Multi-objective optimization scheduling method, device and computer storage medium for offshore micro-energy system

Abstract

The present invention relates to a multi-objective optimization scheduling method, device and computer storage medium for an offshore micro-energy system, the method comprising the following steps: a determining the equipment parameters and pipeline network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system; b establishing a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and pipeline network parameters in step a; c processing the multi-objective optimization scheduling model for the offshore micro-energy system using a variable dimension reduction method and an improved constraint dominance rule; d solving the multi-objective optimization scheduling model for the offshore micro-energy system using an NSGAII algorithm to obtain a scheduling plan for the offshore micro-energy system. The method of the present invention takes into account the natural gas emission and comprehensive energy efficiency indicators of the offshore micro-energy system, and can reduce environmental pollution and improve energy efficiency while ensuring the economic operation of the system.

Description

Technical Field

[0001] The present invention relates to a multi-objective optimization dispatching method, a device and a computer storage medium for an offshore micro-energy system, and belongs to the field of operation optimization of an integrated energy system in a power system. Background Art

[0002] As energy issues become more prominent, an integrated energy system with coordination and complementarity among multiple energy sources as its core is considered an effective way to solve energy problems. Offshore oil and gas engineering refers to an offshore micro-integrated energy system that aims to exploit energy such as oil and natural gas, integrates energy production, transmission, and consumption, and consists of three parts: power system, natural gas system, and thermal system. However, due to the special geographical location and environment of offshore oil and gas engineering, it is different from the integrated energy system in the general sense, mainly reflected in the two aspects that the physical distance between the "source" and the "load" is relatively short and uncomplicated, and the subsystems are weakly coupled.

[0003] Energy waste in offshore oil and gas projects mainly comes from natural gas release, which accounts for about 5.47% of primary energy consumption. In addition, offshore oil and gas projects use different equipment to achieve energy conversion, which inevitably leads to a certain degree of energy depreciation and loss. How to reduce energy waste in offshore oil and gas projects and improve the efficiency of system energy use is of great significance for the safe and economic operation of offshore oil and gas projects, efficient use of energy, and energy conservation and emission reduction. Therefore, how to overcome the shortcomings of existing technologies is an urgent problem to be solved in the field of offshore micro-energy system operation optimization. Summary of the invention

[0004] In response to the above-mentioned prominent problems, the present invention provides a multi-objective optimization scheduling method, device and computer storage medium for an offshore micro-energy system. The method starts from the characteristics of offshore oil and gas projects, takes into account the natural gas emission and comprehensive energy efficiency indicators of the offshore micro-energy system, and can reduce environmental pollution and improve energy efficiency while ensuring the economic operation of the system, thereby providing technical support for the scheduling strategy of the offshore micro-energy system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A first aspect of the present invention provides a multi-objective optimization scheduling method for an offshore micro-energy system, comprising the following steps:

[0007] a. Determine the equipment parameters and pipeline network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system;

[0008] b. Establishing a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and pipeline network parameters in step a;

[0009] c. Using the variable dimension reduction method and the improved constraint control rule to process the multi-objective optimization scheduling model of the offshore micro-energy system;

[0010] d The NSGAII algorithm is used to solve the multi-objective optimization scheduling model of the offshore micro-energy system to obtain the scheduling plan of the offshore micro-energy system.

[0011] In the multi-objective optimization scheduling method for offshore micro-energy systems, preferably, in step a, the equipment parameters include: upper and lower limits of gas turbine unit output, upper and lower limits of ramp rate, efficiency and heat loss coefficient, pollution emission coefficient; upper and lower limits of gas well gas production, upper and lower limits of platform natural gas release, natural gas cost coefficient, natural gas release penalty coefficient; efficiency and rated power parameters of waste heat boilers, gas boilers, and electric boilers; pipeline network parameters include: electrical load and impedance and admittance parameters of transmission lines; gas load and constants related to the length, diameter parameters, and friction constant of the gas pipeline; thermal load.

[0012] In the multi-objective optimization scheduling method for offshore micro-energy systems, preferably, in step b, the process of establishing the multi-objective optimization scheduling model for offshore micro-energy systems is as follows:

[0013] First, the basic data information of the offshore micro-energy system is collected, and then the actual equipment and system are appropriately simplified to establish the corresponding model described in mathematical language, including the objective function and constraints, and then the algorithm is used to solve it.

[0014] The multi-objective optimization scheduling method for offshore micro-energy systems, preferably, in step b, the objective function of the multi-objective optimization scheduling model for offshore micro-energy systems includes:

[0015]

[0016]

[0017] In formula (1), F1 is the first objective function, which represents the comprehensive operating cost of the system, including the pollution emission cost of gas turbines, the pollution emission cost of gas boilers, the gas production cost of gas wells and the penalty cost of natural gas release; Ω gt ,Ω gb and Ω cp They are gas turbine unit assembly, gas boiler assembly and central platform assembly; is the active power output of gas turbine unit i at time t; and are the pollution emission coefficients of gas turbine unit i respectively; is the gas consumption of gas boiler j at time t; is the pollution emission coefficient of gas boiler j; is the gas production of the central platform k at time t; is the gas production cost coefficient of the central platform k; is the natural gas release amount of the central platform k at time t; is the penalty coefficient of natural gas release of the central platform k; ∈ represents the belonging relationship of the set; T is the scheduling period;

[0018] In formula (2), F2 is the second objective function, which indicates the distance of the load rate of the gas turbine unit from the optimal operating range, thereby characterizing the operating state of the gas turbine unit; is the maximum active output of gas turbine unit i; max and χ min They respectively represent the upper and lower limits of the optimal operating range of the gas turbine unit.

[0019] The multi-objective optimization scheduling method for offshore micro-energy systems, preferably, in step b, the constraints of the multi-objective optimization scheduling model for offshore micro-energy systems include: power system constraints, natural gas system constraints, thermal system constraints and coupling link constraints, wherein:

[0020] 1) Power system constraints include:

[0021] Branch flow constraints:

[0022]

[0023] In formula (3), P gh,t is the power flow of branch (g, h) at time t; θ g,t is the phase angle of the power system node g at time t; θ h,t is the phase angle of power system node h at time t; x gh is the reactance of branch (g, h); is the upper limit of the power flow that can be transmitted by branch (g, h);

[0024] Gas turbine unit constraints:

[0025]

[0026] In formula (4), is the lower limit of active output of gas turbine unit i; and are the maximum upward and downward climbing speeds of gas turbine unit i, respectively;

[0027] Node power balance constraints:

[0028]

[0029] In formula (5), g∈h and e∈h represent the branch and electric boiler connected to node h respectively; i∈h represents the gas turbine unit connected to node h; is the electrical load value of node h at time t; is the electric power consumed by the electric boiler e at time t;

[0030] 2) Natural gas system constraints include:

[0031] Natural gas flow constraints:

[0032]

[0033] In formula (6), G mn,t is the flow rate of pipeline (m, n) at time t; C mn is a constant related to the length, diameter and friction coefficient of the pipe (m, n); p n,t is the pressure of gas network node n in period t; p m,t is the pressure of gas network node m in period t;

[0034] Gas well production constraints:

[0035]

[0036] In formula (7), and are the maximum and minimum gas production of gas well k, respectively;

[0037] Nodal pressure constraints:

[0038]

[0039] In formula (8), and are the upper and lower limits of the pressure at the gas network node n, respectively;

[0040] Node natural gas flow balance constraints:

[0041]

[0042] In formula (9), is the amount of natural gas consumed by gas turbine unit i at time t; is the gas load value of node n at time t; k∈n represents the gas well connected to gas node n; m∈n represents the gas pipeline connected to gas node n; i∈n represents the gas turbine unit connected to gas node n; j∈n represents the gas boiler connected to gas node n;

[0043] 3) Thermal system constraints:

[0044]

[0045] In formula (10), is the thermal output power of the waste heat boiler connected to the gas turbine unit i at time t; is the thermal power output of gas boiler j at time t; is the output thermal power of electric boiler e at time t; is the heat load value at time t; Ω eb Assemble for electric boilers;

[0046] 4) Coupling link constraints:

[0047] Gas turbine unit and waste heat boiler constraints:

[0048]

[0049] In formula (11), η gt is the gas turbine unit efficiency; HV gas is the calorific value of natural gas; η l is the heat loss coefficient of the gas turbine unit; η whb is the heat recovery efficiency of the waste heat boiler;

[0050] Gas boiler constraints:

[0051]

[0052] In formula (12), is the upper limit of the output thermal power of gas boiler j; η gb For gas boiler efficiency;

[0053] Electric boiler constraints:

[0054]

[0055] In formula (13), The upper limit of the output thermal power of the electric boiler e; η eb For electric boiler efficiency.

[0056] In the multi-objective optimization scheduling method for offshore micro-energy systems, preferably, the variable dimension reduction method in step c is:

[0057] The actual solution of the multi-objective optimization scheduling model of the offshore micro-energy system is:

[0058]

[0059] Select the variables in equations (5), (9), and (10): and are the dependent variables, respectively and Then equations (5), (9), and (10) are equivalent to equations (15)-(17):

[0060]

[0061]

[0062]

[0063] In formula (15-17), P gt G is the equivalent relation symbol of the gas turbine unit output after variable dimension reduction; well G is the equivalent relationship symbol of gas well production after variable dimension reduction; gb G is the equivalent relationship symbol of the gas consumption of the gas boiler after the variable dimension is reduced; gt is the symbol for the functional relationship between gas consumption and output of gas turbine units; H eb H is the symbol for the functional relationship between the thermal power output and the electrical power consumed by the electric boiler; gb H is the symbol for the functional relationship between the thermal power output and gas consumption of a gas boiler; gt is the symbol of the functional relationship between the heat output power of the waste heat boiler connected to the gas turbine unit and the unit output;

[0064] At the same time, the dependent variable and The boundary constraints of equations (4), (7), and (12) should be satisfied, so that the equality constraints are converted into inequality constraints;

[0065] The improved constraint dominance rule in step (3) is as follows: by recording the violation values ​​of different types of constraints to reduce the speed, the constraints are graded, so as to distinguish between constraints that are easy to satisfy and constraints that are more difficult to satisfy. Constraints of the same level have the same difficulty in solving, and the higher the level, the more difficult the constraint processing method is. When two solutions are not feasible, if one of the solutions has a lower total violation value of the first-level constraints, then this solution is selected. When the total violation values ​​of the first-level constraints of two solutions are equal, it is necessary to compare the higher-level constraint violations until a better solution is selected.

[0066] A second aspect of the present invention provides a multi-objective optimization scheduling device for an offshore micro-energy system, comprising:

[0067] The first processing unit is used to determine the equipment parameters and pipe network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system;

[0068] The second processing unit establishes a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and the pipeline network parameters in step a;

[0069] A third processing unit is used to process the multi-objective optimization scheduling model of the offshore micro-energy system by using a variable dimension reduction method and an improved constraint dominance rule;

[0070] The fourth processing unit is used to solve the multi-objective optimization scheduling model of the offshore micro-energy system by using the NSGAII algorithm to obtain a scheduling plan for the offshore micro-energy system.

[0071] A third aspect of the present invention provides a computer storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the above-mentioned multi-objective optimization scheduling method for offshore micro-energy systems are implemented.

[0072] The present invention adopts the above technical solution, which has the following advantages:

[0073] 1. In order to improve the economic efficiency of the operation of the offshore micro-energy system and reduce the pollution to the environment, the present invention, based on the analysis of the characteristics of offshore oil and gas projects, establishes a two-objective optimization scheduling model for the offshore micro-energy system that takes into account the comprehensive operating cost of the system and the operating status of the gas turbine unit. At the same time, the natural gas emission and comprehensive energy efficiency indicators of the offshore micro-energy system are considered, which can reduce the pollution to the environment and improve energy efficiency while ensuring the economic operation of the system.

[0074] 2. The present invention is also based on the NSGAII algorithm to improve the efficiency of solving multi-objective and multi-constraint problems by reducing variable dimensions and improving the constraint domination principle (ICDP). BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A general flow chart of a multi-objective optimization scheduling method for an offshore micro-energy system provided by an embodiment of the present invention;

[0076] Figure 2 A diagram showing the composition of the system comprehensive operating cost in different scenarios provided by this embodiment of the present invention;

[0077] Figure 3 The distance that the load rate of the gas turbine unit deviates from the optimal operating range in scenario 3 provided in this embodiment of the present invention;

[0078] Figure 4 The amount of natural gas released in different scenarios provided by this embodiment of the present invention;

[0079] Figure 5 The system energy efficiency values ​​in different scenarios provided by this embodiment of the present invention. DETAILED DESCRIPTION

[0080] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work are within the scope of protection of the present invention.

[0081] like Figure 1 As shown, this specific embodiment provides a multi-objective optimization scheduling method for an offshore micro-energy system, comprising the following steps:

[0082] a. Determine the equipment parameters and pipeline network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system;

[0083] b. Establishing a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and pipeline network parameters in step a;

[0084] c. Using the variable dimension reduction method and the improved constraint control rule to process the multi-objective optimization scheduling model of the offshore micro-energy system;

[0085] d The NSGAII algorithm is used to solve the multi-objective optimization scheduling model of the offshore micro-energy system to obtain the scheduling plan of the offshore micro-energy system.

[0086] In this specific implementation, preferably, in step a, the equipment parameters include: equipment parameters include: ① upper and lower limits of gas turbine unit output, upper and lower limits of ramp rate, efficiency and heat loss coefficient, pollution emission coefficient; ② upper and lower limits of gas well gas production, upper and lower limits of platform natural gas release, natural gas cost coefficient, natural gas release penalty coefficient; ③ efficiency and rated power parameters of equipment such as waste heat boilers, gas boilers, and electric boilers; pipeline network parameters include: ① electrical load and impedance and admittance parameters of transmission lines; ② gas load and constants related to the length, pipe diameter parameters, and friction constant of the gas pipeline; ③ thermal load.

[0087] In this specific implementation, preferably, in step b, the process of establishing the multi-objective optimization scheduling model of the offshore micro-energy system is as follows:

[0088] First, basic data information of the offshore micro-energy system is collected, and then based on the laws of physics, the actual equipment and system are appropriately simplified to establish a corresponding model described in mathematical language, including objective functions and constraints, and then the algorithm is used to solve it.

[0089] In this specific implementation, preferably, in step b, the objective function of the multi-objective optimization scheduling model of the offshore micro-energy system includes:

[0090]

[0091]

[0092] In formula (1), F1 is the first objective function, which represents the comprehensive operating cost of the system, including the pollution emission cost of gas turbines, the pollution emission cost of gas boilers, the gas production cost of gas wells and the penalty cost of natural gas release; Ω gt ,Ω gb and Ω cp They are gas turbine unit assembly, gas boiler assembly and central platform assembly; is the active power output of gas turbine unit i at time t; and are the pollution emission coefficients of gas turbine unit i respectively; is the gas consumption of gas boiler j at time t; is the pollution emission coefficient of gas boiler j; is the gas production of the central platform k at time t; is the gas production cost coefficient of the central platform k; is the natural gas release amount of the central platform k at time t; is the penalty coefficient of natural gas release of the central platform k; ∈ represents the belonging relationship of the set; T is the scheduling period;

[0093] In formula (2), F2 is the second objective function, which indicates the distance of the load rate of the gas turbine unit from the optimal operating range, thereby characterizing the operating state of the gas turbine unit; is the maximum active output of gas turbine unit i; max and χ min They respectively represent the upper and lower limits of the optimal operating range of the gas turbine unit.

[0094] In this specific implementation, preferably, in step b, the constraints of the multi-objective optimization scheduling model of the offshore micro-energy system include: power system constraints, natural gas system constraints, thermal system constraints and coupling link constraints, wherein:

[0095] 1) Power system constraints include:

[0096] Branch flow constraints:

[0097]

[0098] In formula (3), P gh,t is the power flow of branch (g, h) at time t; θ g,t is the phase angle of the power system node g at time t; θ h,t is the phase angle of power system node h at time t; x gh is the reactance of branch (g, h); is the upper limit of the power flow that can be transmitted by branch (g, h);

[0099] Gas turbine unit constraints:

[0100]

[0101] In formula (4), is the lower limit of active output of gas turbine unit i; and are the maximum upward and downward climbing speeds of gas turbine unit i, respectively;

[0102] Node power balance constraints:

[0103]

[0104] In formula (5), g∈h and e∈h represent the branch and electric boiler connected to node h respectively; i∈h represents the gas turbine unit connected to node h; is the electrical load value of node h at time t; is the electric power consumed by the electric boiler e at time t;

[0105] 2) Natural gas system constraints include:

[0106] Natural gas flow constraints:

[0107]

[0108] In formula (6), G mn,t is the flow rate of pipeline (m, n) at time t; C mn is a constant related to the length, diameter and friction coefficient of the pipe (m, n); p n,t is the pressure of gas network node n in period t; p m,t is the pressure of gas network node m in period t;

[0109] Gas well production constraints:

[0110]

[0111] In formula (7), and are the maximum and minimum gas production of gas well k, respectively;

[0112] Nodal pressure constraints:

[0113]

[0114] In formula (8), and are the upper and lower limits of the pressure at the gas network node n, respectively;

[0115] Node natural gas flow balance constraints:

[0116]

[0117] In formula (9), is the amount of natural gas consumed by gas turbine unit i at time t; is the gas load value of node n at time t; k∈n represents the gas well connected to gas node n; m∈n represents the gas pipeline connected to gas node n; i∈n represents the gas turbine unit connected to gas node n; j∈n represents the gas boiler connected to gas node n;

[0118] 3) Thermal system constraints:

[0119]

[0120] In formula (10), is the thermal output power of the waste heat boiler connected to the gas turbine unit i at time t; is the thermal power output of gas boiler j at time t; is the output thermal power of electric boiler e at time t; is the heat load value at time t; Ω eb Assemble for electric boilers;

[0121] 4) Coupling link constraints:

[0122] Gas turbine unit and waste heat boiler constraints:

[0123]

[0124] In formula (11), η gt is the gas turbine unit efficiency; HV gas is the calorific value of natural gas; η l is the heat loss coefficient of the gas turbine unit; η whb is the heat recovery efficiency of the waste heat boiler;

[0125] Gas boiler constraints:

[0126]

[0127] In formula (12), is the upper limit of the output thermal power of gas boiler j; η gb For gas boiler efficiency;

[0128] Electric boiler constraints:

[0129]

[0130] In formula (13), The upper limit of the output thermal power of the electric boiler e; ηeb For electric boiler efficiency.

[0131] In this specific implementation, preferably, the variable dimension reduction method in step c is:

[0132] The actual solution of the multi-objective optimization scheduling model of the offshore micro-energy system is:

[0133]

[0134] Select the variables in equations (5), (9), and (10): and are the dependent variables, respectively and Then equations (5), (9), and (10) are equivalent to equations (15)-(17):

[0135]

[0136]

[0137]

[0138] In formula (15-17), P gt G is the equivalent symbol for the output of the gas turbine unit after the variable dimension is reduced; well G is the equivalent relationship symbol for gas well production after variable dimension reduction; gb G is the equivalent relationship symbol for the gas consumption of gas boilers after the variable dimension is reduced; gt It is the symbol representing the functional relationship between gas consumption and output of gas turbine unit; H eb H is the symbol representing the functional relationship between the thermal power output and the electrical power consumed by the electric boiler; gb H is the symbol for the functional relationship between the thermal power output and gas consumption of a gas boiler; gt A symbol that represents the functional relationship between the output thermal power of the waste heat boiler connected to the gas turbine unit and the unit output.

[0139] At the same time, the dependent variable and The boundary constraints of equations (4), (7), and (12) should be satisfied, so that the equality constraints are converted into inequality constraints;

[0140] The constraint dominance rule improved in step (3) is: In NSGAII, the constraint dominance rule is that if both solutions are feasible, the solution with a higher Pareto advantage level will be selected. Otherwise, the solution with a smaller overall constraint violation will be selected to gradually approach the feasible region. It can be seen that the constraint dominance rule uses the constraint violation as a single indicator to reflect the infeasibility of the solution. The efficiency of this rule will be significantly reduced when processing a large number of constraint types. Based on this, the present invention improves the constraint dominance rule by recording the reduction speed of the violation value of different types of constraints, grading the constraints, thereby distinguishing between easy-to-satisfy constraints and difficult-to-satisfy constraints. Constraints of the same level have the same difficulty in solving. The higher the level, the higher the difficulty of the constraint processing method. When both solutions are infeasible, when the total violation value of the first-level constraints of one of the solutions is lower, this solution is selected; when the total violation values ​​of the first-level constraints of the two solutions are equal, it is necessary to compare the higher-level constraint violations until a better solution is selected. It can be seen that in this way, the situations where constraints are easily violated and the number of constraint violations can be easily reduced, and more computing resources can be concentrated on complex constraints. Therefore, the constraint processing efficiency can be improved.

[0141] In this specific implementation, preferably, NSGAII is used to solve and obtain the offshore micro-energy system scheduling plan.

[0142] Application Examples

[0143] This specific application embodiment is designed according to a certain offshore micro-energy system in Bohai Sea, and is composed of a 6-node power system, a 6-node natural gas system and a thermal system. The relevant parameters of the gas turbine are shown in Table 1. The power system nodes 1, 4, and 5 are respectively installed with electric boilers with capacities of 1.2MW, 0.95MW, and 1.1MW. The natural gas system nodes 1 and 2 are respectively installed with gas boilers with maximum thermal powers of 3MW and 4MW. The gas well parameters are shown in Table 2. The efficiency of the gas turbine unit is set to 0.4, the heat loss coefficient of the gas turbine unit is set to 0.3, the heat recovery efficiency of the waste heat boiler is set to 0.47, the combustion efficiency of the gas boiler is set to 0.85, the pollution emission coefficient of the gas boiler is 0.24$ / MW, the electric-thermal conversion efficiency of the electric boiler is set to 0.99, and the calorific value of the natural gas is 9.7kWh / m^3.

[0144] In order to study the impact of different schemes on the operating status of gas turbine units, natural gas release and system energy efficiency level, this application example sets the following three operating scenarios:

[0145] 1) Scenario 1: The heat sources in the system include waste heat boilers and electric boilers

[0146] 2) Scenario 2: The heat sources in the system include waste heat boilers and gas boilers

[0147] 3) Scenario 3: The heat sources in the system include waste heat boilers, electric boilers and gas boilers

[0148] Table 1 Gas turbine unit parameters

[0149]

[0150]

[0151] Table 2 Gas well parameters

[0152]

[0153] The optimization method of this application example is implemented through the following steps:

[0154] (1) Obtain relevant initial data according to the embodiment;

[0155] (2) constructing an optimization model based on relevant initial data;

[0156] (3) Reduce the model's variable dimension and improve constraint control rules;

[0157] (4) Solved by NSGAII.

[0158] Figure 2 The figure shows the composition of the system comprehensive operating cost under different scenarios. It can be seen that in all scenarios, scenario 3 can achieve the minimum comprehensive system operating cost, and the reduction in total system cost is mainly due to the reduction in pollution emission costs of gas turbine units. This shows that the simultaneous use of electric boilers, gas boilers and waste heat boilers can improve the economy of the system and reduce the comprehensive operating costs.

[0159] Figure 3 The load rates of each unit deviate from the optimal operating range when only objective function 2 is considered in scenario 3. This shows that heating through gas boilers can alleviate the pressure of heating in the power system to a certain extent, making the unit load rate further away from the boundary and not exceeding the upper limit of the optimal load rate range.

[0160] Figure 4 is the natural gas emission value under different scenarios. It can be seen that when only considering objective function 1, the natural gas emission in scenarios 2 and 3 is reduced compared with scenario 1, and the natural gas emission in scenario 3 is the smallest, which shows that the presence of a gas boiler in the system can effectively reduce natural gas emission.

[0161] Figure 5The energy efficiency values ​​of the system under different scenarios show that the comprehensive energy efficiency values ​​when only considering objective function 1 are greater than those when only considering objective function 2. This is mainly because objective function 1 comprehensively considers the gas well output cost, natural gas release cost, etc., which will reduce unnecessary energy waste in the system, such as natural gas release, and thus improve the energy efficiency of the system. In addition, by comparing the comprehensive energy efficiency values ​​when only considering objective function 1 under the three scenarios, it can be seen that scenario 1 is the highest, scenario 2 is the lowest, and scenario 3 is between the two. This shows that in terms of the system energy efficiency level, using electric boilers for heating is more efficient than using gas boilers for heating.

[0162] This specific embodiment also provides a multi-objective optimization scheduling device for an offshore micro-energy system, comprising:

[0163] The first processing unit is used to determine the equipment parameters and pipe network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system;

[0164] The second processing unit establishes a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and the pipeline network parameters in step a;

[0165] A third processing unit is used to process the multi-objective optimization scheduling model of the offshore micro-energy system by using a variable dimension reduction method and an improved constraint dominance rule;

[0166] The fourth processing unit is used to solve the multi-objective optimization scheduling model of the offshore micro-energy system by using the NSGAII algorithm to obtain a scheduling plan for the offshore micro-energy system.

[0167] This specific embodiment also provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned multi-objective optimization scheduling method for offshore micro-energy systems are implemented.

[0168] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-objective optimization scheduling method for an offshore micro-energy system, characterized in that: The steps include: a. Determine the equipment parameters and pipeline network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system; b. Establishing a multi-objective optimization scheduling model for the offshore micro-energy system based on the equipment parameters and pipeline network parameters in step a; c. Using the variable dimension reduction method and the improved constraint control rule to process the multi-objective optimization scheduling model of the offshore micro-energy system; d. Using the NSGAII algorithm to solve the multi-objective optimization scheduling model of the offshore micro-energy system, and obtaining a scheduling plan for the offshore micro-energy system; In step b, the process of establishing the multi-objective optimization scheduling model of the offshore micro-energy system is as follows: First, basic data and information of offshore micro-energy systems are collected, and then the actual equipment and systems are appropriately simplified to establish corresponding mathematical models, including objective functions and constraints, and then the algorithms are used to solve them. In step b, the objective function of the multi-objective optimization scheduling model of the offshore micro-energy system includes: In formula (1), F1 is the first objective function, which represents the comprehensive operating cost of the system, including the pollution emission cost of gas turbines, the pollution emission cost of gas boilers, the gas production cost of gas wells and the penalty cost of natural gas release; Ω gt ,Ω gb and Ω cp They are gas turbine unit assembly, gas boiler assembly and central platform assembly; is the active power output of gas turbine unit i at time t; and are the pollution emission coefficients of gas turbine unit i respectively; is the gas consumption of gas boiler j at time t; is the pollution emission coefficient of gas boiler j; is the gas production of the central platform k at time t; is the gas production cost coefficient of the central platform k; is the natural gas release amount of the central platform k at time t; is the penalty coefficient of natural gas release of the central platform k; ∈ represents the belonging relationship of the set; T is the scheduling period; In formula (2), F2 is the second objective function, which indicates the distance that the load rate of the gas turbine unit deviates from the optimal operating range, thereby characterizing the operating state of the gas turbine unit; P i gt,max is the maximum active output of gas turbine unit i; max and χ min They represent the upper and lower limits of the optimal operating range of the gas turbine unit respectively; In step b, the constraints of the multi-objective optimization scheduling model of the offshore micro-energy system include: power system constraints, natural gas system constraints, thermal system constraints and coupling link constraints, wherein: 1) Power system constraints include: Branch flow constraints: In formula (3), P gh,t is the power flow of branch (g, h) at time t; θ g,t is the phase angle of the power system node g at time t; θ h,t is the phase angle of power system node h at time t; x gh is the reactance of branch (g, h); is the upper limit of the power flow that can be transmitted by branch (g, h); Gas turbine unit constraints: In formula (4), P i gt,min is the lower limit of active output of gas turbine unit i; and are the maximum upward and downward climbing speeds of gas turbine unit i, respectively; Node power balance constraints: In formula (5), g∈h and e∈h represent the branch and electric boiler connected to node h respectively; i∈h represents the gas turbine unit connected to node h; is the electrical load value of node h at time t; is the electric power consumed by the electric boiler e at time t; 2) Natural gas system constraints include: Natural gas flow constraints: In formula (6), G mn,t is the flow rate of pipeline (m, n) at time t; C mn is a constant related to the length, diameter and friction coefficient of the pipe (m, n); p n,t is the pressure of gas network node n at time t; p m,t is the pressure of gas network node m in period t; Gas well production constraints: In formula (7), and are the maximum and minimum gas production of gas well k, respectively; Nodal pressure constraints: In formula (8), and are the upper and lower limits of the pressure at the gas network node n, respectively; Node natural gas flow balance constraints: In formula (9), is the amount of natural gas consumed by gas turbine unit i at time t; is the gas load value of node n at time t; k∈n represents the gas well connected to gas node n; m∈n represents the gas pipeline connected to gas node n; i∈n represents the gas turbine unit connected to gas node n; j∈n represents the gas boiler connected to gas node n; 3) Thermal system constraints: In formula (10), is the thermal output power of the waste heat boiler connected to the gas turbine unit i at time t; is the thermal power output of gas boiler j at time t; is the output thermal power of electric boiler e at time t; is the heat load value at time t; Ω eb Assemble for electric boilers; 4) Coupling link constraints: Gas turbine unit and waste heat boiler constraints: In formula (11), η gt is the gas turbine unit efficiency; HV gas is the calorific value of natural gas; η l is the heat loss coefficient of the gas turbine unit; η whb is the heat recovery efficiency of the waste heat boiler; Gas boiler constraints: In formula (12), is the upper limit of the output thermal power of gas boiler j; η gb For gas boiler efficiency; Electric boiler constraints: In formula (13), The upper limit of the output thermal power of the electric boiler e; η eb For electric boiler efficiency.

2. The multi-objective optimization scheduling method for offshore micro-energy systems according to claim 1 is characterized in that: In step a, the equipment parameters include: upper and lower limits of gas turbine unit output, upper and lower limits of ramp rate, efficiency and heat loss coefficient, pollution emission coefficient; upper and lower limits of gas well production, upper and lower limits of platform natural gas release, natural gas cost coefficient, natural gas release penalty coefficient; efficiency and rated power parameters of waste heat boilers, gas boilers, and electric boilers; The network parameters include: electrical load and the impedance and admittance parameters of the transmission line; gas load and constants related to the length, diameter parameters and friction constant of the gas pipeline; and thermal load.

3. The multi-objective optimization scheduling method for offshore micro-energy systems according to claim 2 is characterized in that: The variable dimension reduction method in step c is: The actual solution of the multi-objective optimization scheduling model of the offshore micro-energy system is: Select the variables in equations (5), (9), and (10): and are the dependent variables, respectively and Then equations (5), (9), and (10) are equivalent to equations (15)-(17): In formula (15-17), P gt G is the equivalent relation symbol of the gas turbine unit output after variable dimension reduction; well G is the equivalent relationship symbol of gas well production after variable dimension reduction; gb G is the equivalent relationship symbol of the gas consumption of the gas boiler after the variable dimension is reduced; gt is the symbol for the functional relationship between gas consumption and output of gas turbine units; H eb H is the symbol for the functional relationship between the thermal power output and the electrical power consumed by the electric boiler; gb H is the symbol for the functional relationship between the thermal power output and gas consumption of a gas boiler; gt is the symbol of the functional relationship between the heat output power of the waste heat boiler connected to the gas turbine unit and the unit output; At the same time, the dependent variable and The boundary constraints of equations (4), (7), and (12) should be satisfied, so that the equality constraints are converted into inequality constraints; The improved constraint dominance rule in step c is: by recording the violation value reduction speed of different types of constraints, the constraints are graded, so as to distinguish between constraints that are easy to satisfy and constraints that are more difficult to satisfy. Constraints of the same level have the same difficulty in solving, and the higher the level, the more difficult the constraint processing method is. When two solutions are not feasible, when the total violation value of the first-level constraints of one of the solutions is lower, this solution is selected; when the total violation values ​​of the first-level constraints of two solutions are equal, it is necessary to compare the higher-level constraint violations until a better solution is selected.

4. A multi-objective optimization scheduling device for an offshore micro-energy system, characterized in that: include: The first processing unit is used to determine the equipment parameters and pipe network parameters of the power system, natural gas system and thermal system in the offshore micro-energy system; A second processing unit, based on the equipment parameters and the pipe network parameters in the first processing unit, establishes a multi-objective optimization scheduling model for the offshore micro-energy system; A third processing unit is used to process the multi-objective optimization scheduling model of the offshore micro-energy system by using a variable dimension reduction method and an improved constraint dominance rule; The fourth processing unit is used to solve the multi-objective optimization scheduling model of the offshore micro-energy system by using the NSGAII algorithm to obtain a scheduling plan for the offshore micro-energy system; In the second processing unit, the process of establishing the multi-objective optimization scheduling model of the offshore micro-energy system is as follows: First, basic data and information of offshore micro-energy systems are collected, and then the actual equipment and systems are appropriately simplified to establish corresponding mathematical models, including objective functions and constraints, and then the algorithms are used to solve them. In the second processing unit, the objective function of the multi-objective optimization scheduling model of the offshore micro-energy system includes: In formula (1), F1 is the first objective function, which represents the comprehensive operating cost of the system, including the pollution emission cost of gas turbines, the pollution emission cost of gas boilers, the gas production cost of gas wells and the penalty cost of natural gas release; Ω gt ,Ω gb and Ω cp They are gas turbine unit assembly, gas boiler assembly and central platform assembly; is the active power output of gas turbine unit i at time t; and are the pollution emission coefficients of gas turbine unit i respectively; is the gas consumption of gas boiler j at time t; is the pollution emission coefficient of gas boiler j; is the gas production of the central platform k at time t; is the gas production cost coefficient of the central platform k; is the natural gas release amount of the central platform k at time t; is the penalty coefficient of natural gas release of the central platform k; ∈ represents the belonging relationship of the set; T is the scheduling period; In formula (2), F2 is the second objective function, which indicates the distance that the load rate of the gas turbine unit deviates from the optimal operating range, thereby characterizing the operating state of the gas turbine unit; P i gt,max is the maximum active output of gas turbine unit i; max and χ min They represent the upper and lower limits of the optimal operating range of the gas turbine unit respectively; In the second processing unit, the constraints of the multi-objective optimization scheduling model of the offshore micro-energy system include: power system constraints, natural gas system constraints, thermal system constraints and coupling link constraints, wherein: 1) Power system constraints include: Branch flow constraints: In formula (3), P gh,t is the power flow of branch (g, h) at time t; θ g,t is the phase angle of the power system node g at time t; θ h,t is the phase angle of power system node h at time t; x gh is the reactance of branch (g, h); is the upper limit of the power flow that can be transmitted by branch (g, h); Gas turbine unit constraints: In formula (4), P i gt,min is the lower limit of active output of gas turbine unit i; and are the maximum upward and downward climbing speeds of gas turbine unit i, respectively; Node power balance constraints: In formula (5), g∈h and e∈h represent the branch and electric boiler connected to node h respectively; i∈h represents the gas turbine unit connected to node h; is the electrical load value of node h at time t; is the electric power consumed by the electric boiler e at time t; 2) Natural gas system constraints include: Natural gas flow constraints: In formula (6), G mn,t is the flow rate of pipeline (m, n) at time t; C mn is a constant related to the length, diameter and friction coefficient of the pipe (m, n); p n,t is the pressure of gas network node n at time t; p m,t is the pressure of gas network node m in period t; Gas well production constraints: In formula (7), and are the maximum and minimum gas production of gas well k, respectively; Nodal pressure constraints: In formula (8), and are the upper and lower limits of the pressure at the gas network node n, respectively; Node natural gas flow balance constraints: In formula (9), is the amount of natural gas consumed by gas turbine unit i at time t; is the gas load value of node n at time t; k∈n represents the gas well connected to gas node n; m∈n represents the gas pipeline connected to gas node n; i∈n represents the gas turbine unit connected to gas node n; j∈n represents the gas boiler connected to gas node n; 3) Thermal system constraints: In formula (10), is the thermal output power of the waste heat boiler connected to the gas turbine unit i at time t; is the thermal power output of gas boiler j at time t; is the output thermal power of electric boiler e at time t; is the heat load value at time t; Ω eb Assemble for electric boilers; 4) Coupling link constraints: Gas turbine unit and waste heat boiler constraints: In formula (11), η gt is the gas turbine unit efficiency; HV gas is the calorific value of natural gas; η l is the heat loss coefficient of the gas turbine unit; η whb is the heat recovery efficiency of the waste heat boiler; Gas boiler constraints: In formula (12), is the upper limit of the output thermal power of gas boiler j; η gb For gas boiler efficiency; Electric boiler constraints: In formula (13), The upper limit of the output thermal power of the electric boiler e; η eb For electric boiler efficiency.

5. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-objective optimization scheduling method for offshore micro-energy systems described in any one of claims 1-3 are implemented.

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