Method for calculating capacity demand of biogas power generation system in rural micro-grid capable of independently operating

Abstract

A capacity demand calculation method for a biogas power generation system in a rural micro-grid capable of operating independently comprises the following steps: performing uncertainty quantitative analysis on a rural micro-grid source load containing new energy, and performing typical scene set generation on a net load by using a hyper-Latin sampling method; establishing a source load uncertainty-based rural micro-grid optimization operation model containing the biogas generator set, and substituting the generated typical scenes and the probability of each scene into the optimization operation model; defining a gas storage capacity deviation degree GSD index and a cost growth rate CGR index, and correcting stored gas to determine the installed capacity of the biogas power generation system; establishing a capacity correction model by taking the minimum GSD and CGR as targets, and processing the targets in the model by adopting a linear weighting method; and calculating energy storage demand power and capacity, and determining the installed capacity of the biogas power generation system according to the energy storage demand power and capacity. On the premise of ensuring that the system can operate independently, the investment cost of the system is greatly reduced, the low-carbon electric power optimization capability of the system is improved, and a foundation is laid for continuously optimizing low-carbon electric power.

Description

Technical Field

[0001] The present invention belongs to the field of accurate accounting, carbon reduction and carbon reduction of power system carbon emissions; in particular, it relates to a method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid. Background Art

[0002] In rural microgrids, the output of thermal power generating units has been declining year by year, while the penetration rate of new energy has been increasing year by year, resulting in a serious shortage of system flexibility resources. Efficiently utilizing the widespread biogas resources in rural areas and involving them in rural microgrids can effectively cope with the impact of the randomness of new energy output. How to reasonably select the capacity of biogas generating units will have important theoretical significance and application value.

[0003] Although my country has conducted a certain degree of research in the field of biogas power generation, the capacity demand calculation of biogas generator sets is still in the development stage. In order to ensure the independent operation of rural microgrids and reduce the investment cost of the system, the capacity demand calculation of biogas generator sets has become a top priority. Therefore, the present invention proposes a method for calculating the capacity demand of a biogas power generation system in an independently operating rural microgrid, laying the foundation for improving the rural biogas power generation system. Summary of the invention

[0004] The present invention is aimed at rural microgrids that can operate independently under source and load uncertainty, and provides a method for calculating the capacity demand of a biogas power generation system in an independently operating rural microgrid. On the premise of ensuring that the system can operate independently, the investment cost of the system is greatly reduced, laying the foundation for continued optimization of low-carbon electricity.

[0005] The technical solution of the present invention includes:

[0006] Step 1: Conduct uncertainty quantitative analysis on the source and load of rural microgrids containing new energy, and use the super Latin sampling method to generate a typical scenario set for the net load;

[0007] Step 2: Establish an optimal operation model of a rural microgrid containing biogas generators based on source-load uncertainty, and substitute the typical scenarios and probabilities of each scenario generated in step 1 into the optimal operation model;

[0008] 1. Establish a rural biogas power generation system model:

[0009] Aiming at the reaction process of methanogens in biogas, the unstructured model based on Monod equation is used to describe the steady-state approximate relationship between biogas fermentation rate and different environmental factors under mesophilic or normal temperature conditions.

[0010] 2. Objective function:

[0011] The rural power distribution system optimization operation model takes the minimum total operation cost as the goal and determines the objective function;

[0012] 3. Constraints:

[0013] (1) Constraints of biogas power generation system;

[0014] (2) EB equipment constraints;

[0015] (3) Wind power and photovoltaic constraints;

[0016] (4) Load constraints;

[0017] Step 3: Calculation method of capacity requirement of biogas power generation system;

[0018] Define the gas storage deviation degree GSD index and the cost growth rate CGR index, and correct the gas storage to determine the installed capacity of the biogas power generation system;

[0019] 3.1. Define GSD indicators;

[0020] 3.2. Defining CGR

[0021] 3.3. Establish a gas storage capacity correction model;

[0022] A capacity correction model is established with the goal of minimizing GSD and CGR, and the linear weighting method is used to deal with the two goals in the model;

[0023] 3.4. Calculate the required power and capacity of energy storage.

[0024] The quantitative analysis of the uncertainty of the rural microgrid source and load containing new energy in step 1 of the present invention uses the super Latin sampling method to generate a typical scenario set for the net load, including the following:

[0025] 1: Define parameters and ranges, determine the relevant parameters of temperature, humidity, wind speed, time, user behavior patterns in the simulated power net load scenario, and the value ranges of these parameters;

[0026] 2: Generate a super Latin matrix. After determining the parameters and their ranges, a super Latin sampling method is used to generate a super Latin matrix. This matrix can evenly cover all possible parameter combinations in the multi-dimensional parameter space. Each row represents a possible scenario combination, and the columns correspond to different parameters.

[0027] 3: Scenario generation and selection, through the super Latin square, select the scenarios that are highly representative and evenly distributed and can cover most of the parameter space, and generate the typical net load scenarios and their probabilities of the required quantity;

[0028] 4: Analysis and Validation,Analyze the model output and verify the effectiveness of the Super Latin sampling method in power load scenario reduction, including comparison with actual data, sensitivity analysis, and ensuring the representativeness and coverage of the selected scenarios.

[0029] In step 2 of the present invention, a non-structural model based on the Monod equation is used to establish a rural biogas power generation system model to describe the steady-state approximate relationship between the biogas fermentation rate and different environmental factors under medium or normal temperature conditions, such as substrate concentration, bacterial species, and temperature. The biogas fermentation kinetic model is shown in formulas (1)-(3):

[0030]

[0031] In the formula, G is the maximum growth rate of bacterial biomass at time t under medium or low temperature conditions, used to calculate the biogas production rate in formula (3); t is the biogas production rate of the biogas digester at time t, which is used to calculate the gas storage capacity of the gas storage tank in formula (8); B0 is the biomethane potential, which indicates how much organic matter can be degraded in the anaerobic reaction; S0 is the mass of volatile solids in the fermentation base liquid put into AD. Volatile solids refer to the amount of organic matter in the total solids excluding the inorganic part, which is the most important effective component of microbial fermentation; V AD is the volume of anaerobic tank AD; K d is the microbial fermentation kinetic parameter, used for the calculation of biogas production rate in formula (3); HRT is the hydraulic retention time, that is, the average reaction time of microbial fermentation; α 11 , α 12 , α 21 , α 22 , β 11 , β 12 , β 13 is the microbial fermentation coefficient, which is related to the type of biomass raw materials and the fermentation method; Substituting equations (1)-(2) into equation (3), we can get the biogas production rate G of AD at each moment. t Substituting equations (1)-(2) into equation (3), we can get the biogas production rate G of AD at each moment. t ;

[0032] Equations (4) to (7) represent the heat conduction process during the fermentation and gas production stage. The total heat energy for heating AD comes from the heat recovery of the biogas generator set and the electric heating equipment. According to the steady-state heat conduction law, constraint (6) represents the total heat dissipated by the top and side walls of AD. According to the law of conservation of energy, constraint (7) represents the fermentation temperature in AD changes with heat balance:

[0033]

[0034] In the formula, is the total thermal power used to heat the fermentation base liquid in AD at time t; is the output power of the electric heating equipment at time t; is the thermal power of the biogas generator set at time t; η e It is the electric heat conversion efficiency of electric heating equipment; is the power of the electric heating equipment at time t; K is the heat dissipation power of heat conducted through the surface of AD pool wall; dis is the heat transfer coefficient of AD pool wall; A air-AD is the surface area of ​​AD pool wall; T t is the fermentation temperature in the tank at time t; is the ambient temperature at time t; c AD and r AD are the specific heat capacity and density of the fermentation base solution, respectively;

[0035] In the gas storage stage, the biogas produced by fermentation is transported to BES for storage and standby. It can be seen from equations (1)-(3) that the gas storage capacity is affected by the temperature and dry matter quality factors in AD. The model of BES is different from that of conventional energy storage equipment. When calculating the gas storage capacity of BES at time t+1, the biogas production rate of AD at time t is considered. When calculating the gas storage capacity of BES at time t+1, equation (3) is substituted into equation (8), as shown in equations (8)-(11);

[0036]

[0037] In the formula, is the gas storage capacity of BES at time t; is the maximum air intake rate of the LBP unit; η BES It is the efficiency of BES filling and deflation; is the air intake rate of the biogas generator set at time t;

[0038] In the gas consumption stage, according to the law of conservation of energy, the intake rate of the biogas generator set limits the total output power of the biogas generator set, as shown in formula (8); the heat-to-electricity ratio of the biogas generator set should meet the constraints of its feasible domain of operation, and the auxiliary variable y is introduced tn , assuming represents the set of thermal and electric powers of the nth pole in its feasible domain of operation. Then the electric and thermal powers of the biogas generator set at time t can be expressed by equations (12)-(16); equations (17)-(18) represent the upper and lower limits of the thermal and electric power ramp rates of the biogas generator set;

[0039]

[0040] In the formula, y tn is an auxiliary variable, which constrains the operating state of the biogas generator set within its feasible domain; H up and H downare the upper and lower limits of thermal power ramp for biogas power generation; P up and P down They are the upper and lower limits of biogas power generation ramp respectively; is the active output power of the biogas generator set at time t; η con is the energy conversion efficiency of the biogas generator set; SN is the set of all poles in the feasible domain of the biogas generator set; the gas production, gas storage and gas use process enables the biogas generator set to absorb excess electricity in the distribution network through electric heating equipment, and produce more easily storable biogas through microbial fermentation, which is converted into electrical energy through the biogas generator set when there is a system power shortage in the distribution network to maintain the power balance of the distribution network. The battery-like characteristics of the biogas generator set can effectively improve the peak-shaving and frequency-regulating performance.

[0041] The objective function in step 2 of the present invention is:

[0042]

[0043] In the formula, and are the total costs of large-scale biogas projects, PV, WG, abandoned solar power, abandoned wind power and electric boilers at time t under scenario s; a, b, c are the generator output cost coefficients respectively; are the unit output costs of PV and WG at time t respectively; are the unit penalty costs for abandoning solar power and abandoning wind power at time t respectively; is the unit output cost of EB at time t; are the actual outputs at time t under scenario s for biogas, photovoltaic and wind power, respectively; and are the amount of solar power and wind power abandoned at time t under scenario s, respectively; is the EB electricity consumption at time t under scenario s.

[0044] The constraints in step 2 of the present invention are as follows:

[0045] The generator output in the system is equal to the load demand at all times, as shown in formula (23):

[0046]

[0047] In the formula, is the load at time t under scenario s; is the load reduction at time t under scenario s;

[0048] (1) Constraints of biogas power generation system:

[0049] The biogas power generation system involves two energies, electricity and heat. Under the scheduling mode of "determining electricity by heat", the heat load needs to be determined. Its mathematical model and constraints are as shown in equations (1)-(18);

[0050] (2) EB equipment constraints:

[0051] The EB equipment converts biogas into thermal energy to meet the heat load demand in the system. Its mathematical model and constraints are shown in equations (24)-(26):

[0052]

[0053] Where η EB is the heat generation efficiency of EB; EB,t is the operating status of EB during period t, 1 means operation and 0 means shutdown; They are the upper and lower limits of EB output power respectively; They are the upper and lower limits of the ramp-up of the EB output electric power respectively;

[0054] (3) Wind power and photovoltaic constraints

[0055] Wind power and photovoltaic constraints are shown in equations (27) and (28):

[0056]

[0057] (4) Load constraints

[0058] Considering the incentive demand response IDR, the mathematical model and constraints of the interruptible load IL representing the IDR are shown in equations (29)-(33):

[0059]

[0060] In the formula, is the maximum reduction of IL in period t; is the maximum response efficiency of IL; are the upper and lower limits of the continuous reduction time respectively; ε IL,t is the reduction status of IL in period t, 1 means it has been reduced, and 0 means it has not been reduced.

[0061] The method for calculating the capacity demand of the biogas power generation system in step 3 of the present invention is:

[0062] 3.1、Define the GSD index γ as

[0063]

[0064] Where: is the charging and discharging power of the energy storage during period t; Δt is the time step; N is the total length of the data; E c、E d are the cumulative filling and deflation volumes of the gas storage tank during the assessment period; f c (·),f d (·) are the charging and discharging test functions of energy storage, respectively;

[0065] From the analysis of formula (34), it can be seen that the larger the γ value is, the greater the deviation of energy storage charge and discharge is, and the more unbalanced the charge and discharge are. The GSD index reflects the balance of energy storage inflation and deflation. Severe imbalance of inflation and deflation will result in low energy storage utilization and high configuration capacity.

[0066] 3.2. Define the CGR index σ as:

[0067] σ=(f cost (p E )-F) / F (39)

[0068] Where: f cost (p E ) is the system cost calculation function, p E is the gas storage volume at each time; σ is generally greater than 0; the larger the σ value, the greater the increase in the joint operation cost after the gas storage volume adjustment; the smaller the σ value, the closer the joint operation cost is to the minimum operation cost at the optimal time; the value of σ should be as close to 0 as possible;

[0069] 3.3 Gas storage capacity correction model

[0070] The capacity correction model is established with the minimum GSD and CGR as the objectives, and the linear weighting method is used to process the two objectives in the model, as shown in formula (40);

[0071]

[0072] Where: α1, α2 are the weight values ​​of the two targets; They are GSD and CGR index values ​​respectively

[0073] 3.4 Energy storage demand power and capacity

[0074] Divide according to the continuous inflation and continuous deflation time and form a new time set Γ s ={M l |l=1,2,…,L s}, where L s is the number of continuous inflation / deflation time sets of scene s, M l is the lth energy storage continuous charging / discharging time set, then the maximum gas storage capacity The calculation formula is:

[0075]

[0076] According to formula (41), the capacity of the biogas power generation system in the rural microgrid that can operate independently can be obtained.

[0077] Compared with the prior art, the method of the present invention aims to optimize the operation of rural microgrids, greatly reduce the investment cost of the system while ensuring that the system can operate independently, and improve the low-carbon power optimization capability of the system, laying the foundation for further optimization of low-carbon power. The present invention analyzes the capacity demand of the biogas power generation system and constructs an optimization operation model of the rural microgrid based on uncertainty. By considering the uncertainty of source and load, the super Latin sampling method is used to generate a typical scenario set, and then the uncertainty of source and load is quantitatively analyzed, and a rural microgrid optimization operation model based on this uncertainty is proposed. The model covers the gas production, gas storage, and gas use processes of the biogas power generation system, as well as the constraints related to it, such as the electric-thermal conversion efficiency and energy conservation. In addition, by defining the gas storage deviation degree (GSD) index and the cost growth rate (CGR) index, a gas storage correction model is proposed, and the installed capacity of the biogas power generation system is determined by this, laying the foundation for improving the rural biogas power generation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Graphs are generated for a typical set of scenarios before net load reduction.

[0079] Figure 2 Graphs are generated for a typical set of scenarios after net load reduction.

[0080] Figure 3 It is the probability map of each typical scene. DETAILED DESCRIPTION

[0081] Example

[0082] This embodiment includes the following steps:

[0083] Step 1: Quantitative analysis of source-load uncertainty of rural microgrids containing new energy

[0084] The uncertainty of source load in rural microgrids containing new energy sources increases the difficulty of calculating the capacity demand of biogas generator sets. Therefore, the capacity demand analysis for rural biogas power generation systems should be based on system uncertainty. Therefore, the present invention uses a typical scenario set of net load (the difference between the actual load of the system and the total output of new energy) to quantitatively analyze the uncertainty of source load, substitutes different scenarios into the optimization operation model, and obtains the energy storage capacity set required for all scenarios, which provides a basis for analyzing the energy storage demand capacity. The typical scenario set of net load can better quantify the uncertainty of net load, and the application of super Latin sampling method in the reduction of power net load scenarios can effectively reduce the number of scenarios required for simulation, while maintaining the comprehensiveness and accuracy of power system behavior, and is suitable for situations with large parameter space and limited computing resources. Therefore, the present invention uses the super Latin sampling method to generate a typical scenario set for net load, and the specific steps are as follows:

[0085] 1: Define parameters and ranges. First, determine all relevant parameters (temperature, humidity, wind speed, time, user behavior patterns, etc.) in the simulated power net load scenario, as well as the value ranges of these parameters.

[0086] 2: Generate a Hyper-Latin Hypercube. After determining the parameters and their ranges, a Hyper-Latin Hypercube is generated using the Hyper-Latin sampling method. This matrix can evenly cover all possible parameter combinations in the multi-dimensional parameter space. Each row represents a possible scenario combination, and the columns correspond to different parameters.

[0087] 3: Scenario selection. Through the super Latin square, select scenarios that are representative and evenly distributed. These scenarios should be able to cover most of the parameter space to ensure that the simulation of power loads is both extensive and specific.

[0088] 4: Analysis and Validation. Analyze the model output to validate the effectiveness of the Super Latin sampling approach in reducing power load scenarios. This may include comparisons with actual data, sensitivity analysis, and ensuring representativeness and coverage of the selected scenarios.

[0089] Substituting the generated typical net load scenarios and the probabilities of each scenario into step 2, the uncertainty factors of source and load can be considered in the optimization operation model of the rural microgrid, making the optimization operation results more consistent with the actual operation conditions.

[0090] Step 2: Rural microgrid optimization operation model based on source-load uncertainty

[0091] 1.1 Rural biogas power generation system model

[0092] The present invention focuses on the methanogen reaction process mainly involved in biogas, and adopts a non-structural model based on the Monod equation to describe the steady-state approximate relationship between the biogas fermentation rate and different environmental factors under medium or normal temperature conditions, such as substrate concentration, bacterial species (biomass raw materials), temperature, etc. Therefore, the biogas fermentation kinetic model is shown in formulas (1)-(3):

[0093]

[0094] In the formula, G is the maximum growth rate of bacterial biomass at time t under medium or low temperature conditions, used to calculate the biogas production rate in formula (3); t is the biogas production rate of the anaerobic digester (AD) at time t, which is used to calculate the gas storage capacity of the gas storage tank in formula (8); B0 is the biomethane potential, which indicates how much organic matter can be degraded in the anaerobic reaction; S0 is the mass of volatile solids (VS) in the fermentation base liquid put into AD. Volatile solids refer to the amount of organic matter in the total solids except for the inorganic part, which is the most important effective component of microbial fermentation; V AD is the volume of anaerobic tank AD; K d is the microbial fermentation kinetic parameter, used for the calculation of biogas production rate in formula (3); HRT is the hydraulic retention time, that is, the average reaction time of microbial fermentation; α 11 , α 12 , α 21 , α 22 , β 11 , β 12 , β 13 is the microbial fermentation coefficient, which is related to the type of biomass raw materials and the fermentation method, and is generally obtained through experimental fitting. Substituting equations (1)-(2) into equation (3), the biogas production rate G of AD at each moment can be obtained: t .

[0095] Equations (4) to (7) represent the heat conduction process during the fermentation and gas production stage. The total heat energy for heating the AD comes from the heat recovery of the biogas generator set and the electric heating equipment. According to the law of steady-state heat conduction, constraint (6) represents the total heat dissipated by the top and side walls of the AD. According to the law of conservation of energy, constraint (7) represents that the fermentation temperature in the AD changes with the heat balance.

[0096]

[0097] In the formula, is the total thermal power used to heat the fermentation base liquid in AD at time t; is the output power of the electric heating equipment at time t; is the thermal power of the biogas generator set at time t; η eIt is the electric heat conversion efficiency of electric heating equipment; is the power of the electric heating equipment at time t; K is the heat dissipation power of heat conducted through the surface of AD pool wall; dis is the heat transfer coefficient of AD pool wall; A air-AD is the surface area of ​​AD pool wall; T t is the fermentation temperature in the tank at time t; is the ambient temperature at time t; c AD and r AD are the specific heat capacity and density of the fermentation base liquid respectively.

[0098] In the gas use stage, only the gas storage capacity of the gas tank (Biogas Energy Storage, BES) is considered. However, in the gas storage stage, the biogas produced by fermentation is first transported to the BES for storage and standby. From equations (1)-(3), it can be seen that the gas storage capacity is affected by factors such as temperature and dry matter quality in AD. Therefore, the model of BES is different from that of conventional energy storage equipment. When calculating the gas storage capacity of BES at time t+1, the biogas production rate of AD at time t needs to be considered. Therefore, when calculating the gas storage capacity of BES at time t+1, equation (3) should be substituted into equation (8), as shown in equations (8)-(11).

[0099]

[0100]

[0101] In the formula, is the gas storage capacity of BES at time t; is the maximum air intake rate of the LBP unit; η BES It is the efficiency of BES filling and deflation; is the air intake rate of the biogas generator set at time t.

[0102] In the gas consumption stage, according to the law of conservation of energy, the intake rate of the biogas generator set limits the total output power of the biogas generator set, as shown in formula (8). The heat-to-electricity ratio of the biogas generator set should meet the constraints of its feasible domain. Here, the auxiliary variable y is introduced tn , assuming represents the set of thermal and electric powers of the nth pole in its feasible domain, then the electric and thermal powers of the biogas generator set at time t can be expressed by equations (12)-(16). Equations (17)-(18) represent the upper and lower limits of the thermal and electric power ramp rates of the biogas generator set.

[0103]

[0104] In the formula, y tn is an auxiliary variable, which constrains the operating state of the biogas generator set within its feasible domain; H up and Hdown are the upper and lower limits of thermal power ramp for biogas power generation; P up and P down They are the upper and lower limits of biogas power generation ramp respectively; is the active output power of the biogas generator set at time t; η con is the energy conversion efficiency of the biogas generator set; SN is the set of all poles in the feasible domain of the biogas generator set. The gas production, storage and use process enables the biogas generator set to absorb excess electricity in the distribution network through electric heating equipment, produce more easily storable biogas through microbial fermentation, and convert it into electricity through the biogas generator set when there is a system power shortage in the distribution network to maintain the power balance of the distribution network. The battery-like characteristics of the biogas generator set can effectively improve the peak and frequency regulation performance.

[0105] 1.2 Objective Function

[0106] The optimal operation model of the independently operable rural power distribution system proposed in the present invention aims to minimize the total operating cost, and the objective function is:

[0107]

[0108] In the formula, and are the total costs of large-scale biogas plant (LBP), PV, WG, abandoned solar power, abandoned wind power and electric boiler (EB) at time t under scenario s; a, b, c are the generator output cost coefficients respectively; are the unit output costs of PV and WG at time t respectively; are the unit penalty costs for abandoning solar power and abandoning wind power at time t respectively; is the unit output cost of EB at time t; and are the actual outputs at time t under scenario s for biogas, photovoltaic and wind power, respectively; and are the amount of solar power and wind power abandoned at time t under scenario s, respectively. is the EB electricity consumption at time t under scenario s.

[0109] 1.3 Constraints

[0110] In order to ensure the frequency stability of the system, it is necessary to ensure that the generator output in the system is equal to the load demand at all times, as shown in formula (23):

[0111]

[0112] In the formula, is the load at time t under scenario s; is the load reduction at time t under scenario s.

[0113] (1) Constraints of biogas power generation system

[0114] As can be seen from Section 2.1, the biogas power generation system proposed in this invention involves two energies: electricity and heat. In the dispatching mode of "determining electricity by heat", the heat load must be determined first. Therefore, this invention not only needs to consider the rural distribution network, but also the coupling part with the heat network. Its mathematical model and constraints are shown in equations (1)-(18).

[0115] (2)EB equipment

[0116] EB can convert biogas into thermal energy to meet the heat load demand in the system. Its mathematical model and constraints are shown in equations (24)-(26):

[0117]

[0118] Where η EB is the heat generation efficiency of EB; EB,t is the operating status of EB during period t, 1 means operation and 0 means shutdown; They are the upper and lower limits of EB output power respectively; They are the upper and lower limits of the climbing of EB output electric power respectively.

[0119] (3) Wind power and photovoltaic constraints

[0120] Wind power and photovoltaic constraints are shown in equations (27) and (28):

[0121]

[0122] (4) Load constraints

[0123] Since the research target of this invention is rural power distribution system, only incentive-based demand response (IDR) is considered. The mathematical model and constraint conditions of interruptible load (IL) representing IDR are shown in equations (29)-(33):

[0124]

[0125] In the formula, is the maximum reduction of IL in period t; is the maximum response efficiency of IL; are the upper and lower limits of the continuous reduction time respectively; ε IL,t is the reduction status of IL in period t, 1 means it has been reduced, and 0 means it has not been reduced.

[0126] Step 3: Calculation method for capacity requirement of biogas power generation system

[0127] In the above rural microgrid optimization operation model, the capacity limit of BES is not set. Although this can reduce the system operation cost, it will increase the total investment cost of the system and reduce the biogas utilization rate. In order to avoid the above situation, the present invention proposes the gas storage deviation (Gas Storage Deviation, GSD) index and the cost growth rate (Cost Growth Rate, CGR) index. Finally, the installed capacity of the biogas power generation system is determined based on the corrected gas storage.

[0128] 3.1 GSD index

[0129] In order to describe the degree of charge and discharge balance, the GSD index γ is defined as:

[0130]

[0131] Where: p t E is the charging and discharging power of the energy storage during period t; Δt is the time step; N is the total length of the data; E c 、E d are the cumulative filling and deflation volumes of the gas storage tank during the assessment period; f c (·),f d (·) are the charging and degassing test functions of energy storage, respectively.

[0132] From the analysis of formula (34), it can be seen that the larger the γ value, the greater the deviation of energy storage charge and discharge, and the more unbalanced the charge and discharge capacity. The GSD index reflects the balance of energy storage inflation and deflation. Severe imbalance of inflation and deflation will result in low energy storage utilization and high configuration capacity.

[0133] 3.2 CGR index

[0134] In order to quantify the impact of changes in gas storage on joint operating costs, the CGR index σ is defined as:

[0135] s=(f cost (p E )-F) / F (80)

[0136] Where: f cost (p E ) is the system cost calculation function, p E is the gas storage volume at each time. σ is generally greater than 0. The larger the σ value, the greater the increase in the joint operation cost after the gas storage volume adjustment; the smaller the σ value, the closer the joint operation cost is to the minimum operation cost at the optimal time. Therefore, the value of σ should be as close to 0 as possible.

[0137] 3.3 Gas storage capacity correction model

[0138] During the operation of the system, a large amount of biogas will be continuously charged or released. At this time, the influence of GSD on the determination of the required gas storage capacity is more obvious. Therefore, the present invention mainly establishes a capacity correction model with the minimum GSD and CGR as the goal, and uses a linear weighted method to process the two goals in the model, as shown in formula (40).

[0139]

[0140] Where: α1, α2 are the weight values ​​of the two targets; They are GSD and CGR index values ​​respectively

[0141] 3.4 Energy storage demand power and capacity

[0142] Divide according to the continuous inflation and continuous deflation time and form a new time set Γ s ={M l |l=1,2,…,L s}, where L s is the number of continuous inflation / deflation time sets of scene s, M l is the lth energy storage continuous charging / discharging time set, then the maximum gas storage capacity The calculation formula is:

[0143]

[0144] Adding equation (41) into equation (19), according to the rural microgrid optimization operation model based on source-load uncertainty in step 2, the capacity of the biogas power generation system in the rural microgrid that can operate independently can be obtained.

[0145] This example uses 2000 scenes (such as Figure 1 As shown in the figure, 10 typical scene sets are generated using the method and calculation process in step 1. The generated typical scene sets and their corresponding probabilities are shown in the figure below. Figure 2 , Figure 3 shown.

[0146] The system operation cost before and after correction according to the GAD index and CGR described in step 3 is shown in Table 1.

[0147] Table 1 System operating costs before and after energy storage power correction

[0148]

[0149] It can be seen that the cost increase after correction is not obvious, the maximum increase rate is only 2.02% of typical scenario 3, and the minimum increase rate is 2.06% of typical scenario 8, indicating that the capacity correction method of the biogas power generation system proposed in the present invention can reduce the imbalance degree of charging / discharging of gas storage with a smaller operating cost growth rate. This shows that the present invention can reduce the system investment cost and provide a theoretical basis and application value for low-carbon electricity in rural microgrids.

Claims

1. A method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid, characterized in that: Step 1: Conduct uncertainty quantitative analysis on the source and load of rural microgrids containing new energy, and use the super Latin sampling method to generate a typical scenario set for the net load; Step 2: Establish an optimal operation model of a rural microgrid containing biogas generators based on source-load uncertainty, and substitute the typical scenarios and probabilities of each scenario generated in step 1 into the optimal operation model; 1. Establish a rural biogas power generation system model: Aiming at the reaction process of methanogens in biogas, the unstructured model based on Monod equation is used to describe the steady-state approximate relationship between biogas fermentation rate and different environmental factors under mesophilic or normal temperature conditions.

2. Objective function: The rural power distribution system optimization operation model takes the minimum total operation cost as the goal and determines the objective function; 3. Constraints: (1) Constraints of biogas power generation system; (2) EB equipment constraints; (3) Wind power and photovoltaic constraints; (4) Load constraints; Step 3: Calculation method of capacity requirement of biogas power generation system; Define the gas storage deviation degree GSD indicator and the cost growth rate CGR indicator, and correct the gas storage to determine the installed capacity of the biogas power generation system: 3.

1. Define GSD indicators; 3.

2. Define CGR indicator; 3.

3. Establish a gas storage capacity correction model; A capacity correction model is established with the goal of minimizing GSD and CGR, and the linear weighting method is used to process the goals in the model; 3.

4. Calculate the required power and capacity of energy storage.

2. The method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid according to claim 1 is characterized in that: The quantitative analysis of the uncertainty of the rural microgrid source and load containing new energy in step 1 uses the super Latin sampling method to generate a typical scenario set for the net load, including: (1) Define parameters and ranges, determine the relevant parameters of temperature, humidity, wind speed, time, and user behavior patterns in the simulated power net load scenario, and the value ranges of these parameters; (2) Generate a super Latin matrix. After determining the parameters and their ranges, use the super Latin sampling method to generate a super Latin matrix that can evenly cover all possible parameter combinations in the multidimensional parameter space. Each row represents a possible scenario combination, and the columns correspond to different parameters. (3) Scenario generation and selection: Through the super Latin square, we select scenarios that are highly representative and evenly distributed and can cover most of the parameter space, and generate typical net load scenarios and their probabilities for the required quantity; (4) Analysis and verification: Analyze the model output and verify the effectiveness of the super-Latin sampling method in reducing power load scenarios, including comparison with actual data, sensitivity analysis, and ensuring the representativeness and coverage of the selected scenarios.

3. The method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid according to claim 1 is characterized in that: In the step 2, a non-structural model based on the Monod equation is used to establish a rural biogas power generation system model to describe the steady-state approximate relationship between the biogas fermentation rate and different environmental factors under medium or normal temperature conditions. The biogas fermentation kinetic model is shown in equations (1)-(3): In the formula, G is the maximum growth rate of bacterial biomass at time t under medium or low temperature conditions, used to calculate the biogas production rate in formula (3); t is the biogas production rate of the biogas digester at time t, which is used to calculate the gas storage capacity of the gas storage tank in formula (8); B0 is the biomethane potential, which indicates how much organic matter can be degraded in the anaerobic reaction; S0 is the mass of volatile solids in the fermentation base liquid put into AD. Volatile solids refer to the amount of organic matter in the total solids excluding the inorganic part, which is the most important effective component of microbial fermentation; V AD is the volume of anaerobic tank AD; K d is the microbial fermentation kinetic parameter, used for the calculation of biogas production rate in formula (3); HRT is the hydraulic retention time, that is, the average reaction time of microbial fermentation; α 11 , α 12 , α 21 , α 22 , β 11 , β 12 , β 13 is the microbial fermentation coefficient, which is related to the type of biomass raw materials and the fermentation method; Substituting equations (1)-(2) into equation (3), we can obtain the biogas production rate G at each time point AD t Substituting equations (1)-(2) into equation (3), we can get the biogas production rate G of AD at each moment: t ; Equations (4) to (7) represent the heat conduction process during the fermentation and gas production stage. The total heat energy for heating AD comes from the heat recovery of the biogas generator set and the electric heating equipment. According to the steady-state heat conduction law, constraint (6) represents the total heat dissipated by the top and side walls of AD. According to the law of conservation of energy, constraint (7) represents the fermentation temperature in AD changes with heat balance: In the formula, is the total thermal power used to heat the fermentation base liquid in AD at time t; is the output power of the electric heating equipment at time t; is the thermal power of the biogas generator set at time t; η e It is the electric heat conversion efficiency of electric heating equipment; is the power of the electric heating equipment at time t; K is the heat dissipation power of heat conducted through the surface of AD pool wall; dis is the heat transfer coefficient of AD pool wall; A air-AD is the surface area of ​​AD pool wall; T t is the fermentation temperature in the tank at time t; is the ambient temperature at time t; c AD and r AD are the specific heat capacity and density of the fermentation base solution, respectively; In the gas storage stage, the biogas produced by fermentation is transported to BES for storage and standby. It can be seen from equations (1)-(3) that the gas storage capacity is affected by the temperature and dry matter quality factors in AD. The model of BES is different from that of conventional energy storage equipment. When calculating the gas storage capacity of BES at time t+1, the biogas production rate of AD at time t is considered. When calculating the gas storage capacity of BES at time t+1, equation (3) is substituted into equation (8), as shown in equations (8)-(11); In the formula, is the gas storage capacity of BES at time t; is the maximum air intake rate of the LBP unit; η BES It is the efficiency of BES filling and deflation; is the air intake rate of the biogas generator set at time t; In the gas consumption stage, according to the law of conservation of energy, the intake rate of the biogas generator set limits the total output power of the biogas generator set, as shown in formula (8); the heat-to-electricity ratio of the biogas generator set should meet the constraints of its feasible domain of operation, and the auxiliary variable y is introduced tn , assuming represents the set of thermal and electric powers of the nth pole in its feasible domain of operation. Then the electric and thermal powers of the biogas generator set at time t can be expressed by equations (12)-(16); equations (17)-(18) represent the upper and lower limits of the thermal and electric power ramp rates of the biogas generator set; In the formula, y tn is an auxiliary variable, which constrains the operating state of the biogas generator set within its feasible domain; H up and H down are the upper and lower limits of thermal power ramp for biogas power generation; P up and P down They are the upper and lower limits of biogas power generation ramp respectively; is the active output power of the biogas generator set at time t; η con is the energy conversion efficiency of the biogas generator set; SN is the set of all poles in the feasible domain of the biogas generator set; The gas production, storage and use processes enable the biogas generator set to absorb excess electricity in the distribution network through electric heating equipment, produce more easily storable biogas through microbial fermentation, and convert it into electrical energy through the biogas generator set when there is a system power shortage in the distribution network, thereby maintaining the power balance of the distribution network. The battery-like characteristics of the biogas generator set can effectively improve the peak-shaving and frequency-regulating performance.

4. The method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid according to claim 1 is characterized in that: The objective function in step 2 is: In the formula, and are the total costs of large-scale biogas projects, PV, WG, abandoned solar power, abandoned wind power and electric boilers at time t under scenario s; a, b, c are the generator output cost coefficients respectively; are the unit output costs of PV and WG at time t respectively; are the unit penalty costs for abandoning solar power and abandoning wind power at time t respectively; is the unit output cost of EB at time t; and are the actual outputs at time t under scenario s for biogas, photovoltaic and wind power, respectively; and are the amount of solar power and wind power abandoned at time t under scenario s, respectively; is the EB electricity consumption at time t under scenario s.

5. The method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid according to claim 1 is characterized in that The constraints in step 2 are as follows: The generator output in the system is equal to the load demand at all times, as shown in formula (23): In the formula, is the load at time t under scenario s; is the load reduction at time t under scenario s; (1) Constraints of biogas power generation system: The biogas power generation system involves two energies, electricity and heat. In the "heat-based electricity" scheduling mode, the heat load needs to be determined. Its mathematical model and constraints are as shown in equations (1)-(18); (2) EB equipment constraints: The EB equipment converts biogas into thermal energy to meet the heat load demand in the system. Its mathematical model and constraints are shown in equations (24)-(26): Where η EB is the heat generation efficiency of EB; EB,t is the operating status of EB during period t, 1 means operation and 0 means shutdown; They are the upper and lower limits of EB output power respectively; They are the upper and lower limits of the ramp-up of the EB output electric power respectively; (3) Wind power and photovoltaic constraints Wind power and photovoltaic constraints are shown in equations (27) and (28): (4) Load constraints Considering the incentive demand response IDR, the mathematical model and constraints of the interruptible load IL representing the IDR are shown in equations (29)-(33): In the formula, is the maximum reduction of IL in period t; is the maximum response efficiency of IL; are the upper and lower limits of the continuous reduction time respectively; ε IL,t is the reduction status of IL in period t, 1 means it has been reduced, and 0 means it has not been reduced.

6. The method for calculating the capacity demand of a biogas power generation system in an independently operable rural microgrid according to claim 1 is characterized in that: The method for calculating the capacity requirement of the biogas power generation system in step 3 is: 3.1、Define the GSD index γ as Where: is the charging and discharging power of the energy storage during period t; Δt is the time step; N is the total length of the data; E c 、E d are the cumulative filling and deflation volumes of the gas storage tank during the assessment period; f c (·),f d (·) are the charging and discharging test functions of energy storage, respectively; From the analysis of formula (34), it can be seen that the larger the γ value is, the greater the deviation of energy storage charge and discharge is, and the more unbalanced the charge and discharge are. The GSD index reflects the balance of energy storage inflation and deflation. Severe imbalance of inflation and deflation will result in low energy storage utilization and high configuration capacity. 3.2、Define the CGR index σ as: s=(f cost (p E )-F) / F (39) Where: f cost (p E ) is the system cost calculation function, p E is the gas storage volume at each time; σ is generally greater than 0; the larger the σ value, the greater the increase in the joint operation cost after the gas storage volume adjustment; the smaller the σ value, the closer the joint operation cost is to the minimum operation cost at the optimal time; the value of σ should be as close to 0 as possible; 3.3 Gas storage capacity correction model The capacity correction model is established with the minimum GSD and CGR as the objectives, and the linear weighting method is used to process the two objectives in the model, as shown in formula (40); Where: α1, α2 are the weight values ​​of the two targets; They are GSD and CGR index values ​​respectively 3.4 Energy storage demand power and capacity Divide according to the continuous inflation and continuous deflation time and form a new time set Γ s ={M l |l=1,2,…,L s }, where L s is the number of continuous inflation / deflation time sets of scene s, M l is the lth energy storage continuous charging / discharging time set, then the maximum gas storage capacity The calculation formula is: According to formula (41), the capacity of the biogas power generation system in the rural microgrid that can operate independently can be obtained.

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