Electric power spot market price prediction method based on double games

Abstract

The invention relates to the technical field of electricity market prediction, in particular to an electricity spot market price prediction method based on double games, which comprises the key steps of market transaction price formation mechanism analysis, unit power model construction, electricity selling price decision double game model design and price decision double game optimization problem solving. The power spot market price is dynamically adjusted by simulating strategy interaction between market participants, and Nash equilibrium between a power selling side and a user is realized through an iterative loop solving method and a Yalmip / IPOPT solver, so that an optimal power selling price and an optimal power purchasing quantity decision are obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of power market forecasting, and in particular to a power spot market price forecasting method based on double game. Background Art

[0002] Based on the above-mentioned background technology, the present invention proposes a price pricing method for the electricity spot market based on double game, which aims to achieve dynamic adjustment of prices by simulating the strategic interaction between market participants, so as to more accurately reflect the market supply and demand conditions and improve the efficiency and stability of the electricity spot market.

[0003] In the electricity spot market, the pricing mechanism is crucial to ensure market efficiency and fairness. However, existing pricing methods often fail to fully consider the strategic behavior of market participants and the market supply and demand dynamics. Therefore, a new method is needed to more accurately simulate and predict market prices to adapt to the complexity and dynamics of the electricity market. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for predicting electricity spot market prices based on double game to overcome the problems existing in the current prior art.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] The present application provides a method for predicting electricity spot market prices based on double game, including:

[0007] Obtain data from the power generation company and construct a unit power model; wherein the unit power model includes: a hydropower power model, a wind power model, a photovoltaic power model and a thermal power constraint model;

[0008] Construct a double game model for electricity price decision-making;

[0009] According to the unit power model, an iterative loop solution method is used to nest the Yalmip / IPOPT solver to solve the double game model of electricity price decision until a Nash equilibrium is reached, and a solution result is determined;

[0010] According to the solution results, the forecast adjustment of the electricity spot market price is completed.

[0011] Furthermore, in the method described above, the data of the power generation company include: the net water head of the hydropower unit, the water flow of the hydropower unit, the wind speed, the rated wind speed, the cut-in wind speed, the cut-out wind speed, the rated power of the wind turbine unit, the rated photoelectric conversion efficiency, the radiation intensity of the photovoltaic module, the total area of ​​the photovoltaic module and the power of the thermal power unit.

[0012] Furthermore, in the above method, the hydropower model is:

[0013]

[0014] P HY-min ≤P HY,i ≤P HY-max

[0015] Among them, P HY,i is the power of the hydropower unit at time i, g is the acceleration of gravity, φ is the power generation efficiency of the hydropower unit, is the net water head of power generation at time i, μ i is the water flow at time i, P HY-min is the minimum power of the hydropower unit, P HY-max is the maximum power of the hydroelectric unit.

[0016] Furthermore, in the above method, the wind power model is:

[0017]

[0018] v i =c(-lnβ) 1 / k

[0019]

[0020] Among them, P WT,i is the power of the wind turbine at time i, v i is the wind speed at time i; v r 、v ci 、v cu are rated wind speed, cut-in wind speed and cut-out wind speed respectively, P r is the rated power of the wind turbine, c is the scale parameter, k is the shape parameter, β is a random number that follows a uniform distribution between 0 and 1, E WT is the average wind speed, σ WT is the standard deviation of wind speed, Γ() is the gamma function.

[0021] Furthermore, in the above method, the photovoltaic power model is:

[0022] P PV,i =λ PV ω PV,i S PV

[0023]

[0024] Among them, P PV,i is the predicted photovoltaic power at time i, λ PV is the rated photoelectric conversion efficiency, ωPV,i is the radiation intensity of the photovoltaic module at time i, S PV is the total area of ​​the photovoltaic module, ω max is the maximum value of the radiation intensity of the photovoltaic module, a and b are the shape parameters of the Beta distribution.

[0025] Furthermore, in the above method, the thermal power constraint model is:

[0026] P TH-min ≤P TH,i ≤P TH-max

[0027] P TH,i is the power of the thermal power unit at time i, P TH-max and P TH-min are the maximum and minimum power of thermal power units respectively.

[0028] Furthermore, in the method described above, the dual game model for electricity selling price decision includes: an electricity selling price decision sub-model on the electricity selling side and a user electricity purchase amount decision sub-model.

[0029] Furthermore, in the method described above, the electricity selling price decision sub-model at the electricity selling side is:

[0030] The profit model of electricity sales on the electricity sales side:

[0031]

[0032] in, is the electricity sales side of the thermal power unit i (i=1,2,…,m g ) of electricity sales profit, is the new energy electricity sales side j (j=1,2,…,m f ) of electricity sales profit, p g,i,m,t is the electricity price sold by thermal power generator i to users at time t, q g,i,m,t is the amount of thermal power purchased by the user from the thermal power generator sales side i at time t, p g is the price of thermal power purchased from the wholesale market by the thermal power sales side, p f,j,m,t is the price of electricity sold by the new energy sales side j to users at time t, q f,j,m,t is the amount of electricity that the user purchases from the new energy power sales side j at time t, p f is the electricity price of the new energy sales side purchasing new energy from the renewable energy power plant, and T is the transaction cycle between the sales side and the user;

[0033] The electricity prices for each period are:

[0034]

[0035] in, and are the electricity sales prices of thermal power during valley, flat and peak periods, respectively. and are the electricity prices of new energy electricity during valley, flat and peak periods, respectively. I , T II and T III Corresponding to valley, flat and peak periods respectively;

[0036] The power selling price constraints at different times are:

[0037]

[0038] The power selling price constraint on the power selling side is:

[0039]

[0040] in, and They are the upper limits of the electricity price for thermal power sales during off-peak, flat and peak periods, respectively. and They are the upper limits of electricity sales prices for new energy electricity during off-peak, flat and peak periods respectively.

[0041] Furthermore, in the above method, the user power purchase decision model includes:

[0042] The cost of purchasing electricity on the electricity sales side is:

[0043]

[0044] Among them, C a The cost for users to purchase electricity from the electricity seller;

[0045] The cost for users to buy or sell carbon emission rights is:

[0046]

[0047] Among them, C b represents the cost of users buying or selling carbon emission rights, Q ' m,t is the expected amount of electricity purchased by the user at time t, p c represents the price of carbon emission rights, λ represents the proportion of carbon quota that users can emit;

[0048] The user's electricity satisfaction loss is:

[0049]

[0050] Among them, C d is the loss of electricity satisfaction after user demand response, α mand β m is the user's electricity satisfaction loss parameter, ΔQ m,t is the difference between the actual power consumption and the expected power consumption of the user at time t;

[0051] ΔQ m,t =|Q m,t -Q ′ m,t |

[0052]

[0053] Among them, Q m,t The sum of the electricity purchased by the user from each electricity seller at time t, that is, the actual electricity consumption of the user at time t;

[0054] The user's electricity purchase decision model is:

[0055] C m =C a +C b +C d

[0057] Furthermore, the method described above, according to the unit power model, adopts an iterative loop solution method to nest the Yalmip / IPOPT solver to solve the double game model of electricity price decision, including:

[0058] According to the unit power model, the marginal variable cost per unit of electricity is determined; wherein the marginal variable cost per unit of electricity is formulated as follows:

[0059] M i =(ηαP TH,i +λ HY P HY,i +λ WT P WT,i +λ PV P PV,i ) / P SY,i

[0060] Among them, M i is the marginal variable cost per unit of electricity at time i, η is the standard coal consumption rate of thermal power, α is the unit price of standard coal at time i, and λ HY is the variable cost coefficient of hydropower, λ WT is the variable cost coefficient of wind power, λ PV is the variable cost coefficient of photovoltaics;

[0061] Determine the total cost per unit of electricity based on the marginal variable cost per unit of electricity;

[0062] Determine the adjustment formula for the iterative loop;

[0063] The total electricity cost model is solved by nesting the Yalmip / IPOPT solver using an iterative loop solving method.

[0064] The beneficial effects of the present invention are:

[0065] This application includes the key steps of analyzing the market transaction price formation mechanism, constructing the unit power model, designing the dual game model for power selling price decision and solving the dual game optimization problem for price decision. By simulating the strategic interaction between market participants, the spot electricity market price is dynamically adjusted. Through the iterative loop solution method and Yalmip / IPOPT solver, the Nash equilibrium between the power selling side and the user is achieved, thereby obtaining the optimal power selling price and power purchase volume decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 The present invention provides a flowchart of an embodiment of a method for predicting electricity spot market prices based on double game. DETAILED DESCRIPTION

[0068] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0069] The fundamental reason for the time-based pricing in the medium- and long-term electricity market is that the power supply costs vary in different time periods. Since electric energy cannot be stored on a large scale and the production and consumption of the power industry are completed instantaneously, from the perspective of system balance, the capacity costs and power supply costs of electricity in different time periods are different, which also explains the inherent principle of the price difference of electricity prices in different time periods.

[0070] The cost of electricity supply is the basis for measuring whether the electricity price is reasonable, but the electricity price is ultimately formed through transactions between buyers and sellers. Therefore, the supply and demand scenario is an important factor affecting the price in different time periods. In a perfectly competitive market, when the market clearing price is equal to the marginal cost of power generation, welfare will be maximized. However, when the current electricity market is closer to an oligopolistic market, power generation companies and users can adopt different bidding strategies based on the market supply and demand status to maximize their own welfare. There are three supply and demand scenarios: (1) When the supply and demand of electricity are basically balanced, the two parties in the market are price setters and acceptors for each other, and the quotations are basically determined at the average level. (2) When the market is in short supply, users purchase electricity by continuously raising prices competitively, and power generation companies become the dominant party in the competition and price setter. The highest price users are willing to pay is the loss caused by power shortage. (3) When the market is in oversupply, buyers are price setters, and sellers achieve balance by lowering the electricity selling price. However, if the quotation is lower than the marginal cost, it will result in losses. Therefore, power generation companies will quote at least according to the marginal cost. If there is a large amount of excess supply, in order to avoid unit shutdown, power generation companies will even quote negative electricity prices. Market players will make corresponding adjustments to their bidding strategies based on different supply and demand scenarios, and the final transaction prices in different time periods will vary greatly.

[0071] Figure 1 This is a flowchart provided by an embodiment of a method for predicting electricity spot market prices based on double game in the present invention. Figure 1 , this embodiment may include the following steps:

[0072] S1. Obtain data from power generation companies and construct unit power models; the unit power models include: hydropower power model, wind power model, photovoltaic power model and thermal power constraint model;

[0073] S2. Construct a double game model for electricity price decision-making;

[0074] S3. According to the unit power model, the iterative loop solution method is used to nest the Yalmip / IPOPT solver to solve the double game model of electricity price decision until the Nash equilibrium is reached and the solution result is determined;

[0075] S4. Based on the solution results, complete the forecast adjustment of the electricity spot market price.

[0076] It can be understood that this embodiment includes the key steps of analyzing the market transaction price formation mechanism, constructing the unit power model, designing the dual game model of the power selling price decision and solving the dual game optimization problem of the price decision. By simulating the strategic interaction between market participants, the spot electricity market price is dynamically adjusted. Through the iterative loop solution method and the Yalmip / IPOPT solver, the Nash equilibrium between the power selling side and the user is achieved, thereby obtaining the optimal power selling price and power purchase volume decision.

[0077] Preferably, the data of the power generation company include: the net water head of the hydropower unit, the water flow of the hydropower unit, the wind speed, the rated wind speed, the cut-in wind speed, the cut-out wind speed, the rated power of the wind turbine unit, the rated photoelectric conversion efficiency, the radiation intensity of the photovoltaic module, the total area of ​​the photovoltaic module and the power of the thermal power unit.

[0078] Preferably, the unit power model includes:

[0079] Hydropower Model

[0080]

[0081] Where: P HY,i is the power of the hydropower unit at time i; g is the acceleration of gravity; φ is the power generation efficiency of the hydropower unit; is the net water head of power generation at time i; μ i is the water flow at time i.

[0082] Hydropower power has seasonal fluctuations and is limited by reservoir capacity. The hydropower power constraint is

[0083] P HY-min ≤P HY,i ≤P HY-max

[0084] Where: P HY-min is the minimum power of the hydropower unit; P HY-max is the maximum power of the hydroelectric unit.

[0085] Wind power model

[0086]

[0087] Where: P WT,i is the power of the wind turbine at time i; v i is the wind speed at time i; v r 、v ci 、v cu are rated wind speed, cut-in wind speed, and cut-out wind speed respectively; P r is the rated power of the wind turbine.

[0088] Wind power is mainly affected by wind speed, and the wind speed is generally expressed by Weibull distribution, that is,

[0089] v i =c(-lnβ) 1 / k

[0090]

[0091] Where: c is the scale parameter; k is the shape parameter; β is a random number that follows a uniform distribution between 0 and 1; E WT is the average wind speed; σ WT is the standard deviation of wind speed; Γ() is the gamma function.

[0092] Photovoltaic power model

[0093] P PV,i =λ PV ω PV,i S PV

[0094] Where: P PV,i is the predicted photovoltaic power at time i; PV is the rated photoelectric conversion efficiency; ω PV,i is the radiation intensity of the photovoltaic module at time i; S PV is the total area of ​​PV panels.

[0095] The radiation intensity follows the Beta distribution, and its probability density function is

[0096]

[0097] Where: max is the maximum value of the radiation intensity of the photovoltaic module; a and b are the shape parameters of the Beta distribution.

[0098] Thermal Power Constraint Model

[0099] In this paper, clean energy is given priority, and the power generation of thermal power units is determined by the difference between the total system load and the clean energy power generation, and the constraints are:

[0100] P TH-min ≤P TH,i ≤P TH-max

[0101] Where: P TH,i is the power of the thermal power unit at time i, P TH-max and P TH-min are the maximum and minimum power of thermal power units respectively.

[0102] Preferably, the double game model for electricity price decision-making includes:

[0103] Electricity sales price decision model for electricity sales side

[0104] The electricity sales revenue on the electricity sales side is obtained by selling thermal power (new energy) to various users. The electricity sales cost on the thermal power sales side is formed by purchasing thermal power from the electricity wholesale market. The electricity sales cost on the new energy sales side, mainly wind and solar power, is formed by signing an agreement with a renewable energy power plant to purchase electricity from the power plant.

[0105] The profit of electricity sales on the electricity sales side is the difference between the electricity sales revenue and the electricity sales cost, which can be expressed as

[0106]

[0107] Where: is the electricity sales side of the thermal power unit i (i=1,2,…,m g ) of electricity sales profit, is the new energy electricity sales side j (j=1,2,…,m f ) of electricity sales profit; p g,i,m,t is the electricity price sold by thermal power generator i to users at time t; g,i,m,t is the amount of thermal power purchased by the user from the thermal power generator sales side i at time t; p g The price of thermal power purchased from the wholesale market by the thermal power sales side; p f,j,m,t is the price of electricity sold by the new energy power sales side j to users at time t; f,j,m,t p is the amount of electricity that the user purchases from the new energy power sales side j at time t; f is the electricity price of new energy purchased from renewable energy power plants by the new energy power sales side; T is the transaction cycle between the power sales side and the user.

[0108] The goal of each power sales side is to maximize its power sales profit, so the goal of the thermal power sales side is The goal of the new energy power sales side is

[0109] The constraints on the electricity sales model of the power sales side are mainly reflected in the constraints on the electricity sales price of the power sales side. The electricity sales prices of thermal power sales side and new energy sales side in each period are

[0110]

[0111] Where: and They are the electricity selling prices of thermal power during off-peak, flat and peak periods respectively; and are the electricity prices of new energy electricity during valley, flat and peak periods respectively; T I , T II and T III Corresponding to valley, flat and peak periods respectively.

[0112] The power selling price constraints of each power selling side in different periods are:

[0113]

[0114] In addition, the power selling price constraints of each power selling side are

[0115]

[0116] Where: and are the upper limits of the electricity price of thermal power during off-peak, flat and peak periods, respectively. and They are the upper limits of electricity sales prices for new energy electricity during off-peak, flat and peak periods respectively.

[0117] The upper limit of the electricity price on the thermal power sales side is the minimum of the spot market electricity price in the same period, and the upper limit of the electricity price on the new energy power sales side is the minimum of the spot market electricity price in the same period plus the carbon emission rights price.

[0118] In summary, the optimal power sales models for thermal power sales and new energy power sales are

[0119]

[0120] User electricity purchase decision model

[0121] The goal of electricity purchase by users is to maximize their comprehensive satisfaction with electricity while meeting their electricity demand. The comprehensive satisfaction of users with electricity mainly includes the cost of purchasing electricity from various power sellers, the cost of purchasing or selling carbon emission rights in the carbon market, and the loss of electricity satisfaction.

[0122] Among them, the cost of purchasing electricity from each power sales side is

[0123]

[0124] Where: C a The cost for users to purchase electricity from the electricity seller.

[0125] The cost for users to buy carbon emission rights from the carbon market or sell carbon emission rights to the carbon market is

[0126]

[0127] Where: C b represents the cost of users buying or selling carbon emission rights, Q ′ m,t is the expected amount of electricity purchased by the user at time t, p c represents the price of carbon emission rights, and λ represents the proportion of carbon quota that users can emit, that is, the ratio of total allowed carbon emissions to electricity consumption. If the carbon emissions exceed the allowed carbon emissions, users need to buy carbon emission rights from the carbon market; on the contrary, if the user's carbon emissions are less than the allowed carbon emissions, the excess carbon emission rights can be sold in the carbon market to obtain income.

[0128] In addition, since users adjust their electricity consumption behavior to respond to the demand response of the electricity sales side, the actual electricity consumption at time t may not be equal to the expected electricity consumption, which will cause the loss of user's electricity satisfaction. Therefore, the loss of user's electricity satisfaction needs to be taken into account in the objective function, which can be expressed as

[0129]

[0130] Where: C d is the loss of electricity satisfaction after user demand response, α m and β m is the user's electricity satisfaction loss parameter, ΔQ m,t It is the difference between the actual power consumption and the expected power consumption of the user at time t.

[0131] ΔQ m,t =|Q m,t -Q ′ m,t |

[0132]

[0133] Where: Q m,t It is the total amount of electricity purchased by the user from each power sales party at time t, that is, the actual electricity consumption of the user at time t.

[0134] In summary, the goal of the user's optimal electricity purchasing model can be expressed as maximizing the comprehensive electricity satisfaction, that is, minimizing the comprehensive electricity benefit function including electricity purchase cost, carbon emission right cost, and electricity satisfaction loss, that is,

[0135] C m =C a +C b +C d

[0136] Where: C m Represents the comprehensive electricity benefit function of user m.

[0137] The constraints of user m mainly include purchase-demand balance constraints and purchased electricity quantity constraints.

[0138] In the research scenario of this paper, the user's demand response behavior only considers the load reduction, and does not consider other types of demand response. Therefore, the total amount of electricity purchased by the user in time T should be equal to the actual total load, that is:

[0139]

[0140] When users purchase electricity from the electricity sales side i(j), they cannot exceed the upper and lower limits of the electricity sales side i(j), that is,

[0141]

[0142] Where: are the upper limits of electricity sales at the electricity sales side i and j respectively.

[0143] In summary, the user's optimal electricity purchasing model is:

[0144] minC m

[0145] Preferably, solving the price decision double game optimization problem includes:

[0146] For the dual game model of price decision-making on the electricity sales side proposed in this paper, an iterative loop solution method is adopted to nest the Yalmip / IPOPT solver until the Nash equilibrium between each retailer and the user and the Nash equilibrium among multiple retailers are satisfied.

[0147] First, the system average cost pricing is adopted. The marginal variable cost per unit of electricity at time i can be expressed as

[0148] M i =(ηαP TH,i +λ HY P HY,i +λ WT P WT,i +λ PV P PV,i ) / P SY,i

[0149] Where: M i is the marginal variable cost per unit of electricity at time i, η is the standard coal consumption rate of thermal power, α is the unit price of standard coal at time i, and λ HY is the variable cost coefficient of hydropower, λ WT is the variable cost coefficient of wind power, λ PV is the variable cost coefficient of photovoltaics.

[0150] The total cost per unit of electricity at time i is obtained by adding the time-sharing capacity cost and the time-sharing electricity cost. That is, the pricing model is:

[0151]

[0152] The electricity sales side adjusts the electricity sales price during peak, flat and valley periods according to the difference in the amount of electricity purchased by each user in the two rounds of master-slave bends. The adjustment formula (some subscripts are omitted to simplify the formula) is:

[0153] p s.i =p s-1,i +ΔQη

[0154] Where: p s.i is the electricity selling price adjusted by the electricity purchase amount in the sth round of master-slave game;s-1,i is the electricity selling price on the electricity selling side after the end of the s-1th round of master-slave competition; η represents the step factor for price adjustment on the electricity selling side; ΔZ represents the difference in the amount of electricity purchased by the user from the retailer n in the two rounds before and after.

[0155] ΔZ=|Z s-1 -Z s-2 |

[0156] In specific practice, assume that in a typical electricity spot market, there are 3 power generation companies (including hydropower, wind power and photovoltaic) and 2 power sales companies participating in market transactions. The market is divided into three periods: peak, flat and valley, and the power supply cost and demand are different in each period. The following are the specific example parameters:

[0157] Power supply cost parameters: Hydropower: variable cost coefficient is 0.05 yuan / (MW·h), minimum power is 100MW, maximum power is 500MW. Wind power: variable cost coefficient is 0.03 yuan / (MW·h), rated power is 200MW. Photovoltaic: variable cost coefficient is 0.04 yuan / (MW·h), total area is 10,000 square meters.

[0158] Demand parameters: Peak period: demand is 1000MW, and the power supply cost is 0.10 yuan / (MW·h). Normal period: demand is 800MW, and the power supply cost is 0.08 yuan / (MW·h). Off-peak period: demand is 600MW, and the power supply cost is 0.06 yuan / (MW·h).

[0159] Carbon emission rights price: 20 yuan / t.

[0160] First, the data on power generation efficiency, water flow, wind speed, radiation intensity, etc. of each power generation company are collected and preprocessed. Then, based on the unit power model, the predicted power output of each power generation company in different time periods is calculated. Finally, the double game process between power sales companies and users is simulated to determine the power sales price for each time period.

[0161] The dual game model is solved using an iterative loop solution method combined with the Yalmip / IPOPT solver until the Nash equilibrium is reached.

[0162] Based on the results of the game, market clearing and price adjustments are carried out to ensure supply and demand balance.

[0163] The calculation results are:

[0164] Electricity price: During peak hours, the electricity price is 0.12 yuan / (MW·h). During normal hours, the electricity price is 0.10 yuan / (MW·h). During off-peak hours, the electricity price is 0.08 yuan / (MW·h). Market clearing: During peak hours, the total power supply is 1,000MW, which meets the demand. During normal hours, the total power supply is 800MW, which meets the demand. During off-peak hours, the total power supply is 600MW, which meets the demand. Carbon emission rights trading: The cost for users to purchase carbon emission rights during peak hours is 4,000 yuan, 3,200 yuan during normal hours, and 2,400 yuan during off-peak hours. Comprehensive electricity efficiency: The comprehensive electricity efficiency of users is -3,000 yuan during peak hours, -2,400 yuan during normal hours, and -1,800 yuan during off-peak hours.

[0165] Through case analysis, this application can accurately simulate the supply and demand dynamics and price formation mechanism of the electricity spot market through a double game model. The electricity sales price and market clearing results show that this embodiment can effectively balance market supply and demand, optimize resource allocation, and take into account the impact of carbon emission costs. In addition, the calculation results of the user's comprehensive electricity consumption benefits also verify the effectiveness of the model in improving user electricity satisfaction. This embodiment provides a new pricing and decision support tool for the electricity spot market, which helps to improve the economic efficiency and environmental sustainability of the market.

[0166] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0167] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0168] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0169] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0170] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0171] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0172] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0173] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0174] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for predicting electricity spot market prices based on double game, characterized in that: include: Obtain data from the power generation company and construct a unit power model; wherein the unit power model includes: a hydropower power model, a wind power model, a photovoltaic power model and a thermal power constraint model; Construct a double game model for electricity price decision-making; According to the unit power model, an iterative loop solution method is used to nest the Yalmip / IPOPT solver to solve the double game model of electricity price decision until a Nash equilibrium is reached, and a solution result is determined; According to the solution results, the forecast adjustment of the electricity spot market price is completed.

2. The method according to claim 1, characterized in that The data of the power generation company include: the net water head of the hydropower unit, the water flow of the hydropower unit, the wind speed, the rated wind speed, the cut-in wind speed, the cut-out wind speed, the rated power of the wind turbine unit, the rated photoelectric conversion efficiency, the radiation intensity of the photovoltaic module, the total area of ​​the photovoltaic module and the power of the thermal power unit.

3. The method according to claim 2, characterized in that The hydropower model is: P HY-min ≤P HY,i ≤P HY-max Among them, P HY,i is the power of the hydropower unit at time i, g is the acceleration of gravity, φ is the power generation efficiency of the hydropower unit, is the net water head of power generation at time i, μ i is the water flow at time i, P HY-min is the minimum power of the hydropower unit, P HY-max is the maximum power of the hydroelectric unit.

4. The method according to claim 3, characterized in that The wind power model is: in i =c(-lnβ) 1 / k Among them, P WT,i is the power of the wind turbine at time i, v i is the wind speed at time i; v r 、v ci 、v cu are rated wind speed, cut-in wind speed and cut-out wind speed respectively, P r is the rated power of the wind turbine, c is the scale parameter, k is the shape parameter, β is a random number that follows a uniform distribution between 0 and 1, E WT is the average wind speed, σ WT is the standard deviation of wind speed, Γ() is the gamma function.

5. The method according to claim 4, characterized in that The photovoltaic power model is: P PV,i =λ PV oh PV,i S PV Among them, P PV,i is the predicted photovoltaic power at time i, λ PV is the rated photoelectric conversion efficiency, ω PV,i is the radiation intensity of the photovoltaic module at time i, S PV is the total area of ​​the photovoltaic module, ω max is the maximum value of the radiation intensity of the photovoltaic module, a and b are the shape parameters of the Beta distribution.

6. The method according to claim 5, characterized in that The thermal power constraint model is: P TH-min ≤P TH,i ≤P TH-max P TH,i is the power of the thermal power unit at time i, P TH-max and P TH-min are the maximum and minimum power of thermal power units respectively.

7. The method according to claim 6, characterized in that The dual game model for electricity selling price decision-making includes: an electricity selling price decision-making sub-model on the electricity selling side and a user electricity purchase quantity decision-making sub-model.

8. The method according to claim 7, characterized in that The electricity selling price decision sub-model on the electricity selling side is: The profit model of electricity sales on the electricity sales side: in, is the electricity sales side of the thermal power unit i (i=1,2,…,m g ) of electricity sales profit, is the new energy electricity sales side j (j=1,2,…,m f ) of electricity sales profit, p g,i,m,t is the electricity price sold by thermal power generator i to users at time t, q g,i,m,t is the amount of thermal power purchased by the user from the thermal power generator sales side i at time t, p g is the price of thermal power purchased from the wholesale market by the thermal power sales side, p f,j,m,t is the price of electricity sold by the new energy sales side j to users at time t, q f,j,m,t is the amount of electricity that the user purchases from the new energy power sales side j at time t, p f is the electricity price of the new energy sales side purchasing new energy from the renewable energy power plant, and T is the transaction cycle between the sales side and the user; The electricity prices for each period are: in, and are the electricity sales prices of thermal power during valley, flat and peak periods, respectively. and are the electricity prices of new energy electricity during valley, flat and peak periods, respectively. I , T II and T III Corresponding to valley, flat and peak periods respectively; The power selling price constraints at different times are: The power selling price constraint on the power selling side is: in, and They are the upper limits of the electricity price for thermal power sales during off-peak, flat and peak periods, respectively. and They are the upper limits of electricity sales prices for new energy electricity during off-peak, flat and peak periods respectively.

9. The method according to claim 8, characterized in that The user power purchase decision model includes: The cost of purchasing electricity on the electricity sales side is: Among them, C a The cost for users to purchase electricity from the electricity seller; The cost for users to buy or sell carbon emission rights is: Among them, C b represents the cost of users buying or selling carbon emission rights, Q ′ m,t is the expected amount of electricity purchased by the user at time t, p c represents the price of carbon emission rights, λ represents the proportion of carbon quota that users can emit; The user's electricity satisfaction loss is: Among them, C d is the loss of electricity satisfaction after user demand response, α m and β m is the user's electricity satisfaction loss parameter, ΔQ m,t is the difference between the actual power consumption and the expected power consumption of the user at time t; ΔQ m,t =|Q m,t -Q ′ m,t | Among them, Q m,t It is the sum of the electricity purchased by the user from each power seller at time t, that is, the actual electricity consumption of the user at time t; The user's electricity purchase decision model is: C m =C a +C b +C d 。 10. The method according to claim 9, characterized in that According to the unit power model, the Yalmip / IPOPT solver is nested by an iterative loop solving method to solve the double game model of power price decision, including: According to the unit power model, the marginal variable cost per unit of electricity is determined; wherein the marginal variable cost per unit of electricity is formulated as follows: M i =(ηαP TH,i +λ HY P HY,i +λ WT P WT,i +λ PV P PV,i ) / P SY,i Among them, M i is the marginal variable cost per unit of electricity at time i, η is the standard coal consumption rate of thermal power, α is the unit price of standard coal at time i, and λ HY is the variable cost coefficient of hydropower, λ WT is the variable cost coefficient of wind power, λ PV is the variable cost coefficient of photovoltaics; Determine the total cost per unit of electricity based on the marginal variable cost per unit of electricity; Determine the adjustment formula for the iterative loop; The total electricity cost model is solved by nesting the Yalmip / IPOPT solver using an iterative loop solving method.

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