A method and system for optimizing power spot market trading strategies for joint portfolio optimization

Abstract

The application discloses a kind of power spot market transaction strategy optimization method and system suitable for joint storage body, belong to electrical engineering field.The method is based on master-slave game architecture, constructs the bidding strategy optimization model of joint storage body in power spot market.First, the transaction strategy optimization model of three kinds of subjects, thermal power unit, new energy power station, industry and commerce user is established respectively;Second, the power spot market optimization clearing model is constructed;Third, the market optimization clearing model is equivalently converted into the constraint condition of joint storage body optimization problem by KKT condition, so that the non-convex nonlinear double-layer game model is converted into nonlinear single-layer optimization problem;Finally, the nonlinear term in the above optimization problem is linearized, and a mathematical model easy to solve is formed.In this way, by constructing transaction strategy model and carrying out numerical simulation, the optimal transaction strategy of joint storage body is finally output, which can be used to guide its spot market transaction decision, maximize its economic benefit.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering, and more specifically, relates to a method and system for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities. Technical Background

[0002] Since 2015, my country has gradually implemented spot market pilot programs, encouraging diverse market participants, including thermal power units, new energy power plants, energy storage power plants, and industrial and commercial users, to enter and participate in spot market transactions. With the increasing proportion of renewable energy investment and installed capacity year by year, the uncertainty on both the generation and consumption sides of the power system has significantly increased, leading to greater volatility in spot prices. To mitigate spot price risks and utilize peak-valley price differences for additional arbitrage, market participants can configure energy storage power plants to participate in the electricity spot market as joint distribution and storage entities. Optimizing spot market trading decisions and improving market returns are the direct objectives of joint distribution and storage entities, and are also crucial links in market price discovery and optimal resource allocation. However, the operational constraints of the spot market are complex, and the price formation mechanism is obscure; traditional manual decision-making methods are insufficient to support high-frequency, real-time trading decisions. Therefore, there is an urgent need for an optimization method for electricity spot market trading strategies applicable to joint distribution and storage entities to quantitatively support market participants' trading decisions. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities. It aims to provide a basis for decision-making for market participants to participate in spot market transactions by establishing a mathematical model, simulating the market operation mechanism and optimizing the bidding strategies of the entities.

[0004] To achieve the above objectives, this invention provides a method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities, comprising the following steps:

[0005] S1. Establish a trading strategy optimization problem for each market participant with the goal of maximizing their economic benefits, and establish a trading strategy optimization problem for energy storage power stations with the goal of maximizing market arbitrage for energy storage power stations. Incorporate the trading strategy optimization problem for energy storage power stations into the trading strategy optimization problem for each market participant, and establish a trading strategy optimization model for joint energy storage entities.

[0006] S2. Establish an optimized clearing model for the electricity spot market, with the goal of minimizing system balance costs and the constraints of electricity spot market operation.

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

[0008] S4. The nonlinear single-layer constraint is transformed into a linear constraint through mathematical derivation and linear transformation. The transformed linear constraint is then used as the linear constraint of the trading strategy optimization model of the joint reserve entity. The established trading strategy optimization model of the joint reserve entity is linearized to solve for market trading decisions.

[0009] Further, step S1 includes:

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

[0011] S12. Optimization problem of trading strategy for new energy power plants: The objective function is to maximize power generation revenue (= electricity sales revenue- power generation cost), and the constraint is to ensure the increasing nature of the seller's price curve.

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

[0013] S14. Optimization problem of trading strategy for energy storage power station: The objective function is to maximize market arbitrage (= electricity sales revenue - electricity purchase expenditure - operating cost), and the constraint is to ensure the increasing nature of the seller's price curve and the decreasing nature of the buyer's price curve.

[0014] S15. The optimization problems formed in S14 are incorporated into the optimization problems in S11 to S13 respectively, forming a transaction strategy optimization model for joint allocation and storage of three types of market entities: thermal power units, new energy power plants, and industrial and commercial users.

[0015] Further, step S2 includes:

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

[0017] Further, step S3 includes:

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

[0019] S32. Establish the KKT conditions for the linear optimization model of market clearing, specifically including: ① the original equality constraint; ② the first-order condition of the Lagrange 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 in S32-①~③ with the constraints of the various joint storage entity optimization problems in S15, and transform the two-layer master-slave game into a nonlinear single-layer optimization problem.

[0021] Further, step S4 includes:

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

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

[0024] The present invention also provides an optimization system for electricity spot market trading strategies applicable to joint distribution and storage entities, 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 executable instructions stored in the computer-readable storage medium and execute the above-described method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities.

[0027] In summary, the above-described technical solutions conceived in this invention can achieve the following beneficial effects:

[0028] This invention employs a master-slave game theory architecture, transforming a complex two-level optimization problem into a single-level one. This solves the problem of traditional manual decision-making struggling to handle high-frequency, real-time trading decisions, simplifying the trading strategy optimization process for market participants. By constructing a trading strategy optimization model for joint distribution and storage entities, it supports market participants in optimizing their bidding strategies in the electricity spot market, improving the accuracy of their decisions and maximizing economic benefits. Therefore, this invention has significant practical value and can better serve electricity spot market participants and operators. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating an optimization method for electricity spot market trading strategies applicable to joint distribution and storage entities, provided by the present invention.

[0030] Figure 2 A comparison of transaction profits of joint storage and distribution entities provided in this embodiment of the invention (50% new energy penetration rate scenario).

[0031] Figure 3 Market clearing electricity price comparison provided for embodiments of the present invention (50% renewable energy penetration rate scenario).

[0032] Figure 4 A comparison of transaction profits of joint distribution and storage entities provided in this embodiment of the invention (60% new energy penetration rate scenario).

[0033] Figure 5 Market clearing electricity price comparison provided for embodiments of the present invention (60% renewable energy penetration rate scenario). Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0035] like Figure 1 As shown, the method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities according to the present invention includes the following steps:

[0036] S1. For thermal power units, new energy power plants (considering only photovoltaic and wind power units), industrial and commercial users, and energy storage power plants (taking electrochemical energy storage as an example), establish a trading strategy optimization problem for each market participant with the goal of maximizing the economic benefits of each market participant, and establish a trading strategy optimization problem for energy storage power plants with the goal of maximizing the market arbitrage of energy storage power plants. Integrate the trading strategy optimization problem of energy storage power plants into the trading strategy optimization problem of each market participant, and establish a trading strategy optimization model for joint distribution and storage entities.

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

[0038] S11. Optimization problem of trading strategy for thermal power units: The objective function is to maximize power generation revenue (= electricity sales revenue - power generation cost).

[0039]

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

[0041]

[0042] In the formula, Ω G Let c be the set of thermal power units owned by the Gth thermal power plant. g，b p is the marginal power generation cost of the g-th thermal power unit in section b. g,b，t Let α be the output power of the g-th thermal power unit at time t in segment b. g，b，t Let λ be the seller's bid for the g-th thermal power unit at time t in segment b. t , λ min , λ max These represent the market-clearing electricity price at time t, the upper limit of the market participant's bid, and the lower limit of the bid.

[0043] S12. Optimization problem of trading strategy for new energy power plants: The objective function is to maximize power generation revenue (= electricity sales revenue - power generation cost).

[0044]

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

[0046]

[0047] In the formula, Ω R Let c be the set of wind and solar power units owned by the Rth new energy power station. w c pv Let be the marginal generation cost of the wind / solar turbine, and pw,b,t and ppv,b,t be the output power of the w / pv wind / solar turbine at time t in segment b. w，b,t α pv,b,t These are the seller quotes for the w / pv wind power / photovoltaic power plants at time t in segment b.

[0048] S13. Construct the transaction strategy optimization problem for industrial and commercial users: The objective function is to minimize the electricity purchase cost (= electricity purchase expenditure - electricity utility).

[0049]

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

[0051]

[0052] In the formula, Ω L u is the set of load devices owned by the Lth industrial and commercial user. l，b p represents the marginal power consumption of the l-th load device in section b. l,b，t Let β be the power consumption of the l-th load device at time t in segment b. g，b，t This is the buyer's quote for the first load device at time t in segment b.

[0053] S14. Optimization problem of trading strategy for energy storage power station: The objective function is to maximize market arbitrage (= electricity sales revenue - electricity purchase expenditure - operating cost).

[0054]

[0055] The constraints are to ensure that the seller's price curve is increasing and the buyer's price curve is decreasing.

[0056]

[0057] In the formula, c s The unit charge / discharge cost of an energy storage power station. These represent the charging / discharging power of the energy storage power station at time t in segment b, and α. s，b,t ,β s，b，t These represent the seller's and buyer's quotations for the energy storage power station at time t in segment b.

[0058] S15. The optimization problems formed in S14 are incorporated into the optimization problems in S11 to S13 respectively, forming a transaction strategy optimization model for joint allocation and storage of three types of market entities: thermal power units, new energy power plants, and industrial and commercial users.

[0059] ① For a joint thermal power unit storage entity (thermal power-storage joint venture), the trading strategy optimization problem is as follows:

[0060]

[0061] ② For joint distribution and storage entities of new energy power plants (wind-solar-storage joint ventures), the optimization problem of their trading strategies is as follows:

[0062]

[0063] ③ For the joint allocation entity of industrial and commercial users (Dutch Reserves Union), the optimization problem of its trading strategy is as follows:

[0064]

[0065] S2. Based on the existing basic operating rules of the electricity market, construct an optimized clearing model for the electricity spot market: the objective function is to minimize the system balance cost (= system generation cost - system electricity consumption utility).

[0066]

[0067] The constraints are for the operation of the electricity spot market, and they are as follows:

[0068] ①System power balance constraints;

[0069]

[0070] ② Output constraints of thermal power units;

[0071]

[0072] ③Mountain climbing constraints for thermal power units;

[0073]

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

[0075]

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

[0077]

[0078] ⑥ Charge and discharge power constraints of energy storage power stations;

[0079]

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

[0081]

[0082] In the formula, D t Let t represent the electricity demand of all non-market users. These represent the maximum / minimum declared capacity of thermal power unit g in segment b, and R. g Let g be the absolute value of the maximum climbing power of the thermal power unit. The initial output of thermal power unit g is assumed to be 50% of the installed capacity in this invention's example. These represent the maximum declared capacity of wind power (W) / solar photovoltaic (PV) in segment b, respectively. w，t Q pv,t These represent the maximum renewable capacity of wind power (w) and solar photovoltaic (pv) at time t, respectively. l，bFor commercial user 1, the maximum declared capacity in segment b, D l，t This represents the maximum electricity demand of industrial and commercial user 1 at time t. The maximum charge and discharge power declared by energy storage power station s in segment b. These represent the maximum and minimum charge capacities of the energy storage power station s, respectively. Let λ be the initial charge of the energy storage power station s (assumed to be 50% of the total charge in this example), and η be the charging and discharging efficiency of the energy storage power station (taken as 95% in this example). t , These are the Lagrange dual variables corresponding to the equality constraints; These 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 conditions (KKT conditions), the lower-level electricity spot market optimization clearing model is equivalently transformed into the constraint conditions of the upper-level joint distribution and storage entity's trading strategy optimization model. Thus, the non-convex and nonlinear two-level master-slave game is transformed into a nonlinear single-level constraint condition.

[0084] Specifically, it can be divided into the following 3 steps.

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

[0086]

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

[0088] ① Original equality constraints;

[0089]

[0090] ② First-order conditions for the Lagrange function;

[0091]

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

[0093]

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

[0095]

[0096] For ease of expression, and in accordance with the definitions in power economics, this invention refers to the expression on the left side of the equation as the "supply and demand willingness surplus," defined as Ω; and the expression on the right side of the equation as the "market boundary surplus," defined as Γ. That is:

[0097]

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

[0099] S33. Combine the KKT conditions formed in S32-①~③ with the constraints of the various joint storage entity optimization problems in S15, transforming the two-layer master-slave game into a nonlinear single-layer optimization problem. This step is a simple constraint merging and will not be elaborated here.

[0100] S4. The nonlinear single-layer constraint is transformed into a linear constraint through mathematical derivation and linear transformation. The transformed linear constraint is then used as the linear constraint of the trading strategy optimization model of the joint reserve entity. The established trading strategy optimization model of the joint reserve entity is linearized to solve for market trading decisions.

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

[0102] S41. For the complementary relaxation conditions constructed in S32-③, the Big-M mixed integer relaxation method is used to transform them into mixed integer linear constraints. The complementary relaxation conditions constructed in S32-③ have the following general form:

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

[0104] Here, μ is the dual variable of the constraint p(x). Introducing a binary variable θ∈{0,1} transforms the above nonlinear constraint into a mixed-integer linear constraint:

[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 various joint distribution and storage entity optimization problems in S33, there are still some nonlinear terms (i.e., the product of market clearing price and market entity clearing power):

[0108]

[0109] For this type of nonlinear product term, we simultaneously apply the first-order conditions (S32-②), complementary relaxation conditions (S32-③), and strong duality theorem equations (S32-④) from the KKT conditions, ultimately transforming the nonlinear term into a set of equivalent linear expressions. The specific mathematical derivation and transformation process is as follows.

[0110] ①The optimization problem of the trading strategy of the fire storage consortium obtained from S3 is as follows:

[0111]

[0112] KKT conditions (S32-①~③)

[0113] On the one hand, the product terms need to be processed. Regarding p in S32-② g,b，t In the first-order Lagrange condition, multiplying each term by p simultaneously... g,b,t And by merging them according to the number of declaration segments b, we can get:

[0114]

[0115] To maintain consistency in the formula at t=T, we perform a term-completion transformation on the above equation. First, let:

[0116]

[0117] This leads to an expression with the same form:

[0118]

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

[0120]

[0121] Replacing the previous equation, we get:

[0122]

[0123] Finally, the strong duality theorem of S32-④ is used to replace the above equation.

[0124]

[0125] The product terms in the original objective function can be... Equivalent substitution to a linear expression:

[0126]

[0127] The abbreviation in the last line has the following meaning:

[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 regarding e s，t In the Lagrange first-order condition, multiply each term simultaneously by... e s，t And by merging them according to the number of declaration segments b, we can get:

[0133]

[0134] Meanwhile, the continuity and periodicity of the state of charge of the energy storage power station in the original inequality are constrained:

[0135]

[0136] We can obtain:

[0137]

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

[0139]

[0140] By simultaneously applying the first two rows of constraints of the first-order conditional variation, and based on the above two-step derivation, it can be further transformed into:

[0141]

[0142] Finally, the strong duality theorem of S32-④ is used to replace the above equation.

[0143]

[0144] The product terms in the original objective function can be... Equivalent substitution to a linear expression:

[0145]

[0146] The abbreviation in the last line has the following meaning:

[0147]

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

[0149]

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

[0151] Therefore, the optimization problem of the fire-storage consortium can be transformed 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] ②The optimization problem of the trading strategy of the wind-solar-storage consortium obtained from S3 is as follows:

[0156]

[0157] KKT conditions (S32-①~③)

[0158] Similar to the derivation process in the optimization problem of the combined thermal and solar energy storage entity above, the optimization problem of the combined wind, solar, and energy storage entity can be transformed 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 optimization problem of the trading strategy of the Dutch Federal Reserve consortium obtained from S3 is:

[0163]

[0164] KKT conditions (S32-①~③)

[0165] Similar to the derivation process in the optimization problem of the combined fire and energy storage entity above, the optimization problem of the combined load and energy storage entity can be transformed 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 this invention will be further explained below with reference to a specific testing system.

[0170] The test examples of this invention set up two renewable energy penetration scenarios of 50% and 60%, and the corresponding power generation capacity under each scenario is shown in Table 1.

[0171] Table 1

[0172]

[0173] Simulations were conducted for five scenarios under two penetration rate conditions: ① a perfectly competitive market; ② an independent energy storage scenario; ③ a combined thermal power and energy storage system, where the thermal power generator owns one 250MW coal-fired power unit, one 150MW coal-fired power unit, and one 100MW gas turbine unit; ④ a combined wind power and energy storage system; and ⑤ a combined solar power and energy storage system. The transaction profits of each type of power generator in each scenario were compared with the market-clearing price. Figures 2-5 As shown.

[0174] Depend on Figure 2 , Figure 4 As shown, in various scenarios, the participation of different types of power sources in spot market transactions through joint allocation and storage can significantly improve economic efficiency, verifying the effectiveness of the transaction strategy optimization method.

[0175] Depend on Figure 3 , Figure 5 As shown, in each scenario, compared to a perfectly competitive market, the trading strategies of joint storage entities will cause the market clearing price to rise to varying degrees, thereby increasing trading revenue.

[0176] Those skilled in the art will readily understand that the above description is merely 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 within the scope of protection of the present invention.

Claims

1. A method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities, characterized in that, Includes the following steps: S1. Establish a trading strategy optimization problem for each market participant with the goal of maximizing their economic benefits, and establish a trading strategy optimization problem for energy storage power stations with the goal of maximizing market arbitrage for energy storage power stations. Incorporate the trading strategy optimization problem for energy storage power stations into the trading strategy optimization problem for each market participant, and establish a trading strategy optimization model for joint energy storage entities. S2. Establish an optimal clearing model for the electricity spot market, with the goal of minimizing system balancing costs and the constraints of electricity spot market operation; the objective function is as follows: The constraints are for the operation of the electricity spot market, and they are as follows: ①System power balance constraints; ② Output constraints of thermal power units; ③Mountain climbing constraints for thermal power units; ④ Output constraints of new energy power plants; ⑤ Load constraints for industrial and commercial users; ⑥ Charge and discharge power constraints of energy storage power stations; ⑦ Upper and lower limits, continuity, and periodicity constraints of the state of charge of energy storage power stations; In the formula, Let t represent the electricity demand of all non-market users. These represent the maximum and minimum declared capacities of thermal power unit g in segment b, respectively. Let g be the absolute value of the maximum climbing power of the thermal power unit. The initial output of thermal power unit g. These represent the maximum declared capacity of wind power (W) and solar photovoltaic (PV) in segment b, respectively. Let W represent the maximum renewable capacity of wind power (W) and solar photovoltaic (PV) at time t, respectively. For industrial and commercial users l, the maximum declared capacity in segment b. This represents the maximum electricity demand of industrial and commercial user l at time t. The maximum charge and discharge power declared by energy storage power station s in segment b. These represent the maximum and minimum charge capacities of the energy storage power station s, respectively. Let s be the initial charge of the energy storage power station. It refers to the charging and discharging efficiency of the energy storage power station. These are the Lagrange dual variables corresponding to the equality constraints; These are the Lagrange dual variables corresponding to each inequality constraint; S3. With the joint distribution and storage entity as the upper-level leader and the market operator as the lower-level follower, the power spot market optimization clearing model is transformed into a nonlinear single-layer constraint condition based on KKT conditions and strong duality theory. S4. The nonlinear single-layer constraint is transformed into a linear constraint through linear transformation, and the transformed linear constraint is used as the linear constraint of the trading strategy optimization model of the joint storage entity. The established trading strategy optimization model of the joint storage entity is linearized to solve for market trading decisions.

2. The method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities according to claim 1, characterized in that, Step S1 includes: S11. The objective function established with the goal of maximizing the power generation revenue of thermal power units is as follows: The constraint is to ensure the increasing nature of the seller's price curve: In the formula, Let G be the set of thermal power units owned by the Gth thermal power plant. It is the marginal power generation cost of the g-th thermal power unit in section b. Let be the output power of the g-th thermal power unit at time t in segment b. Let be the seller's offer for the g-th thermal power unit at time t in segment b. These are the upper and lower limits for market participants' quotations, respectively. S12. With the goal of maximizing the power generation revenue of the new energy power plant, the objective function is established as follows: The constraint is to ensure the increasing nature of the seller's price curve: In the formula, Let R be the collection of wind power and photovoltaic units owned by the R-th new energy power station. These represent the marginal generation costs of wind power and solar power units, respectively. Let w and pv represent the output power of the w / p wind / photovoltaic unit at time t in segment b. These are the seller quotes for the w / pv wind power / photovoltaic power plants at time t in segment b; S13. The objective function established with the goal of maximizing the electricity purchase cost for industrial and commercial users is as follows: The constraint is to ensure the decreasing nature of the buyer's price curve: In the formula, This refers to the set of load devices owned by the Lth industrial and commercial user. The marginal power consumption of the l-th load device in section b is given. Let be the power consumption of the l-th load device at time t in segment b. The buyer's quote for the l-th load device at time t in segment b; S14. The objective function established with the goal of maximizing market arbitrage for energy storage power stations is as follows: The constraints are to ensure the increasing nature of the seller's price curve and the decreasing nature of the buyer's price curve: In the formula, The unit charge / discharge cost of an energy storage power station. These represent the charging / discharging power of the energy storage power station at time t in segment b. These are the seller's and buyer's quotations for the energy storage power station at time t in segment b; S15. The optimization problems formed in S14 are incorporated into the optimization problems in S11 to S13 respectively, forming a joint allocation and storage transaction strategy optimization model for three types of market entities: thermal power units, new energy power plants, and industrial and commercial users. ①The trading strategy optimization model for the joint storage entity of thermal power units is as follows: ② For the joint distribution and storage entity of new energy power plants, the optimized trading strategy model is as follows: ③ For joint storage entities of industrial and commercial users, the transaction strategy optimization model is as follows: 。 3. The method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities according to claim 1, characterized in that, Step S3 includes: S31. Establish the Lagrange dual function of the linear optimization model for market clearing. L : S32. Establish the KKT conditions for the linear optimization model of market clearing, specifically including: ① Original equality constraints: ② First-order condition for the Lagrange function: ③ Complementary relaxation conditions for the original inequality constraints: ④ Equivalent strong duality theorem equations for complementary relaxation conditions: S33. Combine the KKT conditions formed in S32-①~③ with the constraints of the various joint storage entity transaction strategy optimization models in S15, and transform the two-layer master-slave game into a nonlinear single-layer constraint.

4. The method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities according to claim 1, characterized in that, Step S4 includes: S41. For the complementary relaxation conditions constructed in S32-③, the mixed integer relaxation method is used to transform them into mixed integer linear constraints. S42. Regarding the nonlinear terms still existing in the objective function of the optimization model for various joint storage entity trading strategies in S33: By simultaneously solving S32-②~④, the nonlinear terms are ultimately transformed into a set of equivalent linear expressions.

5. The method for optimizing electricity spot market trading strategies applicable to joint distribution and storage entities according to claim 4, characterized in that, The complementary relaxation conditions constructed in S32-③ have the following general form: in Constraints The dual variable; Introduce a binary variable This transforms the aforementioned nonlinear constraints into mixed-integer linear constraints: 。 6. A power spot market trading strategy optimization system suitable for joint distribution and storage entities, characterized in that, include: Computer-readable storage media and processors; The computer-readable storage medium is used to store executable instructions; The processor is used to read executable instructions stored in the computer-readable storage medium and execute the power spot market trading strategy optimization method applicable to joint distribution and storage entities as described in any one of claims 1 to 5.

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