Comprehensive energy system considering source-load coordination and optimal scheduling method

Abstract

The invention relates to the technical field of power systems, in particular to a comprehensive energy optimization scheduling system and method considering source-load coordination. An economic dispatching mathematical model is established by taking variables such as fire coal cost of a thermal power generating unit, operation cost of a steam extraction and energy storage device, system operation and maintenance cost, solution loss cost in carbon capture equipment, system wind and light abandoning cost and stepped carbon transaction cost as basic objective functions through an operation decision framework taking a comprehensive energy system as a dispatching basis; basic constraint conditions such as a power balance constraint, a spinning reserve constraint, a wind and light output constraint, a carbon capture device operation constraint and an energy storage constraint are comprehensively considered, and an improved single-target artificial bee colony algorithm is utilized to solve the economic dispatching mathematical model to obtain an optimal solution; therefore, an optimal balance point is found among economy, sustainability and system operation requirements.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems. Background Art

[0002] In an integrated energy system, source-load coordination refers to how to rationally allocate energy supply and energy demand to ensure the balanced and stable operation of the energy system. It mainly involves how to utilize and dispatch different types of energy and loads to minimize costs, improve system efficiency, and ensure energy supply. In a traditional power system, the change of load can be achieved by adjusting the power generation level of power plants. However, in an integrated energy system, there are multiple energy forms, which are inconvenient to coordinate, and more intelligent and flexible scheduling strategies need to be formulated. Summary of the Invention

[0003] To solve the technical problem in the prior art that there are many energy forms in the integrated energy system and it is inconvenient to coordinate, the present invention provides an integrated energy optimal scheduling system and method considering source-load coordination.

[0004] The technical solution adopted by the present invention to achieve the above object is as follows:

[0005] An integrated energy system considering source-load coordination, in which a thermal power unit is connected to a steam extraction energy storage device, a carbon capture device, a hydrogen storage device, a gas storage device, a hydrogen fuel cell, an electric boiler, a wind farm, a photovoltaic power plant, and a load side. The carbon capture device is connected to the hydrogen storage device, the hydrogen storage device is connected to the gas storage device and the hydrogen fuel cell, the gas storage device is connected to the hydrogen fuel cell, the gas storage device is connected to a gas turbine, the gas turbine is connected to the hydrogen fuel cell, the electric boiler is connected to the hydrogen fuel cell, and the hydrogen fuel cell is connected to the load side.

[0006] The load side includes a rigid load, a transferable load, a reducible load, an alternative load, and a flexible load.

[0007] An optimal scheduling method for optimizing the scheduling of any of the integrated energy systems considering source-load coordination includes the following steps:

[0008] Establish an economic dispatch mathematical model of the integrated energy system considering source-load coordination with the lowest operating cost of the integrated energy system as the dispatch objective;

[0009] Establish the operating constraint conditions of the integrated energy system;

[0010] Substitute the economic dispatch mathematical model and constraint conditions of the integrated energy system into an improved single-objective artificial bee colony algorithm to obtain the Pareto optimal solution of the economic dispatch mathematical model of the integrated energy system.

[0011] The step of establishing the economic dispatch mathematical model of the integrated energy system considering source-load coordination:

[0012] minC = min(C c + C r + C A + C ct + C s + C q + C IDR )

[0013] where: C c is the coal consumption cost of the thermal power unit, C r is the operation and maintenance cost, C A is the cost of wind and solar curtailment of the system, C ct is the stepped carbon trading cost, C s is the solution loss cost of the carbon capture equipment, C q is the load shedding cost, C IDR is the IDR resource cost;

[0014]

[0015] where: T is the total number of dispatching cycles, S t is the start-stop state of the thermal power unit at time t, and a, b, and c are the coal consumption cost coefficients of the thermal power unit, P G,t is the output power of the thermal power unit at time t;

[0016]

[0017] where: C GT , C HFC , C se , C P2G , C EB , C CCPP , C WT and C PV are the unit operation and maintenance costs of the gas turbine, hydrogen fuel cell, extraction steam energy storage device, P2G device of the hydrogen fuel cell, electric boiler, carbon capture device, wind farm, and photovoltaic power plant respectively; P HFC,t , P t P2G , P CCPP,t , P WT,t and P PV,t are the electrical output powers of the hydrogen fuel cell, P2G device of the hydrogen fuel cell, carbon capture device, wind farm, and photovoltaic power plant at time t respectively; Q GT,t , Q HFC,t , Q se,t and Q EB,t are the thermal output powers of the gas turbine, hydrogen fuel cell, extraction steam energy storage device, and electric boiler at time t respectively;

[0018]

[0019] Where: c A is the curtailment cost coefficient of wind and light, and are the curtailment of wind and light power at time t, respectively;

[0020]

[0021] Where: is the stepped carbon trading cost at time t;

[0022]

[0023] Where: c s is the cost coefficient of the solvent, ψ is the operating loss of the solvent, M P,t is the CO2 emission captured by the carbon capture equipment at time t;

[0024]

[0025] Where: c q is the unit load shedding cost coefficient, P q,t is the load shedding power of the thermal power unit at time t;

[0026]

[0027] Where: c IDR is the cost coefficient of using IDR resources, P IDR,t is the total amount of IDR resources called at time t;

[0028] The scheduling period of the economic dispatch mathematical model of the integrated energy system is 24h, and the time scale is 15min.

[0029] The above steps establish the operating constraint conditions of the integrated energy system:

[0030] Power balance constraint:

[0031]

[0032] Where: P f,t is the predicted power of the load at time t, N is the type of different thermal power units in the integrated energy system, P i,t is the net output power of thermal power unit i at time t;

[0033] Wind and light output constraint:

[0034]

[0035] Where: and are the maximum predicted outputs of wind and light at time t, respectively;

[0036] Operating constraints of carbon capture device and thermal power unit:

[0037]

[0038] Where: P CCPP,min and P CCPP,max are the minimum output and maximum output of the thermal power unit respectively;

[0039] R down ≤P CCPP,t -P CCPP,t-1 ≤R up

[0040] Where: R down and R up are the down-ramp rate and up-ramp rate of the thermal power unit respectively, and P CCPP,t-1 is the electrical output power of the carbon capture device at time t - 1;

[0041]

[0042] Where: and are the continuous running time and shutdown time of the thermal power unit at time t - 1 respectively, and are the minimum running time and minimum shutdown time of the thermal power unit respectively, S t-1 and S t are the start-stop states of the thermal power unit at time t - 1 and time t respectively;

[0043]

[0044] Where: V f,t and V f,t-1 are the solution volumes of the rich liquid storage tank of the thermal power unit at time t and time t - 1 respectively, V p,t and V p,t-1 are the solution volumes of the lean liquid storage tank of the unit at time t and time t - 1 respectively, V c,t is the solution volume required to discharge CO2 from the storage tank at time t, V ss is the capacity of the storage tank, V f,0 and V f,24 are the initial solution volume and the solution volume after the scheduling period of the rich liquid storage tank respectively, V p,0 and V p,24 are the initial solution volume and the solution volume after the scheduling period of the lean liquid storage tank respectively;

[0045]

[0046] Where: E c,tThe carbon emissions supplied by the liquid storage tank of the thermal power unit at time t, M MEA and M c are the molar mass of the ethanolamine solution and the molar mass of CO2 respectively, ω is the analytical quantity of the regeneration tower, M MEA is the concentration coefficient of the ethanolamine solution, ρ MEA is the density of the ethanolamine solution;

[0047] Gas turbine constraint:

[0048]

[0049] In the formula: G GT,t is the gas power generated by the gas turbine after absorbing natural gas at time t, k gas is the lower calorific value of natural gas combustion, J GT,t is the intake air volume of the gas turbine at time t, P GT,t and Q GT,t are the electrical output power and the thermal output power of the gas turbine at time t respectively, and are the power generation efficiency and the electro-thermal power ratio of the gas turbine respectively, s GT,t is the operating state of the gas turbine at time t, and are the minimum and maximum values of the electrical output power of the gas turbine respectively, and are the lower ramp rate and the upper ramp rate of the electrical output power of the gas turbine respectively;

[0050] Hydrogen fuel cell constraint:

[0051]

[0052] In the formula: P HFC,t , Q HFC,t and are the electrical output power, the thermal output power and the input power of the hydrogen fuel cell at time t respectively, and are the electrical and thermal conversion efficiencies of the hydrogen fuel cell respectively, and are the lower and upper limits of the input power of the hydrogen fuel cell respectively, and are the upper ramp rate and the lower ramp rate of the hydrogen fuel cell respectively;

[0053] Electric boiler constraint:

[0054] Q EB,t = η EB P EB,t

[0055] 0 ≤ Q EB,t≤Q EB,max

[0056] Where: Q EB,t and P EB,t are respectively the heat output power and power consumption of the electric boiler at time t, η EB is the electro-thermal conversion efficiency of the electric boiler, Q EB,max is the maximum heat supply of the electric boiler at time t;

[0057] Extraction steam energy storage device constraint:

[0058] Q y,min ≤Q y,t ≤Q y,max

[0059] 0 ≤ Q se,t ≤Q se,max

[0060] Where: Q y,max and Q y,min are the upper and lower limits of the heat energy storage of the high-temperature energy storage device, Q y,t is the heat energy storage of the high-temperature energy storage device at time t, Q se,max is the maximum heat supply of the high-temperature energy storage device for heating at time t;

[0061] P2G device constraint:

[0062]

[0063] Where: and are the P2G hydrogen consumption power and the upper limit of P2G hydrogen consumption power of the P2G device at time t, is the generated natural gas power of the P2G device at time t, is the upper limit of the output of the P2G device at time t;

[0064] IDR resource constraint:

[0065]

[0066] Where: ΔP IDR,t and ΔP IDR,t-1 are the IDR resource usage at time t and time t - 1, is the maximum response amount of the IDR load, v IDR is the response rate of the IDR load;

[0067] Spinning reserve constraint:

[0068]

[0069] Where: and The upper and lower limits of the net output of the thermal power unit, respectively, and The upper and lower spinning reserve requirements of the integrated energy system at time t, respectively, and P G,t is the total power output of the power plant at time t, and R up and R down The upper and lower ramping rates of the spinning reserve, respectively, and S t is the operating state of the thermal power unit at time t, and U t is the start / stop state of the unit at time t.

[0070] The above steps bring the economic dispatch mathematical model and constraint conditions of the integrated energy system into the improved single-objective artificial bee colony algorithm to obtain the Pareto optimal solution of the economic dispatch mathematical model of the integrated energy system, including the following steps:

[0071] Establish a fitness function:

[0072]

[0073] In the formula: x im is the fitness factor;

[0074] According to the roles of the bee colony, divide them into employed bees, scout bees, and onlooker bees, and initialize the search range, number, and parameters of the colony;

[0075] Divide the entire solution space into v sub-regions, and randomly assign weights φ{Ω1, Ω2,..., Ω v} to each sub-region;

[0076] Divide the employed bees into different groups, each group is responsible for exploring a sub-region and generating a new solution. The calculation formula is as follows:

[0077]

[0078] In the formula: is the new solution after iteration, x im and x jm are the original solutions of the bees in the i-th and j-th regions, respectively, and R is a random number in the interval [0, 1];

[0079] Calculate the fitness value of the new solution, update the weight of each sub-region, and assign the sub-region corresponding to the optimal solution to the bee with the largest weight;

[0080] Calculate the selection probability of the sub-region. The calculation formula is as follows:

[0081]

[0082] In the formula: j = 1, 2,..., n; p jis the selection probability of the sub-region, Ω j is the weight of sub-region j, and n is the number of sub-regions;

[0083] The employed bees explore the sub-regions with large selection probabilities to find new solutions.

[0084] The observing bees judge whether there are solutions to be discarded. The judgment formula is as follows:

[0085]

[0086] In the formula: abs is the absolute value;

[0087] When the solution satisfies the above formula, discard the solution and randomly generate a new solution to replace it;

[0088] Record the solution with the minimum fitness value until the number of iterations reaches the set iteration number value to obtain the optimal solution.

[0089] An apparatus for a comprehensive energy optimization scheduling method considering source-load coordination includes a memory and a processor. The memory is used to store a computer program, and the computer program is used to execute the above method when loaded by the processor.

[0090] A computer-readable storage medium stores a computer program, and the computer program is suitable for executing the above method when loaded by a processor.

[0091] The advantages of the present invention compared with the prior art are as follows:

[0092] An operation decision-making framework based on a comprehensive energy system for scheduling, using variables such as the coal consumption cost of thermal power units, the operation cost of extraction steam energy storage devices, the system operation and maintenance cost, the solution loss cost in carbon capture equipment, the system wind and light abandonment cost, and the stepped carbon trading cost as the basic objective function to establish an economic scheduling mathematical model, and comprehensively considering basic constraint conditions such as power balance constraint, spinning reserve constraint, wind and light output constraint, carbon capture device operation constraint, and energy storage constraint. Use an improved single-objective artificial bee colony algorithm to solve the economic scheduling mathematical model to obtain the optimal solution, so as to find the best balance point among economy, sustainability, and system operation requirements. Description of the Drawings

[0093] Figure 1 is the schematic diagram of a comprehensive energy optimization scheduling system considering source-load coordination of the present invention.

[0094] Figure 2 is the overall flowchart of a comprehensive energy optimization scheduling method considering source-load coordination of the present invention.

[0095] Figure 3 is the flowchart of solving the economic scheduling mathematical model of the comprehensive energy system of the present invention.

[0096] Figure 4 It is the curve graph of the wind power and photovoltaic power output and load prediction results in the embodiment of the present invention.

[0097] Figure 5 It is the bar graph of the system comprehensive operation cost under Scheme 1 and Scheme 2 in the embodiment of the present invention. Detailed implementation manners

[0098] An integrated energy system considering source-load coordination provided by the present invention, as Figure 1 shown, the thermal power unit is connected to the extraction energy storage device, carbon capture equipment, hydrogen storage equipment, gas storage equipment, hydrogen fuel cell, electric boiler, wind farm, photovoltaic power plant and load measurement. The carbon capture equipment is connected to the hydrogen storage equipment. The hydrogen storage equipment is connected to the gas storage equipment and hydrogen fuel cell. The gas storage equipment is connected to the hydrogen fuel cell. The gas storage equipment is connected to the gas turbine. The gas turbine is connected to the hydrogen fuel cell. The electric boiler is connected to the hydrogen fuel cell. The hydrogen fuel cell is connected to the load side; the load side includes rigid load, transferable load, curtailable load, replaceable load and flexible load.

[0099] An optimal scheduling method for optimizing the scheduling of an integrated energy system considering source-load coordination, as Figure 2 shown, includes the following steps:

[0100] Establish an economic dispatch mathematical model of the integrated energy system considering source-load coordination with the lowest operation cost of the integrated energy system as the dispatch goal. The dispatch period of the economic dispatch mathematical model is 24h, and the time scale is 15min;

[0101] minC = min(C c + C r + C A + C ct + C s + C q + C IDR )

[0102] In the formula: C c is the coal combustion cost of the thermal power unit, C r is the operation and maintenance cost, C A is the system wind and light abandonment cost, C ct is the stepped carbon trading cost, C s is the solution loss cost of the carbon capture equipment, C q is the load shedding cost, C IDR is the IDR resource cost;

[0103]

[0104] In the formula: T is the total number of dispatch cycles, S tThe start-stop state of the thermal power unit at time t, and a, b, and c are the coal consumption cost coefficients of the thermal power unit, and P G,t is the output power of the thermal power unit at time t;

[0105]

[0106] In the formula: C GT , C HFC , C se , C P2G , C EB , C CCPP , C WT and C PV are the unit operation and maintenance costs of the gas turbine (GT), hydrogen fuel cell (HFC), extraction steam energy storage device, P2G device of the hydrogen fuel cell, electric boiler (EB), carbon capture power plant (CCPP), wind farm, and photovoltaic power plant respectively; P HFC,t , P t P2G , P CCPP,t , P WT,t and P PV,t are the electrical output powers of the hydrogen fuel cell, P2G device of the hydrogen fuel cell, carbon capture device, wind farm, and photovoltaic power plant at time t respectively; Q GT,t , Q HFC,t , Q se,t and Q EB,t are the thermal output powers of the gas turbine, hydrogen fuel cell, extraction steam energy storage device, and electric boiler at time t respectively;

[0107]

[0108] In the formula: c A is the curtailment cost coefficient of wind and light, and are the curtailment powers of wind and light at time t respectively;

[0109] In order to achieve the goal of reducing the actual CO2 emissions, the present invention introduces a stepped carbon trading mechanism to control the total carbon emissions by allocating carbon emission quotas to each unit. If the actual carbon emissions are greater than the allocated carbon quota, it is necessary to purchase the carbon quota for the excess part. Conversely, the remaining carbon quota can be sold:

[0110]

[0111] In the formula: is the stepped carbon trading cost at time t;

[0112]

[0113] Where: c s is the cost coefficient of the solvent, ψ is the operating loss of the solvent, and M P,t is the CO₂ emission captured by the carbon capture equipment at time t;

[0114]

[0115] Where: c q is the unit load shedding cost coefficient, and P q,t is the load shedding power of the thermal power unit at time t;

[0116]

[0117] Where: c IDR is the resource cost coefficient of using IDR, and P IDR,t is the total amount of IDR resources called at time t.

[0118] Establish the operation constraint conditions of the integrated energy system;

[0119] Power balance constraint:

[0120]

[0121] Where: P f,t is the predicted power of the load at time t, N is the type of different thermal power units in the integrated energy system, and P i,t is the net output power of thermal power unit i at time t;

[0122] Wind and solar power output constraints:

[0123]

[0124] Where: and are the maximum values of the predicted wind and solar power outputs at time t, respectively; the wind and solar power generation output values should be less than their predicted output maximum values;

[0125] Operation constraints of the carbon capture device and the thermal power unit:

[0126]

[0127] Where: P CCPP,min and P CCPP,max are the minimum and maximum outputs of the thermal power unit, respectively;

[0128] R down ≤P CCPP,t -P CCPP,t-1 ≤Rup

[0129] Where: R down and R up are the down-ramp rate and up-ramp rate of the thermal power unit respectively, and P CCPP,t-1 is the electrical output power of the carbon capture device at time t-1;

[0130]

[0131] Where: and are the continuous running time and shutdown time of the thermal power unit at time t-1 respectively, and are the minimum running time and minimum shutdown time of the thermal power unit respectively, S t-1 and S t are the start-stop states of the thermal power unit at time t-1 and time t respectively;

[0132]

[0133] Where: V f,t and V f,t-1 are the solution volumes of the rich liquid storage tank of the thermal power unit at time t and time t-1 respectively, V p,t and V p,t-1 are the solution volumes of the lean liquid storage tank of the unit at time t and time t-1 respectively, V c,t is the solution volume required to discharge CO2 from the storage tank at time t, V ss is the capacity of the storage tank, V f,0 and V f,24 are the initial solution volume and the solution volume after the scheduling period of the rich liquid storage tank respectively, V p,0 and V p,24 are the initial solution volume and the solution volume after the scheduling period of the lean liquid storage tank respectively;

[0134]

[0135] Where: E c,t is the carbon emission supplied by the storage tank of the thermal power unit at time t, M MEA and M c are the molar mass of the ethanolamine solution and the molar mass of CO2 respectively, ω is the stripping amount of the regeneration tower, M MEA is the concentration coefficient of the ethanolamine solution, ρ MEA is the density of the ethanolamine solution;

[0136] Gas turbine constraint:

[0137]

[0138] Where: G GT,t is the gas power generated after the gas turbine absorbs natural gas at time t, k gas is the lower heating value of natural gas, J GT,t is the intake air volume of the gas turbine at time t, P GT,t and Q GT,t are the electrical output power and thermal output power of the gas turbine at time t, respectively, and are the power generation efficiency and electro-thermal power ratio of the gas turbine, respectively, s GT,t is the operating state of the gas turbine at time t, and are the minimum and maximum values of the electrical output power of the gas turbine, respectively, and are the down-ramp rate and up-ramp rate of the electrical output power of the gas turbine, respectively;

[0139] Hydrogen fuel cell constraint:

[0140]

[0141] Where: P HFC,t , Q HFC,t and are the electrical output power, thermal output power and input power of the hydrogen fuel cell at time t, respectively, and are the electrical and thermal conversion efficiencies of the hydrogen fuel cell, respectively, and are the lower and upper limits of the input power of the hydrogen fuel cell, respectively, and are the up-ramp rate and down-ramp rate of the hydrogen fuel cell, respectively;

[0142] Electric boiler constraint:

[0143] Q EB,t = η EB P EB,t

[0144] 0 ≤ Q EB,t ≤ Q EB,max

[0145] Where: Q EB,t and P EB,t are the thermal output power and power consumption of the electric boiler at time t, respectively, η EB is the electro-thermal conversion efficiency of the electric boiler, Q EB,max is the maximum heat supply of the electric boiler at time t;

[0146] When the electrical load demand is low, the extraction steam energy storage device extracts high-temperature steam from the thermal power unit to heat the molten salt and store thermal energy. When the electrical load demand is high, the thermal energy is converted into steam and returned to the steam turbine and the "extraction steam energy storage" device. Constraints of the extraction steam energy storage device:

[0147] Q y,min ≤Q y,t ≤Q y,max

[0148] 0≤Q se,t ≤Q se,max

[0149] Where: Q y,max and Q y,min are the upper and lower limits of the thermal energy storage of the high-temperature energy storage (molten salt) device, Q y,t is the thermal energy storage of the high-temperature energy storage (molten salt) device at time t, Q se,max is the maximum heat supply of the high-temperature energy storage (molten salt) device for heat supply at time t;

[0150] Constraints of the P2G device:

[0151]

[0152] Where: and are the P2G hydrogen consumption power and the upper limit of the P2G hydrogen consumption power of the P2G device at time t, is the generated natural gas power of the P2G device at time t, is the upper limit of the output of the P2G device at time t;

[0153] Demand response constraints, IDR resource constraints. The usage of IDR resources is related to the response speed and response capacity:

[0154]

[0155] Where: ΔP IDR,t and ΔP IDR,t-1 are the usage amounts of IDR resources at time t and t - 1, is the maximum response amount of the IDR load, v IDR is the response rate of the IDR load;

[0156] Spinning reserve constraints:

[0157]

[0158] Where: and are respectively the upper and lower limits of the net output of the thermal power unit, and r t downThe upper and lower spinning reserve requirements of the integrated energy system at time t, respectively, and P G,t is the total power output of the power plant at time t, R up and R down are the upper and lower ramping rates of the spinning reserve, respectively, S t is the operating state of the thermal power unit at time t, U t is the start-stop state of the unit at time t.

[0159] Substitute the economic dispatch mathematical model and constraint conditions of the integrated energy system into the improved single-objective artificial bee colony algorithm to obtain the Pareto optimal solution of the economic dispatch mathematical model of the integrated energy system, as Figure 3 shown, including the following steps:

[0160] Use the penalty function to add the constraints to the objective function as the new fitness function, and establish the fitness function:

[0161]

[0162] In the formula: x im is the fitness factor, that is, the nectar source quality;

[0163] Initialize the sealed population, divide it into employed bees, scout bees and observer bees according to the roles of the bee colony, and initialize the search range, number and parameters of the population;

[0164] Divide the entire solution space into v sub-regions, and randomly assign weights φ{Ω1,Ω2,...,Ω v} to each sub-region;

[0165] Divide the employed bees into different groups, each group is responsible for exploring a sub-region and generating a new solution. The calculation formula is as follows:

[0166]

[0167] In the formula: is the new solution after iteration, x im and x jm are the original solutions of the bees in the i-th and j-th regions respectively, R is a random number in the interval [0, 1];

[0168] Calculate the fitness value of the new solution, update the weight of each sub-region, and assign the sub-region corresponding to the optimal solution to the bee with the largest weight;

[0169] Calculate the selection probability of the sub-region. The selection probability is changed to be determined by the weight of the sub-region. The calculation formula is as follows:

[0170]

[0171] where: j = 1, 2,..., n; p j is the selection probability of the sub-region, and Ω j is the weight of sub-region j, and n is the number of sub-regions;

[0172] The employed bees explore the sub-regions with large selection probabilities to find new solutions.

[0173] The observing bees judge whether there are solutions to be discarded, and the judgment formula is as follows:

[0174]

[0175] where: abs is the absolute value;

[0176] If the solution satisfies the above formula, then discard the solution and randomly generate a new solution to replace it;

[0177] Record the solution with the minimum fitness value until the number of iterations reaches the set iteration number value to obtain the optimal solution.

[0178] An apparatus for a comprehensive energy optimization scheduling method considering source-load coordination includes a memory and a processor. The memory is used to store a computer program, and the computer program is used to execute the above method when loaded by the processor.

[0179] A computer-readable storage medium stores a computer program, and the computer program is suitable for executing the above method when loaded by a processor.

[0180] To verify the effectiveness and applicability of the proposed method, the present invention takes Figure 1 the constructed integrated energy system IES as the research object, sets the maximum iteration limit number to 100 times, and the wind power and photovoltaic output and load prediction result curves are as shown in the appendix Figure 4 as shown, where the detailed parameters of the thermal power units are shown in Table 1 below, and the other parameters are shown in Table 2 below.

[0181] Table 1 Thermal Power Unit Parameters

[0182]

[0183]

[0184] Table 2 Other Parameters

[0185]

[0186] To better verify the integrated energy system optimization scheduling strategy considering source-load coordination proposed by the present invention, two schemes are set for comparative analysis:

[0187] Scenario 1: In the integrated energy system IES, various devices operate independently. The optimal scheduling of the carbon capture power plant system is considered, and demand response resources are not considered.

[0188] Scenario 2: In the integrated energy system IES proposed by the present invention, various devices adopt a combined operation mode. The optimal scheduling of the carbon capture power plant system considering the comprehensive flexible operation mode is considered, and demand response resources are considered.

[0189] The simulation diagram of the target result is as Figure 5 shown.

[0190] Table 3 Calculation Results

[0191]

[0192] In summary, the carbon emissions of 2307.4 t and the comprehensive cost of 12.55 million yuan obtained in Scenario 2 are much lower than the results of Scenario 1. It can be seen that under the condition of meeting the power generation demand and not affecting the economic benefits, by adopting the optimal scheduling strategy of the integrated energy system considering source-load coordination, the power system and the thermal system can meet the demands of the electrical load and the thermal load. And because demand response resources are added to the system, the energy consumption allocation of the system and users is more reasonable, which is more conducive to reducing the comprehensive operation cost of the system, reducing the carbon emissions of the system, and improving the consumption of renewable energy.

[0193] The present invention is described through embodiments. Those skilled in the art know that without departing from the spirit and scope of the present invention, various changes or equivalent replacements can be made to these features and embodiments. In addition, under the teaching of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application belong to the protection scope of the present invention.

Claims

1. An integrated energy system considering source-load coordination, characterized in that The thermal power unit is connected to an extraction steam energy storage device, a carbon capture device, a hydrogen storage device, a gas storage device, a hydrogen fuel cell, an electric boiler, a wind farm, a photovoltaic power plant, and a load side. The carbon capture device is connected to the hydrogen storage device. The hydrogen storage device is connected to the gas storage device and the hydrogen fuel cell. The gas storage device is connected to the hydrogen fuel cell. The gas storage device is connected to a gas turbine. The gas turbine is connected to the hydrogen fuel cell. The electric boiler is connected to the hydrogen fuel cell. The hydrogen fuel cell is connected to the load side.

2. The integrated energy system considering source-load coordination according to claim 1, characterized in that The load side includes rigid loads, shiftable loads, curtailable loads, substitutable loads, and flexible loads.

3. An optimal scheduling method for optimally scheduling any one of the integrated energy systems considering source-load coordination described in claims 1-2, characterized in that, It includes the following steps: Establish an economic dispatch mathematical model of the integrated energy system considering source-load coordination with the goal of minimizing the operating cost of the integrated energy system. Establish the operating constraint conditions of the integrated energy system. Substitute the economic dispatch mathematical model and constraint conditions of the integrated energy system into an improved single-object artificial bee colony algorithm to obtain the Pareto optimal solution of the economic dispatch mathematical model of the integrated energy system.

4. An integrated energy optimization scheduling method considering source-load coordination according to claim 3, characterized in that The step of establishing an economic dispatch mathematical model of the integrated energy system considering source-load coordination: minC = min(C c + C r + C A + C ct + C s + C q + C IDR ) Where: C c is the coal - burning cost of thermal power units, C r is the operation and maintenance cost, C A is the cost of system wind and light curtailment, C ct is the stepped carbon trading cost, C s is the solution loss cost of carbon capture equipment, C q is the load - shedding cost, C IDR is the IDR resource cost; Where: T is the total scheduling cycle number, S t is the start-stop state of the thermal power unit at time t, and a, b, and c are the coal consumption cost coefficients of the thermal power unit, P G,t is the output of the thermal power unit at time t; Where: C GT , C HFC , C se , C P2G , C EB , C CCPP , C WT and C PV are the unit operation and maintenance costs of the gas turbine, hydrogen fuel cell, extraction steam energy storage device, P2G device of the hydrogen fuel cell, electric boiler, carbon capture device, wind farm and photovoltaic power plant respectively; P HFC,t , P t P2G , P CCPP,t , P WT,t and P PV,t are the electrical output powers of the hydrogen fuel cell, P2G device of the hydrogen fuel cell, carbon capture device, wind farm and photovoltaic power plant at time t respectively; Q GT,t , Q HFC,t , Q se,t and Q EB,t are the thermal output powers of the gas turbine, hydrogen fuel cell, extraction steam energy storage device and electric boiler at time t respectively; Where: c A is the curtailment cost coefficient of wind and solar power, and are the curtailment power of wind and solar power at time t, respectively; Wherein: is the stepped carbon trading cost at time t; Where: c s is the cost coefficient of the solvent, ψ is the operating loss of the solvent, and M P,t is the CO2 emission captured by the carbon capture equipment at time t; where: c q is the unit load shedding cost coefficient, P q,t is the load shedding power of the thermal power unit at time t; Where: c IDR is the IDR resource cost coefficient, P IDR,t is the total amount of IDR resources called at time t.

5. A comprehensive energy optimization scheduling method considering source-load coordination according to claim 4, characterized in that, The dispatch period of the economic dispatch mathematical model of the integrated energy system is 24h, and the time scale is 15min.

6. The integrated energy optimization scheduling method considering source-load coordination according to claim 3, characterized in that The step of establishing the operating constraint conditions of the integrated energy system: Power balance constraint: Where: P f,t is the predicted power of the load at time t, N is the type of different thermal power units in the integrated energy system, and P i,t is the net output power of thermal power unit i at time t; Wind and photovoltaic power output constraint: Where: and are the maximum values of the predicted wind and light output powers at time t, respectively; Operating constraints of the carbon capture device and the thermal power unit: Where: P CCPP,min and P CCPP,max are the minimum output and the maximum output of the thermal power unit, respectively; R down ≤P CCPP,t -P CCPP,t-1 ≤R up Where: R down and R up are the down-ramp rate and up-ramp rate of the thermal power unit respectively, and P CCPP,t-1 is the electrical output power of the carbon capture device at time t-1; Wherein: and are respectively the continuous running time and shutdown time of the thermal power unit at the (t - 1)th moment, and are respectively the minimum running time and minimum shutdown time of the thermal power unit; S t-1 and S t are respectively the start-stop states of the thermal power unit at the (t - 1)th moment and the tth moment; Where: V f,t and V f,t-1 are respectively the solution volumes of the rich liquid storage tank of the thermal power unit at time t and t - 1, V p,t and V p,t-1 are respectively the solution volumes of the lean liquid storage tank of the unit at time t and t - 1, V c,t is the solution volume required to discharge CO2 from the storage tank at time t, V ss is the capacity of the storage tank, V f,0 and V f,24 are respectively the initial solution volume of the rich liquid storage tank and the solution volume after the end of the scheduling period, V p,0 and V p,24 are respectively the initial solution volume of the lean liquid storage tank and the solution volume after the end of the scheduling period; Where: E c,t is the carbon emission supplied by the liquid storage tank of the thermal power unit at time t, M MEA and M c are the molar mass of the ethanolamine solution and the molar mass of CO2 respectively, ω is the analytical amount of the regeneration tower, M MEA is the concentration coefficient of the ethanolamine solution, ρ MEA is the density of the ethanolamine solution; Gas turbine constraint: Where: G GT,t is the gas power generated after the gas turbine absorbs natural gas at time t, k gas is the lower calorific value of natural gas for combustion, J GT,t is the intake air volume of the gas turbine at time t, P GT,t and Q GT,t are respectively the electrical output power and the heat output power of the gas turbine at time t, and are respectively the power generation efficiency and the electro-thermal power ratio of the gas turbine, s GT,t is the operating state of the gas turbine at time t, and are respectively the minimum value and the maximum value of the electrical output power of the gas turbine, and are respectively the down-ramp rate and the up-ramp rate of the electrical output power of the gas turbine; Hydrogen fuel cell constraint: Where: P HFC,t , Q HFC,t and are respectively the electrical output power, heat output power and input power of the hydrogen fuel cell at time t, and are respectively the electrical and heat conversion efficiencies of the hydrogen fuel cell, and are respectively the lower and upper limits of the input power of the hydrogen fuel cell, and are respectively the upper and lower ramp rates of the hydrogen fuel cell; Electric boiler constraint: Q EB,t = η EB P EB,t 0 ≤ Q EB,t ≤ Q EB,max Where: Q EB,t and P EB,t are respectively the heat output power and power consumption of the electric boiler at time t, η EB is the electro-thermal conversion efficiency of the electric boiler, and Q EB,max is the maximum heat supply of the electric boiler at time t; Extraction steam energy storage device constraint: Q y,min ≤Q y,t ≤Q y,max 0 ≤ Q se,t ≤ Q se,max Where: Q y,max and Q y,min are the upper and lower limits of the heat energy storage of the high-temperature energy storage device, Q y,t is the heat energy storage amount of the high-temperature energy storage device at time t, Q se,max is the maximum heat supply amount of the high-temperature energy storage device for heat supply at time t; P2G device constraint: Where: and are the hydrogen consumption power and the upper limit of hydrogen consumption power of the P2G device at time t, is the generated natural gas power of the P2G device at time t, is the output upper limit of the P2G device at time t; IDR resource constraint: Where: ΔP IDR,t and ΔP IDR,t-1 are the IDR resource usage at time t and time t - 1, is the maximum response amount of the IDR load, v IDR is the response rate of the IDR load; Spinning reserve constraint: Wherein: and are respectively the upper limit and the lower limit of the net output of the thermal power unit, and are respectively the upper spinning reserve requirement and the lower spinning reserve requirement of the integrated energy system at time t, P G,t is the total output power of the power plant at time t, R up and R down are respectively the upper ramping rate and the lower ramping rate of the spinning reserve, S t is the operating state of the thermal power unit at time t, U t is the start-stop state of the unit at time t.

7. A comprehensive energy optimization scheduling method considering source-load coordination according to claim 3, characterized in that The step of substituting the economic dispatch mathematical model and constraint conditions of the integrated energy system into an improved single-object artificial bee colony algorithm to obtain the Pareto optimal solution of the economic dispatch mathematical model of the integrated energy system includes the following steps: Establish a fitness function: where: x im is the fitness factor; According to the roles of the bee colony, divide them into employed bees, scout bees, and onlooker bees, and initialize the search range, number, and parameters of the colony. Divide the entire solution space into v sub-regions and randomly assign weights φ{Ω1,Ω2,...,Ω v}; Divide the employed bees into different groups, each group is responsible for exploring a sub-region and generating a new solution. The calculation formula is as follows: In the formula: is the new solution after iteration, x im and x jm are the original solutions of the bees in the i-th and j-th regions respectively, and R is a random number in the interval [0, 1]; Calculate the fitness value of the new solution, update the weight of each sub-region, and assign the sub-region corresponding to the optimal solution to the bee with the largest weight. Calculate the selection probability of the sub-region. The calculation formula is as follows: where: j = 1, 2,..., n; p j is the selection probability of the sub-region, Ω j is the weight of the sub-region j, and n is the number of sub-regions; The employed bees explore the sub-regions with large selection probabilities to find new solutions. The onlooker bees judge whether there are solutions to be discarded. The judgment formula is as follows: In the formula: abs is the absolute value; When the solution satisfies the above formula, discard the solution and randomly generate a new solution to replace it; Record the solution with the smallest fitness value until the number of iterations reaches the set iteration number value to obtain the optimal solution.

8. An apparatus for an integrated energy optimal scheduling method considering source-load coordination, characterized in that, It includes a memory and a processor. The memory is used to store a computer program, and the computer program is used to execute the method described in any one of claims 3-7 when loaded by the processor.

9. A computer-readable storage medium, characterized in that, The computer program is stored in the storage medium, and the computer program is suitable for executing the method described in any one of claims 3-7 when loaded by the processor.

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