Method for determining the hourly energy supply capacity of the park's integrated energy system throughout the year

Abstract

The present invention discloses a method for determining the hourly energy supply capacity of a park integrated energy system throughout the year, comprising the following steps: 1. converting the initial investment of each energy conversion technology into each moment, and accumulating it with the operating costs and maintenance costs to obtain a unified quantitative index for each energy conversion technology; 2. calculating the operating power and configuration capacity of each energy conversion technology under constraints; 3. iteratively decoupling and calculating the operating power and configuration capacity of each energy conversion technology in the micro-cooling network, micro-heating network, and microgrid of the park integrated energy system at time τ; 4. iteratively calculating the operating power and configuration capacity of each energy conversion technology within 8760 hours of the year, and outputting the hourly energy supply of the park integrated energy system throughout the year. The present invention can achieve hourly calculation of the energy supply of the park integrated energy system for 8760 hours throughout the year through discretized iterative calculation across moments, providing a quantifiable solution method for energy supply and demand matching.

Description

Technical Field

[0001] The present invention relates to a design and operation method of an integrated energy system, and in particular to a method for determining the hourly energy supply capacity of an integrated energy system in a park throughout the year. Background Art

[0002] Integrated energy utilization within industrial parks is increasingly becoming a focus of research and exploration for scholars both domestically and internationally. Existing research indicates that integrated energy systems within industrial parks can effectively accommodate renewable energy, utilize low-grade energy, and efficiently utilize fossil energy in a cascaded manner. However, in practice, these systems often suffer from low operational efficiency due to design flaws and irrational operation, failing to fully leverage their advantages. In particular, there is a lack of effective methods for determining the types, capacities, and annual operational strategies for energy supply within the 8,760 hours of a year.

[0003] Currently, the solution to optimizing the energy supply configuration of a park's integrated energy system usually uses a large-scale integer programming solution method. This method is applied over 8,760 hours a year, generating a large number of independent variables and dramatically increasing the dimension of the feasible domain, which can reach tens of thousands to hundreds of thousands. This makes the solution process difficult and the results less reliable. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for determining the hourly energy supply capacity of a park integrated energy system throughout the year. Through discrete iterative calculations across time periods, the hourly calculation of the energy supply of the park integrated energy system for 8760 hours a year can be achieved, providing a quantifiable solution method for energy supply and demand matching, and solving the problem of a sharp increase in dimensions brought about by the existing solution process.

[0005] The present invention is achieved in that:

[0006] A method for determining the hourly energy supply capacity of a park integrated energy system throughout the year comprises the following steps:

[0007] Step 1: Convert the initial investment of each energy conversion technology to each moment, and add it with the operating costs and maintenance costs of each energy conversion technology at each moment to obtain a unified quantitative index SCost for each energy conversion technology t，j ;

[0008] Step 2: Calculate the operating power and configuration capacity of each energy conversion technology under the constraints;

[0009] Step 3: Using an iterative method, decouple and calculate the operating power and configuration capacity of each energy conversion technology in the micro-cooling network, micro-heating network, and microgrid of the park's integrated energy system at time τ;

[0010] Step 4: Using an iterative method, calculate the operating power and configuration capacity of each energy conversion technology within 8760 hours of the year, and output the hourly energy supply of the park's integrated energy system throughout the year.

[0011] In step 1, the equation for the unified quantitative index is:

[0012]

[0013] Among them, SCost τ，j is the cost level of energy conversion technology j at time τ, unit: / yuan;

[0014] t = 1, ..., 8760, where 8760 is the total number of hours in a year;

[0015] Q t，j is the output of energy conversion technology j at time t, in kWh;

[0016] Q τ，j is the output of energy conversion technology j at time τ, in kWh;

[0017] α j is the basic electricity fee coefficient;

[0018] α j is the fixed maintenance cost coefficient;

[0019] is the initial investment cost coefficient;

[0020] β j is the variable maintenance cost coefficient;

[0021] ε j is the operating cost coefficient;

[0022] The optimization goal of the unified quantitative index equation (1) is: the sum of the cost levels of each energy conversion technology at time τ SCost τ，total Minimum, that is:

[0023] f=min(SCost τ，total ) (2);

[0024] Establish the constraints of the unified quantitative index equation (1):

[0025]

[0026] Where Q is Q τ，j The column vector of is the independent variable;

[0027]

[0028] A·Q≤b is a linear inequality constraint, which mainly includes coupling constraints between networks: lithium bromide cooling constraint between micro cooling network and micro grid, lithium bromide heating constraint between micro heating network and micro grid;

[0029] A eq Q = b eq It is a linear equality constraint, mainly including energy conservation constraint;

[0030]

[0031] in, is the electrical load of the park at time τ, excluding the power consumed by electric refrigeration and heat pumps;

[0032] is the power consumption of the park's heat pump heating at time τ;

[0033] is the power consumption of the park's electric refrigeration units at time τ;

[0034] is the total cooling load in the park at time τ;

[0035] is the total heat load of the park at time τ;

[0036] C τ，lose is the cooling loss of the park at time τ, and its equation is:

[0037]

[0038] H τ，lose is the heat loss of the park at time τ, and its equation is:

[0039]

[0040] Among them, K represents the average heat transfer coefficient of the transmission pipeline, which is 1.1-1.5W / (m 2 ℃);

[0041] ΔT represents the average temperature difference between the medium inside the pipe network and the surrounding medium outside the pipe network, in °C;

[0042] ∈ represents the heat loss coefficient of each local attachment of the pipe network;

[0043] d i Indicates the nominal diameter of the i-th pipe section, in meters;

[0044] l i It represents the length of the i-th pipe section, in meters;

[0045] lb≤Q≤ub is the upper and lower limit constraints, which mainly include the output upper limit constraints of each energy conversion technology;

[0046] Among them, the transposed vector of the lower limit constraint lb is:

[0047] lb′=|0 0 0 0 0 0 0 0 0 0 0| (8);

[0048] The transposed vector of the upper limit constraint ub is:

[0049] ub′=|10 15 10 15 10 15 10 15 10 15 10 15 10 15 ub8 ub9 ub 10 ub 11 | (9);

[0050] The output at this moment is also constrained by the output at the previous moment, that is:

[0051]

[0052] in, It represents the maximum output increase of energy conversion technology j within one hour; It represents the maximum output drop of energy conversion technology j within one hour.

[0053] Q0 is the initial value of Q;

[0054] Input the prices of large power grid and gas and the basic parameters of each energy conversion technology to calculate the coefficient a in equation (1) j , α j 、 β j and ε j .

[0055] The output Q of the two different energy conversion technologies at time τ τ,1 and Q τ,2 The difference is defined as: ΔQ τ,1-2 =Q τ,1 -Q τ,2 ,when When , the energy supply cost of the energy network composed of these two different energy conversion technologies is the lowest;

[0056] At this time, the energy demand that can be met is Q τ，1 +Q τ，2 , we can get the equation:

[0057]

[0058] Let y = ΔQ τ，1-2 , x=Q τ，2 , then: 2x+y=∑Q τ，j (11);

[0059] Among them, ∑Q τ，j Refers to the total amount of energy supplied by the two different energy conversion technologies at time τ.

[0060] Combining equations (10) and (11) to solve the output Q of two different energy conversion technologies at time τ τ，1 and Q τ，2 ;

[0061] Similarly, solve the output Q of each energy conversion technology j at time τ τ，j .

[0062] Step 3 includes the following sub-steps:

[0063] Step 3.1: Calculate the cooling power consumption E using equations (1), (2) and (3) k，c , waste heat WHU consumed by lithium bromide unit (ARU) refrigeration k，c And the electricity consumed by delivering cooling capacity E c，s ;

[0064] Step 3.2: Calculate the heating power consumption E using equations (1), (2) and (3) k，h , waste heat consumed by lithium bromide unit (ARU) for heating WHU k，h and the amount of electricity consumed to transport heat E h，s ;

[0065] Step 3.3: Calculate the waste heat AWH generated by the gas generator set k ;

[0066] Step 3.4: Determine the waste heat AWH generated by the gas generator set k Waste heat WHU consumed by lithium bromide unit for heating k，h Or the waste heat WHU consumed by the lithium bromide unit k，c The relative size of .

[0067] In step 3.1, the amount of electricity consumed by cooling is E k，c The calculation equation is:

[0068]

[0069] Among them, Q τ，3 represents the output of the electric refrigeration unit at time τ, which is calculated in step 2; COP3 represents the efficiency of the electric refrigeration unit at time τ;

[0070] Waste heat consumed by lithium bromide unit refrigeration WHU k，c The calculation equation is:

[0071]

[0072] Among them, Q τ，2 represents the cooling output of the lithium bromide unit at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ;

[0073] Electricity consumed in delivering cooling capacity E c，s The calculation equation is:

[0074]

[0075] Among them, G1 represents the flow rate in the micro-cooling network, the unit is: t / h;

[0076] R1 represents the average specific friction resistance of the micro-cooling network, unit: Pa / m;

[0077] L1 represents the total length of the micro cooling network pipeline, unit: m;

[0078] ω1 represents the ratio of local resistance to all resistance in the micro-cooling network;

[0079] ρ represents the density of the conveying medium. The conveying medium is water. The unit is: kg / m 3 ;

[0080] η p Denotes the electromechanical efficiency of the conveying equipment used to transport the medium, η p Take 0.5-0.7.

[0081] In step 3.2, the amount of electricity consumed by heating is E k，h The calculation equation is:

[0082]

[0083] Among them, Q τ，4 represents the output of the heat pump at time τ, which is calculated in step 2, and COP4 represents the efficiency of the heat pump at time τ;

[0084] Waste heat consumed by lithium bromide unit for heating WHU k，h The calculation equation is:

[0085]

[0086] Among them, Q τ，2 represents the output of the lithium bromide unit during heating at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ;

[0087] The amount of electricity consumed to transport heat E h，s The calculation equation is:

[0088]

[0089] Among them, G2 represents the transport flow in the microheating network, the unit is: t / h;

[0090] R2 represents the average specific friction resistance of the micro-heating network, in Pa / m;

[0091] L2 represents the total length of the micro-heating network pipeline, unit: m;

[0092] ω2 represents the ratio of local resistance to all resistance in the micro-heating network;

[0093] ρ represents the density of the transport medium in the micro-heating network. The transport medium is water. The unit is: kg / m 3 ;

[0094] η p Denotes the electromechanical efficiency of the conveying equipment used to transport the medium, η p Take 0.5-0.7.

[0095] In step 3.3, the waste heat AWH generated by the gas generator set k The calculation equation is:

[0096]

[0097] Among them, η1 represents the power generation efficiency of the gas generator set, η AWH Indicates the proportion of waste heat from gas generator sets that is utilized.

[0098] In step 3.4, if AWH k >WHU k，c , end the calculation and go to step 4; if AWH k ≤WHU k，c , let Q τ，2 ≤AWH k *COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ during cooling, COP ARU Indicates the refrigeration efficiency of the lithium bromide unit, COP ARU Take 1.2;

[0099] If AWH k >WHU k，h , end the calculation and go to step 4; if AWH k ≤WHU k，h , let Q τ，2 ≤AWH k*COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ when heating, COP ARU Indicates the heating efficiency of the lithium bromide unit, COP ARU Take 1.2;

[0100] Described step 4 comprises the following sub-steps:

[0101] Step 4.1: When τ = 1, The output and configuration capacity of each energy conversion technology are calculated through step 3;

[0102] Step 4.2: When τ = 2, max(Q t，j )=Q 1，j or Q 2，j , The output and configuration capacity of each energy conversion technology are calculated through step 3;

[0103] Step 4.3: At that time, transform equation (1) into equation (17), and calculate the output and configuration capacity of each energy conversion technology through step 3;

[0104]

[0105] Where, t = (1, ..., τ);

[0106] Step 4.4: Repeat step 4.3 and perform iterative calculation until one calculation cycle ends, that is, τ = 8760;

[0107] Step 4.5: After one calculation cycle, τ = 1, transform equation (17) into equation (18), and repeat steps 4.1 to 4.4 to calculate the output and configuration capacity of each energy conversion technology;

[0108]

[0109] Wherein, t = (1, ..., 8760).

[0110] In step 4.5, the detection coefficient ζ is set, and the calculation equation of the detection coefficient ζ is:

[0111]

[0112] Among them, Q′ τ，j represents the output of technology j at time τ in the previous computing cycle; SCost′ t,total represents the sum of the cost levels of each energy conversion technology at time t in the previous calculation cycle;

[0113] When ζ≤ζ0, stop the iterative calculation and ζ0 is set to 0.02.

[0114] The present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0115] Figure 1 It is a logic principle diagram of the method for determining the hourly energy supply capacity of the park integrated energy system throughout the year according to the present invention;

[0116] Figure 2 This is a flowchart of step 3 in the method for determining the hourly energy supply capacity of the park integrated energy system throughout the year according to the present invention;

[0117] Figure 3 This is a flowchart of step 4 in the method for determining the hourly energy supply capacity of the park integrated energy system throughout the year according to the present invention;

[0118] Figure 4 Schematic diagram of the application scope of the park integrated energy system of the method for determining the hourly energy supply capacity of the park integrated energy system throughout the year according to the present invention;

[0119] Figure 5 It is an energy network structure diagram of a park integrated energy system to which the method for determining the hourly energy supply capacity of the park integrated energy system throughout the year of the present invention is applied. DETAILED DESCRIPTION

[0120] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0121] Please see the attached Figure 1 A method for determining the hourly energy supply capacity of a park integrated energy system throughout the year comprises the following steps:

[0122] Step 1: Use the equal annual value method to convert the initial investment of each energy conversion technology to each moment, and add it with the operating costs and maintenance costs of each energy conversion technology at each moment to obtain a unified quantitative index SCost for each energy conversion technology t,j .

[0123] Please see the attached Figure 4 and attached Figure 5A park refers to an industrial park, urban area, block, or community with a certain geographical boundary. Energy production, transmission, consumption, and management can be carried out within the park. The available energy resources mainly include fossil energy, local renewable energy, electricity transmitted by the large power grid, municipal heating network, and local low-grade energy. The park has an independent cooling, heating, and electricity network, and energy exchange occurs at the nodes in the network. Due to the diversity of energy resources in the park's integrated energy system, the output range of each energy resource varies, and the initial investment, operating costs, maintenance costs, and service life of each energy conversion technology are different. By converting the costs of each energy conversion technology throughout its life cycle into a unified quantitative indicator, and conducting a unified optimization analysis on this basis, the energy conversion technologies can be made comparable.

[0124] Energy conversion technology refers to equipment within the park that converts primary energy into secondary energy sources such as cooling, heating, and electricity, which can be transported to the park and directly consumed by users, as well as equipment that converts secondary energy sources into each other. Energy conversion technology j ranges from 1 to 11, representing, respectively, gas-fired generators, the main grid, photovoltaic power generation, and wind power generation in the microgrid; lithium bromide units, electric refrigeration units, and cold / heat storage devices in the micro-cooling network; and heat pumps, boilers, solar thermal power generation, and geothermal energy storage in the micro-heating network. The value of j can be adjusted based on the addition or removal of energy conversion technologies.

[0125] The equation of the unified quantitative index is:

[0126]

[0127] Among them, SCost τ,j is the cost level of energy conversion technology j at time τ, in RMB. The number of j can be adjusted according to the actual integrated energy system of the park.

[0128] t = 1,…,8760, where 8760 is the total number of hours in a year.

[0129] Q t，j is the output of energy conversion technology j at time t, in kWh.

[0130] Q τ，j is the output of energy conversion technology j at time τ, in kWh.

[0131] a j is the basic electricity cost coefficient.

[0132] α j is the fixed maintenance cost coefficient.

[0133] is the initial investment cost coefficient.

[0134] β jis the variable maintenance cost coefficient.

[0135] ε j is the operating cost coefficient.

[0136] The optimization goal of the unified quantitative index equation (1) is: the sum of the cost levels of each energy conversion technology at time τ SCost τ，total Minimum, that is:

[0137] f=min(SCost τ，total ) (2).

[0138] Establish the constraints of the unified quantitative index equation (1):

[0139]

[0140] Where Q is Q τ，j , which is the independent variable.

[0141]

[0142] A·Q≤b is a linear inequality constraint, which mainly includes coupling constraints between networks, such as lithium bromide cooling constraints between the micro cooling network and the microgrid, and lithium bromide heating constraints between the micro heating network and the microgrid.

[0143] A eq Q = b eq It is a linear equality constraint, mainly including energy conservation constraint.

[0144]

[0145] The first row of the matrix represents the microgrid, the second row represents the micro-cooling grid, and the third row represents the micro-heating grid. If other energy grids exist in the energy system, the number of rows in the matrix can be adjusted accordingly. A "1" in the second column of the second row and a "1" in the second column of the third row indicate that the lithium bromide unit is an absorption cooling and heating unit, capable of both cooling in summer and heating in winter, and never operating both year-round. A "1" in the sixth column of the second row and a "1" in the sixth column of the third row indicate that the cold / heat storage device can provide cooling or heating in different seasons, but not simultaneously. A "1" in the first row of the matrix indicates that the energy conversion technology can generate electricity, a "1" in the second row indicates that the energy conversion technology can generate cooling, and a "1" in the third row indicates that the energy conversion technology can generate heat.

[0146]

[0147] in, is the electrical load of the park at time τ in addition to the power consumed by electric refrigeration and heat pumps. The transmission process of the micro-cooling network and micro-heating network requires electricity consumption, so the transmission power consumption term needs to be added to the linear equality constraint.

[0148] is the power consumption of the park heat pump heating at time τ.

[0149] It is the power consumption of the electric refrigeration unit in the park at time τ.

[0150] is the total cooling load in the park at time τ.

[0151] is the total heat load of the park at time τ.

[0152] C τ，lose is the cooling loss of the park at time τ, and its equation is:

[0153]

[0154] H τ，lose is the heat loss of the park at time τ, and its equation is:

[0155]

[0156] Among them, K represents the average heat transfer coefficient of the transmission pipeline, which is about 1.1-1.5W / (m 2 ·℃).

[0157] ΔT represents the average temperature difference between the medium inside the pipe network and the surrounding medium outside the pipe network, in degrees Celsius.

[0158] ∈ represents the heat loss coefficient of each local attachment of the pipe network.

[0159] d i Indicates the nominal diameter of the i-th pipe section, in meters.

[0160] l i Indicates the length of the i-th pipe segment, in meters.

[0161] Micro cooling and heating networks are different from microgrids. In the park, there is almost no loss in the transmission of electricity, but there is a non-negligible loss in the transmission of cooling and heat. Therefore, the transmission cooling loss C needs to be added to the equation constraints of the cooling and heating networks. τ，lose and heat loss H τ，lose .

[0162] lb≤Q≤ub are upper and lower limit constraints, mainly including the output upper limit constraints (natural resource constraints) of various energy conversion technologies, such as the upper limit of installed capacity of photovoltaic power generation.

[0163] Among them, the transposed vector of the lower limit constraint lb is:

[0164] lb′=|0 0 0 0 0 0 0 0 0 0 0| (8).

[0165] In equation (8), “0” means that each energy conversion technology cannot have a negative number during the solution process, that is, it must produce energy or be equal to zero, but cannot absorb energy.

[0166] The transposed vector of the upper limit constraint ub is:

[0167] ub′=|10 15 10 15 10 15 10 15 10 15 10 15 10 15 ub8 ub9 ub 10 ub 11 | (9).

[0168] In equation (9), “10 15 " represents that there is no upper limit for this energy conversion technology, "ub8", "ub9", "ub 10 ", "ub 11 ” represent the upper limits of energy resources in the 8th, 9th, 10th and 11th energy conversion technologies respectively.

[0169] For each energy conversion technology, there is a device climbing (downhill) constraint, that is, the output at this moment is not only subject to the ub upper limit constraint, but also to the output constraint at the previous moment, as shown in equation (9-1).

[0170]

[0171] in, It represents the maximum output increase of energy conversion technology j within one hour.

[0172] It represents the maximum output drop of energy conversion technology j within one hour.

[0173] Q0 is the initial value of Q, which can be any value. To speed up the iteration process, all values in the matrix are set to 0.

[0174] In addition, the input parameters also include the price of large power grid and gas and the basic parameters of various energy conversion technologies, including the installed cost of photovoltaic, wind power, geothermal, etc., the power generation efficiency (COP) (including the power generation efficiency η1 of gas generator sets, the cooling / heating efficiency COP of lithium bromide units, etc. ARU) The proportion of waste heat from gas generator sets that is utilized η AWH , and thus calculate a in equation (1) j , α j 、 β j and ε j Five coefficients.

[0175] Step 2: Calculate the operating power and configuration capacity of each energy conversion technology under the constraints.

[0176] The "output" at each moment is the power of the energy conversion technology at each moment, and the maximum power at all moments (8760 moments) is the configuration capacity.

[0177] Output Q of two different energy conversion technologies at time τ τ，1 and Q τ，2 The difference is defined as: ΔQ τ，1-2 =Q τ，1 -Q τ，2 ,when When the energy supply cost of the energy network composed of these two different energy conversion technologies is the lowest.

[0178] At this time, the energy demand that can be met is Q τ，1 +Q τ，2 , we can get the equation:

[0179]

[0180] Let y = ΔQ τ，1-2 , x=Q τ，2 , then: 2x+y=∑Q τ，j (11).

[0181] Among them, ∑Q τ，j Refers to the total amount of energy supplied by the two different energy conversion technologies at time τ.

[0182] Combining equations (10) and (11) to solve the output Q of two different energy conversion technologies at time τ τ，1 and Q τ，2 .

[0183] Similarly, the output Q of each energy conversion technology j at time τ can be solved τ，j .

[0184] Please see the attached Figure 2 ,Step 3: Using the iterative method, decouple and calculate the operating power and configuration capacity of each energy conversion technology in the micro-cooling network, micro-heating network, and microgrid of the park's integrated energy system at time τ.

[0185] The energy production, transmission, and interaction processes within the park are complex, and the coupling relationship between the micro-cooling network, micro-heating network, and microgrid is strong. The operating power and configuration capacity of each energy conversion technology can be calculated through the decoupling iterative method of the micro-cooling network, micro-heating network, and microgrid at the same time. In actual projects, the gas transmission and distribution network is mostly uniformly constructed, deployed, and transported by gas companies, so the present invention does not include microgrids that supply gas. The coupling relationship between the micro-cooling network, micro-heating network, and microgrid refers to the mutual conversion of energy between different energy networks. When the energy production of a certain energy network changes, the other energy networks also change accordingly, which has a strong coupling characteristic.

[0186] Described step 3 comprises the following sub-steps:

[0187] Step 3.1: Calculate the cooling power consumption E using equations (1), (2) and (3) k，c , waste heat WHU consumed by lithium bromide unit (ARU) refrigeration k，c And the electricity consumed by delivering cooling capacity E c,s .

[0188] Electricity consumed by cooling E k,c The calculation equation is:

[0189]

[0190] Among them, Q τ，3 represents the output of the electric refrigeration unit at time τ, calculated in step 2; COP3 represents the efficiency of the electric refrigeration unit at time τ.

[0191] Waste heat consumed by lithium bromide unit refrigeration WHU k，c The calculation equation is:

[0192]

[0193] Among them, Q τ，2 It represents the cooling output of the lithium bromide unit at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ.

[0194] Electricity consumed in delivering cooling capacity E c，s The calculation equation is:

[0195]

[0196] Among them, G1 represents the delivery flow in the micro-cooling network, and the unit is: t / h.

[0197] R1 represents the average specific friction resistance of the micro cooling network, and its unit is Pa / m.

[0198] L1 represents the total length of the micro-cooling network pipeline (including the water supply pipe and the return pipe), the unit is: m.

[0199] ω1 represents the ratio of local resistance to all resistance in the micro-cooling network. Resistance is divided into local resistance (including resistance caused by elbows, reducers, etc.) and along-the-line resistance.

[0200] ρ represents the density of the conveying medium, usually water, and the unit is: kg / m 3 .

[0201] η p It indicates the electromechanical efficiency of the conveying equipment (usually a water pump) used to convey the medium, and is generally taken as 0.5-0.7.

[0202] Step 3.2: Calculate the heating power consumption E using equations (1), (2) and (3) k,h , waste heat consumed by lithium bromide unit (ARU) for heating WHU k，h and the amount of electricity consumed to transport heat E h，s .

[0203] Heating power consumption E k，h The calculation equation is:

[0204]

[0205] Among them, Q τ,4 represents the output of the heat pump at time τ, which is calculated in step 2; COP4 represents the efficiency of the heat pump at time τ.

[0206] Waste heat consumed by lithium bromide unit for heating WHU k，h The calculation equation is:

[0207]

[0208] Among them, Q τ，2 represents the output of the lithium bromide unit during heating at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ.

[0209] The amount of electricity consumed to transport heat E h，s The calculation equation is:

[0210]

[0211] Among them, G2 represents the delivery flow in the microheating network, and the unit is: t / h.

[0212] R2 represents the average specific friction resistance of the microheating network, and its unit is Pa / m.

[0213] L2 represents the total length of the microheating network pipeline (including the water supply pipe and the return pipe), the unit is: m.

[0214] ω2 represents the ratio of local resistance to all resistance in the microheating network. Resistance is divided into local resistance (including resistance caused by elbows, reducers, etc.) and along-the-line resistance.

[0215] ρ represents the density of the transport medium in the microheating network, usually water, and the unit is: kg / m 3 .

[0216] η p It indicates the electromechanical efficiency of the conveying equipment (usually a water pump) used to convey the medium, and is generally taken as 0.5-0.7.

[0217] Step 3.3: Substitute the equations (1), (2), (3), and E k,c 、E k,h 、E c,s and E h,s As the output Q of the energy conversion technology in the microgrid at time τ τ，1 Under the conditions, the output Q of the energy conversion technology is calculated through step 2. τ,1 , and then through the output Q of energy conversion technology τ,1 Calculate the waste heat AWH generated by gas generator sets k .

[0218] The waste heat AWH generated by the gas generator set k The calculation equation is:

[0219]

[0220] Among them, η1 represents the power generation efficiency of the gas generator set, η AWH Indicates the proportion of waste heat from gas generator sets that is utilized.

[0221] Step 3.4: Determine the waste heat AWH generated by the gas generator set k Waste heat WHU consumed by lithium bromide unit for heating k，h Or the waste heat WHU consumed by the lithium bromide unit k，c Since the cooling and heating of the lithium bromide unit do not occur at the same time, only the waste heat AWH generated by the gas generator set is judged. k with WHU k，c (or WHU k，h )’s relative size.

[0222] If AWH k >WHU k，c , end the calculation and go to step 4; if AWH k ≤WHU k，c , let Q τ，2 ≤AWH k*COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ during cooling, COP ARU Indicates the refrigeration efficiency of the lithium bromide unit, generally taken as 1.2.

[0223] If AWH k >WHU k,h , end the calculation and go to step 4; if AWH k ≤WHU k,h , let Q τ,2 ≤AWH k *COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ when heating, COP ARU Indicates the heating efficiency of the lithium bromide unit, generally taken as 1.2.

[0224] Please see the attached Figure 3 ,Step 4: Use the iterative method to calculate the operating power and configuration capacity of each energy conversion technology within 8760 hours of the whole year, and output the hourly energy supply of the park's integrated energy system throughout the year.

[0225] Described step 4 comprises the following sub-steps:

[0226] Step 4.1: When τ = 1, The output and configuration capacity of each energy conversion technology are calculated through step 3.

[0227] Step 4.2: When τ = 2, max(Q t，j )=Q 1，j or Q 2，j , The output and configuration capacity of each energy conversion technology are calculated through step 3.

[0228] Step 4.3: When τ = τ + 1, transform equation (1) into equation (17) and calculate the output and configuration capacity of each energy conversion technology through step 3.

[0229]

[0230] Wherein, t=(1, ..., τ).

[0231] Step 4.4: Repeat step 4.3 and perform iterative calculation until one calculation cycle ends, that is, τ = 8760.

[0232] Step 4.5: After one calculation cycle, τ = 1, transform equation (17) into equation (18), and repeat steps 4.1 to 4.4 to calculate the output and configuration capacity of each energy conversion technology.

[0233]

[0234] Wherein, t = (1, ..., 8760).

[0235] Park energy supply often exhibits coupling along the time dimension. In step 4.5, a detection coefficient ζ is set as the criterion for determining the evolution of the equation and the termination of the iterations throughout the year during the hourly iterations. This coupling along the time dimension refers to the fact that the configuration results (or operating status) of various energy conversion technologies at different time stages can affect the output during other periods and the maximum annual configuration capacity. This invention addresses this coupling along the time dimension through a finite iteration method, i.e., obtaining a numerical solution to the complex equation through the accumulation of quantities.

[0236] The calculation equation of the detection coefficient ζ is:

[0237]

[0238] Among them, Q′ τ，j represents the output of technology j at time τ in the previous computing cycle; SCost′ t，total It represents the sum of the cost levels of various energy conversion technologies at time t in the previous calculation cycle.

[0239] When ζ≤ζ0, the iterative calculation stops. ζ0 is obtained from error analysis and can be taken as 0.02.

[0240] The present invention is based on a constructed unified quantitative index and proposes a necessary and sufficient condition for the optimization target to reach the optimal state, that is, the first-order derivatives of the unified quantitative index with respect to each energy conversion technology are equal.

[0241] The changing rules of the unified quantitative index include: first, the unified quantitative index presents an increasing law, which can be proved by solving the first-order derivative and second-order derivative of the unified quantitative index; second, the first-order derivative of the unified quantitative index presents the equality principle, which can be proved by the method of finding the conditional extreme value of the Lagrangian function.

[0242] Among them, the proof of the increasing law of unified quantitative indicators is as follows:

[0243] Taking the supply-side technology cost level as an example, the cost level calculation formula of energy conversion technology j at time τ is shown in equation (20).

[0244]

[0245] Solve Q for equation (20)τ，j The first-order derivative of is:

[0246]

[0247] In equation (21), it is difficult to judge Is it greater than 0? In the integrated energy system at the park scale, It is always greater than zero because, Much larger than max 2 (Q t，j ), so that the first term on the right side of the equal sign in equation (21) The absolute value of c is much smaller than j , therefore, SCost τ，j It's Q τ，j An increasing function of .

[0248] Solve Q for equation (20) τ，j The second derivative of is:

[0249]

[0250] Obviously, the second-order derivative It is always greater than zero. Therefore, in the integrated energy system of the park, the marginal technical cost j of energy conversion technology at time τ shows an increasing law.

[0251] The proof of the equality principle of the first-order derivatives of the unified quantitative index is as follows:

[0252] In order to simplify the proof process, two energy conversion technologies are selected for proof, that is, it is assumed that only two energy conversion technologies need to be configured to meet the requirement of minimum technical cost.

[0253] Assuming that the total change of all energy conversion technologies in the optimization process is ΔQ1+ΔQ2, then ΔQ1+ΔQ2 is a constant, the output of the first energy conversion technology is Q1, and the required technology cost level is SCost τ，1 (Q1); the output of the second energy conversion technology is Q2, and the required technology cost level is SCost τ，2 (Q2); the total technical cost level of the two is SCost τ，1 / 2 (Q1, Q2), to avoid unnecessary misunderstanding, the total technical cost level is written as SCost τ，1 (Q1, Q2), its physical meaning does not change, then:

[0254] SCost τ，1 (Q1, Q2) = SCost τ，1 (Q1)+SCost τ，2 (Q2) (23)

[0255] Under the constraints Next, ask SCost τ，1 The conditional extreme value of (Q1, Q2), the Lagrangian function is:

[0256]

[0257] Arranged as:

[0258] F(Q1, Q2, λ) = SCost τ，1 (Q1)+SCost τ，2 (Q2)+λ(Q1+Q2-ΔQ)(25)

[0259] When taking the conditional extreme value (minimum value), there should be:

[0260]

[0261] That is:

[0262] When there are multiple energy conversion technologies, the proof of the conditional extreme value of the minimum optimization objective is the same as above, that is, in the process of optimizing the energy supply of the park's integrated energy system, the principle of equality of the first-order derivative of the unified quantitative indicator is followed when selecting energy conversion technology.

[0263] The above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for determining the hourly energy supply capacity of a park's integrated energy system throughout the year, characterized by: The following steps are involved: Step 1: Convert the initial investment of each energy conversion technology to each moment, and add it with the operating costs and maintenance costs of each energy conversion technology at each moment to obtain a unified quantitative index SCost for each energy conversion technology t，j ; Step 2: Calculate the operating power and configuration capacity of each energy conversion technology under the constraints; Step 3: Using an iterative method, decouple and calculate the operating power and configuration capacity of each energy conversion technology in the micro-cooling network, micro-heating network, and microgrid of the park's integrated energy system at time τ; Step 4: Using an iterative method, calculate the operating power and configuration capacity of each energy conversion technology for 8,760 hours throughout the year, and output the hourly energy supply of the park's integrated energy system throughout the year; In step 1, the equation for the unified quantitative index is: Among them, SCost τ，j is the cost level of energy conversion technology j at time τ, unit: / yuan; t = 1, ..., 8760, where 8760 is the total number of hours in a year; Q t，j is the output of energy conversion technology j at time t, in kWh; Q τ，j is the output of energy conversion technology j at time τ, in kWh; a j is the basic electricity fee coefficient; α j is the fixed maintenance cost coefficient; is the initial investment cost coefficient; β j is the variable maintenance cost coefficient; ε j is the operating cost coefficient; The optimization goal of the unified quantitative index equation (1) is: the sum of the cost levels of each energy conversion technology at time τ SCost τ，total Minimum, that is: f=min(SCOst τ，total ) (2); Establish the constraints of the unified quantitative index equation (1): Where Q is Q τ，j The column vector of is the independent variable; A·Q≤b is a linear inequality constraint, which mainly includes coupling constraints between networks: lithium bromide cooling constraint between micro cooling network and micro grid, lithium bromide heating constraint between micro heating network and micro grid; A eq Q = b eq It is a linear equality constraint, mainly including energy conservation constraint; in, is the electrical load of the park at time τ, excluding the power consumed by electric refrigeration and heat pumps; is the power consumption of the park's heat pump heating at time τ; is the power consumption of the park's electric refrigeration units at time τ; is the total cooling load in the park at time τ; is the total heat load of the park at time τ; C τ，lose is the cooling loss of the park at time τ, and its equation is: H τ，lose is the heat loss of the park at time τ, and its equation is: Among them, K represents the average heat transfer coefficient of the transmission pipeline, which is 1.1-1.5W / (m 2 ℃); ΔT represents the average temperature difference between the medium inside the pipe network and the surrounding medium outside the pipe network, in °C; ∈ represents the heat loss coefficient of each local attachment of the pipe network; d i Indicates the nominal diameter of the i-th pipe section, in meters; l i It represents the length of the i-th pipe section, in meters; lb≤Q≤ub is the upper and lower limit constraints, which mainly include the output upper limit constraints of each energy conversion technology; Among them, the transposed vector of the lower limit constraint lb is: lb′＝|0 0 0 0 0 0 0 0 0 0 0| (8)； The transposed vector of the upper limit constraint ub is: ub′=|10 15 10 15 10 15 10 15 10 15 10 15 10 15 ub8 ub9 ub 10 ub 11 | (9); The output at this moment is also constrained by the output at the previous moment, that is: in, It represents the maximum output increase of energy conversion technology j within one hour; It represents the maximum output drop of energy conversion technology j within one hour; Q0 is the initial value of Q; Input the prices of large power grid and gas and the basic parameters of each energy conversion technology to calculate the coefficient a in equation (1) j , α j 、 β j and ε j ; The output Q of the two different energy conversion technologies at time τ τ，1 and Q τ，2 The difference is defined as: ΔQ τ，1-2 =Q τ，1 -Q τ，2 ,when When , the energy supply cost of the energy network composed of these two different energy conversion technologies is the lowest; At this time, the energy demand that can be met is Q τ，1 +Q τ，2 , we can get the equation: Let y = ΔQ τ，1-2 , x = Q τ，2 , then: 2x + y = ∑Q τ，j (11); Among them, ∑Q τ，j Refers to the total amount of energy supplied by the two different energy conversion technologies at time τ; Combining equations (10) and (11) to solve the output Q of two different energy conversion technologies at time τ τ，1 and Q τ，2 ; Similarly, solve the output Q of each energy conversion technology j at time τ τ，j ; Step 3 includes the following sub-steps: Step 3.1: Calculate the cooling power consumption E using equations (1), (2) and (3) k，c , waste heat WHU consumed by ARU refrigeration of lithium bromide unit k，c And the electricity consumed by delivering cooling capacity E c，s ; Step 3.2: Calculate the heating power consumption E using equations (1), (2) and (3) k，h , waste heat WHU consumed by ARU heating of lithium bromide unit k，h and the amount of electricity consumed to transport heat E h，s ; Step 3.3: Calculate the waste heat AWH generated by the gas generator set k ; Step 3.4: Determine the waste heat AWH generated by the gas generator set k Waste heat WHU consumed by lithium bromide unit for heating k，h Or the waste heat WHU consumed by the lithium bromide unit k，c the relative size of In step 3.4, if AWH k >WHU k，c , end the calculation and go to step 4; if AWH k >WHU k，c , let Q τ，2 ≤AWH k *COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ during cooling, COP ARU Indicates the refrigeration efficiency of the lithium bromide unit, COP ARU Take 1.2; If AWH k >WHU k，h , end the calculation and go to step 4; if AWH k ≤WHU k，h , let Q τ，2 ≤AWH k *COP ARU , and return to step 3.1; where Q τ，2 Indicates the output of the lithium bromide unit at time τ when heating, COP ARU Indicates the heating efficiency of the lithium bromide unit, COP ARU Take 1.

2.

2. The method for determining the annual hourly energy supply capacity of the park integrated energy system according to claim 1 is characterized by: In step 3.1, the amount of electricity consumed by cooling is E k,c The calculation equation is: Among them, Q τ，3 represents the output of the electric refrigeration unit at time τ, which is calculated in step 2; COP3 represents the efficiency of the electric refrigeration unit at time τ; Waste heat consumed by lithium bromide unit refrigeration WHU k，c The calculation equation is: Among them, Q τ，2 represents the cooling output of the lithium bromide unit at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ; Electricity consumed in delivering cooling capacity E c，s The calculation equation is: Among them, G1 represents the flow rate in the micro-cooling network, the unit is: t / h; R1 represents the average specific friction resistance of the micro-cooling network, unit: Pa / m; L1 represents the total length of the micro cooling network pipeline, unit: m; ω1 represents the ratio of local resistance to all resistance in the micro-cooling network; ρ represents the density of the conveying medium. The conveying medium is water. The unit is: kg / m 3 ; η p Denotes the electromechanical efficiency of the conveying equipment used to transport the medium, η p Take 0.5-0.

7.

3. The method for determining the annual hourly energy supply capacity of the park integrated energy system according to claim 1 is characterized by: In step 3.2, the amount of electricity consumed by heating is E k,h The calculation equation is: Among them, Q τ,4 represents the output of the heat pump at time τ, which is calculated in step 2, and COP4 represents the efficiency of the heat pump at time τ; Waste heat consumed by lithium bromide unit for heating WHU k，h The calculation equation is: Among them, Q τ，2 represents the output of the lithium bromide unit during heating at time τ, which is calculated in step 2; COP2 represents the efficiency of the lithium bromide unit at time τ; The amount of electricity consumed to transport heat E h,s The calculation equation is: Among them, G2 represents the transport flow in the microheating network, the unit is: t / h; R2 represents the average specific friction resistance of the micro-heating network, in Pa / m; L2 represents the total length of the micro-heating network pipeline, unit: m; ω2 represents the ratio of local resistance to all resistance in the micro-heating network; ρ represents the density of the transport medium in the micro-heating network. The transport medium is water. The unit is: kg / m 3 ; η p Denotes the electromechanical efficiency of the conveying equipment used to transport the medium, η p Take 0.5-0.

7.

4. The method for determining the annual hourly energy supply capacity of a park integrated energy system according to claim 1 is characterized by: In step 3.3, the waste heat AWH generated by the gas generator set k The calculation equation is: Among them, η1 represents the power generation efficiency of the gas generator set, η AWH Indicates the proportion of waste heat from gas generator sets that is utilized.

5. The method for determining the annual hourly energy supply capacity of the park integrated energy system according to claim 1 is characterized by: Described step 4 comprises the following sub-steps: Step 4.1: When τ = 1, The output and configuration capacity of each energy conversion technology are calculated through step 3; Step 4.2: When τ = 2, max(Q t，j )=Q 1，j or Q 2，j , The output and configuration capacity of each energy conversion technology are calculated through step 3; Step 4.3: When τ = τ + 1, transform equation (1) into equation (17) and calculate the output and configuration capacity of each energy conversion technology through step 3; Where, t = (1, ..., τ); Step 4.4: Repeat step 4.3 and perform iterative calculation until one calculation cycle ends, that is, τ = 8760; Step 4.5: After one calculation cycle, τ = 1, transform equation (17) into equation (18), and repeat steps 4.1 to 4.4 to calculate the output and configuration capacity of each energy conversion technology; Wherein, t = (1, ..., 8760).

6. The method for determining the annual hourly energy supply capacity of the park integrated energy system according to claim 5 is characterized by: In step 4.5, the detection coefficient ζ is set, and the calculation equation of the detection coefficient ζ is: Among them, Q′ τ，j represents the output of technology j at time τ in the previous computing cycle; SCost′ t，total represents the sum of the cost levels of each energy conversion technology at time t in the previous calculation cycle; When ζ≤ζ0, stop the iterative calculation and ζ0 is set to 0.02.

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