Method for optimal dispatching of multi-microgrid with hydrogen-doped gas integrated energy based on nash negotiation

Abstract

The application discloses a hydrogen-containing gas comprehensive energy multi-microgrid optimization scheduling method based on Nash negotiation, hydrogen-containing gas is sent into a gas unit after being mixed with hydrogen, and then a hydrogen energy storage model containing hydrogen-containing gas is established, and a calculation method based on external power carbon emission is supplemented in a ladder type carbon trading mechanism; the uncertainty of the multi-microgrid is measured by calculating the condition risk value of the net interaction cost of the multi-microgrid and the distribution network, a two-stage optimization is implemented on the multi-microgrid by using the Nash negotiation method, the total cost of the multi-microgrid is minimized in the first stage, and the benefits of each microgrid are distributed and maximized in the second stage; and the two-stage optimization model is distributedly solved by improving the alternating direction multiplier method. The method can maximize the benefits of each microgrid under the premise of ensuring that the total cost of the multi-microgrid is the lowest, improves the new energy consumption level, reduces the carbon emission amount, and provides a reference for low-carbon economic scheduling of the comprehensive energy multi-microgrid in an uncertain environment.

Description

Technical Field

[0001] This invention belongs to the field of new energy dispatching technology, specifically involving an optimized dispatching method for multi-microgrid integrated energy sources containing hydrogen-blended natural gas based on Nash negotiations. Background Technology

[0002] Integrated energy systems improve the efficiency and cleanliness of end-use energy consumption, serving as a crucial means to achieve clean energy substitution and sustainable low-carbon energy development. As the number of integrated energy microgrids increases within the same distribution area, adjacent microgrids interconnect and couple, forming multi-microgrid integrated energy systems. When microgrids exchange electricity, they can sell or purchase electricity from other microgrids to improve their operational economy. However, each microgrid belongs to a different stakeholder, and the complex coupling of interests and energy flows among multiple microgrids makes traditional single-microgrid scheduling methods difficult to apply.

[0003] Existing dispatching methods for multi-microgrid systems rarely consider source-load uncertainty, and carbon trading mechanisms focus on the carbon emissions generated by the microgrid's own equipment output, without considering the equivalent carbon emissions from purchased electricity. In the hydrogen utilization phase, hydrogen fuel cell technology is typically used for power generation. Hydrogen fuel cell power generation equipment is relatively simple, has a wide power range, and can effectively cope with the volatility of renewable energy; however, it is expensive and has a low power generation efficiency of only 30%–60%. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized scheduling method for multi-microgrid integrated energy sources containing hydrogen-blended gas based on Nash negotiation, which can improve the absorption of new energy sources and reduce carbon emissions.

[0005] The technical solution adopted in this invention is a multi-microgrid optimization scheduling method for integrated energy resources containing hydrogen-doped natural gas based on Nash negotiations, which is implemented according to the following steps:

[0006] Step 1: Improve the integrated energy microgrid for low-carbon economic efficiency by adding hydrogen to the gas and sending it into the gas turbine unit to establish an integrated energy microgrid model containing hydrogen-added gas.

[0007] Step 2: Calculate the conditional value of risk by the net interaction cost between the multi-microgrid and the distribution network, and implement a two-stage optimization of the multi-microgrid using Nash negotiation theory. The first stage minimizes the total cost of the multi-microgrid, and the second stage allocates benefits and maximizes the interests of each microgrid. Construct a two-stage optimization scheduling model for integrated energy multi-microgrid based on Nash negotiation.

[0008] Step 3: The two-stage optimization scheduling model of integrated energy microgrid based on Nash negotiation is solved by distributed optimization using the improved alternating direction multiplier algorithm. The parameters obtained are the scheduling results.

[0009] The hydrogen energy storage model containing hydrogen-blended gas in step 1 includes the integrated energy microgrid equipment output model, the integrated energy microgrid energy efficiency improvement mechanism model, the single microgrid cost model, and constraints. The integrated energy microgrid equipment output model includes the carbon capture power plant model and the hydrogen energy storage system model containing hydrogen-blended gas.

[0010] The model construction process of the carbon capture power plant is as follows: the output P of the thermal power unit in the carbon capture power plant during time period t. TU,e,i (t) Part of the electricity is supplied to the carbon capture equipment, and the other part flows into the power grid to supply the electrical load; the electrical power P input to the carbon capture equipment during time period t CCS,e,i (t) is further divided into two parts: the consumption of its own fixed electrical power P CCS,e1,i (t) and the electrical power consumption P for processing CO2 CCS,e2,i (t):

[0011]

[0012] A portion of the CO2 captured by the carbon capture equipment is stored, while the remainder is supplied to the power-to-gas conversion equipment. The carbon capture power plant model is as follows:

[0013]

[0014] In the formula, P TU,e,i (t) represents the electrical power output (kW) of the thermal power units in microgrid i (i = 1, 2, 3, ...) during time period t; e TU The carbon emission intensity per unit time, m 3 / (kW·h); V TU,CO2,i (t) represents the volumetric flow rate of CO2 emitted by the thermal power unit during time period t, in m³. 3 / h;λ CCS Energy consumption per unit volume flow rate of CO2 processed by a carbon capture device, (kW·h) / m³ 3 μ CCS For carbon capture efficiency; V CCS1,CO2,i (t) represents the volumetric flow rate of CO2 captured by the carbon capture device; V CCS2,CO2,i (t) is the volumetric flow rate of CO2 supplied to the electro-gas conversion equipment for carbon capture, V CCS,CO2,i (t) represents the volumetric flow rate of CO2 captured by carbon capture.

[0015] The process of constructing a model for a hydrogen energy storage system containing hydrogen-blended fuel gas is as follows:

[0016] 1) A dimensionlessly standardized electrolytic cell model:

[0017] The electrolyzer converts electrical energy into hydrogen energy, and hydrogen is produced by proton exchange electrolysis. The variable efficiency mathematical model is as follows:

[0018]

[0019] In the formula, n EL,H2,i (t) represents the amount of hydrogen produced by the electrolyzer in microgrid i during time period t; n ELN P is the rated capacity of the electrolytic cell; EL,e,i (t) represents the electrical power input to the electrolytic cell during time period t; P EL,eN f(P) is the rated value of the input electrical power to the electrolytic cell; EL,e,i (t) is the efficiency function of the electrolytic cell; a EL b EL c EL These are the efficiency function coefficients;

[0020] The dimensionlessly uniform electrolytic cell model is as follows:

[0021]

[0022] In the formula, n EL,H2,i (t) represents the amount of hydrogen produced by the electrolyzer in microgrid i during time period t, in kmol; P EL,e,i (t) represents the electrical power input to the electrolytic cell during time period t, in kW; m EL,H2,i (t) represents the mass of hydrogen produced by the electrolyzer during time period t, in kg; M H2 ρ is the molar mass of hydrogen gas. H2 P represents the density of hydrogen gas; Δt represents the scheduling unit time; and P represents the density of hydrogen gas. EL,H2,i (t) represents the hydrogen production power of the electrolyzer during time period t;

[0023] 2) A dimensionlessly standardized methane reactor model

[0024] 4H₂ + CO₂ → CH₄ + 2H₂O (5)

[0025] Since chemical reaction equations are usually calculated using the amount of molecular weight, the CO2 volumetric flow rate (equation (2)) supplied to the carbon capture and electro-gas conversion equipment is converted into the amount of substance as follows:

[0026]

[0027] In the formula, m CCS2,CO2,i (t) represents the mass of CO2 supplied to the power-to-gas conversion equipment by carbon capture in microgrid i during time period t, in kg; n CCS2,CO2,i,i (t) represents the amount of CO2 supplied to the electro-gas conversion equipment by carbon capture, in kmol; M CO2 V is the molar mass of CO2. CCS2,CO2,i (t) is the volumetric flow rate of CO2 supplied to the electro-gas conversion equipment for carbon capture; ρ CO2 The density of CO2;

[0028] The amount of CH4 produced in a methane reactor depends on the molar relationship between CO2 and H2. The reaction amount and the amount produced can be determined by the following formula:

[0029]

[0030] In the formula, n MR1,H2,i (t) represents the amount of H2 supplied from the electrolyzer to the methane reactor during time period t; n CCS2,CO2,i (t) represents the amount of CO2 supplied to the methane reactor for carbon capture; n MR,H2,i (t) represents the amount of H2 that participates in the methane reaction during time period t; n MR,CH4,i (t) represents the amount of CH4 produced by the methane reactor during time period t;

[0031] The amount of H2 input to the methane reactor and the rate of CH4 production in the methane reactor are further converted to obtain the power of H2 input and CH4 production, as shown in the following formula:

[0032]

[0033]

[0034] In the formula, P MR,H2,i (t) represents the H2 power input to the methane reactor during time period t, in kW; P MR,g,i (t) represents the CH4 power output from the methane reactor during time period t; η MR,g M represents the output efficiency of the methane reactor during time period t; CH4 ρ is the molar mass of CH4. CH4 The density of CH4;

[0035] 3) Hydrogen storage tank model

[0036] The hydrogen energy loss generated during the filling and discharging process of the hydrogen storage tank is approximated by the filling and discharging efficiency; therefore, the model is as follows:

[0037]

[0038] In the formula, These represent the hydrogen charging / discharging power, efficiency, and capacity of the hydrogen storage tank in microgrid i during time period t.

[0039] 4) Hydrogen-blended gas turbine unit model

[0040] Natural gas is coupled with hydrogen produced by equation (4) within a certain hydrogen blending ratio range and supplied to the gas turbine unit. Since the operation of the gas turbine unit is less affected by the hydrogen blending ratio of 10% to 20%, the electrothermal conversion efficiency of the gas turbine unit is set as a constant. The mathematical model of the hydrogen-blended gas turbine is as follows:

[0041]

[0042]

[0043] In the formula, R GT,i (t) represents the hydrogen blending ratio of the gas turbine; V GT,H2,i (t), V GT,g,i (t) represents the volumetric flow rates of hydrogen and natural gas input to the gas turbine during time period t, i.e., the volume of gas flowing through the pipeline per unit time, in m. 3 / h;HHV CH4 P is the calorific value of methane; GT,g,i (t), P GT,H2,i (t) represents the power output of natural gas and hydrogen input to the gas turbine during time period t, respectively; P GT,e,i (t) represents the electrical power output of the gas turbine during time period t; η GT,e P represents the electrical conversion efficiency of the gas turbine. GT,h,i (t) represents the thermal power output of the gas turbine in microgrid i during time period t, in kW; η GT,h The heat conversion efficiency of the gas turbine;

[0044] The mathematical model for a hydrogen-blended gas boiler is as follows:

[0045]

[0046] P GB,h,i (t)=η GB,h (P GB,g,i (t)+P GB,H2,i (t)) (14)

[0047] In the formula, R GB,i (t) represents the hydrogen blending ratio of the gas-fired boiler; V GB,H2,i (t), V GB,g,i (t) represents the volumetric flow rates of hydrogen and natural gas input to the gas turbine during time period t; P GB,g,i (t) and P GB,H2,i (t) represents the power output of the natural gas and hydrogen input to the gas-fired boiler during time period t, in kW; P GB,h,i (t) represents the thermal power output of the gas-fired boiler during time period t, in kW; η GB,h This refers to the heat conversion efficiency of a gas-fired boiler.

[0048] The proportion of thermal power in the power distribution network during the dispatching period is taken as... The specific calculation model for carbon trading costs is as follows:

[0049]

[0050] m L,i =m GT0,i +m GB0,i +mTU0,i (16)

[0051] In the formula, m GT0,i m GB0,i m TU0,i m L,i These refer to the carbon emission allowances for gas turbines, gas boilers, thermal power units, and total emissions within the microgrid i dispatch cycle; σ e Carbon emission allowance per unit power generation of gas turbine units; σ h Carbon emission quota per unit heating power of gas turbine units; σ p Carbon emission quotas per unit power generation of thermal power units;

[0052]

[0053]

[0054] In the formula, m P,i (t) represents the total actual carbon emissions during the microgrid i scheduling cycle; m GT,i (t), m GB,i (t), m TU,i (t) represents the actual carbon emissions of the microgrid i gas turbine, gas boiler, and thermal power unit during time period t; a i b i c i (i = 1, 2, 3) are the carbon emission coefficients of gas turbine power supply, gas turbine heating supply and thermal power power supply respectively;

[0055]

[0056] In the formula, C CO2,i denoted as ω, where ω is the carbon trading cost of microgrid i; c is the unit price of carbon trading in the market; Υ is the price increase range for each tiered carbon trading system; ω is the length of the carbon emission interval; and ε is the carbon trading incentive coefficient.

[0057] The process of constructing a single microgrid cost model is as follows:

[0058] The goal of microgrid i-optimization operation is the total cost C. i Minimum:

[0059] C i =min(C a,i +C buy,i +C TU,i +C CO2,i +C DR,i +C ES,i -C trade,i +C c,i (20)

[0060] Where C CO2,iThe formulas for calculating carbon trading costs and other costs are as follows:

[0061] 1) The formula for calculating the penalty cost of wind and solar power curtailment is:

[0062]

[0063] In the formula, δ a P represents the penalty coefficient for wind and solar power curtailment. WTI,e,i (t), P WT,e,i (t) represents the wind power input and absorption of the microgrid during time period t, respectively; P PVI,e,i (t), P PV,e,i (t) represents the photovoltaic power input and consumption of the microgrid within time period t;

[0064] 2) The formula for calculating energy purchase cost is:

[0065]

[0066] In the formula, δ buy,e (t), δ sell,e (t) represents the purchase and sale price of electricity during time period t; P buy,e,i (t), P sell,e,i (t) represents the power purchased by microgrid i from the distribution network and the power sold to the distribution network during time period t; δ buy,g (t) represents the gas price during time period t; P buy,g,i (t) represents the gas purchase power of the microgrid during time period t;

[0067] 3) The formula for calculating the start-up, shutdown, and coal consumption costs of thermal power units is as follows:

[0068]

[0069] In the formula, P TU,e,i (t) represents the electrical power output of the thermal power unit in microgrid i during time period t; a4, b4, and c4 are the coal consumption cost coefficients of the thermal power unit, respectively; U TU,i (t) is a binary variable, U TU,i (t) = 1 indicates startup, U TU,i (t) = 1 indicates shutdown; δ TU This is the start-up and shutdown cost coefficient for thermal power units.

[0070] 4) The formula for calculating demand response cost is:

[0071]

[0072] In the formula, δ cut,e δ cut,h These are the cost compensation coefficients for reducing electric heating load; δ tran,e δ tran,hThese are the cost coefficients for compensating for transferred electric heating loads;

[0073] 5) The formula for calculating energy storage operation and maintenance costs is:

[0074]

[0075] In the formula, δ ES1 δ ES2 The charging and discharging operation and maintenance cost coefficients for electric and hydrogen energy storage equipment are respectively, in yuan / (kW·h);

[0076] 6) The formula for calculating electricity trading costs is:

[0077]

[0078] In the formula, P e,ij (t) represents the electrical power exchanged between microgrid i and microgrid j during time period t, P e,ij (t)>0 indicates that microgrid i sells electricity to microgrid j, δ e,ij (t) represents the electricity price for the interaction between microgrid i and microgrid j, in yuan / (kW·h);

[0079] 7) The formula for calculating network access fees is:

[0080]

[0081] In the formula, g e The cost coefficient for internet access fees is expressed in yuan per (kW·h).

[0082] The constraints are expressed as follows:

[0083] The electrical, thermal, gas, and hydrogen balance constraints are expressed as follows:

[0084]

[0085] P GT,h,i (t)+P GB,h,i (t)=P load,h,i (t) (29)

[0086] P buy,g,i (t)+P MR,g,i (t)=P GT,g,i (t)+P GB,g,i (t) (30)

[0087]

[0088] In the formula, P load,e,i (t), P load,h,i (t) represent the electrical and thermal loads of microgrid i, respectively.

[0089] The power output constraints for wind and solar power are expressed as follows:

[0090]

[0091] In the formula, P WTI,e,i (t) represents the wind power input during time period t; P WT,e (t) represents the wind power absorbed by the microgrid during time period t; P PVI,e,i (t) represents the wind power input of the microgrid during time period t; P PV,e,i (t) represents the wind power absorbed by the microgrid during time period t;

[0092] Equipment operating constraints are expressed as follows:

[0093]

[0094] In the formula, These are the upper and lower limits of the output power of device x, respectively; These represent the upper and lower limits of the output power ramp-up for device x, respectively.

[0095] Electricity trading constraints are expressed as follows:

[0096]

[0097] In the formula, This is the upper limit of the electrical power for interaction between microgrid i and other microgrids;

[0098] If transmission power loss is not considered, the sum of the electrical power of all microgrid interactions within time period t is zero:

[0099]

[0100] If transmission power loss is not considered, the sum of all microgrid power transaction costs within time period t is zero:

[0101]

[0102] Step 2 is as follows:

[0103] Step 2.1: For integrated energy microgrids, various uncertainties affect their dispatch results. Conditional value of risk (VoV) is used to calculate the risk losses faced by the integrated energy microgrid. When the actual wind and solar power output is lower than the predicted value, or the actual load is higher than the predicted value, the power trading capacity cannot meet the day-ahead plan, resulting in load shedding losses. The microgrid will purchase the shortfall from the distribution network. Conversely, when the wind and solar power output is higher than the predicted value, or the load is lower than the predicted value, the microgrid will sell the surplus power to the distribution network. The risk loss function f(ζ,x) is represented by the net interaction cost between the microgrid and the distribution network. The conditional value of risk X of microgrid i is then calculated. CVaR,β,i Represented as:

[0104]

[0105] In the formula, N is the number of micronetworks, β is the confidence level, and α is the confidence level. i The risk value cost of microgrid i;

[0106] Equation (37) is relaxed as follows:

[0107]

[0108]

[0109] In the formula, y i,s The conditional value-at-risk cost of microgrid i in scenario s exceeds the value-at-risk cost.

[0110] Step 2.2: Implement two-stage optimization for the multi-micronet network, specifically as follows:

[0111] In a multi-microgrid integrated energy system, each microgrid can be viewed as a competing negotiating unit. The Nash negotiation method is used to solve the problem, and the mathematical model is as follows:

[0112]

[0113] Among them, C 0,i For the maximum possible cost of microgrid i, C 0,i The cost of microgrid i;

[0114] In the first stage, equation (40) is transformed, and the model is as follows:

[0115]

[0116] In the formula, C 0,i p represents the maximum possible cost of microgrid i, i.e., the cost of microgrid i without considering power interaction. s Let be the probability of the s-th typical scenario occurring; S be the number of typical scenarios; and k be the risk preference coefficient, representing the risk attitude of micro-network investors, with a value range of [0,1]. The smaller k is, the higher the risk that micro-network investors are pursuing, as they seek lower costs. The optimal solution is obtained by solving this model.

[0117] Because of P e,ij (t)=-P e,ji (t), the electricity transaction costs will cancel each other out during the summation process, so the sum of the electricity transaction costs of each microgrid is 0, but the grid access cost (Equation (27)) still contains the power interaction variable P. e,ij (t), the objective function of the integrated energy multi-microgrid is to minimize the sum of the operating costs of each microgrid and the risk of electricity purchase and sale:

[0118]

[0119] In the formula, C I To determine the cost of integrated energy microgrids, the optimal solution for interactive power can be obtained in the model. Internet access fee and conditional value of risk

[0120] Second stage: After obtaining the interaction variables of optimal cost and optimal power in microgrid i excluding electricity trading cost, equation (42) can be further transformed into:

[0121]

[0122] Equation (43) uses δ e,ij,s (t) is the optimization variable. The inequality ensures that each microgrid can obtain a benefit. After transforming the objective function of Problem 2, we get:

[0123]

[0124] With δ e,ij,s (t) is the optimization variable, where This is the optimal solution for the first stage;

[0125] Formulas (41)-(44) are two-stage optimization scheduling models for integrated energy microgrids based on Nash negotiations.

[0126] Step 3 is as follows:

[0127] Step 3.1: Solve equation (41) in a distributed manner using the improved alternating direction multiplier algorithm. The specific solution method is as follows:

[0128] 1) Establish the augmented Lagrangian function for microgrid i:

[0129]

[0130] In the formula, λ ij,s (t) represents the Lagrange multiplier of the first-stage optimization model, and ρ is the penalty factor;

[0131] 2) Set the maximum number of iterations l max The value is 100, and the interactive electrical power P between microgrid i and microgrid j during time period t in the initial scenario s is 100. e,ij,s (t)=0, P e,ij,s (t) and the Lagrange multiplier λ ij,s The iterative process is as follows:

[0132]

[0133] 3) Determine if the algorithm has converged, and calculate the original residual and dual residual:

[0134]

[0135]

[0136] In the formula, The original residuals of the electricity trading between microgrid i and microgrid j in the (l+1)th iteration are shown. For the (l+1)th iteration, the dual residual of the power trading between microgrid i and microgrid j is denoted as ;

[0137] The iteration stopping condition is:

[0138]

[0139]

[0140] In the formula, ε pri ε dual These are the upper limits of the original residual and the dual residual, respectively;

[0141] 4) The penalty factor is automatically updated based on the quantitative relationship between the original residual and the dual residual. The dynamic penalty factor is expressed as:

[0142]

[0143] In the formula, ν is the proportionality coefficient of the original residual and the dual residual, and θ1 and θ2 are the acceleration convergence coefficient and the deceleration convergence coefficient, respectively (ν, θ1, θ2>1);

[0144] 5) Output scheduling results: device output of each microgrid, and optimal solution interaction power. Internet access fee and conditional value of risk

[0145] Step 3.2: Obtain the optimal solution from Step 3.1. and Substituting the objective function (44) of the second stage of the integrated energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the improved alternating direction multiplier algorithm is used to solve equation (44) in a distributed manner, and the interactive power between each microgrid is obtained. Electricity trading costs This is the scheduling result.

[0146] The beneficial effects of this invention are:

[0147] 1) In an uncertain environment, a two-stage optimization scheduling model for integrated energy microgrids is established based on the Nash negotiation method. A portion of the hydrogen produced during the electricity-to-hydrogen process is mixed with gas and sent to the gas turbine unit. A hydrogen energy storage system including an electrolyzer, a methane reactor, a hydrogen blending unit, and a hydrogen storage tank is established, which can reduce system costs, reduce carbon emissions, and enable microgrids to share electricity.

[0148] 2) After the introduction of the carbon trading mechanism, the multi-microgrid system can achieve the dual goals of low-carbon and economical operation, which is an effective means to achieve low-carbon operation of integrated energy systems.

[0149] 3) In uncertain environments, CVaR is used to quantify the risk losses faced by integrated energy microgrids. Taking advantage of the fact that the error between the day-ahead forecast of source load and real-time power will cause changes in the net interactive power between the microgrid and the distribution network, the risk loss function is characterized by the net interactive cost between the microgrid and the distribution network. By selecting an appropriate risk coefficient, the risk and economy of the microgrid under uncertain environments can be better measured.

[0150] 4) The improved alternating direction multiplier algorithm can dynamically correct the penalty factor through the quantitative relationship between the original residual and the dual residual, which reduces the randomness of the solution caused by the initial penalty factor and obtains more accurate and effective multi-micronet optimization scheduling results. Attached Figure Description

[0151] Figure 1 This is a schematic diagram illustrating the proportion of thermal power in the power distribution network during the scheduling cycle of this invention.

[0152] Figure 2 This is a schematic diagram showing the changes in the photovoltaic, wind power, and load forecast power generation curves of a microgrid on typical days for one, two, and three days under five scenarios in this embodiment of the invention.

[0153] Figure 3 This is a schematic diagram of the results of electricity trading between microgrids in an embodiment of the present invention;

[0154] Figure 4 This is a schematic diagram of the power optimization results of microgrid 1 in an embodiment of the present invention;

[0155] Figure 5 This is a schematic diagram of the power optimization results of microgrid 2 in an embodiment of the present invention;

[0156] Figure 6 This is a schematic diagram of the power optimization results of microgrid 3 in an embodiment of the present invention;

[0157] Figure 7 This is a schematic diagram illustrating the optimized electricity price transaction results between microgrids in an embodiment of the present invention. Detailed Implementation

[0158] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0159] The integrated energy microgrid established by this invention incorporates four types of energy: electricity, heat, gas, and hydrogen. Its main equipment includes a carbon capture power plant, a hydrogen energy storage system, photovoltaic power, wind turbines, and batteries. All carbon emissions and absorptions involved in the operation of these devices are ultimately traded through the carbon trading market.

[0160] This invention relates to an optimized scheduling method for multi-microgrid integrated energy systems containing hydrogen-doped natural gas, based on Nash negotiations. The method is implemented according to the following steps:

[0161] Step 1: Improve the integrated energy microgrid for low-carbon economic efficiency by adding hydrogen to the gas and sending it into the gas turbine unit to establish an integrated energy microgrid model containing hydrogen-added gas.

[0162] The model construction process of the carbon capture power plant is as follows: the output P of the thermal power unit in the carbon capture power plant during time period t. TU,e,i (t) Part of the electricity is supplied to the carbon capture equipment, and the other part flows into the power grid to supply the electrical load; the electrical power P input to the carbon capture equipment during time period t CCS,e,i (t) is further divided into two parts: the consumption of its own fixed electrical power P CCS,e1,i (t) and the electrical power consumption P for processing CO2 CCS,e2,i (t):

[0163]

[0164] A portion of the CO2 captured by the carbon capture equipment is stored, while the remainder is supplied to the power-to-gas conversion equipment. The carbon capture power plant model is as follows:

[0165]

[0166] In the formula, P TU,e,i (t) represents the electrical power output (kW) of the thermal power units in microgrid i (i = 1, 2, 3, ...) during time period t; e TU The carbon emission intensity per unit time, m 3 / (kW·h); V TU,CO2,i (t) represents the volumetric flow rate of CO2 emitted by the thermal power unit during time period t, in m³. 3 / h;λ CCS Energy consumption per unit volume flow rate of CO2 processed by a carbon capture device, (kW·h) / m³ 3 μ CCS For carbon capture efficiency; V CCS1,CO2,i (t) represents the volumetric flow rate of CO2 captured by the carbon capture device; V CCS2,CO2,i (t) is the volumetric flow rate of CO2 supplied to the electro-gas conversion equipment for carbon capture, V CCS,CO2,i (t) represents the volume of carbon dioxide captured by carbon capture.

[0167] The electro-gas conversion process is refined into electro-hydrogen conversion and methanation processes, and dimensional uniformity is achieved. A hydrogen energy storage system is established, comprising an electrolyzer, a methane reactor, a hydrogen blending unit, and a hydrogen storage tank. The electrolyzer produces hydrogen by electrolyzing water, which is then stored in the hydrogen storage tank. Part of the produced hydrogen reacts with CO2 in the methane reactor to convert it into methane, which is then injected into the natural gas system. The other part is mixed with natural gas in a certain proportion to form hydrogen-blended fuel gas, which is fed into the gas turbine and gas boiler to generate electrical and thermal energy to supply the load, thus realizing hydrogen utilization. Based on this, the model construction process of the hydrogen energy storage system containing hydrogen-blended fuel gas in this invention is as follows:

[0168] 1) A dimensionlessly standardized electrolytic cell model:

[0169] The electrolyzer converts electrical energy into hydrogen energy, and hydrogen is produced by proton exchange electrolysis. The variable efficiency mathematical model is as follows:

[0170]

[0171] In the formula, n EL,H2,i (t) represents the amount of hydrogen produced by the electrolyzer in microgrid i during time period t; n ELN P is the rated capacity of the electrolytic cell; EL,e,i (t) represents the electrical power input to the electrolytic cell during time period t; P EL,eN f(P) is the rated value of the input electrical power to the electrolytic cell; EL,e,i (t) is the efficiency function of the electrolytic cell; a EL b EL c EL These are the efficiency function coefficients;

[0172] Based on the fact that hydrogen production quality can be calculated using both amount of substance and molar mass, and gas volumetric flow rate and density, the relationship between hydrogen production power and the amount of hydrogen produced is derived. By uniformly using power for cost calculation and constraint balancing, dimensional unification is achieved, resulting in the following dimensionally unified electrolyzer model:

[0173]

[0174] In the formula, n EL,H2,i (t) represents the amount of hydrogen produced by the electrolyzer in microgrid i during time period t, in kmol; P EL,e,i (t) represents the electrical power input to the electrolytic cell during time period t, in kW; m EL,H2,i (t) represents the mass of hydrogen produced by the electrolyzer during time period t, in kg; M H2 ρ is the molar mass of hydrogen gas. H2 P represents the density of hydrogen gas; Δt represents the scheduling unit time; and P represents the density of hydrogen gas. EL,H2,i (t) represents the hydrogen production power of the electrolyzer during time period t;

[0175] 2) A dimensionlessly standardized methane reactor model

[0176] 4H₂ + CO₂ → CH₄ + 2H₂O (5)

[0177] Since chemical reaction equations are usually calculated using the amount of molecular weight, the CO2 volumetric flow rate (equation (2)) supplied to the carbon capture and electro-gas conversion equipment is converted into the amount of substance as follows:

[0178]

[0179] In the formula, m CCS2,CO2,i (t) represents the mass of CO2 supplied to the power-to-gas conversion equipment by carbon capture in microgrid i during time period t, in kg; n CCS2,CO2,i,i (t) represents the amount of CO2 supplied to the electro-gas conversion equipment by carbon capture, in kmol; M CO2 V is the molar mass of CO2. CCS2,CO2,i (t) is the volumetric flow rate of CO2 supplied to the electro-gas conversion equipment for carbon capture; ρ CO2 The density of CO2;

[0180] The amount of CH4 produced in a methane reactor depends on the molar relationship between CO2 and H2. The reaction amount and the amount produced can be determined by the following formula:

[0181]

[0182] In the formula, n MR1,H2,i (t) represents the amount of H2 supplied from the electrolyzer to the methane reactor during time period t; n CCS2,CO2,i (t) represents the amount of CO2 supplied to the methane reactor for carbon capture; n MR,H2,i (t) represents the amount of H2 that participates in the methane reaction during time period t; n MR,CH4,i (t) represents the amount of CH4 produced by the methane reactor during time period t;

[0183] Similar to equation (4), the amount of H2 input to the methane reactor and the rate of CH4 production in the methane reactor are further converted to obtain the power of H2 input and CH4 production, as shown in the following equation:

[0184]

[0185]

[0186] In the formula, P MR,H2,i (t) represents the H2 power input to the methane reactor during time period t, in kW; P MR,g,i (t) represents the CH4 power output from the methane reactor during time period t; η MR,g M represents the output efficiency of the methane reactor during time period t; CH4 ρ is the molar mass of CH4. CH4 The density of CH4;

[0187] 3) Hydrogen storage tank model

[0188] The hydrogen energy loss generated during the filling and discharging process of the hydrogen storage tank is approximated by the filling and discharging efficiency; therefore, the model is as follows:

[0189]

[0190] In the formula, E ES,H2,i (t) represents the hydrogen charging / discharging power, efficiency, and capacity of the hydrogen storage tank in microgrid i during time period t;

[0191] 4) Hydrogen-blended gas turbine unit model

[0192] Natural gas is coupled with hydrogen produced by equation (4) within a certain hydrogen blending ratio range and supplied to the gas turbine unit. Since the operation of the gas turbine unit is less affected by the hydrogen blending ratio of 10% to 20%, the electrothermal conversion efficiency of the gas turbine unit is set as a constant. The mathematical model of the hydrogen-blended gas turbine is as follows:

[0193]

[0194]

[0195] In the formula, R GT,i (t) represents the hydrogen blending ratio of the gas turbine; V GT,H2,i (t), V GT,g,i (t) represents the volumetric flow rates of hydrogen and natural gas input to the gas turbine during time period t, i.e., the volume of gas flowing through the pipeline per unit time, in m. 3 / h;HHV CH4 P is the calorific value of methane; GT,g,i (t), P GT,H2,i (t) represents the power output of natural gas and hydrogen input to the gas turbine during time period t, respectively; P GT,e,i (t) represents the electrical power output of the gas turbine during time period t; η GT,e P represents the electrical conversion efficiency of the gas turbine. GT,h,i (t) represents the thermal power output of the gas turbine in microgrid i during time period t, in kW; η GT,h The heat conversion efficiency of the gas turbine;

[0196] The mathematical model for a hydrogen-blended gas boiler is as follows:

[0197]

[0198] P GB,h,i (t)=η GB,h (P GB,g,i (t)+P GB,H2,i (t)) (14)

[0199] In the formula, R GB,i (t) represents the hydrogen blending ratio of the gas-fired boiler; V GB,H2,i (t), V GB,g,i (t) represents the volumetric flow rates of hydrogen and natural gas input to the gas turbine during time period t; P GB,g,i (t) and P GB,H2,i (t) represents the power output of the natural gas and hydrogen input to the gas-fired boiler during time period t, in kW; P GB,h,i (t) represents the thermal power output of the gas-fired boiler during time period t, in kW; η GB,h This refers to the heat conversion efficiency of a gas-fired boiler.

[0200] A carbon trading mechanism was introduced as a microgrid energy efficiency improvement mechanism, and a conventional model was used for demand response; the construction process of the integrated energy microgrid energy efficiency improvement mechanism model is as follows:

[0201] Since purchased electricity may originate from thermal power units, the proportion of thermal power in the distribution network is calculated to obtain the equivalent power of thermal power units in the purchased electricity, thereby determining the carbon emissions generated by purchased electricity. This allows for a more precise quantification of the carbon emissions of the multi-microgrid integrated energy system. The electricity in the studied region's distribution network mainly comes from thermal power, wind power, and hydropower. By reviewing year-on-year data on installed capacity in the region, the proportion of thermal power in the distribution network's electricity during the dispatching cycle is determined to be... like Figure 1 As shown, the specific calculation model for carbon trading costs is as follows:

[0202]

[0203] m L,i =m GT0,i +m GB0,i +m TU0,i (16)

[0204] In the formula, m GT0,i m GB0,i m TU0,i m L,i These refer to the carbon emission allowances for gas turbines, gas boilers, thermal power units, and total emissions within the microgrid i dispatch cycle; σ e Carbon emission allowance per unit power generation of gas turbine units; σ h Carbon emission quota per unit heating power of gas turbine units; σ p Carbon emission quotas per unit power generation of thermal power units;

[0205]

[0206]

[0207] In the formula, m P,i(t) represents the total actual carbon emissions during the microgrid i scheduling cycle; m GT,i (t), m GB,i (t), m TU,i (t) represents the actual carbon emissions of the microgrid i gas turbine, gas boiler, and thermal power unit during time period t; a i b i c i (i = 1, 2, 3) are the carbon emission coefficients of gas turbine power supply, gas turbine heating supply and thermal power power supply respectively;

[0208]

[0209] In the formula, C CO2,i denoted as ω, where ω is the carbon trading cost of microgrid i; c is the unit price of carbon trading in the market; Υ is the price increase range for each tiered carbon trading system; ω is the length of the carbon emission interval; and ε is the carbon trading incentive coefficient.

[0210] The process of constructing a single microgrid cost model is as follows:

[0211] The goal of microgrid i-optimization operation is the total cost C. i Minimum:

[0212] C i =min(C a,i +C buy,i +C TU,i +C CO2,i +C DR,i +C ES,i -C trade,i +C c,i (20)

[0213] Where C CO2,i The formulas for calculating carbon trading costs and other costs are as follows:

[0214] 1) The formula for calculating the penalty cost of wind and solar power curtailment is:

[0215]

[0216] In the formula, δ a P represents the penalty coefficient for wind and solar power curtailment. WTI,e,i (t), P WT,e,i (t) represents the wind power input and absorption of the microgrid during time period t, respectively; P PVI,e,i (t), P PV,e,i (t) represents the photovoltaic power input and consumption of the microgrid within time period t;

[0217] 2) The formula for calculating energy purchase cost is:

[0218]

[0219] In the formula, δ buy,e (t), δ sell,e (t) represents the purchase and sale price of electricity during time period t; P buy,e,i (t), P sell,e,i (t) represents the power purchased by microgrid i from the distribution network and the power sold to the distribution network during time period t; δ buy,g (t) represents the gas price during time period t; P buy,g,i (t) represents the gas purchase power of the microgrid during time period t;

[0220] 3) The formula for calculating the start-up, shutdown, and coal consumption costs of thermal power units is as follows:

[0221]

[0222] In the formula, P TU,e,i (t) represents the electrical power output of the thermal power unit in microgrid i during time period t; a4, b4, and c4 are the coal consumption cost coefficients of the thermal power unit, respectively; U TU,i (t) is a binary variable, U TU,i (t) = 1 indicates startup, U TU,i (t) = 1 indicates shutdown; δ TU This is the start-up and shutdown cost coefficient for thermal power units.

[0223] 4) The formula for calculating demand response cost is:

[0224]

[0225] In the formula, δ cut,e δ cut,h These are the cost compensation coefficients for reducing electric heating load; δ tran,e δ tran,h These are the cost coefficients for compensating for transferred electric heating loads;

[0226] 5) The formula for calculating energy storage operation and maintenance costs is:

[0227]

[0228] In the formula, δ ES1 δ ES2 The charging and discharging operation and maintenance cost coefficients for electric and hydrogen energy storage equipment are respectively, in yuan / (kW·h);

[0229] 6) The formula for calculating electricity trading costs is:

[0230]

[0231] In the formula, P e,ij (t) represents the electrical power exchanged between microgrid i and microgrid j during time period t, P e,ij (t)>0 indicates that microgrid i sells electricity to microgrid j, δe,ij (t) represents the electricity price for the interaction between microgrid i and microgrid j, in yuan / (kW·h);

[0232] 7) The formula for calculating network access fees is:

[0233] Each microgrid exchanges electrical energy through an energy router, and a certain network access fee needs to be paid to the distribution network. The model is as follows:

[0234]

[0235] In the formula, g e The cost coefficient for internet access fees is expressed in yuan per (kW·h).

[0236] The constraints include electricity, heat, gas, and hydrogen balance constraints, wind and solar power output constraints, equipment operation constraints, upper and lower limits for energy purchases, and electricity trading constraints; therefore, electricity trading constraints were considered, as shown below:

[0237] The electrical, thermal, gas, and hydrogen balance constraints are expressed as follows:

[0238]

[0239] P GT,h,i (t)+P GB,h,i (t)=P load,h,i (t) (29)

[0240] P buy,g,i (t)+P MR,g,i (t)=P GT,g,i (t)+P GB,g,i (t) (30)

[0241]

[0242] In the formula, P load,e,i (t), P load,h,i (t) represent the electrical and thermal loads of microgrid i, respectively;

[0243] The power output constraints for wind and solar power are expressed as follows:

[0244]

[0245] In the formula, P WTI,e,i (t) represents the wind power input during time period t; P WT,e (t) represents the wind power absorbed by the microgrid during time period t; P PVI,e,i (t) represents the wind power input of the microgrid during time period t; P PV,e,i (t) represents the wind power absorbed by the microgrid during time period t;

[0246] Equipment operating constraints are expressed as follows:

[0247]

[0248] In the formula, These are the upper and lower limits of the output power of device x, respectively; These represent the upper and lower limits of the output power ramp-up for device x, respectively.

[0249] Electricity trading constraints are expressed as follows:

[0250]

[0251] In the formula, This is the upper limit of the electrical power for interaction between microgrid i and other microgrids;

[0252] If transmission power loss is not considered, the sum of the electrical power of all microgrid interactions within time period t is zero:

[0253]

[0254] If transmission power loss is not considered, the sum of all microgrid power transaction costs within time period t is zero:

[0255]

[0256] Step 2: Calculate the conditional value of risk (VoV) using the net interaction cost between the multi-microgrid and the distribution network. Apply Nash negotiation theory to implement a two-stage optimization of the multi-microgrid. The first stage minimizes the total cost of the multi-microgrid, and the second stage allocates revenue and maximizes the benefits of each microgrid. Construct a two-stage optimization scheduling model for integrated energy multi-microgrids based on Nash negotiation. The specific process is as follows:

[0257] Step 2.1: In the integrated energy multi-microgrid, through the interaction of electricity among the microgrids, the purchase of electricity from the distribution network is reduced, thereby lowering the operating cost of the multi-microgrid, increasing the renewable energy absorption rate, and reducing carbon emissions. When the actual value of wind power and photovoltaic power is lower than the predicted value, or the actual value of load is higher than the predicted value, the power trading capacity cannot meet the day-ahead plan, resulting in load shedding losses. The multi-microgrid will purchase the shortfall in electricity from the distribution network. Conversely, when the wind power and photovoltaic power is higher than the predicted value, or the load is lower than the predicted value, the multi-microgrid will sell the surplus electricity to the distribution network. The risk loss function f(ζ,x) is represented by the net interaction cost between the multi-microgrid and the distribution network, and the conditional risk value cost X of microgrid i is... CVaR,β,i Represented as:

[0258]

[0259] In the formula, N is the number of micronetworks, β is the confidence level, and α is the confidence level. i The risk value cost of microgrid i;

[0260] For ease of calculation, equation (29) is relaxed to:

[0261]

[0262]

[0263] In the formula, y i,s The conditional value-at-risk cost of microgrid i in scenario s exceeds the value-at-risk cost.

[0264] Step 2.2: Model the problem using Nash negotiation theory and transform it into two relatively easy-to-solve sub-problems: minimizing the cost of multiple microgrids and maximizing the revenue of each microgrid. Perform optimization scheduling in two stages. The two-stage optimization for the multiple microgrids is as follows:

[0265] In a multi-microgrid integrated energy system, each microgrid can be viewed as a competing negotiating unit. The Nash negotiation method is used to solve the problem, and the mathematical model is as follows:

[0266]

[0267] Among them, C 0,i For the maximum possible cost of microgrid i, C 0,i The cost of microgrid i;

[0268] The first-stage calculation model is as follows:

[0269]

[0270] In the formula, C 0,i p represents the maximum possible cost of microgrid i, i.e., the cost of microgrid i without considering power interaction. s Let be the probability of the s-th typical scenario occurring; S be the number of typical scenarios; and k be the risk preference coefficient, representing the risk attitude of micro-network investors, with a value range of [0,1]. The smaller k is, the higher the risk that micro-network investors are pursuing, as they seek lower costs. The optimal solution is obtained by solving this model.

[0271] Because of P e,ij (t)=-P e,ji (t), the electricity transaction costs will cancel each other out during the summation process, so the sum of the electricity transaction costs of each microgrid is 0, but the grid access cost (Equation (27)) still contains the power interaction variable P. e,ij (t), the objective function of the integrated energy multi-microgrid is to minimize the sum of the operating costs of each microgrid and the risk of electricity purchase and sale:

[0272]

[0273] In the formula, C ITo address the cost of integrated energy microgrids, the objective function for the first stage is a non-negative weighted sum of convex functions, which remains convex. The optimal solution, interactive power, can be obtained from the model. Internet access fee and conditional value of risk

[0274] Second stage: After obtaining the interaction variables of optimal cost and optimal power in microgrid i excluding electricity trading cost, equation (42) can be further transformed into:

[0275]

[0276] Equation (43) uses δ e,ij,s (t) is the optimization variable. The inequality ensures that each microgrid can obtain a benefit. After transforming the objective function of Problem 2, we get:

[0277]

[0278] With δ e,ij,s (t) is the optimization variable, where This is the optimal solution for the first stage;

[0279] Formulas (41)-(44) are two-stage optimization scheduling models for integrated energy microgrids based on Nash negotiations.

[0280] Step 3: The two-stage optimization scheduling model of integrated energy multi-microgrid based on Nash negotiation is solved using an improved alternating direction multiplier algorithm in a distributed manner. The obtained parameters are the scheduling results. The specific process is as follows:

[0281] Step 3.1: Solve equation (44) in a distributed manner using the improved alternating direction multiplier algorithm. The specific solution method is as follows:

[0282] Considering that the subproblem of minimizing the cost of multiple micronets and the subproblem of maximizing the revenue of each micronet have separable convex functions and constraints, the improved alternating direction multiplier algorithm is used to solve equation (44) in a distributed manner. The specific solution method is as follows:

[0283] 1) Establish the augmented Lagrangian function for microgrid i:

[0284]

[0285] In the formula, λ ij,s (t) represents the Lagrange multiplier of the first-stage optimization model, and ρ is the penalty factor;

[0286] 2) Set the maximum number of iterations l max The value is 100, and the interactive electrical power P between microgrid i and microgrid j during time period t in the initial scenario s is 100. e,ij,s (t)=0, Pe,ij,s (t) and the Lagrange multiplier λ ij,s The iterative process is as follows:

[0287]

[0288]

[0289] 3) Determine if the algorithm has converged, and calculate the original residual and dual residual:

[0290]

[0291]

[0292] In the formula, The original residuals of the electricity trading between microgrid i and microgrid j in the (l+1)th iteration are shown. For the (l+1)th iteration, the dual residual of the power trading between microgrid i and microgrid j is denoted as ;

[0293] The iteration stopping condition is:

[0294]

[0295]

[0296] In the formula, ε pri ε dual These are the upper limits of the original residual and the dual residual, respectively;

[0297] 4) In the conventional ADMM algorithm, the penalty factor ρ is an empirically given constant. If set improperly, it may affect the overall convergence speed of the algorithm. The penalty factor is automatically updated based on the quantitative relationship between the original residual and the dual residual. The dynamic penalty factor is expressed as:

[0298]

[0299] In the formula, ν is the proportionality coefficient of the original residual and the dual residual, and θ1 and θ2 are the acceleration convergence coefficient and the deceleration convergence coefficient, respectively (ν, θ1, θ2>1);

[0300] 5) Output scheduling results: device output of each microgrid, and optimal solution interaction power. Internet access fee and conditional value of risk

[0301] Step 3.2: Obtain the optimal solution from Step 3.1. and Substituting the objective function (44) of the second stage of the integrated energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the improved alternating direction multiplier algorithm is used to solve equation (44) in a distributed manner, and the interactive power between each microgrid is obtained. Electricity trading costs This is the scheduling result.

[0302] Implementation Cases

[0303] A certain integrated energy microgrid was selected as the test system. The system contains three microgrids, MG1, MG2 and MG3. The parameters of each device in the microgrid are shown in Table 1.

[0304] Table 1

[0305]

[0306] The wind and solar power forecast curves and the electric and heat load curves for typical days of each microgrid are as follows: Figure 2 As shown in the table; the electricity and gas purchase prices for each microgrid are shown in Tables 2 and 3; the carbon emission coefficients are shown in Table 4.

[0307] Table 2

[0308]

[0309] Table 4

[0310]

[0311] Assuming the prediction errors for wind power, solar power, and load follow normal distributions with a mean of 0, standard deviations of 0.1, 0.08, and 0.03 respectively, a confidence level of 0.95, and an initial risk coefficient of 0.5, five typical scenarios and their corresponding probabilities are obtained using the first-stage solution method. The simulation example is performed in the MATLAB R2018b compilation environment, using the Yalmip optimization tool for modeling and the Gurobi solver for solving.

[0312] To verify the effectiveness of the proposed two-stage optimization scheduling model for multi-microgrids, four scenarios were set up for comparative analysis, based on whether each microgrid operates independently or collaboratively, and whether the hydrogen energy storage system includes gas turbine units with a hydrogen blending ratio of 15% or conventional gas turbine units. The results are shown in Table 5.

[0313] Table 5

[0314]

[0315] In the model proposed in this invention, the solution result for scenario 1 is the negotiation breakdown point for scenario 2, and the solution result for scenario 3 is the negotiation breakdown point for scenario 4. Based on the solution results, the costs and equipment outputs of each microgrid in the four scenarios are compared, as shown in Table 6:

[0316] Table 6

[0317]

[0318] The following conclusions can be drawn from the data results in Table 6:

[0319] 1) Comparing scenarios 1 and 2, and scenarios 3 and 4, it can be seen that in scenario 2, the cost of each microgrid is reduced by RMB 3516.18, 3584.86, and 3040.13 respectively compared to scenario 1, while the wind and solar power absorption rates are increased by 11.6%, 10.4%, and 10.8% respectively, resulting in a total cost reduction of RMB 10141.17 for the multi-microgrid network. In scenario 4, the cost of each microgrid is reduced by RMB 4944.49, 4652.05, and 2680.08 respectively compared to scenario 3, while the wind and solar power absorption rates are increased by 11.9%, 7.5%, and 7.2% respectively, resulting in a total cost reduction of RMB 12276.62 for the multi-microgrid network. This demonstrates that, considering uncertainty metrics, the distributed scheduling model based on Nash negotiations can prioritize the absorption of new energy sources through energy sharing, reducing the power generation of thermal power units and the output of gas turbine units. Therefore, it reduces the purchase of gas and electricity, lowers the operating costs of each microgrid, and achieves a win-win situation for all microgrids.

[0320] 2) Comparing scenarios 1 and 3, and scenarios 2 and 4, it can be seen that in scenario 3, the cost of each microgrid is reduced by RMB 817.15, RMB 1298.75, and RMB 1316.77 respectively compared to scenario 1, while the wind and solar grid integration rates increase by 1.1%, 3.7%, and 3.8% respectively, resulting in a total cost reduction of RMB 3432.67 for the multi-microgrid network. In scenario 4, the cost of each microgrid is reduced by RMB 2245.46, RMB 2365.94, and RMB 956.72 respectively compared to scenario 2, while the wind and solar grid integration rates increase by 1.4%, 0.8%, and 0.2% respectively, resulting in a total cost reduction of RMB 5568.12 for the multi-microgrid network. This is because, after considering the blending of gas and hydrogen, the curtailed wind and solar power is preferentially converted into hydrogen and natural gas through electricity-to-hydrogen conversion and fed into gas turbine units to supply the electric heating load, promoting wind and solar grid integration while also achieving the recycling of hydrogen energy. Since the electricity generated by electricity-to-hydrogen conversion comes from its own wind and solar power, the purchase of electricity from the distribution network is reduced, thereby saving microgrid costs.

[0321] 3) In Scenario 2, the actual carbon emissions of each microgrid decreased by 1026.65, 1156.56, and 1305.52 kg respectively compared to Scenario 1, resulting in a total reduction of 3488.73 kg in carbon emissions across multiple microgrids. In Scenario 4, the actual carbon emissions of each microgrid decreased by 1043.65, 1197.56, and 1265.75 kg respectively compared to Scenario 3, resulting in a total reduction of 3506.96 kg in carbon emissions across multiple microgrids. This is because after participating in electricity trading, each microgrid exchanges electricity in real time through reasonable negotiations, prioritizing the consumption of new energy sources and reducing the power generation of thermal power units and the output of gas turbine units, thereby effectively reducing carbon emissions and promoting the low-carbon and environmentally friendly operation of multiple microgrids.

[0322] 4) In Scenario 3, the actual carbon emissions of each microgrid decreased by 811.30, 415.33, and 552.87 kg respectively compared to Scenario 1, resulting in a total reduction of 1779.5 kg in carbon emissions across multiple microgrids. In Scenario 4, the actual carbon emissions of each microgrid decreased by 828.30, 456.33, and 513.10 kg respectively compared to Scenario 2, resulting in a total reduction of 1797.73 kg in carbon emissions across multiple microgrids. After hydrogen is added to the gas turbine units, the curtailed wind and solar power is preferentially converted into hydrogen and mixed with natural gas and fed into the gas turbine units via electricity-to-hydrogen conversion. Therefore, the output of thermal power units decreases, the power purchased from the distribution network decreases, and the equivalent carbon emissions of thermal power units decrease. Increased gas purchases due to hydrogen blending lead to higher hydrogen demand due to the fixed blending ratio, resulting in higher electrolyzer output and increased gas turbine power generation. This, in turn, increases the carbon emissions of the gas turbine. However, the fixed heat-to-power ratio of the gas turbine also increases its heat production capacity, while reducing the heat production capacity of the gas boiler and thus reducing its carbon emissions. Nevertheless, due to the lower carbon emissions per unit of power generation from the gas turbine and the increased consumption of renewable energy, the total actual carbon emissions of each microgrid still decrease.

[0323] Based on scenario 4, taking the first typical uncertain scenario as an example for analysis, the results of electricity trading between microgrids are as follows: Figure 3 As shown, the power optimization results within each microgrid are as follows: Figure 4 , Figure 5 , Figure 6 As shown.

[0324] Referring to Table 6, such as Figure 4 , Figure 5 , Figure 6It is known that all three microgrids prioritize the consumption of new energy sources. The hydrogen-blended gas turbine achieves carbon and hydrogen cycling, saving dispatch costs, and has low carbon emissions per unit of power generation. Therefore, the hydrogen-blended gas turbine has the highest output priority and outputs power throughout the dispatch cycle. The three microgrids only purchase electricity from the distribution network during certain periods, without selling electricity back to it. This is because the first-stage optimization of multi-microgrid power trading only considered grid crossing fees. Although selling electricity to the distribution network is profitable, overall, exchanging power with other microgrids is less costly than exchanging power with the distribution network. Therefore, microgrids prioritize exchanging power with other microgrids. However, the constraint that the sum of all microgrid exchanging power must be zero during the dispatch period must also be met. Therefore, microgrids only purchase electricity from the distribution network during dispatch periods when the sum of new energy sources, gas turbines, and electricity purchased from other microgrids is still insufficient to meet the power load.

[0325] Electricity prices for transactions between microgrids, such as Figure 7 As shown, by optimizing the electricity trading prices between microgrids in the second stage, the electricity price that a microgrid sells to other microgrids is higher than the electricity price that it sells to the grid, and the electricity price that it buys from other microgrids is lower than the electricity price that it buys from the grid, thus improving the economic efficiency of each microgrid.

Claims

1. A method for optimal scheduling of multi-microgrid integrated energy systems containing hydrogen-doped natural gas based on Nash negotiation, characterized in that: The specific steps are as follows: Step 1: Improve the integrated energy microgrid for low-carbon economic efficiency by adding hydrogen to the gas and sending it into the gas turbine unit to establish an integrated energy microgrid model containing hydrogen-added gas. The integrated energy microgrid model containing hydrogen-blended gas mentioned in step 1 includes an integrated energy microgrid equipment output model, an integrated energy microgrid energy efficiency improvement mechanism model, a single microgrid cost model, and constraints. The integrated energy microgrid equipment output model includes a carbon capture power plant model and a hydrogen energy storage system model containing hydrogen-blended gas. Step 2: Calculate the conditional value of risk (VoV) using the net interaction cost between the multi-microgrid and the distribution network. Apply Nash negotiation theory to implement a two-stage optimization of the multi-microgrid. The first stage minimizes the total cost of the multi-microgrid, and the second stage allocates revenue and maximizes the benefits of each microgrid. Construct a two-stage optimization scheduling model for integrated energy multi-microgrids based on Nash negotiation. The specific process is as follows: Step 2.1: For integrated energy microgrids, various uncertainties affect their dispatch results. Conditional Value at Risk (VaR) is used to calculate the risk losses faced by the integrated energy microgrid. When the actual wind and solar power output is lower than the predicted value, or the actual load is higher than the predicted value, the power trading capacity cannot meet the day-ahead plan, resulting in load shedding losses. The microgrid will purchase the shortfall power from the distribution network. Conversely, when the wind and solar power output is higher than the predicted value, or the load is lower than the predicted value, the microgrid will sell the surplus power to the distribution network. The net interaction cost between the microgrid and the distribution network is used to characterize the risk loss function. f ( ζ,x Micronet i Conditional risk value cost X CVaR,β,i Represented as: (37) In the formula, N Number of microgrids β For confidence level, α i For micro-network i The risk value cost; Equation (37) is relaxed as follows: (38) (39) In the formula, y i,s For the microgrid in scenario s i The conditional value-at-risk cost exceeds the value of the value-at-risk cost; Step 2.2: Implement two-stage optimization for the multi-micronet network, specifically as follows: In integrated energy microgrids, each microgrid is considered as a competing negotiating unit. Nash negotiation is used to solve the problem, and the mathematical model is as follows: (40) in, C 0,i For micro-network i The maximum possible cost, C 0,i For micro-network i The cost; In the first stage, equation (40) is transformed, and the model is as follows: (41) In the formula, C 0,i For micro-network i The maximum possible cost of a microgrid, without considering power interaction. i The cost, p s Let S be the probability of the s-th typical scenario occurring; S is the number of typical scenarios. k The risk preference coefficient represents the risk attitude of micro-network investors, and its value ranges from [0,1]. k A smaller value indicates that microgrid investors are pursuing lower costs but facing higher risks. The optimal solution is obtained by solving this model. ; because P e,ij ( t )=- P e,ji ( t The electricity transaction costs will cancel each other out during the summation process, so the sum of the electricity transaction costs of each microgrid is 0. However, the grid access fee cost (Equation (27)) still contains the power interaction variable. P e,ij ( t The objective function of the integrated energy microgrid is to minimize the sum of the operating costs of each microgrid and the risk of electricity purchase and sale. (42) In the formula, C I To determine the cost of integrated energy microgrids, the optimal solution for interactive power can be obtained in the model. , Internet access fee and conditional value of risk ; Phase Two: Obtaining the Microgrid i After removing the interaction variables of optimal cost and optimal power (excluding electricity trading costs), equation (42) can be further transformed into: (43) Equation (43) δ e,ij,s ( t To optimize the variables, the inequality ensures that each microgrid can obtain a benefit. After transforming the objective function of Problem 2, we get: (44) by δ e,ij,s ( t ) represents the optimization variable, where , This is the optimal solution for the first stage; Formulas (41)-(44) are two-stage optimization scheduling models for integrated energy microgrids based on Nash negotiations; Step 3: The two-stage optimization scheduling model of integrated energy multi-microgrid based on Nash negotiation is solved in a distributed manner using an improved alternating direction multiplier algorithm. The obtained parameters are the scheduling results. The specific process is as follows: Step 3.1: Solve equation (41) in a distributed manner using the improved alternating direction multiplier algorithm. The specific solution method is as follows: 1) Establish a microgrid i The augmented Lagrangian function: (45) In the formula, λ ij,s ( t ) represents the Lagrange multipliers of the first-stage optimization model. ρ As a penalty factor; 2) Set the maximum number of iterations l max The value is 100, representing the time period in the initial scene s. t Intranet i With micro-network j Interactive electrical power P e,ij,s ( t )=0, P e,ij,s ( t ) and Lagrange multipliers λ ij,s The iterative process is as follows: (46) (47) 3) Determine if the algorithm has converged, and calculate the original residual and dual residual: (48) (49) In the formula, For the first l +1 iteration microgrid i With micro-network j The original residuals of electricity trading; For the first l +1 iteration microgrid i With micro-network j Dual residuals in electricity trading; The iteration stopping condition is: (50) (51) In the formula, ε pri , ε dual These are the upper limits of the original residual and the dual residual, respectively; 4) The penalty factor is automatically updated based on the quantitative relationship between the original residual and the dual residual. The dynamic penalty factor is expressed as: (52) In the formula, ν The scaling factor is the ratio of the original residual to the dual residual.

1. 2 represents the acceleration convergence coefficient and the deceleration convergence coefficient, respectively. ν , 1, 2 > 1); 5) Output scheduling results: device output of each microgrid, and optimal solution interaction power. Internet access fee and conditional value of risk ; Step 3.2: Obtain the optimal solution from Step 3.

1. and Substituting the objective function (44) of the second stage of the integrated energy multi-microgrid two-stage optimization scheduling model based on Nash negotiation, the improved alternating direction multiplier algorithm is used to solve equation (44) in a distributed manner, and the interactive power between each microgrid is obtained. Electricity trading costs This is the scheduling result.

2. The method for optimized scheduling of multi-microgrid integrated energy systems containing hydrogen-doped natural gas based on Nash negotiation as described in claim 1, characterized in that, The carbon capture power plant model construction process is as follows: In a carbon capture power plant, thermal power units are... t Output during the period P TU,e,i ( t A portion of the electricity is supplied to carbon capture equipment, while the other portion flows into the power grid to supply electrical load; the carbon capture equipment... t Power input during time period P CCS,e,i ( t It is further divided into two parts: the consumption of its own fixed electrical power. P CCS,e1,i ( t and the electrical power consumption for CO2 processing P CCS,e2,i ( t ): (1) A portion of the CO2 captured by the carbon capture equipment is stored, while the remainder is supplied to the power-to-gas conversion equipment. The carbon capture power plant model is as follows: (2) In the formula, P TU,e,i ( t (Time period) t Intranet i ( i =1,2,3,…) power output of thermal power units, kW; e TU The carbon emission intensity per unit time, m 3 / (kW·h); V TU,CO2,i ( t (Time period) t Volumetric flow rate of CO2 emitted by internal thermal power units, m 3 / h; λ CCS Energy consumption (kW·h) for a carbon capture device to process a unit volume flow rate of CO2. / m 3 ; µ CCS For carbon capture efficiency; V CCS1,CO2,i ( t () represents the volumetric flow rate of CO2 captured by the carbon capture device; V CCS2,CO2,i ( t The volumetric flow rate of CO2 supplied to the power-to-gas (HPC) equipment for carbon capture. V CCS,CO2,i ( t ) represents the volumetric flow rate of CO2 captured by carbon capture.

3. The method for optimal scheduling of multi-microgrid integrated energy systems containing hydrogen-doped natural gas based on Nash negotiation as described in claim 1, characterized in that, The process of constructing the hydrogen energy storage system model containing hydrogen-doped gas is as follows: 1) A dimensionlessly standardized electrolytic cell model: The electrolyzer converts electrical energy into hydrogen energy, and hydrogen is produced by proton exchange electrolysis. The variable efficiency mathematical model is as follows: (3) In the formula, n EL,H2,i ( t (Time period) t Intranet i The amount of hydrogen produced in the electrolyzer; n ELN This refers to the rated capacity of the electrolytic cell; P EL,e,i ( t (Time period) t The electrical power input to the internal electrolytic cell; P EL,eN The rated value of the electrical power input to the electrolytic cell; f ( P EL,e,i ( t )) is the efficiency function of the electrolyzer; a EL , b EL , c EL These are the efficiency function coefficients; The dimensionlessly uniform electrolytic cell model is as follows: (4) In the formula, n EL,H2,i ( t (Time period) t Intranet i The amount of hydrogen produced in the electrolyzer, in kmol; P EL,e,i ( t (Time period) t The electrical power input to the internal electrolytic cell, in kW; m EL,H2,i ( t (Time period) t Mass of hydrogen produced by the internal electrolyzer, kg; M H2 is the molar mass of hydrogen gas; ρ H2 The density of hydrogen gas; Δ t The scheduling unit time, P EL,H2,i ( t (Time period) t Hydrogen production power of the internal electrolyzer; 2) A dimensionlessly standardized methane reactor model 4H₂ + CO₂ → CH₄ + 2H₂O (5) Since chemical reaction equations are usually calculated using the amount of molecular weight, the CO2 volumetric flow rate (equation (2)) supplied to the carbon capture and electro-gas conversion equipment is converted into the amount of substance as follows: (6) In the formula, m CCS2,CO2,i ( t (Time period) t Intranet i Mass of CO2 supplied to the power-to-gas (H2O) converter by carbon capture, in kg; n CCS2, CO2,i,i ( t The amount of CO2 supplied to the electro-gas conversion equipment for carbon capture, in kmol; M CO2 This represents the molar mass of CO2. V CCS2,CO2,i ( t The volumetric flow rate of CO2 supplied to the power-to-gas (HPC) equipment for carbon capture; ρ CO2 The density of CO2; The amount of CH4 produced in a methane reactor depends on the molar relationship between CO2 and H2. The reaction amount and the amount produced can be determined by the following formula: (7) In the formula, n MR1,H2,i ( t (Time period) t The amount of H2 supplied to the methane reactor by the internal electrolyzer; n CCS2,CO2,i ( t The amount of CO2 supplied to the methane reactor for carbon capture; n MR,H2,i ( t (Time period) t The amount of H2 that participates in the reaction with methane; n MR,CH4,i ( t (Time period) t The amount of CH4 produced by the internal methane reactor; The amount of H2 input to the methane reactor and the rate of CH4 production in the methane reactor are further converted to obtain the power of H2 input and CH4 production, as shown in the following formula: (8) (9) In the formula, P MR,H2,i ( t (Time period) t H2 power input to the methane reactor, kW; P MR,g,i ( t (Time period) t CH4 power output from the internal methane reactor; η MR,g For time period t Output efficiency of the internal methane reactor; M CH4 Here is the molar mass of CH4; ρ CH4 The density of CH4; 3) Hydrogen storage tank model The hydrogen energy loss generated during the filling and discharging process of the hydrogen storage tank is approximated by the filling and discharging efficiency; therefore, the model is as follows: (10) In the formula, , , , , Time periods t Intranet i Hydrogen storage tank filling and discharging power, efficiency, and capacity; 4) Hydrogen-blended gas turbine unit model Natural gas is coupled with hydrogen produced by equation (4) within a certain range of hydrogen blending ratio and supplied to the gas turbine unit. Therefore, the electrothermal conversion efficiency of the gas turbine unit is set as a constant. The mathematical model of the hydrogen-blended gas turbine is as follows: (11) (12) In the formula, R GT,i ( t () represents the hydrogen blending ratio of the gas turbine; V GT,H2,i ( t ), V GT,g,i ( t () are time periods t The volumetric flow rate of hydrogen and natural gas input to the gas turbine, i.e., the volume of gas flowing through the pipeline per unit time, in m³. 3 / h;HHV CH4 The calorific value of methane; P GT,g,i ( t ), P GT,H2,i ( t () are time periods t The gas turbine is internally fed with natural gas and hydrogen power. P GT,e,i ( t (Time period) t The electrical power output of the internal gas turbine; η GT,e The electrical conversion efficiency of the gas turbine; P GT,h,i ( t (Time period) t Intranet i Thermal power output of the gas turbine, kW; η GT,h The heat conversion efficiency of the gas turbine; The mathematical model for a hydrogen-blended gas boiler is as follows: (13) (14) In the formula, R GB,i ( t () represents the hydrogen blending ratio of the gas-fired boiler; V GB, H2,i ( t ), V GB,g,i ( t () are time periods t The volumetric flow rates of hydrogen and natural gas input to the gas turbine; P GB,g,i ( t )and P GB,H2,i ( t () are time periods t The power output of the internally fed gas-fired boiler, consisting of natural gas and hydrogen, is measured in kW. P GB,h,i ( t (Time period) t Thermal power output of internal gas-fired boiler, kW; η GB,h This refers to the heat conversion efficiency of a gas-fired boiler.

4. The method for optimized scheduling of multi-microgrid integrated energy systems containing hydrogen-doped natural gas based on Nash negotiation as described in claim 1, characterized in that, The construction process of the integrated energy microgrid energy efficiency improvement mechanism model is as follows: The proportion of thermal power in the power distribution network during the dispatching period is taken as... ( t The specific calculation model for carbon trading costs is as follows: (15) (16) In the formula, m GT0,i , m GB0,i , m TU0,i , m L,i microgrids i The carbon emission allowances for gas turbines, gas boilers, thermal power units, and total emissions during the dispatch cycle; σ e Carbon emission allowance per unit power generation of gas turbine units; σ h Carbon emission quota per unit heating power of gas turbine units; σ p Carbon emission quotas per unit power generation of thermal power units; (17) (18) In the formula, m P,i ( t (for microgrids) i Total actual carbon emissions during the scheduling cycle; m GT,i ( t ), m GB,i ( t ), m TU,i ( t () are time periods t Intranet i Actual carbon emissions from gas turbines, gas boilers, and thermal power units; a i , b i , c i ( i =1,2,3) are the carbon emission coefficients of gas turbine power supply, gas turbine heating supply and thermal power unit power supply, respectively; (19) In the formula, C CO2,i For micro-network i The carbon trading cost; c is the market price per unit of carbon trading; The price increase range for each tier of carbon trading; The length of the carbon emission range. This is the carbon trading reward coefficient.

5. The method for optimal scheduling of multi-microgrid integrated energy systems containing hydrogen-doped natural gas based on Nash negotiation as described in claim 1, characterized in that, The process of constructing the single microgrid cost model is as follows: microgrid i The goal of optimizing operations is to reduce total cost. C i Minimum: (20) Where C CO2,i The formulas for calculating carbon trading costs and other costs are as follows: 1) The formula for calculating the penalty cost of wind and solar power curtailment is: (21) In the formula, δ a This refers to the penalty coefficient for wind and solar power curtailment. P WTI,e,i ( t ), P WT,e,i ( t () are time periods t The wind power input and consumption of the microgrid within the network; P PVI, e,i ( t ), P PV, e,i ( t (Time period) t Photovoltaic power input and consumption by the internal microgrid; 2) The formula for calculating energy purchase cost is: (22) In the formula, δ buy,e ( t ), δ sell,e ( t () are time periods t The purchase and sale price of electricity; P buy,e,i ( t ) P sell,e,i ( t () are time periods t Intranet i The power of electricity purchased from and sold to the distribution network; δ buy,g ( t )for t Gas prices during different time periods; P buy,g,i ( t (Time period) t Gas purchasing power of the internal microgrid; 3) The formula for calculating the start-up, shutdown, and coal consumption costs of thermal power units is as follows: (23) In the formula, P TU,e,i ( t (Time period) t Intranet i The electrical power output of medium-sized thermal power units; a 4. b 4. c 4 represents the coal consumption cost coefficient for thermal power units; U TU,i (t) is a binary variable. U TU,i (t)=1 indicates startup. U TU,i (t)=1 indicates shutdown; δ TU This is the start-up and shutdown cost coefficient for thermal power units. 4) The formula for calculating demand response cost is: (24) In the formula, δ cut,e , δ cut,h These are respectively the cost coefficients for reducing electric heating load compensation; δ tran,e , δ tran,h These are the cost coefficients for compensating for transferred electric heating loads; 5) The formula for calculating energy storage operation and maintenance costs is: (25) In the formula, δ ES1 , δ ES2 The charging and discharging operation and maintenance cost coefficients for electric and hydrogen energy storage equipment are respectively, in yuan / (kW·h); 6) The formula for calculating electricity trading costs is: (26) In the formula, P e,ij ( t (Time period) t Intranet i With micro-network j The electrical power exchanged between them P e,ij ( t )>0 indicates microgrid i To Micro Network j Electricity sales δ e,ij ( t (for microgrids) i With micro-network j The electricity price between them, in yuan / (kW·h); 7) The formula for calculating network access fees is: (27) In the formula, g e The cost coefficient for internet access fees is expressed in yuan per (kW·h).

6. The method for optimal scheduling of hydrogen-doped natural gas integrated energy multi-microgrid based on Nash negotiation according to claim 5, characterized in that, The constraint condition is expressed as follows: The electrical, thermal, gas, and hydrogen balance constraints are expressed as follows: (28) (29) (30) (31) In the formula, P load,e,i ( t ), P load,h,i ( t ) represent microgrids respectively i The electrical and thermal loads; The power output constraint of wind and solar power is expressed as follows: (32) In the formula, P WTI,e,i ( t (Time period) t Wind power input within; P WT,e ( t (Time period) t Wind power absorbed by the internal microgrid; P PVI, e,i ( t (Time period) t Wind power input to the internal microgrid; P PV, e,i ( t (Time period) t Wind power absorbed by the internal microgrid; Equipment operating constraints are expressed as follows: (33) In the formula, , respectively equipment x The upper and lower limits of the output power; , respectively equipment x The upper and lower limits of output power ramp-up; Electricity trading constraints are expressed as follows: (34) In the formula, For micro-network i Maximum power limit for interaction with other microgrids; If transmission power loss is not considered, then the time period t The sum of the electrical power of all microgrid interactions is zero. (35) If transmission power loss is not considered, then the time period t The sum of all microgrid electricity trading costs is zero: (36)。

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