Electric power spot market transaction strategy optimization method and system suitable for joint distribution and storage main body

Abstract

The invention discloses an electric power spot market transaction strategy optimization method and system suitable for a combined distribution and storage main body, and belongs to the field of electrical engineering. According to the method, a quotation strategy optimization model of a joint distribution and storage main body in an electric power spot market is constructed based on a master-slave game architecture. The method comprises the following steps: firstly, respectively establishing transaction strategy optimization models of three subjects including a thermal power generating unit, a new energy power station and industrial and commercial users; secondly, an electric power spot market optimization clearing model is constructed; thirdly, the market optimization clearing model is equivalently converted into a constraint condition of a joint distribution and storage main body optimization problem through a KKT condition, so that a non-convex nonlinear double-layer game model is converted into a nonlinear single-layer optimization problem; finally, nonlinear terms in the optimization problem are linearized, and a mathematical model easy to solve is formed. Therefore, by constructing the transaction strategy model and performing numerical simulation, the optimal transaction strategy of the combined distribution and storage main body is finally output, the optimal transaction strategy can be used for guiding the spot market transaction decision, and the economic benefit is maximized.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and more specifically, relates to a method and system for optimizing power spot market transaction strategies applicable to a joint distribution and storage entity. Technical Background

[0002] Since 2015, my country has gradually implemented a spot market pilot, promoting multiple market players such as thermal power units, new energy power stations, energy storage power stations, and industrial and commercial users to enter the spot market and participate in transactions. As the proportion of renewable energy investment and installed capacity increases year by year, the uncertainty on both sides of the system has increased significantly, resulting in increased volatility of spot prices. In order to avoid spot price risks and use the peak-valley price difference to achieve additional arbitrage, market players can configure energy storage power stations and participate in the electricity spot market in the form of joint distribution and storage entities. Optimizing spot market trading decisions and increasing market returns are the direct goals of joint distribution and storage entities, and are also important links in market price discovery and resource optimization allocation. However, the operating constraints of the spot market are complex, the price formation mechanism is obscure, and traditional manual decision-making methods are difficult to support high-frequency and real-time trading decisions. Therefore, there is an urgent need for an optimization method for electricity spot market trading strategies suitable for joint distribution and storage entities to quantitatively support the trading decisions of market entities. Summary of the invention

[0003] In view of the defects of the prior art, the purpose of the present invention is to provide a method and system for optimizing electricity spot market trading strategies suitable for joint distribution and storage entities, aiming to provide a decision-making basis for market entities participating in spot market transactions by establishing a mathematical model, simulating the market operation mechanism and optimizing the entity's bidding strategy.

[0004] To achieve the above object, the present invention provides a method for optimizing power spot market trading strategy applicable to a joint distribution and storage entity, comprising the following steps:

[0005] S1. Establish the trading strategy optimization problem of each market entity with the goal of maximizing the economic benefits of each market entity, establish the trading strategy optimization problem of the energy storage power station with the goal of maximizing the market arbitrage of the energy storage power station, incorporate the trading strategy optimization problem of the energy storage power station into the trading strategy optimization problem of each market entity, and establish a trading strategy optimization model for the joint distribution and storage entity;

[0006] S2. Establish an optimal clearing model for the electricity spot market, with the goal of minimizing the system balancing cost and the constraints of the electricity spot market operation as constraints;

[0007] S3. Based on the master-slave game theory, a two-layer game model is established with the joint distribution and storage subject as the upper leader and the market operator as the lower follower; further, through the Karush-Kuhn-Tucker condition (KKT condition), the lower-layer power spot market optimization clearing model is equivalently converted into the constraint conditions in the upper-layer joint distribution and storage subject's trading strategy optimization model, thereby converting the non-convex nonlinear two-layer master-slave game into a nonlinear single-layer constraint condition;

[0008] S4. The nonlinear single-layer constraint conditions are converted into linear constraint conditions through mathematical derivation and linear transformation, and the converted linear constraint conditions are used as the linear constraint conditions of the trading strategy optimization model of the joint storage allocation entity. The established trading strategy optimization model of the joint storage allocation entity is linearized to solve the market trading decision.

[0009] Furthermore, the step S1 comprises:

[0010] S11. Construct the trading strategy optimization problem of thermal power units: the objective function is to maximize the power generation revenue (= power sales revenue - power generation cost), and the constraint is to ensure the increasing nature of the seller's quotation curve.

[0011] S12. Constructing the trading strategy optimization problem of new energy power stations: the objective function is to maximize the power generation revenue (= power sales revenue - power generation cost), and the constraint is to ensure the increasing nature of the seller's quotation curve.

[0012] S13. Construct the trading strategy optimization problem for industrial and commercial users: the objective function is to minimize the electricity purchase cost (= electricity purchase expenditure - electricity use utility), and the constraint is to ensure the decreasing nature of the buyer's quotation curve.

[0013] S14. Constructing the trading strategy optimization problem of energy storage power stations: The objective function is to maximize market arbitrage (= electricity sales revenue - electricity purchase expenditure - operating costs), and the constraints are to ensure the increasing nature of the seller's quotation curve and the decreasing nature of the buyer's quotation curve.

[0014] S15. Incorporate the optimization problem formed in S14 into the optimization problems of S11 to S13, and form trading strategy optimization models for the joint allocation and storage of three types of market entities: thermal power units, new energy power stations, and industrial and commercial users.

[0015] Furthermore, the step S2 comprises:

[0016] Based on the basic operating rules of the existing electricity market, an optimization clearing model for the electricity spot market is constructed: the objective function is to minimize the system balancing cost (= system power generation cost - system electricity utility), and the constraints are the operating constraints of the electricity spot market, which are: ① system power balance constraints; ② thermal power unit output constraints; ③ thermal power unit ramp constraints; ④ output constraints of new energy power stations; ⑤ load constraints of industrial and commercial users; ⑥ charging and discharging power constraints of energy storage power stations; ⑦ upper and lower limits, continuity, and periodicity constraints on the state of charge of energy storage power stations.

[0017] Furthermore, the step S3 comprises:

[0018] S31. Establish the Lagrangian dual function of the market clearing linear optimization model.

[0019] S32. Establish the KKT conditions for the market clearing linear optimization model, which specifically include: ① the original equality constraint; ② the first-order condition of the Lagrangian function; ③ the complementary relaxation condition transformed from the original inequality constraint; ④ the strong duality theorem equation equivalent to the complementary relaxation condition.

[0020] S33. Combine the KKT conditions formed by S32-①~③ into the constraint conditions of the optimization problems of various joint storage entities in S15, and transform the two-layer master-slave game into a nonlinear single-layer optimization problem.

[0021] Furthermore, the step S4 comprises:

[0022] S41. For the complementary relaxation conditions constructed in S32-③, use the mixed integer relaxation method (Big-M) to convert them into mixed integer linear constraints.

[0023] S42. For the nonlinear terms of the objective functions in the optimization problems of various joint storage entities in S33 (i.e., the electricity sales revenue and electricity purchase expenditure contained in the objective functions of each entity in problems S11 to S14 (essentially the product of the market clearing electricity price and the market entity's clearing power), the first-order conditions (S32-②), complementary relaxation conditions (S32-③), and strong duality theorem equation (S32-④) in the KKT conditions are respectively combined, and finally the nonlinear terms are equivalently converted into a set of linear expressions.

[0024] The present invention also provides an electricity spot market transaction strategy optimization system applicable to a joint distribution and storage entity, comprising: a computer-readable storage medium and a processor;

[0025] The computer-readable storage medium is used to store executable instructions;

[0026] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the above-mentioned electricity spot market transaction strategy optimization method applicable to the joint distribution and storage entity.

[0027] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0028] The present invention adopts a master-slave game architecture to transform a complex two-layer optimization problem into a single-layer optimization problem, solving the problem that traditional manual decision-making is difficult to deal with high-frequency and real-time trading decisions, and simplifying the trading strategy optimization process of market participants; by constructing a trading strategy optimization model for the joint distribution and storage subject, it supports market entities to optimize their quotation strategies in the electricity spot market, improves the decision-making accuracy of market participants, and maximizes economic benefits. Therefore, the present invention has significant practical value and can better serve the participants and operators of the electricity spot market. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flow chart of a method for optimizing electricity spot market trading strategies applicable to a joint distribution and storage entity provided by the present invention.

[0030] Figure 2 Comparison of transaction profits of joint storage entities provided in an embodiment of the present invention (50% new energy penetration scenario).

[0031] Figure 3 A comparison of market-clearing electricity prices provided for an embodiment of the present invention (50% new energy penetration scenario).

[0032] Figure 4 Comparison of transaction profits of joint storage entities provided in an embodiment of the present invention (60% new energy penetration scenario).

[0033] Figure 5 A comparison of market-clearing electricity prices provided for an embodiment of the present invention (60% new energy penetration scenario). DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0035] like Figure 1 As shown, the power spot market transaction strategy optimization method applicable to the joint distribution and storage subject of the present invention comprises the following steps:

[0036] S1. For thermal power units, new energy power stations (only photovoltaic and wind power units are considered), industrial and commercial users, and energy storage power stations (taking electrochemical energy storage as an example), the trading strategy optimization problem of each market entity is established with the goal of maximizing the economic benefits of each market entity, and the trading strategy optimization problem of energy storage power stations is established with the goal of maximizing the market arbitrage of energy storage power stations. The trading strategy optimization problem of energy storage power stations is incorporated into the trading strategy optimization problem of each market entity, and a trading strategy optimization model for joint storage entities is established.

[0037] Specifically, it can be divided into the following 5 steps.

[0038] S11. Constructing the trading strategy optimization problem of thermal power units: the objective function is to maximize the power generation revenue (=power sales revenue minus power generation cost).

[0039]

[0040] The constraint is to ensure the incremental nature of the seller's quote curve.

[0041]

[0042] In the formula, Ω G is the set of thermal power units owned by the Gth thermal power plant, c g，b is the marginal power generation cost of the g-th thermal power unit in the b-th segment, p g,b，t is the output power of the g-th thermal power unit at the b-th time t, α g，b，t is the seller's price of the g-th thermal power unit at the b-th time t, λ t ,λ min ,λ max They are the market clearing electricity price at time t and the upper and lower limits of the quotations of market players.

[0043] S12. Constructing the trading strategy optimization problem of new energy power stations: the objective function is to maximize the power generation revenue (=power sales revenue minus power generation cost).

[0044]

[0045] The constraint is to ensure the incremental nature of the seller's quote curve.

[0046]

[0047] In the formula, Ω R is the collection of wind power and photovoltaic units owned by the Rth new energy power station, c w , c pv are the marginal power generation costs of wind power / photovoltaic units, pw,b,t, ppv,b,t are the output power of the w / pvth wind power / photovoltaic unit at the bth time, α w，b,t , α pv,b,t They are the seller’s quotations for the w / pv wind power / photovoltaic power at the b period and time t respectively.

[0048] S13. Constructing the trading strategy optimization problem for industrial and commercial users: the objective function is to minimize the electricity purchase cost (= electricity purchase expenditure minus electricity use utility).

[0049]

[0050] The constraint is to ensure the decreasing nature of the buyer's quote curve.

[0051]

[0052] In the formula, Ω L is the set of load equipment owned by the Lth industrial and commercial user, u l，b is the marginal power utility of the lth load device in section b, p l,b，t is the power consumption of the lth load device at the bth time t, β g，b，t is the buyer's price for the first load device at time t in segment b.

[0053] S14. Constructing the trading strategy optimization problem of energy storage power station: the objective function is to maximize market arbitrage (= electricity sales revenue minus electricity purchase expenditure minus operating costs).

[0054]

[0055] The constraints are to ensure the increasing nature of the seller's quote curve and the decreasing nature of the buyer's quote curve.

[0056]

[0057] In the formula, c s is the unit charging and discharging cost of the energy storage power station, are the charging / discharging power of the energy storage station at the bth segment at time t, α s，b,t , β s，b，t They are the seller / buyer quotes of the energy storage power station at time t in segment b.

[0058] S15. Incorporate the optimization problem formed in S14 into the optimization problems of S11 to S13, and form trading strategy optimization models for the joint allocation and storage of three types of market entities: thermal power units, new energy power stations, and industrial and commercial users.

[0059] ① For the joint storage entity of thermal power units (thermal storage combination), the trading strategy optimization problem is:

[0060]

[0061] ② For the joint distribution and storage entity of new energy power stations (wind, solar and storage), the trading strategy optimization problem is:

[0062]

[0063] ③ For the joint storage entity of industrial and commercial users (load-storage combination), the trading strategy optimization problem is:

[0064]

[0065] S2. Based on the basic operating rules of the existing electricity market, an optimization clearing model for the electricity spot market is constructed: the objective function is to minimize the system balancing cost (= system power generation cost minus system power utility).

[0066]

[0067] The constraints are the operation constraints of the electricity spot market, which are:

[0068] ① System power balance constraints;

[0069]

[0070] ② Output constraints of thermal power units;

[0071]

[0072] ③Climbing constraints of thermal power units;

[0073]

[0074] ④ Output constraints of new energy power stations;

[0075]

[0076] ⑤ Load constraints for industrial and commercial users;

[0077]

[0078] ⑥Charging and discharging power constraints of energy storage power stations;

[0079]

[0080] ⑦ Upper and lower limits, continuity and periodicity constraints of the state of charge of the energy storage power station;

[0081]

[0082] Where D t is the electricity demand of all non-market users at time t, are the maximum / minimum declared capacity of thermal power unit g in section b, R g is the absolute value of the maximum climbing power of thermal power unit g, is the initial output of thermal power unit g (assumed to be 50% of the installed capacity in the calculation example of the present invention), are the maximum declared capacity of wind power w / photovoltaic pv in section b, Q w，t , Q pv,t are the maximum capacity of wind power w / photovoltaic pv at time t, P l，bThe maximum declared capacity of industrial and commercial user 1 in paragraph b, D l，t is the maximum electricity demand of industrial and commercial user 1 at time t, The maximum charging and discharging power reported by the energy storage station s in section b, are the maximum / minimum charge capacity of the energy storage station s, is the initial charge of the energy storage station s (assumed to be 50% of the total charge in the present example), and η is the charging and discharging efficiency of the energy storage station (assumed to be 95% in the present example). t , are the Lagrange dual variables corresponding to the constraints of each equality; are the Lagrange dual variables corresponding to each inequality constraint.

[0083] S3. A master-slave game architecture is adopted, with the joint distribution and storage entity as the (upper) leader and the market operator as the (lower) follower. Through the Karush-Kuhn-Tucker condition (KKT condition), the lower-level electricity spot market optimization clearing model is equivalently converted into the constraint conditions of the upper-level joint distribution and storage entity's trading strategy optimization model, thereby converting the non-convex nonlinear two-layer master-slave game into a nonlinear single-layer constraint condition.

[0084] Specifically, it can be divided into the following three steps.

[0085] S31. Establish the Lagrangian dual function L of the market clearing linear optimization model.

[0086]

[0087] S32. Establish the KKT conditions of the market clearing linear optimization model, including:

[0088] ①Original equality constraint;

[0089]

[0090] ②The first-order condition of Lagrangian function;

[0091]

[0092] ③ Complementary relaxation conditions transformed from the original inequality constraints;

[0093]

[0094] ④The strong duality theorem equation is equivalent to the complementary relaxation condition.

[0095]

[0096] For the convenience of expression, combined with the definition of power economics, the expression on the left side of the equation is called "supply and demand willingness surplus", which is defined as Ω; the expression on the right side of the equation is called "market boundary surplus", which is defined as Γ. That is:

[0097]

[0098] It is easy to understand that Ω=Γ.

[0099] S33, combine the KKT conditions formed by S32-①~③ into the constraints of the optimization problems of various joint storage allocation entities in S15, and transform the double-layer master-slave game into a nonlinear single-layer optimization problem. This step is a simple constraint merging and will not be described in detail here.

[0100] S4. The nonlinear single-layer constraint conditions are converted into linear constraint conditions through mathematical derivation and linear transformation, and the converted linear constraint conditions are used as the linear constraint conditions of the trading strategy optimization model of the joint storage allocation entity. The established trading strategy optimization model of the joint storage allocation entity is linearized to solve the market trading decision.

[0101] Specifically, it can be divided into the following two steps.

[0102] S41. For the complementary relaxation condition constructed in S32-③, use the mixed integer relaxation method (Big-M) to transform it into a mixed integer linear constraint condition. The complementary relaxation condition constructed in S32-③ has the following general form:

[0103] 0≤μ⊥p(x)≥0, that is, μ×p(x)=0, μ≥0, p(x)≥0

[0104] where μ is the dual variable of the constraint p(x). A binary variable θ∈{0,1} is introduced to transform the above nonlinear constraints into mixed integer linear constraints:

[0105] 0≤μ≤θ×M, 0≤p(x)≤(1-θ)×M

[0106] Where M is a sufficiently large constant

[0107] S42. In the objective function of the optimization problem of various joint distribution and storage entities in S33, there are still some nonlinear terms (i.e., the product of the market clearing price and the market entity clearing power):

[0108]

[0109] For this type of nonlinear product terms, the first-order conditions (S32-②), complementary relaxation conditions (S32-③), and strong duality theorem equation (S32-④) in the KKT conditions are combined, and the nonlinear terms are finally converted into a set of linear expressions. The specific mathematical derivation and conversion process is as follows.

[0110] ① The trading strategy optimization problem of the fire storage joint entity obtained by S3 is:

[0111]

[0112] KKT conditions(S32-①~③)

[0113] On the one hand, the product term needs to be processed In S32-②, regarding p g,b，t In the Lagrangian first-order condition, multiply each term by p g,b,t , and merged according to the declared segment number b, we can get:

[0114]

[0115] In order to keep the formula form consistent when t=T, the above formula is transformed into:

[0116]

[0117] Then we can get an expression of the same form:

[0118]

[0119] Furthermore, using the complementary relaxation condition of S32-③, we can obtain:

[0120]

[0121] Substituting the above formula, we can get:

[0122]

[0123] Finally, we use the strong duality theorem of S32-④ to replace

[0124]

[0125] The product term in the original objective function can be Equivalently replaced by a linear expression:

[0126]

[0127] The abbreviation of the last line means as follows:

[0128]

[0129] (i.e., the expression related to g in Γ)

[0130]

[0131] (i.e. the expression related to g in Ω)

[0132] On the other hand, the product terms need to be processed In S32-② about e s，t In the Lagrangian first-order condition, each term is multiplied by e s，t , and merged according to the declared segment number b, we can get:

[0133]

[0134] At the same time, the continuity and periodicity constraints of the charge state of the energy storage power station in the original inequality are:

[0135]

[0136] We can get:

[0137]

[0138] Furthermore, using the complementary relaxation condition of S32-③, we can obtain:

[0139]

[0140] Combine the first two rows of constraints of the first-order conditional variation and, based on the above two-step derivation, further transform it into:

[0141]

[0142] Finally, we use the strong duality theorem of S32-④ to replace

[0143]

[0144] The product term in the original objective function can be Equivalently replaced by a linear expression:

[0145]

[0146] The abbreviation of the last line means as follows:

[0147]

[0148] (i.e., the expression in Γ related to s)

[0149]

[0150] (i.e. the expression related to s in Ω)

[0151] At this point, the optimization problem of the fire-storage joint entity can be converted into a completely linear optimization problem:

[0152]

[0153] KKT conditions(S32-①~③)

[0154] where Γ′=Γ-γ g -γ s , Ω′=Ω-υ g -ω s

[0155] ② The trading strategy optimization problem of the wind-solar-storage joint entity obtained by S3 is:

[0156]

[0157] KKT conditions(S32-①~③)

[0158] Similar to the derivation process in the optimization problem of the thermal energy storage joint entity mentioned above, the optimization problem of the wind, solar and energy storage joint entity can be converted into a completely linear optimization problem:

[0159]

[0160] KKT conditions(S32-①~③)

[0161] where Γ′=Γ-γ s -γ pv -γ s , Ω′=Ω-ω w -ω pv -ω s

[0162] ③ The trading strategy optimization problem of the load-storage joint entity obtained by S3 is:

[0163]

[0164] KKT conditions(S32-①~③)

[0165] Similar to the derivation process in the optimization problem of the combined subject of fire and storage in the above text, the optimization problem of the combined subject of load and storage can be converted into a completely linear optimization problem:

[0166]

[0167] KKT conditions(S32-①~③)

[0168] where Γ′=Γ-γ l -γ s ,Ω′=Ω-w l -ω s

[0169] The specific implementation steps of the present invention are further described below in conjunction with a specific test system.

[0170] The test example of the present invention sets two renewable energy penetration scenarios of 50% and 60%, and the corresponding power installed capacity under each scenario is shown in Table 1.

[0171] Table 1

[0172]

[0173] Under two penetration rate scenarios, five sub-scenarios were simulated respectively: ① fully competitive market scenario; ② independent energy storage scenario; ③ thermal-storage combined entity, in which the thermal power generation entity has a 250MW coal-fired power unit, a 150MW coal-fired power unit, and a 100MW gas-fired unit; ④ wind-storage combined entity; ⑤ photovoltaic-storage combined entity, and the transaction profits of various types of power entities in each scenario were compared with the market clearing electricity price. Figure 2 to Figure 5 shown.

[0174] Depend on Figure 2 , Figure 4 As shown in the figure, in each scenario, various types of power sources participating in spot market transactions in a joint storage manner can greatly improve economic benefits, verifying the effectiveness of the trading strategy optimization method.

[0175] Depend on Figure 3 , Figure 5 As shown in the figure, in each scenario, compared with the perfectly competitive market, the trading strategy of the joint storage entities will cause the market clearing price to increase to varying degrees, thereby increasing trading income.

[0176] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing electric power spot market trading strategies applicable to joint distribution and storage entities, characterized in that: The following steps are involved: S1. Establish the trading strategy optimization problem of each market entity with the goal of maximizing the economic benefits of each market entity, establish the trading strategy optimization problem of the energy storage power station with the goal of maximizing the market arbitrage of the energy storage power station, incorporate the trading strategy optimization problem of the energy storage power station into the trading strategy optimization problem of each market entity, and establish a trading strategy optimization model for the joint distribution and storage entity; S2. Establish an optimal clearing model for the electricity spot market, with the goal of minimizing the system balancing cost and the constraints of the electricity spot market operation as constraints; S3, with the joint distribution and storage entity as the upper leader and the market operator as the lower follower, the optimal clearing model of the electricity spot market is transformed into a nonlinear single-layer constraint condition according to the KKT condition and strong duality theory; S4. The nonlinear single-layer constraint conditions are converted into linear constraint conditions through linear transformation, and the converted linear constraint conditions are used as linear constraint conditions of the trading strategy optimization model of the joint storage allocation entity. The established trading strategy optimization model of the joint storage allocation entity is linearized to solve the market trading decision.

2. The power spot market transaction strategy optimization method applicable to the joint distribution and storage subject according to claim 1 is characterized in that: The step S1 comprises: S11. Taking the maximum power generation revenue of thermal power units as the goal, the objective function established is: The constraints are to ensure the incremental nature of the seller's quote curve: In the formula, Ω G is the set of thermal power units owned by the Gth thermal power plant, c g，b is the marginal power generation cost of the g-th thermal power unit in the b-th segment, p g，b，t is the output power of the g-th thermal power unit at the b-th time t, α g，b，t is the seller's price of the g-th thermal power unit at the b-th time t, λ t ,λ min ,λ max are the market clearing electricity price at time t and the upper and lower limits of the quotations of market players; S12. Taking the maximum power generation revenue of the new energy power station as the goal, the objective function established is: The constraints are to ensure the incremental nature of the seller's quote curve: In the formula, Ω R is the collection of wind power and photovoltaic units owned by the Rth new energy power station, c w , c pv are the marginal power generation costs of wind power and photovoltaic units, respectively, w，b，t , p pv，b，t are the output power of the w / pvth wind power / photovoltaic unit at the bth time t, α w，b，t , α pv，b，t are the seller's quotations of wind power / photovoltaic power in the w / pv stage at the b segment at time t; S13. Taking the maximum electricity purchase cost of industrial and commercial users as the goal, the objective function established is: The constraints are to ensure the decreasing nature of the bid price curve: In the formula, Ω L is the set of load equipment owned by the Lth industrial and commercial user, u l，b is the marginal power utility of the lth load device in section b, p l，b，t is the power consumption of the lth load device at the bth time t, β g，b，t is the buyer's price of the lth load equipment at the bth time t; S14. Taking the maximum market arbitrage of energy storage power stations as the goal, the objective function established is: The constraints are to ensure the increasing nature of the seller's quote curve and the decreasing nature of the buyer's quote curve: In the formula, c s is the unit charging and discharging cost of the energy storage power station, are the charging / discharging power of the energy storage station at the bth segment at time t, α s，b，t , β s，b，t They are the seller / buyer quotes of the energy storage power station at time t in segment b; S15, incorporating the optimization problem formed in S14 into the optimization problems of S11 to S13, respectively, to form the trading strategy optimization models for the joint allocation and storage of three types of market entities: thermal power units, new energy power stations, and industrial and commercial users; ① For the joint storage entity of thermal power units, the trading strategy optimization model is: ② For the joint distribution and storage entities of new energy power stations, the trading strategy optimization model is: ③ For the joint storage entities of industrial and commercial users, the transaction strategy optimization model is:

3. The power spot market transaction strategy optimization method applicable to the joint distribution and storage subject according to claim 1 is characterized in that: Taking the minimum system balance cost as the goal, the objective function constructed is: The constraints are the operation constraints of the electricity spot market, which are: ① System power balance constraints; ② Output constraints of thermal power units; ③Climbing constraints of thermal power units; ④ Output constraints of new energy power stations; ⑤ Load constraints for industrial and commercial users; ⑥Charging and discharging power constraints of energy storage power stations; ⑦ Upper and lower limits, continuity and periodicity constraints of the state of charge of the energy storage power station; Where D t is the electricity demand of all non-market users at time t, are the maximum / minimum declared capacity of thermal power unit g in section b, R g is the absolute value of the maximum climbing power of thermal power unit g, is the initial output of thermal power unit g (assumed to be 50% of the installed capacity in the calculation example of the present invention), are the maximum declared capacity of wind power w / photovoltaic pv in section b, Q w，t , Q pv，t are the maximum capacity of wind power w / photovoltaic pv at time t, P l，b The maximum declared capacity of industrial and commercial users l in paragraph b, D l，t is the maximum electricity demand of industrial and commercial user l at time t, The maximum charging and discharging power reported by the energy storage station s in section b, are the maximum / minimum charge capacity of the energy storage station s, is the initial charge of the energy storage station s, η is the charging and discharging efficiency of the energy storage station, are the Lagrange dual variables corresponding to the constraints of each equality; are the Lagrange dual variables corresponding to each inequality constraint.

4. The power spot market transaction strategy optimization method applicable to the joint distribution and storage subject according to claim 1 is characterized in that: The step S3 comprises: S31. Establish the Lagrangian dual function L of the market clearing linear optimization model: S32. Establish the KKT conditions of the market clearing linear optimization model, including: ①Original equality constraint: ②The first-order condition of Lagrangian function: ③Complementary relaxation conditions for the transformation of the original inequality constraints: ④ Strong duality theorem equation for complementary relaxation conditions: S33. Combine the KKT conditions formed by S32-①~③ into the constraint conditions of the trading strategy optimization model of various joint storage entities in S15, and transform the two-layer master-slave game into a nonlinear single-layer constraint condition.

5. The power spot market transaction strategy optimization method applicable to the joint distribution and storage subject according to claim 1 is characterized in that: The step S4 comprises: S41. For the complementary relaxation conditions constructed in S32-③, use the mixed integer relaxation method to transform them into mixed integer linear constraints; S42. Nonlinear terms still exist in the objective function of the optimization model of trading strategies for various joint storage entities in S33: Combine S32-②~④ respectively, and finally transform the nonlinear terms into a set of linear expressions.

6. The power spot market transaction strategy optimization method applicable to the joint distribution and storage subject according to claim 5 is characterized in that: The complementary relaxation condition constructed in S32-③ has the following general form: 0≤μ⊥p(r)≥0, that is, μ×p(r)=0, μ≥0,p(x)≥0 where μ is the dual variable of the constraint p(x); A binary variable θ∈{0, 1} is introduced to transform the above nonlinear constraints into mixed integer linear constraints:

7. An electricity spot market transaction strategy optimization system suitable for joint distribution and storage entities, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the electricity spot market trading strategy optimization method applicable to the joint distribution and storage entity as described in any one of claims 1 to 6.

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