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

Abstract

The application discloses a kind of power tight balanced scene multi-virtual power plant low-carbon game optimization scheduling method and device, combine virtual power plant internal output equipment unit model, interactive constraint condition between virtual power plant and the interactive constraint condition of virtual power plant and superior distribution network, and give consideration to user side demand response model and reward and punishment type step carbon trading mechanism model, with minimum virtual power plant total operating cost as target, Nash negotiation model of multi-virtual power plant low-carbon game is constructed;The Nash negotiation model of multi-virtual power plant low-carbon game is solved, and the optimal game strategy under cooperation game between virtual power plants is obtained;Optimal game strategy under cooperation game between virtual power plants is used to develop multi-virtual power plant low-carbon game optimization scheduling strategy.The application effectively integrates and utilizes the flexibility resource inside virtual power plant, while giving consideration to long-term low-carbon development goal in the aspect of encouraging excess emission reduction or constraining excessive emission, to realize the overall optimization and low-carbon transformation of multi-virtual power plant.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of virtual power plants, and particularly relates to a multi-virtual power plant low-carbon game optimization scheduling method and device for a power tight balance scene. BACKGROUND

[0002] With the increasing penetration rate of new energy units, the intermittency and randomness of new energy units lead to the occurrence of power tight balance scenes. In particular, under extreme weather and meteorological conditions such as high temperature and no wind in summer, no light in winter, and continuous no wind and no light, local and time-based power supply gaps frequently occur, and the pressure on power supply is significantly increased. In order to cope with this challenge, new aggregators such as virtual power plants (VPPs) actively participate in power scheduling under the guidance and incentives of market mechanisms, and their adjustment potential and value have gradually been widely recognized by the industry and academia. VPPs integrate and optimize various energy resources, realize the synergistic complementation of different types of energy, effectively meet the energy demand of various loads, actively participate in peak clipping and load demand response, and significantly reduce the carbon emission level of the power system. In particular, in a park multi-VPP system, due to the large amount of load demand, high degree of automation, complex load characteristics, and a large number of adjustable flexible resources, the role of VPPs is more prominent.

[0003] Although VPPs have achieved remarkable results in promoting low-carbon development, existing carbon trading models still have obvious deficiencies in encouraging emission reduction and optimizing resource allocation. Traditional carbon trading models mainly rely on a single price mechanism, which has defects in encouraging excess emission reduction or constraining excessive emission, and fails to fully consider long-term low-carbon development goals. At the same time, in the process of virtual power plant low-carbon game optimization scheduling, the lack of consideration of user-side demand response leads to insufficient flexibility of virtual power plants. SUMMARY

[0004] In view of the problems in the prior art, the application provides a multi-virtual power plant low-carbon game optimization scheduling method and device for a power tight balance scene, which can effectively integrate and utilize the flexibility of virtual power plants, and at the same time, consider long-term low-carbon development goals in encouraging excess emission reduction or constraining excessive emission, so as to realize the overall optimization and low-carbon transformation of multi-virtual power plants.

[0005] In order to solve the above technical problems, the application is implemented by the following technical scheme:

[0006] According to a first aspect of the application, a multi-virtual power plant low-carbon game optimization scheduling method for a power tight balance scene is provided, comprising:

[0007] By combining the internal power output equipment unit model of virtual power plants, the interaction constraints between virtual power plants, and the interaction constraints between virtual power plants and the upper-level distribution network, and taking into account both the user-side demand response model and the reward-and-punishment tiered carbon trading mechanism model, a Nash negotiation model for low-carbon game among multiple virtual power plants is constructed with the goal of minimizing the total operating cost of virtual power plants.

[0008] The Nash negotiation model of the low-carbon game among the multiple virtual power plants is solved to obtain the optimal game strategy among the virtual power plants under the cooperative game, and the optimal game strategy includes the electricity volume and electricity price exchanged among the virtual power plants.

[0009] By utilizing the optimal game strategy among the virtual power plants under cooperative game theory, a low-carbon game optimization scheduling strategy for multiple virtual power plants is formulated.

[0010] In one possible implementation of the first aspect, the power output equipment unit model inside the virtual power plant includes a photovoltaic generator unit model, a wind turbine generator unit model, a combined heat and power unit model, a gas boiler model, and an energy storage equipment model.

[0011] The photovoltaic generator model is as follows:

[0012]

[0013] In the formula, For the first i A photovoltaic power generation unit in t Actual output power during the scheduling period; For the first i A photovoltaic power generation unit t Maximum output power during the scheduling period; For the first i Actual solar irradiance of each photovoltaic generator unit; For the first i Standard illuminance for each photovoltaic generator unit; l The external ambient temperature coefficient; This is the actual temperature value of the solar panel; This is the reference temperature value for the solar panel;

[0014] The wind turbine generator model is as follows:

[0015]

[0016] In the formula, For the first i A wind turbine unit t Actual output power during the scheduling period; v l , v ci , v rand v co respectively represent the actual wind speed, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator; represents the rated output power of the wind turbine generator;

[0017] The combined heat and power unit model is:

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, represents the electric power output by the i-th combined heat and power unit during the dispatching period; i represents the natural gas consumption of the i-th gas turbine during the dispatching period; t represents the thermal-electric conversion efficiency of the i-th gas turbine; represents the calorific value of natural gas; i represents the thermal power output by the i-th combined heat and power unit during the dispatching period; t represents the heating coefficient of the i-th waste heat boiler; represents the real-time output power of the i-th gas turbine during the dispatching period; i represents the real-time output power of the i-th gas turbine during the dispatching period; , and i respectively represent the maximum output power, the minimum output power and the maximum climbing power of the i-th gas turbine; t i represents the real-time output power of the i-th gas turbine during the dispatching period; i represents the real-time output power of the i-th gas turbine during the dispatching period; t , and i respectively represent the maximum output power, the minimum output power and the maximum climbing power of the i-th gas turbine; t- i represents the maximum output power, the minimum output power and the maximum climbing power of the i-th gas turbine;

[0023] The gas boiler model is:

[0024]

[0025]

[0026]

[0027] In the formula, represents the electric power output by the i-th combined heat and power unit during the dispatching period; i ​​​​​​​a gas boiler in t the thermal power output in the dispatch period; the first i a gas boiler in t- 1 the thermal power output in the dispatch period; the first i the natural gas consumption of the first t a gas boiler in the dispatch period; i the energy efficiency conversion coefficient of the first , and the maximum output power, the minimum output power and the maximum ramping power of the first i a gas boiler, respectively;

[0028] The storage device model is:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034]

[0035] wherein, the real-time storage capacity of the first i a storage device in t the dispatch period; the real-time storage capacity of the first i a storage device in t -1 the dispatch period; the charging efficiency of the first i a storage device; the discharging efficiency of the first i a storage device; the charging power of the first i a storage device in t the dispatch period; the charging power of the first i a storage device in t- 1 the dispatch period; the discharging power of the first i a storage device in t the dispatch period; the discharging power of the first i a storage device in t- 1 the dispatch period; upper limit of the storage capacity of the mth storage device; i lower limit of the storage capacity of the mth storage device; i upper limit of the charging power of the mth storage device; i lower limit of the charging power of the mth storage device; i upper limit of the discharging power of the mth storage device; i lower limit of the discharging power of the mth storage device; i state variable of the mth storage device charging in the dispatching period; i t state variable of the mth storage device discharging in the dispatching period; when the storage device is charging, i t when the storage device is discharging, initial value of the storage capacity of the mth storage device; i T total dispatching period.

[0036] In a possible implementation manner of the first aspect, the interaction constraint condition of the virtual power plant and the superior power distribution network is:

[0037]

[0038]

[0039]

[0040] wherein, j t j t j t j t when the virtual power plant is purchasing power from the superior power distribution network,​​​​​​​​​​​​​​​​​​​​​​​​​ The value is 1. The value is 0; when the virtual power plant sells electricity to the upstream distribution network, The value is 0. The value is 1; For the first j The upper limit of the power purchase capacity of a virtual power plant from the upper-level distribution network; For the first j The upper limit of the power output of a virtual power plant to the upper-level distribution network.

[0041] In one possible implementation of the first aspect, the interaction constraints between the virtual power plants are as follows:

[0042]

[0043]

[0044]

[0045]

[0046] In the formula, For the first j The virtual power plant and the first k A virtual power plant t Power consumption during the dispatch period in the P2P electricity trading system; For the first j The virtual power plant and the first k A virtual power plant t Thermal energy P2P transaction power during scheduling periods; For the first j The virtual power plant and the first k The permissible power output for P2P transactions of electricity from a virtual power plant; For the first j The virtual power plant and the first k The permissible power of P2P transactions of thermal energy from a virtual power plant.

[0047] In one possible implementation of the first aspect, the user-side demand response model includes a flexible electrical load characteristic model and a flexible thermal load characteristic model, wherein the flexible electrical load characteristic model is:

[0048]

[0049]

[0050]

[0051] In the formula, For the first j A virtual power plant tForecasted electrical load during the dispatch period; For the first j A virtual power plant t The amount of transferable electrical load during the dispatch period; For the first j A virtual power plant t The amount of electricity load that can be reduced during the scheduling period; For the first j The percentage of electrical load that can be transferred by each virtual power plant; For the first j A virtual power plant can reduce the percentage of electricity load;

[0052] The flexible heat load characteristic model is as follows:

[0053]

[0054]

[0055]

[0056] In the formula, For the first j A virtual power plant t Forecasted heat load during the scheduling period; For the first j A virtual power plant t Transferable heat load during the scheduling period; For the first j A virtual power plant t The amount of heat load that can be reduced during the scheduling period; For the first j The percentage of heat load that can be transferred by a virtual power plant; For the first j A virtual power plant can reduce the percentage of heat load.

[0057] In one possible implementation of the first aspect, the reward-and-punishment tiered carbon trading mechanism model is as follows:

[0058]

[0059] In the formula, For the first j A virtual power plant t Carbon trading costs during the scheduling period; For the first in multiple virtual power plants j The difference between the actual carbon emissions of a virtual power plant and its initial carbon allowance; d The length of the carbon emission range corresponding to each step; u This is the base price for carbon trading on that day; α This is the incentive coefficient for low carbon emissions;β This represents the price increase during periods of high carbon emissions.

[0060] In one possible implementation of the first aspect, the Nash negotiation model for the multi-virtual power plant low-carbon game is as follows:

[0061]

[0062] in:

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] In the formula, For the first j The operating costs of a virtual power plant before participating in the Nash negotiations; For the first j The operating costs of a virtual power plant after participating in the Nash negotiations; D The number of virtual power plants; f grid , f gas , f d and f idr These are the costs of purchasing electricity and gas, operating and maintaining power generation equipment units, and comprehensive load demand response. for t Real-time electricity purchase price during the dispatch period; for t Gas purchase volume during the scheduling period; for t Real-time gas price during the dispatch period; For the first i Unit power operation and maintenance cost of each power output equipment unit; N The number of power output units within the virtual power plant; For the first i Each power output unit t Output power during the scheduling period; The unit power compensation cost for time-shiftable electric heating load; for t The electric heating load can be shifted during the scheduling period.

[0069] In a possible implementation manner of the first aspect, the Nash negotiation model of the multi-virtual power plant low-carbon game is solved, and specifically:

[0070] The Nash negotiation model of the multi-virtual power plant low-carbon game is solved by using an alternating direction multiplier method.

[0071] According to the second aspect of the present application, a power tight balancing scenario multi-virtual power plant low-carbon game optimal scheduling device is provided, comprising:

[0072] A construction module is configured to combine a virtual power plant internal output device unit model, an interaction constraint condition between virtual power plants, and an interaction constraint condition between the virtual power plants and a superior power distribution network, and take into account a user side demand response model and a reward and punishment type step carbon trading mechanism model, to construct a Nash negotiation model of a multi-virtual power plant low-carbon game with the minimum total virtual power plant operation cost as the target;

[0073] A solution module is configured to solve the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain optimal game strategies of the virtual power plants in cooperative game, the optimal game strategies including an interactive power and a power price between the virtual power plants;

[0074] A formulation module is configured to formulate a multi-virtual power plant low-carbon game optimal scheduling strategy by using the optimal game strategies of the virtual power plants in cooperative game.

[0075] According to the third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the power tight balancing scenario multi-virtual power plant low-carbon game optimal scheduling method when executing the computer program.

[0076] According to the fourth aspect of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the power tight balancing scenario multi-virtual power plant low-carbon game optimal scheduling method.

[0077] Compared with the prior art, the present application has at least the following beneficial effects:

[0078] The application provides a power tight balance scene multi-virtual power plant low-carbon game optimization scheduling method, a Nash negotiation model of multi-VPP low-carbon game is constructed by combining a VPP internal output equipment unit model, interactive constraint conditions between VPPs and interactive constraint conditions between the VPP and a superior power distribution network, the model not only considers minimization of VPP operation cost, but also considers a user side demand response model and a reward and punishment type step carbon trading mechanism model, and the balance between flexibility and low-carbon development can be realized in VPP optimization scheduling. The user side demand response provides real-time flexibility support for the system by optimizing load distribution, and the reward and punishment type step carbon trading mechanism guides the VPP to realize 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 system operation cost and improve resource utilization efficiency, but also promote the organic unification of short-term scheduling optimization and long-term low-carbon development goals, and provide important protection for efficient, economic and sustainable operation of the VPP. In the power tight balance scene, especially in the face of extreme weather and climate conditions such as high temperature and no wind in summer, no light in winter, and continuous no wind and no light, the method of the application can enhance the stability of the VPP system, effectively alleviate the local and time period power supply gap, and reduce the power supply pressure.

[0079] In order to make the above-mentioned purposes, characteristics and advantages of the present application more obvious and easy to understand, the following preferred embodiments are described in detail below, and the accompanying drawings are described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the specific embodiments. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creating any creative labor.

[0081] Figure 1 The flowchart of the power tight balance scene multi-virtual power plant low-carbon game optimization scheduling method of the present application;

[0082] Figure 2 The multi-VPP basic architecture diagram of the embodiment;

[0083] Figure 3 The iteration convergence results of the algorithm of the embodiment, wherein (a) is the iteration convergence result of the traded power between virtual power plants, and (b) is the iteration convergence result of the traded power price between virtual power plants;

[0084] Figure 4 The VPP1 electric energy optimization scheduling result of the embodiment;

[0085] Figure 5 The VPP1 thermal energy optimization scheduling result of the embodiment;

[0086] Figure 6 Results of energy trading among VPPs for the embodiment, wherein (a) is the result of electricity trading, (b) is the result of thermal energy trading;

[0087] Figure 7 Prices of energy trading among VPPs for the embodiment, wherein (a) is the price of electricity trading, (b) is the price of thermal energy trading;

[0088] Figure 8 Carbon trading costs under different reward coefficients for the embodiment. DETAILED DESCRIPTION

[0089] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the present application will be described below in connection with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0090] First, the technical terms and abbreviations involved in the present application are explained as follows:

[0091] Virtual power plant (VPP);

[0092] Wind turbine (WT);

[0093] Photovoltaic (PV);

[0094] Electricity storage (ES);

[0095] Gas turbine (GT);

[0096] Waste heat boiler (WHB);

[0097] Combined heat and power (CHP);

[0098] Gas boiler (GB);

[0099] Peer-to-peer (P2P).

[0100] As Figure 1As shown, the embodiment of the present application provides a power tight balance scene multi-virtual power plant low-carbon game optimization scheduling method, which realizes low-carbon, efficient and collaborative scheduling of multi-virtual power plants in the power tight balance scene by constructing and solving a Nash negotiation model, and specifically includes the following steps:

[0101] S1, in combination with the virtual power plant internal output equipment unit model, the interaction constraint conditions between virtual power plants and the interaction constraint conditions between virtual power plants and the upper distribution network, and taking into account the user side demand response model and the reward and punishment type step carbon trading mechanism model, a Nash negotiation model for multi-virtual power plant low-carbon game is constructed with the minimum total operation cost of virtual power plant as the target.

[0102] In an implementable manner, the virtual power plant internal output equipment unit model includes a photovoltaic generator set model, a wind power generator set model, a combined heat and power unit model, a gas boiler model and a power storage device model.

[0103] Specifically, with the continuous development of solar application technology, PV has been widely used due to its low pollution and low noise. The photovoltaic generator set model is specifically as follows:

[0104]

[0105] In the formula, is the actual output power of the i th photovoltaic generator set in the scheduling period; i t is the maximum output power of the i th photovoltaic generator set in the scheduling period; i is the actual light intensity of the i th photovoltaic generator set; t is the standard light intensity of the i th photovoltaic generator set; i is the ambient temperature coefficient; i l is the actual temperature value of the battery panel; is the reference temperature value of the battery panel.

[0106] Specifically, WT is an important component of the VPP energy supply side. Through high-speed rotation of the wind turbine set, wind energy is converted into electric energy with a certain standard quality, improving the effective use of clean energy. The wind power generator set model is specifically as follows:

[0107]

[0108] In the formula, is the actual output power of the i th wind power generator set in the scheduling period; i t is the maximum output power of the i th wind power generator set in the scheduling period; v ​​​​​​​l 、 v ci 、 v r and v co are the actual wind speed, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine, respectively; is the rated output power of the wind turbine;

[0109] Specifically, the CHP is mainly composed of a GT and a WHB, generates electric energy and heat energy by burning natural gas, and supplies the generated waste heat to the heat load. The combined heat and power unit model is:

[0110]

[0111]

[0112]

[0113]

[0114] wherein, is the electric power output by the i-th combined heat and power unit during the j-th dispatching period; i is the natural gas consumption of the i-th gas turbine during the j-th dispatching period; t is the thermal-electric conversion efficiency of the i-th gas turbine; is the calorific value of natural gas, which is 9.78 kW·h / m 3 ; i is the heat power output by the i-th combined heat and power unit during the j-th dispatching period; t is the heating coefficient of the i-th waste heat boiler; is the real-time output power of the i-th gas turbine during the j-th dispatching period; i is the real-time output power of the i-th gas turbine during the j-1th dispatching period; are the maximum output power, the minimum output power and the maximum climbing power of the i-th gas turbine, respectively. i t i i t i t- 、 and are the maximum output power, the minimum output power and the maximum climbing power of the i-th gas turbine, respectively. i

[0115] ​​​​​​​​​​​​Specifically, GB utilizes high-temperature steam generated from burning natural gas for direct heating, offering advantages of both environmental friendliness and high efficiency. The specific model of the gas-fired boiler is as follows:

[0116]

[0117]

[0118]

[0119] In the formula, For the first i A gas-fired boiler t Thermal power output during the scheduling period; For the first i A gas-fired boiler t- 1. Thermal power output during the scheduling period; For the first i A gas-fired boiler t The amount of natural gas consumed during the scheduling period; For the first i Energy efficiency conversion coefficient of a gas-fired boiler; , and The first i The maximum output power, minimum output power, and maximum ramp power of each gas-fired boiler.

[0120] Specifically, the energy storage device model is as follows:

[0121]

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] In the formula, For the first i A storage device in t Real-time energy storage capacity during the dispatch period; For the first i A storage device in t- 1. Real-time energy storage capacity during the dispatch period; For the first i The charging efficiency of individual energy storage devices; For the first i Discharge efficiency of individual energy storage devices; the first storage device in the dispatch period; i the first storage device in the dispatch period; t the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; t- the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; t the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; t- the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; the first storage device in the dispatch period, i the first storage device in the dispatch period; t the first storage device in the dispatch period; the first storage device in the dispatch period; i the first storage device in the dispatch period; t the first storage device in the dispatch period; when the storage device is charging, when the storage device is discharging, when the storage device is charging, when the storage device is discharging; the initial value of the storage capacity of the first storage device; i the total dispatch period. T In an implementation manner, the VPP exchanges electric energy with the superior power distribution network through a tie line, and the interaction constraint condition between the virtual power plant and the superior power distribution network is specifically as follows:

[0128]

[0129]

[0130]

[0131]

[0132] In the formula, n is the number of the storage devices, and the first storage device in the dispatch period; j ​A virtual power plant t The amount of electricity purchased from the upper-level distribution network during the dispatching period; For the first j A virtual power plant t The amount of electricity sold to the upper-level distribution network during the dispatching period; For the first j A virtual power plant t Input state variables that interact with the upper-level distribution network during the scheduling period; For the first j A virtual power plant t Output state variables during the scheduling period that interact with the upper-level distribution network; 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 upstream distribution network, The value is 0. The value is 1; For the first j The upper limit of the power purchase capacity of a virtual power plant from the upper-level distribution network; For the first j The upper limit of the power output of a virtual power plant to the upper-level distribution network.

[0133] In one feasible approach, considering the differences and complementarity of the supply and demand balance characteristics of VPPs at different times, VPPs are encouraged to use a peer-to-peer (P2P) approach for electricity and heat trading. The specific interaction constraints between the virtual power plants are as follows:

[0134]

[0135]

[0136]

[0137]

[0138] In the formula, For the first j The virtual power plant and the first k A virtual power plant t Power consumption during the dispatch period in the P2P electricity trading system; For the first j The virtual power plant and the first k A virtual power plant t Thermal energy P2P transaction power during scheduling periods; For the first j The virtual power plant and the first k The permissible power output for P2P transactions of electricity from a virtual power plant; For the first j The virtual power plant and the first kThe allowable value of the thermal energy P2P transaction power of the virtual power plant.

[0139] In an implementation, the VPP user load is classified into rigid load and flexible load in a manner of participating in demand response according to load. The rigid load belongs to uncontrollable load, and the VPP fully responds to the demand of the user and cannot change the energy use mode and energy use time. The flexible load is controllable load, and is classified into transferable load and reducible load according to different adjustment modes. The transferable load refers to the electricity consumption in each time period that can be flexibly adjusted, but the total load amount after transfer remains unchanged. The reducible load refers to the load that can bear a certain interruption or power reduction and operation time reduction, and is partially or wholly reduced according to the supply and demand situation. In the embodiment, the load is classified according to the above-mentioned classification, and the adjustable potential of the load and other flexible resources of the VPP are used 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.

[0140] Specifically, the flexible electric load characteristic model is as follows:

[0141]

[0142]

[0143]

[0144] In the formula, is the electric load prediction value of the i-th virtual power plant in the j-th dispatching period; j is the transferable electric load amount of the i-th virtual power plant in the j-th dispatching period; t is the reducible electric load amount of the i-th virtual power plant in the j-th dispatching period; is the percentage of the transferable electric load of the i-th virtual power plant; j is the percentage of the reducible electric load of the i-th virtual power plant. t Specifically, the flexible thermal load characteristic model is as follows: j t j j

[0145]

[0146]

[0147]

[0148]

[0149] In the formula,​​​​​​​​ the thermal load prediction value of the first virtual power plant in the dispatch period; j the transferable thermal load amount of the first virtual power plant in the dispatch period; t the reducible thermal load amount of the first virtual power plant in the dispatch period; the percentage of transferable thermal load of the first virtual power plant; j the percentage of reducible thermal load of the first virtual power plant. t In an implementation manner, a reward-punishment type step carbon trading mechanism is introduced into the optimal dispatch strategy to guide the VPP to save energy and reduce carbon. The carbon trading mechanism mainly includes three links of initial carbon quota allocation, actual carbon emission calculation and carbon emission trading. The baseline method is adopted to determine the initial carbon emission quota of the system. The power purchased by the multi-VPP from the upper distribution network is all from thermal power generation, so the carbon emission sources in the VPP are mainly divided into three categories: CHP, GB and power purchased from the upper distribution network. Since the CHP can provide both electric energy and heat energy, the initial carbon quota model is: j t j In the formula, j is the initial carbon quota of the virtual power plant; ,

[0150] is the unit power supply and heat supply output carbon quota coefficient, takes 0.728 kg / kWh,

[0151] takes 0.367 kg / kWh; is the conversion coefficient of CHP power generation capacity converted into heat supply capacity, which takes 6 MJ / kWh.

[0152] The actual carbon emission amount is calculated by the following formula in the embodiment: In the formula, is the actual carbon emission amount of the virtual power plant; is the upper distribution network emission factor, which takes 0.581 kg / kWh and is updated in real time according to the latest value released by the Ministry of Ecology and Environment;

[0153] is the natural gas carbon emission coefficient, which takes 2.165 kg / m³.

[0154]

[0155] In the formula, is the actual carbon emission amount of the virtual power plant; is the upper distribution network emission factor, which takes 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 natural gas carbon emission coefficient, which takes 2.165 kg / m³.

[0156] ​​​​In the carbon emissions trading process, this invention proposes a tiered carbon trading mechanism with differentiated pricing based on incentives and penalties to encourage VPPs to conserve energy and reduce carbon emissions. The specific model of this tiered carbon trading mechanism is as follows:

[0157]

[0158] In the formula, For the first j A virtual power plant t The carbon trading cost during the scheduling period is greater than zero when it means that the actual carbon emissions are higher than the initial quota and carbon emission credits need to be purchased; when it is less than zero, it means that the actual carbon emissions are lower than the initial quota and carbon emission credits can be sold to obtain market subsidies. For the first in multiple virtual power plants j Actual carbon emissions of a virtual power plant Compared with the initial carbon quota The difference; d The length of the carbon emission range corresponding to each step; u This is the base price for carbon trading on that day; α This is the incentive coefficient for low carbon emissions; β This represents the price increase during periods of high carbon emissions.

[0159] In one feasible approach, the Nash negotiation model for the multi-virtual power plant low-carbon game is as follows:

[0160]

[0161] in:

[0162]

[0163]

[0164]

[0165]

[0166]

[0167] In the formula, For the first j The operating cost of a virtual power plant before participating in the Nash negotiations, i.e., the breaking point of the Nash negotiations; For the first j The operating costs of a virtual power plant after participating in the Nash negotiations; D The number of virtual power plants; f grid , f gas , f d and fidr Cost and expense of purchasing electricity, purchasing gas, equipment operation and maintenance, and comprehensive load demand response, respectively; is the purchasing electricity power of the nth virtual power plant to the upper power grid in the dispatching period; j is the real-time purchasing electricity price in the dispatching period; t is the gas purchasing amount in the dispatching period; is the real-time gas price in the dispatching period; t is the unit power operation and maintenance expense of the nth power output equipment unit; is the number of power output equipment units in the virtual power plant; t is the output power of the nth power output equipment unit in the dispatching period; is the unit power compensation expense of the time-shiftable electric-thermal load; t is the time-shiftable electric-thermal load in the dispatching period. i S2, solving the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain optimal game strategies of the virtual power plants in cooperative game, the optimal game strategies including electricity and electricity price interacted between the virtual power plants. N Preferably, to reduce the dependence of the solving 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. i t S3, using the optimal game strategies of the virtual power plants in cooperative game to formulate a multi-virtual power plant low-carbon game optimal dispatching strategy, and finally performing virtual power plant low-carbon game optimal dispatching according to the multi-virtual power plant low-carbon game optimal dispatching strategy. The effectiveness of the multi-virtual power plant low-carbon game optimal dispatching method in the power tight balancing scenario is verified below based on example analysis. t

[0168]

[0169] Preferably, to reduce the dependence of the solving 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.

[0170] S3, using the optimal game strategies of the virtual power plants in cooperative game to formulate a multi-virtual power plant low-carbon game optimal dispatching strategy, and finally performing virtual power plant low-carbon game optimal dispatching according to the multi-virtual power plant low-carbon game optimal dispatching strategy.

[0171] The effectiveness of the multi-virtual power plant low-carbon game optimal dispatching method in the power tight balancing scenario is verified below based on example analysis.

[0172] The basic architecture of the multi-virtual power plant is as follows: Figure 2 ​​​​​​As shown in the diagram. WT and PV are renewable energy power supply equipment, providing green electricity to the virtual power plant to meet electricity load demand. ES utilizes the price difference between peak and off-peak hours to achieve price arbitrage through "buying low and selling high," and improves the energy efficiency of the virtual power plant. GT and WHB constitute CHP, realizing heterogeneous energy flow coupling of electricity, heat, and gas, providing electricity and heat to the virtual power plant to meet electricity and heat load demand. GB converts natural gas into heat, supplying the virtual power plant's heat load. Multiple virtual power plants upload the electricity prices and quantities exchanged between them to the information center. When there is a power shortage, the multiple virtual power plants purchase electricity from the upper-level distribution network; when there is a power shortage, they sell excess electricity to the upper-level distribution network.

[0173] A case study was conducted using a multi-virtual power plant with three members as the test system, and the solution was performed using the cplex solver via yalmip. Members VPP1 and VPP3 have wind power units, while member VPP2 has a photovoltaic unit. All three VPP members are configured with identical ES, GT, WHB, and GB generators. In the case study, equipment efficiency is assumed to be constant, and the impact of load factor on efficiency is ignored. The relevant parameters for the multi-VPP are shown in the table below.

[0174] Table 1. Parameters related to multiple VPPs

[0175]

[0176] Solving the Nash negotiation model for low-carbon games involving multiple virtual power plants using the alternating direction multiplier method. Figure 3 For the algorithm's iterative convergence curve, such as Figure 3 As shown in (a), the virtual power plant inter-transaction electricity volume converged after 45 iterations, with a computation time of 286 seconds. Figure 3 As shown in (b), the inter-virtual power plant trading price converges after 27 iterations, with a calculation time of 16 seconds. This indicates that the alternating direction multiplier method used in this invention has good computational efficiency and convergence, and can achieve operational optimization of multiple virtual power plants and each VPP.

[0177] To verify the rationality of the low-carbon game-theoretic optimal scheduling method for multiple virtual power plants, the optimal scheduling results for electricity and heat of VPP1 are plotted as follows: Figure 4 and Figure 5 As shown.

[0178] Depend on Figure 4 It can be seen that during the dispatch periods of 01:00-05:00 and 23:00-24:00, the gas turbine operates at full capacity, and the power generation of VPP1 can meet the load demand, storing any surplus energy. During the dispatch periods of 05:00-09:00 and 13:00-18:00, wind power output decreases. At this time, VPP1 achieves power balance through ES charging and discharging, inter-VPP transactions, and power purchase from the upstream distribution network.

[0179] From Figure 5 it can be seen that in the peak period, the thermal load demand of VPP1 is mainly borne by the GT, and a small part is borne by the GB. This is because the peak period of the thermal and electrical load of VPP1 is generally consistent, and the GT runs in the "electricity determines heat" mode, thus bearing the main share of heat supply. In the 06:00-07:00 and 15:00-22:00 scheduling periods, the heat power interacts between VPPs, because VPP2 and VPP3 have excess heat energy in the above-mentioned scheduling periods, so that the multi-VPP realizes optimized operation through heat interaction.

[0180] In the cooperative operation mode, the electric and heat power interaction between VPPs has a positive effect on improving the comprehensive benefit of operation. In order to analyze the effectiveness based on the present application in depth, the electric and heat trading results between VPPs are plotted in Figure 6 .

[0181] As shown in (a) in Figure 6 , first, the electric energy trading amount between VPPs is investigated. In the 06:00-17:00 scheduling period, the electric load of VPP1 increases, and there is no photovoltaic generator set output, so that there is a power shortage; in the same scheduling period, VPP2 outputs electric energy to VPP1 due to large photovoltaic power generation. VPP3 outputs electric energy to the outside all day, because the electric load of VPP3 is small, and there is a surplus of wind power.

[0182] As shown in (b) in Figure 6 , the heat energy trading between VPPs is investigated. VPP1 needs to purchase a large amount of heat energy in the remaining scheduling periods except for the 00:00-05:00 and 8:00-14:00 scheduling periods, because the heat load of VPP1 is high and cannot realize self-sufficiency. VPP2 outputs heat energy to other VPPs in the whole scheduling period; this is because the heat energy unit capacity of VPP2 is large, and the heat load of VPP2 is small, which can support the heat load demand of other VPPs. The heat supply margin of VPP3 varies with time periods, and can realize supply-demand balance in most scheduling periods.

[0183] The electric and heat real-time trading prices between VPPs obtained by solving through the Nash negotiation model are plotted in Figure 7 . According to Figure 7As shown in (a), the transaction prices among VPPs are all within the range of the distribution network's purchase and sale prices. VPPs can purchase electricity at prices lower than the distribution network's sale price and sell electricity at prices higher than the distribution network's purchase price, thus effectively increasing the revenue of each VPP. This trading model promotes energy mutual assistance and cooperative operation among VPPs, achieving optimized allocation and dispatch of electricity and heat resources on a wider scale. This has positive significance for improving the overall operating efficiency, energy self-sufficiency rate, and operational flexibility of multiple VPPs. According to... Figure 7 As shown in (b), the heat load of each VPP is high during the scheduling period from 18:00 to 24:00, so heat energy exchange between VPPs is required. At this time, setting a higher trading heat price can maximize the benefits of each VPP.

[0184] Figure 8 This shows the curves illustrating how carbon trading costs change with the unit carbon trading price under different incentive coefficients. Figure 8 It can be seen that when each VPP begins to profit from carbon trading, the reward coefficient for low carbon emissions increases. α The higher the price, the greater the returns from carbon trading, thus significantly reducing the carbon emissions of VPPs. When the unit carbon trading price increases to a certain level, the downward trend in carbon trading costs will slow down, or even stop, indicating that the carbon reduction potential of each VPP has been fully realized.

[0185] This invention provides a low-carbon game-theoretic optimization scheduling device for multiple virtual power plants in a tight power balance scenario, specifically comprising the following modules:

[0186] The module is designed to combine the internal power generation 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, while also taking into account the user-side demand response model and the reward-and-punishment tiered 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 among multiple virtual power plants is constructed.

[0187] Specifically, the photovoltaic generator model is as follows:

[0188]

[0189] In the formula, For the first i A photovoltaic power generation unit t Actual output power during the scheduling period; For the first i A photovoltaic power generation unit t Maximum output power during the scheduling period; For the first i Actual solar irradiance of each photovoltaic generator unit; is the standard light intensity of the first photovoltaic generator set; i is the ambient temperature coefficient; l is the actual temperature value of the battery panel; is the reference temperature value of the battery panel.

[0190] Specifically, the wind turbine generator set model is as follows:

[0191]

[0192] In the formula, is the actual output power of the first wind turbine generator set in the dispatching period; i t l , v ci , v r and v co are the actual wind speed, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator set respectively; v 3 is the rated output power of the wind turbine generator set.

[0193] Specifically, the cogeneration unit model is as follows:

[0194]

[0195]

[0196]

[0197]

[0198] In the formula, is the electric power output by the first cogeneration unit in the dispatching period; i t is the natural gas consumption of the first gas turbine in the dispatching period; i t is the thermal-electric conversion efficiency of the first gas turbine; i is the calorific value of natural gas, which is 9.78 kW·h / m 3 ; i is the thermal power output by the first cogeneration unit in the dispatching period; t i is the heating coefficient of the first waste heat boiler; is the actual temperature value of the battery panel;​​​​​​​​​​i the nth gas turbine in the nth scheduling period; t the real-time output power of the nth gas turbine in the nth scheduling period; the first gas turbine in the nth scheduling period; i the real-time output power of the nth gas turbine in the nth scheduling period; t- 1 the real-time output power of the nth gas turbine in the nth scheduling period; , and the maximum output power, the minimum output power and the maximum ramping power of the nth gas turbine, respectively. i

[0199] Specifically, the gas turbine model is as follows:

[0200]

[0201]

[0202]

[0203] wherein, the heat output of the nth gas turbine in the nth scheduling period; i the heat output of the nth gas turbine in the nth scheduling period; t 1 the heat output of the nth gas turbine in the nth scheduling period; the natural gas consumption of the nth gas turbine in the nth scheduling period; i the energy efficiency conversion coefficient of the nth gas turbine; t- , and i the maximum output power, the minimum output power and the maximum ramping power of the nth gas turbine, respectively. t i i

[0204] Specifically, the electricity storage device model is as follows:

[0205]

[0206]

[0207]

[0208]

[0209]

[0210]

[0211] wherein, the heat output of the nth gas turbine in the nth scheduling period;​​​​​​​​i the real-time storage capacity of the i-th storage device in the dispatch period; t the real-time storage capacity of the i-th storage device in the dispatch period; the charging efficiency of the i-th storage device; i the charging efficiency of the i-th storage device; t- 1 the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; 1 the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; t the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i 1 the charging power of the i-th storage device in the dispatch period; t- the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; t the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; t- 1 the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; t the charging power of the i-th storage device in the dispatch period; the charging power of the i-th storage device in the dispatch period; i the charging power of the i-th storage device in the dispatch period; t the charging power of the i-th storage device in the dispatch period; 0 when the i-th storage device is charging, 1 when the i-th storage device is discharging, 0 when the i-th storage device is discharging, 1 when the i-th storage device is charging; the initial value of the storage capacity of the i-th storage device; i the initial value of the storage capacity of the i-th storage device; T the initial value of the storage capacity of the i-th storage device.

[0212] In one implementation, the interaction constraint conditions between the virtual power plant and the upper-level power distribution network are as follows:

[0213]

[0214]

[0215]

[0216] In the formula, For the first j A virtual power plant t The amount of electricity purchased from the upper-level distribution network during the dispatching period; For the first j A virtual power plant t The amount of electricity sold to the upper-level distribution network during the dispatching period; For the first j A virtual power plant t Input state variables that interact with the upper-level distribution network during the scheduling period; For the first j A virtual power plant t Output state variables during the scheduling period that interact with the upper-level distribution network; 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 upstream distribution network, The value is 0. The value is 1; For the first j The upper limit of the power purchase capacity of a virtual power plant from the upper-level distribution network; For the first j The upper limit of the power output of a virtual power plant to the upper-level distribution network.

[0217] Specifically, the interaction constraints between the virtual power plants are as follows:

[0218]

[0219]

[0220]

[0221]

[0222] In the formula, For the first j The virtual power plant and the first k A virtual power plant t Power consumption during the dispatch period in the P2P electricity trading system; For the first j The virtual power plant and the first k A virtual power plant t Thermal energy P2P transaction power during scheduling periods; For the firstj The virtual power plant and the first k The permissible power output for P2P transactions of electricity from a virtual power plant; For the first j The virtual power plant and the first k The permissible power of P2P transactions of thermal energy from a virtual power plant.

[0223] The user-side demand response model includes a flexible electrical load characteristic model and a flexible thermal load characteristic model. Specifically, the flexible electrical load characteristic model is as follows:

[0224]

[0225]

[0226]

[0227] In the formula, For the j-th virtual power plant in t Forecasted electrical load during the dispatch period; For the j-th virtual power plant in t The amount of transferable electrical load during the dispatch period; For the j-th virtual power plant in t The amount of electricity load that can be reduced during the scheduling period; This represents the percentage of electrical load that can be transferred from the j-th virtual power plant. denoted as the percentage of electrical load that can be reduced by the j-th virtual power plant.

[0228] Specifically, the flexible heat load characteristic model is as follows:

[0229]

[0230]

[0231]

[0232] In the formula, For the first j A virtual power plant t Forecasted electrical load during the dispatch period; For the first j A virtual power plant t The amount of transferable electrical load during the dispatch period; For the first j A virtual power plant t The amount of electricity load that can be reduced during the scheduling period; For the first j The percentage of electrical load that can be transferred by each virtual power plant; For the first jA virtual power plant can reduce the percentage of electricity load.

[0233] Specifically, the reward-and-punishment tiered carbon trading mechanism model is as follows:

[0234]

[0235] In the formula, For the first j A virtual power plant t The carbon trading cost during the scheduling period is greater than zero when it means that the actual carbon emissions are higher than the initial quota and carbon emission credits need to be purchased; when it is less than zero, it means that the actual carbon emissions are lower than the initial quota and carbon emission credits can be sold to obtain market subsidies. For the first in multiple virtual power plants j Actual carbon emissions of a virtual power plant Compared with the initial carbon quota The difference; d The length of the carbon emission range corresponding to each step; u This is the base price for carbon trading on that day; α This is the incentive coefficient for low carbon emissions; β This represents the price increase during periods of high carbon emissions.

[0236] Specifically, the Nash negotiation model for the multi-virtual power plant low-carbon game is as follows:

[0237]

[0238] in:

[0239]

[0240]

[0241]

[0242]

[0243]

[0244] In the formula, For the first j The operating cost of a virtual power plant before participating in the Nash negotiations, i.e., the breaking point of the Nash negotiations; For the first j The operating costs of a virtual power plant after participating in the Nash negotiations; D The number of virtual power plants; f grid , f gas , f d and fidr Cost and expense of purchasing electricity, purchasing gas, equipment operation and maintenance, and comprehensive load demand response, respectively; is the purchasing power of the nth virtual power plant to the superior power grid in the dispatch period; j is the real-time purchasing electricity price in the dispatch period; t is the gas purchasing amount in the dispatch period; is the real-time gas price in the dispatch period; t is the unit power operation and maintenance expense of the nth power output equipment unit; is the number of power output equipment units in the virtual power plant; t is the output power of the nth power output equipment unit in the dispatch period; is the unit power compensation expense of the time-shiftable electric-thermal load; t is the time-shiftable electric-thermal load in the dispatch period. i is the purchasing power of the nth virtual power plant to the superior power grid in the dispatch period; N is the real-time purchasing electricity price in the dispatch period; is the gas purchasing amount in the dispatch period; i is the real-time gas price in the dispatch period; t is the unit power operation and maintenance expense of the nth power output equipment unit; is the number of power output equipment units in the virtual power plant; is the output power of the nth power output equipment unit in the dispatch period; t is the unit power compensation expense of the time-shiftable electric-thermal load;

[0245] The solving module is configured to solve the Nash negotiation model of the multi-virtual power plant low-carbon game, and obtain optimal game strategies of the virtual power plants in cooperative game, the optimal game strategies including electricity and electricity price interacted between the virtual power plants.

[0246] Preferably, in order to reduce the dependence of the solving 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.

[0247] The making module is configured to make a multi-virtual power plant low-carbon game optimal dispatch strategy by using the optimal game strategies of the virtual power plants in cooperative game.

[0248] All the related contents of the steps involved in the foregoing embodiment of the method for multi-virtual power plant low-carbon game optimal dispatch in a power tight balancing scenario can be cited as the function description of the function modules corresponding to the functions of the embodiment of the device for multi-virtual power plant low-carbon game optimal dispatch in a power tight balancing scenario, which will not be repeated here. The division of the modules in the embodiment of the application is illustrative, and is only a logical function division. In actual implementation, another division mode can be used. In addition, the function modules in each embodiment of the application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The integrated modules can be realized in the form of hardware or in the form of software function modules.

[0249] ​In another embodiment of the present application, a computer device is provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method process or a corresponding function; the processor in the embodiments of the present application can be used for the operation of the power tight balancing scene multi-virtual power plant low-carbon game optimization scheduling method.

[0250] In another embodiment of the present application, the present application further provides a storage medium, specifically a computer readable storage medium, which is a memory device in the computer device, and is configured to store programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the computer device, and of course can also include the expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed random access memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the power tight balancing scene multi-virtual power plant low-carbon game optimization scheduling method in the above embodiments.

[0251] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage etc.) embodying computer readable program code.

[0252] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0253] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0254] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks. Figure 1 Figure 1 one or more functions specified in the flowchart and / or block diagram block or blocks.

[0255] In this disclosure, the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" mean that a particular feature, structure, material, or characteristic is included in at least one embodiment or example of the present disclosure. Exemplary expressions of the above terms do not necessarily refer to the same embodiment or example in this specification. Moreover, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine or arrange the various embodiments or examples described in this specification and their features in a suitable manner, without contradiction.

[0256] Finally, it should be noted that the above-described embodiments are merely specific embodiments of the present disclosure, which are used to illustrate the technical solutions of the present disclosure, rather than limiting them. The protection scope of the present disclosure is not limited thereto, and although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can make modifications or easily think of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed by the present disclosure, or make equivalent replacements to some of the technical features; and these modifications, changes or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure, and should be covered within the protection scope of the present disclosure.

Claims

1. A power tight balance scenario multi-virtual power plant low-carbon game optimization scheduling method, characterized in that, The method comprises the following steps: The Nash negotiation model of the multi-virtual power plant low-carbon game is constructed by combining a virtual power plant internal output device unit model, an interaction constraint condition between virtual power plants, and an interaction constraint condition between the virtual power plant and a superior distribution network, and taking into account a user side demand response model and a reward and punishment type step carbon trading mechanism model, with the minimum total operation cost of the virtual power plant as the target; The virtual power plant internal output device unit model comprises a photovoltaic generator unit model, a wind power generator unit model, a combined heat and power unit model, a gas boiler model, and a power storage device model; The photovoltaic generator unit model is: In the formula, P PV,i,t is the actual output power of the ith photovoltaic generator set at the tth dispatch period; P STC,i,t Pmax,i(t) is the maximum output power of the ith photovoltaic generator set at the tth dispatch period; I ING,i actual light intensity for the i-th photovoltaic generator set; I STC,i is the standard light intensity for the ith photovoltaic generator set; l is the ambient temperature coefficient; T C is the actual temperature value of the battery panel; T STC Tbatt is the battery plate reference temperature value; The wind power generator unit model is: In the formula, P WT,i,t is the actual output power of the ith wind turbine group at the tth dispatch period; v l , v ci , v r , and v co are the actual wind speed, the cut-in wind speed, the rated wind speed, and the cut-out wind speed of the wind turbine group, respectively; P R is the rated output power of the wind turbine group; The combined heat and power unit model is: P GT,i,min ≤P GT,i,t ≤P GT,i,max - ΔP GT,i ≤ P GT,i,t - P GT,i,t-1 ≤ ΔP GT,i wherein, is the electrical power output by the ith combined heat and power unit at the tth scheduling period; V GT,i,t is the amount of natural gas consumed by the ith gas turbine at the tth scheduling period; η GT,i is the heat-to-electricity conversion efficiency of the ith gas turbine; is the natural gas heating value; is the thermal power output by the ith combined heat and power unit at the tth scheduling period; η WHB,i is the heating coefficient of the i-th waste heat boiler; P GT,i,t is the real-time output power of the i-th gas turbine at the t-th scheduling period; P GT,i,t-1 is the real-time output power of the i-th gas turbine at the t-1-th scheduling period; P GT,i,max , P GT,i,min and ΔP GT,i are the maximum output power, the minimum output power and the maximum ramping power of the i-th gas turbine, respectively; The gas boiler model is: H GB,i,min ≤H GB,i,t ≤H GB,i,max - ΔH GB,i ≤ H GB,i,t - H GB,i,t-1 ≤ ΔH GB,i H GB,i,t is the heat output of the i-th gas boiler at the t-th scheduling period; H GB,i,t-1 is the heat output of the i-th gas boiler at the t-1-th scheduling period; V GB,i,t is the natural gas consumption of the i-th gas boiler at the t-th scheduling period; η GB,i is the energy efficiency conversion coefficient of the i-th gas boiler; H GB,i,max , H GB,i,min and ΔH GB,i are the maximum output power, the minimum output power and the maximum ramping power of the i-th gas boiler, respectively. The power storage device model is: SOC ES,i,min ≤ SOC ES,i,t < SOC ES,i,max SOC ES,i,T = SOC ES,i,0 s ch,i,t P ch,i,min ≤P ch,i,t ≤s ch,i,t P ch,i,max s dis,i,t P dis,i,min ≤P dis,i,t ≤s dis,i,t P dis,i,max s ch,i,t +s dis,i,t ≤1 In the formula, SOC ES,i,t is the real-time storage capacity of the i-th storage device at the t-th scheduling period; SOC ES,i,t-1 is the real-time storage capacity of the i-th storage device at the t-1-th scheduling period; η ch,i ηi is the charging efficiency of the i-th power storage device; η dis,i is the discharge efficiency of the i-th power storage device; P ch,i,t is the charging power of the i-th power storage device at the t-th scheduling period; P ch,i,t-1 is the charging power of the i-th power storage device at the t-1-th scheduling period; P dis,i,t is the discharging power of the i-th power storage device at the t-th scheduling period; P dis,i,t-1 is the discharging power of the i-th power storage device at the t-1-th scheduling period; SOC ES,i,max is the upper limit of the power storage capacity of the i-th power storage device; SOC ES,i,min is a lower limit of the storage capacity of the i-th storage device; P ch,i,max is an upper limit of the charging power of the i-th storage device; P ch,i,min lower limit of charging power for the i-th power storage device; P dis,i,max upper limit of discharging power for the i-th power storage device P dis,i,min is the lower limit of discharging power of the i-th energy storage device; s ch,i,t is the state variable of the i-th energy storage device charging at the t-th scheduling period; s dis,i,t is the state variable of the i-th energy storage device discharging at the t-th scheduling period; s ch,i,t takes the value of 0, s dis,i,t takes the value of 1; s ch,i,t takes the value of 1, s dis,i,t takes the value of 0; SOC ES,i,0 is the initial value of the energy storage capacity of the i-th energy storage device; T is the total scheduling period; The interaction constraint condition between the virtual power plant and the superior distribution network is: 0 < P buy,j,t ≤ s buy,j,t P buy,j,max 0 < P sell,j,t ≤ s sell,j,t P sell,j,max s buy,j,t +s sell,j,t ≤1 In the formula, P buy,j,t is the purchase power of the jth virtual power plant to the upper-level power grid at the tth scheduling period; P sell,j,t is the sale power of the jth virtual power plant to the upper-level power grid at the tth scheduling period; s buy,j,t is the input state variable of the jth virtual power plant interacting with the upper-level power grid at the tth scheduling period; s sell,j,t is the output state variable of the jth virtual power plant interacting with the upper-level power grid at the tth scheduling period; when the virtual power plant purchases power from the upper-level power grid, s buy,j,t takes the value of 1, s sell,j,t takes the value of 0; when the virtual power plant sells power to the upper-level power grid, s buy,j,t takes the value of 0, s sell,j,t takes the value of 1; P buy,j,max is the upper limit of the purchase power of the jth virtual power plant to the upper-level power grid; P sell,j,max Pmaxj is the upper limit of the power sold by the jth virtual power plant to the superior power distribution network; The interaction constraint condition between the virtual power plant and the superior distribution network is: In the formula, is the power of the P2P electricity transaction of the jth virtual power plant and the kth virtual power plant at the tth scheduling period; is the power of the P2P heat transaction of the jth virtual power plant and the kth virtual power plant at the tth scheduling period; is the allowable value of the power of the P2P electricity transaction of the jth virtual power plant and the kth virtual power plant; is the allowable value of the power of the P2P heat transaction of the jth virtual power plant and the kth virtual power plant; The user side demand response model comprises a flexible electric load characteristic model and a flexible thermal load characteristic model, and the flexible electric load characteristic model is: In the formula, is the electrical load prediction value of the jth virtual power plant at the tth scheduling period; is the transferable electrical load amount of the jth virtual power plant at the tth scheduling period; is the reducible electrical load amount of the jth virtual power plant at the tth scheduling period; is the percentage of the transferable electrical load of the jth virtual power plant; is the percentage of the reducible electrical load of the jth virtual power plant; The flexible thermal load characteristic model is: In the formula, is the heat load prediction value of the jth virtual power plant at the tth scheduling period; is the transferable heat load amount of the jth virtual power plant at the tth scheduling period; is the reducible heat load amount of the jth virtual power plant at the tth scheduling period; is the percentage of the transferable heat load of the jth virtual power plant; is the percentage of the reducible heat load of the jth virtual power plant; The reward and punishment type step carbon trading mechanism model is: In the formula, is the carbon trading cost of the jth virtual power plant in the tth scheduling period; E VPP,j is the difference between the actual carbon emissions and the initial carbon quota of the jth virtual power plant in the multi-virtual power plant; d is the length of the carbon emission interval corresponding to each step; u is the base price of carbon trading on the day; α is the reward coefficient when the carbon emission is low; β is the price growth rate when the carbon emission is high; The Nash negotiation model of the multi-virtual power plant low-carbon game is: Wherein: In the formula, Cj is the operation cost of the jth virtual power plant before participating in Nash negotiation; D is the number of virtual power plants; f j Cj is the operation cost of the jth 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 are the cost and expense of purchasing electricity, purchasing gas, unit power operation and maintenance of output equipment units and comprehensive load demand response, respectively; α buy,t P is the real-time electricity purchasing price at the t scheduling period; P gas,t P is the gas purchasing amount at the t scheduling period; β gas,t P is the real-time gas price at the t scheduling period; c i c is the unit power operation and maintenance expense of the ith output equipment unit; N is the number of output equipment units in the virtual power plant; P i,t P is the output power of the ith output equipment unit at the t scheduling period; c eh c is the unit power compensation expense of the time-shiftable electric-thermal load; P eh,t P is the time-shiftable electric-thermal load at the t scheduling period; The Nash negotiation model of the multi-virtual power plant low-carbon game is solved to obtain an optimal game strategy of the virtual power plants in cooperative game, and the optimal game strategy comprises an electric quantity and an electric price interacted between the virtual power plants; An optimal scheduling strategy of the multi-virtual power plant low-carbon game is formulated by using the optimal game strategy of the virtual power plants in cooperative game. 2.The method of claim 1, wherein The Nash negotiation model of the multi-virtual power plant low-carbon game is solved in the following manner: The Nash negotiation model of the multi-virtual power plant low-carbon game is solved by using an alternating direction multiplier method.

3. A device for optimal scheduling of power tight balance scene multi-virtual power plant low-carbon game, characterized in that, The method comprises the following steps: A constructing module is configured to construct a Nash negotiation model of a multi-virtual power plant low-carbon game by combining a virtual power plant internal output device unit model, an interaction constraint condition between virtual power plants, and an interaction constraint condition between the virtual power plant and a superior distribution network, and taking into account a user side demand response model and a reward and punishment type step carbon trading mechanism model, with the minimum total operation cost of the virtual power plant as the target; The virtual power plant internal output device unit model comprises a photovoltaic generator unit model, a wind power generator unit model, a combined heat and power unit model, a gas boiler model, and a power storage device model; The photovoltaic generator unit model is: In the formula, P PV,i,t is the actual output power of the ith photovoltaic generator set at the tth dispatch period; P STC,i,t Pmax,i(t) is the maximum output power of the ith photovoltaic generator set at the tth dispatch period; I ING,i actual light intensity for the i-th photovoltaic generator set; I STC,i is the standard light intensity for the ith photovoltaic generator set; l is the ambient temperature coefficient; T C is the actual temperature value of the battery panel; T STC Tref is the reference temperature value for the battery plate; The wind power generator unit model is: In the formula, P WT,i,t is the actual output power of the ith wind turbine group at the tth dispatch period; v l , v ci , v r , and v co are the actual wind speed, the cut-in wind speed, the rated wind speed, and the cut-out wind speed of the wind turbine group, respectively; P R is the rated output power of the wind turbine group; The combined heat and power unit model is: P GT,i,min ≤P GT,i,t ≤P GT,i,max - ΔP GT,i ≤ P GT,i,t - P GT,i,t-1 ≤ ΔP GT,i wherein, is the electrical power output by the ith combined heat and power unit at the tth scheduling period; V GT,i,t is the amount of natural gas consumed by the ith gas turbine at the tth scheduling period; η GT,i is the heat-to-electricity conversion efficiency of the ith gas turbine; is the natural gas heating value; is the thermal power output by the ith combined heat and power unit at the tth scheduling period; η WHB,i P is the coefficient of performance (COP) of the i-th waste heat boiler. GT,i,t P represents the real-time output power of the i-th gas turbine during the t-hour dispatch period; GT,i,t-1 P represents the real-time output power of the i-th gas turbine during the t-1 scheduling period; GT,i,max P GT,i,min and ΔP GT,i These are the maximum output power, minimum output power, and maximum ramp power of the i-th gas turbine, respectively; The gas boiler model is: H GB,i,min ≤H GB,i,t ≤H GB,i,max - ΔH GB,i ≤ H GB,i,t - H GB,i,t-1 ≤ ΔH GB,i H GB,i,t is the heat output of the i-th gas boiler at the t-th scheduling period; H GB,i,t-1 is the heat output of the i-th gas boiler at the t-1-th scheduling period; V GB,i,t is the natural gas consumption of the i-th gas boiler at the t-th scheduling period; η GB,i is the energy efficiency conversion coefficient of the i-th gas boiler; H GB,i,max , H GB,i,min and ΔH GB,i are the maximum output power, the minimum output power and the maximum ramping power of the i-th gas boiler, respectively; The power storage device model is: SOC ES,i,min ≤ SOC ES,i,t < SOC ES,i,max SOC ES,i,T = SOC ES,i,0 s ch,i,t P ch,i,min ≤P ch,i,t ≤s ch,i,t P ch,i,max s dis,i,t P dis,i,min ≤P dis,i,t ≤s dis,i,t P dis,i,max s ch,i,t +s dis,i,t ≤1 In the formula, SOC ES,i,t is the real-time storage capacity of the i-th storage device at the t-th scheduling period; SOC ES,i,t-1 is the real-time storage capacity of the i-th storage device at the t-1-th scheduling period; η ch,i ηi is the charging efficiency of the i-th power storage device; η dis,i is the discharge efficiency of the i-th power storage device; P ch,i,t is the charging power of the i-th power storage device at the t-th scheduling period; P ch,i,t-1 is the charging power of the i-th power storage device at the t-1-th scheduling period; P dis,i,t is the discharging power of the i-th power storage device at the t-th scheduling period; P dis,i,t-1 is the discharging power of the i-th power storage device at the t-1-th scheduling period; SOC ES,i,max is the upper limit of the power storage capacity of the i-th power storage device; SOC ES,i,min is a lower limit of the storage capacity of the i-th storage device; P ch,i,max is an upper limit of the charging power of the i-th storage device; P ch,i,min lower limit of charging power for the i-th power storage device; P dis,i,max upper limit of discharging power for the i-th power storage device P dis,i,min is the lower limit of discharging power of the i-th energy storage device; s ch,i,t is the state variable of the i-th energy storage device charging at the t-th scheduling period; s dis,i,t is the state variable of the i-th energy storage device discharging at the t-th scheduling period; s ch,i,t takes the value of 0, s dis,i,t takes the value of 1; s ch,i,t takes the value of 1, s dis,i,t takes the value of 0; SOC ES,i,0 is the initial value of the energy storage capacity of the i-th energy storage device; T is the total scheduling period; The interaction constraint condition between the virtual power plant and the superior distribution network is: 0 < P buy,j,t ≤ s buy,j,t P buy,j,max 0 < P sell,j,t ≤ s sell,j,t P sell,j,max s buy,j,t +s sell,j,t ≤1 In the formula, P buy,j,t is the purchase power of the jth virtual power plant to the upper-level power grid at the tth scheduling period; P sell,j,t is the sale power of the jth virtual power plant to the upper-level power grid at the tth scheduling period; s buy,j,t is the input state variable of the jth virtual power plant interacting with the upper-level power grid at the tth scheduling period; s sell,j,t is the output state variable of the jth virtual power plant interacting with the upper-level power grid at the tth scheduling period; when the virtual power plant purchases power from the upper-level power grid, s buy,j,t is valued at 1, s sell,j,t is valued at 0; when the virtual power plant sells power to the upper-level power grid, s buy,j,t is valued at 0, s sell,j,t is valued at 1; P buy,j,max is the upper limit of the purchase power of the jth virtual power plant to the upper-level power grid; P sell,j,max Pmaxj, j = 1, 2,..., N the upper limit of the power sold by the jth virtual power plant to the superior power distribution network; The interaction constraint condition between the virtual power plant and the superior distribution network is: In the formula, is the power of the P2P electricity transaction of the jth virtual power plant and the kth virtual power plant at the tth scheduling period; is the power of the P2P heat transaction of the jth virtual power plant and the kth virtual power plant at the tth scheduling period; is the allowable value of the power of the P2P electricity transaction of the jth virtual power plant and the kth virtual power plant; is the allowable value of the power of the P2P heat transaction of the jth virtual power plant and the kth virtual power plant. The user side demand response model comprises a flexible electric load characteristic model and a flexible thermal load characteristic model, and the flexible electric load characteristic model is: In the formula, is the electrical load prediction value of the jth virtual power plant at the tth scheduling period; is the transferable electrical load amount of the jth virtual power plant at the tth scheduling period; is the reducible electrical load amount of the jth virtual power plant at the tth scheduling period; is the percentage of the transferable electrical load of the jth virtual power plant; is the percentage of the reducible electrical load of the jth virtual power plant; The flexible thermal load characteristic model is: In the formula, is the heat load prediction value of the jth virtual power plant at the tth scheduling period; is the transferable heat load amount of the jth virtual power plant at the tth scheduling period; is the reducible heat load amount of the jth virtual power plant at the tth scheduling period; is the percentage of the transferable heat load of the jth virtual power plant; is the percentage of the reducible heat load of the jth virtual power plant; The reward and punishment type step carbon trading mechanism model is: In the formula, is the carbon trading cost of the jth virtual power plant in the tth scheduling period; E VPP,j is the difference between the actual carbon emissions and the initial carbon quota of the jth virtual power plant in the multi-virtual power plant; d is the length of the carbon emission interval corresponding to each step; u is the base price of carbon trading on the day; a is the reward coefficient when the carbon emission is low; b is the price growth rate when the carbon emission is high; The Nash negotiation model of the multi-virtual power plant low-carbon game is: Wherein: In the formula, Cj is the operation cost of the jth virtual power plant before participating in Nash negotiation; D is the number of virtual power plants; f j Cj is the operation cost of the jth 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 are the cost and expense of purchasing electricity, purchasing gas, unit power operation and maintenance of output equipment unit, and comprehensive load demand response link, respectively; α buy,t P is the real-time purchasing electricity price at the t scheduling period; P gas,t P is the gas purchasing amount at the t scheduling period; β gas,t P is the real-time gas price at the t scheduling period; c i c is the unit power operation and maintenance expense of the ith output equipment unit; N is the number of output equipment units in the virtual power plant; P i,t P is the output power of the ith output equipment unit at the t scheduling period; c eh c is the unit power compensation expense of the time-shiftable electric-thermal load; P eh,t P is the time-shiftable electric-thermal load at the t scheduling period; A solving module is configured to solve the Nash negotiation model of the multi-virtual power plant low-carbon game to obtain an optimal game strategy of the virtual power plants in cooperative game, and the optimal game strategy comprises an electric quantity and an electric price interacted between the virtual power plants; The making module is used for making a multi-virtual power plant low-carbon game optimization scheduling strategy by using the optimal game strategy of the virtual power plants in the cooperative game.

4. An electronic device, comprising: The computer program stored in the memory and executable on the processor realizes the method for multi-virtual power plant low-carbon game optimization scheduling of the power tight balancing scene according to any one of claims 1 or 2 when the processor executes the computer program.

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