Comprehensive energy scheduling method, system and equipment based on biomass gas and electricity-to-gas coupling

Abstract

The invention is suitable for the technical field of integrated energy scheduling, and provides an integrated energy scheduling method, system and device based on biomass gas and electricity-to-gas coupling. The mathematical model comprises a biomass gas and electricity-to-gas coupling model, a combined heat and power generation unit model, an electric heater model, a photo-thermal power station model and an electric energy storage model; a target function and constraint conditions are set, the target function takes the minimum scheduling cost as a target, and the constraint conditions comprise an electric power balance constraint, a thermal power balance constraint, a gas amount balance constraint, a carbon balance constraint, a power-to-gas constraint, a cogeneration unit constraint, an electric heater and photo-thermal power station constraint, a thermal energy storage and electric energy storage constraint and an energy purchase and sale constraint; and determining a low-carbon optimal scheduling scheme of the integrated energy system. According to the invention, the energy utilization rate in the low-carbon operation process of the comprehensive energy system containing biogas is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy dispatching, and in particular to an integrated energy dispatching method, system and equipment based on biomass gas and electricity-to-gas coupling. Background Art

[0002] As a new energy source with large reserves and low pollution, biomass energy supply technology realizes the resource utilization of agricultural production waste such as livestock manure, crops, and straw, which can effectively reduce energy consumption and agricultural pollution. Biomass energy produces biogas after anaerobic fermentation, which contains 25%-50% carbon dioxide and 50%-70% methane. Biogas can be purified to obtain biomass natural gas. According to the latest "Biogas" released by the country on October 1, 2022: Biogas purified to a methane content of more than 93% can be called biomass gas (bio natural gas, BNG), which has almost the same calorific value as natural gas and can be used as pipeline gas.

[0003] There are many methods in the existing technology to improve the economy and low carbon of the integrated energy system. For example, the waste such as crop straw in rural areas is used to produce biogas, and a rural IES with biogas power generation as the main energy input is constructed. On this basis, the start and stop constraints of the biogas cogeneration unit are added to effectively solve the problem of frequent start and stop of the unit, and improve the economy of the ecological agriculture IES. In addition, there is also a source-storage-load collaborative management model driven by biogas, photovoltaic and gas turbine power generation systems; a biogas fermentation kinetic model is introduced, and combined with new energy hydrogen production technology, a rural light-hydrogen-biogas source-storage collaborative IES model is constructed. The above-mentioned technologies introduce biogas to improve the economy and low carbon of IES, but do not consider the improvement of the economy and low carbon of IES by producing biomass gas through biogas purification technology.

[0004] In view of this, there is a need for a comprehensive energy scheduling method, system and equipment based on biomass gas and power-to-gas coupling. Summary of the invention

[0005] The embodiments of the present application provide a comprehensive energy scheduling method, system and equipment based on biomass gas and power-to-gas coupling, which are used to improve the energy utilization rate during the low-carbon operation of the comprehensive energy system containing biogas and realize the economical and environmentally friendly operation of the energy system.

[0006] The first aspect of the embodiment of the present application provides a comprehensive energy scheduling method based on biomass gas and power-to-gas coupling, including:

[0007] Building a mathematical model based on the equipment in the integrated energy system, the mathematical model includes a biomass gas and power-to-gas coupling model, a cogeneration unit model, an electric heater model, a solar thermal power station model, and an electric energy storage model;

[0008] According to the constructed mathematical model, an objective function and constraints are set, wherein the objective function aims to minimize the dispatching cost of the integrated energy system, and the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints;

[0009] A low-carbon optimization scheduling scheme for the integrated energy system is determined according to the objective function and the constraints.

[0010] Furthermore, the mathematical model based on the equipment in the integrated energy system includes:

[0011] Biomass gas and power-to-gas coupling model:

[0012] V BG,t =η BG E BG M BE,t

[0013] Where: V BG,t is the amount of biogas generated at time t, t represents the IES scheduling period, t∈[1,24], η BG is the biomass energy utilization rate, E BG is the gas production factor, M BE,t is the consumption of biomass energy in period t;

[0014]

[0015] P PSA,t =g PSA V BG,t

[0016] Where: V BNG,t is the amount of biomass natural gas produced at time t, To improve the production efficiency of CH4 under PSA process, is the percentage of CH4 in biogas, is the amount of CO2 produced under the PSA process at time t, is the volume ratio of CO2 to CH4 in biogas, To improve the loss rate of CO2 / * / under PSA process, is the density of CO2, P PSA,t is the electrical power required to improve the PSA process at time t, g PSA To improve the electrical power efficiency of the PSA process;

[0017]

[0018] Where: M PSA,CO2 is the total CO2 storage capacity in one IES scheduling cycle, T is the IES scheduling cycle, which is 24;

[0019] M FER,t =η FER (M BE,t ―ρ BG V BG,t )

[0020] Where: M FER,t is the amount of organic fertilizer produced at time t, η FER is the production rate of organic fertilizer in biomass residue, ρ BG is the biogas density;

[0021]

[0022] Where: P2G is the reaction process of producing natural gas by consuming electricity;

[0023]

[0024] Where: V P2G,t is the amount of natural gas synthesized by P2G at time t, η P2G is the P2G synthesis efficiency, P P2G,t is the power consumption of P2G at time t, is the calorific value of natural gas;

[0025]

[0026] Where: is the amount of CO2 consumed by P2G at time t, is the total amount of CO2 consumed by P2G in one day.

[0027] Furthermore, the mathematical model based on the equipment in the integrated energy system includes:

[0028] Combined Heat and Power Unit Model:

[0029]

[0030] Where: P CHP,t is the electric power of CHP at time t, V CHP,t is the gas consumption of CHP at time t, η CHP is the electricity generation efficiency of CHP, r is the heat and power conversion value;

[0031] H CHP,t =μ CHP P CHP,t

[0032] Where: H CHP,tis the heat generation of CHP at time t, μ CHP is the thermal-to-electric ratio of CHP;

[0033]

[0034] Where: is the carbon emission generated by CHP in one day, e CHP and e M They are the carbon emission intensity and carbon emission allocation of CHP respectively.

[0035] Furthermore, the objective function and constraint conditions are set according to the constructed mathematical model, the objective function takes the minimization of the dispatching cost of the integrated energy system as the goal, and the constraint conditions include electric power balance constraint, thermal power balance constraint, gas volume balance constraint, carbon balance constraint, power-to-gas constraint, cogeneration unit constraint, electric heater and solar thermal power station constraint, thermal energy storage and electric energy storage constraint, and energy purchase and sale constraint, including:

[0036] The expression of the objective function is as follows:

[0037] minC total =C OM +C BSE +C CE

[0038] Where: minC total is the IES scheduling cost for one day, C OM , C BSE , C CE They are equipment operation and maintenance costs, system energy purchase and sales costs, and system carbon emission costs.

[0039] Furthermore, the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints, including:

[0040] Electric power balance constraints:

[0041] P CSP,t +P CHP,t +P WT,t +P PV,t +P BUY,t +P ED,t =P EC,t +P LOAD,t +P PSA,t +P EH,t

[0042] +P SELL,t +P P2G,t

[0043] Where: P CSP,t is the electric power of CSP at time t, P WT,t , P PV,t are the electric power of wind turbine and photovoltaic at time t, P BUY,t , P SELL,t are the amount of electricity purchased and sold from the system to the grid at time t, P EC,t and P ED,t is the charge and discharge power of EES at time t, P LOAD,t is the electrical load of IES at time t, P EH,t is the electric power of EH at time t;

[0044] Thermal power balance constraints:

[0045] H CHP,t +H TD2,t +H EH,t =H LOAD,t +H TC2,t

[0046] Where: H TC2,t and H TD2,t are the heat charged and released by TES to the heating network at time t, H EH,t is the heat generation power of EH at time t, H LOAD,t is the heat load of TES at time t;

[0047] Gas balance constraints:

[0048] V BNG,t +V BUY,t +V P2G,t =V LOAD,t +V CHP,t +V SELL,t

[0049] Where: V BUY,t and V SELL,t are the gas sales volume of the system from the grid at time t, V LOAD,t is the gas load of the system at time t;

[0050] Carbon balance constraints:

[0051]

[0052] Where: M BUY,t and M SELL,t They are the carbon quantities purchased and sold by the system in the carbon trading market at time t.

[0053] Furthermore, the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints, including:

[0054] Power-to-gas constraints:

[0055]

[0056] Where: P P2G,down and P P2G,up are the lower and upper limits of P2G power ramp-up, P P2G,max is the maximum value of P2G electric power;

[0057] Cogeneration unit constraints:

[0058]

[0059] Where: P CHP,down and P CHP,up are the lower and upper limits of CHP power climbing, P CHP,max is the maximum value of CHP electric power;

[0060] Electric heaters and CSP power plant constraints:

[0061] 0≤P EH,t ≤P EH,max

[0062]

[0063] Where: P EH,max is the maximum electrical power of EH, P CSP,max is the maximum electric power of CSP, P CSP,down and P CSP,up They are the lower and upper limits of CSP electric power ramp-up respectively.

[0064] Furthermore, the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints, including:

[0065] Thermal and electrical energy storage constraints:

[0066]

[0067] Where: H TC,max and H TD,max are the maximum charging and discharging power of TES, E TES,inv is the capacity of TES, E TES,0 and E TES,T Scheduling the capacity of the start and end time periods for TES;

[0068]

[0069] Where: P EC,max and P ED,max are the maximum values ​​of EES charging and discharging power, E EES,inv is the capacity of EES, E EES,0 and E EES,T are the capacities at the start and end of EES scheduling, respectively.

[0070] Furthermore, the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints, including

[0071] Constraints on purchasing and selling energy:

[0072]

[0073] Where: P BUY,max and P SELL,max are the maximum power of electricity purchased and sold, V BUY,max and V SELL,max are the maximum values ​​of gas purchasing and selling power respectively.

[0074] The second aspect of the embodiment of the present application provides an integrated energy dispatching system based on biomass gas and power-to-gas coupling, including:

[0075] A mathematical model building unit, used to build a mathematical model based on the equipment in the integrated energy system, wherein the mathematical model includes a biomass gas and power-to-gas coupling model, a cogeneration unit model, an electric heater model, a solar thermal power station model, and an electric energy storage model;

[0076] An objective function and constraint setting unit, used for setting an objective function and constraint conditions according to the constructed mathematical model, wherein the objective function takes minimizing the dispatching cost of the integrated energy system as the goal, and the constraint conditions include electric power balance constraint, thermal power balance constraint, gas volume balance constraint, carbon balance constraint, power-to-gas constraint, cogeneration unit constraint, electric heater and solar thermal power station constraint, thermal energy storage and electric energy storage constraint, and energy purchase and sale constraint;

[0077] A scheduling scheme determination unit is used to determine a low-carbon optimization scheduling scheme for the integrated energy system according to the objective function and constraint conditions.

[0078] A third aspect of an embodiment of the present application provides a computer device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein when the processor executes the computer-readable instructions, the steps of the integrated energy scheduling method based on biomass gas and power-to-gas coupling as described in any one of the above are implemented.

[0079] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0080] The present invention constructs a mathematical model based on the equipment in the integrated energy system, and the mathematical model includes a biomass gas and power-to-gas coupling model, a cogeneration unit model, an electric heater model, a solar thermal power station model, and an electric energy storage model; the objective function and constraints are set according to the constructed mathematical model, and the objective function takes the minimum scheduling cost of the integrated energy system as the goal, and the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, cogeneration unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints. The present invention purifies biogas and recovers carbon dioxide, and introduces power-to-gas at the same time. The power-to-gas uses the carbon dioxide recovered by pressure swing adsorption technology as a raw material to produce natural gas, which provides a solution for the low-carbon operation of the integrated energy system containing biogas and reduces the gas purchase volume of the system to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of an embodiment of the integrated energy scheduling method based on biomass gas and power-to-gas coupling in the present invention;

[0082] Figure 2 This is a diagram of the integrated energy system architecture of the present invention;

[0083] Figure 3 This is a flow chart of the improved pressure swing adsorption technology in the present invention;

[0084] Figure 4 This is a schematic diagram of the heat flow model of the solar thermal power station in the present invention;

[0085] Figure 5 It is a load and clean energy power prediction value curve diagram in the present invention;

[0086] Figure 6 This is a diagram of the system power dispatch result in the present invention;

[0087] Figure 7 This is a diagram of the thermal energy scheduling results of the system in the present invention;

[0088] Figure 8 This is a diagram of the gas energy scheduling results of the system in the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0090] In this embodiment, the integrated energy scheduling method based on biomass gas and power-to-gas coupling is used to improve the energy utilization rate during the low-carbon operation of the integrated energy system containing biogas, and realize the economic and environmentally friendly operation of the energy system. In this embodiment, the implementation method can be implemented in the system, on the server, or on the terminal, without specific limitation.

[0091] Embodiment 1

[0092] See also Figure 1 An embodiment of a comprehensive energy scheduling method based on biomass gas and power-to-gas coupling in the present invention comprises the following steps:

[0093] S11. Construct mathematical models based on the equipment in the integrated energy system, including biomass gas and power-to-gas coupling models, cogeneration unit models, electric heater models, solar thermal power station models, and electric energy storage models;

[0094] In this embodiment, Figure 2 As shown in the figure, the equipment of the integrated energy system includes solar thermal power station, electric heater, wind power, photovoltaic, electric energy storage, biomass gas, power-to-gas and cogeneration unit. The mathematical model is constructed according to the equipment, as follows:

[0095] 1. Biomass gas and power-to-gas coupling model:

[0096] The biomass gas modeling is as follows:

[0097] V BG,t =η BG E BG M BE,t

[0098] Where: V BG,t is the amount of biogas generated at time t, t represents the IES scheduling period, t∈[1,24], η BG is the biomass energy utilization rate, E BG is the gas production factor, M BE,t is the consumption of biomass energy in period t.

[0099] The biogas produced by biomass fermentation is purified by the improved PSA process, and the amount of CH4 and CO2 generated is:

[0100] V BNG,t =η CH4 ξ CH4 V BG,t

[0101]

[0102] P PSA,t =g PSA V BG,t

[0103] Where: V BNG,t is the amount of biomass natural gas produced at time t, To improve the production efficiency of CH4 under PSA process, is the percentage of CH4 in biogas, is the amount of CO2 produced under the PSA process at time t, is the volume ratio of CO2 to CH4 in biogas, To improve the loss rate of CO2 / * / under PSA process, is the density of CO2, P PSA,t is the electrical power required to improve the PSA process at time t, g PSA To improve the electrical power efficiency of the PSA process.

[0104] The high-purity CO2 generated by the improved PSA process needs to be stored before it can enter the carbon trading market. The amount of CO2 stored is:

[0105]

[0106] Where: M PSA,CO2 is the total CO2 storage capacity in one IES scheduling cycle, T is the IES scheduling cycle, which is taken as 24.

[0107] The remaining residue from biomass fermentation to produce biogas contains a certain amount of organic fertilizer, which can reduce the operating cost of IES. According to the law of conservation of mass, the amount of organic fertilizer produced is:

[0108] M FER,t =η FER (M BE,t ―ρ BG V BG,t )

[0109] Where: M FER,t is the amount of organic fertilizer produced at time t, η FER is the production rate of organic fertilizer in biomass residue, ρ BG is the biogas density.

[0110] P2G produces natural gas by consuming electricity. The specific reaction process is as follows:

[0111]

[0112] Where: P2G is the reaction process of producing natural gas by consuming electrical energy.

[0113] The relationship between the amount of P2G synthetic natural gas and the consumed electricity is:

[0114]

[0115] Where: V P2G,t is the amount of natural gas synthesized by P2G at time t, η P2G is the P2G synthesis efficiency, P P2G,t is the power consumption of P2G at time t, is the calorific value of natural gas.

[0116] The CO2 generated after biogas purification can be used as the raw material of P2G to produce natural gas. In the P2G reaction, the volume of CO2 consumed and the volume of CH4 generated are the same. The amount of CO2 consumed by P2G is:

[0117]

[0118] Where: is the amount of CO2 consumed by P2G at time t, is the total amount of CO2 consumed by P2G in one day.

[0119] For details, please refer to the above mentioned improved PSA process (improved pressure swing adsorption technology). Figure 3 The specific implementation process is as follows:

[0120] ① The biogas enters the buffer tank 1 with the pressure and temperature set corresponding to CH4, and CH4 is precipitated through the PSA technology; ② The biogas enters the buffer tank 2 with the pressure and temperature set corresponding to CO2, and CO2 is precipitated through the PSA technology; ③ The remaining gas of step ② enters the buffer tank 1, and CH4 is precipitated through the PSA technology together with the gas in the buffer tank 1; ④ The remaining gas of step ① enters the buffer tank 2, and CO2 is precipitated through the PSA process together with the gas in the buffer tank 2; After repeating several times, high-purity CH4 and CO2 can be obtained.

[0121] 2. Combined heat and power unit model:

[0122] CHP generates electricity by burning natural gas. The mathematical formula between the natural gas burned by CHP and the electric power generated is:

[0123]

[0124] Where: P CHP,t is the electric power of CHP at time t, V CHP,t is the gas consumption of CHP at time t, η CHP is the electricity generation efficiency of CHP, and r is the heat and power conversion value.

[0125] When CHP generates electricity, the remaining heat energy is collected by the waste heat device and can be used for heat load. The heat generation power of CHP is:

[0126] H CHP,t =μ CHP PCHP,t

[0127] Where: H CHP,t is the heat generation of CHP at time t, μ CHP is the thermal-electric ratio of CHP.

[0128] In the context of carbon trading, the actual carbon emissions of CHP per day are:

[0129]

[0130] Where: is the carbon emission generated by CHP in one day, e CHP and e M They are the carbon emission intensity and carbon emission allocation of CHP respectively.

[0131] 3. Electric heater model:

[0132] EH can consume electrical energy to generate heat energy. The relationship between the electrical energy consumed by EH and the heat energy generated is:

[0133] H EH,t =η EH P EH,t

[0134] Where: H EH,t and P EH,t are the heat generation power and electrical power of EH at time t, respectively, EH is the heat generation efficiency of EH.

[0135] 4. Solar thermal power station model:

[0136] The solar field, generator and heat storage system together constitute CSP, in which the solar field is responsible for receiving solar energy and converting it into heat, and the heat transfer medium is responsible for transferring the heat to the heat storage system or to the generator for use. The detailed energy flow between the three and between the heat storage system and the heat network is as follows: Figure 4 shown.

[0137] The CSP heat flow model is:

[0138] H SF,t =(1-μ loss )H SOLAR,t

[0139] H SF,t +H TD1,t =H CSP,t +H TC1,t

[0140] P CSP,t =η CSP H CSP,t

[0141] Where: H SF,t and H SOLAR,t are the heat transferred by the heat transfer medium and the heat absorbed by the light field at time t, μ loss is the heat loss rate of the heat transfer medium, H TC1,t and H TD1,t is the heat charge and discharge between TES and CSP at time t, H CSP,t and P CSP,t are the heat consumption and power generation of the CSP generator at time t, η CSP is the power generation efficiency of the CSP generator.

[0142] The charging and discharging power of TES is:

[0143]

[0144] Where: H TC2,t and H TD2,t are the heat charged and released by TES to the heating network at time t, H TC,t and H TD,t They are the charging and releasing heat of TES at time t respectively.

[0145] The TES model is:

[0146]

[0147] Where: E TES,t is the thermal storage capacity of TES at time t, μ TL , η TC and η TD They are the heat loss rate and charging and discharging efficiency of TES respectively.

[0148] 5. Electric energy storage model:

[0149] The EES model is:

[0150]

[0151] Where: E EES,t is the capacity of EES at time t, μ EL is the power loss rate of EES, P EC,t , P ED,t , η EC and η ED They are the charge and discharge power of EES and the charge and discharge efficiency of EES respectively.

[0152] S12. According to the constructed mathematical model, the objective function and constraints are set. The objective function aims to minimize the dispatch cost of the integrated energy system. The constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints;

[0153] 1. The optimization goal of IES is to minimize the IES scheduling cost, which includes equipment operation and maintenance cost, system energy purchase and sale cost, and system carbon emission cost. The objective function of IES is:

[0154] minC total =C OM +C BSE +C CE

[0155] Where: minC total is the IES scheduling cost for one day, C OM , C BSE , C CE They are equipment operation and maintenance costs, system energy purchase and sales costs, and system carbon emission costs.

[0156] The operation and maintenance costs of the above equipment are as follows:

[0157]

[0158] Where: g CF is the unit carbon storage cost coefficient, C n is the daily operation and maintenance cost of device n, g n is the operation and maintenance cost coefficient of device n per unit power, P n,t is the power of equipment n at time t. G TU and W HD are gas turbine unit and waste heat device respectively. The sum of their operation and maintenance costs is the operation and maintenance cost of CHP.

[0159] The energy purchase and sale costs of the above system are as follows:

[0160]

[0161] Where: g EB,t , g GB,t , g BE,t and g CB,t are the unit price coefficients of the system purchasing electricity from the grid, gas from the gas grid, biomass from the external market, and CO2 from the carbon trading market at time t, respectively. ES,t , g GS,t , g FER,t and g CS,tare the unit price coefficients of the system selling electricity to the power grid, selling gas to the gas grid, selling organic fertilizer to the outside world, and selling CO2 to the carbon trading market at time t, respectively. BUY,t , P SELL,t 、V BUY,t 、V SELL,t 、M BUY,t , and M SELL,t They are respectively the amount of electricity purchased from the power grid, the amount of electricity sold to the power grid, the amount of gas purchased from the gas grid, the amount of gas sold to the gas grid, the amount of carbon purchased from the carbon trading market, and the amount of carbon sold to the carbon trading market at time t.

[0162] Carbon emission cost of the above system:

[0163] IES will produce CO2 during operation. The CO2 recovered by the PSA process in the biomass gas model has been stored on site. Therefore, the carbon emissions of IES include: carbon emissions generated by CHP, equivalent carbon emissions generated by electricity purchases, and carbon emissions reduced by P2G. The total carbon emissions of IES are as follows:

[0164]

[0165] Where: e BUY is the equivalent carbon emission intensity of electricity purchase, The equivalent carbon emissions generated by purchasing electricity for the system in one day, is the carbon emissions of IES in one day.

[0166] A tiered carbon emissions trading system is adopted here, as follows:

[0167]

[0168] Where: λ, θ and L are the carbon trading base price, carbon trading price growth rate and carbon trading interval length respectively.

[0169] 2. Constraints:

[0170] 1. Electric power balance constraints:

[0171] P CSP,t +P CHP,t +P WT,t +P PV,t +P BUY,t +P ED,t =P EC,t +P LOAD,t +P PSA,t +P EH,t

[0172] +P SELL,t +P P2G,t

[0173] Where: P CSP,tis the electric power of CSP at time t, P WT,t , P PV,t are the electric power of wind turbine and photovoltaic at time t, P BUY,t , P SELL,t are the amount of electricity purchased and sold from the system to the grid at time t, P EC,t and P ED,t is the charge and discharge power of EES at time t, P LOAD,t is the electrical load of IES at time t, P EH,t is the electric power of EH at time t.

[0174] 2. Thermal power balance constraints:

[0175] H CHP,t +H TD2,t +H EH,t =H LOAD,t +H TC2,t

[0176] Where: H TC2,t and H TD2,t are the heat charged and released by TES to the heating network at time t, H EH,t is the heat generation power of EH at time t, H LOAD,t is the heat load of TES at time t.

[0177] 3. Gas volume balance constraints:

[0178] V BNG,t +V BUY,t +V P2G,t =V LOAD,t +V CHP,t +V SELL,t

[0179] Where: V BUY,t and V SELL,t are the gas sales volume of the system from the grid at time t, V LOAD,t is the gas load of the system at time t.

[0180] 4. Carbon balance constraints:

[0181]

[0182] Where: M BUY,t and M SELL,t They are the carbon quantities purchased and sold by the system in the carbon trading market at time t.

[0183] 5. Power-to-gas constraints:

[0184]

[0185] Where: P P2G,down and P P2G,upare the lower and upper limits of P2G power ramp-up, P P2G,max It is the maximum value of P2G electric power.

[0186] 6. Cogeneration unit constraints:

[0187]

[0188] Where: P CHP,down and P CHP,up are the lower and upper limits of CHP power climbing, P CHP,max is the maximum value of CHP electric power.

[0189] 7. Electric heaters and solar thermal power plant constraints:

[0190] 0≤P EH,t ≤P EH,max

[0191]

[0192] Where: P EH,max is the maximum electrical power of EH, P CSP,max is the maximum electric power of CSP, P CSP,down and P CSP,up They are the lower and upper limits of CSP electric power ramp-up respectively.

[0193] 8. Thermal and electrical energy storage constraints:

[0194]

[0195] Where: H TC,max and H TD,max are the maximum charging and discharging power of TES, E TES,inv is the capacity of TES, E TES,0 and E TES,T Scheduling the capacity of the start and end time periods for TES;

[0196]

[0197] Where: P EC,max and P ED,max are the maximum values ​​of EES charging and discharging power, E EES,inv is the capacity of EES, E EES,0 and E EES,T are the capacities at the start and end of EES scheduling, respectively.

[0198] 9. Constraints on purchasing and selling energy:

[0199]

[0200] Where: P BUY,maxand P SELL,max are the maximum power of electricity purchased and sold, V BUY,max and V SELL,max are the maximum values ​​of gas purchasing and selling power respectively.

[0201] S13. Determine the low-carbon optimization scheduling plan of the integrated energy system based on the objective function and constraints.

[0202] In this embodiment, based on the objective function and constraint conditions determined above, a low-carbon optimization scheduling model of the integrated system, that is, a low-carbon optimization scheduling solution, can be obtained.

[0203] The following is a detailed description based on the experiment:

[0204] 1. Here, we select the integrated energy system in northern China as the research object, select the typical daily load data in winter for verification and analysis, and call CPLEX in the MATLAB environment for optimization and solution. Set the scheduling cycle of the integrated energy system to 24h and the time interval to 1h. The system electricity, heat, and gas load data and the power forecast of wind power, photovoltaic, and solar thermal power stations are as follows: Figure 5 Biomass gas parameters, parameters of various equipment in the integrated energy system, time-of-use electricity prices and other system parameters are shown in Table 1-4:

[0205] Table 1 Biomass gas model parameters

[0206]

[0207] Table 2 System equipment parameters

[0208]

[0209] Table 3 Time-of-use electricity prices

[0210]

[0211] Table 4 Other system parameters

[0212]

[0213]

[0214] 2. Analysis of power dispatch results:

[0215] Depend on Figure 6It can be seen that: from 0 (i.e., 24 periods) to 6 periods, the system mainly relies on CHP, WT and electricity purchased from the power grid to meet the system load demand. At this time, the electricity price is off-peak, and EES is charged during this period. The cost-effectiveness of EH power consumption and heat production is high, so the power of CHP is low. From 7 to 16 periods, the electricity generated by PV, WT, CSP and CHP is sufficient for the system, so the system does not need to purchase electricity from the power grid. Among them, during the peak load period of 12-13, EES discharges for system use, and during the period of 11-14, P2G uses the surplus electricity to produce gas. From 17 to 23 periods, the power of CSP and PV gradually decreases to 0, the power of CHP and WT increases, and the remaining load is provided by the power grid. Among them, during the period of 19-21, the electric load reaches its peak, and EES discharges to the system. At this time, the power of CHP has reached the highest level, but it still cannot meet the load demand, so the system still needs to purchase electricity from the power grid. During the entire dispatch period, EES is charged in periods 4-6 to support its discharge in period 12-13, EES is charged in periods 15 and 17 to support its discharge in period 19-21, and EES is charged in period 24 to meet the constraint that the electric storage energy is equal at the start and end of the dispatch.

[0216] 3. Analysis of thermal energy scheduling results:

[0217] Depend on Figure 7 It can be seen that: in the 1-6 period, the electricity price is at the off-peak time, and the EH heat production is cost-effective, so the EH provides most of the heat to the heat load. In the 1 period, TES provides part of the heat, and the remaining heat is provided by CHP. In the 7-18 period, as the electricity price and electricity load increase, the system no longer uses EH for heating, and CSP stores part of the heat in TES. At this time, the heat load is provided by CHP and TES. In the 19-22 period, the electricity load is large at this time, and CHP works in the "electricity determines heat" mode. The system's heat energy is all provided by CHP, and the excess heat energy is stored in TES. In the 23-24 period, after the heat charging in the previous period, the heat stored in TES is close to the heat storage capacity. Therefore, TES releases heat to the heat network in this period, EH also provides part of the heat, and the remaining heat is provided by CHP, so the CHP thermal power is significantly reduced compared with the previous period.

[0218] 4. Analysis of gas energy dispatch results:

[0219] Depend on Figure 8 It can be seen that the natural gas produced by BNG and P2G cannot meet the gas load and CHP demand in any time period, so the system must purchase gas from the gas grid. CHP consumes the most gas in the 19-22 period, and consumes more gas in the 7-8, 11, 13, 15, and 17 periods. P2G produces gas in the 11-14 period. According to calculations, BNG accounts for 41.6% of the system's gas supply, and the natural gas produced by P2G accounts for 3.7% of the system's gas supply.

[0220] The low-carbon optimization scheduling strategy of the above-mentioned embodiment reduces the carbon emissions of the integrated energy system, improves the economy of the integrated energy system, and at the same time promotes the utilization of biomass energy to a certain extent, providing new ideas for the development of the future integrated energy system.

[0221] Embodiment 2

[0222] An embodiment of an integrated energy dispatching system based on biomass gas and power-to-gas coupling in the present invention comprises the following steps:

[0223] A mathematical model building unit, which is used to build mathematical models based on the equipment in the integrated energy system. The mathematical models include biomass gas and power-to-gas coupling models, cogeneration unit models, electric heater models, solar thermal power station models, and electric energy storage models;

[0224] An objective function and constraint setting unit, used to set an objective function and constraint conditions according to the constructed mathematical model, wherein the objective function takes minimizing the dispatching cost of the integrated energy system as the goal, and the constraint conditions include electric power balance constraint, thermal power balance constraint, gas volume balance constraint, carbon balance constraint, power-to-gas constraint, cogeneration unit constraint, electric heater and solar thermal power station constraint, thermal energy storage and electric energy storage constraint, and energy purchase and sale constraint;

[0225] The scheduling scheme determination unit is used to determine the low-carbon optimization scheduling scheme of the integrated energy system based on the objective function and constraint conditions.

[0226] Embodiment 3

[0227] The present invention provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above method are implemented.

[0228] Those of ordinary skill in the art will appreciate that the units of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. 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.

[0229] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored. In addition, each functional unit in each embodiment of the present invention can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0230] If the integrated 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 technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., which can store program code.

[0231] It can be understood that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A comprehensive energy dispatching method based on biomass gas and power-to-gas coupling, characterized in that: include: Building a mathematical model based on the equipment in the integrated energy system, the mathematical model includes a biomass gas and power-to-gas coupling model, a cogeneration unit model, an electric heater model, a solar thermal power station model, and an electric energy storage model; According to the constructed mathematical model, an objective function and constraints are set, wherein the objective function aims to minimize the dispatching cost of the integrated energy system, and the constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal energy storage and electric energy storage constraints, and energy purchase and sale constraints; A low-carbon optimization scheduling scheme for the integrated energy system is determined according to the objective function and the constraints.

2. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 1 is characterized in that: The mathematical model based on the equipment in the integrated energy system includes: Biomass gas and power-to-gas coupling model: V BG,t =the BG E BG M BE,t Where: V BG,t is the amount of biogas generated at time t, t represents the IES scheduling period, t∈[1,24], η BG is the biomass energy utilization rate, E BG is the gas production factor, M BE,t is the consumption of biomass energy in period t; V BNG,t =the CH4 x CH4 V BG,t P PSA,t =g PSA V BG,t Where: V BNG,t is the amount of biomass natural gas produced at time t, η CH4 To improve the production efficiency of CH4 under PSA process, is the percentage of CH4 in biogas, is the amount of CO2 produced under the PSA process at time t, is the volume ratio of CO2 to CH4 in biogas, To improve the loss rate of CO2 / * / under PSA process, ρ CO2 is the density of CO2, P PSA,t is the electrical power required to improve the PSA process at time t, g PSA To improve the electrical power efficiency of the PSA process; Where: M PSA,CO2 is the total CO2 storage capacity in one IES scheduling cycle, T is the IES scheduling cycle, and is set to "24"; M FER,t =the FER (M BE,t ―r BG V BG,t ) Where: M FER,t is the amount of organic fertilizer produced at time t, η FER is the production rate of organic fertilizer in biomass residue, ρ BG is the biogas density; Where: P2G is the reaction process of producing natural gas by consuming electricity; Where: V P2G,t is the amount of natural gas synthesized by P2G at time t, η P2G is the P2G synthesis efficiency, P P2G,t is the power consumption of P2G at time t, L CH4 is the calorific value of natural gas; Where: is the amount of CO2 consumed by P2G at time t, is the total amount of CO2 consumed by P2G in one day.

3. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 2 is characterized in that: The mathematical model based on the equipment in the integrated energy system includes: Combined Heat and Power Unit Model: Where: P CHP,t is the electric power of CHP at time t, V CHP,t is the gas consumption of CHP at time t, η CHP is the electricity generation efficiency of CHP, r is the heat and power conversion value; H CHP,t =μ CHP P CHP,t Where: H CHP,t is the heat generation of CHP at time t, μ CHP is the thermal-to-electric ratio of CHP; Where: M CHP,CO2 is the carbon emission generated by CHP in one day, e CHP and e M They are the carbon emission intensity and carbon emission allocation of CHP respectively.

4. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 1 is characterized in that: The objective function and constraint conditions are set according to the constructed mathematical model, wherein the objective function takes the minimization of the dispatching cost of the integrated energy system as the goal, and the constraint conditions include electric power balance constraint, thermal power balance constraint, gas volume balance constraint, carbon balance constraint, power-to-gas constraint, cogeneration unit constraint, electric heater and solar thermal power station constraint, thermal energy storage and electric energy storage constraint, and energy purchase and sale constraint, including: The expression of the objective function is as follows: minC total =C OM +C BSE +C CE Where: minC total is the IES scheduling cost for one day, C OM , C BSE , C CE They are equipment operation and maintenance costs, system energy purchase and sales costs, and system carbon emission costs.

5. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 1 is characterized in that: The constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal and electric energy storage constraints, and energy purchase and sales constraints, including: Electric power balance constraints: P CSP,t +P CHP,t +P WT,t +P PV,t +P BUY,t +P ED,t =P EC,t +P LOAD,t +P PSA,t +P EH,t +P SELL,t +P P2G,t Where: P CSP,t is the electric power of CSP at time t, P WT,t , P PV,t are the electric power of wind turbine and photovoltaic at time t, P BUY,t , P SELL,t are the amount of electricity purchased and sold from the system to the grid at time t, P EC,t and P ED,t is the charge and discharge power of EES at time t, P LOAD,t is the electrical load of IES at time t, P EH,t is the electric power of EH at time t; Thermal power balance constraints: H CHP,t +H TD2,t +H EH,t =H LOAD,t +H TC2,t Where: H TC2,t and H TD2,t are the heat charged and released by TES to the heating network at time t, H EH,t is the heat generation power of EH at time t, H LOAD,t is the heat load of TES at time t; Gas balance constraints: V BNG,t +V BUY,t +V P2G,t =V LOAD,t +V CHP,t +V SELL,t Where: V BUY,t and V SELL,t are the gas sales volume of the system from the grid at time t, V LOAD,t is the gas load of the system at time t; Carbon balance constraints: M PSA,CO2,t +M BUY,t =M P2G,CO2,t +M SELL,t Where: M BUY,t and M SELL,t They are the carbon quantities purchased and sold by the system in the carbon trading market at time t.

6. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 5 is characterized in that: The constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal and electric energy storage constraints, and energy purchase and sales constraints, including: Power-to-gas constraints: Where: P P2G,down and P P2G,up are the lower and upper limits of P2G power ramp-up, P P2G,max is the maximum value of P2G electric power; Cogeneration unit constraints: Where: P CHP,down and P CHP,up are the lower and upper limits of CHP power climbing, P CHP,max is the maximum value of CHP electric power; Electric heaters and CSP power plant constraints: 0≤P EH,t ≤P EH,max Where: P EH,max is the maximum electrical power of EH, P CSP,max is the maximum electric power of CSP, P CSP,down and P CSP,up They are the lower and upper limits of CSP electric power ramp-up respectively.

7. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 6 is characterized in that: The constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal and electric energy storage constraints, and energy purchase and sales constraints, including: Thermal and electrical energy storage constraints: Where: H TC,max and H TD,max are the maximum charging and discharging power of TES, E TES,inv is the capacity of TES, E TES,0 and E TES,T Scheduling the capacity of the start and end time periods for TES; Where: P EC,max and P ED,max are the maximum values ​​of EES charging and discharging power, E EES,inv is the capacity of EES, E EES,0 and E EES,T are the capacities at the start and end of EES scheduling, respectively.

8. The comprehensive energy dispatching method based on biomass gas and power-to-gas coupling according to claim 7 is characterized in that: The constraints include electric power balance constraints, thermal power balance constraints, gas volume balance constraints, carbon balance constraints, power-to-gas constraints, combined heat and power unit constraints, electric heater and solar thermal power station constraints, thermal and electric energy storage constraints, and energy purchase and sale constraints, including Constraints on purchasing and selling energy: Where: P BUY,max and P SELL,max are the maximum power of electricity purchased and sold, V BUY,max and V SELL,max are the maximum values ​​of gas purchasing and selling power respectively.

9. An integrated energy dispatching system based on biomass gas and power-to-gas coupling, characterized in that: include: A mathematical model building unit, used to build a mathematical model based on the equipment in the integrated energy system, wherein the mathematical model includes a biomass gas and power-to-gas coupling model, a cogeneration unit model, an electric heater model, a solar thermal power station model, and an electric energy storage model; An objective function and constraint setting unit, used for setting an objective function and constraint conditions according to the constructed mathematical model, wherein the objective function takes minimizing the dispatching cost of the integrated energy system as the goal, and the constraint conditions include electric power balance constraint, thermal power balance constraint, gas volume balance constraint, carbon balance constraint, power-to-gas constraint, cogeneration unit constraint, electric heater and solar thermal power station constraint, thermal energy storage and electric energy storage constraint, and energy purchase and sale constraint; A scheduling scheme determination unit is used to determine a low-carbon optimization scheduling scheme for the integrated energy system based on the objective function and constraint conditions.

10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the steps of the comprehensive energy scheduling method based on biomass gas and power-to-gas coupling as described in any one of claims 1-8 are implemented.

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