Configuration model construction method of multi-element comprehensive industrial park

Abstract

The invention relates to a multi-element comprehensive industrial park comprehensive energy management method, which aims at an industrial park comprehensive energy system, establishes a multi-objective optimization scheduling model and a double-layer optimization model, constructs a source-network-load-storage integrated structure, balances power and consumes renewable energy sources through energy storage equipment, comprehensively considers economical efficiency and carbon emission indexes, and improves the comprehensive energy management efficiency. And in combination with a time-of-use electricity price optimization operation strategy, various configuration schemes are provided for users, the problems of one-sidedness and lack of pertinence in the prior art are solved, the economical efficiency and sustainability of the system are improved, and the power supply and heat supply requirements of the users are met.

Description

Technical Field

[0001] The present application relates to the field of energy management, and in particular to a configuration model construction method, system, computer equipment and computer-readable storage medium for a diversified comprehensive industrial park. Background Art

[0002] With the development of my country's economy, energy demand is increasing day by day. At present, the energy consumption structure is dominated by primary energy such as coal, and the energy utilization rate is low, facing major energy and environmental challenges. Integrated energy system (IES) is usually composed of multiple types of components and integrates multiple energy forms. It is a new generation of energy system under the background of energy system and mechanism reform. The industrial park type integrated energy system has regional and diverse characteristics, and its energy supply is affected by the regional demand side.

[0003] At present, there are some applications of IES capacity configuration and optimization system, most of which are evaluated by configuring technical and economic parameters and forming optimization functions from the perspective of economic benefits. Some schemes adopt a hierarchical control method of multi-energy coordination to achieve an economic energy strategy for the integrated energy cabin, but do not consider multi-objective optimization factors such as carbon emissions. A few schemes use genetic algorithms for configuration optimization for non-industrial users with investment costs, energy costs, unmet load gaps and CO2 as targets, but do not combine planning with operation, resulting in a single optimization algorithm structure that cannot effectively respond to user energy needs. Summary of the invention

[0004] The embodiments of the present application provide a comprehensive energy management method, system, computer device and computer-readable storage medium for a diversified comprehensive industrial park, so as to at least solve the problem of poor flexibility in the configuration and operation strategy of industrial parks in related technologies.

[0005] In a first aspect, an embodiment of the present application provides a method for constructing a configuration model of a diversified comprehensive industrial park, the method comprising:

[0006] According to the system structure information of the exemplary multi-integrated park, multiple operating condition models are established, including: new energy power generation equipment operating condition model, gas turbine operating condition model, gas boiler operating condition model and energy storage equipment operating condition model;

[0007] Based on the operating condition model, a two-layer multi-objective optimization model of the multi-integrated park is configured respectively, and the optimization algorithm and optimization decision-making strategy corresponding to the multi-objective optimization model are configured, wherein the two-layer optimization model is used to output configuration and operation plans, including a configuration layer model and an operation layer model.

[0008] In some embodiments, constructing the configuration layer model includes:

[0009] Determine a first objective function and a first constraint condition for configuring the multi-integrated park according to the operating condition model, historical data of the exemplary multi-integrated park, and expert knowledge, wherein the first objective function is constructed based on an economic evaluation index and a carbon emission evaluation index;

[0010] The configuration layer model is constructed according to the first objective function and the first constraint condition.

[0011] In some embodiments, constructing the operation layer model includes:

[0012] The second objective function is constructed based on fuel cost, electricity purchase cost, grid-connected electricity sales cost and carbon emission cost, fuel consumption, grid-connected electricity purchase, grid-connected electricity sales and carbon emission, as well as technical equipment power and unit maintenance cost of technical equipment;

[0013] Determine a second constraint condition according to load requirements of different energy forms and different types of technical equipment satisfying their own operating characteristics and power limits, wherein the second constraint condition includes an electrical balance constraint, a thermal balance constraint, and an equipment operating characteristic constraint;

[0014] Based on the second objective function and the second constraint condition, the operation layer model is constructed.

[0015] In some embodiments, the optimization algorithm is an NSGA-II algorithm, which is used to perform optimization and solution based on the configured multi-objective optimization model, and generate multiple optimal solution sets of the multi-objective optimization model under the second constraint condition, wherein any optimal solution corresponds to a candidate configuration and operation scheme;

[0016] Through the optimization decision strategy, the configuration and operation plan are determined from the combination of the optimal solutions.

[0017] In some embodiments, the Topsis method is used to standardize the optimal solution set, and the relative closeness of a solution in the optimal solution set to the ideal solution is calculated by the following formula:

[0018]

[0019] Among them: U i is the relative proximity, S i + , S i - are the distances between any solution and the positive and negative ideal solutions, respectively, wherein the closer the relative approximation is to 1, the closer the solution is to the ideal solution;

[0020] The multiple optimal solutions are sorted according to the relative similarity, and a satisfactory solution closest to the ideal solution is obtained according to the sorting result to obtain the candidate configuration and operation plan.

[0021] In some of the embodiments, the candidate configuration and operation schemes include: an economy-dominated scheme, an environmental protection-dominated scheme, and a balanced optimal configuration scheme.

[0022] In some embodiments, the operating condition model of the new energy power generation equipment is a photovoltaic power generation model, which is established based on the power, voltage, and current calculation formulas of the photovoltaic power generation group in the maximum power mode and at the optimal working point;

[0023] The gas turbine model is a gas-steam combined cycle unit model, and the gas turbine operating condition model is established based on the relationship between gas turbine gas consumption and power generation, the relationship between gas turbine waste heat emission and power generation efficiency, the relationship between waste heat boiler high-pressure main steam, low-pressure main steam and gas turbine waste heat emission, steam turbine exhaust heat loss power generation and steam turbine power generation;

[0024] By establishing the relationship between the output value of the gas boiler and the gas consumption, gas calorific value and operating efficiency, the gas boiler operating model is constructed;

[0025] The operating model of the energy storage device is constructed by establishing the mathematical relationship between the capacity of the energy storage device at any time, and the charge and discharge power, charge and discharge efficiency, and self-discharge efficiency.

[0026] In a second aspect, an embodiment of the present application provides a capacity configuration and operation control method for a diversified comprehensive industrial park, the method comprising:

[0027] The two-layer multi-objective optimization model completed by the configuration can dynamically output configuration and operation plans based on the demand data and field data of any multi-industrial park;

[0028] The configuration and operation plan based on the dynamic output is used for capacity configuration and operation control of the diversified comprehensive industrial park.

[0029] In a third aspect, the embodiment of the present application provides a capacity configuration and operation control system for a diversified comprehensive industrial park, the system comprising a planning module and a control module, wherein:

[0030] The planning module is used to dynamically output configuration and operation plans based on demand data and field data of any multi-industrial park through the two-layer multi-objective optimization model completed by the configuration;

[0031] The control module is used for capacity configuration and operation control of the diversified comprehensive industrial park based on the configuration and operation plan of the dynamic output.

[0032] In a fourth aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect above when executing the computer program.

[0033] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect above.

[0034] Compared with the related technologies, the embodiment of the present application provides a diversified and comprehensive industrial park integrated energy management method. For the integrated energy system of the industrial park, a multi-objective optimization scheduling model and a two-layer optimization model are established, and an integrated source-grid-load-storage structure is constructed. Energy storage equipment is used to balance power and absorb renewable energy. Economic and carbon emission indicators are comprehensively considered, and the time-of-use electricity price optimization operation strategy is combined to provide users with a variety of configuration solutions, solve the one-sidedness and lack of specificity of existing methods, improve the economy and sustainability of the system, and meet users' power supply and heating needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0036] Figure 1 It is a flowchart of a method for constructing a multi-integrated industrial park configuration model according to an embodiment of the present application;

[0037] Figure 2 is a schematic diagram of the system structure of a comprehensive industrial park according to an embodiment of the present application;

[0038] Figure 3 is a structural schematic diagram of a double-layer multi-objective optimization model according to an embodiment of the present application;

[0039] Figure 4 It is a schematic diagram of the scheduling result before the operation day of the balanced optimal configuration under the fixed electricity price according to the embodiment of the present application;

[0040] Figure 5 is a schematic diagram comparing the operating conditions of the power subsystem under the time-of-use electricity price according to an embodiment of the present application;

[0041] Figure 6 is a flow chart of a method for capacity configuration and operation control of a diversified comprehensive industrial park according to an embodiment of the present application;

[0042] Figure 7 It is a structural block diagram of a capacity configuration and operation control system of a diversified comprehensive industrial park according to an embodiment of the present application;

[0043] Figure 8 It is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments provided in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0045] Obviously, the drawings described below are only some examples or embodiments of the present application. For ordinary technicians in this field, the present application can also be applied to other similar scenarios based on these drawings without creative work. In addition, it can also be understood that although the efforts made in this development process may be complicated and lengthy, for ordinary technicians in this field related to the content disclosed in this application, some changes in design, manufacturing or production based on the technical content disclosed in this application are just conventional technical means, and should not be understood as insufficient content disclosed in this application.

[0046] Reference to "embodiments" in this application means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0047] Unless otherwise defined, the technical terms or scientific terms involved in this application should be understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the" and the like involved in this application do not indicate a quantitative limitation, and may represent the singular or plural. The terms "include", "comprise", "have" and any of their variations involved in this application are intended to cover non-exclusive inclusions; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units that are not listed, or may also include other steps or units inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0048] With the development of economy, the demand for energy is increasing day by day. However, my country's energy consumption structure is dominated by primary energy such as coal, and the energy utilization rate is low, facing major energy and environmental challenges. Integrated energy system (IES) is usually composed of multiple types of components and integrates multiple energy forms. It is a new generation of energy system under the background of energy system and mechanism reform. The industrial park type integrated energy system has regional and diverse characteristics, and its energy supply is affected by the regional demand side.

[0049] The invention patent "IES adjustable capacity evaluation method, device, equipment and medium based on conditional risk value" (application number: CN202410499554.4) proposes an IES adjustable capacity evaluation method, device, equipment and medium based on conditional risk value. The maximum and minimum conditional risk values ​​of the exchange power between the IES and the upper-level power grid interconnection line at each moment are used as the first objective function, and the maximum and minimum conditional risk values ​​of the cumulative flow of electricity between the IES and the upper-level power grid interconnection line are used as the second objective function. Considering the IES absorption constraints, energy storage equipment operation constraints, energy coupling equipment operation constraints, demand response constraints, power balance constraints and total cost constraints, the adjustable capacity model is solved to obtain the adjustable capacity range of the IES.

[0050] At present, there are some applications of IES capacity configuration and optimization system, most of which are evaluated by configuring technical and economic parameters and forming optimization functions from the perspective of economic benefits. Some schemes adopt a hierarchical control method of multi-energy coordination to achieve an economic energy strategy for the integrated energy cabin, but do not consider multi-objective optimization factors such as carbon emissions. A few schemes use genetic algorithms for configuration optimization for non-industrial users with investment costs, energy costs, unmet load gaps and CO2 as targets, but do not combine planning with operation, resulting in a single optimization algorithm structure that cannot effectively respond to user energy needs.

[0051] The demonstration industrial park mainly includes process industries, e-commerce, star-rated hotels, high-end offices, exhibition centers, etc. In order to solve the various energy needs of users, this application proposes a configuration model construction method and control method for a diversified comprehensive industrial park, taking into account factors such as the terminal energy demand, energy price situation and resource supply conditions in the region, and then formulating a suitable installed capacity and operation strategy. In addition, combined with the characteristics of regional demand-side response, a two-layer optimization theory is used to establish a comprehensive energy system optimization model for a diversified comprehensive industrial park, in which the configuration layer model takes economy and carbon emissions as the goals, and the operation layer takes economy as the goal. Under the two-layer optimization framework, a two-stage rolling optimization is performed to obtain a multi-objective Pareto solution set, and then the multi-objective optimization decision is combined with the time-of-use electricity price to obtain the optimal configuration and operation strategy.

[0052] Figure 1 is a flowchart of a method for constructing a multi-integrated industrial park configuration model according to an embodiment of the present application, such as Figure 1 As shown, the process includes the following steps:

[0053] S101, based on the system structure information of the exemplary multi-integrated park, multiple operating condition models are established, including: a new energy power generation equipment operating condition model, a gas turbine operating condition model, a gas boiler operating condition model and an energy storage equipment operating condition model;

[0054] Before step S101, it is necessary to establish and analyze the mathematical model of the equipment submodule in combination with the unit form and expansion plan of the comprehensive industrial park. Figure 2 is a schematic diagram of the system structure of a comprehensive industrial park according to an embodiment of the present application, such as Figure 2 As shown:

[0055] In a specific embodiment, the IES's new energy power generation equipment is photovoltaic, and the gas turbine uses a combined cycle unit as an electric, heat and gas coupling device to generate electricity by consuming gas and supplying it to the load side. It provides heat to the system while generating electricity. When there is a shortage of heat supply, the shortage of heat energy can be supplemented by a gas boiler. At the same time, the system is equipped with power storage equipment, and when there is a shortage of power supply, it can be supplemented together with the power grid. Due to the diversified and comprehensive characteristics of the demonstration area, the day-ahead load of the park can be predicted based on the load characteristics of the existing users in the demonstration area and the types of plots planned for the subsequent park.

[0056] However, it should be noted that photovoltaic equipment can be used to generate electricity and can be replaced by other forms of energy supply, such as wind power generation, hydropower generation, etc. In this embodiment, a gas-steam combined cycle generator set is selected to improve the power generation efficiency, but the equipment can be replaced only by a gas turbine or a steam turbine for thermal power generation; in addition, the energy storage system in this embodiment adopts an electrochemical energy storage form, and the equipment can be replaced by flywheel energy storage, compressed air energy storage, supercapacitor energy storage, pumped storage, thermal energy storage or other forms of electrochemical energy storage.

[0057] 1) Based on the above Figure 2 The analysis results shown in the figure establish a photovoltaic power generation model based on the power, voltage and current calculation formulas of the photovoltaic generator set in the maximum power mode and at the optimal working point, including:

[0058] Solar photovoltaic cells (PV) use materials with photovoltaic effect to convert light into electricity. Their output power is mainly affected by natural conditions, including ambient temperature and light intensity. The expressions of power PPV(t), voltage VPV(t), and current IPV(t) at the optimal working point t of photovoltaics in maximum power mode are as follows:

[0059] P PV (t) = V PV (t)I PV (t) (1)

[0060]

[0061] ΔT(t)=T A (t)+0.02H T (t)-25 (6)

[0062] ΔV(t)=V PV (t)-V mp (7)

[0063]

[0064] Wherein, Vmp and Imp are the maximum power point voltage and current respectively; VOC is the open circuit voltage; ISC is the short circuit current; HT(t) is the light radiation at time t, α is the current temperature coefficient of the photovoltaic cell module, β is the voltage temperature coefficient of the photovoltaic cell module, and TA(t) is the ambient temperature at time t.

[0065] 2) When the gas turbine model is a gas-steam combined cycle unit model, based on the above Figure 2 The analysis results shown are combined with the relationship between the gas turbine gas consumption and the power generation, the relationship between the gas turbine waste heat emission and the power generation efficiency, the relationship between the waste heat boiler high-pressure main steam, the low-pressure main steam and the gas turbine waste heat emission, the turbine exhaust heat loss power generation, and the steam turbine power generation to establish a gas turbine operating model. Specifically, the establishment of the gas turbine operating model includes:

[0066] The gas-steam combined cycle unit includes three main equipment: gas turbine, waste heat boiler and steam turbine.

[0067] The relationship between gas turbine natural gas consumption and power generation is as follows:

[0068]

[0069] Where: G gas (t) is the gas consumption of the gas turbine, in Nm 3 / h; P GT (t) is the gas turbine output power, kW; a1, b1, c1 are the polynomial fitting coefficients.

[0070]

[0071] The relationship between gas turbine waste heat emission and power generation efficiency is:

[0072]

[0073] Where: Q GT (t) is the exhaust heat of the gas turbine, kW; η GT is the power generation efficiency of the gas turbine; η1 is the heat dissipation loss coefficient of the gas turbine, which is taken as 0.15.

[0074] The waste heat boiler is a key component of the cogeneration system to achieve heat exchange between flue gas and working fluid. The modeling goal is to obtain the heat transfer of the waste heat boiler for steam turbine power calculation. The mathematical model of the high-pressure main steam and low-pressure main steam flow of the waste heat boiler and the waste heat emission of the gas turbine is:

[0075]

[0076] Where: D h(t) is the high-pressure main steam flow rate generated by the waste heat boiler, t / h; D1(t) is the low-pressure main steam flow rate generated by the waste heat boiler, t / h; a3, b3 and a4, b4 are polynomial fitting coefficients.

[0077] The steam turbine in this embodiment is a double-pressure, single-cylinder, supplementary steam, extraction condensing steam turbine. The power loss due to extraction heat supply is:

[0078]

[0079] Where: P h (t) is the power loss of the steam turbine due to steam extraction for heating, kW; Q h，GT (t) is the steam extraction heating power of the steam turbine, kW; h ex is the rated extraction enthalpy of the steam turbine, kj / kg; h out is the rated exhaust enthalpy of the steam turbine, kJ / kg.

[0080] The power generated by the steam turbine is:

[0081] P ST (t) = D h (t)(h in -h out )+D l (t)(h add -h out )-P h (t) (14)

[0082] Where: P ST (t) is the power generated by the steam turbine, kW; h in is the rated steam inlet enthalpy of the steam turbine, kJ / kg; h add is the rated air supply enthalpy of the turbine, kJ / kg.

[0083] 3) Based on Figure 2 The analysis results shown in the figure establish the relationship between the gas boiler output value and gas consumption, gas calorific value and operating efficiency, and construct a gas boiler operating model to establish a gas boiler mathematical model, including:

[0084] Gas boilers are usually used when the waste heat of the main energy supply unit cannot meet the heat load demand. The output mathematical model is as follows:

[0085] Q GB =G N ×Q LHV ×η GB (15)

[0086] Where: Q GB is the gas boiler output, unit is kW; G N is the gas consumption, m3 / h;Q LHV is the calorific value of natural gas, kJ / Nm 3 . η GB For operational efficiency.

[0087] 4) Establish a mathematical model for energy storage equipment

[0088] Energy storage equipment can smooth out the fluctuation characteristics of renewable energy output in 1ES and realize energy translation. At the same time, the demand-side response factor is taken into account to achieve economic improvement while balancing the system power. The state of the energy storage device at time t is related to the state at time t-Δt. The energy storage capacity of this project takes into account the battery's own power attenuation, and its capacity at time t is E Bs (t) is as follows.

[0089]

[0090] Where: P BS.in , P BS.out is the charging and discharging power of the energy storage device, η in , η out are the charging and discharging efficiencies of the device, respectively, and σ is the self-discharge rate of the battery per hour.

[0091] Through the above steps, the comprehensive energy system structure of the industrial park and the models of photovoltaic power generation, gas turbines, gas boilers, energy storage equipment, etc. are established, describing the working principle, input-output relationship and various constraints of each component in the system. Because the two-layer optimization model needs to consider the actual operation and mutual relationship of each device in the system to determine the optimization objectives and constraints, the above steps can serve as the basis for the subsequent establishment of the two-layer optimization model; in addition, it can also assist the subsequent optimization solution algorithm and output decision strategy.

[0092] S102, based on the operating condition model, respectively configure a two-layer multi-objective optimization model of the multi-integrated park, and configure the optimization algorithm and optimization decision-making strategy corresponding to the multi-objective optimization model, wherein the two-layer optimization model includes a configuration layer model and an operation layer model, which are used to output configuration and operation plans.

[0093] In this embodiment, the IES with multiple energy inputs and outputs is optimized and designed, and the corresponding optimization objective function is established according to the index requirements. Through the double-layer optimization strategy control, the best installation plan and operation strategy are obtained.

[0094] In addition, the integrated energy system in the demonstration area of ​​this application is connected to the grid, with the grid as a backup power source, and its power supply reliability is relatively high. However, the system should have a certain degree of independence, try to achieve self-balance within the integrated energy system, and reduce dependence on the grid. In the optimization problem of IES, it is necessary to consider the impact of various factors such as capacity configuration and operation strategy on system performance.

[0095] This application scheme adopts a two-layer optimization structure for solving, and the optimization results of the upper and lower layers in the two-layer optimization model are coupled with each other. According to the characteristics of the demonstration area in the previous section, the configuration layer optimizes the equipment configuration through a multi-objective algorithm to solve the problem of system planning capacity; the operation layer obtains the intraday operation optimization strategy based on the planned capacity of the configuration layer, and the corresponding operating cost is used as the input basis of the configuration layer.

[0096] Figure 3 is a schematic diagram of the structure of a double-layer multi-objective optimization model according to an embodiment of the present application, such as Figure 3 As shown in the figure, the model includes a configuration layer and an operation layer. The configuration layer is mainly used for capacity configuration management of the integrated park, while the operation layer combines optimization algorithms and optimization decision strategies to generate the optimal operation plan that meets the needs.

[0097] The construction process of the configuration layer model includes the following steps:

[0098] Step 1, according to the operating condition model and the historical data and expert knowledge of the exemplary multi-integrated park, determine the evaluation index and the first constraint condition for configuring the multi-integrated park, wherein the evaluation index includes the economic evaluation index and the carbon emission evaluation index;

[0099] Specifically, the configuration economic evaluation index is: the total cost of the energy supply system C at the configuration layer tc C is the annual investment cost of the equipment ec and annual operating cost C oc sum:

[0100] C tc =C ec +C oc (17)

[0101] Among them, the annual operating cost is calculated by adding up the daily operating costs of the year:

[0102]

[0103] Where: C i，tech is the unit price of the i-th equipment, M i，tech is the installed capacity of the i-th equipment, I is the annual interest rate, and n is the service life of the unit.

[0104] Configure carbon emission evaluation indicators: Carbon emissions have received widespread attention from all walks of life in recent years. The pursuit of low-carbon and carbon-free is the trend of future development; distributed energy supply systems will produce a certain amount of CO2 in the process of producing terminal energy. The carbon emissions generated by the system are mainly divided into two parts. The calculation method of the system's carbon dioxide emissions is shown in the formula.

[0105] C ce =∑F f，t .α+∑E grid,t .β (19)

[0106] Where: C ce is the total carbon dioxide emissions of the energy supply system; F f，t 、E grid，t represents the system fuel consumption and the amount of electricity purchased from the power grid; α is the CO2 emission factor of the fuel; β is the CO2 emission factor of electricity purchased from the power grid.

[0107] In addition, the number of IES devices is limited by external conditions such as the installation site and user wishes. The number of different types of devices installed needs to be constrained at the configuration level.

[0108] 0≤ni≤ni max (20)

[0109] Where: n i is the maximum number of equipment of type i that can be installed in the park.

[0110] Step 2, constructing a configuration layer model according to the first objective function and the first constraint condition;

[0111] It can be understood that after determining the first objective function and the first constraint condition, the above configuration layer model can be constructed through an intelligent optimization algorithm (such as a genetic algorithm, a particle swarm optimization algorithm, etc.).

[0112] On the other hand, the construction process of the operational layer model includes the following steps:

[0113] Step 1, based on the price cost of fuel, electricity purchase, grid-connected electricity sales and carbon emissions, fuel consumption, grid-connected electricity purchase, grid-connected electricity sales and carbon emissions, technical equipment power and unit maintenance cost, construct the second objective function;

[0114] The second objective function is shown in the following formula:

[0115]

[0116] Where: C f , C grid，t , C sell , C co2 are fuel, electricity purchase, grid-connected electricity sales and carbon emission price costs respectively; Ff，t 、E grid，t 、E sell,t , E co2,t They are fuel consumption, power purchase from the power grid, power sales to the grid and carbon dioxide emissions; E tech,t is the power of the technical equipment; C' tech It is the unit maintenance cost of technical equipment.

[0117] The second objective function comprehensively considers the expressions of various cost and energy-related quantities. From an economic perspective, it considers fuel costs, electricity purchase costs, grid-connected electricity sales revenue, and technical equipment maintenance costs. These items reflect the economic revenue and expenditure of the system related to energy trading and equipment maintenance during operation. At the same time, carbon emission costs are also considered, reflecting the economic consideration of environmental impact, which meets the requirements for sustainable development of the energy system.

[0118] In the above operation layer model, "technical equipment" refers to various equipment used for energy production, conversion, storage and distribution in the integrated energy system. These equipment have specific technical parameters and operating characteristics, which have an important impact on the system's operating costs, energy efficiency and carbon emissions. Specifically, it can include energy production equipment, energy conversion equipment and energy storage equipment.

[0119] Step 2, according to the load demand of different energy forms, and different types of equipment meet their own operating characteristics and power limits, determine the second constraint condition, wherein the second constraint condition includes electrical balance constraint, thermal balance constraint, and equipment operating characteristic constraint;

[0120] 1) Electricity balance relationship: The electricity balance relationship in the regional energy supply system is shown in the following formula.

[0121] P GT +P ST +P PV +P Grid +P BS =P load (twenty two)

[0122] P Bs =P BS.out z BS.out -P Bs.in z BS.in (twenty three)

[0123] Where: PGT, PST, PPV, PGrid and PBS are the charging and discharging powers of gas turbine, steam turbine, photovoltaic, grid interaction and electric energy storage respectively; ZBS,out and ZBS,in are 0-1 variables, representing the discharge state and charging state respectively; PLoad is the electric load demand of system users.

[0124] 2) Heat balance relationship: The heat balance relationship in the regional energy supply system is shown as follows:

[0125] Q H，ST +Q H，GB =Q H，Load (twenty four)

[0126] Where: QH,ST and QH,GB are the heating outputs of steam turbine and gas boiler respectively; QH,Load is the heat load demand of system users.

[0127] Equipment operating characteristic constraints: To ensure the safe operation of the system, the negative output of the equipment must meet specific operating conditions.

[0128] In addition, the operation of the equipment must also conform to its own characteristics and equipment output constraints:

[0129] P i minσi≤P i ≤P i maxσi σ∈{0,1} (25)

[0130] Where: Pimin and Pimax are the minimum and maximum boundary values ​​of the output of the ith device respectively; σi is a state variable that characterizes whether the ith device is working, which is a 0-1 variable, 0 means not working, and 1 means working.

[0131] The real-time charging and discharging power amplitude and charging and discharging power of the energy storage device must meet the constraints of the stored energy. Considering the service life of the equipment, it is required to meet the principle of conservation of charge and discharge capacity, that is, after a day of charging and discharging, the capacity remains the same.

[0132] Operating constraints of electric energy storage devices:

[0133]

[0134]

[0135] Where: P min BS and P max BS are the lower and upper limits of the battery charging and discharging power respectively; P cap，BS is the maximum storage capacity of the battery; Z BS,sta This is the static state of the battery.

[0136] Through the above steps, the constraint condition can ensure the safe, stable and efficient operation of the integrated energy system (IES), while meeting the user's demand for different energy forms (electricity and heat), and restricting and regulating the equipment operation and energy balance in the system from different aspects.

[0137] Step 3, based on the second objective function and the second constraint, construct the operation layer model.

[0138] Among them, after determining the second objective function and the second constraint condition, the operation layer model can be constructed through the intelligent optimization algorithm and combined with the configuration layer model.

[0139] It can be understood that after determining the two-layer optimization model (including the objective functions and constraints of the configuration layer and the operation layer) and various equipment models, it is necessary to configure the algorithm used to solve the model to obtain the configuration and operation plan; specifically, the NSGA-II algorithm is closely related to the previously established model, and uses the objective functions in the model (such as the economic and carbon emission targets of the configuration layer, the economic targets of the operation layer) and constraints (such as equipment operation characteristic constraints, electrical and thermal balance constraints, etc.) to evaluate individuals in the population, and then select better individuals for subsequent operations.

[0140] Because traditional genetic algorithms have limitations when dealing with multi-objective optimization problems, because there are often conflicts between multiple objectives, it is difficult to accurately measure the pros and cons of individuals with a single fitness function. In this embodiment, based on the multi-objective genetic algorithm such as the NSGA-II algorithm, in the solution process, it no longer relies solely on the fitness function, but sorts and selects the retained individuals according to the non-dominated Rank value and crowding degree.

[0141] Specifically, in multi-objective optimization problems, a solution is called a non-dominated solution (Pareto optimal solution) if there is no other solution that is better than it in all objectives. The NSGA-II algorithm first stratifies the population according to the non-dominated relationship of the individuals, divides all non-dominated individuals into a frontier (Rank 1), and then finds the next non-dominated frontier (Rank 2) from the remaining individuals, and so on. The smaller the Rank value, the better the frontier the individual is in, and the better the individual is. For example, in IES configuration optimization, if a configuration scheme is not inferior to other schemes in both economic and carbon emission objectives, then it belongs to Rank 1; if there are other schemes that are better than it in at least one objective, but it is not inferior to other schemes in the remaining objectives, then it may belong to Rank 2 or higher.

[0142] Furthermore, the crowding distance of each individual is obtained by calculating the sum of the distance differences between the two adjacent individuals on each sub-objective function. Specifically, for each sub-objective function (such as equipment investment cost, carbon emissions and other sub-objectives in the configuration layer), the individuals in the population are first sorted according to the value of the sub-objective function. Then, the crowding distance of the individuals at both ends (boundary individuals) is calculated to be infinite, because they have no adjacent individuals on this sub-objective. For the individual in the middle, its crowding distance is equal to the difference between the values ​​of the sub-objective function of the two adjacent individuals. For example, for the sub-objective of equipment investment cost, assuming that there are three individuals A, B, and C, whose investment costs are 100, 120, and 150 respectively, then the crowding distance of B (on the sub-objective of investment cost) is (150-120) + (120-100) = 50. Finally, the crowding distance of each individual on all sub-objective functions is added to obtain the total crowding distance of the individual. The crowding degree reflects the distribution density of the solutions around the individual. The larger the crowding degree, the sparser the solutions around the individual, and the greater its contribution to diversity in the population.

[0143] In this embodiment, the NSGA-II algorithm is used to find a set of better configuration schemes in the IES optimization configuration process through its non-dominated Rank value and congestion calculation mechanism. These schemes can not only achieve a good balance between goals such as economy and carbon emissions, but also provide a variety of options to meet different practical scenarios and needs. Compared with traditional optimization algorithms, it is more suitable for handling complex multi-objective optimization problems in this application, making the optimization results of the overall scheme more scientific, reasonable and practical, which helps to improve the comprehensive performance of the integrated energy system and achieve efficient use of energy and sustainable development.

[0144] Considering that multi-objective optimization often has inherent conflicts between objectives, for example, in the integrated energy system of an industrial park, it may be difficult to achieve the optimal economic goal and carbon emission goal at the same time, which leads to the optimization result being a set of optimal solutions. Therefore, it is essential to configure a multi-objective optimization decision strategy, which can help decision makers make reasonable choices among many possible solutions.

[0145] Specifically, in practical applications, the Topsis method can be used to standardize the data results and calculate the relative closeness of a solution to the ideal solution. The optimal solution has the shortest geometric distance to the positive ideal solution and the longest geometric distance to the negative ideal solution. The relative closeness formula is as follows:

[0146]

[0147] Where: U i is the relative proximity, S i + , S i- are the distances U between any solution and the positive and negative ideal solutions respectively. i The closer it is to 1, the closer the solution is to the ideal solution. i Sort them and select the satisfactory solution that is closest to the ideal solution according to the sorting.

[0148] In the integrated energy system of industrial parks, the satisfactory solution may be a configuration solution that strikes a good balance between economy and carbon emissions, or a solution selected based on the specific needs of the park (such as high environmental protection requirements or strict cost control), mainly including: economic dominant solution, environmental dominant solution and optimal configuration solution. Users can flexibly choose the configuration solution according to actual needs. Among them, the "optimal configuration solution" does not only perform well on a single goal, but finds a good balance between the two goals of economy and carbon emissions, taking into account economic factors such as system operating costs and equipment investment costs, and strives to reduce the total cost of the integrated energy system and improve economic benefits.

[0149] In an exemplary embodiment, when operating the strategy, the system units are set to operate at 50%-100% load, and the overall scheduling process is from the thermal system to the electrical system. The NSGA-II algorithm is used to solve the IES multi-objective optimization problem of the industrial park. By analyzing the 200 sets of optimization results in the Pareto front solution set, it can be found that due to the different effects of decision variables on different objective functions, for example, when the economic optimum is met, the environmental protection performance is poor. However, in actual planning and construction, it is necessary to weigh the two target values ​​and finally determine the optimal configuration plan. Therefore, it is necessary to select the configuration plan of the Pareto front solution set according to the actual needs of the park.

[0150] In this application, the Topsis method is used to select the Pareto frontier solution set configuration scheme. The system provides three schemes, namely the economic-dominant scheme, the environmental-dominant scheme, and the optimal configuration scheme. The decision variable optimization results of the three installation schemes are shown in Table 1. According to the actual needs and external conditions of the park construction, the final configuration scheme of the park comprehensive energy system is obtained.

[0151] Table 1 Optimization results of decision variables for 3 installation schemes

[0152]

[0153] In addition, considering the optimization of energy storage operation strategy for time-of-use electricity prices, a demand response strategy is also added to this application. The demand response strategy discharges energy storage as a power source under the premise of power balance during peak prices, and charges energy storage by purchasing electricity from the power grid during valley prices and par prices. The energy storage operation strategy selected for this project is to combine demand response with the power balance condition of the park to reduce the energy storage operation cost. The output of each device in the power subsystem of the two operation modes is as follows Figure 4 , Figure 5 shown.

[0154] The output periods of gas combined cycle units and photovoltaic units are almost the same, and the power load basically needs to be met by purchasing additional electricity from the grid. Due to the large power load, the fixed electricity price strategy has almost no chance to charge and replenish the power. After the introduction of the time-of-use electricity price operation strategy optimization, the power is charged by purchasing electricity from the grid during flat and valley electricity prices, and discharged during peak electricity prices to make up for the difference between the combined cycle and photovoltaic units and the load. The economic efficiency of the entire system is improved through the local peak-valley electricity price difference.

[0155] Table 2. Scheduling results before

[0156]

[0157]

[0158] Finally, it should be noted that in order to provide a basis for calculating the objective function and setting constraints, it is also necessary to configure the technical and economic parameters of the energy equipment. Specifically, in the planning process of this application, the equipment and related parameters used mainly include initial investment cost, operation and maintenance cost, efficiency and other technical parameters. The reference values ​​are shown in Tables 3 and 4.

[0159] Table 3 Technical and economic parameters of energy equipment

[0160]

[0161] Table 4 Technical parameters of energy storage equipment

[0162]

[0163] In the planning and operation of IES, electricity and gas prices directly affect the results of planning optimization. Therefore, local electricity and gas prices must be clarified before planning. The natural gas price is 2.38 yuan / Nm3. The reference values ​​of energy price parameters are shown in Table 5.

[0164] Table 5 Energy price parameters

[0165]

[0166] By configuring the technical parameters of energy equipment, basic data is provided for the two-layer optimization model for objective function calculation and constraint setting; it assists the decision-making process, evaluates and compares plans and determines practical feasible plans; it can also adapt to changes in actual conditions, perform dynamic adjustments and optimizations, and improve the flexibility of the plan.

[0167] Through the above steps S101 to S102, by analyzing the energy structure characteristics of the industrial park, the energy supply structure and internal equipment model of the integrated energy system are clarified; energy storage equipment is used as the key equipment for renewable energy consumption and power balance, and the economic and carbon emission indicators are comprehensively considered to establish the IES multi-objective optimization scheduling model. At the same time, the double-layer optimization theory is adopted to construct a multi-dimensional comprehensive industrial park integrated energy system optimization model. The configuration layer takes economy and carbon emissions as the goals, and the operation layer focuses on economic goals, thereby providing users with economic-dominant solutions, environmental-dominant solutions, and optimal configuration solutions to fully meet different actual needs.

[0168] also, Figure 6 is a flow chart of a capacity configuration and operation control method of a diversified comprehensive industrial park according to an embodiment of the present application, such as Figure 6 As shown, the process includes the following steps:

[0169] S601, through the configured two-layer multi-objective optimization model, dynamically outputs configuration and operation plans based on the demand data and field data of any multi-industrial park;

[0170] S602, configuration and operation plan based on dynamic output, is used for capacity configuration and operation control of diversified comprehensive industrial parks.

[0171] Through the above steps S601 to S602, in actual applications, the model will dynamically output configuration and operation plans based on the specific needs of the park, such as fluctuations in energy demand, changes in the operating status of equipment, etc.; the configuration and operation plans based on dynamic output can be effectively used for capacity configuration and operation control of diversified comprehensive industrial parks. In terms of capacity configuration, the appropriate number, type and capacity of equipment can be determined to ensure a stable and reliable energy supply in the park. In terms of operation control, the operating parameters of the equipment, such as power output, charging and discharging strategies, etc., can be optimized to improve energy utilization efficiency and reduce operating costs.

[0172] It should be noted that the steps shown in the above process or the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0173] The embodiment of the present application also provides a capacity configuration and operation control system for a diversified comprehensive industrial park. Figure 7 is a structural block diagram of a capacity configuration and operation control system of a diversified comprehensive industrial park according to an embodiment of the present application, such as Figure 7 As shown, the system includes a planning module 70 and a control module 71, wherein:

[0174] The planning module 70 is used to dynamically output configuration and operation plans based on demand data and field data of any multi-industrial park through the two-layer multi-objective optimization model completed by the configuration;

[0175] The control module 71 is used for capacity configuration and operation control of the diversified comprehensive industrial park based on the configuration and operation plan of dynamic output.

[0176] Through this system, in actual applications, the model will dynamically output configuration and operation plans based on the specific needs of the park, such as fluctuations in energy demand and changes in the operating status of equipment; the configuration and operation plans based on dynamic output can be effectively used for capacity configuration and operation control of diversified comprehensive industrial parks. In terms of capacity configuration, the appropriate number, type and capacity of equipment can be determined to ensure a stable and reliable energy supply in the park. In terms of operation control, the operating parameters of the equipment, such as power output, charging and discharging strategies, etc., can be optimized to improve energy utilization efficiency and reduce operating costs.

[0177] In one embodiment, Figure 8 is a schematic diagram of the internal structure of an electronic device according to an embodiment of the present application, such as Figure 8 As shown, an electronic device is provided, which may be a server, and its internal structure diagram may be as shown in Figure 8 The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a capacity configuration and operation control method for a diversified comprehensive industrial park.

[0178] Those skilled in the art will understand that Figure 8The structure shown in the figure is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0179] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0180] Those skilled in the art should understand that the technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for constructing a configuration model of a multi-integrated industrial park, characterized in that: The method comprises: According to the system structure information of the exemplary multi-integrated park, multiple operating condition models are established, including: new energy power generation equipment operating condition model, gas turbine operating condition model, gas boiler operating condition model and energy storage equipment operating condition model; Based on the operating condition model, a two-layer multi-objective optimization model of the multi-integrated park is configured respectively, and the optimization algorithm and optimization decision-making strategy corresponding to the multi-objective optimization model are configured, wherein the two-layer optimization model is used to output configuration and operation plans, including a configuration layer model and an operation layer model.

2. The method according to claim 1, characterized in that Building the configuration layer model includes: Determine a first objective function and a first constraint condition for configuring the multi-integrated park according to the operating condition model, historical data of the exemplary multi-integrated park, and expert knowledge, wherein the first objective function is constructed based on an economic evaluation index and a carbon emission evaluation index; The configuration layer model is constructed according to the first objective function and the first constraint condition.

3. The method according to claim 2, characterized in that Constructing the operation layer model includes: The second objective function is constructed based on fuel cost, electricity purchase cost, grid-connected electricity sales cost and carbon emission cost, fuel consumption, grid-connected electricity purchase, grid-connected electricity sales and carbon emission, as well as technical equipment power and unit maintenance cost of technical equipment; Determine a second constraint condition according to load requirements of different energy forms and different types of technical equipment satisfying their own operating characteristics and power limits, wherein the second constraint condition includes an electrical balance constraint, a thermal balance constraint, and an equipment operating characteristic constraint; Based on the second objective function and the second constraint condition, the operation layer model is constructed.

4. The method according to claim 1, characterized in that The optimization algorithm is an NSGA-II algorithm, which is used to optimize and solve the multi-objective optimization model based on the configuration, and generate multiple optimal solution sets of the multi-objective optimization model under the second constraint condition, wherein any optimal solution corresponds to a candidate configuration and operation plan; Through the optimization decision strategy, the configuration and operation plan are determined from the combination of the optimal solutions.

5. The method according to claim 4, characterized in that The Topsis method is used to standardize the optimal solution set, and the relative closeness between a solution in the optimal solution set and the ideal solution is calculated by the following formula: Among them: U i is the relative proximity, S i + , S i - are the distances between any solution and the positive and negative ideal solutions, respectively, wherein the closer the relative approximation is to 1, the closer the solution is to the ideal solution; The multiple optimal solutions are sorted according to the relative similarity, and a satisfactory solution closest to the ideal solution is obtained according to the sorting result to obtain the candidate configuration and operation plan.

6. The method according to claim 5, characterized in that The candidate configuration and operation schemes include: an economy-dominated scheme, an environmental protection-dominated scheme and a balanced optimal configuration scheme.

7. The method according to claim 1, characterized in that The operating condition model of the new energy power generation equipment is a photovoltaic power generation model, which is established based on the power, voltage and current calculation formulas of the photovoltaic power generation group when it is in the maximum power mode and at the optimal working point; The gas turbine model is a gas-steam combined cycle unit model, and the gas turbine operating condition model is established based on the relationship between gas turbine gas consumption and power generation, the relationship between gas turbine waste heat emission and power generation efficiency, the relationship between waste heat boiler high-pressure main steam, low-pressure main steam and gas turbine waste heat emission, steam turbine exhaust heat loss power generation and steam turbine power generation; By establishing the relationship between the output value of the gas boiler and the gas consumption, gas calorific value and operating efficiency, the operating model of the gas boiler is constructed; The operating model of the energy storage device is constructed by establishing the mathematical relationship between the capacity of the energy storage device at any time, and the charging and discharging power, the charging and discharging efficiency, and the self-discharging efficiency.

8. A configuration and operation control method for a diversified comprehensive industrial park, characterized in that: The method comprises: The two-layer multi-objective optimization model completed by the configuration can dynamically output configuration and operation plans based on the demand data and field data of any multi-industrial park; The configuration and operation plan based on the dynamic output is used for capacity configuration and operation control of the diversified comprehensive industrial park.

9. A capacity configuration and operation control system for a diversified comprehensive industrial park, characterized in that: The system comprises a planning module and a control module, wherein: The planning module is used to dynamically output configuration and operation plans based on demand data and field data of any multi-industrial park through the two-layer multi-objective optimization model completed by the configuration; The control module is used for capacity configuration and operation control of the diversified comprehensive industrial park based on the configuration and operation plan of the dynamic output.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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