Multi-agent robust optimization scheduling method based on bidirectional master-slave game

Abstract

The invention relates to a multi-body robust optimization scheduling method based on a bidirectional master-slave game, and belongs to the field of comprehensive utilization of energy. A bidirectional master-slave game model is provided, an integrated energy system operator (IESO) is used as a leader to optimize energy pricing, an energy storage operator (ESO) is used as a secondary leader, a load aggregator (LA) is used as a follower, and each main body formulates an energy transaction strategy according to the own state and benefit target to optimize the internal running state of the system, so that various load requirements are met. Uncertain factors are added into a proposed bidirectional master-slave game model, the uncertainty of wind and light output and electricity price of an electricity market is considered, a safety constraint economic optimization model under the worst condition is established, and the problem is converted into an MILP formula by using a strong duality theory. And finally, solving the proposed bidirectional master-slave game model by adopting a golden section method, only exchanging limited energy quantity and price information during transaction, and protecting data information privacy of each subject.

Description

Technical Field

[0001] The present invention belongs to the field of comprehensive energy utilization, and particularly relates to a multi-agent robust optimization scheduling method based on two-way master-slave game. Background Art

[0002] Driven by global climate change and the carbon neutral goal, reducing carbon emissions has become one of the core goals of achieving sustainable development. As important participants in the optimal operation of the energy system, the integrated energy system operator (IESO), energy storage operator (ESO), and load aggregator (LA), their collaborative management is of great significance for improving energy utilization efficiency and reducing carbon emissions. However, the existing multi-agent collaborative management mechanism is not yet perfect, and there is a lack of effective coordination and game mechanisms among IESO, ESO, and LA, resulting in low energy resource allocation efficiency and being unable to fully cope with the increasingly complex scheduling requirements and uncertainties in the energy system.

[0003] As an advanced method for dealing with uncertainty problems, robust optimization is widely applied to the optimization of complex systems with randomness and volatility. Its core idea is to seek the optimal solution within the range of uncertainty, so that the system can still maintain the reliability and robustness of performance under the most unfavorable conditions. In the energy system, the output of wind power and photovoltaic power has high randomness, and the real-time electricity price traded with the superior power grid also changes with market fluctuations. Robust optimization technology provides strong support for new energy consumption, power trading, and system stability by effectively dealing with these uncertainties, promoting the efficient utilization of renewable energy and the optimized development of the energy system. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-agent robust optimization scheduling method based on two-way master-slave game. First of all, it can improve the robustness of the optimization model. By modeling and analyzing uncertain factors, it enhances the adaptability of the system to volatility and ensures the feasibility of the energy scheduling plan in actual operation. Secondly, fully considering uncertainty can prompt the system to better balance risks and benefits in the design stage, optimize resource allocation, and reduce operating costs. At the same time, this method helps to improve the utilization efficiency of renewable energy, reduce carbon emissions, and achieve the goal of green and low-carbon development. In addition, combined with uncertainty analysis, it can formulate electricity price strategies more accurately, promote market stability and user satisfaction.

[0005] To achieve the above object, the technical solution of the present invention is: a multi-agent robust optimization scheduling method based on two-way master-slave game, including:

[0006] Propose a two-way master-slave game model, add uncertain factors, consider the uncertainty of wind and light output and the electricity price in the power market, and establish a security-constrained economic optimization model under the worst case.

[0007] In an embodiment of the present invention, the method further includes:

[0008] Using the strong duality theory to transform the worst - case security - constrained economic optimization model into a MILP formula.

[0009] In an embodiment of the present invention, the method further includes:

[0010] Using the golden section method to solve the transformed MILP formula, and only exchanging a limited amount of energy quantity and price information during transactions to protect the data information privacy of each entity.

[0011] In an embodiment of the present invention, a two - way master - slave game model is proposed, and the specific implementation is as follows:

[0012] Taking the integrated energy system operator IESO as the leader to optimize the energy pricing, whose goal is to maximize its own profit by formulating energy prices; the energy storage operator ESO as the secondary leader, who both purchases energy from the IESO and sells energy to the LA, and its goal is to balance the transaction price and volume between the two to maximize its own profit; the load aggregator LA as the follower, whose goal is to minimize its own energy cost under the given energy price.

[0013] In an embodiment of the present invention, uncertain factors are added, considering the uncertainty of wind and solar power output and the electricity price in the power market, and a worst - case security - constrained economic optimization model is established, which specifically includes:

[0014] Due to the intermittent and uncertain characteristics of natural resources including wind energy and solar energy, the output values of wind power and photovoltaic power show obvious randomness; it is assumed that the power values of natural resources including wind energy and solar energy fall within the interval, where P wt,u and P pv,u represent a set of possible wind and photovoltaic power generation, respectively represent the maximum allowable deviations above and below P wt and P pv ; the electric power balance constraint condition of the integrated energy system without considering the uncertainty of wind and solar power is as follows:

[0015] P buy (t)+P CHP (t)+P wt (t)+P pv (t)-P sell (t)-P e,cha (t)-P el (t)-P e,EL (t)-P e,CCS (t)-P e,ISAC (t)=0

[0016] Wherein: P buy (t), P CHP (t), P wt (t), P pv (t), P sell (t), P e,cha (t), P el (t), P e,EL (t), P e,CCS (t), P e,ISAC (t) are the power purchase power of the IESO from the superior power grid, the power generation power of the CHP, the wind power generation power, the photovoltaic power generation power, the power selling power of the IESO to the superior power grid, the power purchase power of the ESO from the IESO, the power purchase power of the user, the power consumption of the electrolyzer, the power consumption of the carbon capture system, and the power consumption of the ice storage air conditioner at time t, respectively;

[0017] Therefore, the power balance constraint under the worst case of the output of the uncertain parameters of wind power and photovoltaic power is as follows:

[0018]

[0019] Wherein: is the scaling deviation of the wind power generation output, is the scaling deviation of the photovoltaic power generation output;

[0020] In an embodiment of the present invention, it further includes:

[0021] Introduce the dual variable λ 1 , Convert the power balance constraint problem of the output of the uncertain parameters of wind power and photovoltaic power under the worst case into a corresponding dual problem;

[0022]

[0023] Wherein: Γ represents the adjustable parameter of the uncertainty, that is, the uncertainty range;

[0024]

[0025] The original power balance constraint condition can be converted to:

[0026]

[0027] In an embodiment of the present invention, it further includes:

[0028] Establish the objective function for the operation of the integrated energy system considering the electricity price uncertainty as follows:

[0029]

[0030] where: f grid is the cost of power purchase and sale; f gas is the cost of gas purchase; f om is the operation and maintenance cost; f cur is the penalty cost for abandoning wind and light; f CO2 is the reward and punishment ladder carbon trading cost; f CHP , f GB are the total operation costs of the combined heat and power unit and the gas boiler respectively; f sell is the revenue from selling energy to energy storage operators and load aggregators; d 0 is the price deviation coefficient; z 1 , z 2 are the introduced auxiliary variables; the last term in the above formula represents the operation cost generated by the uncertainty of the electricity price in the power market on the electricity purchase and sale price of the IESO. As a penalty term related to the power market, it is added to the total optimization objective function of the IESO to reduce its deviation; among them, the inner max part provides the worst-case scenario of the electricity price, and the outer min part is to minimize the overall cost, that is, to maximize the revenue.

[0031] In an embodiment of the present invention, the security-constrained economic optimization model in the worst case is transformed into a MILP formula by using the strong duality theory, and the specific implementation is as follows:

[0032] The inner max problem is re-relaxed into the following form:

[0033]

[0034] where: δ 1 , δ 2 are the electricity price uncertainty parameters. δ determines the number of time periods when the IES considers the electricity price uncertainty within a scheduling period. δ = 0 means not considering the electricity price uncertainty, and δ = 24 means considering the electricity price uncertainty at all times within the scheduling period; π 1 , π 2 , π 3 , π 4 are the Lagrange multipliers corresponding to the inequality constraints respectively;

[0035] Using the strong duality theory for equivalence, the final transformed Lagrange dual problem is as follows:

[0036]

[0037] The objective function of the Min-Max problem in the operation of the integrated energy system considering electricity price uncertainty is transformed into the objective function of the Min-Min problem through equivalent transformation as:

[0038]

[0039] In an embodiment of the present invention, the golden section method is used to solve the transformed MILP formula. Only a limited amount of energy quantity and price information are exchanged during transactions, protecting the data information privacy of each entity. The specific implementation is as follows:

[0040] Initial parameter setting of the golden section method: The optimization variable is the price p of energy trading, and the domain is [a 1 , b 1 , where a 1 and b 1 are the upper and lower limits of the energy trading price respectively. Calculate two initial search points: α 1 = a 1 + 0.382(b 1 - a 1 ), β 1 = a 1 + 0.618(b 1 - a 1 );

[0041] IESO iteration process: IESO sends α 1 and β 1 to ESO, requiring ESO to adjust its energy purchase strategy and selling price according to the energy price; IESO calculates its own profit f IESO (α 1 ) and f IESO (β 1 ) according to the energy purchase strategy fed back by ESO. Compare the profit values of the two points and update the search interval: If f IESO (α k ) < f IESO (β k ), then update a k+1 = a k , b k+1 = β k , β k+1 = α k , α k+1 = a k+1 + 0.382(b k+1 - a k+1 ); If f IESO (α k ) ≥ f IESO (β k ), then update a k+1 = α k , b k+1 = b k , α k+1 = β k , β k+1 = a k+1 + 0.618(b k+1 - a k+1); Repeat the whole process until b k -a k <τ; where k is the number of iterations and τ is the convergence accuracy;

[0042]

[0043] In the formula: C gas is the natural gas purchase price of IESO; k WHB , k AC , k ISAC , k WT , k PV are the operation and maintenance costs of the waste heat boiler, absorption chiller, ice storage air conditioner, wind turbine generator and photovoltaic generator respectively; k cur is the penalty price per unit of wind and light abandonment; k CHP , k GB are the operation costs of CHP and GB respectively; p el (t), p hl (t), p cl (t) are the electricity, heat and cold energy prices set by IESO for selling to ESO and LA respectively; P e,cha (t), P h,cha (t) are the power of ESO for storing electricity and heat respectively;

[0044] ESO iteration process: Within the energy price range provided by IESO, ESO updates the interactive electric and heat power P e,cha , P h,cha and the interactive energy price p dis according to the obtained energy trading price using the following formula, and returns the optimized energy price and energy purchase strategy to IESO, and at the same time sends the adjusted energy selling price to LA;

[0045]

[0046] In the formula: f ESO is the total revenue of ESO; is the revenue of ESO from selling energy to LA; is the cost of ESO for purchasing energy from IESO; f loss is the operation and use cost of energy storage; f pen is the penalty cost for the energy storage operator to interrupt the supply to users; p e,dis , p h,dis are the electricity and heat selling prices of ESO to LA respectively; P e,dis (t), P h,dis (t) are the discharging and heat releasing powers of ESO respectively; η e,cha , η h,cha are the loss costs of ESO for charging and discharging electricity and heat of energy storage respectively; ηpen The penalty price for the energy storage operator to interrupt the supply to users;

[0047] LA iterative process: After receiving the energy prices transmitted by the IESO and ESO respectively, LA optimizes its energy procurement strategy according to the following formula to minimize the total cost, and transmits the optimal procurement strategy to the IESO and ESO respectively according to the optimization results;

[0048]

[0049] In the formula: f LA is the total cost; is the LA energy procurement cost; f tran is the LA transferable load cost; f cut is the LA curtailable load cost. λ e,tran 、λ h,tran 、λ c,tran are the transfer costs of the electric, heat, and cold loads respectively; P e,tran (t), P h,tran (t), P c,tran (t) are the powers of the electric, heat, and cold transferable loads respectively; λ e,cut 、λ h,cut 、λ c,cut are the curtailment costs of the electric, heat, and cold loads respectively; P e,cut (t), P h,cut (t), P c,cut (t) are the powers of the electric, heat, and cold curtailable loads respectively;

[0050] Through the above golden section method iterative process, the interest goals of the IESO, ESO, and LA in the master-slave game model are coordinated, and at the same time, the balance between profit maximization and cost minimization of the entire energy trading system is achieved.

[0051] The present invention also provides a computer-readable storage medium, on which computer program instructions that can be run by a processor are stored. When the processor runs the computer program instructions, the method steps described above can be implemented.

[0052] Compared with the prior art, the present invention has the following beneficial effects: Firstly, the method of the present invention can improve the robustness of the optimization model. By modeling and analyzing uncertain factors, it enhances the adaptability of the system to volatility and ensures the feasibility of the energy scheduling scheme in actual operation. Secondly, fully considering uncertainty can prompt the system to better balance risks and benefits in the design stage, optimize resource allocation, and reduce operating costs. At the same time, this method helps to improve the utilization efficiency of renewable energy, reduce carbon emissions, and achieve the goal of green and low-carbon development. In addition, combined with uncertainty analysis, it can formulate electricity price strategies more accurately, promote market stability and user satisfaction. Description of the Drawings

[0053] Figure 1 Schematic diagram of the two-way master-slave game optimization framework proposed by the present invention.

[0054] Figure 2 Flow chart of using the golden section distributed algorithm to solve the master-slave game model in this embodiment.

[0055] Figure 3 Iterative convergence results of the master-slave game between IESO, ESO, and LA solved by the distributed trial method based on the golden section search.

[0056] Figure 4 Energy prices set for IESO and ESO.

[0057] Figure 5 Comparison chart of carbon emissions under the influence of different robustness factors. Specific implementation manner

[0058] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0059] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0060] It should be noted that the terms used herein are only for describing specific implementation manners and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular forms are also intended to include the plural forms. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0061] The present invention provides a multi-agent robust optimization scheduling method based on two-way master-slave game, including:

[0062] Propose a two-way master-slave game model, add uncertain factors, consider the uncertainty of wind and light output and electricity market price, and establish a security-constrained economic optimization model in the worst case.

[0063] Use strong duality theory to transform the security-constrained economic optimization model in the worst case into a MILP formula;

[0064] Use the golden section method to solve the transformed MILP formula, and only exchange limited energy quantity and price information during transactions to protect the data information privacy of each subject.

[0065] The following is the specific implementation process of the present invention.

[0066] This embodiment provides a multi-agent robust optimization scheduling method based on two-way master-slave game, including:

[0067] 1. Propose a two-way master-slave game model:

[0068] As shown in Figure 1 , it is a schematic diagram of the proposed two-way master-slave game optimization framework. In this master-slave game model, IESO is the first-layer leader, and its goal is to maximize its own profit by setting energy prices; ESO is the second-layer leader, which buys energy from IESO and sells energy to LA. Its goal is to balance the transaction price and volume between the two to maximize its own profit; LA is the follower, and its goal is to minimize its own energy cost under the given energy price. The entire solution process is based on the golden section method to efficiently achieve the coordination and optimization between the decisions of different agents.

[0069] Propose a model that simultaneously considers the uncertainty of wind and solar power output and electricity price as follows:

[0070] Due to the intermittent and uncertain characteristics of natural resources such as wind energy and solar energy, the output values of wind power and photovoltaic power show obvious randomness. Assume that these power values fall within the interval, where P wt,u and P pv,u represent a set of possible wind and photovoltaic power generation, respectively represent the maximum allowable deviations above and below P wt and P pv . The power balance constraint condition of the integrated energy system without considering the uncertainty of wind and solar power is as follows:

[0071] P buy (t)+P CHP (t)+P wt (t)+P pv (t)-P sell (t)-P e,cha (t)-P el (t)-P e,EL (t)-P e,CCS (t)-P e,ISAC (t)-P (t)=0

[0072] In the formula: P buy (t), P CHP (t), P wt (t), P pv (t), P sell (t), P e,cha (t), P el (t), P e,EL (t), P e,CCS(t), P e,ISAC (t) are the power purchase from the superior power grid by the IESO at time t, the power generation of the CHP, the wind power generation, the photovoltaic power generation, the power sold by the IESO to the superior power grid, the power purchased by the ESO from the IESO, the power purchased by users, the power consumption of the electrolyzer, the power consumption of the carbon capture system, and the power consumption of the ice storage air conditioner, respectively;

[0073] Therefore, the power balance constraint under the worst-case scenario of the output of wind power and photovoltaic power, which are uncertain parameters, is shown as follows:

[0074]

[0075] In the formula: is the scaling deviation of the wind power output, is the scaling deviation of the photovoltaic power output;

[0076] To make the above problem easy to handle, the dual variables λ 1 and are introduced to transform the original problem into the corresponding dual problem.

[0077]

[0078] In the formula: Γ represents the adjustable parameter of uncertainty, that is, the uncertainty range.

[0079]

[0080] The original power balance constraint condition can be transformed into:

[0081]

[0082] The uncertainty of electricity market prices poses challenges to the optimization strategy of integrated energy systems. This uncertainty may lead to suboptimal scheduling decisions, thereby increasing the total operating cost. The objective function for the operation of integrated energy systems considering electricity price uncertainty is established as follows:

[0083]

[0084] In the formula: f grid is the cost of power purchase and sale; f gas is the cost of gas purchase; f om is the operation and maintenance cost; f cur is the penalty cost for abandoning wind and light; f CO2 is the cost of reward and punishment ladder carbon trading; f CHP and f GB are the total operating costs of the CHP unit and the gas boiler, respectively; f sell is the revenue from selling energy to energy storage operators and load aggregators; d 0is the price deviation coefficient; z 1 , z 2 are the introduced auxiliary variables; the last term in the above formula represents the operating cost generated by the uncertainty of the electricity price in the power market on the IESO's purchase and sale electricity prices. As a penalty term related to the power market, it is added to the overall optimization objective function of the IESO to reduce its deviation. Among them, the inner max part provides the worst-case scenario of the electricity price, and the outer min part is to minimize the overall cost, that is, to maximize the revenue.

[0085] To facilitate the solution of the model, auxiliary variables z 1 , z 2 are introduced, and the inner max problem is re-relaxed into the following form:

[0086]

[0087] In the formula: δ 1 , δ 2 are the electricity price uncertainty parameters. δ determines the number of time periods when the IES considers the electricity price uncertainty within a scheduling period. δ = 0 means not considering the electricity price uncertainty, and δ = 24 means considering the electricity price uncertainty at all times within the scheduling period; π 1 , π 2 , π 3 , π 4 are the Lagrange multipliers corresponding to the inequality constraints respectively.

[0088] Using the strong duality theory for equivalence, the final transformed Lagrange dual problem is as follows:

[0089]

[0090] The objective function after the Min-Max problem in the original formula is equivalently transformed into a Min-Min problem is:

[0091]

[0092] 2. Solve the master-slave game model

[0093] As Figure 2 shown, this embodiment uses the golden section distributed algorithm to solve the proposed master-slave game model, including the following steps:

[0094] Initial parameter setting of the golden section method: The optimization variable is the price p of energy trading, and the domain is [a 1 , b 1 , where a 1 , b 1 are the upper and lower limits of the energy trading price respectively. Calculate two initial search points: α 1 = a 1+0.382(b 1 -a 1 ),β 1 =a 1 +0.618(b 1 -a 1 );

[0095] IESO iteration process: IESO sends α 1 and β 1 to ESO, asking ESO to adjust its energy purchase strategy and selling energy price according to the energy price; IESO calculates its own profit f IESO (α 1 ) and f IESO (β 1 ) according to the energy purchase strategy fed back by ESO and the following formula; compares the profit values at two points and updates the search interval: if f IESO (α k ) < f IESO (β k ), then update a k+1 =a k , b k+1 =β k , β k+1 =α k , α k+1 =a k+1 +0.382(b k+1 -a k+1 );if f IESO (α k ) ≥ f IESO (β k ), then update a k+1 =α k , b k+1 =b k , α k+1 =β k , β k+1 =a k+1 +0.618(b k+1 -a k+1 );repeat the whole process until b k -a k <τ; where k is the number of iterations and τ is the convergence accuracy;

[0096]

[0097] In the formula: C gas is the natural gas purchase price of IESO; k WHB , k AC , k ISAC , k WT , k PVThey are the operation and maintenance costs of the waste heat boiler, absorption chiller, ice storage air conditioner, wind turbine generator set, and photovoltaic generator set, respectively; k cur is the penalty price for unit wind and light abandonment; k CHP and k GB are the operating costs of CHP and GB, respectively; p el (t), p hl (t), p cl (t) are the electricity, heat, and cooling energy prices set by IESO for selling to ESO and LA, respectively; P e,cha (t), P h,cha (t) are the power of ESO for storing electrical energy and thermal energy, respectively;

[0098] ESO iteration process: within the energy price range provided by IESO, ESO updates the interactive electrical and thermal power P e,cha and P h,cha as well as the interactive energy price p dis according to the following formula, and returns the optimized energy price and energy purchase strategy to IESO, and at the same time sends the adjusted energy selling price to LA;

[0099]

[0100] In the formula: f ESO is the total revenue of ESO; is the revenue of ESO from selling energy to LA; is the cost of ESO purchasing energy from IESO; f loss is the operating cost of energy storage; f pen is the penalty cost for the energy storage operator to interrupt the supply to users; p e,dis and p h,dis are the prices of ESO selling electrical energy and thermal energy to LA, respectively; P e,dis (t), P h,dis (t) are the discharging and heat releasing powers of ESO, respectively; η e,cha and η h,cha are the loss costs of ESO for charging and discharging electrical energy and thermal energy during energy storage, respectively; η pen is the penalty price for the energy storage operator to interrupt the supply to users.

[0101] LA iteration process: after receiving the energy prices transmitted by IESO and ESO respectively, LA optimizes its energy purchase strategy according to the following formula to minimize the total cost, and transmits the optimal energy purchase strategies to IESO and ESO respectively according to the optimization results.

[0102]

[0103] In the formula: f LA is the total cost; is the LA purchase energy cost; f tran is the LA transferable load cost; f cut is the LA curtailable load cost. λ e,tran 、λ h,tran 、λ c,tran are the electric, heat, and cooling load transfer costs respectively; P e,tran (t), P h,tran (t), P c,tran (t) are the electric, heat, and cooling transferable load powers respectively; λ e,cut 、λ h,cut 、λ c,cut are the electric, heat, and cooling load curtailment costs respectively; P e,cut (t), P h,cut (t), P c,cut (t) are the electric, heat, and cooling curtailable load powers respectively.

[0104] Through the above iteration process of the golden section method, the interest objectives of the IESO, ESO, and LA in the master-slave game model are coordinated. At the same time, the entire energy trading system achieves a balance between maximizing profit and minimizing cost, providing an efficient and feasible solution for the future optimization of the multi-layer energy market.

[0105] Figure 3 is the iterative convergence result of solving the master-slave game between the IESO and the ESO, LA using the distributed trial method based on the golden section search. The proposed algorithm converges after 15 iterations, and the iteration time is 20.452742 seconds. Its convergence residual can be guaranteed within 10 -2 times. The total revenue of the IESO converges to 22,697.9 yuan, the total revenue of the ESO converges to 541.4 yuan, and the total cost of the LA converges to 39,944.4 yuan. This efficient performance not only reflects the strong convergence ability of the algorithm in dealing with complex game scenarios but also verifies its reliability in terms of computational accuracy. More importantly, the design of the algorithm fully considers the data privacy issues of each participant in practical applications, ensuring that the privacy information of the IESO, ESO, and LA is effectively protected during the process of information sharing and decision-making, while achieving the effective scheduling and utilization of resources.

[0106] As Figure 4 shown, as the leader of the ESO and LA, the IESO formulates a unified price strategy and transmits it to the ESO and LA. The ESO and LA determine their energy purchase strategies according to their own optimization objectives and then feedback to the IESO. The IESO then updates the energy price with the goal of maximizing its own interests based on the energy purchase strategies of the ESO and LA. After continuous iteration, the energy price formulated by the IESO is as Figure 4(a) As shown, the figure includes the prices of electric energy, thermal energy, and cold energy. After the IESO determines the energy prices, the ESO, as the leader of the LA, will also determine the energy release price to the LA based on the goal of maximizing its own interests. The energy prices set by the ESO are as Figure 4 (b) As shown, the figure includes the prices of electric energy and thermal energy.

[0107] Figure 5 It is a comparison chart of carbon emissions under the influence of different robustness factors. As the robustness factor increases, the carbon emissions also show a significant increasing trend. A larger robustness factor means that a more conservative coping strategy is adopted for the impact of system uncertainties, thus requiring more energy to suppress the potential adverse effects of these uncertainties. As the energy demand increases, the carbon emissions and operating costs of the system also rise.

[0108] The present invention also provides a computer-readable storage medium, on which computer program instructions capable of being run by a processor are stored. When the processor runs these computer program instructions, the method steps described in any of the above can be implemented.

[0109] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the processes described in Figure 1One or more processes and / or blocks Figure 1 The functions specified in one or more blocks.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 One or more processes and / or blocks Figure 1 The steps of the functions specified in one or more blocks.

[0113] As described above, it is only the preferred embodiment of the present invention, and it is not a limitation of the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A multi-agent robust optimization scheduling method based on two-way master-slave game, characterized in that: include: A two-way master-slave game model is proposed, uncertain factors are added, the uncertainty of wind and solar power output and electricity market prices is considered, and a safety-constrained economic optimization model under the worst case scenario is established.

2. According to claim 1, a multi-agent robust optimization scheduling method based on two-way master-slave game is characterized in that: Also includes: The worst-case safety-constrained economic optimization model is transformed into a MILP formula using strong duality theory.

3. The multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 2 is characterized in that: Also includes: The golden section method is used to solve the converted MILP formula. Only limited energy quantity and price information are exchanged during transactions to protect the data privacy of each subject.

4. The multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 3 is characterized in that: A two-way master-slave game model is proposed, and its specific implementation is as follows: The integrated energy system operator IESO is taken as the leader to optimize energy pricing, and its goal is to maximize its own profits by setting energy prices; the energy storage operator ESO is the secondary leader, which both purchases energy from IESO and sells energy to LA, and its goal is to balance the transaction price and transaction volume between the two to maximize its own profits; the load aggregator LA is the follower, and its goal is to minimize its own energy costs under the established energy price.

5. The multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 4 is characterized in that: By adding uncertain factors, considering the uncertainty of wind and solar power output and electricity market prices, a safety-constrained economic optimization model under the worst-case scenario is established, including: Due to the intermittent and uncertain characteristics of natural resources including wind energy and solar energy, the output values ​​of wind power and photovoltaic power generation show obvious randomness; assuming that the power values ​​of natural resources including wind energy and solar energy fall within In the interval, P wt,u and P pv,u represents a set of possible wind and photovoltaic power generation powers, Respectively represent P wt , P pv The maximum deviation allowed above and below; the power balance constraints of the integrated energy system without considering the uncertainty of wind and solar power are as follows: P buy (t)+P CHP (t)+P wt (t)+P pv (t)-P sell (t)-P e,cha (t)-P el (t)-P e,EL (t)-P e,CCS (t)-P e,ISAC (t)=0 Where: P buy (t), P CHP (t), P wt (t), P pv (t), P sell (t), P e,cha (t), P el (t), P e,EL (t), P e,CCS (t), P e,ISAC (t) are the power purchased by IESO from the upper grid, power generated by CHP, wind power generation, photovoltaic power generation, power sold by IESO to the upper grid, power purchased by ESO from IESO, power purchased by users, power consumption of electrolyzer, power consumption of carbon capture system, and power consumption of ice storage air conditioner at time t; Therefore, the worst-case electric power balance constraints for wind and photovoltaic power output with uncertain parameters are as follows: Where: is the scale deviation of wind power output, is the scaling deviation of the photovoltaic power output.

6. A multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 5, characterized in that: Also includes: Introduce the dual variable λ1, The worst-case power balance constraint problem of wind and photovoltaic power generation output with uncertain parameters is transformed into the corresponding dual problem; Where: Γ represents the adjustable parameter of uncertainty, that is, the uncertainty range; The original power balance constraint can be transformed into:

7. A multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 6, characterized in that: Also includes: The objective function of establishing the operation of the integrated energy system considering the uncertainty of electricity prices is as follows: Where: f grid is the cost of purchasing and selling electricity; gas is the gas purchase cost; f om is the operation and maintenance cost; cur Penalty cost for abandoning wind power; CO2 The carbon trading cost of the reward and punishment ladder; CHP 、f GB are the total operating costs of the cogeneration unit and gas boiler respectively; f sell is the revenue from energy sales to energy storage operators and load aggregators; d0 is the price deviation coefficient; z1 and z2 are introduced auxiliary variables; the last term in the above formula represents the operating cost of IESO’s electricity purchase and sales price due to the uncertainty of electricity prices in the electricity market. As a penalty term related to the electricity market, it is added to the IESO’s total optimization objective function to reduce its deviation; the inner max part provides the worst case of electricity prices, and the outer min part minimizes the overall cost, that is, maximizes the benefits.

8. The multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 7 is characterized in that: The worst-case safety-constrained economic optimization model is transformed into a MILP formula using strong duality theory. The specific implementation is as follows: Relax the inner max problem into the following form: Where: δ1 and δ2 are electricity price uncertainty parameters, δ determines the number of time periods that IES considers electricity price uncertainty in a dispatch cycle, δ = 0 means that electricity price uncertainty is not considered, and δ = 24 means that electricity price uncertainty is considered at all times in the dispatch cycle; π1, π2, π3, and π4 are the Lagrange multipliers corresponding to the inequality constraints respectively; Using strong duality theory for equivalence, the Lagrange dual problem is finally transformed as follows: The objective function of the Min-Max problem in the objective function of the operation of the integrated energy system considering the uncertainty of electricity prices is equivalently transformed into the Min-Min problem:

9. The multi-agent robust optimization scheduling method based on two-way master-slave game according to claim 8 is characterized in that: The golden section method is used to solve the converted MILP formula. Only limited energy and price information are exchanged during transactions to protect the data privacy of each subject. The specific implementation is as follows: Initial parameter setting of the golden section method: the optimization variable is the price p of energy trading, the domain is [a1, b1], a1 and b1 are the upper and lower limits of the energy trading price, and two initial search points are calculated: α1 = a1 + 0.382 (b1-a1), β1 = a1 + 0.618 (b1-a1); IESO iteration process: IESO sends α1 and β1 to ESO, requiring ESO to adjust its energy purchasing strategy and energy selling price according to energy prices; IESO calculates its own profit f according to the energy purchasing strategy fed back by ESO according to the following formula IESO (α1) and f IESO (β1); compare the profit values ​​of the two points and update the search interval: if f IESO (α k )<f IESO (β k ), then update a k+1 =a k ,b k+1 =β k ,β k+1 =α k ,α k+1 =a k+1 +0.382(b k+1 -a k+1 ); if f IESO (α k )≥f IESO (β k ), then update a k+1 =α k ,b k+1 =b k ,α k+1 =β k ,β k+1 =a k+1 +0.618(b k+1 -a k+1 ); Repeat the whole process until b k -a k <τ; where k is the number of iterations and τ is the convergence accuracy; Where: C gas The price of natural gas purchased for IESO; k WHB , k AC , k ISAC , k WT , k PV are the operation and maintenance costs of waste heat boiler, absorption chiller, ice storage air conditioner, wind turbine and photovoltaic generator respectively; k cur k is the penalty price for abandoning wind and solar power; CHP , k GB are the operating costs of CHP and GB respectively; p el (t), p hl (t), p cl (t) the prices of electricity, heating and cooling energy sold to ESO and LA respectively set by IESO; e,cha (t), P h,cha (t) the power to store electrical and thermal energy for the ESO respectively; ESO iteration process: Within the energy price range provided by IESO, ESO updates the interactive electric heating power P according to the following formula based on the energy transaction price obtained e,cha , P h,cha and the interactive energy price p dis The optimized energy price and energy purchase strategy are returned to IESO, and the adjusted energy sales price is sent to LA; Where: f ESO is the total income of ESO; Proceeds from the sale of energy to LA for ESO; The cost of purchasing energy from IESO for ESO; loss is the operating cost of energy storage; f pen The penalty cost for energy storage operators to interrupt supply to users; e,dis 、p h,dis are the prices of electricity and heat sold by ESO to LA respectively; P e,dis (t), P h,dis (t) are ESO discharge and heat release power, respectively; η e,cha , η h,cha are the loss costs of ESO energy storage charging and discharging energy and heat energy respectively; η pen Penalty prices for energy storage operators to charge customers for interrupting supply; LA iteration process: After receiving the energy prices transmitted by IESO and ESO respectively, LA optimizes its energy purchasing strategy according to the following formula to minimize the total cost, and transmits the optimal energy purchasing strategy to IESO and ESO respectively according to the optimization results; Where: f LA is the total cost; is the energy purchase cost of LA; f tran is the LA transferable load cost; f cut LA can reduce the load cost; e,tran , h,tran , c,tran are the electric heating and cooling load transfer costs; P e,tran (t), P h,tran (t), P c,tran (t) are the transferable load power of electric heating and cooling respectively; λ e,cut , h,cut , c,cut are the electric heating and cooling load reduction costs; P e,cut (t), P h,cut (t), P c,cut (t) are the load power that can be reduced by electric heating and cooling; Through the above iterative process of the golden section method, the interests of IESO, ESO and LA in the master-slave game model are coordinated, and the entire energy trading system achieves a balance between profit maximization and cost minimization.

10. A computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, and when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 9 can be implemented.

Images