Transaction decision-making method and system for new energy participating in spot market and green certificate market

Abstract

The present invention relates to a transaction decision method and system for new energy to participate in the spot market and the green certificate market. The method establishes a double-layer transaction decision model for new energy to participate in the spot market and the green certificate market, and reconstructs the double-layer transaction decision model into a single-layer transaction decision model based on the KKT condition; the single-layer transaction decision model is linearly solved by the binary expansion method, which can simultaneously simulate the transaction behavior of new energy in the spot market and the green certificate market, establish a collaborative clearing process for the spot electricity energy market and the green certificate market, and provide support for the collaborative construction of the spot market and the green certificate market. In addition, this method fully considers the transaction positioning and goals of new energy enterprises in different markets, proposes a transaction decision method for new energy enterprises, realizes the collaborative optimization of the transaction behavior of new energy enterprises in the spot market and the green certificate market, and has market design and transaction decision feasibility, and is very practical.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy power market operation, and specifically relates to a transaction decision method and system for new energy to participate in the spot market and the green certificate market. Background Art

[0002] The construction of the electricity spot market is the core link in the construction of the electricity market system and the key means to rationalize the electricity price mechanism. In addition, my country's energy transformation has risen to the national strategic level, and the economic development mode will change, promoting energy development from total expansion to quality and efficiency improvement. Renewable energy generation needs to maintain or occupy a certain proportion in regional power construction. With the gradual increase in the penetration rate of renewable energy represented by wind and solar power and the gradual advancement of the construction of the spot market, it is urgent to establish a market incentive mechanism that can promote the consumption of renewable energy. In March and September 2018, the National Energy Administration issued the "First Draft for Comments on the Renewable Energy Quota System" and the "Second Draft for Comments on the Renewable Energy Quota System", respectively, aiming to adopt the model of mandatory quotas for renewable energy and supporting green certificate transactions, smooth the industry fluctuations caused by subsidy declines and gaps, and start renewable energy quota assessment and green certificate mandatory constraint transactions in a timely manner. The implementation and effective execution of the quota and green certificate trading mechanism will enable renewable energy electricity equivalent to the quota to be traded between regions (grids) to address the differences in renewable energy resources between regions, and will achieve the above-mentioned goals of reducing carbon emissions and increasing the proportion of non-fossil energy, ensuring that renewable energy such as wind power and photovoltaic power generation can be connected to the grid without discrimination and barriers, and encouraging a wider range of social groups to assume more obligations in green development and green consumption. Due to the uncertain characteristics and technical parameters of new energy operation, there is a lack of guidance on the trading behavior of new energy in the spot market and green certificate market.

[0003] Therefore, how to achieve coordinated optimization of the trading behaviors of new energy enterprises in the spot market and the green certificate market to provide support for new energy to further participate in the electricity spot market has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In order to at least solve the above-mentioned problems existing in the prior art, the present invention provides a trading decision method and system for new energy to participate in the spot market and the green certificate market, so as to ensure the coordinated optimization of the trading behaviors of new energy enterprises in the spot market and the green certificate market, and provide support for new energy to further participate in the electricity spot market.

[0005] The technical solution provided by the present invention is as follows:

[0006] On the one hand, a transaction decision method for new energy to participate in the spot market and green certificate market includes:

[0007] Establish a two-tier model for transaction decision-making for new energy participating in the spot market and green certificate market;

[0008] Reconstruct the transaction decision double-layer model into a transaction decision single-layer model based on KKT conditions;

[0009] The binary expansion method is used to linearize the single-layer model of transaction decision-making to assist new energy companies in making transaction decisions in the spot market and green certificate market.

[0010] Optionally, the above-mentioned establishment of a two-tier trading decision model for new energy to participate in the spot market and the green certificate market includes:

[0011] Establish a top-level model for transaction decision-making of new energy enterprises;

[0012] Establish the lower-level model of day-ahead market clearing, the lower-level model of real-time market clearing and the lower-level model of green certificate market clearing.

[0013] Optionally, the above-mentioned establishment of the upper-level model for new energy enterprise transaction decision-making includes:

[0014] Construct the objective function of the upper-level decision-making model with the goal of maximizing the benefits of new energy;

[0015] Determine the constraints of the upper-level decision-making model, which include: quantity and price declaration curve constraints, upper and lower limit constraints on green certificate sales, green certificate sales balance constraints, green certificate sales relationship constraints and green certificate market price forecasts;

[0016] According to the objective function and the constraints, an upper-level model for new energy enterprise transaction decision-making is established.

[0017] Optionally, the above-mentioned establishment of the lower-level model of day-ahead market clearing includes:

[0018] Construct the objective function of the lower-level model of day-ahead market clearing with the goal of maximizing the overall social benefits;

[0019] Determine the constraints of the lower model of the day-ahead market clearing, wherein the constraints of the lower model of the day-ahead market clearing include: power supply and demand balance constraints, unit / station output constraints, user power demand constraints, interconnection line capacity constraints and power angle constraints;

[0020] A day-ahead market clearing lower model is established according to the objective function of the day-ahead market clearing lower model and the constraints of the day-ahead market clearing lower model.

[0021] Optionally, the above-mentioned establishment of a real-time market clearing lower-level model includes:

[0022] Construct the objective function of the lower-level model of real-time market clearing based on multi-scenario new energy output forecast;

[0023] Determine the underlying model constraints for real-time market clearing;

[0024] A real-time market clearing lower layer model is established according to the real-time market clearing lower layer model objective function and the real-time market clearing lower layer model constraint conditions.

[0025] Optionally, the above-mentioned establishment of a lower-level model for clearing the green certificate market includes:

[0026] Based on the competitive Cournot model, the green certificate market clearing price expression is determined;

[0027] According to the green certificate market clearing price expression, a green certificate market clearing lower-level model is established.

[0028] Optionally, the above-mentioned linearization solution of the transaction decision single-layer model by using the binary expansion method includes:

[0029] Linearizing the product between decision quantities in the decision single-layer model and the complementary relaxation of the KKT condition;

[0030] The linearized result is subjected to binary expansion to obtain a solution.

[0031] On the other hand, a transaction decision system for new energy to participate in the spot market and green certificate market includes:

[0032] A two-layer model building module is used to build a two-layer model for trading decisions of new energy participating in the spot market and green certificate market;

[0033] A single-layer model reconstruction module, used to reconstruct the transaction decision double-layer model into a transaction decision single-layer model based on KKT conditions;

[0034] The linearization solution module is used to use the binary expansion method to perform linearization solution on the single-layer model of transaction decision-making, so as to assist new energy enterprises in participating in the transaction decision-making of the spot market and the green certificate market.

[0035] Optionally, the above-mentioned two-layer model building module is specifically used for:

[0036] Establish a top-level model for transaction decision-making of new energy enterprises;

[0037] Establish the lower-level model of day-ahead market clearing, the lower-level model of real-time market clearing and the lower-level model of green certificate market clearing.

[0038] Optionally, the above-mentioned two-layer model building module is further used for:

[0039] Construct the objective function of the upper-level decision-making model with the goal of maximizing the benefits of new energy;

[0040] Determine the constraints of the upper-level decision-making model, which include: quantity and price declaration curve constraints, upper and lower limit constraints on green certificate sales, green certificate sales balance constraints, green certificate sales relationship constraints and green certificate market price forecasts;

[0041] According to the objective function and the constraint conditions, an upper-level model for transaction decision-making of new energy enterprises is established;

[0042] Construct the objective function of the lower-level model of day-ahead market clearing with the goal of maximizing the overall social benefits;

[0043] Determine the constraints of the lower model of the day-ahead market clearing, wherein the constraints of the lower model of the day-ahead market clearing include: power supply and demand balance constraints, unit / station output constraints, user power demand constraints, interconnection line capacity constraints and power angle constraints;

[0044] Establishing a day-ahead market clearing lower model according to the day-ahead market clearing lower model objective function and the day-ahead market clearing lower model constraint conditions;

[0045] Construct the objective function of the lower-level model of real-time market clearing based on multi-scenario new energy output forecast;

[0046] Determine the underlying model constraints for real-time market clearing;

[0047] Establishing a real-time market clearing lower model according to the real-time market clearing lower model objective function and the real-time market clearing lower model constraint conditions;

[0048] Based on the competitive Cournot model, the green certificate market clearing price expression is determined;

[0049] According to the green certificate market clearing price expression, a green certificate market clearing lower-level model is established.

[0050] The beneficial effects of the present invention are:

[0051] The present invention provides a transaction decision method and system for new energy to participate in the spot market and the green certificate market. The method establishes a double-layer transaction decision model for new energy to participate in the spot market and the green certificate market, and reconstructs the double-layer transaction decision model into a single-layer transaction decision model based on the KKT condition; the single-layer transaction decision model is linearly solved by the binary expansion method, which can simultaneously simulate the transaction behavior of new energy in the spot market and the green certificate market, establish a collaborative clearing process for the spot electricity energy market and the green certificate market, and provide support for the collaborative construction of the spot market and the green certificate market. In addition, this method fully considers the transaction positioning and goals of new energy enterprises in different markets, proposes a transaction decision method for new energy enterprises, realizes the collaborative optimization of the transaction behavior of new energy enterprises in the spot market and the green certificate market, and has market design and transaction decision feasibility, and is very practical. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] 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.

[0053] Figure 1 It is a flow chart of a transaction decision method for new energy participating in the spot market and the green certificate market provided by an embodiment of the present invention;

[0054] Figure 2 This is a diagram of the green certificate market clearing quantity and price;

[0055] Figure 3 It is a structural diagram of a trading decision system for new energy participating in the spot market and the green certificate market provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] Figure 1 This is a flow chart of a transaction decision method for new energy participating in the spot market and the green certificate market provided by an embodiment of the present invention. Figure 2 It is a schematic diagram of the clearing quantity and price of the green certificate market.

[0058] like Figure 1 As shown, this embodiment provides a transaction decision method for new energy participating in the spot market and the green certificate market, including the following steps:

[0059] S11. Establish a two-tier model for transaction decision-making for new energy participating in the spot market and green certificate market.

[0060] In this embodiment, the double - layer trading decision model includes a spot market and a green certificate market. The specific market structure is as follows: First, new - energy enterprises submit quantity - price bid curves in the day - ahead market. The dispatching agency clears the day - ahead market and generates multiple new - energy output prediction scenarios and their corresponding occurrence probabilities considering the uncertainty of new energy. Within each scenario, the dispatching agency clears the real - time market based on the day - ahead bid quantity - price curves. Then, new - energy enterprises conduct green certificate trading in the green certificate market. New - energy enterprises face a double - layer trading decision problem in the spot market and the green certificate market. At the upper layer, the enterprise pursues the maximization of market revenue and transmits the bid quantity - price to the lower - layer sub - problem. At the lower layer, the enterprise makes trading decisions in the day - ahead market, real - time market, and green certificate market and transmits the trading results to the upper layer.

[0061] Specifically, a double - layer trading decision model for new - energy participation in the spot market and the green certificate market is established, including: establishing an upper - layer model for new - energy enterprise trading decisions. Establishing a lower - layer model for day - ahead market clearing, a lower - layer model for real - time market clearing, and a lower - layer model for green certificate market clearing.

[0062] And establishing an upper - layer model for new - energy enterprise trading decisions includes: constructing an objective function of the upper - layer decision model with the maximization of new - energy revenue as the goal;

[0063]

[0064] In the formula, R DA j 、R RT j and R TGC j are the revenues of new - energy enterprise j in the day - ahead market, real - time market, and green certificate market respectively, and their expressions are as follows:

[0065]

[0066]

[0067]

[0068] In the formula, T is the number of trading periods covered by the corresponding market; λ DA n,t is the nodal electricity price at node n at time t in the day - ahead market; λ G i,b is the marginal cost corresponding to the b - th segment in the day - ahead bid curve of unit / station i at time t; P DA,G i,b,t is the awarded output of the b - th segment in the day - ahead bid curve of unit / station i at time t; △t is the duration of each trading period; π ω is the probability of scenario ω occurring; W is the set of all scenarios; Ω jis the set of units / stations owned by new energy enterprise j; S n is the unit / field battle set at node n; RT n，ωt is the node electricity price of node n in the scenario ω at the real-time market time t; P RT,G i,b,ω,t is the marked force in the bth segment of the real-time declaration curve of unit / station i at time t in scenario ω; PT ω is the green certificate market clearing price in scenario ω; FT ω Predict the clearing price of the green certificate market in the future in the scenario ω; Q PT j，ω is the number of green certificates sold by new energy enterprise j in the green certificate market in scenario ω; Q FT j，ω is the number of green certificates sold by new energy enterprise j in the scenario ω in the future green certificate market.

[0069] Determine the constraints of the upper-level decision-making model, which include: quantity and price declaration curve constraints, upper and lower limit constraints on green certificate sales, green certificate sales equilibrium constraints, green certificate sales relationship constraints and green certificate market price forecasts; among them,

[0070] 1) Constraints on the quantity and price declaration curve, the expression is as follows:

[0071]

[0072]

[0073] In the formula, α min i,b and α max i,b The upper and lower limits of the price of segment b of the declaration curve of unit / station i; α i,b is the price quoted for the bth segment of the declaration curve of unit / station i. Formula (5) indicates that the unit / station quotation must meet the upper and lower limit constraints of the declared price; Formula (6) indicates that the unit / station quantity price declaration curve must meet the non-monotonic decreasing condition.

[0074] 2) The upper and lower limits of the green certificate sales volume are as follows:

[0075]

[0076] In the formula, Q Gmin j，ω and Q Gmax j，ω Q is the upper and lower limits of the green certificate sales volume of new energy enterprises in the scenario ω; PT j，ωis the sales volume of green certificates of new energy enterprise j in scenario ω. Formula (7) indicates that the sales volume of green certificates of new energy enterprises must meet the upper and lower limits of their green certificate sales volume.

[0077] 3) Green certificate sales volume equilibrium constraint, the expression is as follows:

[0078]

[0079] In the formula, Φ R is the set of new energy enterprises in the green certificate market. Formula (8) indicates that the maximum green certificate sales volume of new energy enterprises must be consistent with the real-time market clearing result.

[0080] 4) The green certificate sales volume constraint is expressed as follows:

[0081]

[0082] Formula (9) indicates that the sum of the current and future green certificate sales of new energy enterprises must be less than their maximum green certificate sales.

[0083] 5) Future green certificate market price forecast, the expression is as follows:

[0084]

[0085] In the formula, γ FT j is the prediction coefficient of new energy enterprise j on the future green certificate market price; 0 TGC It is the penalty price when the supply of green certificates is 0.

[0086] The upper model transforms the decision variable α i,b , Q PT j，ω and Q FT j，ω The lower model then passes the solved clearing price and electricity demand to the upper model.

[0087] Then, based on the objective function and constraints, an upper-level model for new energy enterprise transaction decision-making is established.

[0088] Specifically, the lower-level model of the day-ahead market clearing is established, including: constructing the objective function of the lower-level model of the day-ahead market clearing with the goal of maximizing the overall social benefits; the objective function is to maximize the sum of the overall social benefits during the day-ahead market trading period, and the expression is as follows:

[0089]

[0090] Where P DA,D d,k,t is the winning load of the kth segment in the day-ahead declared curve of user d at time t; DA,Dd,k is the winning bid price for the kth segment in the day-ahead bid curve of user d.

[0091] Determine the constraints of the lower model of the day-ahead market clearing. The constraints of the lower model of the day-ahead market clearing include: power supply and demand balance constraints, unit / station output constraints, user power demand constraints, interconnection line capacity constraints and power angle constraints; among them,

[0092] 1) Electricity supply and demand balance constraint, the expression is as follows:

[0093]

[0094] In the formula, Ψ D n and N n are the set of users at node n and the set of nodes connected to node n; B n,m is the admittance of the line between node n and node m; δ DA n,t and δ DA m,t is the voltage amplitude angle between node n and node m in the day-ahead market at time t.

[0095] 2) Unit / station output constraint, the expression is as follows:

[0096]

[0097] Where P Gmin i,b and P Gmax i,b are the upper and lower limits of the available capacity of the bth section of unit / station i. Formula (13) indicates that the output of unit / station i cannot exceed its upper and lower limits of available capacity.

[0098] 3) User power demand constraint, the expression is as follows:

[0099]

[0100] Where P Dmin d,t and P Dmax d,t are the upper and lower limits of the load demand reported by user d. Formula (14) indicates that the day-ahead market clearing load of user d cannot exceed the upper and lower limits of the load demand reported by it.

[0101] 4) Tie line capacity constraint, the expression is as follows:

[0102]

[0103] Where P Lmaxn,m is the rated capacity of the line between node n and node m. Formula (15) indicates that the power of the line between node n and node m cannot exceed its rated capacity.

[0104] 5) Power angle constraint, the expression is as follows:

[0105]

[0106] Where π is the rated power angle of the node. Formula (16) indicates that the power angle of node n in the day-ahead market cannot exceed its rated power angle.

[0107] Among them, λ DA n,t , μ DA,Gmin i,b,t , μ DA,Gmax i,b,t , μ DA,GTmin i,t , μ DA,GTmax i,t , μ DA,Dmin d,k,t , μ DA,Dmax d,k,t , ν DA,Lmax n,m,t , DA,max n,t , DA,min n,t and DA,δ1 t is the dual variable of the corresponding constraint.

[0108] Then, according to the objective function and constraints of the lower-level model of day-ahead market clearing, a lower-level model of day-ahead market clearing is established with the goal of maximizing the overall social benefits.

[0109] Specifically, a real-time market clearing lower-level model is established, including: constructing the objective function of the real-time market clearing lower-level model based on multi-scenario new energy output forecasts; the real-time market clearing lower-level model is similar to the day-ahead market clearing lower-level model, the main difference being that multiple new energy output forecast scenarios are generated based on the uncertainty characteristics of new energy output. The objective function of the real-time market clearing lower-level model is to maximize the sum of the total social benefits of the real-time market, and the expression is as follows:

[0110]

[0111] Where P RT,D d,k,t,ω is the real-time market bid load of user d at time t in scenario ω; D d,k is the real-time market node price of user d.

[0112] Determine the constraints of the real-time market clearing lower model; the real-time market clearing model constraints are similar to the day-ahead market clearing model constraints and will not be described in detail. Then, based on the objective function and constraints of the real-time market clearing lower model, consider multi-scenario new energy forecasts to establish the real-time market clearing lower model.

[0113] Specifically, the lower-level model of green certificate market clearing is established, including: based on the competitive Cournot model, the green certificate market clearing price expression is determined, and the expression is as follows:

[0114]

[0115] In the formula, α 0 TGC and β 0 TGC It is the inverse function of the Cournot model price function and can be calculated based on the green certificate market parameters. In the Cournot model, there is a linear relationship between the clearing price and clearing volume of the green certificate market, as shown in Figure 2. Figure 2 In the figure, point A represents the highest clearing price of the green certificate market, i.e., λ 0 TGC Point B represents the user's willingness to pay, i.e., θ 0 TGC λ 0 TGC When the supply of green certificates is sufficient, in order to meet the government's renewable energy consumption requirements, the user's willingness to pay is ρ 0 TGC Q 0 D , where θ 0 TGC It can be obtained from historical data, ρ 0 TGC is the market renewable energy quota ratio, Q 0 D is the demand for green certificates. Figure 2 The straight line in is the inverse function of the price function in the Cournot model. Point C represents the market equilibrium point calculated by equation (18).

[0116] Therefore, the values ​​of α0TGC and β0TGC can be obtained as follows:

[0117]

[0118]

[0119]

[0120] Then, based on the green certificate market clearing price expression, the green certificate market clearing lower-level model is established.

[0121] S12. Reconstruct the two-layer transaction decision model into a single-layer transaction decision model based on KKT conditions.

[0122] First, based on the KKT condition, the day-ahead market clearing model can be reconstructed as:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Secondly, the lower-level model of real-time market clearing can be reconstructed into a model similar to the lower-level model of day-ahead market clearing.

[0136] Finally, since the lower-level model of green certificate market clearing does not involve optimization problems, the constraints of the lower-level model of green certificate market clearing can be directly applied to the upper-level model.

[0137] In summary, by reconstructing the lower-level models of the day-ahead market, real-time market and green certificate market clearing, the original two-level trading decision model can be transformed into a single-level trading decision model that includes the original upper-level model and the optimization constraints of the lower-level model through the above method.

[0138] S13. Use the binary expansion method to linearize the single-layer model of transaction decision-making to assist new energy companies in making transaction decisions in the spot market and green certificate market.

[0139] Although the two-layer model has been transformed into a single-layer model, the single-layer model still belongs to a nonlinear optimization problem. Nonlinear factors include: 1) the product between decision variables; 2) the complementary slackness of KKT conditions. Based on binary expansion, the binary expansion method is used to linearize the single-layer model of transaction decision, which mainly includes: linearizing the product between decision quantities and the complementary slackness of KKT conditions in the single-layer model of decision, and performing binary expansion on the result after linearization. Among them, the linearization of the product between decision quantities in the single-layer model of decision is specifically as follows:

[0140] The nonlinear factors brought about by the product of decision variables include: DA n,t P DA,G i,b,t , λ in formula (3) RT n,ω,t P DA,G i,b,t and λ RT n，ω，t P RT，G i，b，ω，t And λ in formula (4) PT ω Q PT j，ω .

[0141] First, for λ DA n,t P DA,G i,b,t Linearization is performed. Based on the strong duality theorem, the equation relationship between the objective function of the original transaction decision model and the current dual model can be obtained as follows:

[0142]

[0143] Secondly, λ in formula (2) DA n,t can be replaced by other variables in formula (22), so R DA j can be converted to:

[0144]

[0145] In order to facilitate the expression of the formula, Λ DA 1 and Λ DA 2 The intermediate variable simplified formula (35) is as follows:

[0146]

[0147]

[0148] According to formula (34), Λ DA 1 It can be expressed as:

[0149]

[0150] According to the complementary slackness constraint formula (25)-formula (33), Λ DA 2 It can be expressed as:

[0151]

[0152] Therefore, the nonlinear R DA j The expression is converted to a linear expression. For nonlinear R RT j The same steps can be used to convert the expression into a linear expression.

[0153] Finally, for λ PT ω Q PT j，ω Linearization is performed. Based on the binary expansion, R TGC j It can be linearized as follows:

[0154]

[0155] The constraints of this linearization method are as follows:

[0156]

[0157]

[0158]

[0159]

[0160]

[0161] In the formula, Q ref j，s Q PT j，ω The sth approximate reference value of ΔQ ref j is the approximate accuracy; ψ PT j，ω，s and η PT j，ω，s are auxiliary variables of the binary expansion method; M PT 0 Q PT j，ωHigh-level; Q max j and Q min are the upper and lower limits of green certificate trading volume of power generation enterprise j; S PT 0 is the order of the approximate reference value.

[0162] The linearization of the complementary slackness of the KKT condition specifically includes:

[0163] The complementary relaxation of the KKT condition mainly exists in equations (25) to (33), which can be expressed as:

[0164] 0≤f(x)⊥g(x)≥0 (46)

[0165] Similar to substep (3.1), introducing binary variables c and higher-order M, we can obtain:

[0166] 0≤f(x)≤Mc (47)

[0167] 0≤g(y)≤M(1-c) (48)

[0168] Through the above method, the complementary slack constraints (25)-(33) are converted into linear constraints. The original nonlinear trading decision two-layer model is converted into a linear trading decision single-layer model through the above steps, which can be solved by the mixed integer linear programming method.

[0169] This embodiment provides a trading decision method for new energy to participate in the spot market and the green certificate market. By establishing a two-layer trading decision model for new energy to participate in the spot market and the green certificate market, the two-layer trading decision model is reconstructed into a single-layer trading decision model based on the KKT condition; the single-layer trading decision model is linearly solved by the binary expansion method, which can simultaneously simulate the trading behavior of new energy in the spot market and the green certificate market, establish a collaborative clearing process for the spot electricity energy market and the green certificate market, and provide support for the collaborative construction of the spot market and the green certificate market. In addition, this method fully considers the trading positioning and goals of new energy enterprises in different markets, proposes a trading decision method for new energy enterprises, realizes the collaborative optimization of the trading behavior of new energy enterprises in the spot market and the green certificate market, and has market design and trading decision feasibility, and is very practical.

[0170] Based on the same general inventive concept, the present application also protects a trading decision system for new energy to participate in the spot market and the green certificate market.

[0171] Figure 3 It is a structural diagram of a trading decision system for new energy participating in the spot market and the green certificate market provided by an embodiment of the present invention.

[0172] like Figure 3As shown, this embodiment provides a transaction decision system for new energy participating in the spot market and the green certificate market, including:

[0173] A two-layer model building module 10 is used to build a two-layer model for transaction decision-making of new energy participating in the spot market and the green certificate market;

[0174] A single-layer model reconstruction module 20, used to reconstruct the transaction decision double-layer model into a transaction decision single-layer model based on the KKT condition;

[0175] The linearization solution module 30 is used to use the binary expansion method to perform linearization solution on the single-layer model of transaction decision-making, so as to assist new energy enterprises in participating in the transaction decision-making of the spot market and the green certificate market.

[0176] This embodiment provides a trading decision system for new energy to participate in the spot market and the green certificate market. By establishing a two-layer trading decision model for new energy to participate in the spot market and the green certificate market, the two-layer trading decision model is reconstructed into a single-layer trading decision model based on the KKT condition; the single-layer trading decision model is linearly solved by the binary expansion method, which can simultaneously simulate the trading behavior of new energy in the spot market and the green certificate market, establish a collaborative clearing process for the spot electricity energy market and the green certificate market, and provide support for the collaborative construction of the spot market and the green certificate market. In addition, this method fully considers the trading positioning and goals of new energy enterprises in different markets, proposes a trading decision method for new energy enterprises, and realizes the collaborative optimization of the trading behavior of new energy enterprises in the spot market and the green certificate market. At the same time, it has market design and trading decision feasibility, and is very practical.

[0177] Furthermore, the two-layer model building module 20 in this embodiment is specifically used for:

[0178] Establish a top-level model for transaction decision-making of new energy enterprises;

[0179] Establish the lower-level model of day-ahead market clearing, the lower-level model of real-time market clearing and the lower-level model of green certificate market clearing.

[0180] Furthermore, the two-layer model building module 20 in this embodiment is also specifically used for:

[0181] Construct the objective function of the upper-level decision-making model with the goal of maximizing the benefits of new energy;

[0182] Determine the constraints of the upper-level decision-making model, which include: quantity and price declaration curve constraints, upper and lower limit constraints on green certificate sales, green certificate sales equilibrium constraints, green certificate sales relationship constraints and green certificate market price forecast;

[0183] According to the objective function and constraints, establish the upper-level model of new energy enterprise transaction decision-making;

[0184] Construct the objective function of the lower-level model of day-ahead market clearing with the goal of maximizing the overall social benefits;

[0185] Determine the constraints of the lower-level model for day-ahead market clearing, which include: power supply and demand balance constraints, unit / station output constraints, user power demand constraints, interconnection line capacity constraints, and power angle constraints;

[0186] According to the objective function and constraint conditions of the lower model of day-ahead market clearing, the lower model of day-ahead market clearing is established;

[0187] Construct the objective function of the lower-level model of real-time market clearing based on multi-scenario new energy output forecast;

[0188] Determine the underlying model constraints for real-time market clearing;

[0189] According to the objective function and constraint conditions of the real-time market clearing lower model, a real-time market clearing lower model is established;

[0190] Based on the competitive Cournot model, the green certificate market clearing price expression is determined;

[0191] According to the green certificate market clearing price expression, a green certificate market clearing lower-level model is established.

[0192] The embodiments of the device part have been introduced and explained in detail in the corresponding method embodiments. Therefore, no specific explanation will be given in the corresponding device part, and they can be understood by reference to each other.

[0193] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0201] 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.

[0202] 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 trading decision-making method for new energy to participate in the spot market and green certificate market, It is characterized in that Bag include: Establish a two-tier model for new energy companies to participate in the spot market and green certificate market, including: establishing an upper-tier model for new energy companies’ trading decisions; establishing lower-tier models for day-ahead market clearing, real-time market clearing, and green certificate market clearing; The establishment of the upper-level model for transaction decision-making of new energy enterprises includes: constructing the objective function of the upper-level model for decision-making with the goal of maximizing the revenue of new energy; determining the constraint conditions of the upper-level model for decision-making, wherein the constraint conditions of the upper-level model for decision-making include: quantity-price declaration curve constraint, upper and lower limit constraints of green certificate sales volume, balance constraint of green certificate sales volume, relationship constraint of green certificate sales volume and prediction of green certificate market price; establishing the upper-level model for transaction decision-making of new energy enterprises according to the objective function of the upper-level model for decision-making and the constraint conditions of the upper-level model for decision-making; The establishment of the lower model of the day-ahead market clearing includes: constructing an objective function of the lower model of the day-ahead market clearing with the goal of maximizing the overall social benefits; determining the constraint conditions of the lower model of the day-ahead market clearing, wherein the constraint conditions of the lower model of the day-ahead market clearing include: power supply and demand balance constraint, unit or station output constraint, user power demand constraint, interconnection line capacity constraint and power angle constraint; establishing the lower model of the day-ahead market clearing according to the objective function of the lower model of the day-ahead market clearing and the constraint conditions of the lower model of the day-ahead market clearing; The establishment of the real-time market clearing lower model includes: constructing a real-time market clearing lower model objective function based on multi-scenario new energy output forecast; determining the real-time market clearing lower model constraint conditions; establishing the real-time market clearing lower model according to the real-time market clearing lower model objective function and the real-time market clearing lower model constraint conditions; The establishment of the lower-level model for clearing the green certificate market includes: determining the green certificate market clearing price expression based on the competitive Cournot model; and establishing the lower-level model for clearing the green certificate market based on the green certificate market clearing price expression; Reconstructing the two-layer trading decision model into a single-layer trading decision model based on KKT conditions includes: reconstructing the lower-layer model of the day-ahead market clearing based on KKT conditions; reconstructing the lower-layer model of the real-time market clearing and the lower-layer model of the green certificate market clearing; The binary expansion method is used to linearize the single-layer model of transaction decision-making to assist new energy The enterprise participates in the trading decision of the spot market and the green certificate market; wherein the use of the binary expansion method to linearize the single-layer model of the trading decision includes: linearizing the product between the decision variables in the single-layer model and the complementary relaxation of the KKT conditions; performing binary expansion on the result after the linearization process; Among them, the objective function of the decision upper model is expressed as: ； In the formula, R DA j , R RT j and R TGC j New energy enterprises j The returns in the day-ahead market, real-time market and green certificate market are expressed as follows: In the formula, T The number of trading sessions covered for the respective market; λ DA n,t For Node n At the day-ahead market time t Node electricity price; λ G i,b For crew / station i At the moment t The first b The marginal cost corresponding to the segment; P DA,G i,b,t For crew / station i At the moment t The first b The force of the paragraph is marked; △t Duration of each trading session; π ω For the scene ω Probability of occurrence; W For all scene sets; Ω j For new energy enterprises j Owned units / stations; S n For the node n The crew / field set; λ RT n,ω,t For the scene ω Midpoint n In real-time market moments t Node electricity price; P RT,G i,b,ω,t For the scene ω Medium unit / station i At the moment t In the real-time declaration curve b The force of the paragraph is marked; λ PT ω For the scene ω The market clearing price of China Green Certificates; λ FT ω For the scene ω The forecast clearing price of the green certificate market in the future; Q PT j,ω For the scene ω New Energy Enterprises j The number of green certificates sold in the green certificate market; Q FT j,ω For the scene ω New Energy Enterprises j The number of green certificates sold in the future green certificate market; Among them, the quantity and price declaration curve constraint is expressed as follows: Wherein, α min i,b and α max i,b are the upper and lower price limits of the i declaration curve of the unit / station for the b th segment; α i,b is the quoted price of the unit / station for the i declaration curve of the b th segment; Among them, the upper and lower limits of the green certificate sales volume are constrained, and the expression is as follows: In the formula, Q Gmin j,ω and Q Gmax j,ω For the scene ω New Energy Enterprises j The upper and lower limits of green certificate sales; Q PT j,ω For the scene ω New Energy Enterprises j Green Certificates sold; Among them, the green certificate sales volume equilibrium constraint is expressed as follows: In the formula, Φ R For the green certificate market new energy enterprise collection; Among them, the green certificate sales volume constraint is expressed as follows: It means that the sum of the current and future green certificate sales of new energy enterprises must be less than their maximum green certificate sales; Among them, the future green certificate market price forecast is expressed as follows: In the formula, γ FT j For new energy enterprises j The prediction coefficient of future green certificate market price; λ 0 TGC It is the penalty price when the supply of green certificates is 0; Among them, the objective function of the lower model of the day-ahead market clearing is expressed as: In the formula, P DA,D d,k,t For users d At the moment t The first k Section winning bid load; λ DA,D d,k For users d The first k The winning bid price for the segment; Among them, the power supply and demand balance constraint is expressed as follows: In the formula, Ψ D n and N n They are respectively at the nodes n The user set and the node n The set of connected nodes; B n,m For Node n and nodes m The admittance of the line between δ DA n,t and δ DA m,t is the node in the day-ahead market n and nodes m At the moment t The voltage amplitude angle; The unit or station output constraint is expressed as follows: In the formula, P Gmin i,b and P Gmax i,b Respectively for units / stations i No. b The upper and lower limits of the segment’s available capacity; The user power demand constraint is expressed as follows: In the formula, P Dmin d,t and P Dmax d,t For users d The upper and lower limits of the reported load demand; The tie line capacity constraint is expressed as follows: In the formula, P Lmax n,m For Node n and nodes m Rated capacity of the line between them; Power angle constraint, the expression is as follows: ； In the formula, π is the node rated power angle; Among them, the objective function of the real-time market clearing lower model is expressed as: In the formula, in the formula, P RT,D d,k,t,ω For the scene ω Medium User d At the moment t Real-time market bidding load; λ D d,k For users d Real-time market node prices; The green certificate market clearing price expression is: In the formula, α 0 TGC and β 0 TGC It is the inverse function of the price function of the Cournot model.

2. A trading decision-making system for new energy to participate in the spot market and green certificate market, It is characterized in that include: A two-layer model building module is used to establish a two-layer model for transaction decisions of new energy participating in the spot market and the green certificate market; specifically used to: establish an upper-layer model for transaction decisions of new energy enterprises; establish a lower-layer model for day-ahead market clearing, a lower-layer model for real-time market clearing, and a lower-layer model for green certificate market clearing; wherein, the establishment of an upper-layer model for transaction decisions of new energy enterprises includes: constructing an objective function of the upper-layer model for decision-making with the goal of maximizing new energy revenue; determining the constraints of the upper-layer model for decision-making, the constraints of the upper-layer model for decision-making include: quantity and price declaration curve constraints, upper and lower limit constraints on green certificate sales volume, green certificate sales volume equilibrium constraints, green certificate sales volume relationship constraints, and green certificate market price forecasts; according to the objective function of the upper-layer model for decision-making and the constraints of the upper-layer model for decision-making, establish an upper-layer model for transaction decisions of new energy enterprises; The establishment of the lower model of the day-ahead market clearing includes: constructing an objective function of the lower model of the day-ahead market clearing with the goal of maximizing the overall social benefits; determining the constraint conditions of the lower model of the day-ahead market clearing, wherein the constraint conditions of the lower model of the day-ahead market clearing include: power supply and demand balance constraint, unit or station output constraint, user power demand constraint, interconnection line capacity constraint and power angle constraint; establishing the lower model of the day-ahead market clearing according to the objective function of the lower model of the day-ahead market clearing and the constraint conditions of the lower model of the day-ahead market clearing; The establishment of the real-time market clearing lower model includes: constructing a real-time market clearing lower model objective function based on multi-scenario new energy output forecast; determining the real-time market clearing lower model constraint conditions; establishing the real-time market clearing lower model according to the real-time market clearing lower model objective function and the real-time market clearing lower model constraint conditions; The establishment of the lower-level model for clearing the green certificate market includes: determining the green certificate market clearing price expression based on the competitive Cournot model; and establishing the lower-level model for clearing the green certificate market based on the green certificate market clearing price expression; The single-layer model reconstruction module is used to reconstruct the trading decision double-layer model into a trading decision single-layer model based on KKT conditions; specifically, it is used to reconstruct the day-ahead market clearing lower-layer model based on KKT conditions; reconstruct the real-time market clearing lower-layer model and the green certificate market clearing lower-layer model; A linearization solution module is used to linearize the single-layer model of transaction decision using a binary expansion method to assist new energy enterprises in participating in transaction decisions in the spot market and the green certificate market; specifically, it is used to linearize the product between decision variables in the single-layer model of decision making and the complementary relaxation of KKT conditions; and binary expansion is performed on the result after the linearization process; Among them, the objective function of the decision upper model is expressed as: ； In the formula, R DA j , R RT j and R TGC j New energy enterprises j The returns in the day-ahead market, real-time market and green certificate market are expressed as follows: In the formula, T The number of trading sessions covered for the respective market; λ DA n,t For Node n At the day-ahead market time t Node electricity price; λ G i,b For crew / station i At the moment t The first b The marginal cost corresponding to the segment; P DA,G i,b,t For crew / station i At the moment t The first b The force of the paragraph is marked; △t Duration of each trading session; π ω For the scene ω Probability of occurrence; W For all scene sets; Ω j For new energy enterprises j Owned units / stations; S n For the node n The crew / field set; λ RT n,ω,t For the scene ω Midpoint n In real-time market moments t Node electricity price; P RT,G i,b,ω,t For the scene ω Medium unit / station i At the moment t In the real-time declaration curve b The force of the paragraph is marked; λ PT ω For the scene ω The market clearing price of China Green Certificates; λ FT ω For the scene ω The forecast clearing price of the green certificate market in the future; Q PT j,ω For the scene ω New Energy Enterprises j The number of green certificates sold in the green certificate market; Q FT j,ω For the scene ω New Energy Enterprises j The number of green certificates sold in the future green certificate market; Among them, the quantity and price declaration curve constraint is expressed as follows: In the formula, α min i,b and α max i,b For crew / station i Report curve b The upper and lower price limits of the segment; α i,b For crew / station i Report curve b Segment quotation; Among them, the upper and lower limits of the green certificate sales volume are constrained, and the expression is as follows: In the formula, Q Gmin j,ω and Q Gmax j,ω For the scene ω New Energy Enterprises j The upper and lower limits of green certificate sales; Q PT j,ω For the scene ω New Energy Enterprises j Green Certificates sold; Among them, the green certificate sales volume equilibrium constraint is expressed as follows: In the formula, Φ R For the green certificate market new energy enterprise collection; Among them, the green certificate sales volume constraint is expressed as follows: It means that the sum of the current and future green certificate sales of new energy enterprises must be less than their maximum green certificate sales; Among them, the future green certificate market price forecast is expressed as follows: In the formula, γ FT j For new energy enterprises j The prediction coefficient of future green certificate market price; λ 0 TGC It is the penalty price when the supply of green certificates is 0; Among them, the objective function of the lower model of the day-ahead market clearing is expressed as: In the formula, P DA,D d,k,t For users d At the moment t The first k Section winning bid load; λ DA,D d,k For users d The first k The winning bid price for the segment; Among them, the power supply and demand balance constraint is expressed as follows: In the formula, Ψ D n and N n They are respectively at the nodes n The user set and the node n The set of connected nodes; B n,m For Node n and nodes m The admittance of the line between δ DA n,t and δ DA m,t is the node in the day-ahead market n and nodes m At the moment t The voltage angle of The unit or station output constraint is expressed as follows: In the formula, P Gmin i,b and P Gmax i,b Respectively for units / stations i No. b The upper and lower limits of the segment’s available capacity; The user power demand constraint is expressed as follows: In the formula, P Dmin d,t and P Dmax d,t For users d The upper and lower limits of the reported load demand; The tie line capacity constraint is expressed as follows: In the formula, P Lmax n,m For Node n and nodes m Rated capacity of the line between them; Power angle constraint, the expression is as follows: ； In the formula, π is the node rated power angle; Among them, the objective function of the real-time market clearing lower model is expressed as: In the formula, in the formula, P RT,D d,k,t,ω For the scene ω Medium User d At the moment t Real-time market bidding load; λ D d,k For users d Real-time market node prices; The green certificate market clearing price expression is: In the formula, α 0 TGC and β 0 TGC It is the inverse function of the price function of the Cournot model.

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