Control method and device of multi-energy complementary power generation system and multi-energy complementary power generation system

Abstract

The invention discloses a control method and device of a multi-energy complementary power generation system and the multi-energy complementary power generation system, and belongs to the field of electronic equipment. The control method of the multi-energy complementary power generation system comprises the following steps: adjusting input parameters of a first model and a second model by taking the minimum sum of variances of charging cost output by the first model and residual load output by the second model as a target, obtaining a first numerical value of a working parameter corresponding to the wind power generation equipment and a second numerical value of a working parameter corresponding to the photovoltaic power generation equipment; predicting a target residual load corresponding to the multi-energy complementary power generation system in the process that the wind power generation equipment works based on the first numerical value and the photovoltaic power generation equipment works based on the second numerical value; and based on the target residual load, controlling the numerical values of the working parameters corresponding to the other power generation devices. The method can be suitable for different distributed energy terminals, improves the energy cooperative operation and control strategy, reduces the system operation cost, and improves the stability, economy and environmental protection of system operation.

Description

Technical Field

[0001] The present application belongs to the field of electronic equipment, and in particular, relates to a control method and device for a multi-energy complementary power generation system and a multi-energy complementary power generation system. Background Art

[0002] Virtual Power Plant (VPP) technology is a technology that can take into account resources such as multiple flexible loads on the demand side and charging and discharging loads of new energy vehicles through advanced information and communication technology and software systems, and can efficiently integrate distributed resources such as distributed energy units and distributed energy storage units. Among the related technologies, existing research on energy Internet technology and virtual power plant technology focuses on the architectural design of energy Internet, such as realizing the plug-and-play function of distributed equipment. The energy required by users can be provided by miniaturized and modular distributed renewable energy sources, which are uniformly allocated and managed by intelligent energy management devices. However, there are still many aspects of the related technologies of energy Internet and virtual power plants that are not perfect. For example, the adaptability of different distributed energy terminals of energy Internet is insufficient, and the coordinated operation and control strategy of multiple distributed energy in virtual power plants is not perfect, resulting in high system operating costs, insufficient stability and poor environmental protection. Summary of the invention

[0003] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a control method, device and multi-energy complementary power generation system, which predicts the target residual load and calculates the output of each distributed energy unit by taking the charging cost and the residual load variance as the minimum, can be applied to different distributed energy terminals, improve the coordinated operation and control strategy of multiple distributed energy in the virtual power plant, reduce the system operation cost, and improve the stability, economy and environmental protection of the system operation.

[0004] In a first aspect, the present application provides a control method for a multi-energy complementary power generation system, the method comprising:

[0005] Taking the sum of the variances of the charging cost output by the first model and the residual load output by the second model as the minimum as the goal, adjusting the input parameters of the first model and the second model to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameters and electricity price information corresponding to the target vehicle, and the second model is pre-constructed based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment;

[0006] Predicting a target residual load corresponding to the multi-energy complementary power generation system during the operation of the wind power generation equipment based on the first numerical value and the operation of the photovoltaic power generation equipment based on the second numerical value;

[0007] Based on the target residual load, the values ​​of the operating parameters corresponding to each of the other power generation equipment are controlled.

[0008] According to the control method of the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the charging cost and the minimum residual load variance as the goal, adjusting the values ​​of the working parameters of the wind power and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy, reduce the abandonment rate of light and wind, and further enable other equipment to have a better power generation strategy while meeting the user's residual load demand, reduce the system operating cost, improve stability and environmental protection, and be suitable for different distributed energy terminals, and improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0009] According to one embodiment of the present application, the second model is pre-constructed based on the operating parameters corresponding to the wind power generation equipment and the operating parameters corresponding to the photovoltaic power generation equipment, and includes:

[0010] Based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment, predicting the combination probability of the wind power generation equipment and the photovoltaic power generation equipment in a plurality of different combination scenarios;

[0011] With the goal of outputting the variance of the residual load corresponding to the multi-energy complementary power generation system when operating under each of the combination scenarios, the second model is constructed based on the combination probability, the scheduling cycle, the total number of combination scenarios and the residual load per unit time period under each of the combination scenarios.

[0012] According to an embodiment of the present application, the residual load per unit time period in each of the combined scenarios is determined based on the following steps:

[0013] Based on the flexible power load in the unit time period, the charging load of the target vehicle in the unit time period, and the predicted residual load corresponding to the wind power generation equipment and the photovoltaic power generation equipment when working in the target combination scenario, determine the residual load in the unit time period under each combination scenario;

[0014] According to an embodiment of the present application, the step of adjusting the input parameters of the first model and the second model with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model to obtain a first value of the operating parameter corresponding to the wind power generation equipment and a second value of the operating parameter corresponding to the photovoltaic power generation equipment includes:

[0015] Determining a first target constraint based on at least one of a power constraint and a target prediction error constraint corresponding to the target vehicle;

[0016] With the goal of minimizing the sum of the variance of the charging cost and the remaining load, based on the first objective constraint, the input parameters of the first model and the second model are adjusted to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment.

[0017] According to an embodiment of the present application, controlling the value of the operating parameter corresponding to each of the other power generation equipment based on the target residual load includes:

[0018] Taking the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model as minimum and satisfying the target residual load as the goal, adjusting the input parameters of the third model and the fourth model to obtain the values ​​of the working parameters corresponding to the other power generation equipment;

[0019] Among them, the fourth model is pre-constructed based on the power generation power of the wind power generation equipment, the power generation power of the photovoltaic power generation equipment, the natural gas price information, the electricity price information, the operation and maintenance coefficient of each equipment, at least one of the number of target vehicles and the remaining load in a unit time period.

[0020] According to an embodiment of the present application, the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model are minimized, and the target residual load is met as the goal, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the working parameters corresponding to the other power generation equipment, including:

[0021] Constructing a second target constraint based on at least one of an energy balance constraint, an equipment operation constraint, an energy purchase and sale constraint, and an energy storage equipment constraint;

[0022] Taking the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost and flexible load scheduling cost output by the fourth model as minimum and satisfying the target residual load as the goal, based on the target residual load and the second target constraint, adjust the input parameters of the third model and the fourth model to obtain the values ​​of the working parameters corresponding to the other power generation equipment.

[0023] In a second aspect, the present application provides a control device for a multi-energy complementary power generation system, the device comprising:

[0024] A first processing module, for adjusting input parameters of the first model and the second model with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model, to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameters and electricity price information corresponding to the target vehicle, and the second model is pre-constructed based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment;

[0025] A second processing module is used to predict the target residual load corresponding to the multi-energy complementary power generation system during the process in which the wind power generation equipment works based on the first numerical value and the photovoltaic power generation equipment works based on the second numerical value;

[0026] The third processing module is used to control the value of the working parameter corresponding to each of the other power generation equipment based on the target residual load.

[0027] According to the control device of the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the charging cost and the minimum residual load variance as the goal, adjusting the numerical values ​​of the working parameters of the wind power and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy, reduce the abandonment rate of light and wind, and further enable other equipment to have a better power generation strategy while meeting the user's residual load demand, reduce the system operating cost, improve stability and environmental protection, be suitable for different distributed energy terminals, and improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0028] In a third aspect, the present application provides a multi-energy complementary power generation system, the system comprising:

[0029] Wind power generation equipment;

[0030] Photovoltaic power generation equipment;

[0031] at least one other power generating device;

[0032] Based on the control device of the multi-energy complementary power generation system as described in the second aspect, the control device of the multi-energy complementary power generation system is electrically connected to the wind power generation equipment, the photovoltaic power generation equipment and the at least one other power generation equipment respectively.

[0033] According to the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the charging cost and the minimum residual load variance as the goal, adjusting the values ​​of the working parameters of the wind power and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy, reduce the abandonment rate of light and wind, and further enable other equipment to have a better power generation strategy while meeting the user's residual load demand, reduce the system operating cost, improve stability and environmental protection, be suitable for different distributed energy terminals, and improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0034] According to one embodiment of the present application, the at least one other power generation equipment includes: an energy storage device and at least one of coal, oil or natural gas fuel power generation equipment.

[0035] In a fourth aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the control method of the multi-energy complementary power generation system as described in the first aspect above is implemented.

[0036] The above one or more technical solutions in the embodiments of the present application have at least one of the following technical effects:

[0037] By taking the charging cost and the minimum residual load variance as the goal, adjusting the values ​​of the working parameters of the wind and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy and reduce the abandonment of light and wind. While meeting the user's residual load needs, it further enables other equipment to have a better power generation strategy, reduce the system operating cost, improve stability and environmental protection, and is suitable for different distributed energy terminals, and improves the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0038] Furthermore, by taking the minimum sum of the variances of the charging cost output by the first model and the remaining load output by the second model as the goal, and adjusting the input parameters of the first model and the second model based on the first objective constraint, while meeting the economic efficiency of the wind power generation equipment and the photovoltaic power generation equipment, the values ​​corresponding to the working parameters with higher stability can be obtained to ensure the stability of the remaining load and the safe and stable operation of the system.

[0039] Furthermore, based on the target residual load, the working values ​​of other power generation equipment except wind power and photovoltaic equipment in the multi-energy complementary power generation system are regulated, which can control the power generation cost and carbon emissions, so as to meet the economy of the multi-energy complementary power generation system while improving the stability and environmental protection of the multi-energy complementary power generation system.

[0040] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0042] Figure 1 This is one of the flow charts of the control method of the multi-energy complementary power generation system provided in the embodiment of the present application;

[0043] Figure 2 This is the second flow chart of the control method of the multi-energy complementary power generation system provided in the embodiment of the present application;

[0044] Figure 3 is a structural schematic diagram of a control device for a multi-energy complementary power generation system provided in an embodiment of the present application;

[0045] Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0047] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0048] In combination with the accompanying drawings, the control method of the multi-energy complementary power generation system, the control device of the multi-energy complementary power generation system, the electronic device and the readable storage medium provided in the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0049] The control method of the multi-energy complementary power generation system may be applied to a terminal, and may be specifically executed by hardware or software in the terminal.

[0050] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or tablet computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad). It should also be understood that in some embodiments, the terminal may not be a portable communication device, but a desktop computer with a touch-sensitive surface (e.g., a touch screen display and / or a touch pad).

[0051] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, a mouse and a joystick.

[0052] The control method of a multi-energy complementary power generation system provided in the embodiment of the present application, the execution subject of the control method of the multi-energy complementary power generation system can be an electronic device or a functional module or functional entity in the electronic device that can implement the control method of the multi-energy complementary power generation system. The electronic devices mentioned in the embodiment of the present application include but are not limited to mobile phones, tablet computers, computers, cameras and wearable devices, etc. The control method of a multi-energy complementary power generation system provided in the embodiment of the present application is described below using an electronic device as an example of the execution subject.

[0053] like Figure 1 As shown, the control method of the multi-energy complementary power generation system includes: step 110, step 120 and step 130.

[0054] The multi-energy complementary power generation system may include: wind power generation equipment, photovoltaic power generation equipment and other power generation equipment.

[0055] Among them, other power generation equipment may include: energy storage equipment and at least one of coal, oil or natural gas fuel power generation equipment.

[0056] The multi-energy complementary power generation system is used to provide electric energy to the tram.

[0057] Step 110: With the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model, the input parameters of the first model and the second model are adjusted to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameter corresponding to the target vehicle and the electricity price information, and the second model is pre-constructed based on the working parameter corresponding to the wind power generation equipment and the working parameter corresponding to the photovoltaic power generation equipment;

[0058] In this step, the target vehicle is a user of a tram participating in the incentive-based demand response.

[0059] The number of target vehicles can be one or more.

[0060] The first model is pre-built based on the operating parameters and electricity price information corresponding to the target vehicle, and can be deployed in the virtual power plant system to calculate the vehicle charging cost.

[0061] The input parameters of the first model may be various parameters when the vehicle is charged, such as the vehicle charging power, the charging coefficient, etc.

[0062] The second model is pre-built based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment, and can be deployed in the virtual power plant system to calculate the variance of the remaining load.

[0063] The input parameters of the second model may include the power generation of wind power generation equipment and photovoltaic power generation equipment.

[0064] The working parameters corresponding to the wind power generation equipment may include: various parameter items when the wind power generation equipment is working, such as: the working state of the wind power generation equipment (including working state or non-working state), and the wind power generation, power generation and the number of working wind power generation equipment in the working state; accordingly, the first value of the working parameter corresponding to the wind power generation equipment is the specific value of each parameter item when the wind power generation equipment is working, such as the wind power generation equipment is working, the wind power generation in the working state is 1000 degrees, the power generation is 500KW and the number of working wind power generation equipment is 5, etc.

[0065] The working parameters corresponding to the photovoltaic power generation equipment may include various parameter items when the photovoltaic power generation equipment is working, the working state of the photovoltaic power generation equipment (including working state or non-working state), and the photovoltaic power generation, power generation power and the number of working photovoltaic power generation equipment in the working state; accordingly, the second value of the working parameter corresponding to the photovoltaic power generation equipment is the specific value of each parameter item when the photovoltaic power generation equipment is working, such as the photovoltaic power generation equipment is working, the photovoltaic power generation in the working state is 1000 degrees, the power generation power is 100KW, and the number of working photovoltaic power generation equipment is 6, etc.

[0066] In some embodiments, the second model is pre-built based on the operating parameters corresponding to the wind power generation equipment and the operating parameters corresponding to the photovoltaic power generation equipment, and may include:

[0067] Based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment, the combination probability of the wind power generation equipment and the photovoltaic power generation equipment in various different combination scenarios is predicted;

[0068] With the goal of outputting the variance of the residual load corresponding to the multi-energy complementary power generation system under each combination scenario, the second model is constructed based on the combination probability, scheduling cycle, the total number of combination scenarios and the residual load per unit time period under each combination scenario.

[0069] In this embodiment, the combination scenario may include: different ratios of the number of wind power generation equipment and photovoltaic power generation equipment in working state, or scenarios such as wind power generation equipment and photovoltaic power generation equipment in working state having different power generation capacities.

[0070] In various different combination scenarios, wind power generation equipment and photovoltaic power generation equipment may have different output ratios in power generation scenarios. The output can be understood as the ability of the power station to output electricity.

[0071] Predicting the combined probability of wind power generation equipment and photovoltaic power generation equipment in a variety of different combination scenarios can be understood as the probability of the nth combination scenario.

[0072] In the actual implementation process, the combination probability can be obtained by prediction based on historical data.

[0073] like Figure 2 As shown, a typical output scenario set can be generated by a dynamic scenario generation method based on the prediction error probability distribution characteristics of wind power generation equipment and photovoltaic power generation equipment and the output results predicted by the renewable energy prediction algorithm to describe the uncertainty of the output of wind power generation equipment and photovoltaic power generation equipment.

[0074] In some embodiments, the input parameters of the first model and the second model can be adjusted with the goal of minimizing the sum of the variance of the charging cost and the remaining load, which can be specifically expressed as:

[0075]

[0076] Among them, F 1 is the minimum value of the first model, which can be used to calculate the minimum charging cost of electric vehicles, F 2 is the minimum value of the second model, which can be used to calculate the minimum value of the remaining user load variance, P e,t is the charging power of the electric vehicle participating in the incentive demand response during period t, r t is the charging electricity price of electric vehicles in period t, Φ is the electricity price discount for participating in incentive-based demand response, η c is the electric vehicle charging coefficient, N is the total number of typical output scenarios of wind and photovoltaic power generation equipment, P n is the probability of wind power and photovoltaic power generation equipment output scenario combination, T is the scheduling period, L r,t,n is the residual load in period t in combined scenario n, and Δt is the duration of a single period.

[0077] Understandably, L r,t,n is the residual load per unit time period under each combination scenario.

[0078] In some embodiments, the remaining load per unit time period in each combination scenario can be determined based on the following steps:

[0079] Based on the flexible electricity load in unit time period, the charging load of the target vehicle in unit time period, and the predicted remaining load corresponding to the wind power generation equipment and photovoltaic power generation equipment working in the target combination scenario, the remaining load in unit time period under each combination scenario is determined.

[0080] In this embodiment, the calculation method of the residual load can be expressed as:

[0081] L r,t,n =L t -L EV,t -P W,t,n -P PV,t,n

[0082] Among them, L t is the flexible electricity load in the time period scheduling model; L EV,t is the charging load of electric vehicles during the period; P W,t,n , P PV,t,n They are respectively the residual load of the virtual power plant system in period t in the wind and solar combination scenario n, where t is the unit period.

[0083] In some embodiments, with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model, adjusting the input parameters of the first model and the second model to obtain a first value of the operating parameter corresponding to the wind power generation equipment and a second value of the operating parameter corresponding to the photovoltaic power generation equipment may include:

[0084] Determining a first target constraint based on at least one of a power constraint and a target prediction error constraint corresponding to the target vehicle;

[0085] With the goal of minimizing the sum of the variance of the charging cost and the remaining load, based on the first objective constraint, the input parameters of the first model and the second model are adjusted to obtain the first value of the working parameter corresponding to the wind power generation equipment and the second value of the working parameter corresponding to the photovoltaic power generation equipment.

[0086] In this embodiment, the power constraint corresponding to the target vehicle can be understood as that after the electric vehicles participating in the demand response participate in the aggregated scheduling, the remaining power of the electric vehicles reaches a certain amount to meet the normal travel needs of the users.

[0087] In the actual execution process, the power constraint corresponding to the target vehicle can be expressed as:

[0088]

[0089] Among them, E i,st is the initial battery charge of the ith electric vehicle when it joins the incentive demand response, E depis the electric vehicle owner who is willing to participate in the incentive-based demand response optimization scheduling. At the end of the virtual power plant optimization scheduling, the electric vehicle state of charge needs to reach the charge capacity, C is the electric vehicle battery capacity, SOC dep It is the battery charge state that electric vehicle users participating in the incentive-based demand response need to achieve when they leave.

[0090] The target prediction error constraint can be understood as the output of wind power generation equipment and photovoltaic power generation equipment being within the range of the prediction error.

[0091] In actual execution, the target prediction error can be expressed as:

[0092]

[0093] Among them, P PV,t is the actual photovoltaic power generation during period t; P PVf,t is the predicted photovoltaic power generation during period t; ΔP PV,t is the prediction error of photovoltaic power generation during period t; P W,t is the actual power generation of wind power in period t; P Wf,t is the predicted wind power generation during period t; ΔP W,t is the wind power prediction error in period t.

[0094] According to the control method of the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the sum of the variances of the charging cost output by the first model and the residual load output by the second model as the minimum, and adjusting the input parameters of the first model and the second model based on the first target constraint, while satisfying the economy of the wind power generation equipment and the photovoltaic power generation equipment, the values ​​corresponding to the working parameters with higher stability can be obtained to ensure the stability of the residual load and the safe and stable operation of the system.

[0095] Step 120, predicting the target residual load corresponding to the multi-energy complementary power generation system during the process in which the wind power generation equipment works based on the first numerical value and the photovoltaic power generation equipment works based on the second numerical value;

[0096] In this step, the target residual load is the residual load excluding the power generation of wind power and photovoltaic power generation equipment.

[0097] The target residual load may be obtained based on a first model and a second model of the first value and the second value.

[0098] In some embodiments, the target residual load may be obtained by weighting the first model and the second model.

[0099] The specific target residual load calculation method can be expressed as:

[0100] minF=λ 1 F1 +λ 2 F 2

[0101] Among them, λ 1 , 2 are the weighting coefficients corresponding to the first model and the second model respectively, and λ 1 +λ 2 =1.

[0102] The weighting factors can be user-defined.

[0103] Continue to refer Figure 2 It can be understood that the calculation results of the first model and the second model can be obtained based on the first value of the working parameter corresponding to the wind power generation equipment and the second value of the working parameter corresponding to the photovoltaic power generation equipment, so as to obtain the target residual load.

[0104] According to the control method of the multi-energy complementary power generation system provided in the embodiment of the present application, the target residual load corresponding to the multi-energy complementary power generation system is predicted based on the working values ​​of the wind power and photovoltaic power generation equipment, so as to meet the economic efficiency and obtain a more stable residual load, so as to facilitate the subsequent optimization of other power generation equipment except the wind power and photovoltaic power generation equipment based on the residual load, so as to improve the stability of the multi-energy complementary power generation system.

[0105] Step 130: Based on the target residual load, control the values ​​of the corresponding operating parameters of other power generation equipment.

[0106] In this step, the various other power generation equipment may include energy storage equipment and coal, oil or natural gas fuel power generation equipment.

[0107] The operating parameters corresponding to each other power generation equipment may be operating parameters related to the power generation of each other power generation equipment.

[0108] For example, the power generation capacity of the gas turbine in the natural gas power generation equipment, the amount of natural gas consumed, etc.

[0109] In some embodiments, based on the target residual load, controlling the values ​​of the operating parameters corresponding to the other power generation equipment may include:

[0110] With the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model being minimized and the target residual load being met as the goal, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the corresponding working parameters of other power generation equipment;

[0111] Among them, the fourth model is pre-constructed based on the power generation power of wind power generation equipment, the power generation power of photovoltaic power generation equipment, natural gas price information, electricity price information, the operation and maintenance coefficient of each equipment, at least one of the number of target vehicles and the remaining load within a unit time period.

[0112] In this embodiment, the third model may be a model that calculates the carbon emissions of all other power generation equipment based on each other power generation equipment, and may be deployed in a virtual power plant system.

[0113] The input parameters of the third model may include the supply of coal, oil or natural gas, etc.

[0114] The fourth model is pre-constructed based on the power generation power of wind power generation equipment, the power generation power of photovoltaic power generation equipment, natural gas price information, electricity price information, the operation and maintenance coefficient of each equipment, at least one of the number of target vehicles and the remaining load within a unit time period. It can be deployed in the virtual power plant system to calculate the cost of renewable energy power generation, natural gas energy consumption cost, equipment operation and maintenance cost and flexible load scheduling cost.

[0115] The input parameters of the fourth model may include the power generation capacity of each power generation device, the price of each fuel, etc.

[0116] During the actual implementation process, the fourth model can be pre-constructed based on at least one of the power generation of wind power generation equipment, the power generation of photovoltaic power generation equipment, natural gas price information, electricity price information, the operation and maintenance coefficient of each equipment and the number of target vehicles and the remaining load within a unit time period.

[0117] In some embodiments, the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model are minimized, which can be specifically expressed as:

[0118]

[0119] Among them, E s,t is carbon emissions, C ren , C fuel , C rep , C fle They are renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load dispatch cost. CO2 is the carbon emission cost, C EV is the compensation cost for electric vehicles participating in incentive-based demand response.

[0120] In this embodiment, the renewable energy power generation cost may mainly include: wind turbine power generation cost and photovoltaic power generation cost; specifically, it may be calculated by the following formula:

[0121]

[0122] Among them, C W is the power generation cost of the wind turbine, C PV is the power generation cost of the photovoltaic unit, P W,t , P PV,t They are the power generation power of wind turbines and photovoltaic generators in each period.

[0123] The fuel cost may include the cost of burning natural gas and the portion of electric energy traded between the virtual power plant optimization dispatch system and the power grid. In some embodiments, the relationship between the fuel cost may be as follows:

[0124] C fuel =C gas,t G g,t +C e,t P e,t

[0125] Among them, C gas,t , C e,t They are the natural gas unit price traded between the VPP (virtual power plant) system and the natural gas grid, and the electricity unit price traded between the VPP system and the grid. g,t The amount of natural gas purchased for the VPP system, P e,t It is the electric power traded between VPP and the grid.

[0126] System operation and maintenance costs may include, but are not limited to: costs incurred by manual inspections, repairs, and equipment wear and tear, which are related to the equipment operation level and mode of operation.

[0127] In some embodiments, the system operation and maintenance cost relationship may be as follows:

[0128]

[0129] Among them, λ PV , wind , MT , GB , EC , WH , AC , P2G They are photovoltaic units, wind turbines, gas turbines, gas boilers, electric refrigerators, waste heat boilers, absorption refrigerators, and power-to-gas equipment. S,i is the operation and maintenance coefficient of the i-th energy equipment, P PV,t , P wind,t , P MT,t , Q GB,t , Q WH,t , Q EC,t , Q AC,t , PP2G,t are the power of photovoltaic unit, wind turbine unit, gas turbine, gas boiler, waste heat boiler, electric refrigerator, absorption refrigerator, and power-to-gas in period t, respectively. S,i,dis,t , P S,i,ch,t are the charging and discharging powers of the i-th energy storage device respectively.

[0130] Flexible loads can include interruptible loads and translational loads.

[0131] In some embodiments, the electricity demand can be reduced during peak load periods and increased during low load periods, and corresponding compensation can be obtained.

[0132] For example, the flexible load dispatch cost can be calculated as follows:

[0133] C fle,t =C elc,t [ω IL P IL,t +σ EL P EL,t ]

[0134] Among them, C fle,t is the flexible load dispatching cost during period t, C elc,t is the VPP electricity price during period t, P IL,t , P EL,t are the interruption and increase of power in period t, ω IL , σ EL They are the unit dispatch cost coefficient of the interrupted equipment and the electricity price discount for user incentive reserve compensation.

[0135] In some embodiments, the compensation cost of electric vehicles participating in the incentive-based demand response can be calculated as follows:

[0136]

[0137] Among them, C EV is the compensation fee for electric vehicles participating in the incentive-based demand response, i is the number of electric vehicles participating in the dispatch, c 1 、c 2 are the electric vehicle power compensation coefficient and the scheduling time compensation coefficient, SOC i,ST is the initial state of charge of the battery when the i-th electric vehicle participates in the dispatch, SOC SET is the compensation threshold for setting the battery status of electric vehicles, T i,ST is the dispatchable time length of the ith electric vehicle participating in demand response, T SET It is to set the compensation threshold for electric vehicles’ participation in dispatching time.

[0138] According to the control method of the multi-energy complementary power generation system provided in the embodiment of the present application, based on the target residual load, the working values ​​of the power generation equipment other than wind power and photovoltaic equipment in the multi-energy complementary power generation system are regulated, so that the power generation cost and carbon emissions can be controlled to meet the economy of the multi-energy complementary power generation system while improving the stability and environmental protection of the multi-energy complementary power generation system.

[0139] In some embodiments, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the working parameters corresponding to other power generation equipment with the goal of minimizing at least one of the carbon emissions output by the third model and the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model, and satisfying the target residual load, which may include:

[0140] Constructing a second target constraint based on at least one of an energy balance constraint, an equipment operation constraint, an energy purchase and sale constraint, and an energy storage equipment constraint;

[0141] With the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost and flexible load scheduling cost output by the fourth model being minimized and meeting the target residual load as the goal, based on the target residual load and the second target constraint, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the corresponding working parameters of other power generation equipment.

[0142] In this embodiment, the energy balance constraint may be that the VPP system should ensure the balance between supply and demand of energy in each scheduling period without considering energy loss.

[0143] In some embodiments, energy balance constraints may include, but are not limited to: electrical energy balance of each device, heat and cold energy balance, and gas supply balance.

[0144] For example, the energy balance constraint can be expressed as follows:

[0145] P GT,t +P SOC,t +P grid,t +P wind,t +P PV,t =P L,t +P EC,IN,t +P P2G,t

[0146]

[0147] G GT,t +G GB,t =G g,t +G P2G,t

[0148] Among them, PGT,t is the power generation of the gas turbine during period t, P grid,t is the amount of electricity purchased or sold by the VPP system to the grid during period t, P L,t is the user load power during period t, P EC,IN,t is the load power of the electric refrigerator during period t, P SOC,t is the charging and discharging power of the energy storage device during period t, P P2G,t is the load power of the power-to-gas equipment during period t, P wind,t , P PV,t are wind power and photovoltaic power output in period t, H L,t , Q C,t are the cooling load and heating load of the VPP system during period t, H HR,t is the thermal power generated by the waste heat recovery device during period t, H GB,t is the thermal power generated by the gas boiler during period t, Q AC,t , H AC,t is the heat power consumed and cold power generated by the absorption refrigerator during period t, Q EC,t is the cooling power generated by the electric refrigerator during period t, Q SOC,t , H SOC,t They are the heat storage tank and the cold storage equipment charging and discharging heat and cold power in period t, G GT,t , G GB,t is the amount of natural gas consumed by gas turbines and gas boilers during the period, G g,t , G P2G,t They are the amount of natural gas purchased from the natural gas grid and the amount of natural gas generated when the power-to-gas equipment is in operation.

[0149] In some embodiments, the equipment operation constraints may include: the ramp power of each equipment output in the VPP system and the upper and lower power limits of the operation.

[0150] For example, a device operation constraint can be expressed as:

[0151]

[0152] Among them, P y,t , P y,max , P y,min ;Q y,t , Q y,max , Q y,min ;H y,t , H y,max , H y,min are respectively the operating power of device y and the upper and lower limits of power, cooling and heating. In some embodiments, the upper and lower limits of the ramping power of the gas turbine and the energy storage device may be defined separately, which may be specifically expressed as:

[0153] -ΔP GT,max ≤Pt+1 -P t ≤ΔP GT,max

[0154] Among them, ΔP GT,max is the upper and lower limits of the ramp power of the gas turbine or energy storage device, P t It is the climbing power of gas turbine or energy storage equipment.

[0155] In some embodiments, energy purchase and sale constraints may include upper and lower limit constraints on purchasing or selling electricity from the power grid and purchasing natural gas.

[0156] For example, the energy purchase and sale constraint can be expressed as:

[0157]

[0158] Among them, P e,min , P e,max are the upper and lower limits of the power purchased or sold by the VPP system, P e,t is the amount of electricity purchased or sold by the power grid during period t, G g,min , G g,max They are the upper and lower limits of natural gas purchased by the VPP system, G g,t is the natural gas bought or sold during period t.

[0159] In some embodiments, the energy storage device constraint can be understood as the charging and discharging power and energy storage capacity of the energy storage device need to be within a certain range, and its expression can be as follows:

[0160]

[0161] Among them, P ch,i is the charging power of the energy storage device, P dis,i is the discharge power of the energy storage device, SOC i , SOC max,i are the current capacity and rated capacity of the i-th energy storage device, α min,i , α max,i are the minimum and maximum energy storage coefficients of the i-th energy storage device; λ ch,i , dis,i are the charging and discharging efficiencies of the i-th energy storage device respectively.

[0162] During the research and development process, the inventors discovered that among the related technologies, the existing research on energy Internet technology and virtual power plant technology focuses more on the architectural design of energy Internet, but the terminal adaptability of different distributed energy sources in the existing energy Internet is insufficient, and the coordinated operation and control strategies of multiple distributed energy sources in virtual power plants are not perfect, which leads to high operating costs, insufficient stability and poor environmental protection of the power generation system.

[0163] In the present application, by adjusting the input parameters of the first model and the second model with the goal of minimizing the charging cost and the variance of the remaining load, so as to adjust the numerical values ​​of the working parameters of the wind power and photovoltaic power generation equipment, it is possible to give priority to the use of clean energy and reduce the abandonment rate of light and wind. On this basis, the target remaining load of the multi-energy complementary power generation system is predicted based on the numerical value, and the predicted value of the target remaining load is obtained, so as to adjust and control the numerical values ​​of the working parameters of other power generation equipment except wind power and photovoltaic power generation equipment, so as to further enable other equipment to have a better power generation strategy while meeting the user's remaining load demand, reduce the system operating cost, improve stability and environmental protection, and be suitable for different distributed energy terminals, so as to improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0164] According to the control method of the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the charging cost and the minimum residual load variance as the goal, adjusting the values ​​of the working parameters of the wind power and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy, reduce the abandonment rate of light and wind, and further enable other equipment to have a better power generation strategy while meeting the user's residual load demand, reduce the system operating cost, improve stability and environmental protection, and be suitable for different distributed energy terminals, and improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0165] The control method of the multi-energy complementary power generation system provided in the embodiment of the present application can be executed by the control device of the multi-energy complementary power generation system. In the embodiment of the present application, the control method of the multi-energy complementary power generation system executed by the control device of the multi-energy complementary power generation system is taken as an example to illustrate the control device of the multi-energy complementary power generation system provided in the embodiment of the present application.

[0166] The embodiment of the present application also provides a control device for a multi-energy complementary power generation system.

[0167] like Figure 3 As shown, the control device of the multi-energy complementary power generation system includes: a first processing module 310 , a second processing module 320 and a third processing module 330 .

[0168] The first processing module 310 is used to adjust the input parameters of the first model and the second model with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model, so as to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameter corresponding to the target vehicle and the electricity price information, and the second model is pre-constructed based on the working parameter corresponding to the wind power generation equipment and the working parameter corresponding to the photovoltaic power generation equipment;

[0169] The second processing module 320 is used to predict the target residual load corresponding to the multi-energy complementary power generation system during the process in which the wind power generation equipment works based on the first value and the photovoltaic power generation equipment works based on the second value;

[0170] The third processing module 330 is used to control the values ​​of the working parameters corresponding to the other power generation equipment based on the target residual load.

[0171] According to the control device of the multi-energy complementary power generation system provided in the embodiment of the present application, by taking the charging cost and the minimum residual load variance as the goal, adjusting the numerical values ​​of the working parameters of the wind power and photovoltaic power generation equipment and predicting the target residual load, and adjusting and controlling other power generation equipment based on the target residual load, it is possible to give priority to the use of clean energy, reduce the abandonment rate of light and wind, and further enable other equipment to have a better power generation strategy while meeting the user's residual load demand, reduce the system operating cost, improve stability and environmental protection, be suitable for different distributed energy terminals, and improve the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0172] In some embodiments, the first processing module 310 may also be used to:

[0173] Based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment, the combination probability of the wind power generation equipment and the photovoltaic power generation equipment in various different combination scenarios is predicted;

[0174] With the goal of outputting the variance of the residual load corresponding to the multi-energy complementary power generation system under each combination scenario, the second model is constructed based on the combination probability, scheduling cycle, the total number of combination scenarios and the residual load per unit time period under each combination scenario.

[0175] In some embodiments, the first processing module 310 may also be used to:

[0176] Based on the flexible power load in a unit time period, the charging load of the target vehicle in a unit time period, and the predicted residual load corresponding to the wind power generation equipment and the photovoltaic power generation equipment when working in the target combination scenario, the residual load in a unit time period under each combination scenario is determined;

[0177] In some embodiments, the first processing module 310 may also be used to:

[0178] Determining a first target constraint based on at least one of a power constraint and a target prediction error constraint corresponding to the target vehicle;

[0179] With the goal of minimizing the sum of the variance of the charging cost and the remaining load, based on the first objective constraint, the input parameters of the first model and the second model are adjusted to obtain the first value of the working parameter corresponding to the wind power generation equipment and the second value of the working parameter corresponding to the photovoltaic power generation equipment.

[0180] In some embodiments, the third processing module 330 may also be used to:

[0181] With the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model being minimized and the target residual load being met as the goal, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the corresponding working parameters of other power generation equipment;

[0182] Among them, the fourth model is pre-constructed based on the power generation power of wind power generation equipment, the power generation power of photovoltaic power generation equipment, natural gas price information, electricity price information, the operation and maintenance coefficient of each equipment, at least one of the number of target vehicles and the remaining load within a unit time period.

[0183] In some embodiments, the third processing module 330 may also be used to:

[0184] Constructing a second target constraint based on at least one of an energy balance constraint, an equipment operation constraint, an energy purchase and sale constraint, and an energy storage equipment constraint;

[0185] With the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost and flexible load scheduling cost output by the fourth model being minimized and meeting the target residual load as the goal, based on the target residual load and the second target constraint, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the corresponding working parameters of other power generation equipment.

[0186] The control device of the multi-energy complementary power generation system in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or it can be other devices other than the terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0187] The control device of the multi-energy complementary power generation system in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an IOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0188] The control device of the multi-energy complementary power generation system provided in the embodiment of the present application can achieve Figures 1 to 3 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0189] The embodiment of the present application also provides a multi-energy complementary power generation system.

[0190] The multi-energy complementary power generation system comprises: a wind power generation device, a photovoltaic power generation device, at least one other power generation device and a control device of the multi-energy complementary power generation system as described in any of the above embodiments.

[0191] Among them, wind power generation equipment is equipment used to generate electricity based on wind energy.

[0192] Photovoltaic power generation equipment is equipment for generating electricity based on light energy.

[0193] Other power generation equipment is equipment used to generate electricity based on energy sources other than wind energy and solar energy.

[0194] The control device of the multi-energy complementary power generation system is electrically connected to the wind power generation equipment, the photovoltaic power generation equipment and at least one other power generation equipment respectively. The control device of the multi-energy complementary power generation system is used to execute the steps of the control method of the multi-energy complementary power generation system described in any of the above embodiments, which will not be repeated in this application.

[0195] In some embodiments, the other power generation equipment may include: energy storage equipment and at least one of coal, oil or natural gas fuel power generation equipment.

[0196] The multi-energy complementary power generation system provided in the embodiment of the present application adjusts the numerical values ​​of the working parameters of the wind and photovoltaic power generation equipment and predicts the target residual load by taking the charging cost and the residual load variance as the minimum, and adjusts and controls other power generation equipment based on the target residual load. It can give priority to the use of clean energy and reduce the abandonment rate of light and wind. While meeting the user's residual load demand, it further enables other equipment to have a better power generation strategy, reduce the system operating cost, improve stability and environmental protection, is suitable for different distributed energy terminals, and improves the coordinated operation and control strategy of multiple distributed energy in virtual power plants.

[0197] In some embodiments, Figure 4 As shown, an embodiment of the present application also provides an electronic device 400, including a processor 401, a memory 402, and a computer program stored in the memory 402 and executable on the processor 401. When the program is executed by the processor 401, each process of the control method embodiment of the above-mentioned multi-energy complementary power generation system is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0198] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0199] The embodiment of the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes of the control method embodiment of the above-mentioned multi-energy complementary power generation system are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0200] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0201] An embodiment of the present application also provides a computer program product, including a computer program, which implements the control method of the above-mentioned multi-energy complementary power generation system when executed by a processor.

[0202] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0203] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the control method embodiment of the above-mentioned multi-energy complementary power generation system, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0204] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0205] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0206] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0207] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

[0208] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0209] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

Claims

1. A control method for a multi-energy complementary power generation system, characterized in that: The multi-energy complementary power generation system includes a wind power generation device, a photovoltaic power generation device and at least one other power generation device; the method includes: Taking the sum of the variances of the charging cost output by the first model and the residual load output by the second model as the minimum as the goal, adjusting the input parameters of the first model and the second model to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameters and electricity price information corresponding to the target vehicle, and the second model is pre-constructed based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment; Predicting a target residual load corresponding to the multi-energy complementary power generation system during the operation of the wind power generation equipment based on the first numerical value and the operation of the photovoltaic power generation equipment based on the second numerical value; Based on the target residual load, the values ​​of the operating parameters corresponding to each of the other power generation equipment are controlled.

2. The control method of the multi-energy complementary power generation system according to claim 1, characterized in that: The second model is pre-constructed based on the operating parameters corresponding to the wind power generation equipment and the operating parameters corresponding to the photovoltaic power generation equipment, and includes: Based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment, predicting the combination probability of the wind power generation equipment and the photovoltaic power generation equipment in a plurality of different combination scenarios; With the goal of outputting the variance of the residual load corresponding to the multi-energy complementary power generation system when operating under each of the combination scenarios, the second model is constructed based on the combination probability, the scheduling cycle, the total number of combination scenarios and the residual load per unit time period under each of the combination scenarios.

3. The control method of the multi-energy complementary power generation system according to claim 2, characterized in that: The remaining load per unit time period in each of the combined scenarios is determined based on the following steps: Based on the flexible power load in the unit time period, the charging load of the target vehicle in the unit time period, and the predicted remaining load corresponding to the wind power generation equipment and the photovoltaic power generation equipment when working in the target combination scenario, the remaining load in the unit time period under each combination scenario is determined.

4. The control method of a multi-energy complementary power generation system according to any one of claims 1 to 3, characterized in that: The step of adjusting the input parameters of the first model and the second model with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment includes: Determining a first target constraint based on at least one of a power constraint and a target prediction error constraint corresponding to the target vehicle; With the goal of minimizing the sum of the variance of the charging cost and the remaining load, based on the first objective constraint, the input parameters of the first model and the second model are adjusted to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment.

5. The control method of a multi-energy complementary power generation system according to any one of claims 1 to 3, characterized in that: The controlling, based on the target residual load, the values ​​of the operating parameters corresponding to each of the other power generation equipment includes: Taking the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model as minimum and satisfying the target residual load as the goal, adjusting the input parameters of the third model and the fourth model to obtain the values ​​of the working parameters corresponding to the other power generation equipment; Among them, the fourth model is pre-constructed based on the power generation power of the wind power generation equipment, the power generation power of the photovoltaic power generation equipment, the natural gas price information, the electricity price information, the operation and maintenance coefficient of each equipment, at least one of the number of target vehicles and the remaining load in a unit time period.

6. The control method of the multi-energy complementary power generation system according to claim 5, characterized in that: The carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost, and flexible load scheduling cost output by the fourth model are minimized, and the target residual load is met as the goal, the input parameters of the third model and the fourth model are adjusted to obtain the values ​​of the working parameters corresponding to the other power generation equipment, including: Constructing a second target constraint based on at least one of an energy balance constraint, an equipment operation constraint, an energy purchase and sale constraint, and an energy storage equipment constraint; Taking the carbon emissions output by the third model and at least one of the renewable energy power generation cost, natural gas energy consumption cost, equipment operation and maintenance cost and flexible load scheduling cost output by the fourth model as minimum and satisfying the target residual load as the goal, based on the target residual load and the second target constraint, adjust the input parameters of the third model and the fourth model to obtain the values ​​of the working parameters corresponding to the other power generation equipment.

7. A control device for a multi-energy complementary power generation system, characterized in that: The multi-energy complementary power generation system includes a wind power generation device, a photovoltaic power generation device and at least one other power generation device; the device includes: A first processing module, for adjusting input parameters of the first model and the second model with the goal of minimizing the sum of the variances of the charging cost output by the first model and the residual load output by the second model, to obtain a first value of the working parameter corresponding to the wind power generation equipment and a second value of the working parameter corresponding to the photovoltaic power generation equipment; wherein the first model is pre-constructed based on the working parameters and electricity price information corresponding to the target vehicle, and the second model is pre-constructed based on the working parameters corresponding to the wind power generation equipment and the working parameters corresponding to the photovoltaic power generation equipment; A second processing module is used to predict the target residual load corresponding to the multi-energy complementary power generation system during the process in which the wind power generation equipment works based on the first numerical value and the photovoltaic power generation equipment works based on the second numerical value; The third processing module is used to control the value of the working parameter corresponding to each of the other power generation equipment based on the target residual load.

8. A multi-energy complementary power generation system, characterized in that: include: Wind power generation equipment; Photovoltaic power generation equipment; at least one other power generating device; Based on the control device of the multi-energy complementary power generation system as claimed in claim 7, the control device of the multi-energy complementary power generation system is electrically connected to the wind power generation equipment, the photovoltaic power generation equipment and the at least one other power generation equipment respectively.

9. The multi-energy complementary power generation system according to claim 8, characterized in that: The at least one other power generation equipment includes: energy storage equipment and at least one of coal, oil or natural gas fuel power generation equipment.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the control method of the multi-energy complementary power generation system as described in any one of claims 1-6 is implemented.

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