CN113077095B - Planned electric quantity determining method based on correction linear declaration and double-layer model - Google Patents
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Abstract
The invention discloses a planned electric quantity determining method based on correction linear declaration and a double-layer model. Comprising the following steps: step 1: firstly linearizing a reporting function of a generator set, and then correcting the linear reporting function through the influence of the quantized planned electric quantity; step 2: constructing a power transaction center planning electric quantity distribution double-layer model, wherein the upper-layer model takes the power transaction center as a main body to optimize a planning electric quantity distribution strategy; the lower layer model takes the generator set as a main body to optimize the self reporting coefficient; an optimal projected amount of power is determined. The method solves the problem of efficient and reasonable distribution of the planned electric quantity in the process of gradually discharging and developing the power consumption plan. Technical support can be provided for determining the planned electric quantity of the electric power transaction center, actual requirements are met, and the operation efficiency of the market can be effectively improved.
Description
Technical Field
The invention relates to the technical field of electric power markets, in particular to a planned electric quantity determining method based on a modified linear declaration and a double-layer model.
Background
In recent years, china continuously advances the reform of an electric power market, but because a large amount of investment cost of a generator set cannot be recovered in the initial stage of the reform, china adopts a plan-market double-rail mechanism, and the plan electric quantity is reduced year by year through orderly releasing a power generation plan. The reasonable planned electric quantity distribution proportion is a key for guaranteeing the smooth transition of the electric power market from a planned mode to a market mode, and the research plan electric quantity determining method is beneficial to improving the social overall benefit and has economic and practical application values.
Disclosure of Invention
The technical scheme adopted for solving the technical problems is as follows: the method for determining the planned electric quantity based on the correction linear declaration and the double-layer model comprises the following specific steps:
assuming that there are n gensets participating in the market, first the ith genset (i=1, 2, cost and reporting parameters are linearized, specifically expressed as:
wherein:representing the marginal power generation cost of the generator set i; />The declaration price of the generator set i is represented; a, a i And b i Representing the cost coefficient of the generator set i; />Representing the reporting coefficient; q (Q) i The power generation amount of the generator set i is shown.
As shown in fig. 1, the declaration of the generator set on the market should be greater than or equal to the corresponding marginal cost on the premise that the generator set is known to be distributed to the planned electric quantity. And further, a modified generator set linear declaration function can be obtained, which is specifically expressed as follows:
wherein:representing the corrected generator set declaration; />Representing the corrected reporting coefficient as a parameter to be determined; />Representing the planned power allocated to the generator set i, specifically expressed as:
wherein: q (Q) P Representing the total planned electrical quantity;the annual energy production upper limit of the ith generator set is indicated.
And (3) making:
and has the following steps:
wherein:represents the market electric quantity of the generator set i, Q M Representing the total market power. And then can obtainBy P MCP Unified representation, there are:
wherein: q (Q) U Representing the total load demand of the user, which is equal to the sum of the planned power and the market power.
Further constructing a double-layer model for planning electric quantity distribution of the electric power transaction center, wherein a schematic diagram of the model is shown in fig. 2. The upper model takes the electric power transaction center as a main body to optimize the distribution strategy of the planned electric quantity; the lower model takes a generator set in the market as a main body to optimize the self reporting coefficient. The method comprises the following specific steps:
(1) The power trading center first determines a planned power quantity Q P In the range, sampling at equal intervals with h as the precision;
(2) Q obtained for each sample P The method for determining the declaration information of each generator set by using the lower model comprises the following steps:
firstly, for the generator set i, firstly, counting an uncertainty set of reporting coefficients of other generator sets according to historical reporting data. Assume that generating set i predicts reporting coefficient of opponentIs +.>
In the plan-market double-track system, the generator set i determines its own reporting coefficient with the maximum of robust yields as a target. Benefit R of generator set i i Can be expressed as:
wherein:and->The electricity selling income of the planned electric quantity and the electricity selling income of the market electric quantity of the generator set i are respectively represented; c (C) i Representing the total power generation cost of the generator set i, c i Representing a fixed cost factor; p (P) P And the purchase price of the planned electric quantity is expressed and calculated by the government.
In the construction of a lower-layer generator set reporting decision model, decision variablesReporting coefficient, random variable +.>Representing an uncertain adversary reporting strategy, the objective function of the genset is to maximize the robust gain +.>Namely:
wherein:the declaration coefficient representing the generator set i is +.>Competitor reporting coefficient is->And obtaining the benefits of the generator set i.
To solve the robust optimization problem, defineThe probability that the generating set profit is not smaller than a certain critical value alpha is expressed as follows:
wherein:a probability density function representing the adversary declaration coefficient. From the robust optimization theory, it is known that +.>Can be obtained by solving the following model:
maxα
wherein: epsilon represents a given confidence level.
In addition, constraints of the underlying model are:
(3) After solving the reporting parameters for each generator set, P is calculated by equation (1) MCP On the basis, the Lena index L is defined to measure the market efficiency, and the market efficiency is specifically expressed as follows:
wherein:and->The average electricity price and the average electricity generation cost of the market are respectively represented.
(4) Comparing the index L under all planned electric quantity sampling values, and taking the Q corresponding to the minimum index L P I.e. the optimal planned power determined by the method.
The beneficial effects of the invention are as follows:
aiming at the defect that the method for determining the planned electric quantity of the electric power trading center is lacking in China at present, the invention provides a method for correcting the linear declaration function of the generator set based on the influence of the planned electric quantity, and finally determining the optimal planned electric quantity.
Drawings
FIG. 1 is a declaration function of a generator set under a plan-market double track system.
Fig. 2 is a schematic diagram of a planned power distribution double-layer optimization model.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the invention; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted. The positional relationship described in the drawings are for illustrative purposes only and are not to be construed as limiting the invention.
The technical scheme adopted for solving the technical problems is as follows: the method for determining the planned electric quantity based on the correction linear declaration and the double-layer model comprises the following specific steps:
assuming that there are n gensets participating in the market, first the ith genset (i=1, 2, cost and reporting parameters are linearized, specifically expressed as:
wherein:representing the marginal power generation cost of the generator set i; />The declaration price of the generator set i is represented; a, a i And b i Representing the cost coefficient of the generator set i; />Representing the reporting coefficient; q (Q) i The power generation amount of the generator set i is shown.
As shown in fig. 1, the declaration of the generator set on the market should be greater than or equal to the corresponding marginal cost on the premise that the generator set is known to be distributed to the planned electric quantity. And further, a modified generator set linear declaration function can be obtained, which is specifically expressed as follows:
wherein:representing the corrected generator set declaration; />Representing corrected reporting coefficientsThe parameters to be determined; />Representing the planned power allocated to the generator set i, specifically expressed as:
wherein: q (Q) P Representing the total planned electrical quantity;the annual energy production upper limit of the ith generator set is indicated.
And (3) making:
and has the following steps:
wherein:represents the market electric quantity of the generator set i, Q M Representing the total market power. And then can obtainBy P MCP Unified representation, there are:
wherein: q (Q) U Representing the total load demand of the user, which is equal to the sum of the planned power and the market power.
Further constructing a double-layer model for planning electric quantity distribution of the electric power transaction center, wherein a schematic diagram of the model is shown in fig. 2. The upper model takes the electric power transaction center as a main body to optimize the distribution strategy of the planned electric quantity; the lower model takes a generator set in the market as a main body to optimize the self reporting coefficient. The method comprises the following specific steps:
(1) The power trading center first determines a planned power quantity Q P In the range, sampling at equal intervals with h as the precision;
(2) Q obtained for each sample P The method for determining the declaration information of each generator set by using the lower model comprises the following steps:
firstly, for the generator set i, firstly, counting an uncertainty set of reporting coefficients of other generator sets according to historical reporting data. Assume that generating set i predicts reporting coefficient of opponentIs +.>
In the plan-market double-track system, the generator set i determines its own reporting coefficient with the maximum of robust yields as a target. Benefit R of generator set i i Can be expressed as:
wherein:and->The electricity selling income of the planned electric quantity and the electricity selling income of the market electric quantity of the generator set i are respectively represented; c (C) i Representing the total power generation cost of the generator set i, c i Representing a fixed cost factor; p (P) P And the purchase price of the planned electric quantity is expressed and calculated by the government.
In the construction of a lower-layer generator set reporting decision model, decision variablesReporting coefficient, random variable +.>Representing an uncertain adversary reporting strategy, the objective function of the genset is to maximize the robust gain +.>Namely:
wherein:the declaration coefficient representing the generator set i is +.>Competitor reporting coefficient is->And obtaining the benefits of the generator set i.
To solve the robust optimization problem, defineThe probability that the generating set profit is not smaller than a certain critical value alpha is expressed as follows:
wherein:a probability density function representing the adversary declaration coefficient. From the robust optimization theory, it is known that +.>Can be obtained by solving the following model:
maxα
wherein: epsilon represents a given confidence level.
In addition, constraints of the underlying model are:
(3) After solving the reporting parameters for each generator set, P is calculated by equation (1) MCP On the basis, the Lena index L is defined to measure the market efficiency, and the market efficiency is specifically expressed as follows:
wherein:and->The average electricity price and the average electricity generation cost of the market are respectively represented.
(4) Comparing the index L under all planned electric quantity sampling values, and taking the Q corresponding to the minimum index L P I.e. the optimal planned power determined by the method.
There are 5 power generating units A, B, C, D, E in a certain region of China, and the power generation cost coefficients are (0.073, 307, 1000), (0.064, 286, 5000), (0.057, 271, 9000), (0.048, 264, 14000) and (0.039, 258, 18000) respectively. The annual energy production of five generator sets is 200, 500, 750, 1000 and 1250 hundred million kWh, respectively. The planned electric quantity of the regional generator set is 2500 hundred million kWh, and the fixed acquisition price is 384 yuan/MWh. The confidence level ε is 0.9 and the total user load requirement is 3000 hundred million kWh. According to the method for determining the planned electric quantity based on the correction linear declaration and the double-layer model, the optimal planned electric quantity distribution result is 930 hundred million kWh, and at the moment, the Lena index L for measuring the market benefit is 0.1344. Compared with the condition that the actual planned electric quantity is 2500 hundred million kWh (corresponding to the index L of the lux index of 0.2015), the electric quantity is reduced by 33.3 percent. The method of the invention is more in line with the actual demands, can effectively reduce market force and improve social benefits.
Claims (1)
1. The method for determining the planned electric quantity based on the modified linear declaration and the double-layer model is characterized by comprising the following steps of:
step 1: firstly linearizing a reporting function of a generator set, and then correcting the linear reporting function through the influence of the quantized planned electric quantity;
step 2: constructing a power transaction center planning electric quantity distribution double-layer model, wherein the upper-layer model takes the power transaction center as a main body to optimize a planning electric quantity distribution strategy; the lower layer model takes the generator set as a main body to optimize the self reporting coefficient; determining an optimal planned electric quantity;
the step 1 specifically comprises the following steps:
assuming that there are n gensets participating in the market, first the ith genset (i=1, 2, cost and reporting parameters are linearized, specifically expressed as:
wherein:representing the marginal power generation cost of the generator set i; p (P) i bid,nor The declaration price of the generator set i is represented; a, a i And b i Representing the cost coefficient of the generator set i; />Representing the reporting coefficient; q (Q) i Representing the power generation amount of the generator set i;
the linear declaration function of the generator set is corrected, and the method is specifically expressed as follows:
wherein: p (P) i bid,* Representing the corrected generator set declaration;representing the corrected reporting coefficient as a parameter to be determined; />Representing the planned power allocated to the generator set i, specifically expressed as:
wherein: q (Q) P Representing the total planned electrical quantity;representing an annual energy production upper limit of the ith generator set;
and (3) making:
and has the following steps:
wherein:represents the market electric quantity of the generator set i, Q M Representing the total market power; and then P can be obtained i bid,* (i=1, 2, … n), with P MCP Unified representation, there are:
wherein: q (Q) U Representing a total load demand of the user, which is equal to a sum of the planned power and the market power;
the step 2 specifically comprises the following steps:
(1) First determining a planned electrical quantity Q P In the range, sampling at equal intervals with h as the precision;
(2) Sampling each oneQ of arrival of P The method for determining the declaration information of each generator set by using the lower model comprises the following steps:
for the generator set i, firstly, counting an uncertainty set of reporting coefficients of other generator sets according to historical reporting data, and assuming that the generator set i predicts the reporting coefficients of opponentsIs +.>
The generating set i determines the self reporting coefficient by taking the maximum robust gain as the target, and the gain R of the generating set i i Expressed as:
wherein:and->The electricity selling income of the planned electric quantity and the electricity selling income of the market electric quantity of the generator set i are respectively represented; c (C) i Representing the aggregate of the generator sets iCost of generating electricity, c i Representing a fixed cost factor; p (P) P The purchase price of the planned electric quantity is expressed and calculated by the government;
in the construction of a lower-layer generator set reporting decision model, decision variablesReporting coefficient, random variable +.>Representing an uncertain adversary reporting strategy, the objective function of the genset is to maximize the robust gain +.>Namely:
wherein:the declaration coefficient representing the generator set i is +.>Competitor reporting coefficient is->The income obtained by the generator set i;
to solve the robust optimization problem, defineThe probability that the generating set profit is not smaller than a certain critical value alpha is expressed as follows:
wherein:probability density function representing opponent declaration coefficient, known according to robust optimization theory, ++>Obtained by solving the following model:
maxα
wherein: epsilon represents a given confidence level;
in addition, constraints of the underlying model are:
(3) After solving the reporting parameters for each generator set, P is calculated by equation (1) MCP On the basis, the Lena index L is defined to measure the market efficiency, and the market efficiency is specifically expressed as follows:
wherein:and->Respectively representing the average electricity price and the average electricity generation cost of the market;
(4) Comparing the index L under all planned electric quantity sampling values, and taking the Q corresponding to the minimum index L P I.e. the optimal planned power.
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Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108711890A (en) * | 2018-06-27 | 2018-10-26 | 广东电网有限责任公司 | Ahead market goes out clearing method, system, device and computer readable storage medium |
CN109508857A (en) * | 2018-09-28 | 2019-03-22 | 国网浙江省电力有限公司经济技术研究院 | A kind of active distribution network multistage programming method |
CN109872250A (en) * | 2019-01-29 | 2019-06-11 | 国电南瑞科技股份有限公司 | A kind of Initial Stages of Electricity Market radix deviation electricity settlement processing method and system |
CN110310173A (en) * | 2019-06-11 | 2019-10-08 | 上海电力学院 | A kind of renewable energy participate in long-term electricity transaction power energy allocation method |
JP2020129345A (en) * | 2019-02-12 | 2020-08-27 | 三菱電機株式会社 | Electric power supply-demand plan creation device and electric power supply-demand plan creation method |
CN112529256A (en) * | 2020-11-24 | 2021-03-19 | 华中科技大学 | Distributed power supply cluster day-ahead scheduling method and system considering multiple uncertainties |
-
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- 2021-04-13 CN CN202110395523.0A patent/CN113077095B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108711890A (en) * | 2018-06-27 | 2018-10-26 | 广东电网有限责任公司 | Ahead market goes out clearing method, system, device and computer readable storage medium |
CN109508857A (en) * | 2018-09-28 | 2019-03-22 | 国网浙江省电力有限公司经济技术研究院 | A kind of active distribution network multistage programming method |
CN109872250A (en) * | 2019-01-29 | 2019-06-11 | 国电南瑞科技股份有限公司 | A kind of Initial Stages of Electricity Market radix deviation electricity settlement processing method and system |
JP2020129345A (en) * | 2019-02-12 | 2020-08-27 | 三菱電機株式会社 | Electric power supply-demand plan creation device and electric power supply-demand plan creation method |
CN110310173A (en) * | 2019-06-11 | 2019-10-08 | 上海电力学院 | A kind of renewable energy participate in long-term electricity transaction power energy allocation method |
CN112529256A (en) * | 2020-11-24 | 2021-03-19 | 华中科技大学 | Distributed power supply cluster day-ahead scheduling method and system considering multiple uncertainties |
Non-Patent Citations (2)
Title |
---|
用户报量不报价模式下电力现货市场需求响应机制与方法;陈雨果;张轩;罗钢;白杨;谭振飞;钟海旺;;电力系统自动化(第09期);1-3 * |
考虑用户有限理性的售电公司峰谷组合电力套餐设计;张智等;电力系统自动化;第45卷(第16期);114-123 * |
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