Two-stage planning method and device for electric-thermal coupling system considering heat network flow

Abstract

The application provides a two-stage planning method of an electricity-heat coupling system considering heat network flow, comprising the following steps: establishing a first-stage investment model, wherein the first-stage investment model comprises a first objective function, and the first objective function is constructed for minimizing the equipment investment cost in a planning period; establishing a second-stage operation mode model, wherein the second-stage operation mode model comprises a second objective function, and the second objective function is constructed for minimizing the equipment investment cost and operation cost in the planning period; setting running environment data in the planning period, and replacing the second-stage operation mode model with corresponding KKT conditions to convert the overall model into a single-level mixed integer optimization problem, and the problem is solved by using a point-in method to obtain a planning scheme suitable for different scenarios. The application adopting the above scheme can realize optimal planning and operation of the system, thereby improving the overall efficiency and performance.

Description

Technical Field

[0001] This application relates to the field of planning technology for multi-energy flow coupled systems, and more particularly to a two-stage planning method and apparatus for electro-thermal coupled systems that take into account the flow rate of the heating network. Background Technology

[0002] An electro-thermal coupling system is a typical integrated energy system in which the power system and the heating system are coupled together through equipment such as combined heat and power (CHP), heat pumps, and electric boilers. Compared with traditional coal-fired boiler heating, CHP units can achieve combined heat and power supply and cascade utilization of heat energy of different grades through waste heat recovery. Its primary energy utilization rate can reach more than 80%, and realizing electro-thermal synergy has become a major practical need for heating areas under the "dual carbon" background.

[0003] Due to limitations in the planning phase, there is very limited room for optimization and improvement in terms of economy, energy efficiency, and environmental protection during the operational phase. Therefore, formulating a reasonable planning and construction scheme is crucial for reducing energy waste, rationally allocating energy demand, and achieving economical system operation. Setting the regulation mode of the heating network to quality regulation with fixed flow values ​​during planning not only increases energy waste but also fails to ensure the rational allocation of energy demand. Furthermore, distributed renewable energy sources such as wind power are uncertain, exhibiting strong randomness and volatility. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, the first objective of this application is to propose a two-stage planning method for an electro-thermal coupling system that takes into account the heat network flow rate. This method solves the technical problem that existing methods do not consider the impact of the heat network flow rate on the electro-thermal coupling system and cannot generate the optimal planning scheme. It realizes the optimal planning and operation of the system, thereby improving the overall efficiency and performance.

[0006] The second objective of this application is to propose a two-stage planning device for an electro-thermal coupling system that takes into account the flow rate of the heating network.

[0007] The third objective of this application is to propose a computer device.

[0008] To achieve the above objectives, the first aspect of this application proposes a two-stage planning method for an electro-thermal coupling system considering heat network flow, comprising: establishing a first-stage investment model, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing equipment investment costs within the planning period; establishing a second-stage operation mode model, wherein the second-stage operation mode model includes a second objective function, which is constructed with the goal of minimizing both equipment investment costs and operating costs within the planning period; setting operating environment data within the planning period, and replacing the second-stage operation mode model with corresponding KKT conditions, transforming the overall model composed of the first-stage investment model and the second-stage operation mode model into a single-level mixed integer optimization problem, and solving it using the in-point method to obtain planning schemes adapted to different scenarios.

[0009] The two-stage planning method for an electro-thermal coupling system considering heat network flow in this application embodiment determines the optimal scheme for the electro-thermal coupling system in the first stage by comprehensively considering investment costs. The goal of this stage is to obtain the optimal planning result under the condition of minimum investment cost. In the second stage, the operating characteristics of the equipment are considered, and the planning result is further optimized based on this. The investment in electricity and heat sources is optimized in the first stage, while the operational issues are optimized in the second stage. In this way, the optimal result can be obtained through the game between the first-stage problem and the second-stage problem. At the same time, the uncertainty of heat network flow and wind power output is taken into account to ensure the rational allocation of energy demand, so as to reduce the uncertainty, randomness and volatility brought by distributed new energy sources such as wind power.

[0010] Optionally, in one embodiment of this application, the first objective function is expressed as:

[0011]

[0012] in, This represents the equipment investment cost in year y. X represents the investment cost of device i. y,i Let i represent the planning state of device i in year y, and define the candidate device set Ω = [Ω TU ,Ω CHP ,Ω ES ,Ω PV ], Ω TU Ω cHP Ω ES Ω PV These are collections of candidate traditional thermal power units, CHP units, energy storage equipment, and distributed photovoltaics.

[0013] Optionally, in one embodiment of this application, the first-stage investment model includes a first constraint, which states that equipment in the investment model cannot be repeatedly constructed. The first constraint is expressed as follows:

[0014]

[0015] Among them, X y,i This represents the planning status of device i in year y.

[0016] Optionally, in one embodiment of this application, the second objective function is expressed as:

[0017]

[0018] Among them, C LL C represents the sum of equipment investment costs and operating costs within the planning period. CHP (·), C CT (·), C WD (·) represents the cost functions for combined heat and power (CHP) units, conventional thermal power units, and wind farms, respectively. These are the outputs of the generator and the wind farm, respectively.

[0019] Optionally, in one embodiment of this application, the second-stage operation mode model includes second constraints, which include operating characteristic equation constraints of the thermal system and operating characteristic equation constraints of the power system. The operating characteristic equation constraints of the thermal system include heat equation constraints of heating facilities in cogeneration units and heating stations, output equation constraints of electric boilers, heat equation constraints of heat generated by cogeneration plants and heat load heat equation constraints, node supply and return water temperature limit equation constraints of heating stations and heat loads, flow continuity equation constraints, flow magnitude limit constraints, flow equality constraints, node mixing equation constraints, and pipeline temperature drop equation constraints. The operating characteristic equation constraints of the power system include power balance equation constraints, transmission line transmission capacity equation constraints, unit output equation constraints, ramping equation constraints, and standby equation constraints.

[0020] Optionally, in one embodiment of this application, the heat equation constraints for heating facilities in combined heat and power units and heating stations are expressed as follows:

[0021]

[0022] in, Indicates the output of the wind farm. This represents the heat generated in electric boiler e. This represents the heat generated in node i of the heating station connection. N represents the collection of combined heat and power units and electric boilers, respectively. HS T represents the set of connection nodes of the heating station.d This represents the set of scheduling period t, where the heat generation of a combined heat and power (CHP) unit is affected by its operational feasible region. Constraints, Feasible Domain Represented as:

[0023]

[0024] in, G represents the output of the generator and the wind farm, respectively. CHP This represents the set of combined heat and power (CHP) units, g.

[0025] The output equation constraint of the electric boiler is expressed as follows:

[0026]

[0027]

[0028]

[0029] in, This represents the heat generated in electric boiler e. Indicates the efficiency of electric heating. Let E represent the consumption of the electric boiler at time t, and let E represent the set of electric boilers e. This indicates the maximum power consumption of the electric boiler. Indicates the start-up and shutdown status of the electric boiler;

[0030] The constraints of the heat equation for the heat generated by the thermal power plant and the heat load equation are expressed as follows:

[0031]

[0032]

[0033] in, This represents the heat generated by the heating station and the load, where c represents the density of water. Let represent the mass flow rate connected to the heating station and the mass flow rate connected to node i, respectively. The temperatures at thermal node i of the supply and return pipelines are N, respectively. LD The set representing heat load i;

[0034] The supply and return water temperature constraint equations for the heating station and heat load nodes are expressed as follows:

[0035]

[0036]

[0037] in, These are the temperatures at the hot node i of the supply and return pipelines, respectively. They are respectively, These are the upper and lower limits of the supply and return water temperatures at the nodes, respectively.

[0038] The flow continuity equation constraint is expressed as:

[0039]

[0040] in, Let i be the set of the pipe start and pipe end points of hot node i. These are the mass flow rates in the water supply and return pipes, respectively. These are the mass flow rates in the load and the heat station, respectively. These are the sets of heat loads and heat stations connected to heat node i, respectively.

[0041] The flow rate limit constraint is expressed as:

[0042]

[0043]

[0044] in, These are the upper and lower limits of the mass flow rate of the water supply pipeline, respectively. These are the upper and lower limits of the mass flow rate of the return water pipeline, respectively. S represents the mass flow rate in the water supply and return pipes, respectively. p Let p represent the set of water supply pipes and return pipes;

[0045] The equal flow constraint is expressed as:

[0046]

[0047] in, These are the mass flow rates in the water supply and return pipes, respectively.

[0048] The nodal mixing equation constraint is expressed as:

[0049]

[0050]

[0051] in, These are the temperatures at the outlets of the water supply pipe p and the return pipe p, respectively. These are the temperatures at the inlets of the water supply pipe p and the return pipe p, respectively.

[0052] The constraint of the pipe temperature drop equation is expressed as:

[0053]

[0054] in, The temperatures at the outlets of the water supply pipe p and the return pipe p are respectively, A p , λ p L p Let p represent the cross-sectional area, thermal conductivity, and length of pipe p, respectively, where ρ is the specific heat capacity and c is the density of water. These represent the mass flow rates in the water supply and return pipes, respectively. Optionally, in one embodiment of this application, the power balance equation constraint is expressed as:

[0055]

[0056] Among them, D b,t This represents the actual electrical load at busbar b. B represents the output of the generator, and B represents the set of electrical loads b.

[0057] The transmission capacity equation constraints for transmission lines are expressed as follows:

[0058]

[0059] Among them, SF b,l D is the offset coefficient of bus b relative to line l. p,t This represents the actual electrical load at busbar b. L represents the output of the generator, and L represents the set of lines l.

[0060] The constraints of the unit output equation are expressed as follows:

[0061]

[0062]

[0063]

[0064]

[0065] in, Indicates the generator's output. These are the lower and upper limits of the power output of generator g, respectively. Indicates the start-up and shutdown status of the unit, G CT Represents a set of generators. Represents the set of candidate generators. This represents the set of candidate combined heat and power (CHP) units;

[0066] The climbing equation constraints are expressed as follows:

[0067]

[0068]

[0069]

[0070]

[0071] Among them, RU g RD g These represent the upward and downward ramp capacities of generator g, respectively, and ru g,t rd g,t Let Δt represent the upward and downward rotational reserve capacity of generator g, and Δt represent the dispatch interval.

[0072] The alternative equation constraints are expressed as follows:

[0073]

[0074] Wherein, SRU and SRD are the upward and downward reserve storage capacities of generator g, respectively.

[0075] To achieve the above objectives, a second aspect of the present invention proposes a two-stage planning device for an electro-thermal coupling system considering the flow rate of a heating network, comprising a first model building module, a second model building module, and a planning scheme generation module, wherein...

[0076] The first model construction module is used to establish the first-stage investment model, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing the equipment investment cost within the planning period;

[0077] The second model construction module is used to build the second-stage operation mode model. The second-stage operation mode model includes a second objective function, which is constructed with the goal of minimizing the equipment investment cost and operating cost within the planning period.

[0078] The planning scheme generation module is used to set the operating environment data within the planning period and replace the second-stage operation mode model with the corresponding KKT conditions. It transforms the overall model composed of the first-stage investment model and the second-stage operation mode model into a single-level mixed integer optimization problem, and solves it using the in-point method to obtain planning schemes adapted to different scenarios.

[0079] Optionally, in one embodiment of this application, the second-stage operation mode model includes second constraints, which include operating characteristic equation constraints of the thermal system and operating characteristic equation constraints of the power system. The operating characteristic equation constraints of the thermal system include heat equation constraints of heating facilities in cogeneration units and heating stations, output equation constraints of electric boilers, heat equation constraints of heat generated by cogeneration plants and heat load heat equation constraints, node supply and return water temperature limit equation constraints of heating stations and heat loads, flow continuity equation constraints, flow magnitude limit constraints, flow equality constraints, node mixing equation constraints, and pipeline temperature drop equation constraints. The operating characteristic equation constraints of the power system include power balance equation constraints, transmission line transmission capacity equation constraints, unit output equation constraints, ramping equation constraints, and standby equation constraints.

[0080] To achieve the above objectives, a third aspect of the present invention provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned two-stage planning method for an electro-thermal coupling system that takes into account the flow rate of the heating network.

[0081] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0082] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0083] Figure 1 This is a flowchart illustrating a two-stage planning method for an electro-thermal coupling system that takes into account the flow rate of a heating network, as provided in Embodiment 1 of this application.

[0084] Figure 2 This is a schematic diagram of a two-stage planning device for an electro-thermal coupling system that takes into account the flow rate of a heating network, provided as an embodiment of this application. Detailed Implementation

[0085] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0086] The following describes, with reference to the accompanying drawings, a two-stage planning method and apparatus for an electro-thermal coupling system taking into account the flow rate of the heating network, according to embodiments of this application.

[0087] Figure 1This is a flowchart illustrating a two-stage planning method for an electro-thermal coupling system that takes into account the flow rate of the heating network, as provided in Embodiment 1 of this application.

[0088] like Figure 1 As shown, the two-stage planning method for the electro-thermal coupled system that takes into account the heat network flow includes the following steps:

[0089] Step 101: Establish the first-stage investment model, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing the equipment investment cost within the planning period;

[0090] In this embodiment, the first-stage investment model consists of an objective function and constraints, as detailed below:

[0091] 1) Establish the objective function of the first-stage investment model:

[0092] The equipment investment cost of the park in year y is as follows:

[0093]

[0094] in, This represents the equipment investment cost in year y. X represents the investment cost of device i. y,i Let i represent the planning state of device i in year y, and define the candidate device set Ω = [Ω TU ,Ω CHP ,Ω ES ,Ω PV ], Ω TU Ω CHP Ω ES Ω PV These are collections of candidate traditional thermal power units, CHP units, energy storage equipment, and distributed photovoltaics.

[0095] 2) To reduce the risks and costs of investing in and operating wind power projects, better utilize wind energy resources, and provide more accurate data and information for power system planning and market operation, uncertainty modeling is performed on the power output of wind turbine generators. This uncertainty modeling is expressed as follows:

[0096]

[0097] Where v represents wind speed, v ci Let v be the minimum starting wind speed of the fan, i.e., the cut-in wind speed; c is the amplitude in the statistical distribution function; and v is the amplitude in the statistical distribution function. c0 This refers to the maximum safe wind speed of the fan, i.e., the cut-off wind speed. Let w represent the power of the wind turbine at time t, b be the shape of the statistical distribution function, and p be the power of the wind turbine at time t. st This refers to the power output of the fan under standard operating conditions.

[0098] 3) Determine the constraints of the first-stage investment model; including:

[0099]

[0100] Among them, X y,i Let represent the planning status of equipment i in year y. The above formula means that equipment in the investment model cannot be built repeatedly.

[0101] Step 102: Establish the second-stage operation model, which includes a second objective function. The second objective function is constructed with the goal of minimizing the equipment investment cost and operating cost within the planning period.

[0102] In this embodiment, the established second-stage operation model consists of an objective function and constraints, as detailed below:

[0103] 1) Establish the objective function for the second-stage operation model:

[0104] The objective function of the lower-level model for the integrated power and heat energy system planning is established with the goal of minimizing the sum of equipment investment costs and operating costs (including generator fuel costs and wind curtailment costs) within the planning period:

[0105]

[0106] Among them, C LL C represents the sum of equipment investment costs and operating costs within the planning period. CHP (·), C CT (·), C WD (·) represent the cost functions for combined heat and power (CHP) units, conventional thermal power units, and wind farms, respectively. These are the outputs of the generator and the wind farm, respectively.

[0107] 2) Determine the constraints of the second-phase operation model; including:

[0108] The constraints on the operating characteristic equations of the thermodynamic system are as follows:

[0109] (1) Heat equation constraints for heating facilities such as combined heat and power units and electric boilers in heating stations:

[0110]

[0111] in, Indicates the output of the wind farm. This represents the heat generated in electric boiler e. This represents the heat generated in node i of the heating station connection. N represents the collection of combined heat and power units and electric boilers, respectively.HS T represents the set of connection nodes of the heating station. d This represents the set of scheduling period t, where the heat generation of a combined heat and power (CHP) unit is affected by its operational feasible region. Constraints, Feasible Domain Represented as:

[0112]

[0113] in, G represents the output of the generator and the wind farm, respectively. CHP This represents the set of combined heat and power (CHP) units, g.

[0114] (2) Output equation constraints for electric boilers:

[0115]

[0116]

[0117]

[0118] in, This represents the heat generated in electric boiler e. Indicates the efficiency of electric heating. Let E represent the consumption of the electric boiler at time t, and let E represent the set of electric boilers e. This indicates the maximum power consumption of the electric boiler. Indicates the start-up and shutdown status of the electric boiler;

[0119] (3) Constraints on the heat generation equation and heat load equation of the thermal power plant:

[0120]

[0121]

[0122] in, This represents the heat generated by the heating station and the load, where c represents the density of water. Let represent the mass flow rate connected to the heating station and the mass flow rate connected to node i, respectively. The temperatures at thermal node i of the supply and return pipelines are N, respectively. LD The set representing heat load i;

[0123] (4) Constraints of the supply and return water temperature limiting equations at the nodes of the heating station and heat load:

[0124]

[0125]

[0126] in, These are the temperatures at the hot node i of the supply and return pipelines, respectively. They are respectively, These are the upper and lower limits of the supply and return water temperatures at the nodes, respectively.

[0127] (5) Constraints of the flow continuity equation:

[0128]

[0129] in, Let i be the set of the pipe start and pipe end points of hot node i. These are the mass flow rates in the water supply and return pipes, respectively. These are the mass flow rates in the load and the heat station, respectively. These are the sets of heat loads and heat stations connected to heat node i, respectively.

[0130] (6) Flow size limit constraint:

[0131]

[0132]

[0133] in, These are the upper and lower limits of the mass flow rate of the water supply pipeline, respectively. These are the upper and lower limits of the mass flow rate of the return water pipeline, respectively. S represents the mass flow rate in the water supply and return pipes, respectively. p Let p represent the set of water supply pipes and return pipes;

[0134] (7) Equal flow constraint:

[0135]

[0136] in, These are the mass flow rates in the water supply and return pipes, respectively; the mass flow rates in the supply and return pipes are continuously circulated, and their values ​​are the same.

[0137] (8) Nodal mixing equation constraints:

[0138]

[0139]

[0140] in, These are the temperatures at the outlets of the water supply pipe p and the return pipe p, respectively. These are the temperatures at the inlets of the supply and return water pipes, respectively. Furthermore, the temperature at the hot spot is the same as the temperature at the inlet of the supply and return pipes.

[0141] (9) Pipeline temperature drop equation constraints:

[0142]

[0143] in, These are the temperatures at the outlets of the water supply pipe p and the return pipe p, respectively. It means that A p , λ p L p Let p represent the cross-sectional area, thermal conductivity, and length of pipe p, respectively, where ρ is the specific heat capacity and c is the density of water. These are the mass flow rates in the water supply and return pipes, respectively.

[0144] The constraints of the operating characteristic equations of the power system are as follows:

[0145] (1) Power balance equation constraints:

[0146]

[0147] Among them, D b,t This represents the actual electrical load at busbar b. B represents the output of the generator, and B represents the set of electrical loads b.

[0148] (2) Constraints on the transmission capacity equation of transmission lines:

[0149]

[0150] Among them, SF b,l D is the offset coefficient of bus b relative to line l. b,t This represents the actual electrical load at busbar b. L represents the output of the generator, and L represents the set of lines l.

[0151] (3) Unit output equation constraints:

[0152]

[0153]

[0154]

[0155]

[0156] in, Indicates the generator's output. These are the lower and upper limits of the power output of generator g, respectively. Indicates the start-up and shutdown status of the unit, G CT Represents a set of generators. Represents the set of candidate generators. This represents the set of candidate combined heat and power (CHP) units;

[0157] (4) Climbing equation constraints:

[0158]

[0159]

[0160]

[0161]

[0162] Among them, RU g RD g These represent the upward and downward ramp capacities of generator g, respectively, and ru g,t rd g,t Let Δt represent the upward and downward rotational reserve capacity of generator g, and Δt represent the dispatch interval.

[0163] (5) Alternate equation constraints:

[0164]

[0165] Wherein, SRU and SRD are the upward and downward reserve storage capacities of generator g, respectively.

[0166] Step 103: Set the operating environment data within the planning period, and replace the second-stage operation mode model with the corresponding KKT conditions. The overall model composed of the first-stage investment model and the second-stage operation mode model is transformed into a single-level mixed integer optimization problem. The in-point method is used to solve the problem and obtain planning schemes that are suitable for different scenarios.

[0167] In this embodiment, the planning period is set to 10 years, with three typical daily data points (summer, winter, and transition season) each with a time step of 1 hour. The data for summer and winter is 91 days / year, and for the transition season it is 183 days / year. The annual growth rates of electricity and heat loads, obtained from the National Electricity and Heat Supply and Demand Situation Analysis Report, are 15.0% and 3.5%, respectively, with a discount rate of 8%.

[0168] In this embodiment, given the planning period, typical day, annual growth rate of electricity and heat load, etc., the corresponding KKT conditions are used to replace the linear second-stage model. The BLCEP model composed of the proposed first-stage investment model and the second-stage model is transformed into a single-level mixed integer linear optimization problem. The interior point method is used to solve the problem, and the investment cost and operating cost adapted to different scenarios are obtained.

[0169] The two-stage planning method for an electro-thermal coupling system considering heat network flow in this application embodiment determines the optimal scheme for the electro-thermal coupling system in the first stage by comprehensively considering investment costs. The goal of this stage is to obtain the optimal planning result under the condition of minimum investment cost. In the second stage, the operating characteristics of the equipment are considered, and the planning result is further optimized based on this. The investment in electricity and heat sources is optimized in the first stage, while the operational issues are optimized in the second stage. In this way, the optimal result can be obtained through the game between the first-stage problem and the second-stage problem. At the same time, the uncertainty of heat network flow and wind power output is taken into account to ensure the rational allocation of energy demand, so as to reduce the uncertainty, randomness and volatility brought by distributed new energy sources such as wind power.

[0170] To achieve the above embodiments, this application also proposes a two-stage planning device for an electro-thermal coupling system that takes into account the flow rate of the heating network.

[0171] Figure 2 This is a schematic diagram of a two-stage planning device for an electro-thermal coupling system that takes into account the flow rate of a heating network, provided as an embodiment of this application.

[0172] like Figure 2 As shown, the two-stage planning device for the electro-thermal coupling system that takes into account the heat network flow includes a first model building module, a second model building module, and a planning scheme generation module, wherein...

[0173] The first model construction module is used to establish the first-stage investment model, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing the equipment investment cost within the planning period;

[0174] The second model construction module is used to build the second-stage operation mode model. The second-stage operation mode model includes a second objective function, which is constructed with the goal of minimizing the equipment investment cost and operating cost within the planning period.

[0175] The planning scheme generation module is used to set the operating environment data within the planning period and replace the second-stage operation mode model with the corresponding KKT conditions. It transforms the overall model composed of the first-stage investment model and the second-stage operation mode model into a single-level mixed integer optimization problem, and solves it using the in-point method to obtain planning schemes adapted to different scenarios.

[0176] Optionally, in one embodiment of this application, the second-stage operation mode model includes second constraints, which include operating characteristic equation constraints of the thermal system and operating characteristic equation constraints of the power system. The operating characteristic equation constraints of the thermal system include heat equation constraints of heating facilities in cogeneration units and heating stations, output equation constraints of electric boilers, heat equation constraints of heat generated by cogeneration plants and heat load heat equation constraints, node supply and return water temperature limit equation constraints of heating stations and heat loads, flow continuity equation constraints, flow magnitude limit constraints, flow equality constraints, node mixing equation constraints, and pipeline temperature drop equation constraints. The operating characteristic equation constraints of the power system include power balance equation constraints, transmission line transmission capacity equation constraints, unit output equation constraints, ramping equation constraints, and standby equation constraints.

[0177] It should be noted that the foregoing explanation of the two-stage planning method embodiment for an electro-thermal coupling system taking into account the heat network flow also applies to the two-stage planning device for an electro-thermal coupling system taking into account the heat network flow in this embodiment, and will not be repeated here.

[0178] To implement the above embodiments, the present invention also proposes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method described in the above embodiments.

[0179] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0180] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0181] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0182] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0183] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0184] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0185] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0186] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A two-stage planning method for an electro-thermal coupled system considering the flow rate of a heating network, characterized in that, Includes the following steps: A first-stage investment model is established, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing the equipment investment cost within the planning period; A second-stage operation model is established, which includes a second objective function. The second objective function is constructed with the goal of minimizing equipment investment costs and operating costs within the planning period. Set the operating environment data within the planning period, and use the corresponding KKT conditions to replace the second-stage operation mode model. Transform the overall model composed of the first-stage investment model and the second-stage operation mode model into a single-level mixed integer optimization problem, and solve it using the interior point method to obtain planning schemes adapted to different scenarios. The first objective function is expressed as: in, This represents the equipment investment cost in year y. This represents the investment cost of device z. Let z represent the planning state of device z in year y, and define the candidate device set. , , , , These are collections of candidate traditional thermal power units, CHP units, energy storage devices, and distributed photovoltaic power. The first stage investment model includes a first constraint, which states that equipment in the investment model cannot be repeatedly constructed. This first constraint is expressed as follows: in, This indicates the planning status of device z in year y; The second objective function is expressed as: Among them, C LL C represents the sum of equipment investment costs and operating costs within the planning period. CHP (·), C CT (·), C WD (·) represent the cost functions for combined heat and power (CHP) units, conventional thermal power units, and wind farms, respectively. , These are the outputs of the generator and the wind farm, respectively. Let w be the power of the wind turbine at time t; The second-stage operation mode model includes second constraints, which include operating characteristic equation constraints for the thermal system and operating characteristic equation constraints for the power system. The operating characteristic equation constraints for the thermal system include heat equation constraints for heating facilities in cogeneration units and heating stations, output equation constraints for electric boilers, heat equation constraints for heat generated by cogeneration plants and heat load heat equation constraints, node supply and return water temperature limit equation constraints for heating stations and heat loads, flow continuity equation constraints, flow magnitude limit constraints, flow equality constraints, node mixing equation constraints, and pipeline temperature drop equation constraints. The operating characteristic equation constraints for the power system include power balance equation constraints, transmission line transmission capacity equation constraints, unit output equation constraints, ramping equation constraints, and reserve equation constraints.

2. The method as described in claim 1, characterized in that, The heat equation constraints for the heating facilities in the combined heat and power units and heating stations are expressed as follows: in, Indicates the output of the wind farm. This represents the heat generated in electric boiler e. Indicates the connection node of the heating station i The heat generated in , These represent the combination of combined heat and power (CHP) units and electric boilers, respectively. This represents the set of connection nodes for heating stations. This represents the set of scheduling period t, where the heat generation of a combined heat and power (CHP) unit is affected by its operational feasible region. limit; The constraint of the pipeline temperature drop equation is expressed as follows: in, , These are the temperatures at the outlets of the water supply and return pipes, respectively. , These are the temperatures at the inlets of the water supply and return pipes, respectively. , , Let p represent the cross-sectional area, thermal conductivity, and length of pipe p, respectively. Here, c represents the specific heat capacity, and c represents the density of water. , These are the mass flow rates in the water supply and return pipes, respectively. , Let i be the set of the pipe start and pipe end points of hot node i. Indicates water supply pipes and return water pipes p A set of.

3. The method as described in claim 1, characterized in that, The power balance equation constraint is expressed as follows: in, This represents the actual electrical load at busbar b. E represents the output of the generator, E represents the set of electric boilers e, and B represents the set of electric loads b. The transmission capacity equation constraint of the transmission line is expressed as follows: in, Let be the offset coefficient of bus b relative to line l. This represents the amount of electricity consumed by the electric boiler e at time t. Let L be the actual value of the electrical load at busbar b, and L represent the set of lines l. The climbing equation constraint is expressed as follows: in, , These represent the upward and downward ramp capacities of generator g, respectively. , For the upward and downward rotational reserve capacity of generator g, Indicates the scheduling interval. , These are the lower and upper limits of the power output of generator g, respectively. This indicates the start-up and shutdown status of the generator unit.

4. A two-stage planning device for an electro-thermal coupling system taking into account the flow rate of a heating network, characterized in that, The apparatus implements the method as described in claim 1, and the apparatus includes a first model construction module, a second model construction module, and a planning scheme generation module, wherein... The first model construction module is used to establish a first-stage investment model, wherein the first-stage investment model includes a first objective function, which is constructed with the goal of minimizing the equipment investment cost within the planning period; The second model construction module is used to establish a second-stage operation mode model, wherein the second-stage operation mode model includes a second objective function, which is constructed with the goal of minimizing equipment investment costs and operating costs within the planning period; The planning scheme generation module is used to set the operating environment data within the planning period and replace the second-stage operation mode model with the corresponding KKT conditions. It transforms the overall model composed of the first-stage investment model and the second-stage operation mode model into a single-level mixed integer optimization problem, solves it using the in-point method, and obtains planning schemes adapted to different scenarios.

5. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the method as described in any one of claims 1-3.

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