Spot market price limit adaptive method based on multi-factor influence layer

Abstract

The invention relates to a spot market price limit adaptive method based on a multi-factor influence layer. The method comprises the following steps: determining a lower limit level of a spot market price limit model; improving the maximum upper limit price control model; establishing price limit influence layer association items; determining an adaptive coefficient; establishing a spot market price limit adaptive model based on a multi-factor influence layer; according to the invention, the influence factor is introduced, so that the dynamic change of the upper limit price can be realized in different scenes, the management and control of the power generation side market power can be effectively enhanced, the reasonable income of the traditional unit is ensured, and the price fluctuation risk brought by the new energy is weakened; real price signals are reflected to the maximum extent to guide reasonable allocation of resources, and peak power generation of a unit is excited to guarantee safe and reliable operation of a system.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy power systems, and in particular relates to a spot market price limit adaptive method based on a multi-factor influence layer. Background Art

[0002] With the acceleration of the construction of new power systems, new energy is experiencing leapfrog development, and large-scale market entry is imperative. At present, the country generally adopts a flat fixed price limit standard to prevent extreme price risks. As the proportion of new energy in the market increases, the operation of the power system will be affected by intermittent output. In addition, conventional units face the risk of reduced guaranteed income and threaten the reliable supply of electricity. Continuing to use a static price limit with a fixed standard will impose greater restrictions on market regulation and is not conducive to risk sharing. Therefore, it is necessary to consider the influencing factors that have a strong linkage with the spot price, study the multi-factor influence layer spot market price limit adaptive model to deal with the risk of high proportion of new energy entering the market, and realize the dynamic tracking of spot prices under different demand scenarios to ensure the safety and stability of the system. Summary of the invention

[0003] The purpose of the present invention is to provide a spot market price limit adaptive method based on a multi-factor influence layer to ensure the benefits of the system and have high economic efficiency in view of the lack of an implementable price limit mechanism for the new power system with new energy as the main body under the current dual carbon background in China.

[0004] The technical solution adopted by the present invention is: a spot market price limit adaptive method based on a multi-factor influence layer, comprising the following steps:

[0005] S1: Determine the lower limit level of the spot market price limit model;

[0006] S2: Improved maximum price ceiling control model;

[0007] S3: Establish price limit impact layer association items;

[0008] S4: Establish the adaptive coefficient;

[0009] S5: Establish a spot market price limit adaptive model based on multiple factor influence layers.

[0010] Specifically, in step S1, the lower limit of the spot market price is fixed as the basic price lower limit of 0 yuan / kwh, and a price upper limit adaptive model is proposed.

[0011] In step S2, the price ceiling control model (RPI-X) is based on direct price control based on performance. Enterprises need to obtain more profits by improving labor productivity without exceeding a given price ceiling. The model is as follows:

[0012] P t =P t-1×(1+RPI-X)

[0013] Where P t The current price level specified by the regulator; P t-1 is the price level of the previous period; RPI is the retail price index, representing inflation; X is the efficiency factor, representing the expected rate of technological progress.

[0014] In step S3, the price limit impact layer correlation items are established by comprehensively considering important factors that have a strong correlation with the spot price, such as the fluctuation of primary energy prices, the proportion of new energy installed capacity in different regions, the high load period in winter and summer, and the safety supply cycle.

[0015] In step S4, the adaptive coefficient is determined by using linear regression and least square method.

[0016] In step S5, the spot market price limit adaptive model based on the multi-factor influence layer is as follows:

[0017]

[0018] Where P max AC is the upper limit price of the price upper limit adaptive model for this period; t-1 is the fuel cost per kWh of the previous period; r is the profit rate of the cost per kWh; ASS p-f is the price limit impact layer associated item; is the power quality coefficient; W i is the adaptive coefficient of each influencing factor; X i is a strong correlation factor of the spot market price limit standard influencing layer; the adaptive coefficient of the influencing factor η in this model is considered to be -1, so in ASS p-f Write it separately in the column.

[0019] Beneficial effects of the present invention: The present invention proposes a spot market price limit adaptive model based on a multi-factor influence layer, and preliminarily designs the spot market upper limit adaptive price under different primary energy prices, new energy contribution rates, and supply and demand situations. The introduction of "influence factors" can achieve dynamic changes in the upper limit price in different scenarios, effectively strengthen the control of market power on the power generation side, ensure the reasonable income of traditional units, and weaken the price volatility risks brought by new energy. At the same time, a "rising and falling" price limit model is established, which maximizes the reflection of real price signals to guide the rational allocation of resources, and encourages peak power generation of units to ensure safe and reliable system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 The present invention is a flow chart for determining the adaptive coefficient by linear regression and least square method;

[0021] Figure 2 This is a model diagram of influence factors for establishing price limit influence layer associated items of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention, and are specifically described in combination with the embodiments below.

[0023] like Figure 1-2 As shown, the present invention comprises the following steps:

[0024] S1: Construct a risk measurement model for power sales companies based on risk value; the specific process is as follows:

[0025] In step S1, the current spot market price limit in my country takes into account the user's affordability and the marginal cost of new energy. Generally, the upper limit of the fixed price range is set to 1.5 yuan / kwh and the lower limit is set to 0 yuan / kwh. In order to encourage new energy to enter the market while preventing vicious competition, the lower limit of the spot market price is fixed to the basic price lower limit of 0 yuan / kwh, and a price cap adaptive model is proposed.

[0026] S2: Construct an optimization target portfolio trading strategy model divided by capacity level; the specific process is as follows:

[0027] The price ceiling control model (RPI-X) is based on direct performance-based price control. Enterprises need to obtain more profits by improving labor productivity without exceeding a given price ceiling. The model is as follows:

[0028] P t =P t-1 ×(1+RPI-X)

[0029] Where P t The current price level specified by the regulator; P t-1 is the price level of the previous period; RPI is the retail price index, representing inflation; X is the efficiency factor, representing the expected rate of technological progress.

[0030] However, the RPI-X model may cause power generation companies to reduce power quality under the condition of the maximum price level limit, which will harm the interests of users and fail to clearly reflect the profitability principle of power generation companies. Therefore, the return on investment price control model (ROR) is introduced, which is expressed as follows:

[0031] R(p,q)=C+S(RB)

[0032] Where R is the revenue function of the enterprise, which is a function of electricity price and power generation; C is the cost of the power generation enterprise; S is the rate of return on investment, which is determined by the national energy policy; and RB is the base of investment return.

[0033] Rearranging the above formula yields:

[0034]

[0035] Finally, the improved maximum price ceiling control model expression is as follows:

[0036] P t =AC t-1 (1+r)(1+RPI-X)

[0037] S3: Establish the price limit impact layer association items. The specific process is as follows:

[0038] In step S3, the price limit impact layer association items are established by comprehensively considering important factors that have a strong correlation with spot prices, such as primary energy price fluctuations, the proportion of new energy installed capacity in different regions, winter and summer high load periods, and safe supply cycles;

[0039] like Figure 2 As shown, it is an influencing factor model diagram of establishing the associated items of the price limit influence layer of the present invention.

[0040]

[0041] Where X1 is the primary energy price change index; P e is the primary energy price of this period; P b is the basic energy price; X2 is the load demand increment index in special periods (winter, summer and safety supply period), and X2 is 0 in other times; Q is the average system load in spring and autumn; Q1 is the load demand increment during the peak load period in winter and summer; Q2 is the load reserve increment during the power supply period; η is the contribution rate of new energy power generation; P0 is the average load level in the flat section of the net load curve; P out It is the peak and valley load value.

[0042] S4: Establish the adaptive coefficient. The specific process is as follows:

[0043] like Figure 1 As shown, the adaptive coefficients W1 and W2 are determined by linear regression and least square method, which is intuitive and efficient.

[0044] The adaptive coefficient and the impact factor together constitute the price limit impact layer:

[0045] ASS p-f =W1X1+W2X2

[0046] S5: Establish a spot market price limit adaptive model based on multiple factor influence layers. The specific process is as follows:

[0047] In step S5, the spot market price limit adaptive model based on the multi-factor influence layer is as follows:

[0048]

[0049] Where P max AC is the upper limit price of the price upper limit adaptive model for this period; t-1 is the last period's fuel cost per kWh (China Electricity Council releases CECI price once a week, and the fuel cost per kWh can be queried according to the quick calculation table of fuel cost per kWh); r is the profit margin of the cost per kWh; ASS p-f is the price limit impact layer associated item; is the power quality coefficient; W i is the adaptive coefficient of each influencing factor; X i is a strong correlation factor of the spot market price limit standard influencing layer; the adaptive coefficient of the influencing factor η in this model is considered to be -1, so in ASS p-f Write it separately in the column.

[0050] This invention proposes a spot market price limit adaptive model based on a multi-factor influence layer, and preliminarily designs the spot market upper limit adaptive price under different primary energy prices, new energy contribution rates, and supply and demand situations. The introduction of "influence factors" can achieve dynamic changes in the upper limit price in different scenarios, effectively strengthen the control of the market power on the power generation side, ensure the reasonable income of traditional units, and weaken the price volatility risks brought by new energy. At the same time, a "rising and falling" price limit model is established, which maximizes the reflection of real price signals to guide the rational allocation of resources, and encourages peak power generation of units to ensure the safe and reliable operation of the system.

[0051] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that fall within the meaning and scope of the elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A spot market price limit adaptive method based on a multi-factor influence layer, characterized in that: The steps include: S1: Determine the lower limit level of the spot market price limit model; S2: Improved maximum price ceiling control model; S3: Establish price limit impact layer association items; S4: Establish the adaptive coefficient; S5: Establish a spot market price limit adaptive model based on multiple factor influence layers.

2. The spot market price limit adaptive method based on multiple factor influence layers according to claim 1, characterized in that: In the step S1, the lower limit of the spot market price is fixed as the basic price lower limit of 0 yuan / kwh, and a price upper limit adaptive model is proposed.

3. The spot market price limit adaptive method based on multiple factor influence layers according to claim 1, characterized in that: In step S2, the price ceiling control model (RPI-X) is based on direct price control based on performance. Enterprises need to obtain more profits by improving labor productivity without exceeding a given price ceiling. The model is as follows: P t =P t-1 ×(1+RPI-X) Where P t The current price level specified by the regulator; P t-1 is the price level of the previous period; RPI is the retail price index, representing inflation; X is the efficiency factor, representing the expected rate of technological progress.

4. The spot market price limit adaptive method based on multiple factor influence layers according to claim 1, characterized in that: In step S3, the price limit impact layer correlation items are established by comprehensively considering important factors that have a strong correlation with the spot price, such as the fluctuation of primary energy prices, the proportion of new energy installed capacity in different regions, the high load period in winter and summer, and the safety supply cycle.

5. The spot market price limit adaptive method based on multiple factor influence layers according to claim 1, characterized in that: In step S4, the adaptive coefficient is determined by using linear regression and least square method.

6. The spot market price limit adaptive method based on multiple factor influence layers according to claim 1, characterized in that: In step S5, the spot market price limit adaptive model based on the multi-factor influence layer is as follows: Where P max AC is the upper limit price of the price upper limit adaptive model for this period; t-1 is the fuel cost per kWh of the previous period; r is the profit rate of the cost per kWh; ASS p-f is the price limit impact layer associated item; is the power quality coefficient; W i is the adaptive coefficient of each influencing factor; X i is a strong correlation factor of the spot market price limit standard influencing layer; the adaptive coefficient of the influencing factor η in this model is considered to be -1, so in ASS p-f Write it separately in the column.

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