New energy power station spot transaction strategy simulation and market optimization operation method and system based on master-slave game

Abstract

The invention discloses a new energy power station spot transaction strategy simulation and market optimization operation method and system based on a master-slave game, and belongs to the field of electrical engineering. According to the method, a master-slave double-layer game architecture is adopted, and a spot market transaction strategy optimization model of each new energy power station is firstly established; secondly, constructing an electric power spot market optimization clearing model; thirdly, the market optimization clearing model is equivalently converted into a constraint condition of a new energy power station optimization problem through a KKT condition, so that a non-convex nonlinear double-layer optimization model is converted into a mathematical programming problem with equilibrium constraint of each new energy power station; and finally, the optimization model of each new energy power station is converted into a constraint condition through the KKT condition, and a ''equalization problem with equalization constraint'' is formed in parallel. Therefore, simulation of transaction strategies of a plurality of new energy power stations can be realized, market optimization clearing is synchronously realized, and the method is used for guiding core businesses such as clearing price prediction and market benefit evaluation of market operators.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and more specifically, relates to a method and system for simulating spot trading strategies and market optimization operations of new energy power stations based on master-slave game. Technical Background

[0002] Since the new round of electricity reform, my country has gradually implemented the spot market pilot, and promoted the participation of renewable energy in spot market transactions, and the bidding of new energy such as wind and solar power and traditional power sources such as thermal power on the same platform. New energy such as wind power and photovoltaic power have the technical and economic characteristics of zero marginal cost. The traditional power market simulation operation method generally regards them as market boundary conditions for "passive optimization", and fails to consider their trading strategies and game behaviors in participating in the spot market; on the other hand, the existing market power analysis methods and regulatory means are generally limited to thermal power units, and have not yet considered new energy power stations with almost negligible marginal costs. With the increasing proportion of renewable energy investment and installed capacity year by year, the uncertainty on both sides of the system has increased significantly, resulting in increased spot price volatility, and its trading strategies and game behaviors will become increasingly important. Therefore, there is an urgent need for a spot trading strategy simulation and market optimization operation method for new energy power stations. 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 simulating spot trading strategies and market optimization operations of new energy power stations based on master-slave game, aiming to establish a mathematical model to numerically simulate the independent optimal bidding strategies of multiple new energy power stations and the market clearing status under corresponding circumstances, so as to provide quantitative support for the core businesses of market operators such as clearing price forecasting and market benefit evaluation.

[0004] To achieve the above object, the present invention provides a method for simulating spot trading strategies and optimizing market operation of new energy power stations based on master-slave game, comprising the following steps:

[0005] S1. Establish a trading strategy optimization model for each new energy power station with the goal of maximizing the power generation revenue of each new energy power station;

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

[0007] S3. Based on the master-slave game theory, a two-level game model is established with the new energy power station as the upper leader and the market operator as the lower follower; further, through the Karush-Kuhn-Tucker condition (KKT condition), the market clearing optimization model at the lower level is equivalently converted into the constraint conditions in the transaction strategy optimization model of each new energy power station at the upper level, thereby forming the "Mathematical Programs with Equilibrium Constraints" problem (MPEC) of each new energy power station;

[0008] S4. Through KKT conditions, the MPEC problems of each new energy power station are converted into single-layer nonlinear constraints, which are combined to form "Equilibrium Problems with Equilibrium Constraints" (EPEC), thereby converting the original complex problem into a single-layer optimization model, which can be solved by the NLP commercial solver, and finally realize the simulation of the trading strategy of the new energy power station and the market clearing operation simulation, which is used to guide the core business of market operators such as clearing price forecasting and market benefit evaluation.

[0009] Furthermore, the step S1 comprises:

[0010] Construct a trading strategy optimization model for each new energy power station: 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] Furthermore, the step S2 comprises:

[0012] Construct an optimal clearing model for the electricity spot market: the objective function is to minimize the system power supply cost (= thermal power generation cost + new energy power station power generation cost + load shedding loss), and the constraints are: ① system power balance constraint; ② thermal power unit output constraint; ③ new energy power station output constraint; ④ load shedding power constraint.

[0013] Furthermore, the step S3 comprises:

[0014] S31. Establish the Lagrangian dual function of the market clearing optimization model.

[0015] S32. Establish the KKT conditions for the market clearing optimization model, which specifically include: ① the constraints of the original problem; ② the first-order conditions of the Lagrangian function; ③ the complementary relaxation conditions transformed from the original inequality constraints, and their equivalent strong duality theorem equations; ④ the non-negativity constraints of the dual variables.

[0016] S33. Combine the KKT conditions formed by S32-①~③ into the constraints of each upper-level optimization problem in S1, and transform the double-layer master-slave game into a nonlinear single-layer constraint, that is, the MPEC problem of each new energy power station.

[0017] Furthermore, the step S4 comprises:

[0018] S41. Reconstruct the MPEC problem of each renewable energy power station and establish the Lagrangian dual function of the trading strategy optimization problem of each renewable energy power station;

[0019] S42. Combine the KKT conditions of each MEPC problem, transform the original complex problem into a single-layer nonlinear optimization problem, and solve it using an NLP commercial solver.

[0020] The present invention also provides a new energy power station spot trading strategy simulation and market power analysis method based on master-slave game, a new energy power station spot trading strategy simulation and market optimization operation system, including: a computer-readable storage medium and a processor;

[0021] The computer-readable storage medium is used to store executable instructions;

[0022] The processor is used to read the executable instructions stored in the computer-readable storage medium to execute the above-mentioned new energy power station spot trading strategy simulation and market optimization operation method based on master-slave game.

[0023] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0024] The present invention adopts a master-slave game architecture to transform a complex two-layer optimization problem into a single-layer optimization problem, realizes the simulation of the trading strategy of new energy power stations and the simulation of the market clearing equilibrium state, and thus provides a scientific basis for the core businesses of market operators such as clearing price prediction and market benefit evaluation. Therefore, the present invention has significant practical value and can better serve the participants and operators of the power spot market. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic flow chart of a method for simulating spot trading strategies and optimizing market operations of new energy power stations based on master-slave game provided by the present invention.

[0026] Figure 2 The embodiment of the present invention provides the impact of the market power concentration of new energy power stations under different renewable energy penetration rates on the market clearing electricity price.

[0027] Figure 3 The embodiment of the present invention provides the distribution trend of market clearing electricity prices as the market power concentration of new energy power stations changes under different renewable energy penetration rates.

[0028] Figure 4 The changing trend of the new energy power station market transaction strategy (i.e., the quotation curve) under different new energy power station market power concentrations provided by the embodiment of the present invention;

[0029] Figure 5 Provided in an embodiment of the present invention is a distribution trend of market economic surplus as the market power concentration of new energy power stations changes under different renewable energy penetration rates. DETAILED DESCRIPTION

[0030] 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.

[0031] like Figure 1 As shown, the method for simulating spot trading strategies and optimizing market operation of new energy power stations based on master-slave game of the present invention comprises the following steps:

[0032] S1. Establish a trading strategy optimization model for each new energy power station with the goal of maximizing the power generation revenue of each new energy power station. Specifically: The objective function is to maximize the power generation revenue (= power sales revenue minus power generation cost):

[0033]

[0034] The constraint is to ensure the incremental nature of the seller's quote curve.

[0035]

[0036] 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 They are the seller’s quotations for the wth / pvth wind power / photovoltaic power at the bth period at the time t.

[0037] S2. Establish an optimization clearing model for the electricity spot market, with the goal of minimizing the system power supply cost and the constraints of the electricity spot market operation. Specifically, the objective function is to minimize the system power supply cost (= thermal power generation cost + new energy power station power generation cost + load shedding loss). In this invention, it is assumed that the existing market power supervision measures are effective, so the thermal power units quote according to their marginal costs:

[0038]

[0039] The constraints are:

[0040] ① System power balance constraints;

[0041]

[0042] ② Output constraints of thermal power units;

[0043]

[0044] ③ Output constraints of new energy power stations;

[0045]

[0046] ④ Load shedding power constraint.

[0047]

[0048] 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, are the maximum declared capacity of wind power w / photovoltaic pv in section b, Q w，t， α pv，t are the maximum capacity of wind power w / photovoltaic pv at time t, is the maximum capacity of the interruptible load. t is the Lagrangian dual variable of the power balance constraint; are the Lagrange dual variables corresponding to each inequality constraint.

[0049] S3. Based on the master-slave game theory, a two-level game model is established with the new energy power station as the upper leader and the market operator as the lower follower; further, through the Karush-Kuhn-Tucker condition (KKT condition), the market clearing optimization model at the lower level is equivalently converted into the constraint conditions in the trading strategy optimization model of each new energy power station at the upper level, thereby establishing the nonlinear trading strategy optimization problem of each new energy power station, which is essentially a "mathematical programming with equilibrium constraints" problem (Mathematical Programs with Equilibrium Constraints, MPEC);

[0050] Specifically, it can be divided into the following three steps.

[0051] S31. Establish the Lagrangian dual function of the market clearing optimization model.

[0052]

[0053] S32. Establish the KKT conditions for the market clearing optimization model, including:

[0054] ① Original problem constraints: as shown in the S2 model.

[0055] ②The first-order condition of Lagrangian function:

[0056]

[0057] ③ The complementary relaxation conditions transformed from the original inequality constraints and their equivalent strong duality theorem equations;

[0058] Complementary slack conditions:

[0059]

[0060] Strong duality theorem equation:

[0061]

[0062] ④Non-negativity constraints on dual variables.

[0063]

[0064] S33. Combine the KKT conditions formed by S32-①~④ into the constraints of the optimization problem of each renewable energy power station in S1, thereby transforming the double-layer master-slave game model of each renewable energy power station into a nonlinear single-layer constraint condition, that is, the MPEC problem of each renewable energy power station.

[0065] S4. Through KKT conditions, the MPEC problems of each renewable energy power station are converted into constraints, and the "Equilibrium Problems with Equilibrium Constraints" (EPEC) are formed together, thereby converting the original complex problem into a single-layer optimization model, which can be solved by the NLP commercial solver, and finally realize the simulation of the trading strategy of renewable energy power stations and the market clearing operation simulation, which is used to guide the core business of market operators such as clearing price forecasting and market benefit evaluation.

[0066] Specifically, it can be divided into the following two steps.

[0067] S41. Reconstruct the MPEC problem of each renewable energy power station and establish the Lagrangian dual function of the trading strategy optimization problem of each renewable energy power station. The reconstructed optimization problem of each renewable energy power station is as follows:

[0068] St:

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] The construction method of its Lagrangian dual function is similar to that of S31 and will not be elaborated here.

[0075] S42. Combine the KKT conditions of each MEPC problem, transform the original complex problem into a single-layer nonlinear optimization problem, and use the NLP commercial solver to solve it. The final optimization problem consists of the following parts:

[0076] ① Original problem equality constraint, as shown in step S2.

[0077] ② Lagrangian first-order conditions for each MPEC problem (i.e., second-order conditions for the original problem):

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] ③ First-order complementary relaxation condition, as shown in step S32-③.

[0097] ④ Second-order complementary relaxation condition:

[0098]

[0099] ⑤Complementary relaxation conditions between first-order and second-order dual variables:

[0100]

[0101] The specific implementation steps of the present invention are further described below in conjunction with a specific test system.

[0102] The test example of the present invention sets two different renewable energy penetration scenarios of 40%, 50%, 60%, 70%, and 80%. The corresponding power installed capacity under each scenario is shown in Table 1.

[0103] Table 1

[0104]

[0105]

[0106] The thermal power units include one 1000MW coal-fired unit, one 600MW coal-fired unit, one 300MW coal-fired unit and one 100MW gas-fired unit.

[0107] Different scenarios are set for the market concentration of new energy power stations under different penetration scenarios, namely: oligopoly market (1 new energy power station), severe oligopoly market (2-5 new energy power stations), mild oligopoly market (6-9 new energy power stations), and competitive market (10 or more new energy power stations). This example numerically simulates different market concentration scenarios under different renewable energy penetration rates, and quantitatively compares the market clearing electricity price and its distribution, the quotation curve of new energy power stations, and the market economic surplus transfer, as shown in the attached manual. Figures 2 to 5 shown.

[0108] 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 simulating spot trading strategies and optimizing market operation of new energy power stations based on master-slave game, characterized in that: The following steps are involved: S1. Establish a trading strategy optimization model for each new energy power station with the goal of maximizing the power generation revenue of each new energy power station; S2. Establish an optimal clearing model for the electricity spot market, with the goal of minimizing the system power supply cost and the constraints of the electricity spot market operation as constraints; S3. With each renewable energy power station as the upper leader and the market operator as the lower follower, the electricity spot market clearing optimization model is transformed into the MPEC problem in the trading strategy optimization model of each renewable energy power station according to the KKT condition and strong duality theory; S4. Through KKT conditions, the MPEC problems are converted into single-layer nonlinear constraints, and all single-layer nonlinear constraints are combined and solved using the NLP commercial solver to obtain the optimal trading strategy and market optimization clearing results of the new energy power station.

2. The method according to claim 1, characterized in that Taking the maximum power generation revenue of the new energy power station as the goal, the objective function constructed is: The constraint is 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 They are the seller’s quotations for the w / pv wind power / photovoltaic power at the b period and time t respectively.

3. The method according to claim 1, characterized in that Taking the minimum system power supply cost as the goal, the objective function constructed is: 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, 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, is the maximum capacity of the interruptible load.

4. The method according to claim 1, characterized in that The constraints in step S2 include: ① System power balance constraints: ② Output constraints of thermal power units: ③ Output constraints of new energy power stations: ④ Load shedding power constraint: 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, 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, is the maximum capacity of the interruptible load.

5. The method according to claim 1, characterized in that The step S3 comprises: S31. Establish the Lagrangian dual function L of the electricity spot market optimization clearing model: S32. Establish the KKT conditions for the optimal clearing model of the electricity spot market, including: ①Original problem constraints: ②The first-order condition of Lagrangian function: ③ The complementary relaxation condition transformed from the original inequality constraint and its equivalent strong duality theorem equation: ④ Non-negativity constraints on dual variables: S33. Combine the KKT conditions formed by S32-①~③ with the constraints in S1, and transform the electricity spot market clearing optimization model into the MPEC problem in the trading strategy optimization model of each new energy power station.

6. The method according to claim 1, characterized in that The step S4 comprises: S41. Reconstruct the MPEC problem of each renewable energy power station and establish the Lagrangian dual function of the trading strategy optimization problem of each renewable energy power station; S42. Combine the KKT conditions of each MEPC problem, transform each MEPC problem into a single-layer nonlinear constraint condition, and solve it using an NLP commercial solver.

7. A new energy power station spot trading strategy simulation and market power analysis method based on master-slave game New energy power station spot trading strategy simulation and market optimization operation system, 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 new energy power station spot trading strategy simulation and market optimization operation method based on master-slave game as described in any one of claims 1 to 6.

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