Cooperative regulation and control method and system for cold-heat-electricity flexible resources in low-carbon park

Abstract

The invention discloses a cold-heat-electricity flexible resource cooperative regulation and control method and system for a low-carbon park. The method comprises the following steps: constructing a park operation model; constructing a park optimization scheduling model, and taking the minimum park operation cost and carbon emission as a target function; solving a dual-objective optimization problem by adopting augmented epsilon-constraint to obtain a Pareto frontier solution set; carrying out evaluation analysis on the solution set by sampling a TOPSIS method and a grey correlation analysis method to obtain an optimal solution; and scheduling the park according to the optimal operation scheme. By optimizing cold-heat-electricity resource regulation and control of the park, dual optimization of the operating cost and the carbon emission of the park is achieved. And an augmented epsilon-constraint method and a TOPSIS method are adopted, so that the solving efficiency of a multi-objective optimization problem and the precision of a solution set are improved, and an optimal regulation and control scheme is finally selected. Meanwhile, carbon capture and electricity-to-gas technologies are combined, so that carbon emission is effectively reduced, and low-carbon transformation and sustainable development of the park are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to a method and system for coordinated regulation of cold-heat-electricity flexible resources in a low-carbon park. Background Art

[0002] The low-carbon development of industrial parks has become an important part of achieving the "dual carbon" goal. Among them, carbon capture equipment is of great significance for the construction of low-carbon industrial parks. It can recycle the carbon dioxide released by the park, and then convert the carbon dioxide into natural gas for system application through power-to-gas equipment. The present invention takes the operating cost of the park and its carbon emissions as the optimization function of multi-objective coordinated regulation, and considers the demand response of electric vehicles and air-conditioning equipment. In order to better obtain the Pareto frontier solution set, the augmented ε-constraint method is used to reconstruct the multi-objective optimization into a single-objective optimization problem.

[0003] Under the "dual carbon" goal, simple single-objective economic scheduling is difficult to meet the optimization needs. It is necessary to consider not only the economic operation of the park, but also the environmental protection of the park, and its carbon emissions. To solve multi-objective algorithms, linear weighted methods and intelligent algorithms are usually used, but both have certain limitations. The linear weighted method is difficult to determine the weight coefficients between objectives. The use of intelligent algorithms to solve multi-objective problems may result in poor distribution of the Pareto frontier, and the solution obtained may not be an ideal solution. Therefore, multi-objective processing methods and the screening of ideal solutions to the Pareto solution set require further research. The present invention uses an augmented ε-constraint method to construct the multi-objective problem as a single-objective optimization problem to obtain the Pareto frontier, and the TOPSIS method and grey correlation analysis method are used for comprehensive decision-making to obtain the ideal solution. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: how to solve the multi-objective optimization problem between operating costs and carbon emissions in the process of low-carbon development of industrial parks. Existing single-objective economic scheduling methods are difficult to take into account both economic efficiency and environmental protection at the same time. Traditional multi-objective optimization algorithms have problems such as difficulty in determining weights and poor Pareto frontier distribution.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for coordinated regulation of cold-heat-electricity flexible resources in a low-carbon park, comprising: constructing a park operation model;

[0007] Construct a park optimization scheduling model, with the minimum park operation cost and carbon emissions as the objective function;

[0008] The augmented ε-constraint is used to solve the bi-objective optimization problem and the Pareto front solution set is obtained.

[0009] The sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution;

[0010] The park is dispatched according to the optimal operation plan.

[0011] As a preferred solution of the low-carbon park cold-heat-electricity flexible resource coordinated control method described in the present invention, wherein: the construction of the park operation model includes that the energy side of the park includes electric energy, natural gas energy, and solar photovoltaic energy;

[0012] The park's energy conversion equipment includes power-to-gas equipment, gas boilers, gas turbines, and electric refrigerators;

[0013] The park load includes electrical load, thermal load and cooling load.

[0014] As a preferred solution of the low-carbon park cold-heat-electricity flexible resource coordinated control method described in the present invention, wherein: the construction of the park optimization scheduling model includes taking the minimum park operation cost as the objective function, and its objective function includes the park's electricity purchase cost, gas purchase cost, power-to-gas equipment operation cost, carbon capture equipment operation cost, carbon sequestration cost, electric refrigerator operation cost, abandoned light penalty cost, demand response compensation cost and carbon trading cost, and optimizing scheduling in combination with the energy efficiency and operation restrictions of each device;

[0015] At the same time, minimizing the park's carbon emissions is taken as the objective function. The carbon emissions are composed of the converted carbon emissions of the park's purchased electricity, the carbon emissions of gas turbines and gas boilers, and the carbon emission reductions of carbon capture equipment. The coordinated scheduling of economy and environmental protection is achieved through a multi-objective optimization method.

[0016] As a preferred scheme of the method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park described in the present invention, the augmented ε-constraint is used to solve the dual-objective optimization problem, including taking the park operating cost as the main objective function and the park carbon emissions as the secondary objective function, dividing the carbon emission value range into multiple sub-intervals, and introducing a slack variable ε so that each sub-interval corresponds to a feasible solution, and traversing and calculating each feasible solution, finally generating a widely covered Pareto frontier solution set, providing a reference for the multi-objective comprehensive optimization of the park scheduling plan.

[0017] As a preferred solution of the method for coordinated regulation of cold, heat and electricity flexible resources in a low-carbon park described in the present invention, the sampling TOPSIS method and the grey correlation analysis method are used to evaluate and analyze the solution set, including performing data standardization processing on each solution in the solution set, calculating the weight and information entropy of each objective function, constructing positive ideal solutions and negative ideal solutions based on the weighted matrix, and comprehensively evaluating the closeness of each solution by calculating the Euclidean distance and grey correlation between each solution and the positive and negative ideal solutions, and finally determining the optimal solution with the shortest distance to the positive ideal solution and the highest correlation.

[0018] As a preferred solution of the low-carbon park cold-heat-electricity flexible resource coordinated control method of the present invention, wherein: the data standardization processing of each solution in the solution set includes setting n objects to be evaluated and m evaluation indicators to construct a data matrix Y=(x ij ) n×m ,n takes N s +1,N s is the number of segments; m is the number of objective functions, and the standardized formula is as follows:

[0019]

[0020] Among them, x ij , Y ij is the function value before and after standardization; max(x j )、min(x j ) represents the maximum and minimum values ​​of the function; i is the sample number, which is used to represent the i-th sample object; j is the indicator number, which is used to represent the j-th evaluation indicator; positive indicators mean that the larger the better, and negative indicators mean that the smaller the better;

[0021] The calculation of the weight and information entropy of each objective function includes calculating each indicator Y ij Information entropy S j :

[0022]

[0023] Calculate each indicator Y ij The weight W j :

[0024]

[0025] Construct the weighted matrix Z of each indicator:

[0026] Z=(z ij ) n×m =(W j ×Y ij ) n×m

[0027] The method of constructing positive ideal solutions and negative ideal solutions based on the weighted matrix includes determining the positive ideal solutions of various indicators according to the weighted matrix Z. and negative ideal solution The objective function is set as the park operation cost and carbon emissions are the less the better and the negative indicators are:

[0028]

[0029] The calculation of the Euclidean distance and grey correlation degree between each solution and the positive and negative ideal solutions includes defining the Euclidean distance of the i-th object as

[0030]

[0031]

[0032] Calculate the grey correlation degree of the object to be evaluated to the positive and negative ideal values. The formula is:

[0033]

[0034] Among them, α is a resolution coefficient used to measure the significance of the difference; are the grey correlation coefficients between positive and negative ideal values ​​respectively; are the grey relational degrees between the ith object and the positive and negative ideal values ​​respectively.

[0035] As a preferred solution of the low-carbon park cold-heat-electricity flexible resource coordinated control method described in the present invention, the closeness of each solution in the comprehensive evaluation includes that the size of the potential value of the object to be evaluated needs to fully consider the Euclidean distance and gray correlation between the object to be evaluated and the positive ideal value, so as to obtain the relative closeness of the object to be evaluated and the ideal value. The formula is:

[0036]

[0037]

[0038] in, are the progress of the i-th object with respect to the positive and negative ideal values, respectively; κ1 and κ2 can be determined by the evaluator according to the situation;

[0039] Calculate the comprehensive progress, the formula is:

[0040]

[0041] Among them, ε i For comprehensive progress.

[0042] A low-carbon park cold-heat-electricity flexible resource coordinated control system, characterized by: including:

[0043] Park operation model building module: build the park operation model;

[0044] Optimization and scheduling model module: build a park optimization and scheduling model, with the minimum park operation cost and carbon emissions as the objective function;

[0045] Augmented ε-constraint solving module: Augmented ε-constraint is used to solve the bi-objective optimization problem and obtain the Pareto frontier solution set;

[0046] Solution set evaluation and analysis module: Sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution;

[0047] Scheduling execution module: schedules the park according to the optimal operation plan.

[0048] Beneficial effects of the invention: The invention achieves dual optimization of park operation costs and carbon emissions by optimizing the park's cooling, heating and electricity resource control. The augmented ε-constraint method and TOPSIS method are used to improve the efficiency of solving multi-objective optimization problems and the accuracy of the solution set, and finally select the optimal control scheme. At the same time, combined with carbon capture and power-to-gas technology, carbon emissions are effectively reduced, helping the park's low-carbon transformation and sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the figures required for use in the description of the embodiments will be briefly introduced below. Obviously, the figures described below are only some embodiments of the present invention. For ordinary technicians in this field, other figures can be obtained based on these figures without paying creative work.

[0050] Figure 1 A flow chart of a method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park provided in the first embodiment of the present invention;

[0051] Figure 2 A park operation model structure diagram of a low-carbon park cold-heat-electricity flexible resource coordinated control method provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the specification drawings. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0053] Example 1, reference Figure 1and Figure 2 , which is an embodiment of the present invention, provides a low-carbon park cold-heat-electricity flexible resource coordinated control method, including:

[0054] S1: Build a park operation model.

[0055] The energy side of the park includes electricity, natural gas, and solar photovoltaics; the park's energy conversion equipment includes power-to-gas equipment, gas boilers, gas turbines, electric refrigerators, etc.; the park's load includes electrical load, heat load, and cooling load. For details, please refer to Figure 2 .

[0056] S2: Construct a park optimization scheduling model with the minimization of park operation costs and carbon emissions as the objective function.

[0057] Considering the economic efficiency and carbon emission, the objective function is to minimize the park operation cost. The objective function 1 can be expressed as:

[0058] minF1=C E +C gas +C P2G +C CCUS +C STO +C ER +C PV +C DR +C CO2

[0059] Among them, F1 is the total cost of park operation; C E The cost of purchasing electricity for the park; C gas C is the gas purchase cost of the park; P2G is the operating cost of the power-to-gas equipment; C CCUS is the operating cost of carbon capture equipment; C STO is the carbon sequestration cost; C ER is the operating cost of the electric refrigerator; C PV C is the penalty cost for abandoning light; DR Compensate for demand response costs; C CO2 The cost of carbon trading.

[0060] Electricity purchase cost:

[0061]

[0062] Where: P E,t The electric energy purchased by the park from the power grid at a certain moment; c E,t It is the unit price of electricity purchased by the park at a certain moment.

[0063] Gas purchase cost: The actual amount of natural gas purchased by the park is the sum of the natural gas consumed by the gas turbine and gas boiler minus the natural gas generated by the power-to-gas equipment:

[0064]

[0065] Where: c CH4 V is the unit price of gas; MT,t V is the volume of natural gas consumed by the gas turbine at a certain moment; GB,t V is the volume of natural gas consumed by the gas boiler at a certain moment; P2G,t It is the volume of carbon dioxide converted into natural gas by the power-to-gas equipment at a certain moment.

[0066] Power-to-gas equipment operating costs:

[0067]

[0068] Where: P P2G,t is the power consumption of the power-to-gas equipment at a certain moment, ω1 is the operation and maintenance coefficient of the power-to-gas equipment; ρ CO2 is the density of carbon dioxide, V P2G,t is the volume of carbon dioxide recovered and utilized by the power-to-gas equipment, and ψ1 is the cost coefficient of converting unit carbon dioxide into methane.

[0069] Carbon capture equipment operating costs:

[0070]

[0071] Where: P CCUS,t is the electric energy consumed by the carbon capture equipment at a certain moment, ω2 is the operation and maintenance coefficient of the carbon capture equipment;

[0072] V CO2,t is the volume of carbon dioxide captured by the carbon capture equipment at a certain moment, and ψ2 is the capture cost coefficient per unit of carbon dioxide.

[0073] (5) Carbon storage costs. Due to the limitations of the power-to-gas equipment, the carbon dioxide produced in the park cannot be completely converted into natural gas. The remaining carbon dioxide needs to be stored:

[0074]

[0075] Where: c sto is the storage cost per unit of carbon dioxide.

[0076] (6) Electric refrigerator operating cost:

[0077]

[0078] Where: ω3 is the operation and maintenance coefficient of the electric refrigerator, P ER,t is the power consumption of the refrigerator at a certain moment; is the cooling power of the electric refrigerator in a certain period of time, and ψ3 is the electric-to-cooling conversion efficiency of the electric refrigerator.

[0079] (7) Penalty cost for abandoning light:

[0080]

[0081] Where: c pv is the unit abandonment penalty cost, To predict the photovoltaic power generation at a certain moment, P PV,t is the actual photovoltaic power generation at a certain moment.

[0082] (8) Demand response compensation costs:

[0083]

[0084] Where: c DR,t is the unit response power compensation cost at a certain moment, P D,t The power required for a certain moment.

[0085] (9) Carbon trading costs:

[0086]

[0087] Where: k CO2 is the carbon market transaction price; T MT,t is the carbon emission of the gas turbine at a certain moment; T GB,t is the carbon emission of the gas boiler at a certain moment; T CCUS,t is the amount of carbon emissions captured by the carbon capture equipment at a certain moment; T f,t Free carbon credits determined for management.

[0088] In addition to considering the park's operating costs, the park's carbon emissions also need to be considered, with the minimum carbon emissions of the park being used as objective function 2:

[0089]

[0090] T E,t =ε E P E,t

[0091] T MT,t =ρ CO2 V MT,t

[0092] T GB,t =ρ CO2 V GB,t

[0093] T CCUS,t =ρ CO2 V CO2,t

[0094] Where: T E,tThe carbon emissions converted from electricity purchased from the power grid at a certain moment; ε E is the carbon emission coefficient per unit of electricity purchased.

[0095] The constraints of the park system constructed by the present invention are as follows:

[0096] 1) Power balance constraints:

[0097] There are three energy flows in the park: electricity, heat and cold:

[0098]

[0099] Where: P ESD,t , P ESC,t They are the charging and discharging amount of the energy storage device at a certain moment respectively; They are electricity, heating and cooling loads respectively; The cooling power provided to a gas turbine at a certain time.

[0100] 2) Power-to-gas equipment constraints:

[0101] P P2G,t =(V P2G,t H g ) / (η EL η MR )

[0102]

[0103] Where: H g is the calorific value of natural gas, η EL , η MR They are the energy efficiencies of the two stages of hydrogen electrolysis and methanogenesis; They are the upper and lower power limits of the power-to-gas equipment respectively.

[0104] 3) Carbon capture equipment constraints:

[0105] P CCUS,t =αρ CO2 V CO2,t

[0106] V CO2,t =η CC (V MT,t +V GB,t )

[0107]

[0108] Where: α is the electrical power required to capture a unit amount of carbon, is the upper and lower limits of the power of the carbon capture equipment, η CC is the carbon capture efficiency; the volume of natural gas consumed by gas turbines and gas boilers is also the volume of carbon dioxide generated.

[0109] 4) Gas turbine equipment constraints:

[0110]

[0111] Where: η e , η h , η c are the electricity, heat and cold conversion efficiency of the gas turbine, are the upper and lower limits of gas turbine power, They are the upper and lower climbing limits of the gas turbine respectively.

[0112] 5) Gas boiler constraints:

[0113]

[0114] Where: η gb is the thermal efficiency of the gas boiler, V GB,t is the volume of natural gas consumed by the gas boiler at a certain moment, It is the upper and lower limits of gas boiler power.

[0115] 6) Electric refrigerator constraints:

[0116]

[0117] P ER,min ≤P ER,t ≤P ER,max

[0118] Where: η ER is the conversion efficiency of the electric refrigerator, P ER,max , P ER,min The upper and lower limits of the electric refrigerator power.

[0119] 7) Electric energy storage constraints:

[0120] S ES,t =S ES,t-1 +η cha P ESC,t -η dis P ESD,t

[0121] 0≤P ESC,t ≤U ESC,t P ESC,max

[0122] 0≤P ESD,t ≤U ESD,t P ESD,max

[0123] 0≤U ESC,t +U ESD,t ≤1

[0124] S ES,min ≤S ES,t ≤S ES,max

[0125] Where: S ES,t is the storage capacity of the energy storage device in a certain period of time, η cha , η dis are the charging and discharging efficiency of the energy storage device, U ESC,t , U ESD,t are the charging and discharging state parameters of the energy storage device, P ESC,max , P ESD,max are the upper limits of charge and discharge power of the energy storage device, S ES,max , S ES,min are the upper and lower limits of the capacity of the electrical energy storage device. The thermal energy storage constraints are similar.

[0126] 8) Demand response constraints:

[0127] The present invention considers the regulation of electric vehicles and air-conditioning equipment in the park, and the specific constraints are as follows:

[0128] Assuming that electric vehicles are charged at standard charging power and time-of-use electricity prices until they are fully charged before being regulated, the constraints on electric vehicles are as follows:

[0129]

[0130] 0≤SOC i,t ≤1

[0131]

[0132] Where: SOC i,t is the state of charge of the ith electric vehicle at time t, η EV is the charging and discharging efficiency of electric vehicles, are the charging and discharging power of the i-th electric vehicle at time t, is the battery capacity of the i-th electric car; They are 0-1 variables representing the charging and discharging status of electric vehicles; They are the upper limit of electric vehicle charging and discharging power; SOC i,t is the state of charge of the i-th car at time t; is the expected value of the state of charge of the i-th electric vehicle when it leaves; is the power demanded by the i-th electric vehicle at time t, is the charging and discharging power of the i-th electric vehicle before regulation; are the arrival and departure times of the i-th electric car respectively.

[0133] Furthermore, the present invention also regulates the air conditioner in the park. When the air conditioner is used for cooling or heating, the human body has a certain acceptance range for the expected ideal ambient temperature, that is, the ambient temperature is allowed to fluctuate within a certain range within the ideal ambient temperature. This feature is used to regulate the air conditioner:

[0134]

[0135]

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] Where: The maximum response charging and discharging power for air conditioning load, is the rated power of the air conditioner, P1 is the power to maintain the lowest indoor temperature acceptable to the human body, P2 is the power to maintain the highest indoor temperature acceptable to the human body, and P0 is the power of the air conditioner to maintain the most comfortable temperature for the human body; Δt is the control period, which is 1 hour. is the rated power operation time of the air conditioner, The power operation time for the air conditioner to maintain the lowest indoor temperature acceptable to human body. The time when the air conditioner stops running; The air conditioner stops running time. In order to maintain the highest acceptable temperature for human body, is the operating time of the air conditioner at rated power; R1 is the equivalent thermal resistance of the air conditioner-building system, C a is the equivalent heat capacity, η ac is the energy efficiency ratio of the air conditioning equipment; T0 is the most comfortable temperature for human senses, T out is the outdoor temperature, I min ,I max The upper and lower limits of the temperature acceptance range for human comfort; The response power of the i-th air conditioner at time t.

[0144] The total demand response power of the park is the sum of the electric vehicle response power and the air conditioning response power:

[0145]

[0146] Where: N is the total number of electric vehicles subject to regulation; K is the total number of air conditioners subject to regulation.

[0147] S3: The augmented ε-constraint is used to solve the bi-objective optimization problem and the Pareto front solution set is obtained.

[0148] Take objective function 1 (F1) as the main objective, put objective function 2 (F2) into the constraints, divide the value interval of secondary objective function 2 into equal parts, make ε traverse each value and solve the corresponding feasible solution, and finally get a set of Pareto frontier solution sets. The objective function is as follows:

[0149] min F=F1+τ(s / r)

[0150] It should be noted that the newly added constraints are as follows:

[0151] F2+s=ε,s≥0

[0152] ε=lb+(k+r) / N s ,k=1,2,...,N s

[0153] r=F2 u -F2 d

[0154] Where: s is the introduced slack variable; F2 u 、F2 d are the maximum and minimum values ​​of objective function 2 respectively; r is the difference between the maximum and minimum values ​​of objective function 2; lb is the minimum value of objective function 2; N s is the number of segments; τ is the coefficient.

[0155] S4: Sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution.

[0156] 1) Data standardization. Assume that there are n objects to be evaluated and m evaluation indicators, and construct a data matrix Y = (x ij ) n×m , the present invention takes N s +1, m is the number of objective functions. The standardization formula is as follows:

[0157]

[0158] Where: x ij , Y ij is the function value before and after standardization; max(x j )、min(x j) represents the maximum and minimum values ​​of the function; a positive indicator means the larger the better; a negative indicator means the smaller the better.

[0159] 2) Calculate each indicator Y ij Information entropy S j :

[0160]

[0161] 3) Calculate each indicator Y ij The weight W j :

[0162]

[0163] 4) Construct the weighted evaluation index Z of each indicator:

[0164] Z=(z ij ) n×m =(W j ×Y ij ) n×m

[0165] 5) Determine the positive ideal solution of each indicator based on the weighted matrix Z and negative ideal solution Since the objective function set by the present invention is that the park operation cost and carbon emissions are negative indicators, the less the better, then:

[0166]

[0167] 6) Define the Euclidean distance of the i-th object as

[0168]

[0169]

[0170] 7) Calculate the grey correlation degree from the object to be evaluated to the positive and negative ideal values:

[0171]

[0172]

[0173]

[0174]

[0175] Wherein: α is a resolution factor used to measure the significance of the difference, and the present invention takes it as 0.5; Grey correlation coefficient between positive and negative ideal values ​​respectively; are the grey relational degrees between the ith object and the positive and negative ideal values ​​respectively.

[0176] 8) After improving the TOPSIS method based on the grey correlation analysis method, the potential value of the object to be evaluated needs to fully consider the Euclidean distance and grey correlation degree between the object to be evaluated and the positive ideal value, so as to obtain the relative closeness between the object to be evaluated and the ideal value:

[0177]

[0178] κ1+κ2=1

[0179] Where: are the progress of the i-th object in contact with the positive and negative ideal values ​​respectively; κ1 and κ2 can be determined by the evaluator according to the situation, and are generally taken as 0.5.

[0180] 9) Calculate the comprehensive evaluation value

[0181]

[0182] Where: ε i It is a comprehensive measure of the progress. The larger its value is, the closer the object to be evaluated is to the positive ideal value, indicating that the optimization effect is better, and vice versa.

[0183] S5: Dispatch the park according to the optimal operation plan.

[0184] It should be noted that the "optimal" of the optimal operation plan is a relative term, which refers to the optimal solution selected by TOPSIS method and grey correlation analysis from the Pareto frontier solution set obtained through the previous multi-objective optimization (operating cost and carbon emissions). In this context, "optimal" does not refer to the absolute minimum value, but the result of a trade-off between multiple objectives. The rationality of this optimal solution comes from the coordinated optimization of flexible resources such as cooling, heating, and electricity, taking into account the dual needs of economy and environmental protection of the actual park.

[0185] Embodiment 2 is the second embodiment of the present invention, which is different from the previous embodiment in that:

[0186] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the current technical solution, can be embodied in the form of a software product. The current computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0187] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, system or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, system or device and execute the instructions), or in conjunction with these instruction execution systems, systems or devices. For the purpose of this specification, "computer-readable medium" can be any system that can contain, store, communicate, propagate or transport a program for use by an instruction execution system, system or device, or in conjunction with these instruction execution systems, systems or devices.

[0188] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic systems), a portable computer disk case (magnetic systems), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber system, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0189] Example 3 is an embodiment of the present invention, which provides a low-carbon park cold-heat-electricity flexible resource coordinated control system, characterized in that it includes a park operation model construction module, an optimization scheduling model module, an augmented ε-constraint solving module, a solution set evaluation and analysis module and a scheduling execution module.

[0190] Park operation model building module: build the park operation model;

[0191] Optimization and scheduling model module: build a park optimization and scheduling model, with the minimum park operation cost and carbon emissions as the objective function;

[0192] Augmented ε-constraint solving module: Augmented ε-constraint is used to solve the bi-objective optimization problem and obtain the Pareto frontier solution set;

[0193] Solution set evaluation and analysis module: Sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution;

[0194] Scheduling execution module: schedules the park according to the optimal operation plan.

[0195] Example 4: The following is an embodiment of the present invention, which provides a method for coordinated regulation of cold-heat-electricity flexible resources in a low-carbon park. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation / comparative experiments.

[0196] In order to verify the effectiveness and innovation of the "cold-heat-electricity" flexible resource coordinated regulation method of the low-carbon park, a simulation experiment was conducted. The experimental goal is to achieve the coordinated optimization of the park's operating costs and carbon emissions under multi-objective optimization by building an operation model and an optimization scheduling model for the park. The experimental objects include an industrial park with complex cold, heat and electricity demands and huge energy consumption. The main energy equipment used in the experiment includes power-to-gas equipment, gas boilers, gas turbines, and carbon capture equipment. At the same time, the charging demand of electric vehicles and air conditioning load regulation in the park are also considered.

[0197] First, we collected data on the park's energy equipment and its loads, including electrical load, heat load, cooling load, etc. The park's main energy sources are electricity, natural gas, and solar photovoltaics. Based on the collected data, we established a balance between the energy side and the load side, taking into account the operating efficiency and conversion efficiency of different energy equipment. When building the model, we paid special attention to the efficiency of the power-to-gas equipment in converting carbon dioxide into natural gas, ensuring that carbon capture and reuse could be reflected in the model.

[0198] Based on the operation model, a scheduling model is constructed, and the objective function is set as the dual optimization of the park operation cost and carbon emissions. Taking into account energy price fluctuations, demand response and carbon trading market prices, the optimization function includes electricity purchase costs, gas purchase costs, equipment operation and maintenance costs, carbon capture and storage costs, etc. The optimization scheduling model is solved by the augmented ε-constraint method, and the Pareto frontier solution set is constructed.

[0199] The augmented ε-constraint method is used to take the park operation cost as the main target and carbon emissions as the secondary target, and the parameter ε value is dynamically adjusted to generate the Pareto frontier solution set. The solution under each set of ε values ​​represents the possible operation scheme of the park under different economic and environmental protection trade-offs. After multiple solutions, multiple sets of Pareto solutions under different operating conditions are obtained.

[0200] The Pareto solution set was analyzed by TOPSIS method and grey correlation analysis method to select the optimal solution. The standardized data was evaluated according to the park's economic and environmental indicators, and the information entropy and weight were calculated. Finally, the optimal operation plan was determined, which can achieve the minimum cost and effectively reduce carbon emissions.

[0201] The cooling, heating and electricity resources of the park are actually dispatched according to the optimal solution. In the simulation experiment, the start and stop and operation parameters of the equipment are dynamically adjusted to respond to real-time load demand and ensure the reasonable distribution of energy flow.

[0202] For details, please refer to Table 1.

[0203] Table 1 Experimental data comparison table

[0204]

[0205]

[0206] Through the analysis of the table data, it can be seen that the method of the present invention has significant advantages in optimizing the cooling-heating-electricity scheduling of the park. Although the traditional scheduling method (Scheme A) can meet the energy needs of the park, its operating cost is high and its carbon emissions are also large. In contrast, based on the optimization scheduling method of the present invention, with the adjustment of the ε value, the operating cost and carbon emissions of the park have been significantly optimized.

[0207] For example, when ε = 0.5 (Scheme D), the power load of the park is reduced to 42MW, while it is 50MW under traditional scheduling. At the same time, the cost of purchasing electricity is reduced from US$2,000 to US$1,800, and carbon emissions are reduced from 15 tons to 8 tons, showing the advantages of this method in optimizing energy allocation. As the value of ε increases, carbon emissions are further reduced. For example, when ε = 0.9 (Scheme F), carbon emissions are only 6 tons and the cost of purchasing electricity is reduced to US$1,700, which illustrates the flexibility of the augmented ε-constraint method in multi-objective optimization.

[0208] It can also be seen from the table that the optimal scheduling scheme can greatly reduce the park's operating costs and environmental impact through reasonable equipment start-stop and load adjustment without sacrificing energy demand. This is in sharp contrast to the traditional scheduling mode, which fails to fully consider load changes and carbon emission control, resulting in energy waste and excessive carbon emissions.

[0209] The innovation of the present invention is mainly reflected in the following points: First, through multi-objective optimization, the economy and environmental protection of the park are comprehensively regulated to ensure the balance between cost and carbon emissions under different ε values; second, the TOPSIS method and gray correlation analysis method are used to conduct in-depth analysis of the solution set to ensure that the selected scheduling scheme has higher scientificity and decision-making basis. In addition, the scheduling method of the present invention is highly flexible and can quickly respond to different operating conditions according to actual load changes and adjustments to carbon emission policies, thereby greatly improving the overall operating efficiency and environmental protection effect of the park.

[0210] In summary, the experimental data show that the method of the present invention can not only significantly reduce the operating costs of the park, but also show excellent results in carbon emission reduction. These innovations and actual results verify the effectiveness and practicality of this method in the scheduling and management of low-carbon parks.

[0211] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for coordinated regulation of cold-heat-electricity flexible resources in a low-carbon park, characterized in that: include: Build a park operation model; Construct a park optimization scheduling model, with the minimum park operation cost and carbon emissions as the objective function; The augmented ε-constraint is used to solve the bi-objective optimization problem and the Pareto front solution set is obtained. The sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution; The park is dispatched according to the optimal operation plan.

2. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 1, characterized in that: The construction of the park operation model includes that the energy side of the park includes electric energy, natural gas energy, and solar photovoltaic energy; The park's energy conversion equipment includes power-to-gas equipment, gas boilers, gas turbines, and electric refrigerators; The park load includes electrical load, thermal load and cooling load.

3. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 2, characterized in that: The construction of the park optimization scheduling model includes taking the minimization of the park operation cost as the objective function, wherein the objective function includes the park's electricity purchase cost, gas purchase cost, power-to-gas equipment operation cost, carbon capture equipment operation cost, carbon sequestration cost, electric refrigerator operation cost, abandoned solar penalty cost, demand response compensation cost and carbon trading cost, and optimizing the scheduling in combination with the energy efficiency and operation restrictions of each device; At the same time, minimizing the park's carbon emissions is taken as the objective function. The carbon emissions are composed of the converted carbon emissions of the park's purchased electricity, the carbon emissions of gas turbines and gas boilers, and the carbon emission reductions of carbon capture equipment. The coordinated scheduling of economy and environmental protection is achieved through a multi-objective optimization method.

4. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 3, characterized in that: The augmented ε-constraint is used to solve the dual-objective optimization problem, including taking the park operation cost as the main objective function, the park carbon emissions as the secondary objective function, dividing the carbon emissions value interval into multiple sub-intervals, and introducing a slack variable ε, so that each sub-interval corresponds to a feasible solution, and traversing and calculating each feasible solution, and finally generating a widely covered Pareto frontier solution set, which provides a reference for the multi-objective comprehensive optimization of the park scheduling plan.

5. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 4, characterized in that: The sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set, including performing data standardization on each solution in the solution set, calculating the weight and information entropy of each objective function, constructing positive ideal solutions and negative ideal solutions based on a weighted matrix, and calculating the Euclidean distance and grey correlation between each solution and the positive and negative ideal solutions, comprehensively evaluating the closeness of each solution, and finally determining the optimal solution with the shortest distance and the highest correlation to the positive ideal solution.

6. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 5, characterized in that: The data standardization process for each solution in the solution set includes setting n objects to be evaluated and m evaluation indicators, constructing a data matrix Y=(x ij ) n×m ,n takes N s +1,N s is the number of segments; m is the number of objective functions, and the standardized formula is as follows: Among them, x ij , Y ij is the function value before and after standardization; max(x j )、min(x j ) represents the maximum and minimum values ​​of the function; i is the sample number, which is used to represent the i-th sample object; j is the indicator number, which is used to represent the j-th evaluation indicator; positive indicators mean that the larger the better, and negative indicators mean that the smaller the better; The calculation of the weight and information entropy of each objective function includes calculating each indicator Y ij Information entropy S j : Calculate each indicator Y ij The weight W j : Construct the weighted matrix Z of each indicator: From=(from ij ) n×m =(In j ×Y ij ) n×m The method of constructing positive ideal solutions and negative ideal solutions based on the weighted matrix includes determining the positive ideal solutions of various indicators according to the weighted matrix Z. and negative ideal solution The objective function is set as the park operation cost and carbon emissions are the less the better as negative indicators, then: The calculation of the Euclidean distance and grey correlation degree between each solution and the positive and negative ideal solutions includes defining the Euclidean distance of the i-th object as Calculate the grey correlation degree of the object to be evaluated to the positive and negative ideal values. The formula is: Among them, α is a resolution factor used to measure the significance of the difference; are the grey correlation coefficients between positive and negative ideal values ​​respectively; are the grey relational degrees between the ith object and the positive and negative ideal values ​​respectively.

7. The method for coordinated control of cold-heat-electricity flexible resources in a low-carbon park according to claim 6, characterized in that: The comprehensive evaluation of the closeness of each solution includes that the size of the potential value of the object to be evaluated needs to fully consider the Euclidean distance and gray correlation between the object to be evaluated and the positive ideal value, so as to obtain the relative closeness between the object to be evaluated and the ideal value. The formula is: in, are the progress of pasting the i-th object with positive and negative ideal values ​​respectively; It can be determined by the evaluator according to the circumstances; Calculate the comprehensive progress, the formula is: Among them, ε i For comprehensive progress.

8. A low-carbon park cold-heat-electricity flexible resource coordinated control system using the method according to any one of claims 1 to 7, characterized in that: Park operation model building module: build the park operation model; Optimization and scheduling model module: build a park optimization and scheduling model, with the minimum park operation cost and carbon emissions as the objective function; Augmented ε-constraint solving module: Augmented ε-constraint is used to solve the bi-objective optimization problem and obtain the Pareto frontier solution set; Solution set evaluation and analysis module: Sampling TOPSIS method and grey correlation analysis method are used to evaluate and analyze the solution set to obtain the optimal solution; Scheduling execution module: schedules the park according to the optimal operation plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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