Light-biogas virtual power plant two-stage low-carbon scheduling method and system based on data driving

Abstract

The invention provides a light-biogas virtual power plant two-stage low-carbon scheduling method and system based on data driving. The method comprises the following steps: constructing a biogas output model according to an energy output mechanism and a biogas resource application condition of a biogas energy system; the method comprises the following steps: constructing a biogas energy system thermal transmission model of gas flowing in a gas pipeline of a biogas gas turbine by combining gas transmission characteristics of a biogas energy system according to the operation stability requirement of the biogas gas turbine; considering gas-electricity conversion characteristics, and constructing a power transmission model of the biogas energy system; the method comprises the following steps: establishing a biogas output initial uncertainty fuzzy set based on a biogas energy system power transmission and heat transmission model; and according to the day-ahead carbon reduction benefit and the real-time scheduling cost of the light-biogas virtual power plant, constructing a light-biogas virtual power plant two-stage optimal low-carbon scheduling objective function and constraint conditions, and generating the light-biogas virtual power plant two-stage low-carbon scheduling method. According to the method, the utilization scheme of the distributed photovoltaic and biogas resources in the rural light-biogas virtual power plant can be adaptively corrected, and the low-carbon economic level is improved while stable energy support is provided for the operation of the virtual power plant under the condition that multiple uncertainties occur in the distributed photovoltaic and biogas resources.

Description

Technical Field

[0001] The present invention relates to the field of power system dispatching, and in particular to a two-stage low-carbon dispatching method and system for a solar-biogas virtual power plant based on data-driven. Background Art

[0002] As the construction of the new power system enters the implementation stage, the types of business that virtual power plants participate in as an emerging market subject are constantly expanding. Virtual power plants use advanced control, communication, and information collection technologies to aggregate and efficiently allocate a variety of distributed resources across regions on a large scale, giving full play to the complementarity and flexibility of rural biomass and distributed renewable resources, which not only meets their own energy needs, but also participates in grid dispatching and operation as a whole, as well as energy markets, peak regulation, frequency regulation, and standby auxiliary service markets. As a virtual subject, the external characteristics of virtual power plants are manifested as a virtual response power adjustment range. In the context of excessive access capacity of a high proportion of distributed resources in rural areas, the energy consumption behavior of various types of distributed resources in virtual power plants is highly uncertain, which puts forward higher requirements for the rationality of grid-side dispatching under low-carbon demand. Therefore, studying the low-carbon dispatching method of virtual power plants is of great significance to improving the economy, reliability, and safety of the system. Therefore, it is necessary to adaptively correct the utilization of distributed photovoltaic and biogas resources, and provide stable energy support for the operation of virtual power plants while achieving low-carbon economy under the condition of multiple uncertainties in distributed photovoltaic and biogas. Summary of the invention

[0003] The purpose of the present invention is to provide a data-driven two-stage low-carbon scheduling method and system for a solar-biogas virtual power plant to solve the problem of low-carbon scheduling of solar-biogas virtual power plants. In the case of multiple uncertainties in distributed photovoltaic and biogas, it provides stable energy support for the operation of virtual power plants while improving the level of low-carbon economy.

[0004] The technical solution to achieve the purpose of the present invention is:

[0005] A two-stage low-carbon dispatch method for a solar-biogas virtual power plant based on data-driven, including:

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

[0008] Based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system, a thermal transmission model of the biogas energy system is generated;

[0009] Based on the theory of generalized phasor modeling method, combined with the thermal transmission model of biogas energy system, and considering the gas-electricity conversion characteristics, the power transmission model of biogas energy system is constructed;

[0010] Based on the power transmission model and thermal transmission model of biogas energy system, the flexibility of gas turbine in biogas energy system is defined as the set of all allowable power distributions within the time range, and the initial uncertain fuzzy set of biogas output is established.

[0011] According to the photovoltaic output characteristics in 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, considering the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, the two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant are constructed, and the optimal low-carbon scheduling scheme of the solar-biogas virtual power plant is determined.

[0013] A two-stage low-carbon dispatching system for a solar-biogas virtual power plant that implements the data-driven two-stage low-carbon dispatching method for a solar-biogas virtual power plant comprises a biogas output model construction unit, a thermodynamic model construction unit for gas flow in a gas pipeline of a biogas gas turbine, a biogas energy system thermal transmission 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 solar-biogas virtual power plant optimal low-carbon dispatching scheme generation unit; wherein:

[0014] The biogas output model building unit is used to build a biogas output model according to the energy output mechanism of the biogas energy system and the application of biogas resources and the biogas production process;

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

[0016] The biogas energy system thermal transmission model building unit generates a biogas energy system thermal transmission model based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system;

[0017] The biogas energy system power transmission model construction unit is based on the generalized phasor modeling method theory, combined with the biogas energy system thermal transmission model, and considers the gas-electricity conversion characteristics to construct the biogas energy system power transmission model;

[0018] The initial uncertain fuzzy set construction unit of biogas output is based on the power transmission model and thermal transmission model of the biogas energy system, considering that the flexibility of the gas turbine of the biogas energy system is defined as the set of all allowable power distributions within the time range, and establishing the initial uncertain fuzzy set of biogas output;

[0019] The distributed photovoltaic output model building unit fits the distributed photovoltaic output model according to the photovoltaic output characteristics in rural areas;

[0020] The optimal low-carbon scheduling scheme generation unit of the solar-biogas virtual power plant is based on the distributed photovoltaic output model and the initial uncertain fuzzy set of biogas output, considers the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, constructs the two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant, and determines the optimal low-carbon scheduling scheme of the solar-biogas virtual power plant.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) the present invention can establish a power transmission model of the biogas energy system based on the energy output mechanism of the biogas energy system and the application of biogas resources in rural areas, combined with the gas transmission characteristics of the biogas energy system, based on the mass conservation, momentum conservation equations and the generalized phasor modeling method theory while taking into account the gas-to-electricity conversion characteristics; (2) the present invention provides a quantitative model for describing the flexibility of the gas turbine in the biogas energy system through the power transmission and heat transmission models of the biogas energy system; (3) the present invention establishes a two-stage optimal low-carbon scheduling objective function and constraint conditions for the solar-biogas virtual power plant in view of the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, generates a two-stage low-carbon scheduling method for the solar-biogas virtual power plant based on data-driven, realizes adaptive correction of the utilization plan of distributed photovoltaic and biogas resources in rural areas, and provides stable energy support for the operation of the solar-biogas virtual power plant while improving the low-carbon economy when multiple uncertainties occur in distributed photovoltaic and biogas. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0023] Figure 1It is an implementation flow chart of a two-stage low-carbon scheduling method of a solar-biogas virtual power plant based on data-driven provided in an embodiment of the present invention.

[0024] Figure 2 It is a flow chart of solving a two-stage robust problem by combining a column constraint generation algorithm with an ADMM algorithm provided by an embodiment of the present invention.

[0025] Figure 3 It is a flow chart of parameter over-limit correction in the operation of a biogas electricity and heat transfer model based on the generalized phasor modeling method theory provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0027] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0028] Combination Figure 1 , a two-stage low-carbon dispatch method for solar-biogas virtual power plants based on data-driven, including:

[0029] 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;

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

[0031] Based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system, a thermal transmission model of the biogas energy system is generated;

[0032] Based on the theory of generalized phasor modeling method, combined with the thermal transmission model of biogas energy system, and considering the gas-electricity conversion characteristics, the power transmission model of biogas energy system is constructed;

[0033] Based on the power transmission and heat transmission model of biogas energy system, the flexibility of gas turbine in biogas energy system is defined as the set of all allowable power distributions within the time range, and the initial uncertain fuzzy set of biogas output is established.

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

[0035] According to the photovoltaic and biogas output model, considering the day-ahead carbon reduction benefits and real-time dispatching costs of the solar-biogas virtual power plant, the two-stage optimal low-carbon dispatching objective function and constraints of the solar-biogas virtual power plant are constructed, and the optimal low-carbon dispatching scheme of the solar-biogas virtual power plant is determined.

[0036] In a possible implementation, based on the photovoltaic and biogas output models, in order to achieve the day-ahead carbon reduction benefits and real-time dispatch costs of the photovoltaic-biogas virtual power plant, a two-stage optimal low-carbon dispatch objective function and constraints for the photovoltaic-biogas virtual power plant are constructed, and the optimal low-carbon dispatch scheme for the photovoltaic-biogas virtual power plant is determined, including:

[0037] 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; according to the operation stability requirements of the biogas gas turbine, a thermal transmission model of the biogas energy system and a power transmission model of the biogas energy system are constructed to form an initial uncertain fuzzy set of biogas, and a distributed photovoltaic output model is fitted according to the photovoltaic output characteristics of rural areas; according to the photovoltaic and biogas output models, a two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant are constructed for the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, and the initial value of the two-stage low-carbon scheduling objective function consisting of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained.

[0038] 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; according to the operation stability requirements of the biogas gas turbine, a thermal transmission model of the biogas energy system and a power transmission model of the biogas energy system are constructed to form an initial uncertain fuzzy set of biogas, and a distributed photovoltaic output model is fitted according to the photovoltaic output characteristics of rural areas; according to the photovoltaic and biogas output models, a two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant are constructed for the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, and the revised value of the two-stage low-carbon scheduling objective function consisting of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained.

[0039] 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; according to the operation stability requirements of the biogas gas turbine, a thermal transmission model of the biogas energy system and a power transmission model of the biogas energy system are constructed to form an initial uncertain fuzzy set of biogas, and a distributed photovoltaic output model is fitted according to the photovoltaic output characteristics of rural areas; according to the photovoltaic and biogas output models, a two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant are constructed for the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, and the final value of the two-stage low-carbon scheduling objective function consisting of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained.

[0040] According to the scheme set, based on the photovoltaic and biogas output, for the day-ahead carbon reduction benefits and real-time scheduling costs of the photovoltaic-biogas virtual power plant, the optimal value of the two-stage low-carbon scheduling objective function consisting of the day-ahead scheduling optimization objective function and the implementation scheduling optimization objective function is obtained, and the best scheduling scheme in the scheme set is determined.

[0041] Combination Figure 3 In a possible implementation, 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 as described below.

[0042] The average degradation rate of the substrate within the sampling time t is expressed as:

[0043]

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

[0045]

[0046] Among them, α is the enzyme synthesis rate constant, which indicates the production rate of enzyme molecules in the biogas fermentation substrate; β is the enzyme degradation rate constant, which indicates the degradation rate of enzyme molecules; k d is the substrate concentration decay constant, S0 is the substrate concentration in the initial stage, χ S is the weight factor of organic matter in input materials.

[0047] Substituting into the equation, the expression of the gas production rate at sampling time t is:

[0048]

[0049] In order to ensure the stable operation of the biogas gas turbine, the gas transmission rate and gas production rate of the gas turbine are set to be in a linear relationship.

[0050] v tr,t =μ tr v S,t

[0051] In a possible implementation, according to the requirements of biogas gas turbine operation stability, combined with the gas transmission characteristics of the biogas energy system, based on the mass conservation and momentum conservation equations, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed as follows.

[0052] Combined with the linear assumption of the gas transmission rate and 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 pipeline of the biogas gas turbine can be expressed by the mass conservation equation and the momentum conservation equation, which can be expressed as follows:

[0055]

[0056] In a possible implementation, a thermal transmission model of a biogas energy system is generated based on a thermodynamic model of gas flow in a gas pipeline of a biogas gas turbine and combined with the heat conduction characteristics of the biogas energy system. The thermal transmission model of the biogas energy system is as follows:

[0057] Substituting the gas flow rate into the gas mass equation, the thermal transfer model of the biogas energy system can be obtained as follows:

[0058]

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

[0060]

[0061] In a possible implementation, the generalized phasor modeling method theory is combined with the thermal transmission model of the biogas energy system, and the gas-electricity conversion characteristics are considered to construct the power transmission model of the biogas energy system.

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

[0063]

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

[0065]

[0066] Then, the power transmission model equation of the biogas energy system is reversed into a time domain function, which is expressed as

[0067]

[0068] In a possible implementation, based on the power transmission and heat transmission model of the biogas energy system, the flexibility of the gas turbine of the biogas energy system is considered to be defined as the set of all allowable power distributions within 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 biogas energy system, the flexibility of gas turbine in biogas energy system is considered to be defined as the set of all allowable power distributions within the time range, in preparation for the uncertain fuzzy set in real-time scheduling.

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

[0071]

[0072] Among them, α i The amount of energy dissipated by the pipeline transmission.

[0073] In a possible implementation, according to the photovoltaic output characteristics in rural areas, the distributed photovoltaic output model is fitted as follows:

[0074]

[0075] Among them, r t is the environmental impact factor at time t, α PV , β PV and εPV is the fitting parameter.

[0076] In a possible implementation, the day-ahead dispatch model is established based on the day-ahead carbon reduction benefits of the solar-biogas virtual power plant and takes the lowest carbon penalty cost of load energy as the goal:

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

[0078] F=minF1+minF2

[0079] When the solar-biogas virtual power plant supplies regional user loads, the biogas energy system generates a certain amount of carbon emissions during the biogas power generation operation stage. At the same time, when there is a load shortage, it is necessary to supply the user load through the form of public grid electricity purchase, which will generate corresponding carbon penalty costs. Considering the carbon reduction benefits of the virtual power plant to the region, the day-ahead scheduling objective function is set to minimize the carbon penalty cost of load energy consumption.

[0080] minF1=C Gbio P Gbio,t +C Grid P Grid,t +C bio (Q Gbio,t -Q bio,t )-C d V sto,t

[0081] Among them, C Gbio Carbon penalty cost for biogas power generation, P Gbio,t is the power generation of biogas gas turbine at time t, C Grid The carbon penalty cost for purchasing electricity from the public grid, P Grid,t is the amount of electricity purchased from the public grid by the user at time t, C bio is the carbon penalty cost for heat transmission dissipation, Q bio,t is the heat load demand of the user at time t, C d To save biogas and reduce carbon, V sto,t is the biogas storage capacity at time t.

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

[0083]

[0084] Among them, μ Gbio is the unit working ratio, η burn is the gas efficiency of biogas gas turbine, P g is the full capacity of the biogas gas turbine in the solar-biogas virtual power plant, m Gbio,t is the biogas consumption, μtran The efficiency of heat transfer.

[0085] A. Biogas system network transmission constraints

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

[0087]

[0088] B. Thermal Energy Transfer Constraints

[0089] Q Gbio,min ≤Q Gbio ≤Q Gbio,max

[0090] In a possible implementation, considering the uncertainty caused by random generation of renewable energy 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 established as

[0091] Considering the uncertainty of rural distributed photovoltaic output and biogas output of biogas fermentation system, the data-driven real-time scheduling method is based on the day-ahead scheduling optimization model to derive the hourly benchmark operation strategy to provide guidance for the operation plan of inflexible units, including determining the on-off status of generators and managing transactions between multi-energy systems and external energy networks. Considering the uncertainty caused by random generation of renewable energy and uncontrollable load, the scheduling of multi-energy systems 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 sets

[0093] Affected by the light intensity and temperature, the active power output of distributed photovoltaics in rural areas fluctuates over time. The active output of distributed photovoltaics connected to the node is expressed by a robust box-type uncertain set as follows:

[0094]

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

[0096] In the solar-biogas virtual power plant, there are distributed biogas resources in various forms. Due to the volatility of biomass output, the biogas output speed in the biogas fermentation output link is affected by many factors. The robust box-type uncertainty set of biogas output is expressed as follows:

[0097]

[0098] in, is the biogas production rate benchmark value at the i-th node position in period t, is the corresponding maximum prediction deviation, ζ i,t is the uncertainty coefficient, and ζ i,t ∈[0,1].

[0099] The objective function of the real-time scheduling stage is to minimize the scheduling cost. The objective function expression is:

[0100]

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

[0102] 2) Constraints

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

[0104]

[0105] Among them, 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 denote the real and imaginary parts of the system admittance matrix, θ mk is the voltage phase angle difference between nodes m and k.

[0106] Voltage inequality constraints, that is, the voltage inequality constraints of each node are

[0107] Pr{U min ≤U mk ≤U max}≥β U ,

[0108] Among them, Pr represents the event probability set, U min Indicates the lower limit of node voltage, U max represents the upper limit of node voltage, β U Grid voltage confidence level.

[0109] Fluctuating distributed photovoltaics will cause voltage offsets at access nodes. To ensure the quality of grid power, the voltage offset inequality constraints of each node are set as follows:

[0110] χ m,t ≤x grid,max

[0111]

[0112] Among them, χ m,t represents the voltage offset generated by distributed photovoltaic on node m at time t, χ grid,max Indicates the maximum allowable voltage deviation of the distribution network, U Line Indicates the feeder voltage.

[0113] This embodiment also provides a data-driven two-stage low-carbon scheduling system for a solar-biogas virtual power plant, including a biogas output model construction unit, a thermodynamic model construction unit for gas flow in a gas pipeline of a biogas gas turbine, a biogas energy system thermal transmission 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 solar-biogas virtual power plant optimal low-carbon scheduling scheme generation unit; wherein:

[0114] The biogas output model building unit is used to build a biogas output model according to the energy output mechanism of the biogas energy system and the application of biogas resources and the biogas production process;

[0115] The thermodynamic model construction unit of the gas flow in the gas pipeline of the biogas gas turbine is based on the biogas output model, based on the mass conservation and momentum conservation equations, according to the requirements of the biogas gas turbine operation stability, combined with the gas transmission characteristics of the biogas energy system, and to construct the thermodynamic model of the gas flow in the gas pipeline of the biogas gas turbine;

[0116] The biogas energy system thermal transmission model building unit generates a biogas energy system thermal transmission model based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system;

[0117] The biogas energy system power transmission model construction unit is based on the generalized phasor modeling method theory, combined with the biogas energy system thermal transmission model, and considers the gas-electricity conversion characteristics to construct the biogas energy system power transmission model;

[0118] The initial uncertain fuzzy set construction unit of biogas output is based on the power transmission model and thermal transmission model of the biogas energy system, considering that the flexibility of the gas turbine of the biogas energy system is defined as the set of all allowable power distributions within the time range, and establishing the initial uncertain fuzzy set of biogas output;

[0119] The distributed photovoltaic output model building unit fits the distributed photovoltaic output model according to the photovoltaic output characteristics in rural areas;

[0120] The optimal low-carbon scheduling scheme generation unit of the solar-biogas virtual power plant is based on the distributed photovoltaic output model and the initial uncertain fuzzy set of biogas output, considers the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, constructs the two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant, and determines the optimal low-carbon scheduling scheme of the solar-biogas virtual power plant.

[0121] Example

[0122] This embodiment provides an implementation step of a two-stage low-carbon scheduling method for a solar-biogas virtual power plant based on data-driven, which specifically includes:

[0123] Combination Figure 2 , the two-stage robust problem is solved by combining the column constraint generation algorithm with the ADMM algorithm, including:

[0124] Step 201, initialization phase, initializing the upper and lower limits of convergence, the number of iterations and the allowable deviation;

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

[0126] Step 203, data preprocessing stage, generates initial uncertainty probability and constructs output uncertainty set;

[0127] Optionally, based on the power transmission and heat transmission model of the biogas energy system, all allowable power distribution sets of the gas turbines of the biogas energy system within the 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] Among them, α i The amount of energy dissipated by the pipeline transmission.

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

[0132]

[0133] Among them, rt is the environmental impact factor at time t, α PV , β PV and ε PV is the fitting parameter.

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

[0135]

[0136] in, is the benchmark value of the active power that the photovoltaic power plant at the i-th node can output in period t, is the corresponding maximum prediction deviation, ξ i,t is the uncertainty coefficient, and ξ i,t ∈[0,1].

[0137] In the solar-biogas virtual power plant, there are distributed biogas resources in various forms. Due to the volatility of biomass output, the biogas output speed in the biogas fermentation output link is affected by many factors. The robust box-type uncertainty set of biogas output is expressed as follows:

[0138]

[0139] in, is the biogas production rate benchmark value at the i-th node position in period t, is the corresponding maximum prediction deviation, ζ i,t is the uncertainty coefficient, and ζ i,t ∈[0,1].

[0140] Step 204, solving and optimizing the carbon emission penalty cost of the day-ahead scheduling, checking the deviation, verifying the biogas resource reserves, and determining whether the scheduling behavior is feasible;

[0141] Optionally, the time domain function constraint for the biogas power transmission model is

[0142]

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

[0144] Pr{U min ≤U mk ≤U max}≥β U ,

[0145] Among them, Pr represents the event probability set, U min Indicates the lower limit of node voltage, U maxrepresents the upper limit of node voltage, β U Grid voltage confidence level.

[0146] Fluctuating distributed photovoltaics will cause voltage offsets at access nodes. To ensure the quality of grid power, the voltage offset inequality constraints of each node are set as follows:

[0147] x m,t ≤x grid,max

[0148]

[0149] Among them, χ m,t represents the voltage offset generated by distributed photovoltaic on node m at time t, χ grid,max Indicates the maximum allowable voltage deviation of the distribution network, U Line Indicates the feeder voltage.

[0150] Step 205, determining the uncertain probability set and reallocating the node scheduling amount, optimizing and solving the overall objective function;

[0151] Step 206, update the number of iterations and determine whether the parameter exceeds the limit.

[0152] Combination Figure 3 The parameter over-limit correction process of the biogas electricity and heat transfer model based on the generalized phasor modeling method theory includes:

[0153] Step 301, from the perspective of time domain and frequency domain, the biogas power and heat energy transmission model is subjected to dimensionality reduction processing, so as to intuitively and efficiently mine additional energy supply information of the biogas energy system;

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

[0155]

[0156] Among them, μ max is the maximum degradation rate at the upper limit of substrate concentration, S t is the average organic waste concentration in livestock manure during anaerobic fermentation during time t, k S is the substrate half-saturation constant, k E is the enzyme concentration influencing factor, is the average enzyme concentration during time t, is the average carbon-nitrogen ratio of the substrate in the tank during time t, k CN It is the factor affecting the carbon-nitrogen ratio of the substrate in the tank.

[0157]

[0158] Among them, α is the enzyme synthesis rate constant, which indicates the production rate of enzyme molecules in the biogas fermentation substrate; β is the enzyme degradation rate constant, which indicates the degradation rate of enzyme molecules; k d is the substrate concentration decay constant, S0 is the substrate concentration in the initial stage, χ S is the weight factor of organic matter in input materials.

[0159] Substituting into the equation, the expression of the gas production rate at sampling time t is:

[0160]

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

[0162] v tr,t =μ tr v S,t

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

[0164]

[0165] Substituting the gas flow rate into the gas mass equation, the thermal transfer model of the biogas energy system can be obtained as follows:

[0166]

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

[0168]

[0169] Based on the generalized phasor modeling method theory, the power transmission model in the biogas energy system can adopt the same modeling principle, and the power line is equivalent to the thermal transmission network model. The dynamic function is expressed as

[0170]

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

[0172]

[0173] Then, the power transmission model equation of the biogas energy system is reversed into a time domain function, which is expressed as

[0174]

[0175] Step 302, comparing and calculating the model information in combination with the real-time operation of the solar-marine virtual power plant;

[0176] Step 303, considering the operation constraints of the virtual power plant, feedback and correction of the additional information of the biogas energy system energy supply;

[0177] Step 304, determine the post-correction processing status of the biogas energy system, and input the optimization problem for iterative solution.

[0178] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0179] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0180] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0181] The units described as separation modules may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0182] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0183] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned data-driven light-marsh virtual power plant two-stage low-carbon scheduling method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0184] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A two-stage low-carbon scheduling method for a solar-biogas virtual power plant based on data-driven, characterized in that: include: 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; On the basis of the biogas production model, according to the requirements of biogas gas turbine operation stability, combined with the gas transmission characteristics of the biogas energy system, based on the mass conservation and momentum conservation equations, a thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed; Based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system, a thermal transmission model of the biogas energy system is generated; Based on the theory of generalized phasor modeling method, combined with the thermal transmission model of biogas energy system, and considering the gas-electricity conversion characteristics, the power transmission model of biogas energy system is constructed; Based on the power transmission model and thermal transmission model of biogas energy system, the flexibility of gas turbine in biogas energy system is defined as the set of all allowable power distributions within the time range, and the initial uncertain fuzzy set of biogas output is established. According to the photovoltaic output characteristics in rural areas, a distributed photovoltaic output model is fitted; According to the distributed photovoltaic output model and the initial uncertain fuzzy set of biogas output, considering the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, the two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant are constructed, and the optimal low-carbon scheduling scheme of the solar-biogas virtual power plant is determined.

2. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The biogas production model is: Among them, v S,t is the average degradation rate of the substrate within the sampling time t, μ max is the maximum degradation rate at the upper limit of substrate concentration, k S is the substrate half-saturation constant, χ S is the weight factor of organic matter in the input materials, S0 is the substrate concentration in the initial stage, k d is the substrate concentration decay constant, k E is the enzyme concentration influencing factor, is the average carbon-nitrogen ratio of the substrate in the tank during time t, k CN is the influencing factor of the carbon-nitrogen ratio of the substrate in the tank, α is the enzyme synthesis rate constant, which indicates the production rate of enzyme molecules in the biogas fermentation substrate, β is the enzyme degradation rate constant, which indicates the degradation rate of enzyme molecules, M i,in,t It is the substance injected into the biogas fermentation process at time t.

3. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The gas transmission characteristics of the biogas energy system are based on the assumption that the gas transmission rate of the gas turbine is linearly related to the degradation rate, that is: v tr,t =μ tr v S,t Among them, μ tr is the linear proportionality coefficient, v tr,t is the gas delivery rate, v S,t is the average degradation rate of the substrate within the sampling time t, that is, the gas production rate.

4. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine is constructed as follows: Among them, v tr is the fitting function of gas transmission rate, p g is the generator output function, ρ g is the gas density, D g is a numerical parameter, is the fitting parameter phase angle, λ and g are numerical parameters, t is time, and x is the independent variable of the fitting function.

5. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 4 is characterized in that: The thermal transmission model of the biogas energy system is: Among them, m g is the gas flow rate of the gas pipeline per unit time, v tr is the fitting function of gas transmission rate, p g is the generator output function, ρ g is the gas density, A g is the cross-sectional area of ​​the gas pipeline, R is the conversion coefficient, T g Transfer heat to the gas pipeline, D g is a parameter, λ and g are data parameters, t is time, and x is the independent variable of the fitting function.

6. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The power transmission model of the biogas energy system is: Among them, α1, α2, β1, β2 are numerical parameters related to pipeline characteristics, ψ and ζ are port fitting functions of the transmission model, the time derivative is eliminated by Fourier transform, and the space derivative is eliminated by using the equivalence of two ports, and the algebraic relationship between the generalized quantity and the intensive quantity at both ends is established as follows: Among them, ψ0 and ζ0 are the reference values ​​of the transmission model ports, and A, B, C, and D are the transmission matrix parameters; Then, the power transmission model equation of the biogas energy system is reversed into a time domain function, which is expressed as: Among them, n is the number of biogas generator sets, and i is the i-th unit.

7. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The initial uncertain fuzzy set of biogas output is: K i ={[ξ i (k)]∈R m |ψ i (k)=a i ψ i (k-1)+Y i |x i (k)} Among them, α i is the output parameter of the i-th biogas generator set.

8. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The distributed photovoltaic output model is: Among them, r t is the environmental impact factor at time t, α PV , β PV and ε PV is the fitting parameter, Γ is the gamma function.

9. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 1 is characterized in that: The two-stage optimal low-carbon scheduling objective function of the solar-biogas virtual power plant includes day-ahead scheduling with a time step of 1 hour and real-time scheduling with a time step of 15 minutes, namely: F=minF1+minF2 Among them, F1 is the day-ahead scheduling objective function, and F2 is the real-time scheduling objective function.

10. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 9 is characterized in that: The day-ahead dispatch objective function aims to minimize the carbon penalty cost of load energy consumption, that is, minF1=C Gbio P Gbio,t +C Grid P Grid,t +C bio (Q Gbio,t -Q bio,t )-C d V sto,t Among them, C Gbio Carbon penalty cost for biogas power generation, P Gbio,t is the power generation of biogas gas turbine at time t, C Grid The carbon penalty cost for purchasing electricity from the public grid, P Grid,t is the amount of electricity purchased from the public grid by the user at time t, C bio is the carbon penalty cost for heat transmission dissipation, Q bio,t is the heat load demand of the user at time t, Q Gbio,t is the heat output of the biogas generator set, is the heat load demand of the user at time t, C d To save biogas and reduce carbon, V sto,t is the biogas storage capacity at time t; Among them, μ Gbio is the unit working ratio, η burn is the gas efficiency of biogas gas turbine, P g is the full capacity of the biogas gas turbine in the solar-biogas virtual power plant, m Gbio,t is the biogas consumption, μ tran is the efficiency of heat energy transfer; The constraints of the day-ahead scheduling objective function include biogas system network transmission constraints and heat energy transmission constraints.

11. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 10 is characterized in that: The biogas system network transmission constraints are: The heat transfer constraint is: Q Gbio,min ≤Q Gbio ≤Q Gbio,max .

12. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 9 is characterized in that: The real-time scheduling objective function is: Among them, ρ i,t is the probability value of the output uncertainty variable of node i, C i,t is the unit scheduling cost of node i, y i,t is the real-time control value of node i, P L,i,t is the node load demand, f(v i,t ) is the absolute offset function of the output active power; The constraints of the real-time scheduling objective function include power flow equality constraints, voltage inequality constraints and voltage offset inequality constraints for each node.

13. The data-driven two-stage low-carbon scheduling method for solar-biogas virtual power plants according to claim 12 is characterized in that: The power flow equality constraint is: Among them, 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 denote the real and imaginary parts of the system admittance matrix, 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 , Among them, Pr represents the event probability set, U min Indicates the lower limit of node voltage, U max represents the upper limit of node voltage, β U Grid voltage confidence level; The inequality constraints for the voltage offset of each node are: x m,t ≤x grid,max Among them, χ m,t represents the voltage offset generated by distributed photovoltaic on node m at time t, χ grid,max Indicates the maximum allowable voltage deviation of the distribution network, U Line Indicates the feeder voltage.

14. A two-stage low-carbon dispatching system for a solar-biogas virtual power plant that implements the data-driven two-stage low-carbon dispatching method for a solar-biogas virtual power plant according to any one of claims 1 to 13, characterized in that: It includes a biogas output model construction unit, a thermodynamic model construction unit for gas flow in the gas pipeline of a biogas gas turbine, a biogas energy system thermal transmission 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 photovoltaic-biogas virtual power plant optimal low-carbon scheduling plan generation unit; among which: The biogas output model building unit is used to build a biogas output model according to the energy output mechanism of the biogas energy system and the application of biogas resources and the biogas production process; The thermodynamic model construction unit of the gas flow in the gas pipeline of the biogas gas turbine is based on the biogas output model, based on the mass conservation and momentum conservation equations, according to the requirements of the biogas gas turbine operation stability, combined with the gas transmission characteristics of the biogas energy system, and to construct the thermodynamic model of the gas flow in the gas pipeline of the biogas gas turbine; The biogas energy system thermal transmission model building unit generates a biogas energy system thermal transmission model based on the thermodynamic model of gas flow in the gas pipeline of the biogas gas turbine and the heat conduction characteristics of the biogas energy system; The biogas energy system power transmission model construction unit is based on the generalized phasor modeling method theory, combined with the biogas energy system thermal transmission model, and considers the gas-electricity conversion characteristics to construct the biogas energy system power transmission model; The initial uncertain fuzzy set construction unit of biogas output is based on the power transmission model and thermal transmission model of the biogas energy system, considering that the flexibility of the gas turbine of the biogas energy system is defined as the set of all allowable power distributions within the time range, and establishing the initial uncertain fuzzy set of biogas output; The distributed photovoltaic output model building unit fits the distributed photovoltaic output model according to the photovoltaic output characteristics in rural areas; The optimal low-carbon scheduling scheme generation unit of the solar-biogas virtual power plant is based on the distributed photovoltaic output model and the initial uncertain fuzzy set of biogas output, considers the day-ahead carbon reduction benefits and real-time scheduling costs of the solar-biogas virtual power plant, constructs the two-stage optimal low-carbon scheduling objective function and constraints of the solar-biogas virtual power plant, and determines the optimal low-carbon scheduling scheme of the solar-biogas virtual power plant.

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