Multi - energy Complementary and Collaborative Optimization Method for Distribution Networks with High - proportion Renewable Energy Access

Abstract

The present invention discloses a multi - energy complementary collaborative optimization method for a distribution network with a high proportion of renewable energy access, which is characterized in that it includes constructing a multi - energy complementary system MEIS model of electricity - heat - gas - hydrogen; determining partial parameter values in the above - mentioned MEIS model; establishing operating constraint conditions of the MEIS model; taking the total economic cost in the MEIS model as the objective function, combining the operating constraint conditions of the MEIS model, and using the space branch - and - bound method to solve the objective function with the minimum function value as the goal; the multi - energy complementary collaborative optimization method provided by the present invention can achieve the refined utilization of hydrogen energy and the multi - energy dynamic coupling of the distribution network, and can further improve the economy, low - carbon performance and flexibility of the multi - energy complementary system MEIS.

Description

Technical Field

[0001] The present invention relates to the technical field of energy utilization, and particularly relates to a multi-energy complementary collaborative optimization method for a distribution network with a high proportion of renewable energy access. Background Art

[0002] To properly address the severe problems of the increasingly depleted fossil fuels and environmental pollution, many countries have started to implement energy development strategies. In the energy network, the distribution network directly faces end-users and is an important public infrastructure for serving people's livelihood. The construction of a multi-energy complementary system (MEIS) in the distribution network provides a new solution for optimizing energy supply and improving energy efficiency. As a key element of MEIS, the multi-energy collaborative optimization of the distribution network has become one of the current research hotspots.

[0003] MEIS mainly includes various energy forms such as electricity, heat, and gas. Among them, electric energy includes various renewable energies such as wind and light, and the penetration rate of renewable energy is gradually increasing. With the development of the times, people's demand for hydrogen energy is increasing day by day, and it is an irresistible trend to incorporate hydrogen energy into MEIS. At the same time, the continuous emergence of emerging technologies enables different types of energy conversion and storage devices to play an increasingly important role in MEIS, the coupling of each energy network is closer, and flexible resources are more abundant. In addition, the transmission differences of different energy networks make the heat network and the gas network exhibit dynamic characteristics, specifically manifested as storing and releasing energy in the energy network in the form of pipe energy storage, with greater flexible scheduling potential.

[0004] The natural gas to hydrogen (G2H) device successfully developed by the Institute of Nuclear Science and Technology of Sichuan University uses liquid metal to assist methane cracking, which can achieve zero-carbon emission, low cost, and high-efficiency hydrogen production from natural gas, with a conversion rate of over 90%, and has broad application prospects in MEIS. However, the current research on introducing G2H into the MEIS optimization scheduling and carrying out refined utilization of hydrogen energy is still blank. The advanced compressed air energy storage system (AA-CAES) has unique characteristics of multi-energy combined storage / supply, and it has a long lifespan, low cost, and zero-carbon emission. However, there are few research results on the role of the advanced compressed air energy system AA-CAES in the MEIS optimization scheduling. Secondly, there are still few studies that simultaneously consider the dynamic characteristics of the heat network and the gas network, and most studies are based on the dynamic characteristics of the transmission network and consider the dynamic characteristics of the medium-pressure or high-pressure gas network. There are few studies on the dynamic coupling characteristics between the heat distribution network (HDN) and the low-pressure gas distribution network (L-GDN) based on the distribution network. In addition, the calculation example is an important basis for verifying the optimal operation strategy of MEIS. Only a calculation example that conforms to reality, is reasonable and effective can reflect the true feasibility of the research results. However, the coupling nodes between different energy networks in the calculation examples applied in the existing research have the characteristics of randomness, and there is a phenomenon that the coupling of each energy network is not reasonable enough.

[0005] In summary, there is an urgent need to construct a collaborative optimization scheduling method for multi-energy complementary in a distribution network with a high proportion of renewable energy access, which combines G2H for refined hydrogen energy utilization, considers the characteristics of multi-energy storage / supply of the advanced compressed air energy system AA-CAES, and targets the dynamic coupling of multiple energies in the distribution network. Additionally, a reasonable and effective MEIS model that takes into account the spatial relationship of each energy network, conforms to the actual situation is required. Summary of the Invention

[0006] The object of the present invention is to provide a collaborative optimization method for multi-energy complementary in a distribution network with a high proportion of renewable energy access.

[0007] To this end, the technical solution of the present invention is as follows:

[0008] A collaborative optimization method for multi-energy complementary in a distribution network with a high proportion of renewable energy access, including

[0009] Constructing a multi-energy complementary system MEIS model of electricity-heat-gas-hydrogen;

[0010] Determining the operating parameters and operating costs of each energy device in the above MEIS model, the penalty cost of renewable energy, the real-time electricity price of the external power grid, the carbon trading parameters, the scheduling interval and scheduling cycle of the MEIS model, the predicted power of multi-energy load, and the predicted power of renewable energy;

[0011] Establishing the operating constraint conditions of the electricity-heat-gas-hydrogen MEIS model;

[0012] Taking the total economic cost in the MEIS model as the objective function, combining with the operating constraint conditions of the MEIS model, aiming at the minimum function value, using the space branch and bound method to solve this objective function; after solving, obtaining the total economic cost, operation and maintenance cost, energy purchase cost, carbon trading cost, the operating conditions and energy supply power of photovoltaic power plants PV, wind turbines WT, advanced compressed air energy storage systems AA-CAES, combined heat and power plants CHP, gas turbines GT, heat pumps HP, gas wells GS, electrolyzers E2H, methane reactors H2G, natural gas to hydrogen equipment G2H, and the external power grid, so as to obtain a scheduling plan within a scheduling cycle.

[0013] Furthermore, when building the multi-energy complementary system MEIS model of electricity-heat-gas-hydrogen, it is necessary to design the coupling nodes of the electricity, heat, gas, and hydrogen energy systems according to the lengths of various distribution network lines or pipelines.

[0014] Furthermore, the operating constraints of the electro-thermal-gas-hydrogen MEIS model include the operating constraints of the advanced compressed air energy storage system AA-CAES, the electrical-hydrogen coupling E2H-H2G-G2H, the gas turbine GT, the combined heat and power CHP, the heat pump HP, the gas well GS, the power distribution network PDN, the heat distribution network HDN considering dynamic characteristics, the low-pressure gas distribution network L-GDN considering dynamic characteristics, and the upper and lower limits of the multi-energy complementary system MEIS.

[0015] Furthermore, the operating constraints of the advanced compressed air energy storage system AA-CAES are as follows:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033] In the formula, is the total power consumption of the compressor; and β u,rThey are respectively the power consumption, inlet air temperature, adiabatic efficiency, and pressure ratio of the r - stage compressor; is the mass flow rate of air of the compressor; ψ is the air adiabatic index; R g is the ideal gas constant; N com is the total number of stages of the compressor; is the total heat energy collected by the cooler; is the heat energy collected by the cooler equipped after the r - stage compressor; is the outlet air temperature of the r - stage compressor; c air is the specific heat capacity at constant pressure of air; T am is the ambient temperature; is the total electrical energy released by the turbine; and γ u,r are respectively the power generation, inlet air temperature, adiabatic efficiency, and expansion ratio of the r - stage turbine; the mass flow rate of air of the turbine; N tur is the total number of stages of the turbine; is the total heat energy consumed by the heater; is the heat energy consumed by the heater equipped after the r - stage turbine; is the temperature in the gas storage chamber; is the outlet air temperature of the r - stage turbine; and are respectively the working state flags of the compressor and the turbine; is the pressure in the gas storage chamber; is the heat storage amount in the heat storage system; V air and are respectively the volume of the gas storage chamber and the initial gas storage chamber pressure; is the heat supplied by the advanced compressed air energy system AA - CAES to the heat distribution network HDN, is the heat storage amount of the initial heat storage tank; is the heat storage amount of the initial heat storage tank.

[0034] Furthermore, the operation constraints of the electrical - hydrogen coupling E2H - H2G - G2H are:

[0035]

[0036]

[0037]

[0038]

[0039]

[0040]

[0041] In the formula, is the power consumption of the electrolyzer E2H, is the amount of hydrogen generated by the electrolyzer E2H allocated to H2G, is the amount of hydrogen generated by the electrolyzer E2H stored in the hydrogen storage tank, is the amount of hydrogen generated by the electrolyzer E2H directly supplied to the hydrogen load, is the amount of hydrogen supplied by the hydrogen storage tank to the methane reactor H2G, is the amount of natural gas generated by the methane reactor H2G, is the natural gas consumption of the natural gas to hydrogen conversion device G2H, is the amount of hydrogen generated by the natural gas to hydrogen conversion device G2H stored in the hydrogen storage tank, is the amount of hydrogen generated by the natural gas to hydrogen conversion device G2H directly supplied to the hydrogen load, is the hydrogen storage capacity of the hydrogen storage tank, is the initial hydrogen storage capacity of the hydrogen storage tank, η E2H is the electrolyzer E2H electricity to hydrogen conversion efficiency, HHV is the hydrogen calorific value, is the Sabatier reaction coefficient, is the hydrogen density, η G2H is the natural gas to hydrogen conversion efficiency of the natural gas to hydrogen conversion device G2H, is the natural gas to hydrogen reaction coefficient, is the hydrogen input efficiency of the hydrogen storage tank, is the hydrogen output efficiency of the hydrogen storage tank, is the hydrogen load at node i.

[0042] Furthermore, the operating constraints of the combined heat and power CHP are:

[0043]

[0044]

[0045]

[0046] In the formula, are the power generation amount and heat generation amount of the combined heat and power CHP respectively, is the gas consumption of the combined heat and power CHP, are the upward ramp rate and downward ramp rate of the combined heat and power CHP respectively, are the electrical efficiency and thermal efficiency of the combined heat and power CHP respectively, HGV is the high calorific value of natural gas;

[0047] The operating constraints of the gas turbine GT are:

[0048]

[0049]

[0050] Wherein, is the power generation of the gas turbine GT, is the gas consumption of the gas turbine GT, are the upward ramp rate and downward ramp rate of the gas turbine GT, respectively. is the electrical efficiency of the gas turbine GT;

[0051] The operation constraints of the heat pump HP are:

[0052]

[0053] Wherein, is the heat output of the heat pump, is the power consumption of the heat pump, η HP is the heat efficiency of the heat pump;

[0054] The operation constraints of the gas well GS are:

[0055]

[0056] Wherein, is the gas supply of the gas well GS, are the minimum and maximum values of the gas supply of the gas well GS, respectively;

[0057] The operation constraints of the power distribution network PDN are:

[0058]

[0059]

[0060]

[0061]

[0062] Wherein, is the reactive power compensation of the static var generator SVG, C j,t is the value of the shunt capacitor / reactance SCR, P ij,t 、Q ij,t are the active power flow and reactive power flow of line ij, U j,t is the electrical node voltage, and are the active electrical load and reactive electrical load at electrical node i, K ij,t is the tap ratio of the on-load tap changer OLTC on line ij, r ij and x ij are the resistance and reactance of line ij, U sl is the voltage of the slack electrical node, ΩOLTC is the set of lines equipped with the on-load tap-changer OLTC, and are the actual output power of the PV power station and the wind turbine respectively, is the power purchased from the external power grid;

[0063] The HDN operation constraints considering dynamic characteristics are:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] In the formula, and are the outlet water temperatures of the supply pipe ij and the return pipe ij respectively, and are the inlet water temperatures of the supply pipe and the return pipe respectively, and are the supply water temperature and the return water temperature at the heat node i respectively, ρ w is the hot water density, l ij is the diameter of the heat network pipe, d ij,heat is the length of the heat network pipe, and are the circulating water mass flow rates of the supply pipe and the return pipe respectively, and are the unit conversion quantities of the transmission delay times of the heat network supply pipe and the return pipe respectively, Δt represents the time step, c w is the constant pressure specific heat of the circulating water, ξ p is the temperature loss coefficient of the heat network pipe, is the circulating water mass flow rate at the heat node i, F(i) and T(i) represent the sets of heat network pipes with the starting node i and the ending node i respectively, is the heat load at the hot node i. represents rounding down the data;

[0075] L-GDN operation constraints considering dynamic characteristics:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086] p i,t ≥p j,t (55)

[0087]

[0088]

[0089]

[0090] In the formula, is the average gas pressure of the gas pipeline ij, p i,t is the gas pressure at the gas node i, and are the natural gas volumes entering and leaving the gas pipeline respectively, is the pipeline storage gas volume of the initial gas pipeline, D ij is the pipeline storage constant, is the length of the gas pipeline, d ij,gas is the diameter of the gas pipeline, R is the ideal gas constant, T is the natural gas temperature, Z is the compressibility factor, ρ0 is the ideal gas density, Ω gas is the set of gas pipelines, is the average gas flow rate of the gas pipeline, C ij is the pipeline coefficient, f ij is the friction coefficient, is the compression ratio of the natural gas compressor, is the gas load;

[0091] The upper and lower limits of the operation of the multi - energy complementary system MEIS are:

[0092] X min ≤ X t ≤ X max (59)

[0093] Wherein, X i,t is a certain operation variable in the energy equipment or energy network, including X min and X max are the minimum and maximum values of the operation variable respectively.

[0094] Furthermore, the total economic cost is the sum of the operation and maintenance cost, the energy purchase cost, and the carbon trading cost. The expression of the total economic cost is:

[0095]

[0096] Wherein, f, f oc , f buy and are the total economic cost, the operation and maintenance cost, the energy purchase cost, and the carbon trading cost respectively.

[0097] Furthermore, the operation and maintenance cost includes the operation and maintenance costs of combined heat and power CHP, gas turbine GT, heat pump HP, advanced compressed air energy storage system AA - CAES, gas well GS, electrolyzer E2H, methane reactor H2G, and natural gas to hydrogen G2H; The representation method of the operation and maintenance cost f oc is:

[0098]

[0099] Wherein, C HP , C CAES , C CHP , C GT , C E2H , C H2G , C G2H , C s and C q are the cost coefficients of heat pump, advanced compressed air energy storage system AA - CAES, combined heat and power CHP, gas turbine GT, electrolyzer E2H, methane reactor H2G, natural gas to hydrogen equipment G2H, gas well GS, and abandoned wind / solar respectively; is the gas supply of the gas well GS; and are the predicted power output of the photovoltaic power station PV and the wind turbine WT respectively; and are the actual electricity output of the PV power station PV and the wind turbine WT, respectively; is the electricity purchased from the external power grid; N f 、N u 、N g 、N w 、N c 、N q 、N m 、N n and N x are the number of electric nodes coupled to the heat pump HP, compressed air energy storage system AA-CAES, combined heat and power CHP, gas turbine GT, electrical hydrogen coupling equipment E2H-H2G-G2H, gas well GS, PV power station PV, wind turbine WT, and the external power grid, respectively;

[0100] The energy purchase cost is the cost generated by purchasing electricity from the external power grid, and the expression of the energy purchase cost is:

[0101]

[0102] In the formula, is the real-time electricity price for purchasing electricity from the external power grid, is the electricity purchased from the external power grid, T N is a scheduling period.

[0103] Furthermore, the carbon trading cost is the cost of trading the CO2 absorption or emission involved in the operation of the multi-energy complementary system MEIS through the carbon trading market finally. The expression of the carbon trading cost is:

[0104]

[0105]

[0106]

[0107]

[0108] In the formula, is the stepped carbon trading cost at time t; and are the actual carbon emissions and carbon emission quota of the multi-energy complementary system MEIS, respectively; and are the carbon emission coefficients of electricity purchased from the external grid, combined heat and power CHP and gas turbine GT, gas load, and methane reactor H2G, respectively; χ grid 、χ gas and χ gload are the carbon emission quota coefficients of electricity purchased from the external grid, combined heat and power CHP and gas turbine GT, gas load, respectively; N glis the number of gas nodes connected with gas load; α and are the carbon trading base price, the carbon price growth rate, and the length of the carbon emission interval respectively.

[0109] Furthermore, in the process of solving the objective function, it is necessary to linearize the non-linear terms in the operation constraints of the advanced compressed air energy storage system AA-CAES and the operation constraints of the power distribution network PDN, and then use gurobi to bilinearize the quadratic programming problem.

[0110] Compared with the existing technology, the multi-energy complementary collaborative optimization method for power distribution networks with high proportion of renewable energy access provided by the present invention utilizes the characteristics of multi-energy storage / supply of the advanced compressed air energy storage system AA-CAES and the characteristics of zero carbon emission, high efficiency, and low cost of natural gas to hydrogen G2H. At the same time, it can take into account the dynamic characteristics of the heat distribution network HDN and the low-pressure gas distribution network L-GDN, and can realize the refined utilization of hydrogen energy and the multi-energy dynamic coupling of the power distribution network, which can further improve the economy, low carbon, and flexibility of the multi-energy complementary system MEIS; at the same time, a practical, reasonable and effective electrical-thermal-gas-hydrogen MEIS example considering the spatial relationship of the multi-energy network is constructed, which can provide a more systematic and comprehensive example analysis platform for relevant scientific research personnel, and is more conducive to reflecting the actual operation status of the actual multi-energy complementary system MEIS. Description of the Drawings

[0111] Figure 1 is the flow chart of the multi-energy complementary collaborative optimization method for power distribution networks with high proportion of renewable energy access provided by the present invention.

[0112] Figure 2 is the schematic diagram of the flexible operation mode of electrical-hydrogen coupled energy supply.

[0113] Figure 3 is the example diagram of the electrical-thermal-gas-hydrogen MEIS model.

[0114] Figure 4 is the multi-energy load prediction curve.

[0115] Figure 5 is the schematic diagram of the comparison between the renewable energy prediction curve and the real-time electricity price of the external power grid.

[0116] Figure 6 is the schematic diagram of the simulation of the electricity dispatch result.

[0117] Figure 7 is the schematic diagram of the simulation of the heat dispatch result.

[0118] Figure 8 is the schematic diagram of the simulation of the gas energy dispatch result.

[0119] Figure 9 is the schematic diagram of the simulation of the hydrogen energy dispatch result.

[0120] Figure 10 Schematic diagram of the tap ratios of each OLTC at peak and trough times of the electrical load. Specific implementation manners

[0121] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, but the following embodiments are by no means any limitation to the present invention.

[0122] A multi - energy complementary collaborative optimization method for a distribution network with a high proportion of renewable energy access, characterized by including

[0123] 1) Construct a multi - energy complementary system MEIS model of electricity - heat - gas - hydrogen; during the construction of this model, it is necessary to select the grid structures of various distribution networks, and then reasonably design the coupling nodes of the electricity, heat, gas, and hydrogen energy systems according to the lengths of various distribution network lines or pipelines, as well as the coupling nodes between the multi - energy complementary system MEIS and the external network. The coupling nodes are set through energy devices, where the energy devices include wind turbines WT, photovoltaic power stations PV, gas wells GS, gas turbines GT, combined heat and power plants CHP, heat pumps HP, advanced compressed air energy storage systems AA - CAES, electrolyzers E2H, methane reactors H2G, natural gas to hydrogen equipment G2H, hydrogen storage tanks HST, static var generators SVG, shunt capacitors / reactors SCR, and on - load tap - changing switches OLTC;

[0124] 2) Determine the operating parameters and operating costs of each energy device in the above - mentioned MEIS model, the penalty cost of renewable energy, the real - time electricity price of the external power grid, the carbon trading parameters, the scheduling interval and scheduling cycle of the MEIS model, the multi - energy load forecasting power, and the renewable energy forecasting power; where the total energy supply power of MEIS is equal to the sum of the multi - energy load forecasting power, the MEIS energy loss power, and the net energy storage power of the energy storage device. At the same time, the total energy supply power of MEIS is also equal to the sum of the total energy supply powers of various energy devices, the external power grid supply power, and the renewable energy forecasting power;

[0125] 3) Establish the operating constraint conditions for the electricity - heat - gas - hydrogen MEIS model; including the operating constraints of the advanced compressed air energy storage system AA - CAES, the electrical - hydrogen coupling E2H - H2G - G2H operating constraints, the gas turbine GT operating constraints, the combined heat and power plant CHP operating constraints, the heat pump HP operating constraints, the gas well GS operating constraints, the distribution network PDN operating constraints, the heat distribution network HDN operating constraints considering dynamic characteristics, the low - pressure gas distribution network L - GDN operating constraints considering dynamic characteristics, and the upper and lower limit operating constraints of the multi - energy complementary system MEIS.

[0126] 4) Take the total economic cost in the MEIS model as the objective function, combine the operation constraints of the MEIS model, and use the space branch and bound method to solve this objective function with the goal of minimizing the function value; after solving, obtain the total economic cost, operation and maintenance cost, energy purchase cost, carbon trading cost, the operating conditions and energy supply power of the photovoltaic power station PV, wind turbine WT, advanced compressed air energy storage system AA-CAES, combined heat and power CHP, gas turbine GT, heat pump HP, gas well GS, electrolyzer E2H, methane reactor H2G, natural gas to hydrogen equipment G2H, and external power grid, so as to obtain a scheduling plan within a scheduling period.

[0127] The total economic cost is the sum of the operation and maintenance cost, energy purchase cost, and carbon trading cost. The expression of the total economic cost is:

[0128]

[0129] In the formula, f, f oc , f buy and are the total economic cost, operation and maintenance cost, energy purchase cost, and carbon trading cost respectively;

[0130] During the solution process of the objective function, it is necessary to linearize the non-linear terms in the operation constraints of the advanced compressed air energy storage system AA-CAES and the operation constraints of the distribution network PDN, and then use gurobi to bilinearize the quadratic programming problem.

[0131] It should be further noted that the operation constraints of the advanced compressed air energy storage system AA-CAES are:

[0132]

[0133]

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] wherein, is the total power consumption of the compressor; and β u,r are respectively the power consumption, inlet air temperature, adiabatic efficiency and pressure ratio of the r-th stage compressor; is the mass flow rate of air of the compressor; ψ is the adiabatic index of air; R g is the ideal gas constant; N com is the total number of stages of the compressor; is the total heat energy collected by the cooler; is the heat energy collected by the cooler equipped after the r-th stage compressor; is the outlet air temperature of the r-th stage compressor; c air is the specific heat capacity at constant pressure of air; T am is the ambient temperature; is the total electric energy released by the turbine; and γ u,r are respectively the power generation, inlet air temperature, adiabatic efficiency and expansion ratio of the r-th stage turbine; the mass flow rate of air of the turbine; N tur is the total number of stages of the turbine; is the total heat energy consumed by the heater; is the heat energy consumed by the heater equipped after the r-th stage turbine; is the temperature in the gas storage chamber; is the outlet air temperature of the r-th stage turbine; and are respectively the working state flags of the compressor and the turbine; is the pressure in the gas storage chamber; is the heat storage amount in the heat storage system; V air and are respectively the volume of the gas storage chamber and the initial gas storage chamber pressure; is the heat supplied by the advanced compressed air energy system AA-CAES to the heat distribution network HDN, is the heat storage amount of the initial heat storage tank; is the heat storage capacity of the initial heat storage tank.

[0150] The operating constraints of the electrical hydrogen coupling E2H-H2G-G2H are as follows:

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] In the formula, is the power consumption of the electrolyzer E2H, is the amount of hydrogen generated by the electrolyzer E2H allocated to H2G, is the amount of hydrogen generated by the electrolyzer E2H stored in the hydrogen storage tank, is the amount of hydrogen generated by the electrolyzer E2H directly supplied to the hydrogen load, is the amount of hydrogen supplied by the hydrogen storage tank to the methane reactor H2G, is the amount of natural gas generated by the methane reactor H2G, is the natural gas consumption of the natural gas to hydrogen conversion equipment G2H, is the amount of hydrogen generated by the natural gas to hydrogen conversion equipment G2H stored in the hydrogen storage tank, is the amount of hydrogen generated by the natural gas to hydrogen conversion equipment G2H directly supplied to the hydrogen load, is the hydrogen storage capacity of the hydrogen storage tank, is the initial hydrogen storage capacity of the hydrogen storage tank, η E2H is the electrolyzer E2H hydrogen conversion efficiency from electricity, HHV is the hydrogen calorific value, is the Sabatier reaction coefficient, is the hydrogen density, η G2H is the natural gas to hydrogen conversion efficiency of the natural gas to hydrogen conversion equipment G2H, is the natural gas to hydrogen reaction coefficient, is the hydrogen input efficiency of the hydrogen storage tank, is the hydrogen output efficiency of the hydrogen storage tank, is the hydrogen load at node i.

[0158] The operating constraints of the combined heat and power CHP are as follows:

[0159]

[0160]

[0161]

[0162] In the formula, are the electricity generation and heat generation of the combined heat and power (CHP) respectively, is the gas consumption of the combined heat and power (CHP), are the upward and downward ramping rates of the combined heat and power (CHP) respectively, are the electrical efficiency and thermal efficiency of the combined heat and power (CHP) respectively, and HGV is the higher heating value of natural gas;

[0163] The operating constraints of the gas turbine (GT) are:

[0164]

[0165]

[0166] In the formula, is the electricity generation of the gas turbine (GT), is the gas consumption of the gas turbine (GT), are the upward and downward ramping rates of the gas turbine (GT) respectively. is the electrical efficiency of the gas turbine (GT);

[0167] The operating constraints of the heat pump (HP) are:

[0168]

[0169] In the formula, is the heat generation of the heat pump, is the power consumption of the heat pump, and η HP is the thermal efficiency of the heat pump;

[0170] The operating constraints of the gas well (GS) are:

[0171]

[0172] In the formula, is the gas supply of the gas well (GS), are the minimum and maximum values of the gas supply of the gas well (GS) respectively;

[0173] The operating constraints of the power distribution network (PDN) are:

[0174]

[0175]

[0176]

[0177]

[0178] In the formula, is the reactive power compensation of the static var generator SVG, C j,t is the value of the shunt capacitor / reactance SCR, P ij,t , Q ij,t are the active power flow and reactive power flow of line ij respectively, U j,t is the voltage of the electrical node, and are the active electrical load and reactive electrical load at electrical node i respectively, K ij,t is the tap ratio of the on-load tap changer OLTC on line ij, r ij and x ij are the resistance and reactance of line ij respectively, U sl is the voltage of the slack electrical node, Ω OLTC is the set of lines equipped with on-load tap changers OLTC, and are the actual output power of the photovoltaic power station and the wind turbine respectively, is the power purchased from the external power grid;

[0179] The HDN operation constraints considering dynamic characteristics are:

[0180]

[0181]

[0182]

[0183]

[0184]

[0185]

[0186]

[0187]

[0188]

[0189]

[0190] In the formula, and are the outlet water temperatures of the supply water pipe ij and the return water pipe ij respectively, and are the inlet water temperatures of the supply water pipe and the return water pipe respectively, and are the supply water temperature and return water temperature at the hot node i, ρ w is the density of hot water, l ij is the diameter of the heat network pipeline, d ij,heat is the length of the heat network pipeline, and are the mass flow rates of the circulating water in the supply pipeline and return pipeline respectively, and are the unit conversion amounts of the transmission delay times of the heat network supply pipeline and return pipeline respectively, Δt represents the time step, c w is the constant pressure specific heat of the circulating water, ξ p is the temperature loss coefficient of the heat network pipeline, is the mass flow rate of the circulating water at the hot node i, F(i) and T(i) represent the sets of heat network pipelines with the first node being i and the last node being i respectively, is the heat load at the hot node i, represents rounding down the data;

[0191] L-GDN operation constraints considering dynamic characteristics:

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202] p i,t ≥p j,t (55)

[0203]

[0204]

[0205]

[0206] In the formula, is the average air pressure of the air pipeline ij, p i,t is the air pressure of the air node i, and are the natural gas volumes entering and leaving the air pipeline respectively, is the gas storage volume in the initial air pipeline, D ij is the pipe storage constant, is the length of the air pipeline, d ij,gas is the diameter of the air pipeline, R is the ideal gas constant, T is the natural gas temperature, Z is the compression factor, ρ0 is the ideal gas density, Ω gas is the set of air pipelines, is the average air flow rate of the air pipeline, C ij is the pipeline coefficient, f ij is the friction coefficient, is the compression ratio of the natural gas compressor, is the air load;

[0207] The upper and lower limits of the operation of the multi - energy complementary system MEIS are:

[0208] X min ≤X t ≤X max (59)

[0209] In the formula, X i,t is a certain operation variable in the energy equipment or energy network, including X min and X max are the minimum and maximum values of this operation variable respectively.

[0210] It should be further noted that the operation and maintenance costs include the operation and maintenance costs of combined heat and power CHP, gas turbine GT, heat pump HP, advanced compressed air energy storage system AA - CAES, gas well GS, electrolyzer E2H, methane reactor H2G, and natural gas to hydrogen G2H; the operation and maintenance cost f oc is expressed as:

[0211]

[0212] In the formula, C HP 、C CAES 、C CHP 、C GT 、C E2H 、C H2G 、C G2H 、C s and C qare the cost coefficients of heat pump, advanced compressed air energy storage system AA-CAES, combined heat and power CHP, gas turbine GT, electrolyzer E2H, methane reactor H2G, natural gas to hydrogen equipment G2H, gas well GS, and curtailed wind / solar power respectively; is the gas supply of gas well GS; and are the predicted power output of photovoltaic power station PV and wind turbine WT respectively; and are the actual power output of photovoltaic power station PV and wind turbine WT respectively; is the electric energy purchased from the external power grid; N f 、N u 、N g 、N w 、N c 、N q 、N m 、N n and N x are the numbers of heat pump HP, advanced compressed air energy storage system AA-CAES, combined heat and power CHP, gas turbine GT, electrical hydrogen coupling equipment E2H-H2G-G2H, gas well GS, photovoltaic power station PV, wind turbine WT, and electrical nodes coupled with the external power grid respectively;

[0213] The energy purchase cost is the cost generated by purchasing electric energy from the external power grid, and the expression of the energy purchase cost is:

[0214]

[0215] In the formula, is the real-time electricity price for purchasing electricity from the external power grid, is the electric energy purchased from the external power grid, T N is a scheduling period.

[0216] It should be further noted that the carbon trading cost is the cost of CO2 absorption or emission involved in the operation of the multi-energy complementary system MEIS and finally traded through the carbon trading market. The expression of the carbon trading cost is:

[0217]

[0218]

[0219]

[0220]

[0221] In the formula, is the stepped carbon trading cost at time t; and are the actual carbon emissions and carbon emission allowance of the multi - energy complementary system MEIS, respectively; and are the carbon emission coefficients of externally purchased electricity, combined heat and power CHP, gas turbine GT, gas load, and methane reactor H2G, respectively; χ grid 、χ gas and χ gload are the carbon emission allowance coefficients of externally purchased electricity, combined heat and power CHP, gas turbine GT, and gas load, respectively; N gl is the number of gas nodes connected with gas loads; α and are the carbon trading base price, carbon price growth rate, and the length of the carbon emission interval, respectively.

[0222] Example:

[0223] During the introduction of the following examples, when introducing each energy equipment, abbreviations are used, including wind turbine (WT), photovoltaic power station (PV), gas well (GS), gas turbine (GT), combined heat and power (CHP), heat pump (HP), advanced compressed air energy storage system (AA - CAES), electrolyzer (E2H), methane reactor (H2G), natural gas to hydrogen equipment (G2H), hydrogen storage tank (HST), static var generator (SVG), shunt capacitor / reactance (SCR), on - load tap - changer (OLTC), power distribution network (PND), heat distribution network (HDN), low - pressure gas distribution network (L - GDN), compressed air energy storage system (AA - CAES), electrical - hydrogen coupling (E2H - H2G - G2H), multi - energy complementary system (MEIS).

[0224] Taking a 33 - node PDN, an 8 - node HDN, an 11 - node L - GDN, and a 2 - node hydrogen system as examples, the present invention is further illustrated. Through reasonable coupling of different distribution networks and nodes, and by organically combining various energy equipment, an electrical - thermal - gas - hydrogen MEIS example is designed, as shown in the appendix Figure 3 shown. In this example, the scheduling period is selected as 24h, the scheduling time interval is 1h, and the penalty cost for renewable energy is 100 $ / MW. The parameters of each energy equipment are shown in Table 1, the carbon trading parameters are shown in Table 2, the multi - energy load forecasting curve is shown in the appendix Figure 4 and the renewable energy forecasting curve and the real - time electricity price of the external power grid are shown in the appendix Figure 5 .

[0225] To analyze the flexible operation mode of AA - CAES and E2H - H2G - G2H coupled energy supply and the role of the dynamic characteristics of HDN and L - GDN in MEIS, 6 scenarios are set, as shown in Table 3. Among them, "√" means considered, and "×" means not considered.

[0226] Table 4 shows the coordinated optimization results under 6 scenarios. Compared with Scenario 1, the total economic cost in Scenario 2 decreased by 14.70% and the carbon emissions decreased by 19.84%. This indicates that AA-CAES has good multi-energy supply / reserve capabilities, which can significantly reduce the economic cost and CO2 emissions of MEIS. Compared with Scenario 2, the total economic cost in Scenario 3 decreased by 4.48% and the carbon emissions decreased by 4.69%. This shows that the flexible operation mode of E2H-H2G-G2H coupled energy supply can reasonably coordinate the output of each unit and utilize hydrogen energy in a refined manner, thus improving the economy and low-carbon performance of MEIS. Compared with Scenario 3, both the total economic cost and carbon emissions in Scenario 4 decreased slightly, with limited optimization space; while compared with Scenario 3, the economic cost in Scenario 5 decreased by 2.98% and the carbon emissions decreased by 5.39%, with obvious optimization in economy and low-carbon performance. This is because the gas network in the designed example belongs to a low-pressure gas network, with short gas network pipelines and small node pressures, resulting in limited gas storage potential in pipelines. Relying solely on the dynamic characteristics of L-GDN cannot fully provide sufficient optimization space for MEIS. The output cost of GS is still relatively high, and the ability to adjust CO2 emissions is also insufficient. Therefore, the optimization space for economic cost and carbon emissions when only considering the dynamic characteristics of HDN is greater than that when only considering the dynamic characteristics of L-GDN. Scenario 6 comprehensively considers the dynamic characteristics of the multi-energy network and has the lowest total economic cost and the smallest carbon emissions compared with Scenarios 3, 4, and 5. This is because there is a coupling relationship between the dynamic characteristics of L-GDN and HDN. Considering both dynamic characteristics can further explore the collaborative optimization potential between HDN and L-GDN and improve the economy and low-carbon performance of MEIS.

[0227] Compared with Scenario 1, the economic cost in Scenario 6 decreased by 20.99% and the carbon emissions decreased by 27.81%, verifying the superiority of the proposed MEIS optimization scheduling method in terms of low-carbon performance and economy. At the same time, the energy purchase cost in Scenario 6 is the smallest, and compared with Scenario 1, its energy purchase cost decreased by 85.15%, greatly reducing the dependence on the external power grid. In addition, Scenario 6 comprehensively utilizes the combined heat and power supply / reserve of AA-CAES, the multi-energy hydrogen conversion and hydrogen storage of E2H-H2G-G2H, and the pipeline gas storage characteristics shown by the dynamic characteristics of the multi-energy network, improving the scheduling flexibility of MEIS.

[0228] Appendix Figures 6 - 9 They are respectively the scheduling results of electricity, heat, gas, and hydrogen energy finally output by using the MEIS scheduling method proposed in the present invention. Figure 10 They are the tap ratios of each OLTC at the peak and trough moments of the electricity load. Thus, the scheduling plan of MEIS within a scheduling cycle can be obtained.

[0229] Table 1 Energy Equipment Parameters

[0230]

[0231] Table 2 Carbon trading parameters

[0232]

[0233] Table 3 Scenario settings

[0234]

[0235] Table 4 Coordination optimization results under different scenarios

[0236]

Claims

1. A distribution network multi-energy complementary collaborative optimization method for high-proportion renewable energy access, characterized in that including Constructing a multi - energy complementary system MEIS model of electricity - heat - gas - hydrogen; when building the multi - energy complementary system MEIS model of electricity - heat - gas - hydrogen, coupling nodes of the electricity, heat, gas, and hydrogen energy systems need to be designed according to the lengths of various distribution network lines or pipelines; Determining the operating parameters and operating costs of each energy device in the above - mentioned MEIS model, the penalty cost of renewable energy, the real - time electricity price of the external power grid, the carbon trading parameters, the scheduling interval and scheduling period of the MEIS model, the multi - energy load prediction power, and the renewable energy prediction power; Establishing the operating constraint conditions of the electricity - heat - gas - hydrogen MEIS model; the operating constraints of the electricity - heat - gas - hydrogen MEIS model include the operating constraints of the advanced compressed air energy storage system AA - CAES, the electrical - hydrogen coupling E2H - H2G - G2H operating constraints, the gas turbine GT operating constraints, the combined heat and power CHP operating constraints, the heat pump HP operating constraints, the gas well GS operating constraints, the power distribution network PDN operating constraints, the heat distribution network HDN operating constraints considering dynamic characteristics, the low - pressure gas distribution network L - GDN operating constraints considering dynamic characteristics, and the upper and lower limit constraints of the multi - energy complementary system MEIS operation; the electrical - hydrogen coupling E2H - H2G - G2H operating constraints are: Wherein, is the power consumption of the electrolyzer E2H, is the amount of hydrogen produced by the electrolyzer E2H allocated to H2G, is the amount of hydrogen produced by the electrolyzer E2H stored in the hydrogen storage tank, is the amount of hydrogen produced by the electrolyzer E2H directly supplied to the hydrogen load, is the amount of hydrogen supplied by the hydrogen storage tank to the methane reactor H2G, is the amount of natural gas produced by the methane reactor H2G, is the natural gas consumption of the natural gas to hydrogen conversion device G2H, is the amount of hydrogen produced by the natural gas to hydrogen conversion device G2H stored in the hydrogen storage tank, is the amount of hydrogen produced by the natural gas to hydrogen conversion device G2H directly supplied to the hydrogen load, is the hydrogen storage capacity of the hydrogen storage tank, is the initial hydrogen storage capacity of the hydrogen storage tank, η E2H is the electrolysis to hydrogen efficiency of the electrolyzer E2H, HHV is the calorific value of hydrogen, is the Sabatier reaction coefficient, is the hydrogen density, η G2H is the natural gas to hydrogen conversion efficiency of the natural gas to hydrogen conversion device G2H, is the natural gas to hydrogen conversion reaction coefficient, is the hydrogen input efficiency of the hydrogen storage tank, is the hydrogen output efficiency of the hydrogen storage tank, is the hydrogen load at node i; Taking the total economic cost in the MEIS model as the objective function, combining with the operating constraint conditions of the MEIS model, aiming at minimizing the function value, using the space branch - and - bound method to solve this objective function; after solving, the total economic cost, operation and maintenance cost, energy purchase cost, carbon trading cost, the operating conditions and energy supply powers of the photovoltaic power station PV, wind turbine WT, advanced compressed air energy storage system AA - CAES, combined heat and power CHP, gas turbine GT, heat pump HP, gas well GS, electrolyzer E2H, methane reactor H2G, natural gas - to - hydrogen equipment G2H, and external power grid are obtained, so as to obtain a scheduling plan within a scheduling period.

2. The multi - energy complementary collaborative optimization method for distribution network with high - proportion renewable energy access according to claim 1, wherein, The operating constraints of the advanced compressed air energy storage system AA - CAES are: In the formula, is the total power consumption of the compressor; and β u,r are respectively the power consumption, inlet air temperature, adiabatic efficiency and pressure ratio of the r - stage compressor; is the mass air flow rate of the compressor; ψ is the adiabatic index of air; R g is the ideal gas constant; N com is the total number of stages of the compressor; is the total thermal energy collected by the cooler; is the thermal energy collected by the cooler equipped after the r-th stage compressor; is the outlet air temperature of the r-th stage compressor; c air is the specific heat capacity at constant pressure of air; T am is the ambient temperature; is the total electric energy released by the turbine; and γ u,r are respectively the generated electricity, inlet air temperature, adiabatic efficiency and expansion ratio of the r-th stage turbine; The air mass flow rate of the turbine; N tur is the total number of stages of the turbine; is the total thermal energy consumed by the heater; The thermal energy consumed by the heater equipped after the r-th stage turbine; T t s is the temperature in the gas storage chamber; is the outlet air temperature of the r-th stage turbine; and are respectively the working state flags of the compressor and the turbine; is the pressure in the gas storage chamber; is the stored heat in the thermal storage system; V air and are respectively the volume of the gas storage chamber and the initial gas storage chamber pressure; is the heat supplied by the advanced compressed air energy system AA-CAES to the heat distribution network HDN, is the stored heat in the initial heat storage tank; is the stored heat in the initial heat storage tank.

3. The collaborative optimization method for multi - energy complementary in a distribution network with high - proportion renewable energy access according to claim 1, characterized in that The combined heat and power CHP operating constraints are: Wherein, are the electricity generation and heat generation of the combined heat and power (CHP), respectively, is the gas consumption of the combined heat and power (CHP), are the upward ramp rate and downward ramp rate of the combined heat and power (CHP), respectively, are the electrical efficiency and thermal efficiency of the combined heat and power (CHP), respectively, and HGV is the high calorific value of natural gas; The gas turbine GT operating constraints are: In the formula, is the power generation of the gas turbine GT, is the gas consumption of the gas turbine GT, are the upward ramp rate and downward ramp rate of the gas turbine GT respectively, is the electrical efficiency of the gas turbine GT; The heat pump HP operating constraints are: In the formula, is the heat output of the heat pump, is the power consumption of the heat pump, and η HP is the heat efficiency of the heat pump; The gas well GS operating constraints are: In the formula, is the gas supply volume of gas well GS, are respectively the minimum value and the maximum value of the gas supply volume of gas well GS; The power distribution network PDN operating constraints are: In the formula, is the reactive power compensation of the static var generator SVG, C j,t is the value of the shunt capacitor / reactance SCR, P ij,t , Q ij,t are the active power flow and reactive power flow of line ij respectively, U j,t is the electrical node voltage, and are the active electrical load and reactive electrical load at electrical node i respectively, K ij,t is the tap ratio of the on-load tap changer OLTC on line ij, r ij and x ij are the resistance and reactance of line ij respectively, U sl is the voltage of the slack electrical node, Ω OLTC is the set of lines equipped with on-load tap changers OLTC, and are the actual output power of the photovoltaic power station and the wind turbine respectively, is the power purchased from the external power grid; The HDN operating constraints considering dynamic characteristics are: In the formula, and are the outlet water temperatures of the supply pipeline ij and the return pipeline ij respectively, and are the inlet water temperatures of the supply pipeline and the return pipeline respectively, and are the supply temperature and the return temperature at the heat node i respectively, ρ w is the hot water density, l ij is the diameter of the heat network pipeline, d ij,heat is the length of the heat network pipeline, and are the circulating water mass flow rates of the supply pipeline and the return pipeline respectively, and are the unit conversion quantities of the transmission delay times of the heat network supply pipeline and the return pipeline respectively, Δt represents the time step, c w is the constant pressure specific heat of the circulating water, ξ p is the temperature loss coefficient of the heat network pipeline, is the circulating water mass flow rate at the heat node i, F(i) and T(i) represent the sets of heat network pipelines with the first node being i and the last node being i respectively, is the heat load at the heat node i, represents rounding down the data; The L - GDN operating constraints considering dynamic characteristics: In the formula, is the average gas pressure of gas pipeline ij, p i,t is the gas pressure of gas node i, and are the natural gas volumes entering and discharging from the gas pipeline respectively, is the gas storage volume in the initial gas pipeline, D ij is the pipe storage constant, is the length of the gas pipeline, d ij,gas is the diameter of the gas pipeline, R is the ideal gas constant, T is the natural gas temperature, Z is the compressibility factor, ρ0 is the ideal gas density, Ω gas is the set of gas pipelines, is the average gas flow rate of the gas pipeline, C ij is the pipeline coefficient, f ij is the friction coefficient, is the compression ratio of the natural gas compressor, is the gas load; The upper and lower limit constraints of the multi - energy complementary system MEIS operation are: X min ≤X t ≤X max (59) where X i,t is an operating variable in an energy device or energy network, including C j,t , K ij,t , p i,t , X min and X max are the minimum and maximum values of the operating variable, respectively.

4. The multi-energy complementary collaborative optimization method for distribution network with high proportion of renewable energy access according to claim 1, characterized in that, The total economic cost is the sum of the operation and maintenance cost, energy purchase cost, and carbon trading cost, and the expression of the total economic cost is: where f, f oc , f buy and are the total economic cost, operation and maintenance cost, energy purchase cost, and carbon trading cost, respectively.

5. The multi-energy complementary collaborative optimization method for distribution network with high proportion of renewable energy access according to claim 4, wherein The operation and maintenance costs include those of combined heat and power (CHP), gas turbine (GT), heat pump (HP), advanced compressed air energy storage system (AA-CAES), gas well (GS), electrolyzer (E2H), methane reactor (H2G), and natural gas to hydrogen conversion (G2H); the operation and maintenance cost f oc is expressed as: Wherein, C HP , C CAES , C CHP , C GT , C E2H , C H2G , C G2H , C s and C q are the cost coefficients of the heat pump, the advanced compressed air energy storage system AA-CAES, the combined heat and power CHP, the gas turbine GT, the electrolyzer E2H, the methane reactor H2G, the natural gas to hydrogen equipment G2H, the gas well GS, and the curtailed wind / solar power, respectively; is the gas supply of the gas well GS; and are the predicted power output of the photovoltaic power station PV and the wind turbine WT, respectively; and are the actual power output of the photovoltaic power station PV and the wind turbine WT, respectively; is the electric energy purchased from the external power grid; N f , N u , N g , N w , N c , N q , N m , N n and N x are the numbers of the heat pump HP, the advanced compressed air energy storage system AA-CAES, the combined heat and power CHP, the gas turbine GT, the electrical hydrogen coupling equipment E2H-H2G-G2H, the gas well GS, the photovoltaic power station PV, the wind turbine WT, and the electrical nodes coupled with the external power grid, respectively; The energy purchase cost is the cost generated by purchasing electric energy from the external power grid, and the expression of the energy purchase cost is: Wherein, is the real-time electricity price for purchasing electricity from the external power grid, is the electric energy purchased from the external power grid, and T N is a scheduling period.

6. The multi - energy complementary collaborative optimization method for distribution network with high - proportion renewable energy access according to claim 5, wherein, The carbon trading cost is the cost of trading the CO2 absorption or emission involved in the operation of the multi - energy complementary system MEIS through the carbon trading market in the end, and the expression of the carbon trading cost is: In the formula, is the stepped carbon trading cost at time t; and are the actual carbon emissions and carbon emission allowance of the multi - energy complementary system MEIS respectively; θ grid 、θ gas 、θ gload and θ H2G are the carbon emission coefficients of external power purchase, combined heat and power generation CHP and gas turbine GT, gas load, and methane reactor H2G respectively; χ grid 、χ gas and χ gload are the carbon emission allowance coefficients of external power purchase, combined heat and power generation CHP and gas turbine GT, gas load respectively; N gl is the number of gas nodes connected with gas load; α and are the carbon trading base price, carbon price growth rate and the length of the carbon emission interval respectively.

7. The multi - energy complementary collaborative optimization method for distribution networks with high - proportion renewable energy access according to claim 6, characterized in that, In the process of solving the objective function, it is necessary to linearize the non - linear terms in the operating constraints of the advanced compressed air energy storage system AA - CAES and the power distribution network PDN operating constraints, and then use gurobi to perform bilinearization on the quadratic programming problem.

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