Two-stage low-carbon scheduling method and system for light-methane virtual power plant based on data driving

Abstract

The present application provides a kind of based on data-driven light-moat virtual power plant two-stage low carbon scheduling method and system.The method comprises: according to the energy output mechanism of biogas energy system and the application of biogas resources, construct biogas output model;According to the operation stability requirement of biogas gas turbine, combined with the gas transmission characteristics of biogas energy system, construct the heat transfer model of gas flow in the gas pipeline of biogas gas turbine of biogas energy system;Considering gas-electricity conversion characteristics, construct biogas energy system power transmission model;Based on the power transmission and heat transfer model of biogas energy system, establish the initial uncertainty fuzzy set of biogas output;According to the day-ahead carbon reduction benefit and real-time scheduling cost of light-moat virtual power plant, construct light-moat virtual power plant two-stage optimal low carbon scheduling objective function and constraint condition, generate light-moat virtual power plant two-stage low carbon scheduling method.The present application can adaptively correct the utilization scheme of distributed photovoltaic and biogas resources in rural light-moat virtual power plant, and under the condition of multiple uncertainties of distributed photovoltaic and biogas, provide stable energy support for virtual power plant operation while improving the level of low-carbon economy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system dispatching, and particularly relates to a two-stage low-carbon scheduling method and system for a light-methane virtual power plant based on data driving. BACKGROUND

[0002] With the construction of new power systems entering the implementation stage, virtual power plants as a new market participant are expanding their business. Virtual power plants aggregate and efficiently deploy various types of distributed resources across regions through advanced control, communication, and information collection technologies, fully utilizing the complementarity and flexibility of rural biomass and distributed renewable resources to meet their own energy needs and participate in grid dispatching, energy markets, peak shaving, frequency regulation, and standby auxiliary service markets. As a virtual entity, the aggregated external characteristics of virtual power plants are represented by the virtual response power adjustment range. In the context of high-capacity distributed resources in rural areas, the energy consumption behavior of various types of distributed resources in virtual power plants is highly uncertain, and higher requirements are placed on the rationality of grid-side scheduling under low-carbon demand. Therefore, it is important to study low-carbon scheduling methods for virtual power plants to improve the economy, reliability, and security of the system. Therefore, it is necessary to adaptively correct the utilization of distributed photovoltaic and biogas resources, and to provide stable energy support for virtual power plant operation while achieving low-carbon economy under multiple uncertainties of distributed photovoltaic and biogas resources. SUMMARY

[0003] The present application aims to provide a two-stage low-carbon scheduling method and system for a light-methane virtual power plant based on data driving to solve the problem of low-carbon scheduling of light-methane virtual power plants, and to provide stable energy support for virtual power plant operation while improving the level of low-carbon economy under multiple uncertainties of distributed photovoltaic and biogas resources.

[0004] The technical solution to achieve the present application is as follows:

[0005] A two-stage low-carbon scheduling method for a light-methane virtual power plant based on data driving, comprising:

[0006] According to the energy output mechanism of the biogas energy system and the application of biogas resources, a biogas output model is constructed according to the biogas production process;

[0007] Based on the biogas output model, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed based on the mass and momentum conservation equations, according to the biogas gas turbine operation stability requirements and the gas transmission characteristics of the biogas energy system;

[0008] According to the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine, and in combination with the heat conduction characteristics of the biogas energy system, a heat transfer model of the biogas energy system is generated;

[0009] Based on the generalized phase modeling method theory, in combination with the heat transfer model of the biogas energy system, and considering the gas-electricity conversion characteristics, a power transmission model of the biogas energy system is constructed;

[0010] Based on the power transmission model and the heat transfer model of the biogas energy system, and considering that the flexibility of the biogas energy system gas turbine is defined as all allowable power distribution sets within a time range, an initial uncertain fuzzy set of biogas output is established;

[0011] According to the photovoltaic output characteristics of rural areas, a distributed photovoltaic output model is fitted;

[0012] According to the distributed photovoltaic output model and the initial uncertain fuzzy set of biogas output, and considering the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the light-biogas virtual power plant are constructed to determine the optimal low-carbon scheduling scheme of the light-biogas virtual power plant.

[0013] A light-biogas virtual power plant two-stage low-carbon scheduling system for implementing the data-driven light-biogas virtual power plant two-stage low-carbon scheduling method includes a biogas output model construction unit, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine construction unit, a biogas energy system heat transfer model construction unit, a biogas energy system power transmission model construction unit, a biogas output initial uncertain fuzzy set construction unit, a distributed photovoltaic output model construction unit, and a light-biogas virtual power plant optimal low-carbon scheduling scheme generation unit; wherein:

[0014] The biogas output model construction unit is used to construct a biogas output model according to the energy output mechanism of the biogas energy system and the application situation of the biogas resource, and according to the biogas production process;

[0015] The thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine construction unit is used to construct, on the basis of the biogas output model, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine according to the biogas gas turbine operation stability requirement, in combination with the gas transmission characteristics of the biogas energy system, and based on the mass conservation and momentum conservation equations;

[0016] The biogas energy system heat transfer model construction unit is used to generate a heat transfer model of the biogas energy system according to the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine, and in combination with the heat conduction characteristics of the biogas energy system;

[0017] The power transmission model construction unit of the biogas energy system is constructed based on the generalized phase modeling method theory, in combination with a thermal transmission model of the biogas energy system, and considering gas-electricity conversion characteristics.

[0018] The initial uncertain fuzzy set construction unit of the biogas output is constructed based on the power transmission model and the thermal transmission model of the biogas energy system, and considering that the flexibility of the gas turbine of the biogas energy system is defined as all allowable power distribution sets within a time range, to establish an initial uncertain fuzzy set of the biogas output.

[0019] The distributed photovoltaic output model construction unit is constructed according to the photovoltaic output characteristics of the rural area, and a distributed photovoltaic output model is fitted.

[0020] The optimal low-carbon scheduling scheme generation unit of the light-biogas virtual power plant is constructed according to the distributed photovoltaic output model and the initial uncertain fuzzy set of the biogas output, considering the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, to construct a two-stage optimal low-carbon scheduling objective function and constraint conditions of the light-biogas virtual power plant, and determine the optimal low-carbon scheduling scheme of the light-biogas virtual power plant.

[0021] Compared with the prior art, the present application has the following beneficial effects: (1) the present application can establish a power transmission model of the biogas energy system according to the energy output mechanism of the biogas energy system and the application situation of the biogas resource in the rural area, in combination with the gas transmission characteristics of the biogas energy system, based on the mass conservation equation, momentum conservation equation and generalized phase modeling method theory, and taking into account the gas-electricity conversion characteristics; (2) the present application provides a quantitative model for the flexibility description of the gas turbine of the biogas energy system through the power transmission and thermal transmission models of the biogas energy system; (3) the present application establishes a two-stage optimal low-carbon scheduling objective function and constraint conditions of the light-biogas virtual power plant in view of the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, generates a data-driven two-stage low-carbon scheduling method of the light-biogas virtual power plant, and realizes adaptive correction of the utilization scheme of the distributed photovoltaic and biogas resources in the rural area, so as to provide stable energy support for the operation of the light-biogas virtual power plant and improve the low-carbon economy under the condition of multiple uncertainties of the distributed photovoltaic and biogas. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative labor.

[0023] Figure 1is an implementation flowchart of a two-stage low-carbon scheduling method based on data-driven optical-methane virtual power plant provided by the embodiment of the present application.

[0024] Figure 2 is a flowchart of a method for solving a two-stage robust problem by combining a column constraint generation algorithm with an ADMM algorithm provided by the embodiment of the present application.

[0025] Figure 3 is a parameter out-of-limit correction flowchart in the operation of a biogas electricity and heat transmission model based on the generalized phasor modeling method theory provided by the embodiment of the present application. DETAILED DESCRIPTION

[0026] In the following description, specific details are set forth such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, persons skilled in the art will understand that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0027] In order to make the objects, technical solutions and advantages of the present application clearer, the following will be described by specific embodiments in conjunction with the accompanying drawings.

[0028] In conjunction with Figure 1 A two-stage low-carbon scheduling method based on data-driven optical-methane virtual power plant includes:

[0029] According to the energy output mechanism of the biogas energy system and the application situation of the biogas resource, a biogas output model is constructed according to the biogas production process;

[0030] According to the operation stability requirement of the biogas gas turbine, in combination with the gas transmission characteristics of the biogas energy system, a thermodynamic model of the gas flow in the gas pipeline of the biogas gas turbine is constructed based on the mass conservation and momentum conservation equations;

[0031] According to the thermodynamic model of the gas flow in the gas pipeline of the biogas gas turbine, in combination with the heat conduction characteristics of the biogas energy system, a heat transmission model of the biogas energy system is generated;

[0032] Based on the generalized phasor modeling method theory, in combination with the heat transmission model of the biogas energy system, a power transmission model of the biogas energy system is constructed by considering the gas-electricity conversion characteristics;

[0033] Based on the power transmission and heat transmission models of the biogas energy system, by considering that the flexibility of the biogas energy system gas turbine is defined as all allowable power distribution sets within a time range, a biogas output initial uncertainty fuzzy set is established;

[0034] According to the photovoltaic output characteristics of rural areas, a distributed photovoltaic output model is fitted, a biogas output model is combined with a biogas gas turbine to form a biogas output model;

[0035] According to the photovoltaic and biogas output models, considering the day-ahead carbon reduction benefit and real-time scheduling cost of the photovoltaic-biogas virtual power plant, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the photovoltaic-biogas virtual power plant are constructed to determine the optimal low-carbon scheduling scheme of the photovoltaic-biogas virtual power plant.

[0036] In a possible implementation, according to the photovoltaic and biogas output models, for the day-ahead carbon reduction benefit and real-time scheduling cost of the photovoltaic-biogas virtual power plant, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the photovoltaic-biogas virtual power plant are constructed to determine the optimal low-carbon scheduling scheme of the photovoltaic-biogas virtual power plant, including:

[0037] According to the energy output mechanism of the biogas energy system and the application situation of the biogas resource, a biogas output model is constructed according to a biogas production process; according to the operation stability requirement of the biogas gas turbine, a heat transfer model and a power transfer model of the biogas energy system are constructed to form an initial uncertain fuzzy set of the biogas; according to the photovoltaic output characteristics of rural areas, a distributed photovoltaic output model is fitted; according to the photovoltaic and biogas output models, for the day-ahead carbon reduction benefit and real-time scheduling cost of the photovoltaic-biogas virtual power plant, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the photovoltaic-biogas virtual power plant are constructed to obtain an initial value of the day-ahead scheduling optimization objective function and the two-stage low-carbon scheduling objective function formed by the implementation scheduling optimization objective function.

[0038] According to the energy output mechanism of the biogas energy system and the application situation of the biogas resource, a biogas output model is constructed according to a biogas production process; according to the operation stability requirement of the biogas gas turbine, a heat transfer model and a power transfer model of the biogas energy system are constructed to form an initial uncertain fuzzy set of the biogas; according to the photovoltaic output characteristics of rural areas, a distributed photovoltaic output model is fitted; according to the photovoltaic and biogas output models, for the day-ahead carbon reduction benefit and real-time scheduling cost of the photovoltaic-biogas virtual power plant, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the photovoltaic-biogas virtual power plant are constructed to obtain a correction value of the day-ahead scheduling optimization objective function and the two-stage low-carbon scheduling objective function formed by the implementation scheduling optimization objective function.

[0039] According to the energy output mechanism of the biogas energy system and the application of the biogas resource, a biogas output model is constructed according to a biogas production process; according to the operation stability requirement of the biogas gas turbine, a heat transfer model and a power transfer model of the biogas energy system are constructed to form an initial uncertain fuzzy set of the biogas, and a distributed photovoltaic output model is fitted according to the photovoltaic output characteristics of the rural area; according to the photovoltaic and biogas output models, a two-stage optimal low-carbon scheduling objective function and constraint conditions of the light-biogas virtual power plant are constructed for the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, and a final value of the two-stage low-carbon scheduling objective function composed of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained.

[0040] According to the scheme set, the optimal value of the two-stage low-carbon scheduling objective function composed of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained for the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, and the optimal scheduling scheme in the scheme set is determined.

[0041] In combination Figure 3 In a possible implementation manner, the biogas output model is constructed according to the energy output mechanism of the biogas energy system and the application of the biogas resource according to the biogas production process.

[0042] The average degradation speed of the substrate in the sampling t time is expressed as

[0043]

[0044] wherein, μ max is the maximum degradation rate under the upper limit of the substrate concentration, S t is the average organic waste concentration in the livestock manure in the anaerobic fermentation in the t time, k S is the substrate half-saturation constant, k E is the enzyme concentration influence factor, is the average enzyme concentration in the t time, is the average substrate carbon-nitrogen ratio in the tank in the t time, k CN is the substrate carbon-nitrogen ratio influence factor.

[0045]

[0046] wherein, α is the synthesis rate constant of the enzyme, indicating the enzyme molecule output rate in the biogas fermentation substrate, β is the degradation rate constant of the enzyme, indicating the degradation rate of the enzyme molecule, k d is the substrate concentration decay constant, S0 is the substrate concentration in the initial stage, χ S is the organic matter weight factor of the input material.

[0047] Substituting the above equation into the equation (1), the expression of the gas production rate at time t is obtained as

[0048]

[0049] To ensure the stable operation of the biogas gas turbine, the gas delivery rate of the gas turbine is set to be linearly related to the gas production rate.

[0050] v tr,t = μ tr v S,t

[0051] In one possible implementation, according to the requirement of the stable operation of the biogas gas turbine, and in combination with the gas transmission characteristics of the biogas energy system, a thermodynamic model of the gas flow in the gas delivery pipeline of the biogas gas turbine is constructed based on the mass conservation equation and the momentum conservation equation, and is expressed as follows.

[0052] In combination with the linear assumption of the gas delivery rate and the gas production rate of the biogas gas turbine,

[0053] v tr,t = μ tr v S,t

[0054] The thermodynamics of the gas flow in the gas delivery pipeline of the biogas gas turbine can be represented by the mass conservation equation and the momentum conservation equation, and can be expressed as

[0055]

[0056] In one possible implementation, according to the thermodynamic model of the gas flow in the gas delivery pipeline of the biogas gas turbine, and in combination with the heat conduction characteristics of the biogas energy system, a thermodynamic transmission model of the biogas energy system is generated, and the thermodynamic transmission model of the biogas energy system is as follows.

[0057] Substituting the gas flow rate into the gas mass equation, the thermodynamic transmission model of the biogas energy system is obtained as

[0058]

[0059] The heat conduction equation in the biogas energy system can be written as

[0060]

[0061] In one possible implementation, according to the generalized phasor modeling method theory, in combination with the thermodynamic transmission model of the biogas energy system, and considering the gas-electricity conversion characteristics, a power transmission model of the biogas energy system is constructed, and is expressed as

[0062] Based on the theory of generalized phasor modeling method, the power transmission model in biogas energy system can use the same modeling principle, and the power line is equivalent to the heat transmission network model. The dynamic function is represented as

[0063]

[0064] wherein, α1, α2, β1, β2 are parameters related to the characteristics of the pipeline, the time derivative is eliminated by applying Fourier transform, and the spatial derivative is eliminated by using two-port equivalence, and the algebraic relationship between the generalized quantity and the intensive quantity at both ends can be derived as

[0065]

[0066] Further, the equation of the power transmission model of the biogas energy system is reversed to the time domain function, which is represented as

[0067]

[0068] In one possible implementation, based on the power transmission and heat transmission model of the biogas energy system, the flexibility of the gas turbine in the biogas energy system is defined as the set of all allowable power distribution in the time range, and the initial uncertain fuzzy set of biogas output is established as

[0069] Based on the power transmission and heat transmission model of the biogas energy system, the flexibility of the gas turbine in the biogas energy system is defined as the set of all allowable power distribution in the time range, and the initial uncertain fuzzy set of biogas output is established as

[0070] K i ={[ξ i (k)]∈R m |ψ i (k)=a i ψ i (k-1)+Y i ξ i (k)}

[0071]

[0072] wherein, α i is the energy dissipation of pipeline transmission.

[0073] In one possible implementation, according to the characteristics of the rural photovoltaic output, the distributed photovoltaic output model is fitted as

[0074]

[0075] wherein, r t is the environmental influence factor at time t, α PV , β PV and εPV to fit the parameters.

[0076] In a possible implementation, the day-ahead scheduling model with the lowest load energy carbon penalty cost as the target is established according to the light-biogas virtual power plant day-ahead carbon reduction benefit.

[0077] A two-stage scheduling optimization model based on data driving is divided into day-ahead scheduling with a time step of 1 hour and real-time scheduling with a time step of 15 minutes. The model is

[0078] F = min F1 + min F2

[0079] In the process of supplying the regional user load of the light-biogas virtual power plant, the biogas energy system produces a certain amount of carbon emissions during the biogas power generation operation stage, and at the same time, when there is a shortage of load, the user load needs to be supplied in the form of public network power purchase, at this time, the corresponding carbon penalty cost will be generated. Considering the carbon reduction benefit of the virtual power plant to the region, the day-ahead scheduling objective function is set to have the lowest load energy carbon penalty cost as the target.

[0080] min F1 = C Gbio P Gbio,t +C Grid P Grid,t +C bio (Q Gbio,t -Q bio,t )-C d V sto,t

[0081] Wherein, C Gbio is the biogas power generation carbon penalty cost, P Gbio,t is the biogas gas turbine power generation capacity at t, C Grid is the public network power purchase carbon penalty cost, P Grid,t is the user public network power purchase capacity at t, C bio is the carbon penalty cost of heat transfer dissipation, Q bio,t is the user heat load demand at t, C d is the biogas storage carbon reduction benefit, V sto,t is the biogas storage capacity at t.

[0082] The biogas gas turbine power generation capacity and heat energy output model can be expressed as

[0083]

[0084] Wherein, μ Gbio is the unit operation ratio, η burn is the biogas gas turbine gas efficiency, P g is the full-load capacity of the biogas gas turbine in the light-biogas virtual power plant, m Gbio,t is the biogas consumption, and μtran for heat energy transmission efficiency.

[0085] A. Biogas system network transmission constraints

[0086] Considering the influence of gas pressure controller, the transmission dynamics in the network transmission branch can be expressed as

[0087]

[0088] B. Heat energy transmission constraints

[0089] Q Gbio,min ≤Q Gbio ≤Q Gbio,max

[0090] In one possible implementation, considering the uncertainty brought by random renewable power generation and uncontrollable load, the scheduling of the multi-energy system should be further adjusted on a smaller time scale, and the real-time scheduling function of the virtual power plant is expressed as

[0091] Considering the uncertainty of rural distributed photovoltaic output and biogas output of biogas fermentation system, based on the data-driven real-time scheduling method, the hourly benchmark operation strategy is derived on the basis of the day-ahead scheduling optimization model, which provides guidance for the operation plan of inflexible units, including determining the on-off state of the generator and managing the transaction between the multi-energy system and the external energy network. Considering the uncertainty brought by random renewable power generation and uncontrollable load, the scheduling of the multi-energy system should be further adjusted on a smaller time scale. In order to improve the optimization performance, the data-driven robust optimization method is used to optimize the decision-making process.

[0092] 1) Data-driven uncertainty fuzzy set

[0093] Affected by light intensity and temperature, the active power output of distributed photovoltaic in rural areas fluctuates with time. The active power output of the node-attached distributed photovoltaic is expressed by a robust box-type uncertainty set as follows

[0094]

[0095] wherein, is the benchmark value of the active power output of the photovoltaic at the i-th node location in the t-th period, is the corresponding maximum prediction deviation amount, ξ i,t is the uncertainty coefficient, and ξ i,t ∈[0,1].

[0096] In the light-biogas virtual power plant, there are various forms of distributed biogas resources. Due to the fluctuation of biological material output, the biogas output speed of the biogas fermentation output link is affected by various factors. The robust box-type uncertainty set of biogas output is expressed as

[0097]

[0098] wherein, is the biogas production rate reference value at time period t for the ith node location, is the corresponding maximum prediction deviation amount, ζ i,t is the uncertainty coefficient, and ζ i,t ∈ [0, 1].

[0099] The real-time scheduling stage has the minimum scheduling cost as the objective function. The objective function expression is

[0100]

[0101] wherein, ρ i,t is the output uncertainty variable probability value of node i, C i,t is the unit scheduling cost of node i, y i,t is the real-time regulation amount of node i, P L,i,t is the node load demand.

[0102] 2) Constraint conditions

[0103] The power distribution energy grid should satisfy the power flow equation constraint

[0104]

[0105] wherein, P m represents the injected active power at node m, Q m represents the injected reactive power at node m, U m is the voltage amplitude at node m, U k is the voltage amplitude at node k, G mk and B mk represent the real part and the imaginary part of the system admittance matrix, respectively, θ mk is the voltage phase angle difference of nodes m and k.

[0106] The voltage inequality constraint, i.e., the inequality constraint condition of the voltage opportunity of each node, is

[0107] Pr{U min ≤ U mk ≤ U max} ≥ β U ,

[0108] wherein, Pr represents the event probability set, U min represents the lower limit of the node voltage, U max represents the upper limit of the node voltage, and β U is the grid voltage confidence level.

[0109] The distributed photovoltaic fluctuation will cause voltage deviation at the access nodes, and in order to ensure the power quality of the power grid, the voltage deviation inequality constraint condition of each node is set

[0110] χ m,t ≤x grid,max

[0111]

[0112] χ m,t represents the voltage deviation of the distributed photovoltaic at the node m at the time t, χ grid,max represents the maximum voltage deviation allowed by the distribution network, U Line represents the feeder voltage.

[0113] The embodiment also provides a data-driven two-stage low-carbon scheduling system of a light-mire virtual power plant, comprising a mire output model construction unit, a thermodynamic model construction unit of gas flow in a gas pipeline of a mire gas turbine, a mire energy system heat transfer model construction unit, a mire energy system power transmission model construction unit, a mire output initial uncertain fuzzy set construction unit, a distributed photovoltaic output model construction unit and a light-mire virtual power plant optimal low-carbon scheduling scheme generation unit; wherein:

[0114] The mire output model construction unit is used for constructing a mire output model according to a mire production process flow according to an energy output mechanism of a mire energy system and a mire resource application situation;

[0115] The thermodynamic model construction unit of gas flow in a gas pipeline of a mire gas turbine is used for constructing a thermodynamic model of gas flow in a gas pipeline of a mire gas turbine based on the mire output model, according to a mire gas turbine operation stability requirement, combining a mire energy system gas transmission characteristic and based on a mass conservation equation and a momentum conservation equation;

[0116] The mire energy system heat transfer model construction unit is used for generating a mire energy system heat transfer model according to the thermodynamic model of gas flow in a gas pipeline of a mire gas turbine, combining a mire energy system heat conduction characteristic;

[0117] The mire energy system power transmission model construction unit is used for constructing a mire energy system power transmission model based on a generalized phase modeling method theory, combining the mire energy system heat transfer model and considering a gas-electricity conversion characteristic;

[0118] The mire output initial uncertain fuzzy set construction unit is used for establishing a mire output initial uncertain fuzzy set based on the mire energy system power transmission model and the heat transfer model and considering defining flexibility of a mire energy system gas turbine as all allowable power distribution sets in a time range;

[0119] The distributed photovoltaic output model construction unit fits a distributed photovoltaic output model according to photovoltaic output characteristics of rural areas;

[0120] The optimal low-carbon scheduling scheme generation unit of the light-methane virtual power plant constructs a two-stage optimal low-carbon scheduling objective function and constraint conditions of the light-methane virtual power plant according to the distributed photovoltaic output model and the initial uncertain fuzzy set of the methane output, considers day-ahead carbon reduction benefits and real-time scheduling costs of the light-methane virtual power plant, and determines the optimal low-carbon scheduling scheme of the light-methane virtual power plant.

[0121] Embodiment

[0122] The embodiment provides an implementation step of a data-driven two-stage low-carbon scheduling method of a light-methane virtual power plant, and specifically includes the following steps.

[0123] In combination Figure 2 The two-stage robust problem is solved by using a method combining a column constraint generation algorithm and an ADMM algorithm, and specifically includes the following steps.

[0124] Step 201, initialization stage, initializing convergence upper and lower limit values, iteration times and allowable deviation;

[0125] Step 202, setting pre-scheduling decision variables;

[0126] Step 203, data preprocessing stage, generating an initial uncertain probability and constructing an output uncertain set;

[0127] Optionally, based on a power transmission and heat transmission model of the methane energy system, all allowable power distribution sets of the gas turbine of the methane energy system in a time range are considered,

[0128] K i ={[ξ i (k)]∈R m |ψ i (k)=a i ψ i (k-1)+Y i ξ i (k)}

[0129]

[0130] Wherein, α i is the energy dissipation amount of pipeline transmission.

[0131] According to the photovoltaic output characteristics of rural areas, the distributed photovoltaic output model is fitted as

[0132]

[0133] Wherein, rt is the environmental impact factor at time t, α PV , β PV and ε PV are fitting parameters.

[0134] Affected by the intensity of light and temperature, the active power output of distributed photovoltaic in rural areas fluctuates with time. The active power output of the node access distributed photovoltaic is expressed by a robust box-type uncertain set as follows

[0135]

[0136] wherein, is the reference value of the active power output of the photovoltaic at the i-th node position at time t, is the corresponding maximum prediction deviation amount, ξ i,t is an uncertain coefficient, and ξ i,t ∈[0, 1].

[0137] In the light-mire virtual power plant, there are various forms of distributed mire resources. Due to the fluctuation of biological material output, the mire output speed in the mire fermentation output link is affected by various factors. The mire output robust box-type uncertain set is expressed as follows

[0138]

[0139] wherein, is the reference value of the mire output speed at the i-th node position at time t, is the corresponding maximum prediction deviation amount, ζ i,t is an uncertain coefficient, and ζ i,t ∈[0, 1].

[0140] Step 204, the carbon emission penalty cost of day-ahead scheduling is solved and optimized, and the deviation is checked, the mire resource reserve is checked, and it is determined whether the scheduling behavior is feasible;

[0141] Optionally, the time domain function constraint of the mire power transmission model is

[0142]

[0143] The node voltage inequality constraint, that is, the inequality constraint condition of the voltage opportunity of each node is

[0144] Pr{U min ≤U mk ≤U max}≥β U ,

[0145] wherein, Pr represents an event probability set, U min represents the lower limit of the node voltage, and U maxUpper limit of node voltage, β U Grid voltage confidence level.

[0146] The fluctuating distributed photovoltaic will cause voltage deviation effect at the access node, in order to protect the power quality of the power grid, set the voltage deviation inequality constraint condition of each node

[0147] x m,t ≤x grid,max

[0148]

[0149] Wherein, χ m,t represents the voltage deviation generated by the distributed photovoltaic at node m at t time, χ grid,max represents the maximum voltage deviation allowed by the distribution network, U Line represents the feeder voltage.

[0150] Step 205, determine the uncertain probability set and redistribute the node scheduling quantity, optimize and solve the overall objective function;

[0151] Step 206, update the iteration number, and judge the parameter out-of-limit condition.

[0152] Combined with Figure 3 , the parameter out-of-limit correction process of the biogas electricity and heat transmission model running based on the generalized phase modeling method theory includes:

[0153] Step 301, from the time domain and frequency domain, the biogas electricity and heat transmission model is processed by dimension reduction, and additional information of the biogas energy system is intuitively and efficiently mined;

[0154] Optionally, the average degradation rate expression of the substrate within the sampling t time is

[0155]

[0156] Wherein, μ max is the maximum degradation rate under the upper limit of the substrate concentration, S t is the average organic waste concentration in the livestock manure in the anaerobic fermentation within t time, k S is the substrate half-saturation constant, k E is the enzyme concentration influence factor, is the average enzyme concentration within t time, is the average substrate carbon-nitrogen ratio in the tank within t time, k CN is the substrate carbon-nitrogen ratio influence factor in the tank.

[0157]

[0158] Where, α is the synthesis rate constant of enzyme, indicating the production rate of enzyme molecules in biogas fermentation substrate, β is the degradation rate constant of enzyme, indicating the degradation rate of enzyme molecules, k d is the substrate concentration decay constant, S0 is the initial stage substrate concentration, χ S is the organic matter weight factor of input material.

[0159] Substituting, the sampling t time gas production rate expression is

[0160]

[0161] In order to ensure the stable operation of the biogas gas turbine, the gas turbine gas rate and gas production rate are set to be in linear relationship.

[0162] v tr,t = μ tr v S,t

[0163] The thermodynamics of gas flow in the gas pipeline of the biogas gas turbine can be represented by the mass conservation equation and the momentum conservation equation, which can be represented as

[0164]

[0165] Substituting the gas flow rate into the gas mass equation, the thermodynamic transmission model of the biogas energy system is

[0166]

[0167] The heat conduction equation in the biogas energy system can be written as

[0168]

[0169] Based on the theory of generalized phasor modeling method, the power transmission model in the biogas energy system can use the same modeling principle, which is to equivalent the power line to the heat transmission network model. The dynamic function is represented as

[0170]

[0171] Where, α1, α2, β1, β2 are parameters related to the characteristics of the pipeline. The time derivative is eliminated by applying Fourier transform, and the spatial derivative is eliminated by using two-port equivalence. The algebraic relationship between the generalized quantities and the intensive quantities at both ends can be derived as

[0172]

[0173] Further, the biogas energy system power transmission model equation is reversed to the time domain function, which is represented as

[0174]

[0175] In step 302, the model information is compared and calculated in combination with the real-time operation of the light-methane virtual power plant.

[0176] In step 303, the additional information of the biogas energy system is fed back and corrected considering the operation constraints of the virtual power plant.

[0177] In step 304, the corrected treatment of the biogas energy system is determined, and the optimization problem is iteratively solved.

[0178] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software function unit. In addition, the specific name of each functional unit and module is only for easy distinction, and does not limit the protection scope of the application. The specific working process of the unit and module in the above system can be referred to the corresponding process in the foregoing method embodiments, which will not be described here.

[0179] In the above embodiments, the description of each embodiment has its own emphasis, and the parts not described or recorded in detail in a certain embodiment can be referred to the related description of other embodiments.

[0180] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0181] The units described as separate modules can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment.

[0182] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0183] The integrated module / unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. The computer program can implement the steps of each of the above-mentioned data-driven two-stage low-carbon scheduling methods for optical-methane virtual power plants when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, an executable file, or some intermediate form, etc. The computer-readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. that can carry the computer program code. It should be noted that the contents included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0184] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A two-stage low-carbon scheduling method based on data-driven optical-moat virtual power plant, characterized in that, The application relates to a method for constructing an optimal low-carbon scheduling scheme of a light-biogas virtual power plant. According to the energy output mechanism of a biogas energy system and the application situation of biogas resources, a biogas output model is constructed according to a biogas production process; On the basis of the biogas output model, according to the operation stability requirements of a biogas gas turbine, in combination with the gas transmission characteristics of the biogas energy system, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed based on the mass conservation and momentum conservation equations; According to the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine, in combination with the heat conduction characteristics of the biogas energy system, a heat transfer model of the biogas energy system is generated; Based on the generalized phase modeling method theory, in combination with the heat transfer model of the biogas energy system, and considering the gas-electricity conversion characteristics, a power transmission model of the biogas energy system is constructed; Based on the power transmission model and the heat transfer model of the biogas energy system, by defining the flexibility of the biogas energy system gas turbine as all allowable power distribution sets within a time range, a biogas output initial uncertain fuzzy set is established; According to the light-biogas virtual power plant, a distributed photovoltaic output model is fitted; According to the distributed photovoltaic output model and the biogas output initial uncertain fuzzy set, considering the day-ahead carbon reduction benefit and real-time scheduling cost of the light-biogas virtual power plant, a light-biogas virtual power plant two-stage optimal low-carbon scheduling target function and constraint conditions are constructed to determine the optimal low-carbon scheduling scheme of the light-biogas virtual power plant; The thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed as follows: where v tr is a fit function for the gas feed rate, p g is a generator set output function, p g is a gas density, D g is a numerical parameter, is a fit parameter phase angle, l and g are numerical parameters, t is time, and x is a fit function argument; The light-biogas virtual power plant two-stage optimal low-carbon scheduling target function includes day-ahead scheduling with a time step of 1 hour and real-time scheduling with a time step of 15 minutes, namely: F = min F1 + min F2 Wherein, F1 is the day-ahead scheduling target function, and F2 is the real-time scheduling target function; The biogas output initial uncertain fuzzy set is as follows: K i = { [ζ i (k)] ∈ R m |ψ i (k) = a i ψ i (k-1) + Y i ζ i (k) wherein a i is the output parameter of the i-th biogas generator set; The day-ahead scheduling target function takes the minimum load energy carbon penalty cost as the target, namely minF1 = C Gbio P Gbio,t +C Grid P Grid,t +C bio (Q Gbio,t -Q bio,t )-C d V sto,t wherein C Gbio is the carbon penalty cost of biogas power generation, P Gbio,t is the biogas gas turbine power generation capacity at time t, C Grid is the carbon penalty cost of public grid electricity purchase, P Grid,t is the user public grid electricity purchase capacity at time t, C bio is the carbon penalty cost of heat energy transmission dissipation, Q bio,t is the user heat load demand capacity at time t, Q Gbio,t is the biogas power generator output heat, is the user heat load demand capacity at time t, C d is the carbon reduction benefit of biogas storage, V sto,t is the biogas storage capacity at time t; wherein μ Gbio is the machine work ratio, η burn is the biogas gas turbine gas efficiency, P g is the biogas gas turbine full-load capacity in the light-biogas virtual power plant, m Gbio,t is the biogas consumption, μ tran is the thermal energy transmission efficiency; The constraint conditions of the day-ahead scheduling target function include biogas system network transmission constraints and heat energy transmission constraints.

2. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The biogas output model is as follows: where v S,t is the average degradation rate of substrate in time t, μ max is the maximum degradation rate at the upper limit of substrate concentration, k S is the half-saturation constant of substrate, χ S is the organic matter weight factor of input material, S0 is the initial stage substrate concentration, k d is the substrate concentration decay constant, k E is the enzyme concentration influence factor, N CN,t is the average substrate carbon-nitrogen ratio in the tank in time t, k CN is the substrate carbon-nitrogen ratio influence factor in the tank, α is the synthesis rate constant of enzyme, indicating the enzyme molecule output rate in the substrate of biogas fermentation, β is the degradation rate constant of enzyme, indicating the degradation rate of enzyme molecules, M i,in,t is the material injected into the biogas fermentation link at time t.

3. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The gas transmission characteristics of the biogas energy system are assumed to have a linear relationship between the gas turbine gas transmission rate and the degradation rate, namely: v tr,t = μ tr v S,t Where, μ tr v is a linear proportionality coefficient tr,t v is the gas delivery rate. S,t The average degradation rate of the substrate within the sampling time t is the gas production rate.

4. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The heat transfer model of the biogas energy system is as follows: where m g is the gas flow rate of the gas pipeline per unit time, v tr is the fitting function of the gas transmission rate, p g is the generator set output function, p g is the gas density, A g is the cross-sectional area of the gas pipeline, R is the conversion coefficient, T g is the heat transfer of the gas pipeline, D g is a parameter, and g and t are data parameters, t is time, and x is the independent variable of the fitting function.

5. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, wherein The power transmission model of the biogas energy system is as follows: Wherein, alpha1, alpha2, beta1, beta2 are numerical parameters related to the pipeline characteristics, and psi, zeta are transmission model port fitting functions, the time derivative is eliminated by Fourier transform, and the spatial derivative is eliminated by using the two-port equivalence to establish the algebraic relationship between the generalized quantities and the intensive quantities at both ends as follows: Wherein, psi0, zeta0 are transmission model port reference values, and A, B, C, D are transmission matrix parameters; Then, the power transmission model equation of the biogas energy system is reversed to a time domain function, which is expressed as: Wherein, n is the number of biogas generators, and i is the i th unit.

6. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The distributed photovoltaic output model is as follows: where r t is the environmental impact factor at time t, α PV , β PV and ε PV are fitting parameters, and Γ is the gamma function.

7. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The biogas system network transmission constraints are as follows: The heat energy transport constraint is: Q Gbio,min ≤ Q Gbio ≤ Q Gbio,max .

8. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 1, characterized in that, The real-time scheduling target function is as follows: wherein, ρ i,t is the output uncertainty variable probability value of node i, C i,t is the unit scheduling cost of node i, y i,t is the real-time regulation amount of node i, P L,i,t is the load demand amount of node, f(v i,t ) is the absolute offset function of output active power The constraint conditions of the real-time scheduling target function include power flow equation constraints, voltage inequality constraints and node voltage offset inequality constraints.

9. The data-driven based optical-methane virtual power plant two-stage low-carbon scheduling method according to claim 8, characterized in that, The power flow equation constraints are as follows: where P m represents the injected real power at node m, Q m represents the injected reactive power at node m, U m is the voltage magnitude at node m, U k is the voltage magnitude at node k, G mk and B mk represent the real and imaginary parts of the system admittance matrix, respectively, and θ mk is the voltage phase angle difference between nodes m and k. The voltage inequality constraint is Pr{U min ≤U mk ≤U max}≥β U , where Pr represents a set of event probabilities, U min represents a lower node voltage limit, U max represents an upper node voltage limit, β U a grid voltage confidence level; The node voltage offset inequality constraint condition is χ m,t ≤χ grid,max where χ m,t represents the distributed photovoltaic generated voltage offset at node m at time t, χ grid,max represents the maximum voltage offset allowed by the distribution network, U Line represents the feeder voltage. 10.A photobioreactor (PBR) virtual power plant (VPP) two-stage low-carbon scheduling system for implementing the data-driven photobioreactor (PBR) VPP two-stage low-carbon scheduling method of any one of claims 1-9, wherein, The unit comprises a biogas output model construction unit, a thermodynamic model construction unit of gas flow in a gas pipeline of a biogas gas turbine, a biogas energy system heat transfer model construction unit, a biogas energy system power transmission model construction unit, a biogas output initial uncertain fuzzy set construction unit, a distributed photovoltaic output model construction unit and a light-biogas virtual power plant optimal low-carbon scheduling scheme generation unit; wherein: The biogas output model construction unit is used to construct a biogas output model according to the energy output mechanism of the biogas energy system and the application of the biogas resource, and according to the biogas production process; The thermodynamic model construction unit of gas flow in a gas pipeline of a biogas gas turbine is used to construct a thermodynamic model of gas flow in a gas pipeline of a biogas gas turbine based on the biogas output model, according to the biogas gas turbine operation stability requirement, combined with the gas transmission characteristics of the biogas energy system, and based on the mass conservation and momentum conservation equations; The biogas energy system heat transfer model construction unit is used to generate a biogas energy system heat transfer model according to the thermodynamic model of gas flow in a gas pipeline of a biogas gas turbine, combined with the heat conduction characteristics of the biogas energy system; The biogas energy system power transmission model construction unit is used to construct a biogas energy system power transmission model based on the generalized phase modeling method theory, combined with the biogas energy system heat transfer model, and considering the gas-electricity conversion characteristics; The biogas output initial uncertain fuzzy set construction unit is used to establish a biogas output initial uncertain fuzzy set by defining the flexibility of the biogas energy system gas turbine as all allowable power distribution sets within a time range based on the biogas energy system power transmission model and the heat transfer model; The distributed photovoltaic output model construction unit is used to fit a distributed photovoltaic output model according to the photovoltaic output characteristics of rural areas; The light-biogas virtual power plant optimal low-carbon scheduling scheme generation unit is used to construct a light-biogas virtual power plant two-stage optimal low-carbon scheduling objective function and constraint conditions according to the distributed photovoltaic output model and the biogas output initial uncertain fuzzy set, considering the light-biogas virtual power plant day-ahead carbon reduction benefit and real-time scheduling cost, and determine the light-biogas virtual power plant optimal low-carbon scheduling scheme.

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