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CN118676895B - A safe low-carbon operation and energy sharing method and device between multiple energy systems - Google Patents

A safe low-carbon operation and energy sharing method and device between multiple energy systems Download PDF

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CN118676895B
CN118676895B CN202410648991.8A CN202410648991A CN118676895B CN 118676895 B CN118676895 B CN 118676895B CN 202410648991 A CN202410648991 A CN 202410648991A CN 118676895 B CN118676895 B CN 118676895B
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侯恺
曲嘉伟
徐凯文
赵瑞锋
郭文鑫
卢建刚
赵敏
贾宏杰
朱乐为
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Abstract

The invention discloses a safe low-carbon operation and energy sharing method and device among multiple energy systems, wherein the device comprises an information processing module, an energy strategy analysis module, a strategy rapid generation module and an energy sharing module, wherein the information processing module is used for realizing all information perception of the energy systems, partial information perception of other interconnected energy systems and total information deduction of other energy systems based on incomplete information, the energy strategy analysis module is used for establishing an economic-safe-environmental protection collaborative optimization model of each energy system based on output information of the information processing module, the strategy rapid generation module is used for realizing rapid generation of an optimal strategy, and the energy sharing module is used for realizing energy sharing among the energy systems. The method and the device have the advantages that the collaborative optimization of environmental protection, safety and economic optimization is achieved, the calculation efficiency is obviously improved, and the iteration times of the algorithm are fewer and the convergence precision is higher under the same deployment environment and the same convergence precision.

Description

Safe low-carbon operation and energy sharing method and device among multiple energy systems
Technical Field
The invention relates to the technical field of comprehensive energy system safety promotion, in particular to a method and a device for safe low-carbon operation and energy sharing among multiple energy systems.
Background
With the development and application of integrated energy systems, the scheduling optimization of the energy systems has been converted from single-objective optimization to balanced operation optimization scheduling of various desired objectives. In the coupling interaction process of all subsystems of the energy system, multi-main-body optimization scheduling research generally surrounds optimization targets such as economy, stability, reliability, environmental friendliness and the like of the energy system so as to construct a collaborative optimization strategy considering multiple uncertainty indexes. For example, the multi-objective optimization model established by literature can realize the improvement of safety while meeting the requirements of ensuring the operation economic benefit, and the thermodynamic equilibrium equations of the network source side and the load side in the comprehensive energy system are deduced by literature, so that the quality collaborative operation of the energy system is promoted by taking the lowest operation cost and the highest energy equilibrium efficiency as single objective functions. The system energy storage grid-connected equipment is considered in literature, the membership function is adopted to perform normalized solution on the system multi-objective function, the literature analyzes energy flows and information flows of users, energy supply enterprises, functional equipment and energy system service providers in the energy system, and demand side management is performed from three aspects of load transferability, load shedding and adjustable thermal load so as to improve triple promotion of system economy, energy supply efficiency and environmental benefit. The existing optimizing equipment targets are developed from original source-load equipment to source-network-load-storage related facilities, renewable energy sources and electricity storage systems are brought into a system optimizing and dispatching range in a literature, the energy utilization level of the energy systems is improved, the non-convex problem of combined optimization of original energy generators, energy storage and other facilities and the energy systems is converted into convex optimization in the literature, and the improvement of solving efficiency is realized.
However, for a multi-energy system, most of documents only aim at an internal optimization method of the system and cannot be applied to a large-scale regional comprehensive energy system comprising a plurality of independent main bodies, but the multi-comprehensive system main body optimization method aiming at multi-energy lacks representation of energy interaction of the energy main body and related economic and environmental characteristic quantitative relationships, and on the other hand, the existing multi-main body control method capable of describing the economic and environmental characteristic quantitative relationships lacks effective combination of various targets of the system, so that the requirements of the three are met, and multi-main body balanced optimization under multi-target optimization is realized, so that the problem to be solved in the large-scale popularization and construction process of the current multi-energy system is urgently solved.
With the continuous development of multiple energy coupling systems, the optimal control of the multiple energy systems faces multiple uncertainties, especially the coordination safety optimization problem among the multiple energy systems and the energy distribution problem.
Disclosure of Invention
The invention aims to provide a safe low-carbon operation and energy sharing method and device among multiple energy systems, which aim to solve the problems that 1) how to realize optimal energy optimization control under the condition of unequal information of each energy system operated independently, 2) how to generate an optimal optimization control scheme in effective time due to the fact that economic and safe optimization among the multiple energy systems is a complex mixed integer optimization problem, and 3) how to formulate an optimal energy sharing strategy aiming at the optimal optimization scheme.
In order to achieve the purpose of the invention, the technical scheme provided by the invention is as follows:
First aspect
The application provides a safe low-carbon operation and energy sharing device among multiple energy systems, which comprises an information processing module, an energy strategy analysis module, a strategy rapid generation module and an energy sharing module;
the information processing module is used for realizing all information perception of the self energy system, partial information perception of other interconnected energy systems and all information deduction of other energy systems based on incomplete information;
the energy strategy analysis module is used for establishing an economic-safety-environmental protection collaborative optimization model of each energy system based on the output information of the information processing module;
The strategy rapid generation module is used for realizing the rapid generation of the optimal strategy through a self-adaptive augmentation Lagrangian method;
The energy sharing module is used for realizing energy sharing among the energy systems.
Second aspect
Correspondingly, the invention also provides a safe low-carbon operation and energy sharing method among the multi-energy systems, which is carried out by using the device.
Compared with the prior art, the invention has the beneficial effects that:
The method and the device have the advantages that the collaborative optimization of environmental protection, safety and economic optimization is achieved, the calculation efficiency is obviously improved, the iteration number of the algorithm can reach below 2/3 of the prior art under the same deployment environment and the same convergence precision, and the higher the convergence precision is, the better the technical effect of the scheme is.
Drawings
Fig. 1 is a schematic diagram of a device module structure according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a CIES operation architecture according to an embodiment of the present application;
FIG. 3 is a schematic diagram of power interaction of scenario 2 in an embodiment of the present application;
FIG. 4 is a schematic diagram of thermal energy interaction of scenario 2 according to an embodiment of the present application;
FIG. 5 is a graph showing the comparison of the iteration number of the present application with the prior art.
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The device provided by the invention inherits an economic-safety-environmental protection cooperative control strategy among multiple energy sources and an energy sharing technology suitable for the condition of non-information peer-to-peer.
As shown in fig. 1, the embodiment provides a device for safe low-carbon operation and energy sharing among multiple energy systems, which comprises an information processing module, an energy strategy analysis module, a strategy rapid generation module and an energy sharing module;
the information processing module is used for realizing all information perception of the self energy system, partial information perception of other interconnected energy systems and all information deduction of other energy systems based on incomplete information;
Specifically, the information processing module comprises an energy information perception sub-module and an incomplete information deduction sub-module;
the energy information perception submodule is specifically used for:
(1) And collecting primary data from different energy devices (such as a cogeneration unit CHP, an air source heat pump Ashp and an electric refrigerating unit EC), including air inflow, power consumption and the like.
(2) Device operation constraint generation generates other important data such as electric power, thermal power, heat generation amount and output amount based on the acquired data and a preset model. Comprising the following steps:
1) Non-hydrogen energy device constraints, applying equation (1) to calculate the output of each non-hydrogen energy device (e.g., CHP), including electrical power, gas input, thermal power, and cold output;
Wherein CHP is a cogeneration unit, ashp is an air source heat pump, gshp is a ground source heat pump, EC is an electric refrigerating unit, AC is an absorption refrigerating unit, P, G, H and O respectively represent electric power, gas input, heat power and cold output, eta represents the efficiency of the device, and mu CHP is the heat conversion coefficient of the CHP;
Where C represents the capacity of the device, P min represents the lower limit of the device's electrical force, The climbing upper limit rate of the CHP unit is set, and t represents a time interval;
2) The hydrogen energy equipment constraint comprises the operation characteristics and efficiency models of the fuel cell and the electrolytic tank equipment, and the output power and the input power of the hydrogen energy equipment constraint are calculated according to formulas (3), (4) and (5);
The operating characteristics of a fuel cell (SOFC) are established based on the following equation:
Wherein the SOFC is a fuel cell, eta SOFC,E is the electrical conversion efficiency of the SOFC, eta SOFC,T is the thermal energy conversion efficiency of the SOFC; Inputting hydrogen power for the SOFC at time t; Output electrical power at time t for the SOFC; The output thermal power at time t for the SOFC;
An operation model of the electrolytic cell (EL) is established based on the following formula:
wherein EL is an electrolytic cell, and eta EL is the energy conversion efficiency of EL; output hydrogen power at time t for EL; The input electric power of EL at time t, q H2 is the hydrogen energy heating value.
Wherein, in order to infer the overall information of other energy systems from the incomplete information, the incomplete information deduction submodule is specifically used for firstly collecting the real-time and historical data of the self and other interconnected energy systems and preprocessing necessary data to ensure the reliability and consistency of the data, then extracting key characteristics of the data by utilizing a characteristic engineering method and supplementing the incomplete information by utilizing a data enhancement technology, and further constructing a data deduction method based on random forests, finally integrating the inferred complete information into an energy information sensing module, wherein the incomplete information deduction problem is expressed as that
Wherein e (t) is a measurement error of a terminal sensor, alpha k (t) is a start-stop state of internal equipment of other energy systems, P k (t) is power of equipment k, P (t) is output electric power of the energy systems, and m is total number of the equipment.
The energy strategy analysis module is used for establishing an economic-safety-environmental protection collaborative optimization model of each energy system based on the output information of the information processing module;
the energy strategy analysis module is specifically configured to:
(1) Based on an information processing module, constructing a three-bit collaborative energy analysis optimization model, wherein the model comprises three targets of economy, environmental protection and safety;
Economic objectives:
In the formula, Is the running cost of the whole system, T max is the typical total time of day, T is the running time of the system, k is the equipment index, m is the maximum number of equipment, Q gas is the purchasing power of natural gas, C gas is the price of natural gas, omega k is the maintenance cost of equipment k,The output power at the time t; representing the cost of energy transactions between the nth campus master and the nth' campus master;
environmental protection goal:
The environmental objectives of the operational optimization are to minimize CO 2 emissions during system operation, the optimization objective functions are as follows:
Wherein f E is the CO 2 emission of the whole system, alpha e and alpha gas are the carbon dioxide emission coefficients of external electricity purchasing and natural gas combustion respectively, P e is the consumption of electric power, and G gas is the consumption of natural gas;
Security objective:
the safety objective of the operational optimization is to minimize energy loss during system operation, and the optimization objective function is as follows:
Wherein, f R is energy loss during system operation, beta H and beta E are load reduction coefficients of thermal load and electric load respectively, and R E and R H are optimal reduction amounts of electric energy and heat energy respectively;
And (3) implementing multi-target weighting, namely integrating a plurality of targets by a weighting method, and analyzing the change relation among the plurality of targets by using a traditional linear weighting method. However, the conventional linear weighting method can explore the change relation among a plurality of targets, which is based on the analysis of mass weight coefficients and cannot meet the requirement of time control. For this purpose, the invention directly enters the sensitivity analysis into the objective function to directly find an acceptable approximate multi-objective equalization solution:
Wherein x is a continuous decision variable, G, H, g, h is a coefficient matrix of an optimization model, lambda 1、λ2、λ3 is a weight coefficient of an objective function, gamma 1、γ2、γ3 is a weight coefficient of an objective function gradient, and c f、c▽f is a normalization coefficient of the objective function and the objective function gradient. This formula means that the scenario when one object will change strongly with another object is not an optimal solution (because the absolute value of the gradient of the strongly changing object will be extremely large). In practical scheduling control this often means that a very small cost effort may lead to a huge objective function improvement. The utility function is introduced and converted into a simple target optimization form.
(3) The utility function is used for converting the multi-objective problem into a double-objective optimization problem, so that the computational complexity is remarkably reduced. The method comprises the following steps:
Wherein uc i (x) represents a utility function, which is used for representing the preference relation of two targets, and the formula means that any number of hydrogen-containing comprehensive energy bodies can be converted into a double-target optimization problem, so that the calculation complexity is remarkably reduced; And Representing the optimal solution when lambda takes 0 and 1, respectively, the weight lambda being a coefficient of a decision variable, M being a very large positive number, z being a binary variable of the dual variable eta, x being a set of consecutive decision variables.
The strategy rapid generation module is used for realizing the rapid generation of the optimal strategy through a self-adaptive augmentation Lagrangian method;
In the prior art, an ADMM algorithm is generally adopted for calculation, so that multi-main-body multi-target collaborative optimization is realized. The algorithm flow is to split the original problem into a plurality of sub-problems by constructing an augmented Lagrangian function, adopt asynchronous iteration among the sub-problems, update corresponding dual variables and finally realize the solution of the original problem in a mode of common convergence.
(1) Problem solving the problem is solved by splitting the optimization problem into a number of sub-problems by constructing an augmented lagrangian function using the ADMM algorithm, converting the multi-park objective function (11) into the form of the Nash bargaining, but is essentially a non-convex nonlinear optimization problem. Converting the problem (11) into two alternating iterative convex optimization problems;
Sub-problem 1 solving optimal electric and thermal energy transaction amount between each garden
Where N is the total number of campus entities,Representing the optimized value of the main body n of the Nash park, and solving the problem to obtain the optimal electric and thermal energy trading volume among the parks;
Sub-problem 2 determining independent optimal solutions for each park at a game break point
In the formula,The game breaking point representing the park main body n is a constant and can be obtained through independent optimization solution of a single park;
(2) Constructing a Lagrangian function, carrying out asynchronous iteration on the split sub-problems by utilizing iteration of strategy information to realize game decision output, and updating dual variables;
where ρ is an increasing functional penalty factor, z takes the iteration result of x and feeds it back into x, For the economic cost after the (k+1) th iteration, f i(Xi) represents the value of the multi-principal objective function,Representing the average of the objective function solution after the kth iteration,And e k+1 is the iteration step length of the optimal solution for the iteration gap after the kth iteration. Repeatedly updating the information to realize information interaction;
the result of the target cost can be obtained by solving the function:
In the formula, c i is an iteration constraint value, two variables exist in buying and selling two kinds of energy behaviors due to the transaction of energy, the variables are multiplied in solving, auxiliary variables are introduced to express, and the following solving formula is satisfied:
Where P n,n',t and P n',n,t are the electric power flowing from park n to park n 'and the electric power flowing from park n' to park n, respectively, and H n,n',t and H n',n,t are the thermal power flowing from park n to park n 'and the thermal power flowing from park n' to park n, respectively.
(3) The original residual error and the dual residual error are used as the convergence standard of the judging algorithm, iteration is ended after the preset precision is reached, if the two conditions are met, the iteration is ended:
Where ε ori and ε dual are the nominal convergence accuracies of the original residual and the dual residual.
The energy sharing module is used for realizing energy sharing among the energy systems. The method aims at the problem of revenue distribution after sharing cooperation of the multi-energy system. The energy system main body participates in energy sharing, and energy is given/obtained, because the energy sharing reduces the energy dependence on a main power grid. It is generally believed that providing energy is believed to contribute more than obtaining the same amount of energy. The energy sharing module adopts a nonlinear function based on natural logarithms to quantify the contribution of comprehensive energy systems (Community INTEGRATED ENERGY SYSTEM, CIES) of different parks in energy sharing, and further, CIES (common information system) mutually negotiates with each contribution as bargained capability, so that the energy transaction price among CIES is determined, and the benefits of energy sharing are fairly distributed.
The energy sharing module is specifically configured to:
(1) Quantification of energy contribution in a multi-energy system, the energy sharing of each CIES is considered as its contribution to the system. This step involves evaluating the total amount of energy provided and received by each CIES, which data will be used for subsequent bargained capability evaluations.
In the formula,Is the energy value that the nth CIES internally provides to campus n' as a whole when participating in optimization; is the energy value obtained by the inside of the nth CIES when participating in optimization;
(2) Constructing a nonlinear function reflecting the bargained capability of each CIES in energy sharing by using natural logarithm and exponential functions, wherein the function quantifies the bargained capability of each CIES based on the energy provided and received by each CIES;
Wherein d n represents an energy factor; represents the total supplied energy maximum in each CIES; overall maximum energy is obtained;
wherein the nonlinear function of the quantized bargained capability size constructed based on the exponential function has the following functional features:
1) Any target subject that obtains objective values in the sharing obtains a proposed value;
2) The CIES of zero energy sharing is zero-proposal value, namely the bargaining capacity is always non-negative;
3) The greater the CIES target subject contribution, the greater the perceived value obtained and the greater the contribution of the provided energy than the received energy;
(3) The energy sharing equalization model construction comprises the following steps:
Establishing an equilibrium objective function, namely establishing a logarithmic objective function to express a gain maximization objective after CIES sharing in the alliance based on a Nash negotiation model;
Conversion optimization problem-due to the strictly monotonic increasing and convex nature of natural logarithms, the maximization problem is converted into the minimization problem to simplify the solving process, and the objective function of the logarithmic energy sharing balance model based on the Nash negotiation model can be expressed as:
wherein U CIES1、UCIES2、UCIESn is the energy sharing balance cost under Nash negotiations of the 1 st, 2 nd and n th parks respectively, The energy sharing balance costs for the 1 st, 2 nd and n th campus non-nash negotiations are respectively. Namely, taking the maximization of the improved benefits after the CIES alliance participates in target sharing as a target, the inequality ensures that the target value of each function sharing contribution can obtain benefits,
Because the natural logarithm is a strictly monotonically increasing convex function, the logarithm is obtained by the opposite equation, the problem of solving the maximum value can be converted into the problem of solving the minimum value so as to be convenient for solving, and the equation is obtained after the conversion.
(4) And implementing an optimal strategy, namely solving the converted minimization problem by using a proper mathematical optimization technology, and determining an optimal energy trading strategy and a corresponding price of each CIES. And the result application is that the energy flow and pricing among CIES are adjusted according to the solving result, so that the benefit of energy sharing is distributed fairly and effectively.
With the multiple parks shown in fig. 2 as the game master, economy, security, environmental protection are optimization objectives.
Table 1 scene comparison
In order to verify the effectiveness of the provided optimization method, 4 scenes are solved respectively, and the solving results are as follows:
TABLE 2 comparison of economic cost and carbon emissions for the system
As can be seen from the table, if cooperative game is adopted among a plurality of parks, the overall and individual park economic costs are obviously reduced, for example, in the scene 1 and the scene 2, the total park economic cost is reduced by 3.3369 multiplied by 10 8 yuan, the CIES1 economic cost is reduced by 1.9963 multiplied by 10 8 yuan, the carbon emission is reduced to a certain extent, which means that the total energy consumption is reduced, the energy utilization rate is improved, and by adopting cooperative game in the parks, not only the energy consumption cost in the parks is reduced, but also the carbon emission in the parks is reduced, for example, compared with the scene 1 and the scene 4, the CIES1 economic cost is reduced by nearly half, and the carbon emission is reduced by 3.5811 multiplied by 10 8 kg.
In fig. 3-5 positive values indicate supplied energy and negative values indicate received energy. In the aspect of electric energy supply, when CIES2 is in a range of 0:00-5:00, electric energy supply is insufficient, CIES1 is used for supplying electric energy to CIES2, when CIES3 is in a range of 0:00-7:00, electric energy supply is also performed by CIES1 for supplying electric energy to CIES2, but when CIES is in a range of 8:00-17:00, electric energy supply is performed by CIES3, fluctuation of CIES2 and CIES3 is large in a range of 11:00-22:00, and energy interaction is frequent. In the aspect of heat energy supply, the energy supply of three CIES (common information element) is gentle from 0:00 to 8:00, and the heat energy fluctuation is large and the energy interaction is frequent along with the working time of entering the daytime from 8:00 to 18:00. CIES1 and CIES3 are often used as energy consumers for energy consumption, and the cost of two parks is high and the load demand is high. Overall, the CIES can realize complementary mutual assistance of energy, and the stable energy supply of the system is facilitated.
In addition, a safe low-carbon operation and energy sharing method among multiple energy systems is provided, and the method is performed by the device.
The foregoing details of the optional implementation of the embodiment of the present invention have been described in conjunction with the accompanying drawings, but the embodiment of the present invention is not limited to the specific details of the foregoing implementation, and various simple modifications may be made to the technical solution of the embodiment of the present invention within the scope of the technical concept of the embodiment of the present invention, where all the simple modifications belong to the protection scope of the embodiment of the present invention.

Claims (9)

1.一种多能源系统间的安全低碳运行及能源共享装置，其特征在于，包括信息处理模块、能源策略分析模块、策略快速生成模块以及能源共享模块；1. A safe and low-carbon operation and energy sharing device among multiple energy systems, characterized by comprising an information processing module, an energy strategy analysis module, a strategy rapid generation module and an energy sharing module; 所述信息处理模块，用于实现自身能源系统的全部信息感知、其他互联能源系统部分信息感知以及基于不完全信息的其他能源系统全信息推演；The information processing module is used to realize the full information perception of its own energy system, the partial information perception of other interconnected energy systems, and the full information deduction of other energy systems based on incomplete information; 所述能源策略分析模块，用于基于信息处理模块的输出信息，建立各能源系统的经济-安全-环保性协同优化模型；The energy strategy analysis module is used to establish an economic-safety-environmental protection collaborative optimization model for each energy system based on the output information of the information processing module; 所述策略快速生成模块，用于通过自适应增广拉格朗日法，实现最优策略的快速生成；The strategy rapid generation module is used to achieve rapid generation of optimal strategies through an adaptive augmented Lagrangian method; 所述能源共享模块，用于实现各能源系统间的能源共享；The energy sharing module is used to realize energy sharing among various energy systems; 所述不完全信息推演子模块，具体用于首先通过收集自身及其他互联能源系统的实时和历史数据，并进行数据预处理以确保数据的可靠性和一致性，随后，利用特征工程方法提取数据的关键特征，并通过数据增强技术补充不完全的信息；并进一步构建基于随机森林的数据推演方法，最终，这些推断的完全信息被集成到能源信息感知模块，其中，不完全信息推演问题被表达为：The incomplete information deduction submodule is specifically used to first collect the real-time and historical data of itself and other interconnected energy systems, and perform data preprocessing to ensure the reliability and consistency of the data. Then, the key features of the data are extracted using feature engineering methods, and the incomplete information is supplemented by data enhancement technology; and a data deduction method based on random forest is further constructed. Finally, the inferred complete information is integrated into the energy information perception module, wherein the incomplete information deduction problem is expressed as: 式中，e(t)为终端传感器的测量误差；αk(t)为其他能源系统内部设备的启停状态；pk(t)为设备k的功率；P(t)是能源系统的输出电功率，m为设备总数。Where e(t) is the measurement error of the terminal sensor; αk (t) is the start/stop status of other devices in the energy system; pk (t) is the power of device k; P(t) is the output power of the energy system, and m is the total number of devices. 2.根据权利要求1所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，所述信息处理模块包括能源信息感知子模块和不完全信息推演子模块；2. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 1, characterized in that the information processing module comprises an energy information perception submodule and an incomplete information deduction submodule; 所述能源信息感知子模块，具体用于：The energy information sensing submodule is specifically used for: (1)数据采集；(1) Data collection; (2)设备运行约束生成，包括：(2) Generation of equipment operation constraints, including: 非氢能设备约束：应用公式(1)计算各非氢能设备的输出，包括电力、燃气输入、热力和冷输出；Non-hydrogen energy equipment constraints: Formula (1) is used to calculate the output of each non-hydrogen energy equipment, including electricity, gas input, heat and cold output; 式中，CHP为热电联产机组、Ashp为空气源热泵、Gshp为地源热泵、EC为电制冷机组、AC为吸收式制冷机组，P、G、H和O分别表示电力、燃气输入、热力和冷输出；η表示设备的效率；μCHP是CHP的热转换系数；Where, CHP is a combined heat and power unit, Ashp is an air source heat pump, Gshp is a ground source heat pump, EC is an electric refrigeration unit, AC is an absorption refrigeration unit, P, G, H and O represent electricity, gas input, heat and cold output respectively; η represents the efficiency of the equipment; μ CHP is the heat conversion coefficient of CHP; 式中，C表示设备的容量，Pmin代表设备电出力下限,为CHP机组爬坡上限率，t表示时间间隔；In the formula, C represents the capacity of the equipment, P min represents the lower limit of the equipment's power output, is the upper limit rate of the CHP unit ramp, and t represents the time interval; 氢能设备约束：包括燃料电池和电解槽设备的运行特性和效率模型，按照公式(3)、(4)和(5)计算其输出功率和输入功率；Hydrogen energy equipment constraints: including the operating characteristics and efficiency models of fuel cell and electrolyzer equipment, and calculating their output power and input power according to formulas (3), (4) and (5); 燃料电池的运行特性基于下式建立：The operating characteristics of a fuel cell are established based on the following equation: Pt SOFC＝ηSOFC,ETt SOFC (3)P t SOFC = η SOFC, E T t SOFC (3) Ht SOFC＝ηSOFC,TTt SOFC (4)H t SOFC = η SOFC,T T t SOFC (4) 式中，SOFC为燃料电池，ηSOFC,E为SOFC的电转换效率；ηSOFC,T为SOFC的热能转换效率；为SOFC在时间t的输入氢功率；为SOFC在时间t的输出电功率；为SOFC在时间t的输出热功率；Wherein, SOFC is a fuel cell, η SOFC,E is the electrical conversion efficiency of SOFC; η SOFC,T is the thermal energy conversion efficiency of SOFC; is the input hydrogen power of SOFC at time t; is the output power of SOFC at time t; is the output thermal power of SOFC at time t; 电解槽的运行模型基于下式建立：The operation model of the electrolyzer is based on the following formula: 式中，EL为电解槽，ηEL为EL的能量转换效率；为EL在时间t的输出氢功率；为EL在时间t的输入电功率；qH2为氢能热值。Where EL is the electrolytic cell, η EL is the energy conversion efficiency of EL; is the output hydrogen power of EL at time t; is the input electrical power of EL at time t; q H2 is the calorific value of hydrogen. 3.根据权利要求2所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，所述能源策略分析模块，具体用于：3. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 2, characterized in that the energy strategy analysis module is specifically used to: (1)基于信息处理模块，构建三位协同能源分析优化模型，所述模型包括经济性、环保性和安全性三个目标；(1) Based on the information processing module, a three-dimensional collaborative energy analysis optimization model is constructed, wherein the model includes three objectives: economy, environmental protection, and safety; (2)通过加权方法整合多个目标，使用线性加权法分析多个目标间的变化关系；(2) Integrate multiple objectives through weighted methods and use linear weighted methods to analyze the change relationship between multiple objectives; (3)使用效用函数转化多目标问题为双目标优化问题。(3) Use the utility function to transform the multi-objective problem into a bi-objective optimization problem. 4.根据权利要求3所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，4. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 3 is characterized in that: 经济性目标：Economic goals: 式中，是整个系统的运行成本；Tmax为典型日总时间；t代表系统运行时刻；k为设备索引，m为设备最大数量；Qgas是天然气的购买力；Cgas是天然气的价格，ωk表示设备k的维护成本，为t时刻的输出功率；代表第n个园区主体与第n’个园区主体之间的能量交易成本；In the formula, is the operating cost of the entire system; T max is the total time of a typical day; t represents the system operating time; k is the device index, m is the maximum number of devices; Q gas is the purchasing power of natural gas; C gas is the price of natural gas, ω k represents the maintenance cost of device k, is the output power at time t; represents the energy transaction cost between the nth park entity and the n'th park entity; 环保性目标：Environmental goals: 运行优化的环保性目标是将系统运行期间的CO2排放降至最低，优化目标函数如下：The environmental protection goal of operation optimization is to minimize CO2 emissions during system operation. The optimization objective function is as follows: 式中，fE是整个系统的CO2排放量；αe和αgas分别是外部购电和天然气燃烧的二氧化碳排放系数，Pe为电力的消耗量；Ggas为天然气的消耗量；Where f E is the CO 2 emission of the entire system; α e and α gas are the carbon dioxide emission coefficients of external electricity purchase and natural gas combustion, respectively; P e is the electricity consumption; G gas is the natural gas consumption; 安全性目标：Security goals: 运行优化的安全性目标是将系统运行期间的能源损失降到最低，优化目标函数如下：The safety goal of operation optimization is to minimize the energy loss during system operation. The optimization objective function is as follows: 式中，fR是系统运行期间的能源损失；βH和βE分别是热负荷、电负荷的负荷削减系数，RE和RH分别为电能、热能的最优削减量。Where fR is the energy loss during system operation; βH and βE are the load reduction factors of thermal load and electrical load, respectively; RE and RH are the optimal reduction amounts of electrical energy and thermal energy, respectively. 5.根据权利要求4所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，所述策略快速生成模块为通过构造增广拉格朗日函数，将原问题拆分为若干子问题，采用子问题间异步迭代，并更新相应的对偶变量，最终达到共同收敛的方式实现对原问题的求解。5. According to the safe and low-carbon operation and energy sharing device among multiple energy systems according to claim 4, it is characterized in that the strategy rapid generation module constructs an augmented Lagrangian function, splits the original problem into several sub-problems, adopts asynchronous iteration between sub-problems, and updates the corresponding dual variables, and finally achieves the solution to the original problem in a common convergence manner. 6.根据权利要求5所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，所述能源共享模块采用一种基于自然对数的非线性函数来量化不同园区综合能源系统CIES在能源共享中的贡献大小，进而，CIES之间以各自贡献为议价能力进行相互谈判，从而确定他们之间的能源交易价格。6. According to claim 5, the safe and low-carbon operation and energy sharing device among multiple energy systems is characterized in that the energy sharing module adopts a nonlinear function based on natural logarithm to quantify the contribution of different park integrated energy systems CIES in energy sharing, and then, CIES negotiate with each other based on their respective contributions as bargaining power to determine the energy transaction price between them. 7.根据权利要求6所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，所述能源共享模块，具体用于：7. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 6, characterized in that the energy sharing module is specifically used for: (1)能源共享的贡献度模型：(1) Contribution model of energy sharing: 式中，是第n个CIES在参与优化时内部总体向园区n’提供的能量值；是第n个CIES在参与优化时内部总体获得的能量值；In the formula, is the energy value provided by the nth CIES to the park n' when participating in the optimization; is the total internal energy value obtained by the nth CIES when participating in the optimization; (2)使用自然对数和指数函数构建一个反映各CIES在能源共享中议价能力大小的非线性函数，此函数基于各CIES提供和接收的能量来量化其议价能力；(2) Using natural logarithms and exponential functions, a nonlinear function is constructed to reflect the bargaining power of each CIES in energy sharing. This function quantifies the bargaining power of each CIES based on the energy provided and received. 式中，dn代表能量因子；代表各CIES中总体提供能量最大值；总体获得能量的最大值；In the formula, d n represents the energy factor; Represents the maximum overall energy provided by each CIES; The maximum value of overall energy obtained; (3)能源共享均衡模型构建，包括如下：(3) Construction of energy sharing equilibrium model, including the following: 建立均衡目标函数：基于纳什谈判模型，构建一个对数目标函数来表达联盟中CIES共享后的收益最大化目标；Establishing equilibrium objective function: Based on the Nash negotiation model, a logarithmic objective function is constructed to express the goal of maximizing the benefits after CIES sharing in the alliance; 转换优化问题：由于自然对数的严格单调递增和凸性质，将最大化问题转换为最小化问题，以简化求解过程，基于纳什谈判模型的对数能量共享均衡模型的目标函数可以被表达为：Conversion optimization problem: Due to the strictly monotonically increasing and convex nature of the natural logarithm, the maximization problem is converted into a minimization problem to simplify the solution process. The objective function of the logarithmic energy sharing equilibrium model based on the Nash negotiation model can be expressed as: 式中，UCIES1、UCIES2、UCIESn分别为第1、2、n个园区纳什谈判下的能量共享均衡成本，分别为第1、2、n个园区非纳什谈判下的能量共享均衡成本，式即以CIES联盟参与目标共享后提升的收益最大化为目标，不等式确保每个函数共享贡献的目标值都能获得收益，Where U CIES1 , U CIES2 , and U CIESn are the energy sharing equilibrium costs of the 1st, 2nd, and nth parks under Nash negotiation, respectively. are the energy sharing equilibrium costs of the 1st, 2nd, and nth parks under non-Nash negotiation, respectively. The formula is to maximize the benefits of CIES alliance participation after target sharing. The inequality ensures that the target value of each function sharing contribution can obtain benefits. 由于自然对数为严格单调递增凸函数，因此，对式取对数，可以将求最大值问题转换为求最小值问题便于求解，转换后得式；Since the natural logarithm is a strictly monotonically increasing convex function, taking the logarithm of the formula can convert the maximum value problem into a minimum value problem for easy solution. After the conversion, we get the formula; (4)求解转换后的最小化问题，确定每个CIES的最优能源交易策略和相应的价格，根据求解结果调整各CIES之间的能源流动和定价，确保公平有效地分配能源共享的收益。(4) Solve the converted minimization problem, determine the optimal energy trading strategy and corresponding price for each CIES, and adjust the energy flow and pricing between CIES based on the solution results to ensure fair and effective distribution of the benefits of energy sharing. 8.根据权利要求7所述的多能源系统间的安全低碳运行及能源共享装置，其特征在于，8. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 7, characterized in that: 式基于指数函数构建的量化议价能力大小的非线性函数具有以下功能特征：The nonlinear function of quantifying bargaining power based on the exponential function has the following functional characteristics: 1)任何在共享中获取客观值的目标主体都获得议价值；1) Any target subject that obtains objective value in sharing obtains bargaining value; 2)零能量共享的CIES为零议价值，即议价能力始终非负；2) CIES with zero energy sharing has zero bargaining value, that is, the bargaining power is always non-negative; 3)CIES目标主体贡献越大，获得的议价值越大，且提供能源比接受能源的贡献更大。3) The greater the contribution of the CIES target subject, the greater the negotiation value obtained, and the contribution of providing energy is greater than that of receiving energy. 9.一种多能源系统间的安全低碳运行及能源共享方法，其特征在于，所述方法利用如权利要求1-8中任一项所述的装置进行。9. A method for safe low-carbon operation and energy sharing among multiple energy systems, characterized in that the method is performed using a device as described in any one of claims 1-8.
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