CN111950782A - Power market scheduling method based on fuzzy evaluation - Google Patents
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Abstract
The invention relates to a power market scheduling method based on fuzzy evaluation, which comprises the following steps: s1: establishing an electric power market evaluation index system, and establishing an evaluation index hierarchical model, wherein indexes of all layers in the evaluation index hierarchical model are correlated; s2: constructing a judgment matrix, and determining fuzzy weight vectors of each level of evaluation indexes; s3: acquiring a membership matrix of an evaluation index grading model; s4: calculating fuzzy comprehensive evaluation scores of all the power market rules by using the primary evaluation indexes, and selecting an optimal power market rule according to the scores; s5: the optimal power market rule is implemented, and the dispatching of each user in the power market is realized.
Description
Technical Field
The invention relates to power market rule evaluation, in particular to a power market scheduling method based on fuzzy evaluation.
Background
Since 2015, according to a system architecture of 'managing the middle and releasing two ends', the construction process of the electric power market in China is accelerated, diversified market main patterns are formed, and the consciousness of market main bodies is continuously enhanced.
Up to now, 28 trading institutions in the national grid management area are all built, a public and transparent trading platform is built, the participation quantity and range of market main bodies are gradually enlarged, the market trading electric quantity is continuously increased, the intra-provincial and inter-provincial trading varieties are gradually enriched, and the consumption level of clean energy is continuously improved. At present, the middle-long term electric power trading in China tends to be mature after years of development, relevant rules of the middle-long term trading in China have been issued by each province, market modes are established, and trading is carried out, so that dispatching coordination of each user (including a power generation party, a power purchasing party, an investor and a supervision organization) and each resource in the electric power market is realized.
However, according to the current situation, according to incomplete statistics, the transaction rules of each province are more than 300 in total, the difference between the transaction rules is large, and along with the more frequent transaction between the provinces, a complete power market comprehensive assessment index system needs to be established urgently to evaluate the rule scheme of the power market, so that the optimal power market rule is selected or formulated, the overall and unified power market scheduling scheme is realized, the power market transaction requirements in and between the provinces are met, the power market economic benefits are improved, and particularly the economic benefits of the inter-province power market transaction are improved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a fuzzy evaluation-based power market scheduling method for improving the economic benefit of a power market.
The purpose of the invention can be realized by the following technical scheme:
a power market scheduling method based on fuzzy evaluation comprises the following steps:
s1: establishing an electric power market evaluation index system, and establishing an evaluation index hierarchical model, wherein indexes of all layers in the evaluation index hierarchical model are correlated;
s2: constructing a judgment matrix, and determining fuzzy weight vectors of each level of evaluation indexes;
s3: acquiring a membership matrix of an evaluation index grading model;
s4: calculating fuzzy comprehensive evaluation scores of all the power market rules by using the primary evaluation indexes, and selecting an optimal power market rule according to the scores;
s5: and implementing the optimal power market rule to realize the scheduling of each user in the power market.
Further, the evaluation index grading model is divided into three grades, including a first-grade evaluation index, a second-grade evaluation index and a third-grade evaluation index.
Further, the step S2 specifically includes:
s21: selecting a secondary evaluation index;
s22: performing empowerment analysis on the secondary evaluation index based on a Delphi method;
s23: establishing a judgment matrix according to the hierarchical analysis weighting result;
s24: performing consistency verification on the judgment matrix, if the judgment matrix passes the consistency verification, executing the step S25, otherwise, returning to execute the step S22;
s25: and solving the eigenvector corresponding to the maximum characteristic root of the judgment matrix, and determining fuzzy weight vectors of evaluation indexes at all levels.
Further, the consistency verification is calculated as:
wherein, C.R. is the consistency ratio of the judgment matrix, C.I. is the consistency index of the judgment matrix, and R.I. is the same-order average consistency index of the judgment matrix;
and if the consistency ratio C.R. of the matrix is less than 0.1, the matrix is judged to pass consistency verification.
Further, the step S4 specifically includes:
s41: acquiring a first-level evaluation index fuzzy weight vector and a membership matrix;
s42: calculating to obtain a fuzzy comprehensive evaluation result vector;
s43: calculating the evaluation score of each power market rule by adopting the maximum membership degree or weighted average;
s44: and selecting the power market rule with the highest evaluation score as the optimal power market rule.
Furthermore, the calculation formula of the fuzzy comprehensive evaluation result vector F is:
wherein (a)11，…,a1n) Is a fuzzy weight vector of a first-level evaluation index,
is a membership matrix, n is the number of first-level evaluation indexes, m is the number of second-level evaluation indexes, a11Fuzzy weight of the first primary evaluation index, a1nFuzzy weight of nth primary evaluation index, b11As membership associated weight between the first primary evaluation index and the first secondary evaluation index, b1nAs membership associated weight between the nth primary evaluation index and the first secondary evaluation index, bm1A membership associated weight before the first primary evaluation index and the m secondary evaluation index, bmnThe nth primary evaluation index and the mth secondary evaluation indexPrevious membership association weights.
Further, the first-level evaluation index comprises an electric power market index, and the electric power market index comprises a fairness index, a safety index, an economic index and an environmental protection index.
Further, the secondary indexes include an active index, an electric power supply and demand index, a development index, an electric power resource optimization index, a benefit index and a renewable energy consumption index, the fairness index is respectively correlated with the active index, the electric power supply and demand index, the development index and the electric power resource optimization index, the safety index is respectively correlated with the active index, the electric power supply and demand index, the electric power resource optimization index and the renewable energy consumption index, the economy index is respectively correlated with the active index, the electric power supply and demand index, the development index, the electric power resource optimization index and the benefit index, and the environmental protection index is respectively correlated with the electric power supply and demand index, the electric power resource optimization index and the renewable energy consumption index.
More particularly, the three-level indexes include:
transaction frequency, transaction electric quantity, transaction price and market participation degree which are respectively associated with the active indexes;
market demand-supply ratio, Top-m share index and HHI index associated with the power demand-supply index, respectively;
the power generation amount of the power generation party, the power purchase amount of the power purchase party and the potential investment amount of the investment supplier are respectively associated with the development indexes;
market extra-high voltage trading indexes, green development indexes and clean energy inter-provincial electric power trading volume which are respectively associated with the electric power resource optimization indexes;
the loss and the benefit of the power generation party and the cost change of the power purchasing party are respectively associated with the benefit indexes;
the total renewable energy and the non-hydroelectric consumption liability weight respectively associated with the renewable energy consumption index.
The calculation formula of the power generation side profit and loss is as follows:
power generation party's profit and loss (price of power on the net pole-price of power for market trade) x quantity of electricity for market trade
The calculation formula of the cost change of the electricity purchasing party is as follows:
cost change of power purchasing party (catalog price of electricity-market price of electricity) x market trading electricity quantity
Compared with the prior art, the invention has the following advantages:
1) according to the invention, various power market rules of each province power market can be evaluated through fuzzy evaluation, and the evaluation score is finally obtained, so that the optimal power market rule is selected as the inter-province power market transaction rule, the function of scheduling individual users of the power market is played, and the economic benefit of the inter-province power market transaction is finally improved;
2) according to the method, an evaluation index grading model is constructed according to an electric power market index system construction principle and an electric power market rule formulation principle, fairness, safety, economy and environmental protection evaluation indexes are selected as first-level evaluation indexes, an electric power market comprehensive evaluation index system with wide dimensionality and strong comprehensiveness is established, an evaluation result with high reliability and credibility is obtained by utilizing fuzzy comprehensive evaluation, and the effectiveness of the method is improved.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
As shown in fig. 1, the invention provides a power market scheduling method based on fuzzy evaluation, which comprises the following steps:
s1: establishing an electric power market evaluation index system, establishing an evaluation index hierarchical model, and correlating indexes of all layers in the evaluation index hierarchical model;
s2: constructing a judgment matrix, and determining fuzzy weight vectors of each level of evaluation indexes;
the method specifically comprises the following steps:
s21: selecting a secondary evaluation index;
s22: performing empowerment analysis on the secondary evaluation index based on a Delphi method;
s23: establishing a judgment matrix according to the hierarchical analysis weighting result;
s24: performing consistency verification on the judgment matrix, if the judgment matrix passes the consistency verification, executing the step S25, otherwise, returning to execute the step S22;
wherein, the calculation formula of the consistency verification is as follows:
wherein, C.R. is the consistency ratio of the judgment matrix, C.I. is the consistency index of the judgment matrix, and R.I. is the same-order average consistency index of the judgment matrix; and if the consistency ratio C.R. of the matrix is less than 0.1, the matrix is judged to pass consistency verification.
S25: and solving the eigenvector corresponding to the maximum characteristic root of the judgment matrix, and determining fuzzy weight vectors of evaluation indexes at all levels.
S3: acquiring a membership matrix of an evaluation index grading model;
s4: calculating fuzzy comprehensive evaluation scores of all the power market rules by using the primary evaluation indexes, and selecting an optimal power market rule according to the scores;
the method specifically comprises the following steps:
s41: acquiring a first-level evaluation index fuzzy weight vector and a membership matrix;
s42: calculating to obtain a fuzzy comprehensive evaluation result vector;
s43: calculating the evaluation score of each power market rule by adopting the maximum membership degree or weighted average;
s44: and selecting the power market rule with the highest evaluation score as the optimal power market rule.
The calculation formula of the fuzzy comprehensive evaluation result vector F is as follows:
wherein (a)11，…,a1n) Is a fuzzy weight vector of a first-level evaluation index,
is a membership matrix, n is the number of first-level evaluation indexes, m is the number of second-level evaluation indexes, a11Fuzzy weight of the first primary evaluation index, a1nFuzzy weight of nth primary evaluation index, b11As membership associated weight between the first primary evaluation index and the first secondary evaluation index, b1nAs membership associated weight between the nth primary evaluation index and the first secondary evaluation index, bm1A membership associated weight before the first primary evaluation index and the m secondary evaluation index, bmnThe nth primary evaluation index and the membership associated weight before the mth secondary evaluation index are used as the membership associated weight.
S5: and implementing the optimal power market rule to realize the scheduling of each user in the power market.
The evaluation index grading model is divided into three grades, including a first-grade evaluation index, a second-grade evaluation index and a third-grade evaluation index. The first-level evaluation index comprises an electric power market index, and the electric power market index comprises a fairness index, a safety index, an economic index and an environmental protection index.
The secondary indexes include an activity index, an electric power supply and demand index, a development index, an electric power resource optimization index, a benefit index and a renewable energy consumption index.
The fairness index is correlated with the power supply and demand index, the safety index is correlated with the power resource optimization index and the development index, the economic index is correlated with the active index and the benefit index, and the environmental protection index is correlated with the renewable energy consumption index.
The three-level indexes comprise: transaction frequency, transaction electric quantity, transaction price and market participation degree which are respectively associated with the active indexes; market demand-supply ratio, Top-m share index and HHI index associated with the power demand-supply index, respectively; the power generation amount of the power generation party, the power purchase amount of the power purchase party and the potential investment amount of the investment supplier are respectively associated with the development indexes; market extra-high voltage trading indexes, green development indexes and clean energy inter-provincial electric power trading volume which are respectively associated with the electric power resource optimization indexes; the loss and the benefit of the power generation party and the cost change of the power purchasing party are respectively associated with the benefit indexes; the total renewable energy and the non-hydroelectric consumption liability weight respectively associated with the renewable energy consumption index.
Wherein, the calculation formula of the power generation side profit and loss is as follows:
power generation party's profit and loss (price of power on the net pole-price of power for market trade) x quantity of electricity for market trade
The calculation formula of the cost change of the power purchasing party is as follows:
cost change of power purchasing party (catalog price of electricity-market price of electricity) x market trading electricity quantity
The specific calculation example given in this embodiment for a certain power market rule evaluation is as follows:
firstly, a third-level evaluation index is obtained, and a second-level evaluation index is constructed through the correlation between the third-level evaluation index and the second-level evaluation index.
Then determining fuzzy weight vectors of the evaluation indexes, and weighting by using an analytic hierarchy process based on an expert consultation method aiming at each stage of evaluation indexes.
In this embodiment, the judgment matrix a is established based on the established secondary evaluation index by using an expert consulting method11：
Calculated and judged matrix A11The consistency index c.r. = c.i./r.i. = 0 < 0.1, pass consistency check.
And (3) using MATLAB to obtain a feature vector corresponding to the maximum feature root, and obtaining a secondary index association weight vector after normalization treatment:
T21＝(0.21，0.13，0.21，0.19)，T22＝(0.56，0.18，0.12)，
T23＝(0.56，0.21，0.16)，T24＝(0.14，0.39，0.39)，
T25＝(0.14，0.45)，T26＝(0.25，0.50)。
weight vector of primary index:
T11＝(0.24，0.52，0.15，0.09)。
and then obtaining a membership matrix of the first-level index and the second-level index, and finally obtaining a fuzzy evaluation result vector of the power market:
and finally, calculating the evaluation score of each power market rule by adopting the maximum membership degree or weighted average.
In the weighted average calculation, the weights of all items in the fuzzy evaluation result vector of the power market are (0.74,0.86,0.93,0.92,0.59 and 0.75), the value is obtained by associating the weight vector with the secondary index, and the final evaluation score is 1.23 points;
the maximum membership simulation result shows that: the weights of the four primary indexes of the electric power market are respectively 0.24, 0.52, 0.15 and 0.09, then the weight of the safety index is the largest, which can be regarded as the largest influence factor for determining the final result, and the score can be evaluated according to the safety index, which indicates that the electric power market needs to ensure the electric power safety in the market, and safe and reliable electric power supply and transaction are the foundation for the existence and development of the electric power market.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A power market scheduling method based on fuzzy evaluation is characterized by comprising the following steps:
s1: establishing an electric power market evaluation index system, and establishing an evaluation index hierarchical model, wherein indexes of all layers in the evaluation index hierarchical model are correlated;
s2: constructing a judgment matrix, and determining fuzzy weight vectors of each level of evaluation indexes;
s3: acquiring a membership matrix of an evaluation index grading model;
s4: calculating fuzzy comprehensive evaluation scores of all the power market rules by using the primary evaluation indexes, and selecting an optimal power market rule according to the scores;
s5: and implementing the optimal power market rule to realize the scheduling of each user in the power market.
2. The electric power market scheduling method based on fuzzy evaluation according to claim 1, wherein the evaluation index grading model is divided into three grades, including a first-grade evaluation index, a second-grade evaluation index and a third-grade evaluation index.
3. The fuzzy evaluation based power market scheduling method according to claim 2, wherein the step S2 specifically comprises:
s21: selecting a secondary evaluation index;
s22: performing empowerment analysis on the secondary evaluation index based on a Delphi method;
s23: establishing a judgment matrix according to the hierarchical analysis weighting result;
s24: performing consistency verification on the judgment matrix, if the judgment matrix passes the consistency verification, executing the step S25, otherwise, returning to execute the step S22;
s25: and solving the eigenvector corresponding to the maximum characteristic root of the judgment matrix, and determining fuzzy weight vectors of evaluation indexes at all levels.
4. The fuzzy evaluation-based power market scheduling method according to claim 3, wherein the consistency verification is calculated by:
wherein, C.R. is the consistency ratio of the judgment matrix, C.I. is the consistency index of the judgment matrix, and R.I. is the same-order average consistency index of the judgment matrix;
and if the consistency ratio C.R. of the matrix is less than 0.1, the matrix is judged to pass consistency verification.
5. The fuzzy evaluation based power market scheduling method according to claim 2, wherein the step S4 specifically comprises:
s41: acquiring a first-level evaluation index fuzzy weight vector and a membership matrix;
s42: calculating to obtain a fuzzy comprehensive evaluation result vector;
s43: calculating the evaluation score of each power market rule by adopting the maximum membership degree or weighted average;
s44: and selecting the power market rule with the highest evaluation score as the optimal power market rule.
6. The power market scheduling method based on fuzzy evaluation according to claim 5, wherein the calculation formula of the fuzzy comprehensive evaluation result vector F is:
wherein (a)11，…,a1n) Is a fuzzy weight vector of a first-level evaluation index,
is a membership matrix, n is the number of first-level evaluation indexes, m is the number of second-level evaluation indexes, a11Fuzzy weight of the first primary evaluation index, a1nFor the nth first-level evaluation index modelWeight of blur, b11As membership associated weight between the first primary evaluation index and the first secondary evaluation index, b1nAs membership associated weight between the nth primary evaluation index and the first secondary evaluation index, bm1A membership associated weight before the first primary evaluation index and the m secondary evaluation index, bmnThe nth primary evaluation index and the membership associated weight before the mth secondary evaluation index are used as the membership associated weight.
7. The fuzzy evaluation-based power market scheduling method according to claim 2, wherein the primary evaluation index comprises a power market index, and the power market index comprises a fairness index, a safety index, an economy index and an environmental protection index.
8. The fuzzy evaluation-based power market scheduling method of claim 7, the secondary indexes comprise an active index, an electric power supply and demand index, a development index, an electric power resource optimization index, a benefit index and a renewable energy consumption index, the fairness index is respectively correlated with an active index, an electric power supply and demand index, a development index and an electric power resource optimization index, the safety index is respectively correlated with an active index, an electric power supply and demand index, an electric power resource optimization index and a renewable energy consumption index, the economic index is respectively correlated with an active index, an electric power supply and demand index, a development index, an electric power resource optimization index and a benefit index, the environmental protection index is respectively correlated with the power supply and demand index, the power resource optimization index and the renewable energy consumption index.
9. The electric power market scheduling method based on fuzzy evaluation according to claim 8, wherein the three-level indexes comprise:
transaction frequency, transaction electric quantity, transaction price and market participation degree which are respectively associated with the active indexes;
market demand-supply ratio, Top-m share index and HHI index associated with the power demand-supply index, respectively;
the power generation amount of the power generation party, the power purchase amount of the power purchase party and the potential investment amount of the investment supplier are respectively associated with the development indexes;
market extra-high voltage trading indexes, green development indexes and clean energy inter-provincial electric power trading volume which are respectively associated with the electric power resource optimization indexes;
the loss and the benefit of the power generation party and the cost change of the power purchasing party are respectively associated with the benefit indexes;
the total renewable energy and the non-hydroelectric consumption liability weight respectively associated with the renewable energy consumption index.
10. The electric power market scheduling method based on fuzzy evaluation according to claim 9, wherein the generator profit and loss is calculated by:
power generation party's profit and loss (price of power on the net pole-price of power for market trade) x quantity of electricity for market trade
The calculation formula of the cost change of the electricity purchasing party is as follows:
the change of the power purchasing cost is (catalog electricity price-market trade electricity price) multiplied by market trade electricity quantity.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580978A (en) * | 2020-12-17 | 2021-03-30 | 佰聆数据股份有限公司 | Power market member credit evaluation and credit label generation method |
| CN113554339A (en) * | 2021-08-05 | 2021-10-26 | 国网山东省电力公司经济技术研究院 | New energy power generator credit evaluation method and device, equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6047274A (en) * | 1997-02-24 | 2000-04-04 | Geophonic Networks, Inc. | Bidding for energy supply |
| US20040039622A1 (en) * | 2002-05-03 | 2004-02-26 | Masiello Ralph D. | Valuing and optimizing scheduling of generation assets |
| CN101751645A (en) * | 2008-12-19 | 2010-06-23 | 北京科东电力控制系统有限责任公司 | Method for searching and determining trading channel in three-class power market |
| CN105426994A (en) * | 2015-11-13 | 2016-03-23 | 国家电网公司 | Optimization selection method of power distribution network alternative construction projects |
| CN110738405A (en) * | 2019-09-29 | 2020-01-31 | 南方电网科学研究院有限责任公司 | Method and device for evaluating effectiveness of power market and storage medium |
-
2020
- 2020-07-31 CN CN202010757940.0A patent/CN111950782A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6047274A (en) * | 1997-02-24 | 2000-04-04 | Geophonic Networks, Inc. | Bidding for energy supply |
| US20040039622A1 (en) * | 2002-05-03 | 2004-02-26 | Masiello Ralph D. | Valuing and optimizing scheduling of generation assets |
| CN101751645A (en) * | 2008-12-19 | 2010-06-23 | 北京科东电力控制系统有限责任公司 | Method for searching and determining trading channel in three-class power market |
| CN105426994A (en) * | 2015-11-13 | 2016-03-23 | 国家电网公司 | Optimization selection method of power distribution network alternative construction projects |
| CN110738405A (en) * | 2019-09-29 | 2020-01-31 | 南方电网科学研究院有限责任公司 | Method and device for evaluating effectiveness of power market and storage medium |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580978A (en) * | 2020-12-17 | 2021-03-30 | 佰聆数据股份有限公司 | Power market member credit evaluation and credit label generation method |
| CN112580978B (en) * | 2020-12-17 | 2023-06-16 | 佰聆数据股份有限公司 | Power market member credit evaluation and credit label generation method |
| CN113554339A (en) * | 2021-08-05 | 2021-10-26 | 国网山东省电力公司经济技术研究院 | New energy power generator credit evaluation method and device, equipment and storage medium |
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