Virtual power plant scheduling method based on stochastic programming and fuzzy satisfaction

Abstract

The invention provides a virtual power plant scheduling method based on stochastic programming and fuzzy satisfaction, and the method comprises the steps: obtaining historical operation data of a virtual power plant, and determining uncertain factors and power plant operation parameters according to the historical operation data; constructing a stochastic programming model according to the uncertainty factors, wherein the stochastic programming model is used for simulating a probability change trend of a stochastic scene along with the uncertainty factors Constructing a virtual power plant model according to the power plant operation parameters for simulating the change trend of the net income of the virtual power plant along with the power plant operation parameters; constructing a target constraint group, wherein the target constraint group at least comprises a demand response constraint, a power balance constraint and an electric energy interaction constraint of the virtual power plant; and based on the stochastic programming model and the virtual power plant model, a profit optimization model is constructed through a p-robus method, target scheduling parameters are solved and obtained, and the virtual power plant is controlled to operate. The method solves the problem that in the prior art, the operation cost of the virtual power plant is increased when scheduling is carried out due to the fact that a risk avoidance method is adopted to solve uncertain factors.

Description

Technical Field

[0001] The present invention relates to the field of virtual power plant scheduling, and in particular to a virtual power plant scheduling method based on random programming and fuzzy satisfaction, a scheduling device, a computer-readable storage medium and a virtual power plant scheduling system. Background Art

[0002] The impact of climate change on renewable energy (RES) cannot be ignored. In order to ensure the reliable operation of the power system, the integration of distributed photovoltaic power generation, wind power generation, renewable energy generation and other systems to form a "virtual" power plant that can be uniformly dispatched is called a virtual power plant (VPP). A typical VPP consists of multiple distributed energy resources (DER), and the VPP system can maximize profits by participating in power dispatch. VPP creates flexible dispatch of aggregated client resources, enabling them to participate in the market and integrate the complementary characteristics of various resources to operate the system stably under different deterministic and random dispatch processes. Despite the advantages of DER, the inherent uncertainty of RES brings some challenges to the system related to technical, commercial and regulatory requirements, which requires a new system optimization method. In addition, the instability of the load model and the uncertainty of market prices will affect the operation plan. Due to the uncertainty of load and generation parameters and the uncertainty of market prices, inaccurate dispatch results may result. System operators have different attitudes towards overcoming uncertainty. In robust optimization, risk aversion is a reliable solution to cover up the impact of uncertainty, but under a rigid pessimistic attitude, operating costs will increase sharply. In order to make multi-objective decisions without strict risk-averse or risk-taking strategies, a new model must be provided to optimize the best decision based on the cost function and the degree of regret. Summary of the invention

[0003] The main purpose of this application is to provide a virtual power plant scheduling method, scheduling device, computer-readable storage medium and virtual power plant scheduling system based on random programming and fuzzy satisfaction, so as to at least solve the problem that the risk avoidance method used in the prior art to solve the uncertainty factors will increase the operating cost of the virtual power plant during scheduling.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a scheduling method for a virtual power plant based on stochastic programming and fuzzy satisfaction is provided, comprising: obtaining historical operating data of the virtual power plant, determining uncertainty factors and power plant operating parameters based on the historical operating data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operating parameters include at least the power interaction between the virtual power plant and the main power grid and the operating cost of the virtual power plant; constructing a stochastic programming model based on the uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of random scenarios with the uncertainty factors, and the random scenarios are composed of at least one of the uncertainty factors; according to the A virtual power plant model is constructed based on the power plant operating parameters, and the virtual power plant model is used to simulate the changing trend of the net profit of the virtual power plant with the changing trend of the power plant operating parameters; a target constraint group is constructed, and the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group includes at least the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant; a profit optimization model is constructed based on the stochastic programming model and the virtual power plant model by the p-robust method, and the profit optimization model is solved based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and the operation of the virtual power plant is controlled according to the target scheduling parameters, and the target scheduling parameters include all parameters in the power plant operating parameters.

[0005] Optionally, constructing a random programming model according to the uncertainty factors includes: determining a corresponding value range according to each of the uncertainty factors to obtain a target interval; constructing a plurality of the random scenarios according to each of the uncertainty factors through a scenario generation method, wherein at least one of the uncertainty factors corresponding to different random scenarios is different; based on the uncertainty factors included in each of the random scenarios, determining parameters of a probability density function under different random scenarios through the historical operation data to obtain target parameters, wherein the target parameters at least include a variance and a mean of the uncertainty factors; constructing a first target formula according to the target parameters and the uncertainty factors, wherein the first target formula is used to calculate the probability of the uncertainty factors under each of the random scenarios: Wherein, ε is the uncertainty factor, δ is the variance, and ò0 is the mean; the first objective formulas are combined to obtain the stochastic programming model.

[0006] Optionally, before jointly establishing the first objective formulas, the method further includes: determining the probability of each of the random scenarios according to the probability corresponding to each of the uncertainty factors to obtain a target probability; and screening the random scenarios based on the target probability by using a hybrid linear programming scenario reduction method:

[0007] 0≤ρ s (n1,n2,n3,...,n i )≤1; where N i is the number of scenes, w s (n1,n2,n3,…,n i ) is a binary variable for selecting each random scene, ρ s (n1,n2,n3,…,n i ) is the probability of each random scene.

[0008] Optionally, constructing a virtual power plant model according to the power plant operating parameters includes: determining a first electricity price, a second electricity price, a first quantity, a second quantity, a first cost, a second cost, a third cost, a fourth cost and a fifth cost according to the power plant operating parameters, wherein the first electricity price is the electricity price at which the virtual power plant sells electricity to the main grid, the second electricity price is the electricity price at which the virtual power plant sells electricity to users, the first quantity is the quantity of electricity sold by the virtual power plant to the main grid, the second quantity is the quantity of electricity sold by the virtual power plant to the users, the first cost is the cost of the virtual power plant purchasing electricity from the main grid, the second cost is the cost of the virtual power plant responding to user demand, the third cost is the cost of the thermal power unit of the virtual power plant, the fourth cost is the cost of the wind power unit of the virtual power plant, and the fifth cost is the cost of the photovoltaic unit of the virtual power plant; constructing a first target formula according to the first electricity price, the second electricity price, the first quantity and the second quantity, the first target formula being used to simulate the change trend of the revenue of the virtual power plant with the first electricity price, the second electricity price, the first quantity and the second quantity: Among them, R s is the revenue of the virtual power plant, N Bus is the number of power exchanges between the virtual power plant and the main grid, λ u is the first electricity price, λ t,s is the second electricity price, is the first electrical quantity, is the second electricity quantity, T is the total number of collection periods; a second target formula is constructed according to the first cost, the second cost, the third cost, the fourth cost and the fifth cost, and the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first cost, the second cost, the third cost, the fourth cost and the fifth cost: Among them, C s is the expenditure of the virtual power plant, For the first cost, For the second cost, For the third cost, For the fourth cost, is the fifth cost; combining the first target formula and the second target formula to obtain the virtual power plant model: OF s =ρ s ×(R s -C s ), where OF s is the net profit of the virtual power plant.

[0009] Optionally, before constructing the second target formula according to the first cost, the second cost, the third cost, the fourth cost and the fifth cost, the method further includes: determining a third quantity of electricity according to the power plant operating parameters, and constructing a third target formula according to the third quantity of electricity, wherein the third target formula is used to simulate a variation trend of the third cost with the third quantity of electricity, and the third quantity of electricity is the power generation of the thermal power unit: Wherein, a, b and c are different operating cost coefficients of the thermal power unit, is the third electrical quantity, I g,t,s QT is a binary variable representing the operating status of the thermal power unit. g,t,s is the start-up and shutdown cost of the thermal power unit, N g is the number of thermal power units; a fourth quantity is determined according to the power plant operating parameters, and a fourth target formula is constructed according to the fourth quantity, wherein the fourth target formula is used to simulate the changing trend of the fourth cost with the fourth quantity, and the fourth quantity is the power generation of the wind turbine unit: Among them, λ WT is the operating cost coefficient of the wind turbine, is the fourth electrical quantity, N WT is the number of wind turbines; a fifth quantity is determined according to the power plant operating parameters, and a fifth target formula is constructed according to the fifth quantity. The fifth target formula is used to simulate the change trend of the fifth cost with the fifth quantity, and the fifth quantity is the power generation of the photovoltaic unit: Among them, λ PV is the operating cost coefficient of the photovoltaic unit, is the fifth electrical quantity, N pv is the number of photovoltaic units; a first power and a third electricity price are determined according to the operating parameters of the power plant, and a sixth target formula is constructed according to the first power and the third electricity price. The sixth target formula is used to simulate the changing trend of the second cost with the first power and the third electricity price. The first power is the power of the demand response of the virtual power plant, and the third electricity price is the price at which the virtual power plant responds to the user's demand: in, is the price of the demand response, ΔOk d is the power of the kth demand response, d is the number of demand responses, and k is the time step of the demand response.

[0010] Optionally, constructing the target constraint group includes: constructing a power balance constraint of the virtual power plant corresponding to the virtual power plant model to obtain a first target constraint: in, For the electrical load, is the total power of demand response, satisfying The demand response constraint of the virtual power plant corresponding to the virtual power plant model is constructed to obtain the second objective constraint: in, is the minimum value of demand response power, is the maximum value of the first demand response power, is the maximum value of the kth demand response power; constructing the electric energy interaction constraint of the virtual power plant corresponding to the virtual power plant model, and obtaining the third objective constraint: in, is the benchmark power generation, is the benchmark electricity purchase amount, base electricity price; and obtaining the target constraint group by combining the first target constraint, the second target constraint and the third target constraint.

[0011] Optionally, constructing a revenue optimization model based on the stochastic programming model and the virtual power plant model by a p-robust method includes: constructing a revenue optimization model based on the stochastic programming model and the virtual power plant model:

[0012] LL≤OF≤UL;

[0015] Among them, p is the robustness index, UL is the upper limit of the preset interval, and LL is the lower limit of the preset interval. Estimated optimal solution.

[0016] According to another aspect of the present application, a scheduling device for a virtual power plant based on random programming and fuzzy satisfaction is provided, the device comprising: an acquisition unit, for acquiring historical operating data of the virtual power plant, and determining uncertainty factors and power plant operating parameters based on the historical operating data, the uncertainty factors at least including at least one of RES output power, virtual power plant load and market price, and the power plant operating parameters at least including the power interaction between the virtual power plant and the main power grid and the operating cost of the virtual power plant; a first construction unit, for constructing a random programming model based on the uncertainty factors, the random programming model being used to simulate the probability change trend of random scenarios with the uncertainty factors, the random scenarios being composed of at least one of the uncertainty factors; a second construction unit, for constructing a random programming model based on the uncertainty factors The power plant operating parameters construct a virtual power plant model, and the virtual power plant model is used to simulate the changing trend of the net profit of the virtual power plant with the power plant operating parameters; a third construction unit is used to construct a target constraint group, and the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group includes at least the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant; a calculation unit is used to construct a revenue optimization model based on the stochastic programming model and the virtual power plant model through the p-robust method, solve the revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and control the operation of the virtual power plant according to the target scheduling parameters, and the target scheduling parameters include all parameters in the power plant operating parameters.

[0017] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the method described in any one of the devices where the computer-readable storage medium is located is controlled.

[0018] According to another aspect of the present application, a virtual power plant scheduling system is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of the described methods.

[0019] By applying the technical solution of the present application, taking into account the impact of the uncertainty of RES itself during the operation of the virtual power plant on the system, the probabilities of different scenarios are determined based on the probability density function of the uncertain factors based on random programming, and then the virtual power plant model is constructed according to the changes in the income and expenditure during the operation of the virtual power plant with the parameters. Then, based on the p-robust method and the principle of the robust model, the robustness level is expressed as the maximum allowable regret, and the solution is performed through the maximum-minimum optimization structure and the fuzzy satisfaction scheme to ensure that the net profit is maximized and the operating risk is minimized at the same time. This method solves the problem that the risk avoidance method used in the prior art to solve the uncertainty factors will increase the operating cost of the virtual power plant during scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A hardware structure block diagram of a mobile terminal according to a scheduling method for a virtual power plant based on random programming and fuzzy satisfaction provided in an embodiment of the present application is shown;

[0021] Figure 2 A flow chart of a virtual power plant scheduling method based on random programming and fuzzy satisfaction provided according to an embodiment of the present application is shown;

[0022] Figure 3 A structural block diagram of a scheduling device for a virtual power plant based on stochastic programming and fuzzy satisfaction provided according to an embodiment of the present application is shown.

[0023] The above drawings include the following reference numerals:

[0024] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0029] Virtual Power Plant (VPP) is a special form of power plant in the energy Internet. It is not a real physical power plant, but a "virtual" power plant that integrates various distributed energy sources through advanced control, metering, communication and other technologies to form a unified dispatchable "virtual" power plant.

[0030] p-robust method: A robust method that emphasizes maintaining relatively stable and reliable performance in the face of various uncertainties, interference, noise, or data changes.

[0031] As introduced in the background technology, the risk-averse attitude under robust optimization in the prior art is a reliable solution to cover up the impact of uncertainty, but the operating costs will increase sharply under a rigid pessimistic attitude. In order to solve the problem that the risk-averse method used in the prior art to solve uncertainty factors will lead to increased operating costs of virtual power plants during scheduling, the embodiments of the present application provide a scheduling method, scheduling device, computer-readable storage medium and virtual power plant scheduling system for a virtual power plant based on random programming and fuzzy satisfaction.

[0032] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0033] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 1 is a hardware structure block diagram of a mobile terminal of a scheduling method for a virtual power plant based on random programming and fuzzy satisfaction according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0034] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the display method of device information in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0035] In this embodiment, a scheduling method for a virtual power plant based on random planning and fuzzy satisfaction is provided, which runs on a mobile terminal, a computer terminal or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 2 is a flow chart of a scheduling method for a virtual power plant based on random programming and fuzzy satisfaction according to an embodiment of the present application. Figure 2As shown, the method comprises the following steps:

[0037] Step S201, obtaining historical operation data of the virtual power plant, and determining uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0038] Specifically, the present application considers scheduling from two aspects: the uncertainty factors of virtual power plant operation and the power plant operation parameters. RES output power (output power of renewable energy), virtual power plant load and at least one of the market price are used as the above-mentioned uncertainty factors. The above-mentioned power plant operation parameters at least include the power interaction between the above-mentioned virtual power plant and the main power grid and the operation cost of the above-mentioned virtual power plant.

[0039] Step S202, constructing a stochastic programming model according to the above uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, wherein the random scenario is composed of at least one of the above uncertainty factors;

[0040] Specifically, a stochastic programming method is used to manage uncertainty risks. According to the probability density function (PDF) of each variable, the uncertainty of RES output power, load and market price is modeled through a scenario generation method to obtain the above-mentioned random scenarios. The above-mentioned stochastic programming model is used to simulate the probability change trend of the random scenarios with the above-mentioned uncertainty factors.

[0041] Step S203, constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters;

[0042] Specifically, the revenue function and the cost function are described respectively by the target formula of each scenario, and the first electricity price λ is determined according to the above power plant operating parameters. u , the second electricity price λ t,s , First Electricity Second power First cost Second cost The third cost Fourth cost and the fifth cost The net profit OF of the virtual power plant is calculated based on the revenue function and cost function. s With the changing trend of the above power plant operating parameters.

[0043] Step S204, constructing a target constraint group, the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant;

[0044] Specifically, according to the first electric quantity The third power Fourth Electricity Fifth power Second power Total power of demand response and power load Get the above power balance constraint; according to the minimum value of demand response power The maximum value of the first demand response power and the maximum value of the kth demand response power Get demand response constraints; based on the benchmark power generation Baseline electricity purchase Base electricity price and the second electricity price λ t,s Get the power interaction constraints.

[0045] Step S205, constructing a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solving the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controlling the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0046] Specifically, according to the principle of robust model, a considerable threshold may be required to propose a reliable solution. To reduce this threshold, the p-robust method uses the expected robustness level to represent the maximum allowable regret. In order to obtain the best trade-off solution that satisfies both the regret level and the expected profit, the target dispatch parameters of the virtual power plant are controlled according to the target dispatch parameters through the minimum-maximum optimization structure and the fuzzy satisfaction method.

[0047] Through this embodiment, first, the historical operation data of the virtual power plant is obtained, and the uncertainty factors and the power plant operation parameters are determined according to the above historical operation data, the above uncertainty factors at least include at least one of the RES output power, the virtual power plant load and the market price, and the above power plant operation parameters at least include the power interaction between the above virtual power plant and the main power grid and the operation cost of the above virtual power plant; then, a random programming model is constructed according to the above uncertainty factors, and the above random programming model is used to simulate the probability change trend of random scenarios with the above uncertainty factors, and the above random scenarios are composed of at least one of the above uncertainty factors; then, a virtual power plant model is constructed according to the above power plant operation parameters, and the above virtual power plant model is constructed according to the above uncertainty factors. The plant model is used to simulate the changing trend of the net profit of the above-mentioned virtual power plant with the operating parameters of the above-mentioned power plant; then, a target constraint group is constructed, and the above-mentioned target constraint group includes the constraint conditions of the above-mentioned virtual power plant model, and the above-mentioned target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the above-mentioned virtual power plant; finally, based on the above-mentioned random programming model and the above-mentioned virtual power plant model, a profit optimization model is constructed by the p-robust method, and the above-mentioned profit optimization model is solved based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and control the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operating parameters. The present application takes into account the impact of the uncertainty of RES itself during the operation of the virtual power plant on the system, determines the probabilities of different scenarios based on the probability density function of the uncertain factors based on stochastic programming, and then constructs a virtual power plant model according to the changes in the income and expenditure during the operation of the virtual power plant with the parameters. Then, based on the p-robust method and the principle of the robust model, the robustness level is expressed as the maximum allowable regret, and the solution is obtained through the maximum-minimum optimization structure and the fuzzy satisfaction scheme to ensure that the net profit is maximized and the operating risk is minimized at the same time. This method solves the problem that the risk avoidance method used in the prior art to solve the uncertainty factors will increase the operating cost of the virtual power plant during scheduling.

[0048] In order to establish a stochastic programming model, in an optional implementation, the above step S202 includes:

[0049] Step S2021, determining the corresponding value range according to each of the above uncertainty factors to obtain a target interval;

[0050] Specifically, the upper and lower limits of the values ​​corresponding to the above-mentioned uncertainty factors are determined based on historical data to obtain the above-mentioned target range.

[0051] Step S2022, constructing a plurality of the random scenarios according to the uncertainty factors by using a scenario generation method, wherein different random scenarios correspond to at least one different uncertainty factor;

[0052] Specifically, the uncertainties of RES output power, load, and market price are modeled through a scenario generation method according to the probability density function of each variable.

[0053] Step S2023, based on the uncertainty factors included in each of the random scenarios, determine the parameters of the probability density function under the different random scenarios through the historical operation data to obtain target parameters, where the target parameters at least include the variance and mean of the uncertainty factors;

[0054] Specifically, based on at least one of the above uncertainty factors among the RES output power, virtual power plant load and market price in the above random scenario, the variance and mean of the above uncertainty factors are fitted based on historical operation data to obtain the above target parameters.

[0055] Step S2024: construct a first target formula according to the target parameters and the uncertainty factors. The first target formula is used to calculate the probability of the uncertainty factors in each of the random scenarios:

[0056]

[0057] Among them, ε is the above uncertainty factor, δ is the above variance, and ò0 is the above mean.

[0058] Step S2025, combining the above first objective formulas to obtain the above stochastic programming model.

[0059] In order to reduce the computational complexity, in an optional implementation, before combining the first objective formulas, the method further includes:

[0060] Step S301, determining the probability of each of the above random scenarios according to the above probabilities corresponding to the above uncertainty factors, and obtaining a target probability;

[0061] Specifically, the above-mentioned probability corresponding to the uncertainty factor is calculated according to the above-mentioned first target formula, and then the corresponding probability of each of the above-mentioned random scenarios is determined according to the probability of the uncertainty factor, and the above-mentioned target probability is calculated.

[0062] Step S302, based on the above target probability, the above random scenarios are screened by a hybrid linear programming scenario reduction method:

[0063]

[0064]

[0065] Among them, N i is the number of scenes, w s (n1,n2,n3,…,n i) is a binary variable for selecting each of the above random scenarios, ρ s (n1,n2,n3,…,n i ) is the probability of each of the above random scenarios.

[0066] Specifically, there are many random scenarios and corresponding probabilities determined based on the above method, and the computational burden of exhaustively enumerating all scenarios in the actual calculation process is high. Therefore, this application adopts a scenario reduction method to reduce the total number of scenarios, specifically, a mixed integer linear programming scenario reduction method is used to solve the above formula to minimize the number and probability of scenarios.

[0067] In order to construct a virtual power plant model, in an optional implementation, the above step S203 includes:

[0068] Step S2031, determining the first electricity price, the second electricity price, the first electricity quantity, the second electricity quantity, the first cost, the second cost, the third cost, the fourth cost and the fifth cost according to the operating parameters of the power plant, the first electricity price being the electricity price at which the virtual power plant sells electricity to the main grid, the second electricity price being the electricity price at which the virtual power plant sells electricity to users, the first electricity quantity being the electricity quantity sold by the virtual power plant to the main grid, the second electricity quantity being the electricity quantity sold by the virtual power plant to the main grid, the first cost being the cost at which the virtual power plant purchases electricity from the main grid, the second cost being the cost at which the virtual power plant responds to demand for users, the third cost being the cost of the thermal power unit of the virtual power plant, the fourth cost being the cost of the wind power unit of the virtual power plant, and the fifth cost being the cost of the photovoltaic unit of the virtual power plant;

[0069] Specifically, the electricity price λ of the virtual power plant for selling electricity to the main grid is determined according to the above power plant operating parameters. u , we get the first electricity price mentioned above, the electricity price λ that the virtual power plant sells electricity to users t,s , the second electricity price is obtained, and the amount of electricity sold by the virtual power plant to the main grid is The amount of electricity sold by the virtual power plant to the main power grid is obtained. The cost of the virtual power plant purchasing electricity from the main grid is The first cost is obtained, the cost of the virtual power plant responding to user demand The second cost is the cost of the thermal power unit of the virtual power plant. The third cost mentioned above is the cost of the wind turbine in the virtual power plant. The fourth cost is the cost of the photovoltaic unit of the virtual power plant. The fifth cost mentioned above is obtained.

[0070] Step S2032, constructing a first target formula according to the first electricity price, the second electricity price, the first quantity and the second quantity, wherein the first target formula is used to simulate the change trend of the revenue of the virtual power plant with the first electricity price, the second electricity price, the first quantity and the second quantity:

[0071]

[0072] Among them, R s is the revenue of the virtual power plant, N Bus is the number of power exchanges between the virtual power plant and the main power grid, λ u is the first electricity price mentioned above, λ t,s is the second electricity price mentioned above, is the first electrical quantity mentioned above, is the second electrical quantity mentioned above, T is the total number of collection periods;

[0073] Step S2033, constructing a second target formula according to the first cost, the second cost, the third cost, the fourth cost and the fifth cost, wherein the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first cost, the second cost, the third cost, the fourth cost and the fifth cost:

[0074]

[0075] Among them, C s For the expenditure of the above virtual power plant, is the first cost mentioned above, is the second cost mentioned above, For the third cost mentioned above, For the fourth cost mentioned above, For the fifth cost mentioned above;

[0076] Step S2034, combining the first target formula and the second target formula to obtain the virtual power plant model:

[0077] OF s =ρ s ×(R s -C s );

[0078] Among them, OF s is the net profit of the above virtual power plant.

[0079] In the above embodiment, the present application establishes a virtual power plant revenue model based on the income obtained by the virtual power plant from exchanging electricity with the main power grid and selling electricity to users, the cost of the virtual power plant power generation group and demand response, and determines the net profit trend of the above virtual power plant model as the above power plant operating parameters change.

[0080] In order to simulate the cost variation trend with the amount of electricity, in an optional implementation, the above step S2033 includes:

[0081] Step S20331, determining a third amount of electricity according to the power plant operation parameters, and constructing a third target formula according to the third amount of electricity, wherein the third target formula is used to simulate the variation trend of the third cost with the third amount of electricity, and the third amount of electricity is the power generation of the thermal power unit:

[0082]

[0083] Among them, a, b and c are different operating cost coefficients of the above thermal power units. is the third electrical quantity mentioned above, I g,t,s QT is a binary variable representing the operating status of the thermal power unit. g,t,s is the start-up and shutdown cost of the above thermal power unit, N g is the number of the above-mentioned thermal power units;

[0084] Step S20332, determining a fourth quantity of electricity according to the power plant operating parameters, and constructing a fourth target formula according to the fourth quantity of electricity, wherein the fourth target formula is used to simulate the variation trend of the fourth cost with the fourth quantity of electricity, and the fourth quantity of electricity is the power generation of the wind turbine:

[0085]

[0086] Among them, λ WT is the operating cost coefficient of the above wind turbine, is the fourth electrical quantity mentioned above, N WT is the number of the above wind turbines;

[0087] Step S20333, determining a fifth amount of electricity according to the power plant operation parameters, and constructing a fifth target formula according to the fifth amount of electricity, wherein the fifth target formula is used to simulate the variation trend of the fifth cost with the fifth amount of electricity, and the fifth amount of electricity is the power generation of the photovoltaic unit:

[0088]

[0089] Among them, λ PV is the operating cost coefficient of the above photovoltaic unit, is the fifth electrical quantity mentioned above, N pv is the number of the above-mentioned photovoltaic units;

[0090] Step S20334, determining the first power and the third electricity price according to the operating parameters of the power plant, and constructing a sixth target formula according to the first power and the third electricity price, the sixth target formula is used to simulate the variation trend of the second cost with the first power and the third electricity price, the first power is the power of the demand response of the virtual power plant, and the third electricity price is the price for the virtual power plant to respond to the user's demand:

[0091]

[0092] in, is the price of the above demand response, is the power of the kth demand response.

[0093] In the above embodiment, the present application determines the third electricity quantity, the fourth electricity quantity, the fifth electricity quantity, the first power and the third electricity price according to the above power plant operating parameters, and constructs corresponding formulas respectively, which are used to simulate the cost trend of each part of the above virtual power plant with the above parameters.

[0094] In order to construct the target constraint group, in an optional implementation, the above step S204 includes:

[0095] Step S2041, constructing the power balance constraint of the virtual power plant corresponding to the virtual power plant model, and obtaining the first target constraint:

[0096]

[0097] in, For the power load, is the total power of demand response, satisfying

[0098] Step S2042, constructing the demand response constraint of the virtual power plant corresponding to the virtual power plant model to obtain the second target constraint:

[0099]

[0100] in, is the minimum value of demand response power, is the maximum value of the first demand response power, is the maximum value of the kth demand response power;

[0101] Specifically, the above formulas are based on the first step of the incentive response, the minimum response required by the DRP, and the range of demand response that changes gradually with price, as shown in the above formula, to determine the maximum value of a single adjustment.

[0102] Step S2042: construct the electric energy interaction constraint of the virtual power plant corresponding to the virtual power plant model to obtain the third objective constraint:

[0103]

[0104] in, is the benchmark power generation, is the benchmark electricity purchase amount, Base electricity price;

[0105] Step S2043, combining the first objective constraint, the second objective constraint and the third objective constraint to obtain the objective constraint group.

[0106] In order to construct a revenue optimization model, in an optional implementation, the above step S205 includes:

[0107] Step S2051: construct a revenue optimization model based on the above stochastic programming model and the above virtual power plant model:

[0108]

[0109] Among them, p is the robustness index, UL is the upper limit of the preset interval, and LL is the lower limit of the preset interval. Estimated optimal solution.

[0110] Specifically, the present application sets up to use the expected robustness level to represent the maximum allowable regret of the decision, determines the minimized regret as the uncertainty indicator in the optimization model, and performs modeling according to the above formula.

[0111] Furthermore, in order to ensure that the optimal trade-off between the regret level and the expected profit is met, the minimum-maximum structure and fuzzy satisfaction method are used to determine the optimal trade-off, as shown in the following formula:

[0112]

[0113] in, is the minimum value of the objective function under the robustness level.

[0114] Furthermore, for the degree of regret and expected profit of each solution, a value between (0,1) is assigned to each objective function according to the above formula. Further determination of the value of regret rate in each scenario can be obtained by assuming p = ∞ to obtain a purely random optimization result. According to the results of the infeasible problem, the value of p is gradually reduced to reach the minimum value. In order to obtain the optimal solution while pursuing profit maximization and operating risk minimization, a two-stage minimum-maximum optimization structure is implemented using the minimum function, taking profit maximization and risk minimization processes into consideration. Solve for the final target scheduling parameters.

[0115] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0116] The embodiment of the present application also provides a scheduling device for a virtual power plant based on random planning and fuzzy satisfaction. It should be noted that the scheduling device for a virtual power plant based on random planning and fuzzy satisfaction in the embodiment of the present application can be used to execute the scheduling method for a virtual power plant based on random planning and fuzzy satisfaction provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have been described will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0117] The following introduces a scheduling device for a virtual power plant based on random programming and fuzzy satisfaction provided in an embodiment of the present application.

[0118] Figure 3 is a structural block diagram of a scheduling device for a virtual power plant based on random programming and fuzzy satisfaction according to an embodiment of the present application. Figure 3 As shown, the device comprises:

[0119] An acquisition unit 10 is used to acquire historical operation data of the virtual power plant, and determine uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0120] Specifically, the present application considers scheduling from two aspects: the uncertainty factors of virtual power plant operation and the power plant operation parameters. RES output power (output power of renewable energy), virtual power plant load and at least one of the market price are used as the above-mentioned uncertainty factors. The above-mentioned power plant operation parameters at least include the power interaction between the above-mentioned virtual power plant and the main power grid and the operation cost of the above-mentioned virtual power plant.

[0121] A first construction unit 20 is used to construct a stochastic programming model according to the above uncertainty factors, wherein the above stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, and the above random scenario is composed of at least one of the above uncertainty factors;

[0122] Specifically, a stochastic programming method is used to manage uncertainty risks. According to the probability density function (PDF) of each variable, the uncertainty of RES output power, load and market price is modeled through a scenario generation method to obtain the above-mentioned random scenarios. The above-mentioned stochastic programming model is used to simulate the probability change trend of the random scenarios with the above-mentioned uncertainty factors.

[0123] A second construction unit 30 is used to construct a virtual power plant model according to the power plant operation parameters, wherein the virtual power plant model is used to simulate the change trend of the net income of the virtual power plant with the power plant operation parameters;

[0124] Specifically, the revenue function and the cost function are described respectively by the target formula of each scenario, and the first electricity price λ is determined according to the above power plant operating parameters. u , the second electricity price λ t,s , First Electricity Second power First cost Second cost The third cost Fourth cost and the fifth cost The net profit OF of the virtual power plant is calculated based on the revenue function and cost function. s With the changing trend of the above power plant operating parameters.

[0125] A third construction unit 40 is used to construct a target constraint group, wherein the target constraint group includes constraint conditions of the virtual power plant model, and the target constraint group includes at least a demand response constraint, a power balance constraint, and a power interaction constraint of the virtual power plant;

[0126] Specifically, according to the first electric quantity The third power Fourth Electricity Fifth power Second power Total power of demand response and power load Get the above power balance constraint; according to the minimum value of demand response power The maximum value of the first demand response power and the maximum value of the kth demand response power Get demand response constraints; based on the benchmark power generation Baseline electricity purchase Base electricity price and the second electricity price λ t,s Get the power interaction constraints.

[0127] The calculation unit 50 is used to construct a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solve the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and control the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0128] Specifically, according to the principle of robust model, a considerable threshold may be required to propose a reliable solution. To reduce this threshold, the p-robust method uses the expected robustness level to represent the maximum allowable regret. In order to obtain the best trade-off solution that satisfies both the regret level and the expected profit, the target dispatch parameters of the virtual power plant are controlled according to the target dispatch parameters through the minimum-maximum optimization structure and the fuzzy satisfaction method.

[0129] Through this embodiment, the acquisition unit acquires the historical operation data of the virtual power plant, determines the uncertainty factors and the power plant operation parameters according to the above historical operation data, the above uncertainty factors include at least one of the RES output power, the virtual power plant load and the market price, and the above power plant operation parameters include at least the power interaction between the above virtual power plant and the main power grid and the operation cost of the above virtual power plant; the first construction unit constructs a random programming model according to the above uncertainty factors, and the above random programming model is used to simulate the probability change trend of random scenarios with the above uncertainty factors, and the above random scenarios are composed of at least one of the above uncertainty factors; the second construction unit constructs a virtual power plant model according to the above power plant operation parameters, and the above virtual power plant model is used to simulate the probability change trend of random scenarios with the above uncertainty factors. The pseudo power plant model is used to simulate the changing trend of the net profit of the above-mentioned virtual power plant with the operating parameters of the above-mentioned power plant; the third construction unit constructs a target constraint group, and the above-mentioned target constraint group includes the constraint conditions of the above-mentioned virtual power plant model, and the above-mentioned target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the above-mentioned virtual power plant; the calculation unit constructs a profit optimization model based on the above-mentioned random programming model and the above-mentioned virtual power plant model through the p-robust method, solves the above-mentioned profit optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controls the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operating parameters. The present application takes into account the impact of the uncertainty of RES itself during the operation of the virtual power plant on the system, determines the probabilities of different scenarios based on the probability density function of the uncertain factors based on stochastic programming, and then constructs a virtual power plant model according to the changes in the income and expenditure during the operation of the virtual power plant with the parameters. Then, based on the p-robust method and the principle of the robust model, the robustness level is expressed as the maximum allowable regret, and the solution is obtained through the maximum-minimum optimization structure and the fuzzy satisfaction scheme to ensure that the net profit is maximized and the operating risk is minimized at the same time. This method solves the problem that the risk avoidance method used in the prior art to solve the uncertainty factors will increase the operating cost of the virtual power plant during scheduling.

[0130] In order to establish a stochastic programming model, in an optional embodiment, the first building unit includes:

[0131] A first determination subunit is used to determine the corresponding value range according to each of the above uncertainty factors to obtain a target interval;

[0132] A first construction subunit is used to construct a plurality of the random scenarios according to the uncertainty factors through a scenario generation method, wherein at least one of the uncertainty factors corresponding to different random scenarios is different;

[0133] A second determination subunit is used to determine the parameters of the probability density function under the different random scenarios through the historical operation data based on the uncertainty factors included in each of the random scenarios, and obtain target parameters, where the target parameters at least include the variance and mean of the uncertainty factors;

[0134] The calculation subunit is used to construct a first target formula according to the target parameters and the uncertainty factors, and the first target formula is used to calculate the probability of the uncertainty factors in each of the random scenarios:

[0135]

[0136] Among them, ε is the above uncertainty factor, δ is the above variance, and ò0 is the above mean;

[0137] The second construction subunit is used to combine the above-mentioned first objective formulas to obtain the above-mentioned stochastic programming model.

[0138] In order to establish a stochastic programming model, in an optional implementation, the apparatus further includes:

[0139] A determination unit, used to determine the probability of each of the above random scenarios according to the above probabilities corresponding to each of the above uncertainty factors, and obtain a target probability;

[0140] Specifically, the above-mentioned probability corresponding to the uncertainty factor is calculated according to the above-mentioned first target formula, and then the corresponding probability of each of the above-mentioned random scenarios is determined according to the probability of the uncertainty factor, and the above-mentioned target probability is calculated.

[0141] A screening unit is used to screen the above random scenarios based on the above target probability by a hybrid linear programming scenario reduction method:

[0142]

[0143]

[0144] Among them, N i is the number of scenes, w s (n1,n2,n3,…,n i ) is a binary variable for selecting each of the above random scenarios, ρ s (n1,n2,n3,…,n i ) is the probability of each of the above random scenarios.

[0145] In order to construct a virtual power plant model, in an optional implementation, the second construction unit includes:

[0146] The third determination subunit determines the first electricity price, the second electricity price, the first electricity quantity, the second electricity quantity, the first cost, the second cost, the third cost, the fourth cost and the fifth cost according to the operating parameters of the power plant, the first electricity price is the electricity price at which the virtual power plant sells electricity to the main grid, the second electricity price is the electricity price at which the virtual power plant sells electricity to users, the first electricity quantity is the electricity quantity sold by the virtual power plant to the main grid, the second electricity quantity is the electricity quantity sold by the virtual power plant to the main grid, the first cost is the cost of the virtual power plant purchasing electricity from the main grid, the second cost is the cost of the virtual power plant responding to demand for users, the third cost is the cost of the thermal power unit of the virtual power plant, the fourth cost is the cost of the wind power unit of the virtual power plant, and the fifth cost is the cost of the photovoltaic unit of the virtual power plant;

[0147] The third construction subunit is used to construct a first target formula according to the first electricity price, the second electricity price, the first quantity and the second quantity, and the first target formula is used to simulate the change trend of the revenue of the virtual power plant with the first electricity price, the second electricity price, the first quantity and the second quantity:

[0148]

[0149] Among them, R s is the revenue of the virtual power plant, N Bus is the number of power exchanges between the virtual power plant and the main power grid, λ u is the first electricity price mentioned above, λ t,s is the second electricity price mentioned above, is the first electrical quantity mentioned above, is the second electrical quantity mentioned above, T is the total number of collection periods;

[0150] The fourth construction subunit is used to construct a second target formula according to the first cost, the second cost, the third cost, the fourth cost and the fifth cost, and the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first cost, the second cost, the third cost, the fourth cost and the fifth cost:

[0151]

[0152] Among them, C s For the expenditure of the above virtual power plant, is the first cost mentioned above, is the second cost mentioned above, For the third cost mentioned above, For the fourth cost mentioned above, For the fifth cost mentioned above;

[0153] The first combined subunit combines the first objective formula and the second objective formula to obtain the virtual power plant model:

[0154] OF s =ρ s ×(R s -C s );

[0155] Among them, OF s is the net profit of the above virtual power plant.

[0156] In order to simulate the cost variation trend with the amount of electricity, in an optional implementation, the fourth construction subunit includes:

[0157] The first construction module is used to determine the third quantity according to the operating parameters of the power plant, and to construct a third target formula according to the third quantity. The third target formula is used to simulate the variation trend of the third cost with the third quantity. The third quantity is the power generation of the thermal power unit:

[0158]

[0159] Among them, a, b and c are different operating cost coefficients of the above thermal power units. is the third electrical quantity mentioned above, I g,t,s QT is a binary variable representing the operating status of the thermal power unit. g,t,s is the start-up and shutdown cost of the above thermal power unit, N g is the number of the above-mentioned thermal power units;

[0160] The second construction module is used to determine a fourth quantity of electricity according to the operating parameters of the power plant, and to construct a fourth target formula according to the fourth quantity of electricity. The fourth target formula is used to simulate the changing trend of the fourth cost with the fourth quantity of electricity. The fourth quantity of electricity is the power generation of the wind turbine:

[0161]

[0162] Among them, λ WT is the operating cost coefficient of the above wind turbine, is the fourth electrical quantity mentioned above, N WT is the number of the above wind turbines;

[0163] The third construction module is used to determine the fifth quantity according to the operating parameters of the power plant, and to construct a fifth target formula according to the fifth quantity. The fifth target formula is used to simulate the change trend of the fifth cost with the fifth quantity. The fifth quantity is the power generation of the photovoltaic unit:

[0164]

[0165] Among them, λ PV is the operating cost coefficient of the above photovoltaic unit, is the fifth electrical quantity mentioned above, N pv is the number of the above-mentioned photovoltaic units;

[0166] The fourth construction module is used to determine the first power and the third electricity price according to the operating parameters of the power plant, and to construct a sixth target formula according to the first power and the third electricity price. The sixth target formula is used to simulate the changing trend of the second cost with the first power and the third electricity price. The first power is the power of the demand response of the virtual power plant, and the third electricity price is the price for the virtual power plant to respond to the user's demand:

[0167]

[0168] in, is the price of the above demand response, is the power of the kth demand response.

[0169] In order to construct the target constraint group, in an optional implementation, the third construction unit includes:

[0170] The fifth construction subunit is used to construct the power balance constraint of the virtual power plant corresponding to the virtual power plant model to obtain the first target constraint:

[0171]

[0172] in, For the electrical load, is the total power of demand response, satisfying

[0173] The demand response constraint of the virtual power plant corresponding to the virtual power plant model is constructed to obtain the second objective constraint:

[0174]

[0175] in, is the minimum value of demand response power, is the maximum value of the first demand response power, is the maximum value of the kth demand response power;

[0176] The sixth construction subunit is used to construct the electric energy interaction constraint of the virtual power plant corresponding to the virtual power plant model to obtain the third target constraint:

[0177]

[0178] in, is the benchmark power generation, is the benchmark electricity purchase amount, Base electricity price;

[0179] The second joint sub-unit jointly combines the first objective constraint, the second objective constraint and the third objective constraint to obtain the objective constraint group.

[0180] The above-mentioned virtual power plant scheduling device based on random programming and fuzzy satisfaction includes a processor and a memory, and the above-mentioned acquisition unit, the first construction unit, the second construction unit, the third construction unit and the calculation unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize the corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in the form of any combination.

[0181] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and the operating cost of the virtual power plant can be improved and reduced by adjusting the kernel parameters.

[0182] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0183] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the scheduling method of the virtual power plant based on random planning and fuzzy satisfaction.

[0184] Specifically, the scheduling method of virtual power plant based on stochastic programming and fuzzy satisfaction includes:

[0185] Step S201, obtaining historical operation data of the virtual power plant, and determining uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0186] Step S202, constructing a stochastic programming model according to the above uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, wherein the random scenario is composed of at least one of the above uncertainty factors;

[0187] Step S203, constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters;

[0188] Step S204, constructing a target constraint group, the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant;

[0189] Step S205, constructing a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solving the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controlling the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0190] An embodiment of the present invention provides a processor, which is used to run a program, wherein the scheduling method of a virtual power plant based on random planning and fuzzy satisfaction is executed when the program is running.

[0191] Specifically, the scheduling method of virtual power plant based on stochastic programming and fuzzy satisfaction includes:

[0192] Step S201, obtaining historical operation data of the virtual power plant, and determining uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0193] Step S202, constructing a stochastic programming model according to the above uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, wherein the random scenario is composed of at least one of the above uncertainty factors;

[0194] Step S203, constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters;

[0195] Step S204, constructing a target constraint group, the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant;

[0196] Step S205, constructing a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solving the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controlling the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0197] An embodiment of the present invention provides a virtual power plant scheduling system, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, the following steps are implemented in a scheduling method for a virtual power plant based at least on random programming and fuzzy satisfaction:

[0198] Step S201, obtaining historical operation data of the virtual power plant, and determining uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0199] Step S202, constructing a stochastic programming model according to the above uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, wherein the random scenario is composed of at least one of the above uncertainty factors;

[0200] Step S203, constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters;

[0201] Step S204, constructing a target constraint group, the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant;

[0202] Step S205, constructing a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solving the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controlling the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0203] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0204] Step S201, obtaining historical operation data of the virtual power plant, and determining uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant;

[0205] Step S202, constructing a stochastic programming model according to the above uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the above uncertainty factors, wherein the random scenario is composed of at least one of the above uncertainty factors;

[0206] Step S203, constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters;

[0207] Step S204, constructing a target constraint group, the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group at least includes the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant;

[0208] Step S205, constructing a revenue optimization model based on the above-mentioned stochastic programming model and the above-mentioned virtual power plant model through the p-robust method, solving the above-mentioned revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method to obtain the target scheduling parameters and controlling the operation of the above-mentioned virtual power plant according to the above-mentioned target scheduling parameters, and the above-mentioned target scheduling parameters include all parameters in the above-mentioned power plant operation parameters.

[0209] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0210] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0211] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0212] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0213] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0214] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0215] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0216] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0217] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0218] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0219] The scheduling method of a virtual power plant based on stochastic programming and fuzzy satisfaction of the present application first obtains historical operating data of the virtual power plant, and determines uncertainty factors and power plant operating parameters based on the above historical operating data, wherein the above uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the above power plant operating parameters include at least the power interaction between the above virtual power plant and the main power grid and the operating cost of the above virtual power plant; then, a stochastic programming model is constructed based on the above uncertainty factors, and the above stochastic programming model is used to simulate the probability change trend of random scenarios with the above uncertainty factors, and the above random scenarios are composed of at least one of the above uncertainty factors; then, a virtual power plant is constructed based on the above power plant operating parameters. A power plant model, wherein the virtual power plant model is used to simulate the changing trend of the net profit of the virtual power plant with the operating parameters of the power plant; then, a target constraint group is constructed, wherein the target constraint group includes the constraint conditions of the virtual power plant model, and the target constraint group includes at least the demand response constraint, power balance constraint and power interaction constraint of the virtual power plant; finally, a revenue optimization model is constructed based on the stochastic programming model and the virtual power plant model through the p-robust method, and the target scheduling parameters are obtained by solving the revenue optimization model based on the minimum-maximum structural optimization and fuzzy satisfaction method, and the operation of the virtual power plant is controlled according to the target scheduling parameters, wherein the target scheduling parameters include all the parameters in the power plant operating parameters. The present application takes into account the impact of the uncertainty of RES itself during the operation of the virtual power plant on the system, determines the probabilities of different scenarios based on the probability density function of the uncertain factors based on stochastic programming, and then constructs a virtual power plant model according to the changes in the income and expenditure during the operation of the virtual power plant with the parameters. Then, based on the p-robust method and the principle of the robust model, the robustness level is expressed as the maximum allowable regret, and the solution is obtained through the maximum-minimum optimization structure and the fuzzy satisfaction scheme to ensure that the net profit is maximized and the operating risk is minimized at the same time. This method solves the problem that the risk avoidance method used in the prior art to solve the uncertainty factors will increase the operating cost of the virtual power plant during scheduling.

[0220] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A scheduling method for a virtual power plant based on stochastic programming and fuzzy satisfaction, characterized in that: include: Acquire historical operation data of the virtual power plant, and determine uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant; Constructing a stochastic programming model according to the uncertainty factors, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario along with the uncertainty factors, wherein the random scenario is composed of at least one of the uncertainty factors; Constructing a virtual power plant model according to the power plant operating parameters, wherein the virtual power plant model is used to simulate the changing trend of the net income of the virtual power plant with the power plant operating parameters; Constructing a target constraint group, wherein the target constraint group includes constraint conditions of the virtual power plant model, and the target constraint group includes at least a demand response constraint, a power balance constraint, and an electric energy interaction constraint of the virtual power plant; Based on the stochastic programming model and the virtual power plant model, a revenue optimization model is constructed by the p-robust method. Based on the minimum-maximum structural optimization and fuzzy satisfaction method, the revenue optimization model is solved to obtain target scheduling parameters and the operation of the virtual power plant is controlled according to the target scheduling parameters. The target scheduling parameters include all parameters in the power plant operation parameters.

2. The method according to claim 1, characterized in that A stochastic programming model is constructed according to the uncertainty factors, including: Determine the corresponding value range according to each of the uncertainty factors to obtain a target interval; Constructing a plurality of the random scenarios according to the uncertainty factors by using a scenario generation method, wherein at least one of the uncertainty factors corresponding to different random scenarios is different; Based on the uncertainty factors included in each of the random scenarios, determine the parameters of the probability density function under the different random scenarios through the historical operation data to obtain target parameters, where the target parameters at least include the variance and mean of the uncertainty factors; A first target formula is constructed according to the target parameter and the uncertainty factor, and the first target formula is used to calculate the probability of the uncertainty factor in each of the random scenarios: Among them, ε is the uncertainty factor, δ is the variance, is the mean value; The first objective formulas are combined to obtain the stochastic programming model.

3. The method according to claim 2, characterized in that Before combining the first target formulas, the method further includes: Determine the probability of each of the random scenarios according to the probability corresponding to each of the uncertainty factors to obtain a target probability; The random scenarios are screened based on the target probability by a hybrid linear programming scenario reduction method: <h2 style=";text-align:left;direction:ltr">ρ<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (n1,n2,n3,…,n<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> )≤w<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (n1,n2,n3,…,n<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> ); <h2 style=";text-align:left;direction:ltr">0≤ρ<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (n1,n2,n3,…,n<h2 style=";text-align:left;direction:ltr"> i <h2 style=";text-align:left;direction:ltr"> )≤1; Among them, N i is the number of scenes, ws(n1,n2,n3,…,ni) is a binary variable for selecting each random scene, ρ s (n1,n2,n3,…,n i ) is the probability of each random scene.

4. The method according to claim 1, characterized in that: Constructing a virtual power plant model according to the power plant operation parameters, including: Determine a first electricity price, a second electricity price, a first quantity of electricity, a second quantity of electricity, a first cost, a second cost, a third cost, a fourth cost and a fifth cost according to the operating parameters of the power plant, wherein the first electricity price is the electricity price at which the virtual power plant sells electricity to the main power grid, the second electricity price is the electricity price at which the virtual power plant sells electricity to users, the first quantity of electricity is the quantity of electricity sold by the virtual power plant to the main power grid, the second quantity of electricity is the quantity of electricity sold by the virtual power plant to the users, the first cost is the cost of the virtual power plant purchasing electricity from the main power grid, the second cost is the cost of the virtual power plant responding to demand for users, the third cost is the cost of the thermal power units of the virtual power plant, the fourth cost is the cost of the wind power units of the virtual power plant, and the fifth cost is the cost of the photovoltaic units of the virtual power plant; A first target formula is constructed according to the first electricity price, the second electricity price, the first quantity of electricity, and the second quantity of electricity. The first target formula is used to simulate the change trend of the revenue of the virtual power plant with the first electricity price, the second electricity price, the first quantity of electricity, and the second quantity of electricity: Among them, R s is the revenue of the virtual power plant, N Bus is the number of power exchanges between the virtual power plant and the main grid, λ u is the first electricity price, λ t,s is the second electricity price, is the first electrical quantity, is the second electrical quantity, T is the total number of collection periods; A second target formula is constructed based on the first cost, the second cost, the third cost, the fourth cost and the fifth cost, and the second target formula is used to simulate the change trend of the expenditure of the virtual power plant with the first cost, the second cost, the third cost, the fourth cost and the fifth cost: Among them, C s is the expenditure of the virtual power plant, For the first cost, For the second cost, For the third cost, For the fourth cost, for said fifth cost; The first objective formula and the second objective formula are combined to obtain the virtual power plant model: OF s =ρ s ×(R s -C s ); Among them, OF s is the net profit of the virtual power plant.

5. The method according to claim 4, characterized in that Before constructing a second target formula according to the first cost, the second cost, the third cost, the fourth cost and the fifth cost, the method further includes: A third quantity of electricity is determined according to the power plant operation parameters, and a third target formula is constructed according to the third quantity of electricity. The third target formula is used to simulate the variation trend of the third cost with the third quantity of electricity. The third quantity of electricity is the power generation of the thermal power unit: Wherein, a, b and c are different operating cost coefficients of the thermal power unit, is the third electrical quantity, I g,t,s QT is a binary variable representing the operating status of the thermal power unit. g,t,s is the start-up and shutdown cost of the thermal power unit, N g is the number of thermal power units; A fourth quantity of electricity is determined according to the power plant operation parameters, and a fourth target formula is constructed according to the fourth quantity of electricity. The fourth target formula is used to simulate the variation trend of the fourth cost with the fourth quantity of electricity, where the fourth quantity of electricity is the power generation of the wind turbine: Among them, λ WT is the operating cost coefficient of the wind turbine, is the fourth electrical quantity, N WT is the number of wind turbines; A fifth quantity of electricity is determined according to the power plant operation parameters, and a fifth target formula is constructed according to the fifth quantity of electricity. The fifth target formula is used to simulate the variation trend of the fifth cost with the fifth quantity of electricity. The fifth quantity of electricity is the power generation of the photovoltaic unit: Among them, λ PV is the operating cost coefficient of the photovoltaic unit, is the fifth electrical quantity, N pv is the number of photovoltaic units; A first power and a third electricity price are determined according to the power plant operation parameters, and a sixth target formula is constructed according to the first power and the third electricity price. The sixth target formula is used to simulate the variation trend of the second cost with the first power and the third electricity price. The first power is the power of the demand response of the virtual power plant, and the third electricity price is the price at which the virtual power plant responds to the demand for users: in, is the price of the demand response, is the power of the kth demand response, d is the number of demand responses, and k is the time step of the demand response.

6. The method according to claim 1, characterized in that Build a target constraint group, including: Constructing the power balance constraint of the virtual power plant corresponding to the virtual power plant model to obtain the first objective constraint: in, For the electrical load, is the total power of demand response, satisfying The demand response constraint of the virtual power plant corresponding to the virtual power plant model is constructed to obtain the second objective constraint: in, is the minimum value of demand response power, is the maximum value of the first demand response power, is the maximum value of the kth demand response power; The electric energy interaction constraint of the virtual power plant corresponding to the virtual power plant model is constructed to obtain the third objective constraint: in, is the benchmark power generation, is the benchmark electricity purchase amount, Base electricity price; The objective constraint group is obtained by combining the first objective constraint, the second objective constraint and the third objective constraint.

7. The method according to claim 1, characterized in that A revenue optimization model is constructed based on the stochastic programming model and the virtual power plant model by using a p-robust method, including: Construct a revenue optimization model based on the stochastic programming model and the virtual power plant model: Among them, p is the robustness index, UL is the upper limit of the preset interval, and LL is the lower limit of the preset interval. Estimated optimal solution.

8. A scheduling device for a virtual power plant based on stochastic programming and fuzzy satisfaction, characterized in that: The device comprises: an acquisition unit, configured to acquire historical operation data of the virtual power plant, and determine uncertainty factors and power plant operation parameters according to the historical operation data, wherein the uncertainty factors include at least one of RES output power, virtual power plant load and market price, and the power plant operation parameters include at least power interaction between the virtual power plant and the main power grid and the operation cost of the virtual power plant; A first construction unit is used to construct a stochastic programming model according to the uncertainty factor, wherein the stochastic programming model is used to simulate the probability change trend of a random scenario with the uncertainty factor, and the random scenario is composed of at least one of the uncertainty factors; A second construction unit is used to construct a virtual power plant model according to the power plant operation parameters, wherein the virtual power plant model is used to simulate the change trend of the net income of the virtual power plant with the power plant operation parameters; A third construction unit is used to construct a target constraint group, wherein the target constraint group includes constraint conditions of the virtual power plant model, and the target constraint group includes at least a demand response constraint, a power balance constraint, and an electric energy interaction constraint of the virtual power plant; A computing unit is used to construct a revenue optimization model based on the stochastic programming model and the virtual power plant model through a p-robust method, solve the revenue optimization model based on minimum-maximum structural optimization and fuzzy satisfaction method to obtain target scheduling parameters and control the operation of the virtual power plant according to the target scheduling parameters, wherein the target scheduling parameters include all parameters in the power plant operation parameters.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

10. A virtual power plant dispatching system, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include methods for executing any one of claims 1 to 7.

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