Hybrid energy storage-based multi-comprehensive energy operation optimization method and device

Abstract

The present application relates to the technical field of power system, and more particularly to a multi-comprehensive energy (IES) operation optimization method and device based on hybrid energy storage, the method comprising: collecting basic parameters of multiple target devices in a comprehensive energy system, and constructing a device model corresponding to each target device, wherein the target devices include power-to-gas devices (P2G), combined heat and power unit devices (CHP units), gas boiler devices and fuel cell devices; based on the device model of each target device, constructing a comprehensive energy optimization scheduling model with the minimum economic cost and pollution gas emission as the target; creating constraint conditions corresponding to each target device according to the comprehensive energy optimization scheduling model; and solving the comprehensive energy optimization scheduling model based on the constraint conditions through a particle swarm algorithm. The present application comprehensively integrates multiple target devices, and constructs a comprehensive energy optimization scheduling model, which can improve energy recycling rate, strengthen the coordinated operation ability between systems, and reduce system economic cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, and in particular to a multi-comprehensive energy operation optimization method and device based on hybrid energy storage. BACKGROUND

[0002] The material basis for human survival is energy, but with the progress of social technology production, environmental problems have grown out year by year, accompanied by related problems of non-renewable resources such as fossil fuels, so the importance of sustainable renewable energy is particularly prominent. The reason why China's traditional energy system cannot realize the advantages and complementarity or replacement between energies is that the coordinated operation ability between China's traditional energy systems is weak, the energy recycling rate is not high, and the overall energy system has weak system stability and other related problems. Therefore, the comprehensive energy system is the inevitable requirement for China to realize the transformation of the energy system, so as to realize the efficient recycling of sustainable energy and the green and efficient energy consumption demand. SUMMARY

[0003] The embodiment of the present application provides a multi-comprehensive energy operation optimization method based on hybrid energy storage, which aims to improve the energy recycling rate in the energy system, enhance the coordinated operation ability between systems, and reduce the system economic cost.

[0004] In a first aspect, the embodiment of the present application provides a multi-comprehensive energy operation optimization method based on hybrid energy storage, comprising the following steps:

[0005] Collecting basic parameters of a plurality of target devices in a comprehensive energy system, and constructing a device model corresponding to each of the target devices, wherein the target devices include an electric-to-gas device, a combined heat and power unit device, a gas-fired boiler device, and a fuel cell device;

[0006] Based on the device model of each of the target devices, a comprehensive energy optimization scheduling model is constructed, with the minimum economic cost and pollution gas emission as the target;

[0007] According to the comprehensive energy optimization scheduling model, a constraint condition corresponding to each of the target devices is created;

[0008] Based on the constraint condition, the comprehensive energy optimization scheduling model is solved by a particle swarm algorithm.

[0009] In a second aspect, the embodiment of the present application provides a multi-comprehensive energy operation optimization device based on hybrid energy storage, comprising:

[0010] A device modeling module is configured to collect basic parameters of a plurality of target devices in a comprehensive energy system, and construct a device model corresponding to each of the target devices, wherein the target devices include an electric-to-gas device, a combined heat and power unit device, a gas-fired boiler device, and a fuel cell device;

[0011] The optimization model creation module is used to construct a comprehensive energy optimization scheduling model based on the equipment models of each target device, with the goal of minimizing economic costs and pollutant emissions.

[0012] The condition constraint module is used to create constraint conditions corresponding to each of the target devices based on the integrated energy optimization scheduling model;

[0013] The calculation module is used to solve the integrated energy optimization scheduling model based on the constraints using the particle swarm optimization algorithm.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps in the multi-energy integrated operation optimization method based on hybrid energy storage provided in embodiments of the present invention.

[0015] In this embodiment of the invention, basic parameters of multiple target devices in an integrated energy system are collected to construct equipment models corresponding to each target device. These target devices include power-to-gas conversion equipment, combined heat and power (CHP) units, gas-fired boilers, and fuel cells. Based on the equipment models of each target device, an integrated energy optimization scheduling model is constructed with the objectives of minimizing economic cost and pollutant emissions. Constraints are created for each target device according to the integrated energy optimization scheduling model. Based on these constraints, the integrated energy optimization scheduling model is solved using a particle swarm optimization algorithm. It is evident that the integrated energy optimization scheduling model provided in this application includes various energy storage devices. Furthermore, the model, which aims to minimize economic cost and pollutant emissions, considers the natural gas system and imposes constraints on various energy storage devices based on the integrated energy optimization scheduling model. Finally, the integrated energy optimization scheduling model is solved using a particle swarm optimization algorithm. This approach can compensate for the shortcomings of low energy recycling rates and weak inter-system coordination capabilities in my country's traditional energy systems. Moreover, the optimized integrated energy system can improve energy utilization and reduce system economic costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of the multi-energy integrated operation optimization method based on hybrid energy storage provided in the embodiments of the present invention;

[0018] Figure 2 is a whole energy flow structure diagram of a comprehensive energy system provided by an embodiment of the present application;

[0019] Figure 3 is a flowchart of step S101 in the method; Figure 1

[0020] Figure 4 is a flowchart of step S103 in the method; Figure 1

[0021] Figure 5 is a flowchart of step S104 in the method; Figure 1

[0022] Figure 6 is a structural schematic diagram of a multi-comprehensive energy operation optimization device based on hybrid energy storage provided by an embodiment of the present application;

[0023] Figure 7 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0025] As shown in Figure 1 , the method comprises the following steps: Figure 1 is a flowchart of a multi-comprehensive energy operation optimization method based on hybrid energy storage provided by an embodiment of the present application, comprising the following steps:

[0026] S101, collecting basic parameters of a plurality of target devices in a comprehensive energy system, and constructing a device model corresponding to each target device, wherein the target devices include an electric-to-gas device, a combined heat and power unit device, a gas-fired boiler device and a fuel cell device.

[0027] ​​​The electronic device on which the multi-comprehensive energy operation optimization method based on hybrid energy storage is provided in the embodiment can be connected with other electronic devices for data transmission and the like in a wired connection mode or a wireless connection mode. The wireless connection mode can include, but is not limited to, 3G / 4G connection, WiFi (Wireless-Fidelity) connection, Bluetooth connection, WiMAX (Worldwide Interoperability for Microwave Access) connection, Zigbee (low-power local area network protocol, also known as Z-Wave protocol) connection, UWB (Ultra Wideband) connection, and other now known or future developed wireless connection modes.

[0028] In the embodiment, the comprehensive energy system internally includes various devices, and the overall energy flow structure diagram of the comprehensive energy system can be constructed based on the energy flow relationship of each device in the comprehensive energy system. As shown in the figure, the input side of the comprehensive energy system is connected with a power grid and a gas grid, the gas grid includes a natural gas system, and the power grid includes a power system. The comprehensive energy system internally includes an electric energy device, a gas energy device and a thermal energy device. The electric energy device can include an energy storage battery and a power-to-gas device (P2G). The gas energy device can include a fuel cell device and a combined heat and power unit (CHP unit). The thermal energy device can include a gas-fired boiler and a heat storage device, and the output side of the system outputs an electric load and a thermal load, respectively. The energy storage battery and the fuel cell can convert electric energy to generate an electric load. The power-to-gas device can realize electric-gas conversion. The CHP unit and the gas-fired boiler can realize gas-heat conversion to generate a thermal load. Figure 2 The above basic parameters can refer to part of the data required for model construction. In the comprehensive energy system, the devices required for modeling can be taken as target devices, and the device modeling can be performed by acquiring the basic parameters of each target device to obtain the mathematical model corresponding to each target device, which can include, but is not limited to, the mathematical models of the power-to-gas device, the combined heat and power unit, the gas-fired boiler and the fuel cell device.

[0029] S102, based on the device model of each target device, constructing a comprehensive energy optimization scheduling model with the minimum economic cost and pollution gas emission as the target.

[0030] In order to realize comprehensive energy optimization scheduling, reduce system economic cost and reduce pollution gas emission, the mathematical models of the above-mentioned target devices can be comprehensively considered to construct a comprehensive energy optimization scheduling model with the minimum economic cost and pollution gas emission as the target.

[0031]

[0032] ​S103, creating constraint conditions corresponding to each target device according to the comprehensive energy optimization scheduling model.

[0033] The constraint conditions can refer to constraint conditions of the comprehensive energy system of the hybrid energy storage, including constraint conditions of the natural gas system, the power system and other related systems. The other related systems can include constraint conditions of the energy storage device in the coupling exchange during system operation and input and output constraint conditions of the energy center. After the comprehensive energy optimization scheduling model is constructed, corresponding constraint conditions can be constructed for the power system, the natural gas system and other energy storage devices according to the constructed comprehensive energy optimization scheduling model.

[0034] S104, solving the comprehensive energy optimization scheduling model by using the particle swarm algorithm based on the constraint conditions.

[0035] The particle swarm algorithm (PSO) is an evolutionary computation technique for solving optimization problems. In the particle swarm algorithm, the behavior of each particle is a kind of symbiotic cooperation, and each particle is affected by other particles in the group. The basic idea of the particle swarm algorithm is to find the optimal solution through the cooperation and information sharing between individuals in the group. The particle swarm algorithm is initialized as a group of random particles, and the optimal solution is found through iteration. Compared with the genetic algorithm, the particle swarm algorithm has the advantages of simplicity and easy implementation, and does not require many parameters to be adjusted. It has been widely used in function optimization, neural network training, fuzzy system control and other application fields of genetic algorithm. In each iteration, the particle updates itself by tracking the historical individual optimal position (P bestd ) and the global historical optimal position (g bestd ).

[0036] Specifically, after collecting the basic parameters of the device model of each target device, an initial group of 200-250 specifications can be generated based on the corresponding basic parameters, so that most of them are uniformly distributed in the required solution area, and the position and speed of each particle in the initial group are recorded. The initial position and speed are used as the P bestd and g bestd of the first iteration, and in each subsequent iteration, the particle updates its speed and position by using the position update formula and the speed update formula.

[0037] More specifically, after each position and velocity update, the particle fitness value can be calculated. Based on this value, it can be determined whether the algorithm is more optimized. If it is, the velocity and position corresponding to the most optimized particle fitness value are used as the current individual optimal position and the global optimal position for updating. Whether the particle fitness value is more optimized can include the calculated particle fitness value being smaller than others. After continuous iteration, when a preset termination condition is met, the algorithm can terminate, and the final particle fitness value is output as the optimal fitness value, serving as the optimal solution for the integrated energy optimization scheduling model. This model achieves minimum network loss, economic cost loss, and minimum pollutant emissions for the hybrid energy storage integrated energy system.

[0038] In this embodiment of the invention, models are constructed by collecting basic parameters of the power-to-gas conversion equipment, combined heat and power (CHP) unit equipment, gas boiler equipment, and fuel cell equipment in the integrated energy system. Based on the equipment models of each target equipment, an integrated energy optimization scheduling model is constructed with the goal of minimizing economic cost and pollutant emissions. The integrated energy optimization scheduling model considers the natural gas system, the power system, and other energy storage equipment. Corresponding constraints are created according to the integrated energy optimization scheduling model, and the model is solved using a particle swarm optimization algorithm. It is evident that the integrated energy optimization scheduling model provided in this application includes various energy storage equipment, and the construction of the integrated energy optimization scheduling model with the goal of minimizing economic cost and pollutant emissions considers the natural gas system. Conditional constraints are imposed on various energy storage equipment based on the integrated energy optimization scheduling model, and finally, the integrated energy optimization scheduling model is solved using a particle swarm optimization algorithm. This approach can compensate for the shortcomings of low energy recycling efficiency and weak inter-system coordination capabilities in my country's traditional energy system. Furthermore, the optimized integrated energy system can improve energy utilization efficiency and reduce system economic costs.

[0039] Optional, such as Figure 3 As shown, Figure 3 This is provided by the embodiments of the present invention. Figure 1 The flowchart of S101 in the middle is as follows: Figure 3 As shown, it includes the following steps:

[0040] S301. Obtain the basic parameters of the electro-gas conversion equipment and construct an electro-gas conversion model, which includes an electro-thermal conversion model and a methanation model.

[0041] The electro-thermal conversion model is as follows:

[0042]

[0043]

[0044] In the formula, electrical power consumed by electrolysis for k period; hydrogen production power consumed in electrolysis for k period; hydrogen consumption power in the methanation device for k period; hydrogen production power in the methanation device for k period; μ EL conversion coefficient of the methanation process; μ 2G conversion coefficient of the electrical heat conversion.

[0045] Specifically, the above-mentioned P2G basic parameters can refer to some data involved in the P2G process, for example: the electrical power consumed by electrolysis in the P2H process, the hydrogen production power consumed in the electrolysis process, the conversion coefficient of the methanation process, etc. Since the conversion coefficient of the whole P2G process is too low, and considering that the low-carbon benefits of hydrogen should be maximized, the whole process can be divided into two processes of P2H and methanation, and therefore the P2H model and the methanation model can be constructed respectively. After obtaining the P2G basic parameters, the P2H model shown in the above-mentioned formulas (1) and (2) can be constructed to obtain the relationship between the parameters in the P2H process.

[0046] The methanation model is:

[0047]

[0048]

[0049] In the formula, carbon dioxide re-produced in k period entering the device; carbon dioxide sealed into the P2G device in k period; L ME methane heat value; electrical power for producing carbon dioxide; total gas produced in k period; electrical power consumed in the methanation device conversion process for k period.

[0050] Specifically, the above-mentioned P2G basic parameters also include some basic parameters in the methanation process. Based on the obtained basic parameters in the methanation process, the methanation model shown in the above-mentioned formulas (3) and (4) can be constructed to obtain the relationship between the parameters in the methanation process. And it is shown that the raw material of CO2 methanation comes from CCS (Carbon Capture and Storage), which embodies the coupling of P2G and CCS.

[0051] S302, obtaining the basic parameters of the combined heat and power unit device, and constructing a combined heat and power unit model;

[0052] PHS,i,k = P CHP,i,k + K v,i H CHP,i,k (5)

[0053] wherein, P HS,i,k is the electric power after conversion of k period; P CHP,i,k is the heat generation power of the cogeneration unit equipment in k period; K v,i is the ratio of the heat and power of the steam heat and power unit equipment; H CHP,i,k is the power generation of the cogeneration unit equipment in k period.

[0054] Specifically, the heat and electric energy generated by the cogeneration unit equipment is operated in a way of consuming natural gas, and the output is related to the characteristics of the cogeneration unit equipment. The cogeneration unit equipment also includes cogeneration basic parameters, and based on the obtained cogeneration basic parameters, the cogeneration unit equipment model shown in formula (5) can be constructed to obtain the relationship between the electric and heat output and each cogeneration basic parameter. The above-mentioned electric power after conversion can refer to the sum of the heat and electric energy generated by the heat and power unit equipment, that is, the output data of the heat and power unit equipment.

[0055] S303, obtaining boiler basic parameters of the gas boiler equipment, and constructing a gas boiler model;

[0056]

[0057] wherein, F GB,k is the gas consumption of the gas boiler equipment at k moment; Q GB,k is the heat transfer power of the gas boiler equipment at k moment; μ GB,k is the thermal power coefficient of the gas boiler equipment at k moment.

[0058] Specifically, the above-mentioned gas boiler equipment is a backup heat source of the entire integrated energy system, and when the heat energy required by the entire integrated energy system is insufficient, the required heat energy can be supplemented by the gas boiler equipment. After collecting the boiler basic parameters, the gas boiler model shown in formula (6) above can be constructed based on the boiler basic parameters to obtain the relationship between the heat transfer power of the gas boiler equipment, the thermal power coefficient of the gas boiler equipment and the gas consumption of the gas boiler equipment. The heat transfer power of the gas boiler equipment can refer to the output data when the gas boiler equipment is converted into heat energy.

[0059] S304, obtaining cell basic parameters of the fuel cell equipment, and constructing a fuel cell model;

[0060] P FC,k = V FC,ME,k L ME μ FC (7)

[0061] P (k) = P (k) + P (k) + P (k) + P (k) (7) FC,k P (k) is the heat power generated by the fuel cell device in the k period; V FC,ME,k P (k) is the amount of natural gas consumed by the fuel cell device in operation in the k period; μ FC P (k) is the power generation coefficient of the fuel cell device; L ME P (k) is the heat value of methane.

[0062] Specifically, the fuel cell device is a device for converting chemical energy in fuel into electrical energy. By collecting the basic parameters of the fuel cell, the fuel cell model shown in the above formula (7) can be constructed, and the relationship between the heat power generated by the fuel cell device and the natural gas consumed by the fuel cell device in operation, the power generation coefficient and the heat value of methane can be obtained from the fuel cell model. The heat power generated by the fuel cell device can refer to the output data of the fuel cell device.

[0063] In the embodiment, after collecting the basic parameters of the electric-gas conversion device, the combined heat and power unit device, the gas boiler device and the fuel cell device respectively, the device models of the electric-gas conversion device, the combined heat and power unit device, the gas boiler device and the fuel cell device are constructed based on the basic parameters. The basic parameters of multiple target devices are considered in the comprehensive energy system including electric energy, gas energy and heat energy, and the economic cost of the energy storage device can be reduced by introducing the electric-gas conversion mode, and the emission of pollutant gas will also be reduced, so that the economic cost and the emission of pollutant gas of the comprehensive energy system can be controlled to the minimum.

[0064] Optionally, Figure 1 In step S102, constructing the comprehensive energy optimization scheduling model with the minimum economic cost and pollutant gas emission as the target specifically includes constructing an economic cost model and constructing a pollutant gas emission model, wherein:

[0065] Constructing the economic cost model:

[0066]

[0067] P (k) = P (k) + P (k) + P (k) + P (k) (7) NG P (k) = μ eg P (k) = P (k) + P (k) + P (k) + P (k) (7) P2G P (k) = μ

[0068] P (k) = P (k) + P (k) + P (k) + P (k) (7) cos k minE is the minimum economic cost; H ep P (k) is the peak-valley electricity price in the k period; H gp P (k) is the natural gas price in the k period; P (k) is the carbon dioxide price in the k period; P e P (k) is the electricity purchase amount in the k period; P g P (k) is the gas purchase amount in the k period; Pnloss (k) is the network loss of k period; b is the conversion coefficient of converting carbon dioxide into natural gas; K is the scheduling period; μ eg is the operation cost coefficient of the electric-gas conversion equipment; P P2G (k) is the output power of the electric-gas conversion equipment in k period; P NG (k) is the operation cost in k period.

[0069] A pollution gas emission model is constructed:

[0070]

[0071] minV mission is the minimum pollution gas emission; i is the number of pollution gas types generated in the process of purchasing electricity from the power grid; γ e,i,k represents the pollution coefficient of the ith pollution gas generated in the process of purchasing electricity from the power grid in k period; γ g,j,k represents the emission coefficient of the jth new energy in the process of burning natural gas at k moment; N represents the types of pollution gas generated in the whole system operation process; and n represents the number of energy centers.

[0072] Specifically, after comprehensively considering the equipment models of the above-mentioned various target equipment, the constructed comprehensive energy optimization scheduling model contains natural gas system and electrical system modules. Secondly, the comprehensive energy optimization scheduling model is established to minimize the economic cost and pollution gas emission. Meanwhile, in the comprehensive energy optimization scheduling model, the loss part of the power system network loss and economic cost can also be reduced as the target. Therefore, the finally constructed economic cost model contains electricity, natural gas, economic loss and operation cost, as shown in formula (8), formula (9) is obtained by deducing through combining the equipment models of various target equipment. And based on the multiple data of pollution gas generated in the system operation process of electric energy, gas energy and heat energy, a pollution gas emission model is constructed, as shown in formula (10), the relationship between the minimum pollution gas emission and each data of pollution gas can be obtained.

[0073] In the embodiment, the economic cost model with the minimum economic cost as the target and the pollution gas emission model with the minimum pollution gas emission as the target are constructed by considering the natural gas system and other related equipment, and the comprehensive energy optimization scheduling model is finally solved by the particle swarm algorithm. The comprehensive energy optimization scheduling model constructed in the application can make up for the low energy recycling rate and weak system coordination operation ability of the traditional energy system in China, and can also improve the energy utilization rate through the comprehensive energy system, and the economic cost of the energy storage equipment can be reduced through the introduction of the electric-gas conversion mode, and the pollution gas emission will also be reduced.

[0074] As Figure 4As shown, Figure 4 is provided by the embodiment of the present application Figure 1 The flow chart of step S103 in the embodiment of the present application, step S103 includes:

[0075] S401, creating a constraint condition of a power system in a system runtime integrated energy system.

[0076] S402, creating a constraint condition of a natural gas system in a system runtime integrated energy system.

[0077] S403, creating a constraint condition of an energy storage device in a coupling link in a system runtime and an input / output constraint condition of an energy center.

[0078] Specifically, the constraint conditions can be respectively constructed for the natural gas system, the power system and other related systems in the integrated energy system with mixed energy storage.

[0079] In the entire system running process, step S401 specifically includes:

[0080] creating a running voltage constraint and a power constraint of the power system in a system runtime;

[0081] The running voltage constraint is:

[0082]

[0083] The power constraint is:

[0084]

[0085] In the formula, is the maximum value of the node voltage in per unit value; is the minimum value of the node voltage in per unit value; V i is the node voltage in per unit value; is the power value of the maximum power transmission line; S ij is the power value of the power transmission line.

[0086] Specifically, the power system needs to be in a rated range when running in a rated state, and the specified voltage running value needs to have a certain range requirement, as shown in the running voltage constraint of the above formula (11). At the same time, in the entire system running process, the transmission power of the line cannot exceed the upper limit of the line transmission power, and the power constraint is shown in the above formula (12).

[0087] In the entire system running process, step S402 specifically includes:

[0088] creating a natural gas transportation channel pressure constraint and a gas compressor compression ratio constraint of the natural gas system in a system runtime integrated energy system;

[0089] wherein the natural gas transportation channel pressure constraint is:

[0090] P min ≤P i ≤P max (13)

[0091] the gas compressor compression ratio constraint is:

[0092] K min ≤K i ≤K max (14)

[0093] wherein P min and P max are the minimum and maximum values of the natural gas transportation channel pressure respectively; K min and K max are the minimum and maximum values of the gas compressor compression ratio respectively.

[0094] Specifically, based on the policy of ensuring the supply of natural gas, it is also necessary to control the pressure in the pipeline to be maintained within a specified range, and therefore it is necessary to construct a natural gas transportation channel pressure constraint, as shown in the above formula (13). At the same time, the gas compressor also needs to maintain a certain range of compression ratio in order to maintain the normal operation of the system, and the compression ratio constraint is shown in the above formula (14).

[0095] In the entire system operation process, step S403 specifically comprises:

[0096] creating the first type of energy center input / output constraint, the second type of energy center input / output constraint, the third type of energy center input / output constraint, and the energy storage device constraint in the energy center in the coupling link during system operation.

[0097] In the entire system operation process, the constraints of the coupling link include device capacity constraints, energy storage device constraints, and different types of energy center input / output constraints,

[0098] wherein the first type of energy center input / output constraint is:

[0099]

[0100] the second type of energy center input / output constraint is:

[0101]

[0102] the third type of energy center input / output constraint is:

[0103]

[0104] wherein, and These represent the minimum and maximum values ​​of electrical energy input, respectively. and These represent the minimum and maximum values ​​of the input natural gas; I, II, and III are the first, second, and third types of energy centers, respectively; gs, es, and hs are gas energy, electrical energy, and thermal energy, respectively. The thermoelectric coefficient of a combined heat and power unit; η is the maximum power of gas energy; GB The thermal power coefficient of the gas-fired boiler equipment; η is the maximum power of electrical energy. AC L represents the electrical power of the combined heat and power unit. e,I To transmit power through pipelines.

[0105] In this embodiment, by constructing constraints on the input and output of the natural gas system, the power system, the energy storage device, and the energy center in the coupling links of the integrated energy system with hybrid energy storage, it is beneficial for the system to achieve normal operation.

[0106] refer to Figure 5 As shown, Figure 5 This is provided by the embodiments of the present invention. Figure 1 The flowchart of step S104 is as follows:

[0107] S501. Initialize the integrated energy optimization scheduling model using the particle swarm optimization algorithm to obtain n particles representing the solution, each particle including velocity and position.

[0108] In the particle swarm optimization (PSO) algorithm, an initial particle swarm is randomly generated, containing multiple n particles. The performance of each particle is determined by its fitness value, which is based on minimizing economic cost loss and minimizing power system network loss. The particles search for the optimal solution within a specified space. Typically, 200-250 particles are randomly generated, with the majority of these n particles evenly distributed within the desired solution region. Each particle has both velocity and position, represented by a coordinate vector. Each initially generated particle can represent a different solution to the integrated energy optimization scheduling model.

[0109] S502. Calculate the initial fitness value of the integrated energy optimization scheduling model based on each particle, and determine the historical optimal position of the individual particle and the historical global optimal position of the particle swarm based on the initial fitness value.

[0110] Specifically, according to the relevant parameter information of the particles in the initial group for reducing the power system network loss and economic cost loss, the relevant curve is obtained through data calculation, and the initial fitness value F is calculated according to the position of each particle of the curve fitness Then, the sizes of the initial fitness values can be compared, and finally the historical individual optimal position of the particle at the initial time and the historical global optimal position of the particle group are obtained, for example: the population size particle number n = 3, the initial fitness values of the curve calculated based on the positions of the three particles are 100, 120 and 130.5 respectively, the three initial fitness values are compared, and the position of the particle corresponding to the largest initial fitness value is selected as the historical individual optimal position of the particle at the initial time and the historical global optimal position of the particle group.

[0111] S503, based on the position update formula and the velocity update formula, iterative calculation is performed, and the particle fitness value of each iteration is calculated, the historical individual optimal position of the particle and the historical global optimal position of the particle group are updated based on the fitness value in the iteration process, and the velocity is updated.

[0112] The position update formula is:

[0113] V id (k+1) = V id (k)ω k +c1rand1(P bestd -x id (k))+c2rand2(g bestd -x id (k)) (18)

[0114] The velocity update formula is:

[0115] x id (k+1) = x id (k) + V id (k) (19)

[0116] Wherein, V id (k) is the particle velocity of the current iteration, ω k is an inertia weight greater than 0; C1, C2 are acceleration factors; rand1, rand2 are random natural numbers, ranging from 0 to 1; P bestd , g bestd are the historical global optimal position and the historical individual optimal position of the current iteration in turn; x id (k) is the position of the particle at the current iteration.

[0117] Specifically, by iteratively calculating according to the above formulas (18) and (19), the position and velocity of each particle can be updated. After each iteration, the fitness value can be calculated based on the position of the particle corresponding to the current iteration number. If the fitness value of the particle's current position is higher than the historical fitness value, the particle's current position can be updated to the historical best position. Similarly, the fitness value of the particle's current position can be compared with the fitness value corresponding to the historical global best position to update the historical global best position. Furthermore, the velocity can be updated based on the position in each iteration.

[0118] S504. Determine if the iteration of the particle swarm algorithm has reached the preset termination condition.

[0119] S505. If the iteration of the particle swarm optimization algorithm reaches the preset termination condition, the optimal fitness value is output. The optimal fitness value is the optimal solution of the integrated energy optimization scheduling model.

[0120] The preset termination condition can include the maximum number of iterations / algorithm convergence. If the number of iterations of the particle swarm optimization algorithm reaches the maximum number of iterations or the algorithm converges, the optimal fitness value will be output. The optimal fitness value will be used as the optimal solution of the integrated energy optimization scheduling model. The optimal solution corresponds to the minimum network loss, economic cost loss, and minimum pollutant emissions of the multi-integrated energy system with hybrid energy storage.

[0121] If the particle swarm optimization algorithm does not reach the maximum number of iterations or the algorithm does not converge, return to step S502 to continue the iteration until the optimal solution is found.

[0122] In this embodiment of the invention, the provided integrated energy optimization scheduling model includes power-to-gas conversion equipment, combined heat and power (CHP) units, gas-fired boilers, and fuel cells. The integrated energy optimization scheduling model, aimed at minimizing economic costs and pollutant emissions, comprehensively considers both the power system and the natural gas system. Based on the integrated energy optimization scheduling model, constraints are imposed on the power system, natural gas system, energy storage devices, and the input and output of the energy center. Finally, the optimal solution of the integrated energy optimization scheduling model is obtained using a particle swarm optimization algorithm. The optimal solution corresponds to the minimum network loss, economic cost loss, and pollutant emissions of the multi-energy integrated system with hybrid energy storage. Therefore, this application can compensate for the shortcomings of low energy recycling efficiency and weak inter-system coordination in my country's traditional energy system. Furthermore, the optimized integrated energy system can improve energy utilization and reduce system economic costs.

[0123] like Figure 6 As shown, Figure 6 This is a modular structure diagram of a multi-energy integrated operation optimization device based on hybrid energy storage provided in an embodiment of the present invention. The device 600 includes:

[0124] The device modeling module 601 is configured to collect basic parameters of a plurality of target devices in the integrated energy system, and construct a device model corresponding to each target device, wherein the target devices include an electricity-to-gas device, a combined heat and power device, a gas boiler device, and a fuel cell device.

[0125] The optimization model creating module 602 is configured to construct an integrated energy optimization scheduling model aiming at minimizing economic cost and pollution gas emission based on the device model of each target device.

[0126] The condition constraint module 603 is configured to create a constraint condition corresponding to each target device according to the integrated energy optimization scheduling model.

[0127] The calculation module 604 is configured to solve the integrated energy optimization scheduling model by a particle swarm algorithm based on the constraint condition.

[0128] Optionally, Figure 6 The device modeling module 601 includes:

[0129] The first modeling unit 6011 is configured to obtain electricity-to-gas basic parameters of the electricity-to-gas device, and construct an electricity-to-gas model, wherein the electricity-to-gas model includes an electricity-to-heat model and a methanation model.

[0130] The electricity-to-heat model is:

[0131]

[0132]

[0133] In the formula, P k is the electricity power consumed for electrolysis in the k period; P k is the hydrogen production power consumed in the electrolysis process in the k period; P k is the power of hydrogen consumed in the methanation device in the k period; P k is the power of hydrogen produced in the methanation device in the k period; EL μ is a conversion coefficient of the methanation process; 2G μ is a conversion coefficient of the electricity-to-heat process;

[0134] The methanation model is:

[0135]

[0136]

[0137] In the formula, P k is the electricity power consumed for electrolysis in the k period; P k is the hydrogen production power consumed in the electrolysis process in the k period; P k is the power of hydrogen consumed in the methanation device in the k period; ME L is a gas amount of the carbon dioxide re-produced in the k period and entering the device; and L is a gas amount of the carbon dioxide sealed into the electricity-to-gas device in the k period. electric power for generating carbon dioxide; total gas amount generated in k period; electric power consumed in k period in the methanation device conversion process;

[0138] The second modeling unit 6012 is configured to acquire the combined heat and power basic parameters of the combined heat and power unit device, and construct a combined heat and power unit model.

[0139] P HS,i,k = P CHP,i,k + K v,i H CHP,i,k

[0140] In the formula, P HS,i,k is the electric power after conversion in k period; P CHP,i,k is the heat generation power of the combined heat and power unit device in k period; K v,i is the ratio of the heat and power of the steam type heat and power unit device; H CHP,i,k is the electric power of the combined heat and power unit device in k period;

[0141] The third modeling unit 6013 is configured to acquire the boiler basic parameters of the gas boiler device, and construct a gas boiler model.

[0142]

[0143] In the formula, F GB,k is the gas consumption of the gas boiler device in k period; Q GB,k is the heat transfer power of the gas boiler device in k period; μ GB,k is the thermal power coefficient of the gas boiler device in k period;

[0144] The fourth modeling unit 6014 is configured to acquire the cell basic parameters of the fuel cell device, and construct a fuel cell model.

[0145] P FC,k = V FC,ME,k L ME μ FC

[0146] In the formula, P FC,k is the heat power generated by the fuel cell device in k period; V FC,ME,k is the natural gas consumption required by the fuel cell device in k period; μ FC is the electric generation coefficient of the fuel cell device; L ME is the methane heat value.

[0147] Optionally, the integrated energy optimization scheduling model comprises an economic cost model and a pollution gas emission amount model, Figure 6 The optimization model creating module 602 is further configured to:

[0148] An economic cost model is constructed:

[0149]

[0150] P NG (k)=μ eg P P2G (k)

[0151] minE cos k is the minimum economic cost; H ep (k) is the peak-valley electricity price in the k period; H gp (k) is the natural gas price in the k period; is the carbon dioxide price in the k period; P e (k) is the electricity purchase quantity in the k period; P g (k) is the gas purchase quantity in the k period; P nloss (k) is the network loss in the k period; b is the conversion coefficient of carbon dioxide into natural gas; K is the scheduling period μ eg is the operation cost coefficient of the electricity-to-gas equipment; P P2G (k) is the output power of the electricity-to-gas equipment in the k period; P NG (k) is the operation cost in the k period;

[0152] A pollution gas emission model is constructed:

[0153]

[0154] minV mission is the minimum pollution gas emission; i is the number of pollution gas types generated in the process of purchasing electricity from the power grid; γ e,i,k represents the pollution coefficient of the i-th pollution gas generated in the process of purchasing electricity from the power grid in the k period; γ g,j,k represents the emission coefficient of the j-th new energy in the process of burning natural gas at the k moment; N represents the types of pollution gases generated in the whole system operation process; n represents the number of energy centers.

[0155] Optionally, Figure 6 The condition constraint module 603 comprises:

[0156] A first constraint unit 6031 is configured to create constraint conditions of the power system in the integrated energy system during system operation;

[0157] A second constraint unit 6032 is configured to create constraint conditions of the natural gas system in the integrated energy system during system operation;

[0158] The third constraint unit 6033 is configured to create an energy storage device constraint condition and an energy center input / output constraint condition in a coupling link of system runtime.

[0159] Optionally, the first constraint unit 6031 is further configured to create an operation voltage constraint and a power constraint of the power system in the system runtime.

[0160] The operation voltage constraint is:

[0161]

[0162] The power constraint is:

[0163]

[0164] In the formula, is a maximum value of the node voltage in per unit; is a minimum value of the node voltage in per unit; i is the node voltage in per unit; is a power value of the maximum power transmission line; and ij is the power value of the power transmission line.

[0165] The second constraint unit 6032 is further configured to create a natural gas transmission channel pressure constraint and a gas compressor compression ratio constraint of a natural gas system in the comprehensive energy system in the system runtime.

[0166] The natural gas transmission channel pressure constraint is:

[0167] P min ≤P i ≤P max

[0168] The gas compressor compression ratio constraint is:

[0169] K min ≤K i ≤K max

[0170] In the formula, P min and P max are a minimum value and a maximum value of the natural gas transmission channel pressure respectively; K min and K max are a minimum value and a maximum value of the gas compressor compression ratio respectively.

[0171] The third constraint unit 6033 is further configured to create a first type of energy center input / output constraint, a second type of energy center input / output constraint, a third type of energy center input / output constraint and an energy storage device constraint in the energy center in the coupling link of the system runtime.

[0172] The first type of energy center input-output constraint is:

[0173]

[0174] The second type of energy center input-output constraint is:

[0175]

[0176] The third type of energy center output-input constraint is:

[0177]

[0178] In the formula, and respectively represent the minimum and maximum values of the electric energy input; and are respectively the minimum and maximum values of the input natural gas; I, II, III are respectively the first type, the second type, and the third type of energy center; gs, es, hs are respectively the gas energy, the electric energy, and the thermal energy; is the thermal-electric coefficient of the combined heat and power unit; is the maximum power of the gas energy; η GB is the thermal power coefficient of the gas boiler equipment; is the maximum power of the electric energy; η AC is the electric power of the combined heat and power unit; L e,I is the pipeline transmission power;

[0179] The energy storage device constraint is:

[0180]

[0181] In the formula, x represents the energy type, including the gas energy, the electric energy, and the thermal energy; respectively represent the energy supply of the energy storage device at the k+1 period and the k period; δ x is the self-loss coefficient of the energy storage device; and are respectively the charging and discharging power at the k period; η x,c and η x,d are respectively the charging coefficient and the discharging coefficient; and respectively represent the minimum and maximum values of the energy that can be stored by the energy storage device; μ x is a natural coefficient between 0 and 1; Δk is a scheduling period; represents the device state and the energy at the beginning and the end of the period.

[0182] Optionally, Figure 6 The calculation module 604 in the formula includes:

[0183] The initialization unit 6041 is configured to initialize the integrated energy optimization scheduling model by using the particle swarm algorithm to obtain n particles representing solutions, each of which includes a velocity and a position;

[0184] The calculation unit 6042 is configured to calculate an initial fitness value of the integrated energy optimization scheduling model based on each particle, and determine a historical individual optimal position of the particle and a historical global optimal position of the particle swarm based on the initial fitness value;

[0185] The iteration unit 6043 is configured to perform iterative calculation based on a position update formula and a velocity update formula respectively, and calculate a particle fitness value of each iteration, update the historical individual optimal position of the particle and the historical global optimal position of the particle swarm based on the fitness value in the iteration process, and perform velocity update;

[0186] The position update formula is as follows:

[0187] V id (k+1) = V id (k) ω k +c1rand1(P bestd -x id (k))+c2rand2(g bestd -x id (k))

[0188] The velocity update formula is as follows:

[0189] x id (k+1) = x id (k)+V id (k)

[0190] wherein, V id (k) is the particle velocity of the current iteration, ω k is an inertia weight greater than 0; C1 and C2 are acceleration factors; rand1 and rand2 are random natural numbers in the range of [0, 1]; P bestd and g bestd are the historical global optimal position and the historical individual optimal position of the current iteration in sequence; and x id (k) is the position of the particle of the current iteration.

[0191] The judgment unit 6044 is configured to judge whether the iteration of the particle swarm algorithm reaches a preset ending condition.

[0192] The output unit 6045 is configured to output an optimal fitness value if the iteration of the particle swarm algorithm reaches the preset ending condition, and the optimal fitness value is the optimal solution of the integrated energy optimization scheduling model.

[0193] The iteration unit 6043 is further configured to continue to perform the updating of the speed and the position until an optimal solution is found if the iteration of the particle swarm algorithm does not reach the preset ending condition.

[0194] The device for operating a multi-comprehensive energy based on hybrid energy storage provided by the embodiment of the present application can realize each implementation manner of the method for operating a multi-comprehensive energy based on hybrid energy storage and the corresponding beneficial effects, and thus the description is not repeated here.

[0195] As shown in Figure 7 , Figure 7 A structural diagram of an electronic device is provided by the embodiment of the present application. As shown in Figure 7 , the structural diagram comprises a processor 701, a memory 702, a network interface 703, and a computer program stored in the memory 702 and executable on the processor 701, wherein:

[0196] The processor 701 is configured to call the computer program stored in the memory 702 to perform the following steps:

[0197] Collecting basic parameters of a plurality of target devices in a comprehensive energy system, and constructing a device model corresponding to each target device, wherein the target devices include an electric-gas conversion device, a combined heat and power unit device, a gas boiler device, and a fuel cell device;

[0198] Based on the device model of each target device, a comprehensive energy optimization scheduling model is constructed, with the minimum economic cost and pollution gas emission as the target;

[0199] According to the comprehensive energy optimization scheduling model, constraint conditions corresponding to each target device are created;

[0200] Based on the constraint conditions, the comprehensive energy optimization scheduling model is solved by a particle swarm algorithm.

[0201] Optionally, the comprehensive energy system includes a device for electric transmission, a device of a combined heat and power unit, and a device for heat transmission, and the processor 701 performs the collecting of the basic parameters of the plurality of target devices in the comprehensive energy system and the constructing of the device model corresponding to each target device, including:

[0202] Obtaining electric-gas conversion basic parameters of the electric-gas conversion device, and constructing an electric-gas conversion model, wherein the electric-gas conversion model includes an electric-heat conversion model and a methanation model;

[0203] The electric-heat conversion model is:

[0204]

[0205]

[0206] In the formula, the power consumed by the electrolysis device in the k period; the power consumed by the electrolysis device in the k period; the power consumed by the methanation device in the k period; the power consumed by the methanation device in the k period; EL the conversion coefficient of the methanation process; 2G the conversion coefficient of the methanation process;

[0207] The methanation model is:

[0208]

[0209]

[0210] wherein, the amount of gas re-emitted into the device in the k period; the amount of gas sealed into the device in the k period; ME the heat value of the methane; the power consumed by the electrolysis device in the k period; the total amount of gas generated in the k period; the power consumed by the methanation device in the k period;

[0211] Obtain the basic parameters of the combined heat and power unit device, and construct a combined heat and power unit model;

[0212] P HS,i,k = P CHP,i,k + K v,i H CHP,i,k

[0213] wherein, P HS,i,k the power after conversion in the k period; P CHP,i,k the heat generation power of the combined heat and power unit device in the k period; K v,i the ratio of the heat and power of the steam type combined heat and power unit device; H CHP,i,k the power generation power of the combined heat and power unit device in the k period;

[0214] Obtain the basic parameters of the gas-fired boiler device, and construct a gas-fired boiler model;

[0215]

[0216] wherein, F GB,k the gas consumption of the gas-fired boiler device in the k period; Q GB,k the heat transfer power of the gas-fired boiler device in the k period; μ GB,k the heat power coefficient of the gas-fired boiler device in the k period;

[0217] obtaining a cell basic parameter of a fuel cell device, and constructing a fuel cell model;

[0218] P FC,k = V FC,ME,k L ME μ FC

[0219] wherein, P FC,k is a heat power generated by the fuel cell device in a k period; V FC,ME,k is a natural gas consumption required by the fuel cell device in the k period; μ FC is a power generation coefficient of the fuel cell device; L ME is a methane heat value.

[0220] Optionally, the integrated energy system comprises a power system and a natural gas system formed by each target device, and the processor 701 executes the integrated energy optimization scheduling model constructed with the minimum economic cost and pollution gas emission as the target, comprising:

[0221] The integrated energy optimization scheduling model comprises an economic cost model and a pollution gas emission model, wherein:

[0222] constructing the economic cost model:

[0223]

[0224] P NG (k) = μ eg P P2G (k)

[0225] wherein, minE cos k is a minimum economic cost; H ep (k) is a peak-valley electricity price in the k period; H gp (k) is a natural gas price in the k period; is a carbon dioxide price in the k period; P e (k) is a power purchase amount in the k period; P g (k) is a gas purchase amount in the k period; P nloss (k) is a network loss in the k period; b is a conversion coefficient of converting carbon dioxide into natural gas; K is a scheduling period; μ eg is an operation cost coefficient of the electricity-to-gas device; P P2G (k) is an output power of the electricity-to-gas device in the k period; P NG (k) is an operation cost in the k period;

[0226] constructing the pollution gas emission model:

[0227]

[0228] minV mission is the minimum pollution gas emission; i is the number of pollution gas types generated in the process of purchasing electricity from the power grid; γ e,i,k represents the pollution coefficient of the i-th pollution gas generated by purchasing electricity from the power grid in the k period; γ g,j,k represents the emission coefficient of the j-th new energy source in the process of burning natural gas at the k moment; N represents the number of pollution gas types generated in the whole system operation process; and n represents the number of energy centers.

[0229] Optionally, the processor 701 executes to create constraint conditions corresponding to each target device according to the integrated energy optimization scheduling model, including:

[0230] creating a constraint condition of a power system in the integrated energy system during system operation;

[0231] creating a constraint condition of a natural gas system in the integrated energy system during system operation;

[0232] creating a constraint condition of an energy storage device in a coupling link during system operation and a constraint condition of input and output of an energy center.

[0233] Optionally, the processor 701 executes to create a constraint condition of a power system in the integrated energy system during system operation, including:

[0234] creating a running voltage constraint and a power constraint of the power system during system operation;

[0235] The running voltage constraint is:

[0236]

[0237] The power constraint is:

[0238]

[0239] wherein, is a maximum value of a node voltage in a per unit value; is a minimum value of the node voltage in the per unit value; V i is the node voltage in the per unit value; is a power value of a maximum power transmission line; S ij is the power value of the power transmission line.

[0240] Optionally, the processor 701 executes to create a constraint condition of a natural gas system in the integrated energy system during system operation, including:

[0241] creating a natural gas transportation channel pressure constraint and a gas compressor compression ratio constraint of the natural gas system in the integrated energy system during system operation;

[0242] wherein the natural gas transportation channel pressure constraint is:

[0243] P min ≤P i ≤P max

[0244] the gas compressor compression ratio constraint is:

[0245] K min ≤K i ≤K max

[0246] wherein P min and P max are the minimum and maximum values of the natural gas transportation channel pressure; K min and K max are the minimum and maximum values of the gas compressor compression ratio.

[0247] Optionally, the processor 701 performs the creation of the energy storage device constraint condition and the energy center input and output constraint condition in the coupling link of the system runtime, including:

[0248] the creation of the first type of energy center input and output constraint, the second type of energy center input and output constraint, the third type of energy center input and output constraint, and the energy storage device constraint in the coupling link of the system runtime;

[0249] wherein the first type of energy center input and output constraint is:

[0250]

[0251] the second type of energy center input and output constraint is:

[0252]

[0253] the third type of energy center input and output constraint is:

[0254]

[0255] wherein, and respectively represent the minimum and maximum values of the electric energy input; and are the minimum and maximum values of the input natural gas; I, II, III are the first type, the second type, and the third type of energy center, respectively; gs, es, hs are the gas energy, the electric energy, and the heat energy, respectively; is the heat and power coefficient of the combined heat and power unit; is the maximum power of the gas energy; η GB is the heat power coefficient of the gas boiler device; ηmax is the maximum power of the electric energy; AC η is the electric power of the combined heat and power unit; e,I η is the pipeline transportation power;

[0256] The energy storage device constraint is:

[0257]

[0258] In the formula, x represents the energy type, including gas energy, electric energy and heat energy; respectively represent the energy supply of the energy storage device at the k+1 period and the k period; δ x is the self-loss coefficient of the energy storage device; and respectively are the charging and discharging power at the k period; η x,c and η x,d respectively are the charging coefficient and the discharging coefficient; and respectively represent the minimum and maximum values of the energy that can be stored by the energy storage device; μ x is a natural coefficient between 0 and 1; Δk is a scheduling period; represent the device state and the energy at the beginning and the end of the period.

[0259] Optionally, the processor 701 performs solving the integrated energy optimization scheduling model by using the particle swarm algorithm, and the solving includes:

[0260] The integrated energy optimization scheduling model is initialized by using the particle swarm algorithm, and n particles representing solutions are obtained, each particle including a velocity and a position;

[0261] An initial fitness value of the integrated energy optimization scheduling model is calculated based on each particle, and a historical individual optimal position of the particle and a historical global optimal position of the particle swarm are determined based on the initial fitness value;

[0262] An iterative calculation is performed based on a position updating formula and a velocity updating formula of the particle, a particle fitness value of each iteration is calculated, the historical individual optimal position of the particle and the historical global optimal position of the particle swarm are updated based on the fitness value in the iteration process, and the velocity is updated;

[0263] The position updating formula is:

[0264] V id (k+1) = V id (k)ω k +c1rand1(P bestd -x id (k))+c2rand2(g bestd -x id (k))

[0265] The velocity update formula is:

[0266] x id (k+1)=x id (k)+V id (k)

[0267] Wherein, V id (k) is the particle velocity of the current iteration, omega k is an inertial weight greater than 0; C1, C2 are acceleration factors; rand1, rand2 are random natural numbers, the range is in [0,1]; P bestd , g bestd are the historical global optimal position and the historical individual optimal position of the current iteration in turn; x id (k) is the position of the particle of the current iteration;

[0268] It is judged whether the iteration of the particle swarm algorithm reaches the preset ending condition;

[0269] If the iteration of the particle swarm algorithm reaches the preset ending condition, the optimal fitness value is output, and the optimal fitness value is the optimal solution of the comprehensive energy optimization scheduling model;

[0270] If the iteration of the particle swarm algorithm does not reach the preset ending condition, the updating of the velocity and the position is continuously executed until the optimal solution is found.

[0271] The embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize each process of the embodiment of the present application, and the same technical effect can be achieved, to avoid repetition, here is not repeated.

[0272] It should be noted that only 701-703 with components are shown in the figure, but it should be understood that all the shown components are not required to be implemented, and more or less components can be alternatively implemented. Among them, the electronic device herein is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions, and the hardware thereof includes but is not limited to microprocessor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), digital signal processor (DSP), embedded device, etc.

[0273] The electronic device 700 can be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The electronic device 700 can interact with a user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, and the like.

[0274] The memory 702 includes at least one type of readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, and the like), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 702 can be an internal storage unit of the electronic device, such as a hard disk or a memory of the electronic device. In other embodiments, the memory 702 can also be an external storage device of the electronic device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Of course, the memory 702 can include both the internal storage unit and the external storage device of the electronic device. In this embodiment, the memory 702 is generally used to store an operating system and various application programs installed in the electronic device, such as program codes of a multi-comprehensive energy operation optimization method based on hybrid energy storage.

[0275] The processor 701 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 701 is generally used to control the overall operation of the electronic device. In this embodiment, the processor 701 is used to execute program codes or process data stored in the memory 701, such as program codes of a multi-comprehensive energy operation optimization method based on hybrid energy storage.

[0276] The network interface 703 can include a wireless network interface or a wired network interface, and the network interface 703 is generally used to establish a communication connection between the electronic device 700 and other electronic devices.

[0277] The embodiment of the present application also provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by the processor 701 to implement each process of an embodiment of a multi-comprehensive energy operation optimization method based on hybrid energy storage provided by the embodiment of the present application, and can achieve the same technical effects. To avoid repetition, details are not described here.

[0278] A person of ordinary skill in the art can understand that all or part of the process in the embodiment of the mixed energy storage based multi-comprehensive energy operation optimization method can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium, and when the program is executed, the program can include the process of each embodiment of the method. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), or the like.

[0279] The terms "first", "second", and the like in the specification and claims of the present application or in the above-described drawings are used to distinguish different objects, rather than to describe a specific order. Referring to "embodiments" herein means that the specific features, structures, or characteristics described in conjunction with the embodiments can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it mutually exclusive or alternative to other embodiments. A person of ordinary skill in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0280] The above only discloses the preferred embodiments of the present application, and of course cannot limit the scope of the rights of the present application, so equivalent changes made in accordance with the claims of the present application are still within the scope of the present application.

Claims

1. A hybrid energy storage based multi-comprehensive energy operation optimization method, characterized in that, The method comprises the following steps: Collecting basic parameters of a plurality of target devices in a comprehensive energy system, and constructing a device model corresponding to each of the target devices, wherein the target devices include an electricity-to-gas device, a combined heat and power unit device, a gas boiler device, and a fuel cell device; Based on the device model of each of the target devices, a comprehensive energy optimization scheduling model is constructed, with the minimum economic cost and pollution gas emission as the target; According to the comprehensive energy optimization scheduling model, constraint conditions corresponding to each of the target devices are created; Based on the constraint conditions, the comprehensive energy optimization scheduling model is solved by a particle swarm algorithm; Wherein, the collecting basic parameters of a plurality of target devices in a comprehensive energy system, and constructing a device model corresponding to each of the target devices, comprises: Obtaining electricity-to-gas basic parameters of the electricity-to-gas device, and constructing an electricity-to-gas model, wherein the electricity-to-gas model includes an electricity-to-heat model and a methanation model; The electricity-to-heat model is: wherein Pelek is the electrical power consumed for electrolysis for the k period; Pehk is the hydrogen production power consumed during electrolysis for the k period; Pehk is the hydrogen production power consumed during electrolysis for the k period; Pehk is the hydrogen production power consumed during electrolysis for the k period; Xmeth is the conversion factor for the methanation process; Xelec is the conversion factor for the electrical heat transfer; The methanation model is: wherein is the amount of gas entering the plant from the re- production of carbon dioxide for the k period; is the amount of gas sequestered into the plant for the k period; is the heat value of methane; is the electric power produced for the k period; is the total amount of gas produced for the k period; is the electric power consumed in the conversion process in the methanation unit for the k period; Obtaining combined heat and power basic parameters of the combined heat and power unit device, and constructing a combined heat and power unit model; wherein, is the electric power converted to k period; is the heat generation power of the cogeneration unit device for k period; is the ratio of the heat and power of the steam type heat and power unit device; is the power generation of the cogeneration unit device for k period; Obtaining boiler basic parameters of the gas boiler device, and constructing a gas boiler model; wherein is the gas consumption consumed by the gas boiler plant at the k moment; is the heat transfer power of the gas boiler plant at the k moment; is the thermal power coefficient of the gas boiler plant at the k moment; Obtaining cell basic parameters of the fuel cell device, and constructing a fuel cell model; wherein is the thermal power generated by the fuel cell plant during the k period; is the amount of natural gas consumed by the fuel cell plant to operate during the k period; is the power generation coefficient of the fuel cell plant; is the methane heat value.

2. The method of claim 1, wherein, The comprehensive energy system includes a power system and a natural gas system formed by each of the target devices, and the comprehensive energy optimization scheduling model is constructed with the minimum economic cost and pollution gas emission as the target, comprising: The comprehensive energy optimization scheduling model includes an economic cost model and a pollution gas emission model, wherein: The economic cost model is constructed: wherein, is the minimum economic cost; is the peak-valley electricity price of k period; is the natural gas price of k period; is the carbon dioxide price of k period; is the electricity purchase quantity of k period; is the gas purchase quantity of k period; is the network loss of k period; b is the conversion coefficient of carbon dioxide into natural gas; K is the dispatching period; is the operation cost coefficient of the electricity-to-gas equipment; is the output power of the electricity-to-gas equipment of k period; is the operation cost of k period; The pollution gas emission model is constructed: In the formula: is the minimum pollution gas emission; i is the number of pollution gas types generated in the process of purchasing electricity from the power grid; represents the pollution coefficient of the ith pollution gas generated from the power grid at the k period; represents the emission coefficient of the jth new energy source in the process of burning natural gas at the k moment; N represents the types of pollution gases generated in the whole system operation process; n represents the number of energy centers.

3. The method of claim 2, wherein, According to the comprehensive energy optimization scheduling model, constraint conditions corresponding to each of the target devices are created, comprising: Creating a constraint condition of the power system in the comprehensive energy system during system operation; Creating a constraint condition of the natural gas system in the comprehensive energy system during system operation; Creating a constraint condition of an energy storage device in a coupling link and an energy center input-output constraint condition during system operation.

4. The method of claim 3, wherein, The constraint condition of the power system in the comprehensive energy system during system operation comprises: Creating a running voltage constraint and a power constraint of the power system during system operation; The running voltage constraint is: The power constraint is: wherein is the maximum value of the node voltage in per unit; is the minimum value of the node voltage in per unit; is the node voltage in per unit; is the power value of the maximum power transmission line; is the power value of the power transmission line.

5. The method of claim 3, wherein, The constraint condition of the natural gas system in the comprehensive energy system during system operation comprises: Creating a natural gas transportation channel pressure constraint and a gas compressor compression ratio constraint of the natural gas system in the comprehensive energy system during system operation; The natural gas transportation channel pressure constraint is: The gas compressor compression ratio constraint is: wherein and are the minimum and maximum values of the pressure in the natural gas transport channel, respectively; and are the minimum and maximum values of the compression ratio of the gas compressor, respectively.

6. The method of claim 3, wherein, The constraint condition of the energy storage device in the coupling link and the energy center input-output constraint condition during system operation comprises: Creating a first-type energy center input-output constraint, a second-type energy center input-output constraint, a third-type energy center input-output constraint, and an energy storage device constraint in the coupling link during system operation; The first-type energy center input-output constraint is: The second-type energy center input-output constraint is: The third type of energy center output input constraint is: wherein, and respectively represent the minimum and maximum values of the electric energy input; and respectively represent the minimum and maximum values of the input natural gas; I, II, CXXIII are respectively the first, second and third energy centers in turn; is the thermal-electric coefficient of the cogeneration unit; is the maximum power of the gas energy; is the thermal power coefficient of the gas boiler device; is the maximum power of the electric energy; is the electric power of the cogeneration unit; is the pipeline transmission power; The energy storage device constraint is: In the formula, x represents energy type, including gas energy, electric energy, and thermal energy; , E(k+1) and E(k) represent energy supply of energy storage device in k+1 period and k period respectively; is self-loss coefficient of energy storage device; and P(k) and P(k) represent charging and discharging power in k period respectively; and C and D represent charging and discharging coefficients respectively; and Emin and Emax represent minimum and maximum energy storage of energy storage device respectively; is natural coefficient between 0 and 1; is a scheduling period; represents that device state and energy are equal at the beginning and end of the period.

7. The method of claim 1, wherein, The solving of the comprehensive energy optimization scheduling model by the particle swarm algorithm comprises: The comprehensive energy optimization scheduling model is initialized by the particle swarm algorithm to obtain n particles representing solutions, each particle comprising a velocity and a position; Based on each particle, an initial fitness value of the comprehensive energy optimization scheduling model is calculated, and a historical individual optimal position of the particle and a historical global optimal position of the particle swarm are determined based on the initial fitness value; Based on a position update formula and a velocity update formula, iterative calculation is performed, and a particle fitness value of each iteration is calculated, and the historical individual optimal position of the particle and the historical global optimal position of the particle swarm are updated based on the fitness value in the iteration process, and velocity updating is performed; The position update formula is: The velocity update formula is: wherein, is the particle velocity for the current iteration number, is an inertia weight greater than 0; C1, C2 are acceleration factors; , is a random natural number in the range [0, 1]; , are, in turn, the historical global optimum position and the historical individual optimum position for the current iteration number; is the position of the particle for the current iteration number; It is judged whether the iteration of the particle swarm algorithm reaches a preset ending condition; If the iteration of the particle swarm algorithm reaches the preset ending condition, an optimal fitness value is output, and the optimal fitness value is an optimal solution of the comprehensive energy optimization scheduling model; If the iteration of the particle swarm algorithm does not reach the preset ending condition, the updating of the velocity and the position is continuously performed until an optimal solution is found.

8. A hybrid energy storage based multi-comprehensive energy operation optimization device for performing the method of any one of claims 1-7, characterized in that, Comprise: The device modeling module is configured to collect basic parameters of a plurality of target devices in the comprehensive energy system, and construct device models corresponding to the target devices, wherein the target devices include an electric-to-gas device, a combined heat and power unit device, a gas-fired boiler device, and a fuel cell device. The optimization model creation module is configured to construct a comprehensive energy optimization scheduling model aiming to minimize economic cost and pollution gas emission based on the device models of the target devices. The constraint condition module is configured to create constraint conditions corresponding to the target devices according to the comprehensive energy optimization scheduling model. The calculation module is configured to solve the comprehensive energy optimization scheduling model by a particle swarm algorithm based on the constraint conditions.

9. An electronic device, comprising: Comprise: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps in the multi-comprehensive energy operation optimization method based on hybrid energy storage according to any one of claims 1 to 7 when executing the computer program.

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