Power tight balance scene multi-virtual power plant low-carbon game optimization scheduling method and device

Abstract

The invention discloses a power tight balance scene multi-virtual power plant low-carbon game optimization scheduling method and device, and the method comprises the steps: combining an internal output equipment unit model of a virtual power plant, an interaction constraint condition between virtual power plants, and an interaction constraint condition between the virtual power plants and a superior power distribution network; a user side demand response model and a reward and punishment type stepped carbon transaction mechanism model are taken into consideration, and a Nash negotiation model of a multi-virtual power plant low-carbon game is constructed by taking the minimum total operation cost of the virtual power plant as a target; solving the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain an optimal game strategy of the virtual power plants under the cooperative game; and making a multi-virtual power plant low-carbon game optimization scheduling strategy by using the optimal game strategy of the virtual power plants under the cooperative game. According to the method, flexible resources in the virtual power plant are effectively integrated and utilized, and meanwhile, a long-term low-carbon development target is taken into account in the aspect of exciting excess emission reduction or constraining excess emission reduction, so that overall optimization and low-carbon transformation of the multiple virtual power plants are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual power plants, and specifically relates to a low-carbon game optimization scheduling method and device for multiple virtual power plants in a tight power balance scenario. Background Art

[0002] The penetration rate of new energy generators is constantly increasing, and their intermittent and random nature leads to frequent power tight balance scenarios. Especially under extreme climate and meteorological conditions such as high temperature and no wind in summer, no light during the late winter peak, and continuous no wind and no light, local and periodic power supply gaps frequently occur, and the pressure on power supply security has increased significantly. In order to meet this challenge, new aggregators such as virtual power plants (VPPs) have actively participated in power dispatching under the guidance and incentives of market mechanisms, and their regulation potential and value have gradually been widely recognized by the industry and academia. VPPs integrate and optimize multiple energy resources to achieve the synergistic complementarity of different types of energy, not only effectively meet the energy needs of various loads, but also actively participate in peak shaving and valley filling and load demand response, significantly reducing the carbon emission level of the power system. Especially in the multi-VPP system in the park, the role of VPPs is more prominent due to the large load demand, high degree of automation, complex load characteristics, and a large number of adjustable flexibility resources.

[0003] Although VPP has achieved remarkable results in promoting low-carbon development, the existing carbon trading model still has obvious deficiencies in incentivizing emission reduction and optimizing resource allocation. The traditional carbon trading model mainly relies on a single price mechanism, which has defects in incentivizing excess emission reduction or constraining excessive emissions, and fails to fully consider the long-term low-carbon development goals. At the same time, in the process of optimizing the scheduling of low-carbon game of virtual power plants, failure to consider the demand response on the user side will lead to insufficient flexibility of virtual power plants. Summary of the invention

[0004] In response to the problems existing in the prior art, the present invention provides a low-carbon game optimization scheduling method and device for multiple virtual power plants in a tight power balance scenario, which can effectively integrate and utilize the flexibility resources within the virtual power plant, while taking into account the long-term low-carbon development goals in terms of incentivizing excess emission reductions or constraining excessive emissions, so as to achieve overall optimization and low-carbon transformation of multiple virtual power plants.

[0005] In order to solve the above technical problems, the present invention is implemented by the following technical solutions: According to a first aspect of the present invention, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is provided, comprising: Combining the internal output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and taking into account the user-side demand response model and the reward-penalty ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, a Nash negotiation model for low-carbon game of multiple virtual power plants is constructed; Solving the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain the optimal game strategy between virtual power plants under the cooperative game, wherein the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants; By utilizing the optimal game strategy among the virtual power plants under cooperative game, a low-carbon game optimization scheduling strategy for multiple virtual power plants is formulated.

[0006] In a possible implementation of the first aspect, the internal output equipment unit model of the virtual power plant includes a photovoltaic generator unit model, a wind generator unit model, a cogeneration unit model, a gas boiler model and a power storage device model; The photovoltaic generator model is:

[0007] In the formula, For the i Photovoltaic generators in t The actual output power during the scheduling period; For the i Photovoltaic generators in t Maximum output power during the scheduling period; For the i The actual light intensity of each photovoltaic generator set; For the i Standard light intensity of each photovoltaic generator set; l is the external environment temperature coefficient; is the actual temperature of the solar panel; is the reference temperature value of the solar panel; The wind turbine generator model is:

[0008] In the formula, For the i Wind turbines in t The actual output power during the dispatch period; v l , v ci , v r and v co They are the actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine generator set; is the rated output power of the wind turbine generator set; The cogeneration unit model is:

[0009]

[0010]

[0011]

[0012] In the formula, For the i Combined heat and power units in t The electric power output during the dispatch period; For the i Gas turbines in t The amount of natural gas consumed during the dispatch period; For the i The heat-to-electricity conversion efficiency of each gas turbine; is the calorific value of natural gas; For the i Combined heat and power units in t Thermal power output during the scheduling period; For the i The heating coefficient of the waste heat boiler; For the i Gas turbines in t Real-time output power during the scheduling period; For the i Gas turbines in t- 1Real-time output power during the scheduling period; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas turbine; The gas boiler model is:

[0013]

[0014]

[0015] In the formula, For the i Gas boilers in t Thermal power output during the scheduling period; For the i Gas boilers in t- 1. Thermal power output during the scheduling period; For the iGas boilers in t The amount of natural gas consumed during the dispatch period; For the i Energy efficiency conversion factor of each gas boiler; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas boiler; The power storage device model is:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] In the formula, For the i Power storage devices in t Real-time storage capacity during the dispatch period; For the i Power storage devices in t -1Real-time storage capacity during the dispatch period; For the i Charging efficiency of each storage device; For the i Discharge efficiency of each storage device; For the i Power storage devices in t Charging power during the scheduling period; For the i Power storage devices in t- 1 Charging power during the scheduling period; For the i Power storage devices in t Discharge power during the dispatch period; For the i Power storage devices in t- 1Discharge power during the dispatch period; For the i The upper limit of the storage capacity of each storage device; For the i The lower limit of the storage capacity of each storage device; For the i The upper limit of the charging power of each power storage device; For thei The lower limit of the charging power of each power storage device; For the i The upper limit of the discharge power of each power storage device; For the i The lower limit of the discharge power of each power storage device; For the i Power storage devices in t State variables of charging during the scheduling period; For the i Power storage devices in t The state variables of the discharge during the scheduling period; when the storage device is charging, The value is 0. The value is 1; when the storage device is discharging, The value is 1. The value is 0; For the i The initial value of the storage capacity of each storage device; T The total scheduling period.

[0022] In a possible implementation manner of the first aspect, the interaction constraint condition between the virtual power plant and the upper-level distribution network is:

[0023]

[0024]

[0025] In the formula, For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; For the j Virtual power plants in t The power sold to the upper-level distribution network during the dispatch period; For the j Virtual power plants in t Input state variables for interaction with the upper distribution network during the dispatch period; For the j Virtual power plants in t The output state variables of the interaction with the upper-level distribution network during the dispatch period; when the virtual power plant purchases electricity from the upper-level distribution network, The value is 1. The value is 0; when the virtual power plant sells electricity to the upper distribution network, The value is 0. The value is 1; For the j The upper limit of power that a virtual power plant can purchase from the upper distribution network; For thej The power limit of a virtual power plant sold to the upper distribution network.

[0026] In a possible implementation manner of the first aspect, the interaction constraint condition between the virtual power plants is:

[0027]

[0028]

[0029]

[0030] In the formula, For the j A virtual power plant and k Virtual power plants in t The power of P2P electric energy trading during the dispatch period; For the j A virtual power plant and k Virtual power plants in t Thermal energy P2P trading power during the scheduling period; For the j A virtual power plant and k The permissible value of the P2P trading power of a virtual power plant; For the j A virtual power plant and k The allowable value of the thermal energy P2P trading power of a virtual power plant.

[0031] In a possible implementation manner of the first aspect, the user-side demand response model includes a flexible electric load characteristic model and a flexible thermal load characteristic model, and the flexible electric load characteristic model is:

[0032]

[0033]

[0034] In the formula, For the j Virtual power plants in t The predicted value of electric load during the dispatch period; For the j Virtual power plants in t The amount of transferable electric load during the dispatch period; For the j Virtual power plants in t The amount of electricity load that can be reduced during the dispatch period; For the jThe percentage of electrical load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce electrical load; The flexible heat load characteristic model is:

[0035]

[0036]

[0037] In the formula, For the j Virtual power plants in t Thermal load forecast value during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be transferred during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be reduced during the scheduling period; For the j The percentage of thermal load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce thermal load.

[0038] In a possible implementation of the first aspect, the reward-penalty type tiered carbon trading mechanism model is:

[0039] In the formula, For the j Virtual power plants in t Carbon trading costs during the dispatch period; It is the first among many virtual power plants j The difference between the actual carbon emissions of a virtual power plant and the initial carbon quota; d The length of the carbon emission interval corresponding to each step; u The carbon trading base price for the day; α The bonus factor for low carbon emissions; β is the price increase under high carbon emissions.

[0040] In a possible implementation of the first aspect, the Nash negotiation model of the multi-virtual power plant low-carbon game is:

[0041] in:

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, For the j The operating cost of a virtual power plant before it participates in Nash negotiation; For the j The operating cost of a virtual power plant after participating in Nash negotiation; D is the number of virtual power plants; f grid , f gas , f d and f idr They are the costs of purchasing electricity, purchasing gas, operating and maintaining output equipment and units, and responding to comprehensive load demand; for t Real-time electricity purchase price during dispatch period; for t Gas purchase volume during the dispatch period; for t Real-time gas price during dispatch period; For the i Unit power operation and maintenance cost of each output equipment unit; N is the number of output equipment units within the virtual power plant; For the i Output equipment units in t Output power during the dispatch period; It is the unit power compensation cost of the time-shiftable electric heating load; for t The electric and heating loads can be time-shifted during the scheduling period.

[0047] In a possible implementation of the first aspect, solving the Nash negotiation model of the multi-virtual power plant low-carbon game is specifically: The alternating direction multiplier method is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game.

[0048] According to a second aspect of the present invention, a low-carbon game optimization scheduling device for multiple virtual power plants in a power tight balance scenario is provided, comprising: A construction module is used to combine the output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and take into account the user-side demand response model and the reward-penalty ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, to build a Nash negotiation model for low-carbon game among multiple virtual power plants; A solution module is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain the optimal game strategy between virtual power plants under the cooperative game, wherein the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants; A formulation module is used to utilize the optimal game strategy between the virtual power plants under the cooperative game to formulate a low-carbon game optimization scheduling strategy for multiple virtual power plants.

[0049] According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for optimizing the scheduling of multiple virtual power plants using low-carbon game in a tight power balance scenario is implemented.

[0050] According to a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the low-carbon game optimization scheduling method for multiple virtual power plants in a tight power balance scenario.

[0051] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention provides a multi-virtual power plant low-carbon game optimization scheduling method in a tight power balance scenario. By combining the VPP internal output equipment unit model, the interaction constraints between VPPs, and the interaction constraints between VPPs and the upper-level distribution network, a Nash negotiation model of multi-VPP low-carbon game is constructed. The model not only considers the minimization of VPP operating costs, but also takes into account the user-side demand response model and the reward-punishment type ladder carbon trading mechanism model, which can achieve a balance between flexibility and low-carbon development in VPP optimization scheduling. User-side demand response provides real-time flexibility support for the system by optimizing load distribution, and the reward-punishment type ladder carbon trading mechanism guides VPP to achieve efficient emission reduction and long-term low-carbon transformation through differentiated incentives and constraints. The synergistic effect of the two can not only effectively reduce the system operating cost and improve resource utilization efficiency, but also promote the organic unity of short-term scheduling optimization and long-term low-carbon development goals, providing important guarantees for the efficient, economical and sustainable operation of VPP. In the scenario of tight power balance, especially when facing extreme climate and meteorological conditions such as high temperature and no wind in summer, no light during the late winter peak, and continuous no wind and no light, the method of the present invention can enhance the stability of the VPP system, effectively alleviate local and periodic power supply gaps, and reduce the pressure of power supply.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the specific implementation modes of the present invention, the drawings required for use in the description of the specific implementation modes will be briefly introduced below. Obviously, the drawings described below are some implementation modes of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a flow chart of a low-carbon game optimization scheduling method for multiple virtual power plants in a tight power balance scenario of the present invention; Figure 2 This is a diagram of the basic architecture of multi-VPP in the embodiment; Figure 3 : The iterative convergence results of the algorithm of the embodiment, where (a) is the iterative convergence result of the transaction volume between virtual power plants, and (b) is the iterative convergence result of the transaction price between virtual power plants; Figure 4 This is the result of the power optimization scheduling of Example VPP1; Figure 5 This is the result of thermal energy optimization scheduling of Example VPP1; Figure 6 The energy trading results between VPPs in the embodiment, where (a) is the electric energy trading result, and (b) is the thermal energy trading result; Figure 7 is the energy trading price between the VPPs in the embodiment, where (a) is the electricity trading price, (b) is the heat trading price; Figure 8 Schematic diagram of carbon trading cost under different reward coefficients. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] First, the technical terms and abbreviations involved in the present invention are explained as follows: Virtual power plant (VPP); Wind turbine (WT); Photovoltaic (PV) generator set; Electricity storage (ES); Gas turbine (GT); Waste heat boiler (WHB); Combined heat and power (CHP) Gas boiler (GB); Peer-to-peer (P2P).

[0057] like Figure 1 As shown, the embodiment of the present invention provides a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario, which realizes low-carbon, efficient and coordinated scheduling of multiple virtual power plants in a power tight balance scenario by constructing and solving the Nash negotiation model, and specifically includes the following steps: S1. Combining the internal output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and taking into account the user-side demand response model and the reward-and-punishment type ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, a Nash negotiation model for low-carbon game of multiple virtual power plants is constructed.

[0058] In one achievable manner, the internal output equipment unit model of the virtual power plant includes a photovoltaic generator unit model, a wind generator unit model, a cogeneration unit model, a gas boiler model and a power storage device model.

[0059] Specifically, with the continuous development of solar energy application technology, PV has been widely used due to its low pollution and low noise. The photovoltaic generator model is as follows:

[0060] In the formula, For the i Photovoltaic generators in t The actual output power during the dispatch period; For the i Photovoltaic generators in t Maximum output power during the scheduling period; For the i The actual light intensity of each photovoltaic generator set; For the i Standard light intensity of each photovoltaic generator set; l is the external environment temperature coefficient; is the actual temperature of the solar panel; It is the reference temperature of the solar panel.

[0061] Specifically, WT is an important component of the VPP energy supply side. Through the high-speed rotation of the wind turbine group, wind energy is converted into electrical energy with a certain standard quality, which improves the effective use of clean energy. The wind turbine model is as follows:

[0062] In the formula, For the i Wind turbines in t The actual output power during the dispatch period; v l , v ci , v r and v co They are the actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine generator set; is the rated output power of the wind turbine generator set; Specifically, CHP is mainly composed of GT and WHB, which generates electricity and heat by burning natural gas, and supplies the waste heat to the heat load. The cogeneration unit model is:

[0063]

[0064]

[0065]

[0066] In the formula, For the i Combined heat and power units in t The electric power output during the dispatch period; For the i Gas turbines in t The amount of natural gas consumed during the dispatch period; For the i The heat-to-electricity conversion efficiency of each gas turbine; is the calorific value of natural gas, which is 9.78 kW·h / m 3 ; For the i Combined heat and power units in t Thermal power output during the scheduling period; For the i The heating coefficient of the waste heat boiler; For the iGas turbines in t Real-time output power during the scheduling period; For the i Gas turbines in t- 1Real-time output power during the scheduling period; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas turbine.

[0067] Specifically, GB generates high-temperature steam by burning natural gas to directly provide heat, which is environmentally friendly and highly efficient. The gas boiler model is as follows:

[0068]

[0069]

[0070] In the formula, For the i Gas boilers in t Thermal power output during the scheduling period; For the i Gas boilers in t- 1. Thermal power output during the scheduling period; For the i Gas boilers in t The amount of natural gas consumed during the dispatch period; For the i Energy efficiency conversion factor of each gas boiler; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas boiler.

[0071] Specifically, the power storage device model is:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] In the formula, For the iPower storage devices in t Real-time storage capacity during the dispatch period; For the i Power storage devices in t- 1Real-time storage capacity during the dispatch period; For the i Charging efficiency of each storage device; For the i Discharge efficiency of each storage device; For the i Power storage devices in t Charging power during the scheduling period; For the i Power storage devices in t- 1 Charging power during the scheduling period; For the i Power storage devices in t Discharge power during the dispatch period; For the i Power storage devices in t- 1Discharge power during the dispatch period; For the i The upper limit of the storage capacity of each storage device; For the i The lower limit of the storage capacity of each storage device; For the i The upper limit of the charging power of each power storage device; For the i The lower limit of the charging power of each power storage device; For the i The upper limit of the discharge power of each power storage device; For the i The lower limit of the discharge power of each power storage device; For the i Power storage devices in t The state variables of charging during the scheduling period, For the i Power storage devices in t The state variables of the discharge during the scheduling period; when the storage device is charging, The value is 0. The value is 1; when the storage device is discharging, The value is 1. The value is 0; For the i The initial value of the storage capacity of each storage device; T The total scheduling period.

[0078] In one possible implementation, the VPP exchanges power with the upper-level distribution network through a tie line. The interaction constraints between the virtual power plant and the upper-level distribution network are as follows:

[0079]

[0080]

[0081] In the formula, For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; For the j Virtual power plants in t The power sold to the upper-level distribution network during the dispatch period; For the j Virtual power plants in t Input state variables for interaction with the upper distribution network during the dispatch period; For the j Virtual power plants in t The output state variables of the interaction with the upper-level distribution network during the dispatch period; when the virtual power plant purchases electricity from the upper-level distribution network, The value is 1. The value is 0; when the virtual power plant sells electricity to the upper distribution network, The value is 0. The value is 1; For the j The upper limit of power that a virtual power plant can purchase from the upper distribution network; For the j The power limit of a virtual power plant sold to the upper distribution network.

[0082] In one possible implementation, considering the differences and complementarity of the supply and demand balance characteristics of VPPs in different time periods, VPPs are encouraged to use P2P methods for electricity and heat energy trading. The interaction constraints between the virtual power plants are as follows:

[0083]

[0084]

[0085]

[0086] In the formula, For the j A virtual power plant and k Virtual power plants in t The power of P2P electric energy trading during the dispatch period; For the j A virtual power plant and k Virtual power plants int Thermal energy P2P trading power during the scheduling period; For the j A virtual power plant and k The permissible value of the P2P trading power of a virtual power plant; For the j A virtual power plant and k The allowable value of the thermal energy P2P trading power of a virtual power plant.

[0087] In one achievable method, VPP user loads are divided into two categories: rigid loads and flexible loads according to the way the loads participate in demand response. Among them, rigid loads are uncontrollable loads, and VPP fully responds to user demand and cannot change its energy usage mode and time; flexible loads are controllable loads, and according to their different adjustment methods, they are divided into two categories: transferable loads and reducible loads. Transferable loads refer to loads that can be flexibly adjusted in each time period, but the total load of the entire cycle after the transfer must remain unchanged from that before the transfer. Reducible loads refer to loads that can withstand certain interruptions or power reductions, and reduce operating time, and they can be partially or completely reduced according to supply and demand conditions. This implementation classifies the loads as described above, and uses the adjustable potential of the loads and other flexibility resources of the VPP to optimize the supply and demand balance of the VPP. The user-side demand response model includes a flexible electric load characteristic model and a flexible thermal load characteristic model.

[0088] Specifically, the flexible electric load characteristic model is as follows:

[0089]

[0090]

[0091] In the formula, For the j Virtual power plants in t The predicted value of electric load during the dispatch period; For the j Virtual power plants in t The amount of transferable electric load during the dispatch period; For the j Virtual power plants in t The amount of electricity load that can be reduced during the dispatch period; For the j The percentage of electrical load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce electrical load.

[0092] Specifically, the flexible heat load characteristic model is as follows:

[0093]

[0094]

[0095] In the formula, For the j Virtual power plants in t Thermal load forecast value during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be transferred during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be reduced during the scheduling period; For the j The percentage of thermal load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce thermal load.

[0096] In one feasible way, the reward-and-punishment tiered carbon trading mechanism is incorporated into the optimization scheduling strategy to guide VPPs to save energy and reduce carbon emissions. The carbon trading mechanism mainly includes three links: initial carbon quota allocation, actual carbon emissions calculation, and carbon emissions trading. This implementation method uses the baseline method to determine the initial carbon emission quota of the system. Multiple VPPs purchase electricity from the upper distribution network from thermal power generation. Therefore, the carbon emission sources in VPP are mainly divided into three categories: CHP, GB, and upper distribution network purchases. Since CHP can provide both electricity and heat, the initial carbon quota model is:

[0097] In the formula, is the initial carbon quota for the virtual power plant; , The carbon quota coefficient for power and heat output of the unit, Take 0.728 kg / kWh, Take 0.367 kg / kWh; The conversion factor for converting CHP power generation into heat supply is 6 MJ / kWh.

[0098] This implementation method uses the following formula to calculate the actual carbon emissions:

[0099] In the formula, is the actual carbon emissions of the virtual power plant; The emission factor of the upper-level distribution network is 0.581 kg / kWh, and is updated in real time according to the latest value released by the Ministry of Ecology and Environment; is the carbon emission coefficient of natural gas, which is taken as 2.165 kg / m³.

[0100] In the carbon emission trading link, the present invention proposes a reward-and-punishment tiered carbon trading mechanism with interval differentiated pricing to encourage VPPs to save energy and reduce carbon emissions. The reward-and-punishment tiered carbon trading mechanism model is as follows:

[0101] In the formula, For the j Virtual power plants in t The carbon trading cost during the scheduling period, when greater than zero, indicates that the actual carbon emissions are higher than the initial quota and it is necessary to purchase carbon emission quotas; when less than zero, it indicates that the actual carbon emissions are lower than the initial quota and carbon emission quotas can be sold to obtain market subsidies. It is the first among many virtual power plants j Actual carbon emissions from virtual power plants With initial carbon quota The difference between d The length of the carbon emission interval corresponding to each step; u The carbon trading base price for the day; α The bonus factor for low carbon emissions; β is the price increase under high carbon emissions.

[0102] In one achievable manner, the Nash negotiation model of the multi-virtual power plant low-carbon game is:

[0103] in:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, For the j The operating cost of a virtual power plant before it participates in Nash negotiation, i.e. the breaking point of Nash negotiation; For the j The operating cost of a virtual power plant after participating in Nash negotiation; D is the number of virtual power plants; f grid , f gas , fd and f idr They are the costs of electricity purchase, gas purchase, equipment operation and maintenance, and comprehensive load demand response; For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; for t Real-time electricity purchase price during dispatch period; for t Gas purchase volume during the dispatch period; for t Real-time gas price during dispatch period; For the i Unit power operation and maintenance cost of each output equipment unit; N is the number of output equipment units within the virtual power plant; For the i Output equipment units in t Output power during the dispatch period; It is the unit power compensation cost of the time-shiftable electric heating load; for t The electric and heating loads can be time-shifted during the scheduling period.

[0109] S2. Solve the Nash negotiation model of the low-carbon game of multiple virtual power plants to obtain the optimal game strategy between virtual power plants under the cooperative game, and the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants.

[0110] Preferably, in order to reduce the dependence of the solution process on the internal data of the VPP and protect the privacy of each VPP, the alternating direction multiplier method is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game.

[0111] S3. Utilize the optimal game strategy between the virtual power plants under the cooperative game to formulate a low-carbon game optimization scheduling strategy for multiple virtual power plants. Finally, perform low-carbon game optimization scheduling for virtual power plants according to the low-carbon game optimization scheduling strategy for multiple virtual power plants.

[0112] Based on case analysis, the effectiveness of the low-carbon game optimization scheduling method of multiple virtual power plants in the power tight balance scenario is verified.

[0113] The basic architecture of multiple virtual power plants is as follows Figure 2As shown. Among them, WT and PV are new energy power supply equipment, which provide green electricity to the virtual power plant to meet the demand of electricity load. ES takes advantage of the difference in electricity prices during peak and valley periods, realizes price arbitrage through "low-absorbing and high-releasing", and improves the energy efficiency of the virtual power plant. GT and WHB constitute CHP, realize the coupling of heterogeneous energy flows of electricity, heat and gas, and provide electricity and heat energy to the virtual power plant to meet the demand of electricity load and heat load. GB converts natural gas into heat energy and supplies the heat load of the virtual power plant. Multiple virtual power plants upload the electricity prices and electricity quantities interacting between virtual power plants to the information center. When there is a shortage of electricity, multiple virtual power plants purchase electricity from the superior distribution network; when there is sufficient electricity, they sell excess electricity to the superior distribution network.

[0114] A case study is conducted using a multi-virtual power plant consisting of three members as the test system, and the cplex solver is called through yalmip for solution. The new energy units of members VPP1 and VPP3 are wind power, and the new energy unit of member VPP2 is photovoltaic. The three VPP members are all equipped with the same type of ES, GT, WHB and GB. In the example, it is assumed that the equipment efficiency is constant and the influence of load rate on efficiency is ignored. The relevant parameters of multi-VPP are shown in the following table.

[0115] Table 1 Multi-VPP related parameters

[0116] The Nash negotiation model of multi-virtual power plant low-carbon game is solved using the alternating direction multiplier method. Figure 3 is the algorithm iteration convergence curve, such as Figure 3 As shown in (a) in Figure 2, the transaction volume between virtual power plants converges after 45 iterations, and the calculation time is 286 seconds. Figure 3 As shown in (b), the transaction price between virtual power plants converges after 27 iterations, and the calculation time is 16 seconds. This shows that the alternating direction multiplier method adopted in the present invention has good calculation efficiency and convergence, and can realize the operation optimization of multiple virtual power plants and each VPP.

[0117] In order to verify the rationality of the low-carbon game optimization scheduling method of multiple virtual power plants, the electricity and heat optimization scheduling results of VPP1 are plotted as follows: Figure 4 and Figure 5 shown.

[0118] Depend on Figure 4 It can be seen that during the 01:00-05:00 and 23:00-24:00 scheduling periods, the gas turbine is in full power state, the power generation of VPP1 can meet the load demand, and the surplus power is stored. During the 05:00-09:00 and 13:00-18:00 scheduling periods, the wind power output decreases. At this time, VPP1 achieves power balance through ES charging and discharging, inter-VPP transactions, and power purchase from the upper distribution network.

[0119] Depend on Figure 5 It can be seen that during the peak period, the heat load demand of VPP1 is mainly borne by GT, and a small part is borne by GB. This is because the peak periods of heat and electricity loads of VPP1 are generally consistent, and GT operates in the "heating based on electricity" mode, so it bears the main share of heating. During the 06:00-07:00 and 15:00-22:00 scheduling periods, thermal power interaction is carried out between VPPs. This is because there is excess thermal energy in VPP2 and VPP3 during the above scheduling periods, so multiple VPPs achieve optimized operation through thermal mutual assistance.

[0120] In the cooperative operation mode, the mutual assistance of electricity and heat power between VPPs has a positive effect on improving the comprehensive benefits of operation. In order to deeply analyze the effectiveness of the present invention, the electricity and heat transaction results between VPPs are plotted on Figure 6 .

[0121] like Figure 6 As shown in (a), we first examine the amount of electricity traded between VPPs. During the 06:00-17:00 dispatch period, the electricity load of VPP1 increased, and there was no output from the photovoltaic generator set, resulting in power shortage; during the same dispatch period, VPP2 exported electricity to VPP1 due to the large photovoltaic generation. VPP3 exported electricity throughout the day because the electricity load of VPP3 was small and there was surplus wind power generation.

[0122] like Figure 6 As shown in (b) in the figure, the heat energy transaction between VPPs is examined. VPP1 has a balance between heat supply and demand during the 00:00-05:00 and 8:00-14:00 scheduling periods, and a large amount of heat energy needs to be purchased in the remaining scheduling periods. This is because VPP1 has a high heat load and cannot achieve self-sufficiency in heat energy. VPP2 sends heat energy to other VPPs throughout the scheduling cycle; this is because the capacity of VPP2's thermal energy unit is large and its own heat load is small, which can support the heat load demand of other VPPs. The margin of VPP3's heat supply varies from time to time, and supply and demand can be balanced in most scheduling periods.

[0123] The real-time transaction prices of electricity and heat between VPPs obtained by solving the Nash negotiation model are plotted on Figure 7 In. According to Figure 7(a) in the figure shows that the transaction electricity prices between VPPs are within the range of the distribution network's electricity purchase price and electricity sales price. The electricity transactions between VPPs can purchase electricity at a price lower than the distribution network's electricity sales price, and can also sell electricity at a price higher than the distribution network's electricity purchase price, thus effectively increasing the profits between VPPs. This trading model has promoted energy mutual assistance and cooperative operation between VPPs, and achieved the optimal allocation and scheduling of electricity and heat resources in a wider range. This has positive significance for improving the comprehensive operating benefits, energy self-sufficiency and operating flexibility of multiple VPPs. According to Figure 7 (b) in the figure shows that the heat load of each VPP is high during the scheduling period of 18:00-24:00, so thermal energy exchange between VPPs is required. At this time, setting a higher transaction heat price can maximize the benefits of each VPP.

[0124] Figure 8 The figure shows the curve of carbon trading cost changing with the unit carbon trading price under different reward coefficients. Figure 8 It can be seen that when each VPP starts to profit from carbon trading, the reward coefficient for low carbon emissions α The higher the carbon trading price, the higher the benefits of carbon trading, which significantly reduces the carbon emissions of VPPs. When the unit carbon trading price increases to a certain level, the downward trend of carbon trading costs will become slower and slower, or even no downward trend, indicating that the carbon reduction potential of each VPP has been fully tapped.

[0125] The embodiment of the present invention provides a low-carbon game optimization scheduling device for multiple virtual power plants in a power tight balance scenario, which specifically includes the following modules: A construction module is used to combine the internal output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and take into account the user-side demand response model and the reward-and-punishment type ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, to construct a Nash negotiation model for low-carbon game of multiple virtual power plants.

[0126] Specifically, the photovoltaic power generation group model is as follows:

[0127] In the formula, For the i Photovoltaic generators in t The actual output power during the dispatch period; For the i Photovoltaic generators in t Maximum output power during the scheduling period; For the i The actual light intensity of each photovoltaic generator set; For the iStandard light intensity of each photovoltaic generator set; l is the external environment temperature coefficient; is the actual temperature of the solar panel; It is the reference temperature of the solar panel.

[0128] Specifically, the wind turbine generator set model is as follows:

[0129] In the formula, For the i Wind turbines in t The actual output power during the dispatch period; v l , v ci , v r and v co They are the actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine generator set; is the rated output power of the wind turbine.

[0130] Specifically, the cogeneration unit model is:

[0131]

[0132]

[0133]

[0134] In the formula, For the i Combined heat and power units in t The electric power output during the dispatch period; For the i Gas turbines in t The amount of natural gas consumed during the dispatch period; For the i The heat-to-electricity conversion efficiency of each gas turbine; is the calorific value of natural gas, which is 9.78 kW·h / m 3 ; For the i Combined heat and power units in t Thermal power output during the scheduling period; For the i The heating coefficient of the waste heat boiler; For the i Gas turbines in t Real-time output power during the scheduling period; For the i Gas turbines in t- 1Real-time output power during the scheduling period; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas turbine.

[0135] Specifically, the gas boiler model is as follows:

[0136]

[0137]

[0138] In the formula, For the i Gas boilers in t Thermal power output during the scheduling period; For the i Gas boilers in t- 1. Thermal power output during the scheduling period; For the i Gas boilers in t The amount of natural gas consumed during the dispatch period; For the i Energy efficiency conversion factor of each gas boiler; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas boiler.

[0139] Specifically, the power storage device model is:

[0140]

[0141]

[0142]

[0143]

[0144]

[0145] In the formula, For the i Power storage devices in t Real-time storage capacity during the dispatch period; For the i Power storage devices int- 1Real-time storage capacity during the dispatch period; For the i Charging efficiency of each storage device; For the i Discharge efficiency of each storage device; For the i Power storage devices in t Charging power during the scheduling period; For the i Power storage devices in t- 1 Charging power during the scheduling period; For the i Power storage devices in t Discharge power during the dispatch period; For the i Power storage devices in t- 1Discharge power during the dispatch period; For the i The upper limit of the storage capacity of each storage device; For the i The lower limit of the storage capacity of each storage device; For the i The upper limit of the charging power of each power storage device; For the i The lower limit of the charging power of each power storage device; For the i The upper limit of the discharge power of each power storage device; For the i The lower limit of the discharge power of each power storage device; For the i Power storage devices in t The state variables of charging during the scheduling period, For the i Power storage devices in t The state variables of the discharge during the scheduling period; when the storage device is charging, The value is 0. The value is 1; when the storage device is discharging, The value is 1. The value is 0; For the i The initial value of the storage capacity of each storage device; T The total scheduling period.

[0146] In one possible implementation, the interaction constraints between the virtual power plant and the upper-level distribution network are as follows:

[0147]

[0148]

[0149] In the formula, For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; For the j Virtual power plants in t The power sold to the upper-level distribution network during the dispatch period; For the j Virtual power plants in t Input state variables for interaction with the upper distribution network during the dispatch period; For the j Virtual power plants in t The output state variables of the interaction with the upper-level distribution network during the dispatch period; when the virtual power plant purchases electricity from the upper-level distribution network, The value is 1. The value is 0; when the virtual power plant sells electricity to the upper distribution network, The value is 0. The value is 1; For the j The upper limit of power that a virtual power plant can purchase from the upper distribution network; For the j The power limit of a virtual power plant sold to the upper distribution network.

[0150] Specifically, the interaction constraints between the virtual power plants are as follows:

[0151]

[0152]

[0153]

[0154] In the formula, For the j A virtual power plant and k Virtual power plants in t The power of P2P electric energy trading during the dispatch period; For the j A virtual power plant and k Virtual power plants in t Thermal energy P2P trading power during the scheduling period; For the j A virtual power plant and k The permissible value of the P2P trading power of a virtual power plant; For the j A virtual power plant and kThe allowable value of the thermal energy P2P trading power of a virtual power plant.

[0155] The user-side demand response model includes a flexible electric load characteristic model and a flexible thermal load characteristic model. Specifically, the flexible electric load characteristic model is as follows:

[0156]

[0157]

[0158] In the formula, For the jth virtual power plant t The predicted value of electric load during the dispatch period; For the jth virtual power plant t The amount of transferable electric load during the dispatch period; For the jth virtual power plant t The amount of electricity load that can be reduced during the dispatch period; is the percentage of the transferable electric load of the jth virtual power plant; is the percentage of electrical load that can be reduced by the jth virtual power plant.

[0159] Specifically, the flexible heat load characteristic model is as follows:

[0160]

[0161]

[0162] In the formula, For the j Virtual power plants in t The predicted value of electric load during the dispatch period; For the j Virtual power plants in t The amount of transferable electric load during the dispatch period; For the j Virtual power plants in t The amount of electricity load that can be reduced during the dispatch period; For the j The percentage of electrical load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce electrical load.

[0163] Specifically, the reward-penalty type tiered carbon trading mechanism model is as follows:

[0164] In the formula, For the j Virtual power plants in t The carbon trading cost during the scheduling period, when greater than zero, indicates that the actual carbon emissions are higher than the initial quota and it is necessary to purchase carbon emission quotas; when less than zero, it indicates that the actual carbon emissions are lower than the initial quota and carbon emission quotas can be sold to obtain market subsidies. It is the first among many virtual power plants j Actual carbon emissions from virtual power plants With initial carbon quota The difference between d The length of the carbon emission interval corresponding to each step; u The carbon trading base price for the day; α The bonus factor for low carbon emissions; β is the price increase under high carbon emissions.

[0165] Specifically, the Nash negotiation model of the multi-virtual power plant low-carbon game is:

[0166] in:

[0167]

[0168]

[0169]

[0170]

[0171] In the formula, For the j The operating cost of a virtual power plant before it participates in Nash negotiation, i.e. the breaking point of Nash negotiation; For the j The operating cost of a virtual power plant after participating in Nash negotiation; D is the number of virtual power plants; f grid , f gas , f d and f idr They are the costs of electricity purchase, gas purchase, equipment operation and maintenance, and comprehensive load demand response; For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; for t Real-time electricity purchase price during dispatch period; for tGas purchase volume during the dispatch period; for t Real-time gas price during dispatch period; For the i Unit power operation and maintenance cost of each output equipment unit; N is the number of output equipment units within the virtual power plant; For the i Output equipment units in t Output power during the dispatch period; It is the unit power compensation cost of the time-shiftable electric heating load; for t The electric and heating loads can be time-shifted during the scheduling period.

[0172] A solution module is used to solve the Nash negotiation model of the low-carbon game of multiple virtual power plants to obtain the optimal game strategy between virtual power plants under the cooperative game, and the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants.

[0173] Preferably, in order to reduce the dependence of the solution process on the internal data of the VPP and protect the privacy of each VPP, the alternating direction multiplier method is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game.

[0174] A formulation module is used to utilize the optimal game strategy between the virtual power plants under the cooperative game to formulate a low-carbon game optimization scheduling strategy for multiple virtual power plants.

[0175] All relevant contents of each step involved in the embodiment of the aforementioned method for low-carbon game optimization scheduling of multiple virtual power plants in a tight power balance scenario can be referred to the functional description of the functional modules corresponding to the low-carbon game optimization scheduling device for multiple virtual power plants in a tight power balance scenario in the embodiment of the present invention, and will not be repeated here. The division of modules in the embodiment of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, the functional modules in the various embodiments of the present invention can be integrated into a processor, or they can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0176] In another embodiment of the present invention, a computer device is provided, the computer device including a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario.

[0177] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed random access memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario in the above embodiment.

[0178] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, 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, optical storage, etc.) containing computer-usable program code.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] In the present invention, the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0183] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present invention, which are used to illustrate the technical solutions of the present invention rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the aforementioned embodiments, those of ordinary skill in the art should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the aforementioned embodiments within the technical scope disclosed by the present invention, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the protection scope of the present invention.

Claims

1. A low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario, characterized in that: include: Combining the internal output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and taking into account the user-side demand response model and the reward-penalty ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, a Nash negotiation model for low-carbon game of multiple virtual power plants is constructed; Solving the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain the optimal game strategy between virtual power plants under the cooperative game, wherein the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants; By utilizing the optimal game strategy among the virtual power plants under cooperative game, a low-carbon game optimization scheduling strategy for multiple virtual power plants is formulated.

2. According to claim 1, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The output equipment unit model of the virtual power plant includes a photovoltaic generator unit model, a wind generator unit model, a cogeneration unit model, a gas boiler model and a power storage device model; The photovoltaic generator model is: In the formula, For the i Photovoltaic generators in t The actual output power during the dispatch period; For the i Photovoltaic generators in t Maximum output power during the scheduling period; For the i The actual light intensity of each photovoltaic generator set; For the i Standard light intensity of each photovoltaic generator set; l is the external environment temperature coefficient; is the actual temperature of the solar panel; is the reference temperature value of the solar panel; The wind turbine generator model is: In the formula, For the i Wind turbines in t The actual output power during the scheduling period; v l , v ci , v r and v co They are the actual wind speed, cut-in wind speed, rated wind speed and cut-out wind speed of the wind turbine generator set; is the rated output power of the wind turbine generator set; The cogeneration unit model is: In the formula, For the i Combined heat and power units in t The electric power output during the dispatch period; For the i Gas turbines in t The amount of natural gas consumed during the dispatch period; For the i The heat-to-electricity conversion efficiency of each gas turbine; is the calorific value of natural gas; For the i Combined heat and power units in t Thermal power output during the scheduling period; For the i The heating coefficient of the waste heat boiler; For the i Gas turbines in t Real-time output power during the scheduling period; For the i Gas turbines in t- 1Real-time output power during the scheduling period; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas turbine; The gas boiler model is: In the formula, For the i Gas boilers in t Thermal power output during the scheduling period; For the i Gas boilers in t- 1. Thermal power output during the scheduling period; For the i Gas boilers in t The amount of natural gas consumed during the dispatch period; For the i Energy efficiency conversion factor of each gas boiler; , and Respectively i The maximum output power, minimum output power and maximum climbing power of each gas boiler; The power storage device model is: In the formula, For the i Power storage devices in t Real-time storage capacity during the dispatch period; For the i Power storage devices in t -1Real-time storage capacity during the dispatch period; For the i Charging efficiency of each storage device; For the i Discharge efficiency of each storage device; For the i Power storage devices in t Charging power during the scheduling period; For the i Power storage devices in t- 1 Charging power during the scheduling period; For the i Power storage devices in t Discharge power during the dispatch period; For the i Power storage devices in t- 1Discharge power during the dispatch period; For the i The upper limit of the storage capacity of each storage device; For the i The lower limit of the storage capacity of each storage device; For the i The upper limit of the charging power of each power storage device; For the i The lower limit of the charging power of each power storage device; For the i The upper limit of the discharge power of each power storage device; For the i The lower limit of the discharge power of each power storage device; For the i Power storage devices in t State variables of charging during the scheduling period; For the i Power storage devices in t The state variables of the discharge during the scheduling period; when the storage device is charging, The value is 0. The value is 1; when the storage device is discharging, The value is 1. The value is 0; For the i The initial value of the storage capacity of each storage device; T The total scheduling period.

3. According to claim 2, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The interaction constraints between the virtual power plant and the upper-level distribution network are: In the formula, For the j Virtual power plants in t The power purchased from the upper-level distribution network during the dispatch period; For the j Virtual power plants in t The power sold to the upper-level distribution network during the dispatch period; For the j Virtual power plants in t Input state variables for interaction with the upper distribution network during the dispatch period; For the j Virtual power plants in t Output state variables for interaction with the upper distribution network during the dispatch period; When a virtual power plant purchases electricity from a higher-level distribution network, The value is 1. The value is 0; when the virtual power plant sells electricity to the upper distribution network, The value is 0. The value is 1; For the j The upper limit of power that a virtual power plant can purchase from the upper distribution network; For the j The power limit of a virtual power plant sold to the upper distribution network.

4. According to claim 3, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The interaction constraints between the virtual power plants are: In the formula, For the j A virtual power plant and k Virtual power plants in t The power of P2P electric energy trading during the dispatch period; For the j A virtual power plant and k Virtual power plants in t Thermal energy P2P trading power during the scheduling period; For the j A virtual power plant and k The permissible value of the P2P trading power of a virtual power plant; For the j A virtual power plant and k The allowable value of the thermal energy P2P trading power of a virtual power plant.

5. According to claim 4, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The user-side demand response model includes a flexible electric load characteristic model and a flexible thermal load characteristic model. The flexible electric load characteristic model is: In the formula, For the j Virtual power plants in t The predicted value of electric load during the dispatch period; For the j Virtual power plants in t The amount of transferable electric load during the dispatch period; For the j Virtual power plants in t The amount of electricity load that can be reduced during the dispatch period; For the j The percentage of electrical load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce electrical load; The flexible heat load characteristic model is: In the formula, For the j Virtual power plants in t Thermal load forecast value during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be transferred during the dispatch period; For the j Virtual power plants in t The amount of heat load that can be reduced during the scheduling period; For the j The percentage of thermal load that can be shifted by a virtual power plant; For the j The percentage by which a virtual power plant can reduce thermal load.

6. According to claim 5, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The reward-penalty type tiered carbon trading mechanism model is: In the formula, For the j Virtual power plants in t Carbon trading costs during the dispatch period; It is the first among many virtual power plants j The difference between the actual carbon emissions of a virtual power plant and the initial carbon quota; d The length of the carbon emission interval corresponding to each step; u The carbon trading base price for the day; α The bonus factor for low carbon emissions; β is the price increase under high carbon emissions.

7. The method for optimizing the scheduling of multiple virtual power plants in a power tight balance scenario based on low-carbon game according to claim 6 is characterized in that: The Nash negotiation model of the multi-virtual power plant low-carbon game is: in: In the formula, For the j The operating cost of a virtual power plant before it participates in Nash negotiation; For the j The operating cost of a virtual power plant after participating in Nash negotiation; D is the number of virtual power plants; f grid , f gas , f d and f idr They are the costs of purchasing electricity, purchasing gas, operating and maintaining output equipment and units, and responding to comprehensive load demand; for t Real-time electricity purchase price during dispatch period; for t Gas purchase volume during the dispatch period; for t Real-time gas price during dispatch period; For the i Unit power operation and maintenance cost of each output equipment unit; N is the number of output equipment units within the virtual power plant; For the i Output equipment units in t Output power during the dispatch period; It is the unit power compensation cost of the time-shiftable electric heating load; for t The electric and heating loads can be shifted during the scheduling period.

8. According to claim 1, a low-carbon game optimization scheduling method for multiple virtual power plants in a power tight balance scenario is characterized in that: The Nash negotiation model of the multi-virtual power plant low-carbon game is solved as follows: The alternating direction multiplier method is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game.

9. A low-carbon game optimization dispatching device for multiple virtual power plants in a power tight balance scenario, characterized in that: include: A construction module is used to combine the output equipment unit model of the virtual power plant, the interaction constraints between virtual power plants, and the interaction constraints between the virtual power plant and the upper-level distribution network, and take into account the user-side demand response model and the reward-penalty ladder carbon trading mechanism model, with the goal of minimizing the total operating cost of the virtual power plant, to build a Nash negotiation model for low-carbon game among multiple virtual power plants; A solution module is used to solve the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain the optimal game strategy between virtual power plants under the cooperative game, wherein the optimal game strategy includes the amount of electricity and electricity price interacted between virtual power plants; A formulation module is used to utilize the optimal game strategy between the virtual power plants under the cooperative game to formulate a low-carbon game optimization scheduling strategy for multiple virtual power plants.

10. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a low-carbon game optimization scheduling method for multiple virtual power plants in a tight power balance scenario as described in any one of claims 1 to 8.

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