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

Abstract

The present invention discloses a method and device for safe and low-carbon operation and energy sharing among multiple energy systems, wherein the device includes 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 to realize the full information perception of the energy system, the partial information perception of other interconnected energy systems, and the full information deduction of other energy systems based on incomplete information; the energy strategy analysis module is used to establish an economic, safety, and environmental protection collaborative optimization model for each energy system based on the output information of the information processing module; the strategy rapid generation module is used to realize the rapid generation of the optimal strategy; the energy sharing module is used to realize energy sharing among various energy systems. While achieving collaborative optimization of environmental protection, safety, and economic optimization, the present invention also significantly improves the computing efficiency; under the same deployment environment and the same convergence accuracy, the algorithm of the present invention has fewer iterations and higher convergence accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of safety enhancement of integrated energy systems, and in particular to a method and device for safe low-carbon operation and energy sharing among multiple energy systems. Background Art

[0002] With the development and application of integrated energy systems, the dispatch optimization of energy systems has been transformed from single-objective optimization to balanced operation optimization dispatch with multiple expected objectives. In the process of coupling and interaction among the subsystems of the energy system, multi-agent optimization dispatch research generally focuses on optimization objectives such as energy system economy, stability, reliability and environmental friendliness to build a collaborative optimization strategy that considers multiple uncertainty indicators. For example, a multi-objective optimization model established in some literature achieves improved safety while ensuring the economic benefits of operation; some literature derives the thermodynamic equilibrium equations on the source and load sides of the network in the integrated energy system, and takes the lowest operating cost and the highest energy balance efficiency as the single objective function, which promotes the quantitative and qualitative coordinated operation of the energy system. Some literature considers the system energy storage grid-connected equipment, and uses the membership function to normalize and solve the system's multi-objective function; some literature analyzes the energy flow and information flow of users, energy supply enterprises, functional equipment and energy system service providers in the energy system, and conducts demand-side management from three aspects: transferable load, load shedding load and adjustable thermal load, so as to improve the system's economy, energy supply efficiency and environmental benefits. The existing optimization equipment objectives have also developed from the original source-load equipment to source-grid-load-storage related facilities; some literature has included renewable energy and power storage systems into the scope of system optimization and scheduling, improving the energy utilization level of the energy system; some literature has transformed the non-convex problem of joint optimization of the original energy generators, energy storage and other facilities with the energy system into convex optimization, thereby achieving improved solution efficiency.

[0003] However, for multi-energy systems, most literature only focuses on optimization methods within the system and cannot be applied to large-scale regional integrated energy systems containing multiple independent entities. As for the optimization methods of multi-energy multi-integrated system entities, on the one hand, there is a lack of representation of the quantitative relationship between energy interactions and related economic and environmental characteristics of energy entities. On the other hand, the existing multi-agent control methods that can describe the quantitative relationship between economic and environmental characteristics lack an effective combination of multiple system objectives. How to meet the three requirements and achieve multi-agent balanced optimization under multi-objective optimization is an urgent problem to be solved in the current large-scale promotion and construction of multi-energy systems.

[0004] With the continuous development of multi-energy coupling systems, the optimization control of multi-energy systems faces various uncertain risks, especially the coordinated safety optimization and energy distribution problems among multi-energy systems. Summary of the invention

[0005] The present invention aims to provide a method and device for safe and low-carbon operation and energy sharing among multiple energy systems, in the hope of solving the following problems: 1) How can each independently operating energy system achieve optimal energy optimization control under the condition of information asymmetry; 2) The economic and safety optimization among multiple energy systems is a complex mixed integer optimization problem, how to generate the best optimization control scheme within an effective time; 3) How to formulate the best energy sharing strategy for the best optimization scheme.

[0006] To achieve the purpose of the present invention, the technical solution provided by the present invention is specifically as follows:

[0007] First aspect

[0008] The present application provides a safe and low-carbon operation and energy sharing device between multiple energy systems, including an information processing module, an energy strategy analysis module, a strategy rapid generation module, and an energy sharing module;

[0009] 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;

[0010] 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;

[0011] The strategy rapid generation module is used to achieve rapid generation of optimal strategies through an adaptive augmented Lagrangian method;

[0012] The energy sharing module is used to realize energy sharing among various energy systems.

[0013] Second aspect

[0014] Corresponding to the above-mentioned device, the present invention also provides a method for safe and low-carbon operation and energy sharing among multiple energy systems, and the method is carried out using the above-mentioned device.

[0015] Compared with the prior art, the present invention has the following beneficial effects:

[0016] While achieving collaborative optimization of environmental protection, safety and economic optimization, the present invention also significantly improves computing efficiency; under the same deployment environment and the same convergence accuracy, the number of iterations of the algorithm of the present invention can be less than 2 / 3 of that of the prior art, and the higher the convergence accuracy, the better the technical effect of the scheme of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the device module structure provided in an embodiment of the present application;

[0018] Figure 2This is a schematic diagram of the CIES operation architecture in the embodiment of this application;

[0019] Figure 3 This is a schematic diagram of power interaction in scenario 2 in an embodiment of the present application;

[0020] Figure 4 This is a schematic diagram of thermal energy interaction in scenario 2 in an embodiment of the present application;

[0021] Figure 5 A schematic diagram comparing the number of iterations of this application and the prior art. DETAILED DESCRIPTION

[0022] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The device provided by the present invention inherits an economic-safety-environmental protection collaborative control strategy among multiple energy entities and an energy sharing technology suitable for non-information peer situations.

[0024] like Figure 1 As shown, this embodiment provides a safe and low-carbon operation and energy sharing device between multiple energy systems, including an information processing module, an energy strategy analysis module, a strategy rapid generation module and an energy sharing module;

[0025] 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;

[0026] Specifically, the information processing module includes an energy information perception submodule and an incomplete information deduction submodule;

[0027] The energy information sensing submodule is specifically used for:

[0028] (1) Data collection: Collect primary data from different energy equipment (such as combined heat and power units (CHP), air source heat pumps (Ashp), and electric refrigeration units (EC), including air intake, power consumption, etc.)

[0029] (2) Equipment operation constraint generation: Based on the collected data and the preset model, other important data such as electrical power, thermal power, heat generation and output are generated. Including:

[0030] 1) Non-hydrogen energy equipment constraints: Formula (1) is used to calculate the output of each non-hydrogen energy equipment (such as CHP), including electricity, gas input, heat and cooling output;

[0031]

[0032] 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 transfer coefficient of CHP;

[0033]

[0034] In the formula, C represents the capacity of the equipment, P min Represents the lower limit of the equipment's electrical output. is the upper limit rate of the CHP unit ramp, and t represents the time interval;

[0035] 2) Hydrogen energy equipment constraints: including the operating characteristics and efficiency models of fuel cells and electrolyzers, and calculating their output power and input power according to formulas (3), (4) and (5);

[0036] The operating characteristics of a fuel cell (SOFC) are established based on the following equation:

[0037]

[0038]

[0039] Where, SOFC is a fuel cell, η SOFC,E is the electricity 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;

[0040] The operation model of the electrolyzer (EL) is based on the following formula:

[0041]

[0042] 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 electric power of EL at time t; q H2 is the calorific value of hydrogen.

[0043] Among them, in order to infer the comprehensive information of other energy systems from incomplete information, 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 necessary 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 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, where the incomplete information deduction problem is expressed as

[0044]

[0045] Where e(t) is the measurement error of the terminal sensor; α k (t) is the start / stop status of other internal equipment of the energy system; p k (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.

[0046] 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;

[0047] The energy strategy analysis module is specifically used for:

[0048] (1) Based on the information processing module, a three-dimensional collaborative energy analysis and optimization model is constructed, wherein the model includes three objectives: economy, environmental protection, and safety;

[0049] Economic goals:

[0050]

[0051] 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 operation 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 equipment k, is the output power at time t; represents the energy transaction cost between the nth park entity and the n'th park entity;

[0052] Environmental goals:

[0053] The environmental protection goal of operation optimization is to minimize CO2 emissions during system operation. The optimization objective function is as follows:

[0054]

[0055] In the formula, f E is the CO2 emission of the entire system; α e and α gas are the carbon dioxide emission coefficients of external electricity purchase and natural gas combustion, P e is the power consumption; G gas is the consumption of natural gas;

[0056] Security goals:

[0057] The safety goal of operation optimization is to minimize the energy loss during system operation. The optimization objective function is as follows:

[0058]

[0059] In the formula, f R is the energy loss during system operation; β H and β E are the load reduction factors for thermal load and electrical load, R E and R H are the optimal reduction amounts of electrical energy and thermal energy respectively;

[0060] Implement multi-objective weighting: Integrate multiple objectives through weighted methods, and use traditional linear weighted methods to analyze the changing relationship between multiple objectives. However, although the traditional linear weighted method can explore the changing relationship between multiple objectives, it is based on the analysis of massive weight coefficients and cannot meet the time requirements of control. To this end, the present invention directly incorporates sensitivity analysis into the objective function to directly obtain an acceptable approximate multi-objective equilibrium solution:

[0061]

[0062] Where x is a continuous decision variable; G, H, g, and h are the coefficient matrices of the optimization model, λ1, λ2, and λ3 are the weight coefficients of the objective function; γ1, γ2, and γ3 are the weight coefficients of the objective function gradient; c f 、c ▽f is the normalization coefficient of the objective function and the objective function gradient. This formula means that the situation when one of the objectives changes dramatically with the other objective is not the optimal solution (because the absolute value of the gradient of the drastically changing objective will be extremely large). In actual scheduling control, this often means that a huge improvement in the objective function can be achieved with minimal cost. The utility function is introduced to transform it into a simple objective optimization form.

[0063] (3) Use the utility function to transform the multi-objective problem into a dual-objective optimization problem, significantly reducing the computational complexity. The details are as follows:

[0064]

[0065] In the formula, uc i (x) represents the utility function, which is used to express the preference relationship between the two parties. This formula means that for any number of hydrogen-containing comprehensive energy entities, it can be transformed into a dual-objective optimization problem, which significantly reduces the computational complexity; η and ξ are dual variable vectors; and They represent the optimal solutions when λ is 0 and 1 respectively; the weight λ is the coefficient of a decision variable; M is a very large positive number, z is a binary variable of the dual variable η, and x is a set of continuous decision variables.

[0066] The strategy rapid generation module is used to achieve rapid generation of optimal strategies through an adaptive augmented Lagrangian method;

[0067] The existing technology usually uses the ADMM algorithm for calculation to achieve multi-agent multi-objective collaborative optimization. The algorithm process is to construct an augmented Lagrangian function, split the original problem into several sub-problems, use asynchronous iteration between sub-problems, and update the corresponding dual variables, and finally achieve a solution to the original problem in a common convergence manner.

[0068] (1) Problem solving: Using the ADMM algorithm, the optimization problem is divided into several sub-problems by constructing an augmented Lagrangian function, and the multi-park objective function (11) is converted into the form of Nash bargaining, but its essence is a non-convex nonlinear optimization problem. Problem (11) is converted into two alternating iterative convex optimization problems;

[0069] Sub-problem 1: Solve the optimal electricity and heat energy trading volume between parks

[0070]

[0071] In the formula, N is the total number of park entities, represents the optimal value of the subject n participating in the Nash park. By solving this problem, the optimal electricity and heat energy trading volume between each park can be obtained;

[0072] Sub-problem 2: Determine the independent optimal solution for each park at the breaking point of the game

[0073]

[0074] In the formula, The game breaking point representing the park entity n is a constant and can be obtained by independent optimization of a single park;

[0075] (2) Asynchronous iterative solution: Construct a Lagrangian function and use the iteration of strategy information to realize the game decision output. Perform asynchronous iteration on the split sub-problems and update the dual variables at the same time.

[0076]

[0077] In the formula, ρ is the added function penalty factor, z takes the iterative result of x and feeds it back to x, is the economic cost after the k+1th iteration, f i (X i ) represents the value of the multi-agent objective function, represents the average value of the objective function solution after the kth iteration, is the iteration gap after the kth iteration, e k+1 is the iterative step length of the optimal solution. This is repeated to achieve information interaction;

[0078] The target cost result can be obtained by solving the function:

[0079]

[0080] In the formula, c i To iterate the constraint value, since it involves energy transactions, there will be two variables in the buying and selling of two energy behaviors. The variables will increase exponentially when solving. Auxiliary variables are introduced here to express them and satisfy the following solution formula:

[0081]

[0082] Where P n,n’,t and P n’,n,t are the electric power from park n to park n' and the electric power from park n' to park n; H n,n’,t and H n’,n,t They are the heat power flowing from park n to park n' and the heat power flowing from park n' to park n.

[0083] (3) The original residual and the dual residual are used as the criteria for judging the convergence of the algorithm. The iteration ends when the predetermined accuracy is reached. If both meet the following conditions, the iteration ends:

[0084]

[0085] In the formula, ε ori and ε dual Specify the convergence precision for the primal and dual residuals.

[0086] The energy sharing module is used to realize energy sharing among various energy systems. It is aimed at the problem of profit distribution after the cooperation of multiple energy systems. The energy system entities participate in energy sharing, and giving / obtaining energy are all contributions, because this kind of energy sharing reduces the energy dependence on the main power grid. It is generally believed that providing energy is considered to be a greater contribution than obtaining the same amount of energy. The energy sharing module uses a nonlinear function based on natural logarithms to quantify the contribution of different community integrated energy systems (CIES) in energy sharing. Then, CIES negotiate with each other based on their respective contributions as bargaining power, so as to determine the energy transaction price between them and fairly distribute the benefits of energy sharing.

[0087] The energy sharing module is specifically used for:

[0088] (1) Energy sharing contribution model: Quantification of energy contribution: In a multi-energy system, the energy sharing of each CIES is regarded as its contribution to the system. This step involves evaluating the total amount of energy provided and received by each CIES, which will be used for subsequent bargaining power evaluation.

[0089]

[0090] 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;

[0091] (2) Energy mapping function construction: A nonlinear function that reflects the bargaining power of each CIES in energy sharing is constructed using natural logarithm and exponential functions. This function quantifies the bargaining power of each CIES based on the energy provided and received by the CIES.

[0092]

[0093] Where, d n represents the energy factor; Represents the maximum overall energy provided by each CIES; The maximum value of overall energy obtained;

[0094] Among them, the nonlinear function of quantifying bargaining power based on the exponential function has the following functional characteristics:

[0095] 1) Any target subject that obtains objective value in sharing obtains bargaining value;

[0096] 2) CIES with zero energy sharing has zero bargaining value, that is, the bargaining power is always non-negative;

[0097] 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;

[0098] (3) Construction of energy sharing equilibrium model, including the following:

[0099] 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;

[0100] 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:

[0101]

[0102] Where U CIES1 , U CIES2 , U CIESn are the energy sharing equilibrium costs of the 1st, 2nd, and nth parks under Nash negotiation, are the energy sharing equilibrium costs of the 1st, 2nd, and nth parks under non-Nash negotiation. The formula is to maximize the benefits of the CIES alliance after participating in the target sharing. The inequality ensures that the target value of each function sharing contribution can obtain benefits.

[0103] Since the natural logarithm is a strictly monotonically increasing convex function, taking the logarithm of the equation can convert the maximum value problem into a minimum value problem for easy solution, and the equation is obtained after the conversion.

[0104]

[0105] (4) Implement the optimal strategy: Use appropriate mathematical optimization techniques to solve the converted minimization problem and determine the optimal energy trading strategy and corresponding price for each CIES. Application of results: 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.

[0106] by Figure 2 The multiple parks shown in the figure are the game entities, and economy, safety and environmental protection are the optimization goals.

[0107] Table 1 Scenario comparison

[0108]

[0109] In order to verify the effectiveness of the proposed optimization method, four scenarios are solved respectively, and the solution results are shown as follows:

[0110] Table 2 Comparison of system economic costs and carbon emissions

[0111]

[0112] It can be seen from the table that if multiple parks adopt cooperative game, the economic cost of the park as a whole and a single park will be significantly reduced. For example, in scenario 1 and scenario 2, the total park economic cost is reduced by 3.3369×10 8 Yuan, CIES1 economic cost is reduced by 1.9963×10 8 Yuan, and carbon emissions have been reduced to a certain extent, indicating that the overall energy use has been reduced and the energy utilization rate has been improved; the adoption of cooperative game within the park will not only reduce the energy cost within the park, but also reduce the carbon emissions of the park. For example, in the comparison between scenario 1 and scenario 4, the economic cost of CIES1 is reduced by nearly half, and carbon emissions are reduced by 3.5811×10 8 kg.

[0113] Figure 3-5 Positive values ​​indicate energy supply, and negative values ​​indicate energy acceptance. In terms of power supply, CIES2 has insufficient power supply from 0:00 to 5:00, and CIES1 supplies power to CIES2; CIES3 has insufficient power supply from 0:00 to 7:00, and CIES1 also supplies power to CIES2; but from 8:00 to 17:00, CIES is supplied by CIES3; CIES2 and CIES3 fluctuate greatly from 11:00 to 22:00, and energy interaction is relatively frequent. In terms of heat supply, the energy supply of the three CIES from 0:00 to 8:00 is relatively smooth. As the daytime working hours begin, from 8:00 to 18:00, the heat energy fluctuates greatly, and energy interaction is relatively frequent. CIES1 and CIES3 often consume energy as energy demanders, and the costs of the two parks are relatively high, and the load demand is high. Overall, CIES can achieve energy complementarity and mutual assistance, which is conducive to the stable energy supply of the system.

[0114] In addition, a method for safe, low-carbon operation and energy sharing among multiple energy systems is provided, wherein the method is performed using the device as described above.

[0115] The optional implementation modes of the embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above implementation modes. Within the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical scheme of the embodiments of the present invention, and these simple modifications all belong to the protection scope of the embodiments of the present invention.

Claims

1. A safe low-carbon operation and energy sharing device among multiple energy systems, characterized in that: It includes information processing module, energy strategy analysis module, strategy rapid generation module and energy sharing module; 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; 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; The strategy rapid generation module is used to achieve rapid generation of optimal strategies through an adaptive augmented Lagrangian method; The energy sharing module is used to realize energy sharing among various energy systems; 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: Where e(t) is the measurement error of the terminal sensor; α k (t) is the start / stop status of other internal equipment of the energy system; p k (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. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 1 is characterized in that: The information processing module includes an energy information perception submodule and an incomplete information deduction submodule; The energy information sensing submodule is specifically used for: (1) Data collection; (2) Generation of equipment operation constraints, including: 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 cooling output; 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 transfer coefficient of CHP; In the formula, C represents the capacity of the equipment, P min Represents the lower limit of the equipment's electrical output. is the upper limit rate of the CHP unit ramp, and t represents the time interval; 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); The operating characteristics of a fuel cell are established based on the following equation: P t SOFC =the SOFC,E T t SOFC (3) H t SOFC =the SOFC,T T t SOFC (4) Where, SOFC is a fuel cell, η SOFC,E is the electricity 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; The operation model of the electrolyzer is based on the following formula: 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 electric power of EL at time t; q H2 is the calorific value of hydrogen.

3. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 2 is characterized in that: The energy strategy analysis module is specifically used for: (1) Based on the information processing module, a three-dimensional collaborative energy analysis and optimization model is constructed, wherein the model includes three objectives: economy, environmental protection, and safety; (2) Integrate multiple objectives through weighted methods and use linear weighted methods to analyze the change relationship between multiple objectives; (3) Use the utility function to transform the multi-objective problem into a bi-objective optimization problem.

4. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 3 is characterized in that: Economic goals: 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 running 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 equipment k, is the output power at time t; represents the energy transaction cost between the nth park entity and the n'th park entity; Environmental goals: The environmental protection goal of operation optimization is to minimize CO2 emissions during system operation. The optimization objective function is as follows: In the formula, f E is the CO2 emission of the entire system; α e and α gas are the carbon dioxide emission coefficients of external electricity purchase and natural gas combustion, P e is the power consumption; G gas is the consumption of natural gas; Security goals: The safety goal of operation optimization is to minimize the energy loss during system operation. The optimization objective function is as follows: In the formula, f R is the energy loss during system operation; β H and β E are the load reduction factors for thermal load and electrical load, R E and R H are the optimal reduction amounts of electrical energy and thermal energy, respectively.

5. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 4 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 a solution to the original problem in a common convergence manner.

6. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 5 is characterized in that: The energy sharing module uses a nonlinear function based on natural logarithms to quantify the contribution of different park integrated energy systems (CIES) in energy sharing. Then, CIES negotiate with each other based on their respective contributions as bargaining power to determine the energy transaction price between them.

7. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 6 is characterized in that: The energy sharing module is specifically used for: (1) Contribution model of energy sharing: 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) 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. Where, d n represents the energy factor; Represents the maximum overall energy provided by each CIES; The maximum value of overall energy obtained; (3) Construction of energy sharing equilibrium model, including the following: 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; 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: Where U CIES1 , U CIES2 , U CIESn are the energy sharing equilibrium costs of the 1st, 2nd, and nth parks under Nash negotiation, 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. 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) 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. The safe low-carbon operation and energy sharing device among multiple energy systems according to claim 7 is characterized in that: The nonlinear function of quantifying bargaining power based on the exponential function has the following functional characteristics: 1) Any target subject that obtains objective value in sharing obtains bargaining value; 2) CIES with zero energy sharing has zero bargaining value, that is, the bargaining power is always non-negative; 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. A method for safe low-carbon operation and energy sharing among multiple energy systems, characterized in that: The method is performed using the device according to any one of claims 1 to 8.

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