责任公司 | Energy industry chain monitoring and early warning system |
CN116894164A (en) * | 2023-09-11 | 2023-10-17 | 湖北华中电力科技开发有限责任公司 | Intelligent management method for data of power platform |
CN116894164B (en) * | 2023-09-11 | 2023-11-28 | 湖北华中电力科技开发有限责任公司 | Intelligent management method for data of power platform |
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