Master-slave game method, device and equipment for aggregator and sub-micro-energy network, and medium

Abstract

The invention discloses a master-slave game method, device and equipment for an aggregator and a sub-micro-energy network, and a medium, and belongs to the technical field of power system planning. Comprising the steps that a master-slave game model with an aggregator and a sub-micro-energy network set as a main body is established, and the model comprises an aggregator layer and a sub-micro-energy network layer; aggregator layer optimization is carried out by taking the lowest total operation cost of an aggregator in a single scheduling day as a target; performing sub-micro-energy network layer optimization by taking the lowest total operation cost of a single scheduling day of the sub-micro-energy network set as a target; and obtaining a game result of the aggregator and the sub-micro-energy network set based on the optimization results of the aggregator layer and the sub-micro-energy network layer. According to the method, the master-slave game model with the aggregator and the sub-micro-energy network set as the main body is established, the benefits of the two parties are maximized, the sub-micro-energy networks are guided to increase the generating capacity of clean new energy and carry out carbon emission quota transaction through a carbon emission quota excitation and transaction mechanism, and the overall carbon emission level of the micro-energy network system is effectively reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning, and relates to a master-slave game method, device, equipment and medium between an aggregator and a sub-micro energy network. Background Art

[0002] The global energy supply faces challenges such as resource depletion, environmental pollution, and energy security. Renewable energy, mainly wind and light, is gradually becoming a research hotspot in the field of energy production and energy consumption. As an emerging form of renewable energy utilization, the micro-grid integrated energy system can effectively integrate a variety of distributed power generation sources such as wind power, photovoltaics, and micro-turbines, coordinate the supply and demand of electricity, gas, heat, and cold energy within the system, and has the advantages of clean, low-carbon, safe, and efficient. In addition, the multi-micro-grid system composed of interconnected micro-grids in adjacent areas has attracted widespread attention from researchers because of its higher renewable energy absorption rate and stronger ability to resist external interference and handle uncertainty. However, compared with a single micro-grid system, the structure of the multi-micro-grid system is more complex, and the energy interaction between each sub-micro-grid faces problems such as competition and transaction settlement. There are also conflicts of interest between operators and users within each micro-grid. Summary of the invention

[0003] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a master-slave game method, device, equipment and medium between aggregators and sub-micro energy networks taking carbon incentives into consideration, which can achieve a balance of interests between aggregators and sub-micro energy networks, and at the same time guide users of each sub-micro energy network in the region to trade carbon emission quotas.

[0004] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0005] In a first aspect, the present invention provides a master-slave game method between an aggregator and a sub-micro energy network, comprising:

[0006] Establish a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer;

[0007] Optimizing the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single dispatch day;

[0008] Optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day;

[0009] Based on the optimization results of the aggregator layer and the sub-micro energy network layer, obtaining the game results of the aggregator and the sub-micro energy network set;

[0010] The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

[0011] Furthermore, the master-slave game model with the aggregator and the sub-micro-energy network set as the main body includes:

[0012] ,

[0013] in, represents the master-slave game model, represents the set of game players, Represents the strategy set of the game subject; Represents the set of cost functions of the game players; represents the regional dispatch center of the aggregator, Micro-Energy Network , , , The number of sub-micro-energy grids in the sub-micro-energy grid layer; represents the operation strategy set of the regional dispatch center, Micro-Energy Network The running strategy set; Micro-Energy Network The amount of electricity purchased during period t; Micro-Energy Network The amount of gas purchased in period t; Micro-Energy Network Hezi Micro Energy Network The amount of electric energy interaction in period t; Micro-Energy Network Hezi Micro Energy Network The amount of thermal energy interaction in period t; Micro-Energy Network The electrical load during period t is: Micro-Energy Network The gas load in period t, Micro-Energy Network Heat load during period t; Micro-Energy Network Maintenance cost in period t; represents the cost function of the regional dispatch center, Micro-Energy Network The cost function of .

[0014] Furthermore, the aggregator layer optimization is performed with the goal of minimizing the total operating cost of the aggregator on a single dispatch day, including:

[0015] ,

[0016] C buy RDC = ∑ t = 1 T ∑ i = 1 N m [ p e ( t ) P buy e ( t , i ) + p g ( t ) P buy g ( t , i )] ,

[0017] C loss RDC = 1 2 ∑ t = 1 T ∑ i = 1 N m ∑ j = 1 , j ≠ i N m [ p e ( t ) P i , j e ( t ) l loss e + p h ( t ) P i , j h ( t ) l loss h ] ,

[0018] in, represents the total purchased energy cost; represents the energy interaction loss cost between sub-micro-energy grids; represents the electricity price during period t; represents the gas price in period t; Represents the electric energy interaction loss coefficient between sub-micro energy grids; represents the thermal energy interaction loss coefficient between sub-micro energy grids; T represents a scheduling day.

[0019] Furthermore, the constraints optimized at the aggregator level include energy purchase constraints, energy interaction constraints, and energy supply and demand margin constraints;

[0020] Ziwei Energy Network The energy purchase constraint is:

[0021] ,

[0022] in: Micro-Energy Network The upper limit of electricity purchase; Micro-Energy Network The upper limit of gas energy purchase;

[0023] Ziwei Energy Network The energy interaction constraint is:

[0024] ,

[0025] in, Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of electrical energy interaction between them; Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of thermal energy interaction between them;

[0026] Ziwei Energy Network The energy supply and demand margin constraint is:

[0027] ,

[0028] ,

[0029] ,

[0030]

[0031] in, Indicates the type of energy, including electrical energy , Qi Energy and heat ; k represents energy coupling equipment, including cogeneration, gas boilers, micro gas turbines and waste heat boilers; K represents sub-micro energy grid The number of energy-coupled devices; Micro-Energy Network China Energy The ratio of demand to its maximum supply in period t; Micro-Energy Network China Energy The upper limit of supply and demand margin; Indicates the sub-micro energy network in time period t Energy The actual demand; Indicates the sub-micro energy network in time period t China Energy The maximum supply of Indicates the sub-micro energy network in time period t Energy coupling device k to energy The actual demand; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Demand; Indicates the sub-micro energy network in time period t Energy coupling device k to energy The maximum supply of Indicates the sub-micro energy network in time period t Energy Purchase volume; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Supply quantity.

[0032] Furthermore, the sub-micro energy grid layer optimization is performed with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day, including:

[0033] ,

[0034] C buy SMIES ( i ) = ∑ t = 1 T [ p e ( t ) P buy e ( t , i ) + p g ( t ) P buy g ( t , i )] ,

[0035] C om SMIES ( i ) = ∑ t = 1 T ∑ s = 1 N s [ λ CHP om P CHP ( t , i ) + λ GB om P GB ( t , i ) + λ MT om P MT ( t , i ) + λ WHB om P WHB ( t , i ) ] ,

[0036] C IDR SMIES ( i ) = ∑ t = 1 T [ λ e power Δ P load e ( t , i ) + λ g power Δ P load g ( t , i ) + λ h power Δ P load h ( t , i ) ] ,

[0037] C i nteract SMIES ( i ) = ∑ t = 1 T ∑ j = 1 , j ≠ i N m [ p int Eract e ( t ) P i , j e ( t ) + p int Eract g ( t ) P i , j g ( t ) ] ,

[0038] ,

[0039] in, Micro-Energy Network The cost of purchasing energy; Micro-Energy Network The operation and maintenance costs of energy coupling equipment; Micro-Energy Network The comprehensive demand response compensation costs; Micro-Energy Network Energy interaction benefits; Micro-Energy Network of carbon emission quota trading income; represents the maintenance cost coefficient of CHP, represents the maintenance cost coefficient of the gas boiler, represents the maintenance cost coefficient of the micro gas turbine, Indicates the maintenance cost coefficient of the waste heat boiler; represents the electric load demand response compensation coefficient, represents the gas load demand response compensation coefficient, represents the heat load demand response compensation coefficient; Micro-Energy Network The amount of power demand response, Micro-Energy Network The amount of gas energy demand response, Micro-Energy Network The amount of heat demand response; It represents the interactive price of electric energy between sub-micro-energy grids; It represents the interactive price of gas energy between sub-micro-energy grids; Indicates the transaction price of carbon emission quotas between sub-micro-energy grids; Micro-Energy Network Sold to Ziweineng.com of carbon emission quotas, Then it means sub-micro energy network Sold to Ziweineng.com of carbon emission quotas.

[0040] Further, the constraints of the sub-micro energy grid layer optimization include operation constraints of energy coupling devices within the sub-micro energy grid;

[0041] Ziwei Energy Network The operating constraints of the internal energy coupling device are:

[0042] ,

[0043] in, Micro-Energy Network The upper limit of input power for CHP; Micro-Energy Network The upper limit of input power of gas boilers; Micro-Energy Network The upper limit of input power of micro gas turbines; Micro-Energy Network The upper limit of input power of waste heat boiler.

[0044] Furthermore, the carbon emission quota of the sub-micro energy network includes an initial carbon emission quota and an incentive carbon emission quota;

[0045] Ziwei Energy Network The initial carbon emission quota is:

[0046] ,

[0047] in, Indicates the carbon emission quota per unit of electrical load power, Micro-Energy Network The historical electricity load; Indicates the carbon emission quota per unit gas load power, Micro-Energy Network Historical gas load; Indicates the carbon emission quota per unit heat load power, Micro-Energy Network Historical gas load; Represents a historical scheduling day;

[0048] Equivalent load curve of micro-energy grid system after source-load balance It is expressed as:

[0049] ,

[0050] in, represents the grid load curve, Represents the clean new energy power generation output curve;

[0051] For preset thresholds ,

[0052] if , Ziwei Energy Network No incentive carbon emission quota was obtained during period t;

[0053] if , if the micro-energy network If the load is reduced during the period t, the incentive carbon emission quota will be obtained;

[0054] if , if the micro-energy network If the load behavior is increased during the period t, incentive carbon emission quotas will be obtained.

[0055] In a second aspect, the present invention further provides a master-slave game device between an aggregator and a sub-micro-energy network, the device comprising:

[0056] A master-slave game model building module is used to build a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer;

[0057] An aggregator layer optimization module, used to optimize the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single scheduling day;

[0058] A sub-micro energy grid layer optimization module, used for optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day;

[0059] A game result acquisition module, used for acquiring the game result of the aggregator and the sub-micro energy network set based on the optimization results of the aggregator layer and the sub-micro energy network layer;

[0060] The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

[0061] In a third aspect, the present invention further provides a computer device, comprising:

[0062] Memory for storing computer programs;

[0063] The processor is used to execute the computer program to implement the steps of the master-slave game method between the aggregator and the sub-micro energy network.

[0064] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the above-mentioned master-slave game method between the aggregator and the sub-micro energy network.

[0065] Compared with the prior art, the present invention has the following beneficial effects:

[0066] The master-slave game method between aggregators and sub-micro-energy networks provided by the present invention establishes a master-slave game model with aggregators and sub-micro-energy networks as the main body, and optimizes the aggregator layer and the sub-micro-energy network layer respectively with a scheduling day as the cycle, so that the overall interests of the aggregator and multiple sub-micro-energy networks are balanced, and the interests of both parties are maximized; The master-slave game method of the present invention guides each sub-micro-energy grid user to use more green energy by setting a carbon emission quota incentive policy; by setting a carbon emission quota trading mechanism, the carbon emission quota trading income is used as the optimization parameter of the sub-micro-energy grid layer, guiding each sub-micro-energy grid user to use the excess carbon emission quota for carbon trading with other sub-micro-energy grid users, effectively reducing the overall carbon emission level of the multi-micro-energy grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flowchart of a master-slave game method between an aggregator and a sub-micro-energy network provided in an embodiment of the present invention;

[0068] Figure 2 Schematic diagram of the framework of the master-slave game model in an embodiment of the present invention;

[0069] Figure 3 Schematic diagram of a low-carbon management framework on the user side of a multi-micro energy grid system in an embodiment of the present invention;

[0070] Figure 4 A schematic diagram of a sub-micro energy network game result in an embodiment of the present invention;

[0071] Figure 5 A schematic diagram of the structure of a master-slave game device between an aggregator and a sub-micro-energy network provided in an embodiment of the present invention;

[0072] Figure 6 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. The embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0074] The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0075] Embodiment 1:

[0076] like Figures 1 to 4 As shown, an embodiment of the present invention provides a master-slave game method between an aggregator and a sub-micro energy network. Figure 1The flowchart is a flowchart of the master-slave game method between the aggregator and the sub-micro energy network. This flowchart only shows the logical sequence of the method described in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different Figure 1 The steps shown or described are accomplished in the order shown.

[0077] The master-slave game method between the aggregator and the sub-micro energy network provided in this embodiment can be applied to the terminal, and can be executed by the master-slave game device between the aggregator and the sub-micro energy network. The device can be implemented by software and / or hardware, and the device can be integrated in the terminal.

[0078] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps 1 to 4. Among them:

[0079] Step 1: Establish a master-slave game model with aggregators and sub-micro-energy networks as the main body. The model includes an aggregator layer and a sub-micro-energy network layer.

[0080] In the multi-micro-grid system, there are aggregators and multiple autonomously operated sub-micro-grid systems. Each sub-micro-grid system has non-autonomous multi-type energy coupling equipment and multi-type loads such as electricity, gas, and heat. The multi-energy coupled micro-grid includes energy coupling equipment such as power and gas supply sources, cogeneration, gas boilers, micro gas turbines, and waste heat boilers. The multi-energy flow complementary micro-grid system consists of three loads: cold, heat, and electricity. Electric energy can be converted into heat energy through electric heat pumps, natural gas can be converted into heat energy through gas boilers, and can also be converted into electricity and heat energy through cogeneration equipment.

[0081] (1) Combined heat and power (CHP)

[0082] The CHP unit is an energy supply system that integrates power generation and heat supply. Compared with the traditional distribution system, CHP recovers the waste heat of gas units through waste heat recovery devices, realizing the cascade utilization of energy, and has many advantages such as reliable energy supply, energy saving and emission reduction. The CHP unit mainly includes a gas turbine and a waste heat boiler, and its mathematical model is:

[0083] ,

[0084] in, is the thermal power generated by the waste heat boiler of the CHP unit during period t; is the CHP power generation during period t; is the consumption of CHP natural gas in period t; is the calorific value of natural gas; , They are the upper and lower limits of CHP power generation respectively; and are the thermal-to-electric ratio and generator efficiency, respectively.

[0085] (2) Gas boiler (GB)

[0086] Gas boilers are important equipment for converting natural gas into thermal energy. They are heat sources in the thermal energy network system, but loads in the gas network. The output model of gas boilers is:

[0087] ,

[0088] in, and They represent the gas consumption power and heating power of the gas boiler at time t respectively; Indicates the energy conversion efficiency of gas boiler; Indicates the calorific value of the gas boiler at time t; Indicates the natural gas consumption of the gas boiler at time t.

[0089] The main constraints of gas boilers include output constraints and ramp constraints, including:

[0090] ,

[0091] in, and Respectively represent the minimum and maximum output of the gas boiler, Indicates the ramp rate of the gas boiler.

[0092] (3) Microturbine (MT)

[0093] Micro gas turbines have been widely used in the field of micro-energy grid integrated energy systems in recent years due to their small size, light weight, convenient and flexible installation, and pollution-free emissions. The high-temperature gas generated during power generation can be recycled through waste heat boilers to achieve "cogeneration" and improve resource utilization. The mathematical model of a micro gas turbine is expressed as:

[0094] ,

[0095] in: the rate at which natural gas is consumed by the microturbine; The lower calorific value of natural gas is generally 9.7 kWh / m 3 ; represents the natural gas power consumed by the micro gas turbine; and They represent the electrical power and thermal power output by the micro gas turbine respectively; and They represent the gas-to-electricity and gas-to-heat conversion efficiencies of the micro gas turbine, respectively; is the heat loss coefficient of the micro gas turbine.

[0096] (4) Waste Heat Boiler (WHB)

[0097] The waste heat boiler can collect the waste heat generated during the power generation process of the micro gas turbine to meet the thermal energy demand of the system. Its mathematical model is expressed as follows:

[0098] ,

[0099] in: Indicates the thermal power output by the waste heat boiler; Indicates the efficiency of the waste heat boiler.

[0100] The present invention not only involves the decision-making and scheduling of the aggregator, but also takes into account the renewable energy output, carbon trading and user load of each sub-micro-grid system. Figure 2 As shown, the master-slave game model of the present invention adopts a two-layer structure, including an aggregator layer and a sub-micro energy grid layer. The aggregator layer includes a regional dispatch center and a carbon supervision center, and the sub-micro energy grid layer includes multiple sub-micro energy grids managed by the aggregator.

[0101] In terms of energy supply, the regional dispatch center of the aggregator is connected to the upper energy grid including the distribution network, gas distribution network, and heat distribution network, and obtains real-time electricity price information while purchasing energy; it receives information such as the output of distributed renewable energy, the operating status of energy coupling equipment, and controllable load uploaded by each sub-micro energy grid system in the region, and sends energy purchase and energy interaction instructions between sub-micro energy grids to each sub-micro energy grid to achieve centralized coordinated dispatch of multiple micro energy grid systems. Each sub-micro energy grid is connected to each other through energy pipelines, and energy transactions are carried out between each sub-micro energy grid according to the energy interaction instructions issued by the regional dispatch center to achieve "energy mutual assistance and resource sharing".

[0102] Yiziweineng.com As an example, the master-slave game model of the embodiment of the present invention includes:

[0103] ,

[0104] in, represents the master-slave game model, represents the set of game players, Represents the strategy set of the game subject; Represents the set of cost functions of the game players; represents the regional dispatch center of the aggregator, Micro-Energy Network , , , The number of sub-micro-energy grids in the sub-micro-energy grid layer; represents the operation strategy set of the regional dispatch center, Micro-Energy Network The running strategy set; Micro-Energy Network The amount of electricity purchased during period t; Micro-Energy Network The amount of gas purchased in period t; Micro-Energy Network Hezi Micro Energy Network The amount of electric energy interaction in period t; Micro-Energy Network Hezi Micro Energy Network The amount of thermal energy interaction in period t; , and Sub-micro-energy network Electricity, gas and heat loads during period t; Micro-Energy Network Maintenance cost in period t; represents the cost function of the regional dispatch center, Micro-Energy Network The cost function of .

[0105] Game subject of the master-slave game model of the present invention Also includes other Therefore, the strategy set of the game subject is , the cost function set of the game subject , the operation strategy set of the regional dispatch center Also includes other Related parameters of each micro energy network.

[0106] Step 2: Optimize the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single scheduling day.

[0107] In an embodiment of the present invention, the aggregator layer optimization includes minimizing the cost function of the regional dispatch center :

[0108] ,

[0109] C buy RDC = ∑ t = 1 T ∑ i = 1 N m [ p e ( t ) P buy e ( t , i ) + p g ( t ) P buy g ( t , i )] ,

[0110] C loss RDC = 1 2 ∑ t = 1 T ∑ i = 1 N m ∑ j = 1 , j ≠ i N m [ p e ( t ) P i , j e ( t ) l loss e + p h ( t ) P i , j h ( t ) l loss h ] ,

[0111] in, represents the total purchased energy cost; represents the energy interaction loss cost between sub-micro-energy grids; represents the electricity price during period t; represents the gas price in period t; Represents the electric energy interaction loss coefficient between sub-micro energy grids; represents the thermal energy interaction loss coefficient between sub-micro energy grids; T represents a scheduling day.

[0112] The constraints optimized at the aggregator level include energy purchase constraints, energy interaction constraints, and energy supply and demand margin constraints.

[0113] Ziwei Energy Network The energy purchase constraint is:

[0114] ,

[0115] in: Micro-Energy Network The upper limit of electricity purchase; Micro-Energy Network The gas energy purchase limit.

[0116] Ziwei Energy Network The energy interaction constraint is:

[0117] ,

[0118] in, Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of electrical energy interaction between them; Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of thermal energy interaction between them.

[0119] Ziwei Energy Network The energy supply and demand margin constraint is:

[0120] ,

[0121] ,

[0122] ,

[0123]

[0124] in, Indicates the type of energy, including electrical energy , Qi Energy and heat ; k represents energy coupling equipment, including cogeneration, gas boilers, micro gas turbines and waste heat boilers; K represents sub-micro energy grid The number of energy-coupled devices; Micro-Energy Network China Energy The ratio of demand to its maximum supply in period t; Micro-Energy Network China Energy The upper limit of supply and demand margin; Indicates the sub-micro energy network in time period t Energy The actual demand; Indicates the sub-micro energy network in time period t China Energy The maximum supply of Indicates the sub-micro energy network in time period t Energy coupling device k to energy The actual demand; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Demand; Indicates the sub-micro energy network in time period t Energy coupling device k to energy The maximum supply of Indicates the sub-micro energy network in time period t Energy Purchase volume; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Supply quantity.

[0125] Step 3: Optimize the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day.

[0126] In an embodiment of the present invention, the sub-micro energy grid layer optimization includes minimizing the cost function of each sub-micro energy grid :

[0127] ,

[0128] C buy SMIES ( i ) = ∑ t = 1 T [ p e ( t ) P buy e ( t , i ) + p g ( t ) P buy g ( t , i )] ,

[0129] C om SMIES ( i ) = ∑ t = 1 T ∑ s = 1 N s [ λ CHP om P CHP ( t , i ) + λ GB om P GB ( t , i ) + λ MT om P MT ( t , i ) + λ WHB om P WHB ( t , i ) ] ,

[0130] C IDR SMIES ( i ) = ∑ t = 1 T [ λ e power Δ P load e ( t , i ) + λ g power Δ P load g ( t , i ) + λ h power Δ P load h ( t , i ) ] ,

[0131] C i nteract SMIES ( i ) = ∑ t = 1 T ∑ j = 1 , j ≠ i N m [ p int Eract e ( t ) P i , j e ( t ) + p int Eract g ( t ) P i , j g ( t ) ] ,

[0132] ,

[0133] in, Micro-Energy Network The cost of purchasing energy; Micro-Energy Network The operation and maintenance costs of energy coupling equipment; Micro-Energy Network The comprehensive demand response compensation costs; Micro-Energy Network Energy interaction benefits; Micro-Energy Network of carbon emission quota trading income; , , and They represent the maintenance cost coefficients of cogeneration, gas boiler, micro gas turbine and waste heat boiler respectively; , and They represent the demand response compensation coefficients for electricity, gas and heat loads respectively; , and Sub-micro-energy network Demand response for electricity, gas and heat; It represents the interactive price of electric energy between sub-micro-energy grids; It represents the interactive price of gas energy between sub-micro-energy grids; Indicates the transaction price of carbon emission quotas between sub-micro-energy grids; Micro-Energy Network Sold to Ziweineng.com of carbon emission quotas, Then it means sub-micro energy network Sold to Ziweineng.com of carbon emission quotas.

[0134] The constraint conditions for sub-micro energy grid layer optimization include operation constraints of energy coupling devices within the sub-micro energy grid;

[0135] Ziwei Energy Network The operating constraints of the internal energy coupling device are:

[0136] ,

[0137] in, , , and Sub-micro-energy network The upper limit of input power for combined heat and power, gas boilers, micro gas turbines and waste heat boilers.

[0138] The carbon emission quota of the sub-micro energy grid of the present invention includes an initial carbon emission quota and an incentive carbon emission quota.

[0139] like Figure 3 As shown in the figure, the carbon regulation center of the aggregator first determines the total carbon emission quota of the micro-energy grid system based on the quarterly or annual historical energy consumption of the region; then, the initial carbon emission quota of each sub-micro-energy grid is allocated based on the carbon emission of each sub-micro-energy grid on the previous scheduling day. The regional scheduling center is used for short-term low-carbon management. It performs low-carbon optimization management of user cycle energy consumption and carbon emission accounting through the data energy management center of each sub-micro-energy grid to determine the trading plan of carbon emission quotas between sub-micro-energy grids.

[0140] The present invention adopts a baseline method based on power generation to determine the initial carbon emission quota of the sub-micro energy grid users; The initial carbon emission quota is:

[0141] ,

[0142] in, Indicates the carbon emission quota per unit of electrical load power, Micro-Energy Network The historical electricity load; Indicates the carbon emission quota per unit gas load power, Micro-Energy Network Historical gas load; Indicates the carbon emission quota per unit heat load power, Micro-Energy Network Historical gas load; Indicates the previous historical scheduling day of T.

[0143] When the sub-micro energy grid is in operation, the energy coupling devices inside will produce certain carbon emissions, which will consume the carbon emission quotas obtained.

[0144] The carbon emission calculation formula of the Submicro Energy Network is as follows:

[0145] M i = ∑ t = 1 T d g [ P CHP ( t ) + P GB ( t ) + P MT ( t ) + P WHB ( t )] ,

[0146] in, is the micro-energy network at time t carbon emissions; is the carbon emission coefficient of natural gas combustion.

[0147] For the trading of carbon emission quotas, the party with excess carbon emission quotas can obtain income by selling excess carbon emission quotas, but when the actual carbon emissions are greater than the quotas held, the purchase cost of carbon emission quotas will increase. When the carbon emission quota trading mechanism is introduced into the micro-energy grid, especially for thermal power units in the sub-micro-energy grid, it can effectively reduce the overall carbon emission level. The carbon trading mechanism is a market-based measure developed to achieve the purpose of energy conservation and emission reduction. It is a trading mechanism that achieves the ultimate goal of reducing carbon emissions through reasonable allocation and legal trading of carbon emission rights.

[0148] As the main body of the optimization, the users of the sub-micro-grid will participate in the interaction of the micro-grid system from the perspective of their own interests. In order to achieve the goal of energy conservation and emission reduction, the present invention introduces a carbon emission quota incentive mechanism for the users of the sub-micro-grid, taking into account the emission reduction of the users, and rewards them with an appropriate amount of carbon emission quotas. Users can sell the obtained carbon emission quotas to reduce their own energy costs and improve their enthusiasm for participating in scheduling.

[0149] The factors that affect the optimization of the micro-grid are mainly the peak-to-valley difference of the grid load and the peak-to-valley difference of the clean new energy power generation output. And clean new energy power generation output curve The peak and valley periods are often not synchronized. To describe the above phenomenon, the equivalent load curve after source-load balance is defined. The peak and valley periods are as follows:

[0150] ,

[0151] Equivalent load curves for different power grid conditions curve, set up corresponding carbon emission quota incentive mechanism, including:

[0152] if ( is a small positive number), indicating that the source and charge are basically balanced at this time. No incentive carbon emission quotas were obtained;

[0153] if , indicating that clean new energy generation is insufficient. If the micro-grid If the load is reduced during the period t, the incentive carbon emission quota will be obtained;

[0154] if , indicating that there is an oversupply of clean new energy. If the load behavior is increased during the period t, incentive carbon emission quotas will be obtained.

[0155] Energy usage can be adjusted to increase or decrease loads during certain periods of time, such as equipment that needs to be charged.

[0156] By adopting the carbon emission quota incentive policy, users of the sub-micro energy network can not only play the role of "peak shaving and valley filling", but also consume as little thermal power as possible, increase the power generation of clean new energy, and help reduce carbon emissions.

[0157] Step 4: Based on the optimization results of the aggregator layer and the sub-micro energy network layer, obtain the game results of the aggregator and the sub-micro energy network set.

[0158] Figure 4 Sub-micro-energy network The game results show that the game results include the sub-micro energy network The output of various energy coupling devices within the network and the trading volume of carbon emission quotas between sub-micro energy grids, etc.

[0159] Embodiment 2:

[0160] Based on the same inventive concept as in Example 1, the embodiment of the present invention also provides a master-slave game device between an aggregator and a sub-micro-energy network for implementing the master-slave game method between the aggregator and the sub-micro-energy network. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the master-slave game device embodiment of the aggregator and the sub-micro-energy network provided below can refer to the limitations of the coastal wave forecasting method above, and will not be repeated here.

[0161] like Figure 5 As shown, an embodiment of the present invention provides a master-slave game device between an aggregator and a sub-micro-energy network, including:

[0162] A master-slave game model building module is used to build a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer;

[0163] An aggregator layer optimization module, used to optimize the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single scheduling day;

[0164] A sub-micro energy grid layer optimization module, used for optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day;

[0165] A game result acquisition module, used for acquiring the game result of the aggregator and the sub-micro energy network set based on the optimization results of the aggregator layer and the sub-micro energy network layer;

[0166] The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

[0167] Embodiment 3:

[0168] The embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Figure 6 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the master-slave game method between the aggregator and the sub-micro energy network in the aforementioned embodiment is implemented.

[0169] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0170] Embodiment 4:

[0171] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the following method are implemented:

[0172] Establish a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer;

[0173] Optimizing the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single dispatch day;

[0174] Optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day;

[0175] Based on the optimization results of the aggregator layer and the sub-micro energy network layer, obtaining the game results of the aggregator and the sub-micro energy network set;

[0176] The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

[0177] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products, and therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0178] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A master-slave game method between an aggregator and a sub-micro-energy network, characterized in that: include: Establish a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer; Optimizing the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single dispatch day; Optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day; Based on the optimization results of the aggregator layer and the sub-micro energy network layer, obtaining the game results of the aggregator and the sub-micro energy network set; The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

2. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 1 is characterized in that: The master-slave game model with the aggregator and the sub-micro-energy network set as the main body includes: ， in, represents the master-slave game model, represents the set of game players, Represents the strategy set of the game subject; Represents the set of cost functions of the game players; represents the regional dispatch center of the aggregator, Micro-Energy Network , , , The number of sub-micro-energy grids in the sub-micro-energy grid layer; represents the operation strategy set of the regional dispatch center, Micro-Energy Network The running strategy set; Micro-Energy Network The amount of electricity purchased during period t; Micro-Energy Network The amount of gas purchased in period t; Micro-Energy Network Hezi Micro Energy Network The amount of electric energy interaction in period t; Micro-Energy Network Hezi Micro Energy Network The amount of thermal energy interaction in period t; Micro-Energy Network The electrical load during period t is: Micro-Energy Network The gas load in period t, Micro-Energy Network Heat load during period t; Micro-Energy Network Maintenance cost in period t; represents the cost function of the regional dispatch center, Micro-Energy Network The cost function of .

3. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 2 is characterized in that: The aggregator layer optimization is performed with the goal of minimizing the total operating cost of the aggregator on a single dispatch day, including: ， ， ， in, represents the total purchased energy cost; represents the energy interaction loss cost between sub-micro-energy grids; represents the electricity price during period t; represents the gas price in period t; Represents the electric energy interaction loss coefficient between sub-micro energy grids; represents the thermal energy interaction loss coefficient between sub-micro energy grids; T represents a scheduling day.

4. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 3 is characterized in that: The constraints optimized at the aggregator level include energy purchase constraints, energy interaction constraints, and energy supply and demand margin constraints; Ziwei Energy Network The energy purchase constraint is: ， in: Micro-Energy Network The upper limit of electricity purchase; Micro-Energy Network The upper limit of gas energy purchase; Ziwei Energy Network The energy interaction constraint is: ， in, Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of electrical energy interaction between them; Micro-Energy Network With Ziwei Energy Network The upper limit of the amount of thermal energy interaction between them; Ziwei Energy Network The energy supply and demand margin constraint is: ， ， ， ， in, Indicates the type of energy, including electrical energy , Qi Energy and heat ; k represents energy coupling equipment, including cogeneration, gas boilers, micro gas turbines and waste heat boilers; K represents sub-micro energy grid The number of energy-coupled devices; Micro-Energy Network China Energy The ratio of demand to its maximum supply in period t; Micro-Energy Network China Energy The upper limit of supply and demand margin; Indicates the sub-micro energy network in time period t Energy The actual demand; Indicates the sub-micro energy network in time period t China Energy The maximum supply of Indicates the sub-micro energy network in time period t Energy coupling device k to energy The actual demand; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Demand; Indicates the sub-micro energy network in time period t Energy coupling device k to energy The maximum supply of Indicates the sub-micro energy network in time period t Energy Purchase volume; Indicates the sub-micro energy network at time t Pair Micro Energy Network Energy Supply quantity.

5. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 2 is characterized in that: The sub-micro-energy network layer is optimized with the goal of minimizing the total operating cost of the sub-micro-energy network set on a single dispatch day, including: ， ， ， ， ， ， in, Micro-Energy Network The cost of purchasing energy; Micro-Energy Network The operation and maintenance costs of energy coupling equipment; Micro-Energy Network The comprehensive demand response compensation costs; Micro-Energy Network Energy interaction benefits; Micro-Energy Network of carbon emission quota trading income; represents the maintenance cost coefficient of CHP, represents the maintenance cost coefficient of the gas boiler, represents the maintenance cost coefficient of the micro gas turbine, Indicates the maintenance cost coefficient of the waste heat boiler; represents the electric load demand response compensation coefficient, represents the gas load demand response compensation coefficient, represents the heat load demand response compensation coefficient; Micro-Energy Network The amount of power demand response, Micro-Energy Network The amount of gas energy demand response, Micro-Energy Network The amount of heat demand response; It represents the interactive price of electric energy between sub-micro-energy grids; It represents the interactive price of gas energy between sub-micro-energy grids; Indicates the transaction price of carbon emission quotas between sub-micro-energy grids; Micro-Energy Network Sold to Ziweineng.com of carbon emission quotas, Then it means sub-micro energy network Sold to Ziweineng.com of carbon emission quotas.

6. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 5 is characterized in that: The constraint conditions for sub-micro energy grid layer optimization include operation constraints of energy coupling devices within the sub-micro energy grid; Ziwei Energy Network The operating constraints of the internal energy coupling device are: ， in, Micro-Energy Network The upper limit of input power for CHP; Micro-Energy Network The upper limit of input power of gas boilers; Micro-Energy Network The upper limit of input power of micro gas turbines; Micro-Energy Network The upper limit of input power of waste heat boiler.

7. The master-slave game method between the aggregator and the sub-micro-energy network according to claim 5 is characterized in that: The carbon emission quota of the Sub-Micro Energy Network includes the initial carbon emission quota and the incentive carbon emission quota; Ziwei Energy Network The initial carbon emission quota is: ， in, Indicates the carbon emission quota per unit of electrical load power, Micro-Energy Network The historical electricity load; Indicates the carbon emission quota per unit gas load power, Micro-Energy Network Historical gas load; Indicates the carbon emission quota per unit heat load power, Micro-Energy Network Historical gas load; Represents a historical scheduling day; Equivalent load curve of micro-energy grid system after source-load balance It is expressed as: ， in, represents the grid load curve, Represents the clean new energy power generation output curve; For preset thresholds , if , Ziwei Energy Network No incentive carbon emission quota was obtained during period t; if , if the micro-energy network If the load is reduced during the period t, the incentive carbon emission quota will be obtained; if , if the micro-energy network If the load behavior is increased during the period t, incentive carbon emission quotas will be obtained.

8. A master-slave game device between an aggregator and a sub-micro-energy network, characterized in that: include: A master-slave game model building module is used to build a master-slave game model with aggregators and sub-micro-energy networks as the main body, and the model includes an aggregator layer and a sub-micro-energy network layer; An aggregator layer optimization module, used to optimize the aggregator layer with the goal of minimizing the total operating cost of the aggregator on a single scheduling day; A sub-micro energy grid layer optimization module, used for optimizing the sub-micro energy grid layer with the goal of minimizing the total operating cost of the sub-micro energy grid set on a single scheduling day; A game result acquisition module, used for acquiring the game result of the aggregator and the sub-micro energy network set based on the optimization results of the aggregator layer and the sub-micro energy network layer; The game results include the output of energy coupling equipment within the sub-micro energy grid and the trading volume of carbon emission quotas between sub-micro energy grids.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor is used to execute the computer program to implement the steps of the master-slave game method between the aggregator and the sub-micro energy network according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the master-slave game method between an aggregator and a sub-micro energy network described in any one of claims 1 to 7 are implemented.

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