A method for collaborative planning of an electrothermal coupling system and a terminal

Abstract

This invention discloses a collaborative planning method and terminal for an electrothermal coupling system. The method uses minimizing investment data as the objective function and constrains the state and quantity of candidate equipment to establish an investment model for the electrothermal coupling system. Historical load data and historical wind turbine data of the electrothermal coupling system are clustered to obtain clustering scenarios, and wind turbine output is predicted based on these scenarios. A steady-state operation model of the electrothermal coupling system is established with minimizing operating cost as the objective function. The objective function for collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model. Based on the objective function, the prediction results, and the constraints of the steady-state operation model, the collaborative planning scheme for the electrothermal coupling system is solved. Therefore, by optimizing the heating structure, wind power absorption in the system can be promoted, and the grid connection capacity of wind turbines in the electrothermal coupling system can be enhanced. Furthermore, heat network reconfiguration can provide additional flexibility to support the supply and demand balance of heat load.

Description

Technical Field

[0001] This invention relates to the field of electrothermal coupling system planning technology, and in particular to a collaborative planning method and terminal for electrothermal coupling systems. Background Technology

[0002] Combined heat and power (CHP) units are widely used due to their advantages in energy conservation, emission reduction, and environmental improvement. The widespread deployment of CHP units has made the interconnection between power and heating systems closer.

[0003] How to fully tap the synergistic potential of electric heating systems to achieve rational and economical equipment planning is a key focus for many researchers. To ensure the rationality of equipment planning results, it is usually necessary to consider multiple typical operating scenarios throughout the year, such as winter scenarios with high heat loads and summer scenarios with low heat loads. However, existing research treats the heating network topology in each scenario as fixed, requiring seasonal reconfiguration through appropriate pipe switching to meet seasonal changes in heat load. This seasonal reconfiguration is achieved manually to meet peak shaving and wind power consumption needs. Therefore, ignoring heating network reconfiguration in planning will lead to several problems: firstly, the planning will be unrealistic, resulting in unreasonable planning outcomes; secondly, the flexibility of the heating network cannot be fully utilized, leading to unnecessary planning and operating costs.

[0004] Some scholars have conducted research on the economic and safe operation of heating network reconfiguration. However, there are no publicly available results on the collaborative planning problem of electrothermal coupled systems. To fill this gap, it is urgent to propose a collaborative planning method for electrothermal coupled systems that considers heating network reconfiguration. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a collaborative planning method and terminal for an electrothermal coupling system, which can take into account the flexibility resources brought by the reconfiguration of the heat network to the system in the planning, and ensure the economy and rationality of the planning scheme of the electrothermal coupling multi-energy flow system.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A collaborative planning method for an electrothermal coupling system includes the following steps:

[0008] The investment data of the electrothermal coupling system is obtained, and an objective function is established with the goal of minimizing the investment data. The state and quantity of candidate devices are constrained as constraints to establish an investment model for the electrothermal coupling system.

[0009] The historical load data and historical fan data of the electrothermal coupling system are clustered to obtain clustering scenarios, and the fan output is predicted based on the clustering scenarios.

[0010] A steady-state operation model for the electrothermal coupling system is established with the minimum operating cost as the objective function.

[0011] An objective function for the collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model. Based on the objective function of the collaborative planning of the electrothermal coupling system, the result of the wind turbine output prediction, and the constraints of the steady-state operation model, the collaborative planning scheme of the electrothermal coupling system is solved.

[0012] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0013] A collaborative planning terminal for an electrothermal coupling system includes 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 various steps of the collaborative planning method for an electrothermal coupling system described above.

[0014] The beneficial effects of this invention are as follows: An investment model for the electrothermal coupling system is established with the minimum investment data as the objective function and the state and quantity constraints of candidate equipment as constraints; historical load data and historical wind turbine data of the electrothermal coupling system are clustered to obtain clustering scenarios, and wind turbine output is predicted based on these clustering scenarios; a steady-state operation model for the electrothermal coupling system is established with the minimum operating cost as the objective function; an objective function for collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model, and the collaborative planning scheme of the electrothermal coupling system is solved based on the objective function, the above prediction results, and the constraints of the steady-state operation model. Therefore, by optimizing and adjusting the heating structure, the wind power consumption of the system can be promoted, the grid connection capacity of wind turbines in the electrothermal coupling system can be enhanced, and support can be provided for the construction of a new power system based on new energy sources; furthermore, the reconfiguration of the heating network can provide additional flexibility to support the supply and demand balance of heat load, while assisting the grid side in peak shaving through multi-energy complementarity, thereby avoiding unnecessary equipment investment during the planning period and helping to achieve the goals of improving quality and efficiency, energy conservation and emission reduction. Attached Figure Description

[0015] Figure 1 This is a flowchart of a collaborative planning method for an electrothermal coupling system according to an embodiment of the present invention;

[0016] Figure 2 This is a schematic diagram of a collaborative planning terminal of an electrothermal coupling system according to an embodiment of the present invention;

[0017] Label Explanation:

[0018] 1. A collaborative planning terminal for an electrothermal coupling system; 2. A memory; 3. A processor. Detailed Implementation

[0019] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0020] Please refer to Figure 1 This invention provides a collaborative planning method for an electrothermal coupling system, comprising the following steps:

[0021] The investment data of the electrothermal coupling system is obtained, and an objective function is established with the goal of minimizing the investment data. The state and quantity of candidate devices are constrained as constraints to establish an investment model for the electrothermal coupling system.

[0022] The historical load data and historical fan data of the electrothermal coupling system are clustered to obtain clustering scenarios, and the fan output is predicted based on the clustering scenarios.

[0023] A steady-state operation model for the electrothermal coupling system is established with the minimum operating cost as the objective function.

[0024] An objective function for the collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model. Based on the objective function of the collaborative planning of the electrothermal coupling system, the result of the wind turbine output prediction, and the constraints of the steady-state operation model, the collaborative planning scheme of the electrothermal coupling system is solved.

[0025] As described above, the beneficial effects of this invention are as follows: An investment model for the electrothermal coupling system is established with the minimum investment data as the objective function and the state and quantity constraints of candidate equipment as constraints; historical load data and historical wind turbine data of the electrothermal coupling system are clustered to obtain clustering scenarios, and wind turbine output is predicted based on these clustering scenarios; a steady-state operation model for the electrothermal coupling system is established with the minimum operating cost as the objective function; an objective function for collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model, and the collaborative planning scheme of the electrothermal coupling system is solved based on the objective function, the above prediction results, and the constraints of the steady-state operation model. Therefore, by optimizing and adjusting the heating structure, the wind power consumption of the system can be promoted, the grid connection capacity of wind turbines in the electrothermal coupling system can be enhanced, and support can be provided for the construction of a new power system based on new energy sources; furthermore, the reconfiguration of the heating network can provide additional flexibility to support the supply and demand balance of heat load, and at the same time, it can assist the grid side in peak shaving through multi-energy complementarity, thereby avoiding unnecessary equipment investment during the planning period and helping to achieve the goals of improving quality and efficiency, energy conservation and emission reduction.

[0026] Furthermore, establishing an objective function with the goal of minimizing the aforementioned investment data includes:

[0027] The objective function for establishing the investment model of the electrothermal coupling system is:

[0028]

[0029] In the formula, C inv This indicates the investment cost over the entire planning period. This represents the investment cost of the wind turbine in year y. This represents the investment cost of the combined heat and power unit in year y. y represents the investment cost of the electric boiler in year y, and NY represents the planning period.

[0030] As described above, establishing the objective function of the investment model for the electrothermal coupling system facilitates the subsequent generation of the objective function for the collaborative planning of the electrothermal coupling system.

[0031] Furthermore, the constraints on the state and number of candidate devices include:

[0032] The state constraints of the candidate devices are:

[0033]

[0034]

[0035]

[0036] In the formula, This represents the planning status of the i-th candidate wind turbine at the a-th candidate installation location in year y. This indicates the planning status of the i-th candidate cogeneration unit at the a-th candidate installation location in year y. w represents the planning status of the i-th candidate electric boiler at the a-th candidate installation location in year y. C cg represents the set of candidate wind turbine units. C ε represents the set of candidate cogeneration units. C Let A represent the set of candidate electric boilers. i This represents the set of candidate planning positions for the i-th candidate device;

[0037] The constraint on the number of candidate devices is:

[0038]

[0039]

[0040]

[0041] As described above, setting corresponding constraints based on the status and quantity of candidate devices facilitates the generation of collaborative planning schemes for the subsequent electrothermal coupling system.

[0042] Furthermore, the objective function of minimizing operating cost includes:

[0043] The objective function for establishing the operating model of the electrothermal coupling system is:

[0044]

[0045] In the formula, This represents the system's operating cost during time period t. Let be the power generation cost of the i-th cogeneration generator unit during time period t. Let be the power generation cost of the i-th non-cogeneration generator unit during time period t. Let be the power generation cost of the i-th wind turbine during time period t. Let represent the active power of the i-th combined heat and power unit during time period t. This represents the thermal power of the i-th combined heat and power unit during time period t. This represents the electrical output of the i-th wind turbine during time period t. Let represent the electrical output of the i-th non-cogeneration unit during time period t.

[0046] As described above, establishing the objective function of the electrothermal coupling system operation model facilitates the subsequent generation of the objective function for the electrothermal coupling system collaborative planning.

[0047] Furthermore, the objective function of minimizing operating cost also includes:

[0048] Determine the constraints for the steady-state operation model of the electrothermal coupling system:

[0049] Determine the active power constraints of non-cogeneration units, wind turbine units, operating characteristic equation constraints of cogeneration units, operating characteristic equation constraints of electric boilers, ramp-up constraints of active power for both non-cogeneration units and cogeneration units, power system constraints, heating system constraints, and heating network topology constraints of the electrothermal coupling system.

[0050] As described above, determining the constraints of the steady-state operation model of the electrothermal coupling system facilitates the subsequent generation of the objective function for the collaborative planning of the electrothermal coupling system.

[0051] Furthermore, the objective function for the collaborative planning of the electrothermal coupling system, combining the investment model and the steady-state operation model, includes:

[0052] Establish the objective function for the collaborative planning of the electrothermal coupling system:

[0053]

[0054] In the formula, S y φ represents the set of typical days selected in year y. s This represents the number of days in a typical scenario s.

[0055] Furthermore, based on the objective function of the electrothermal coupling system collaborative planning, the results of the fan output prediction, and the constraints of the steady-state operation model, the solution for the collaborative planning scheme of the electrothermal coupling system includes:

[0056] Using the branch and bound method, based on the objective function of the electrothermal coupling system collaborative planning, and according to the results of the wind turbine output prediction and the constraints of the steady-state operation model, the collaborative planning scheme of the electrothermal coupling system is obtained.

[0057] The collaborative planning scheme for the electrothermal coupling system includes the planned models, locations, and timing of the fans, cogeneration units, and electric boilers within the electrothermal coupling system.

[0058] As described above, the branch and bound method can be used to solve the collaborative planning scheme of the electro-thermal integrated energy system, ensuring the economy and rationality of the planning scheme of the electro-thermal coupled multi-energy flow system.

[0059] Please refer to Figure 2 Another embodiment of the present invention provides a collaborative planning terminal for an electrothermal coupling system, 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 various steps of the collaborative planning method for an electrothermal coupling system described above.

[0060] The above-described collaborative planning method and terminal for an electrothermal coupling system can consider the flexibility resources brought by the reconfiguration of the heat network during the planning process, ensuring the economy and rationality of the planning scheme for the electrothermal coupling multi-energy flow system. The following is a detailed description of specific implementation methods:

[0061] Example 1

[0062] Please refer to Figure 1 A collaborative planning method for an electrothermal coupling system includes the following steps:

[0063] S1. Obtain the investment data of the electrothermal coupling system, establish an objective function with the goal of minimizing the investment data, and constrain the state and quantity of candidate devices as constraints to establish an investment model for the electrothermal coupling system.

[0064] S11. Establish the objective function for the investment model of the electrothermal coupling system:

[0065]

[0066] in:

[0067]

[0068]

[0069]

[0070] In the formula, C inv This indicates the investment cost over the entire planning period. This represents the investment cost of the wind turbine in year y. This represents the investment cost of the combined heat and power unit in year y. Let y represent the investment cost of the electric boiler in year y, and NY represent the planning period. i Let represent the set of candidate planning positions for the i-th candidate device. δ represents the investment price of the i-th candidate device. y Denotes the discount factor (δ) for year y. NY (representing the discount factor in year NY), satisfying δ y = 1 / (1+d)y-1 (d is the discount rate, which can be obtained from the bank), γ i,y This represents the depreciation rate of the i-th device from the planning in year y to the end of the planning period.

[0071] S12. Constraining the status and quantity of candidate devices as constraints includes:

[0072] The state constraints of the candidate devices are:

[0073]

[0074]

[0075]

[0076] In the formula, This represents the planning status of the i-th candidate wind turbine at the a-th candidate installation location in year y. This indicates the planning status of the i-th candidate cogeneration unit at the a-th candidate installation location in year y. w represents the planning status of the i-th candidate electric boiler at the a-th candidate installation location in year y. C cg represents the set of candidate wind turbine units. C ε represents the set of candidate cogeneration units. C Let A represent the set of candidate electric boilers. i This represents the set of candidate planning positions for the i-th candidate device;

[0077] The constraint on the number of candidate devices is:

[0078]

[0079]

[0080]

[0081] S2. Cluster the historical load data and historical fan data of the electrothermal coupling system to obtain clustering scenarios, and predict the fan output based on the clustering scenarios.

[0082] S3. Establish a steady-state operation model for the electrothermal coupling system with the objective function of minimizing operating cost.

[0083] S31. Establish the objective function for the operation model of the electrothermal coupling system:

[0084]

[0085] In the formula, This represents the system's operating cost during time period t. Let be the power generation cost of the i-th cogeneration generator unit during time period t. Let be the power generation cost of the i-th non-cogeneration generator unit during time period t. Let be the power generation cost (essentially the cost of wind curtailment) of the i-th wind turbine during time period t. Let represent the active power of the i-th combined heat and power unit during time period t. This represents the thermal power of the i-th combined heat and power unit during time period t. This represents the electrical output of the i-th wind turbine during time period t. Let represent the electrical output of the i-th non-cogeneration unit during time period t.

[0086] in:

[0087]

[0088] In the formula, a 0,g a 1,g a 2,g This represents the cost factor for the g-th CHP unit, which can be obtained from the unit's manufacturer's manual; b 0,i b 1,i These are the cost constant coefficients, linear coefficients, and quadratic coefficients for the i-th non-CHP generator set, respectively, which can be obtained from the manufacturer's manual for the non-CHP generator set. σ represents the upper limit of the predicted power of wind power i in time period t, obtained from the wind power prediction module. i The cost coefficient (penalty cost factor) of the i-th wind turbine unit can be obtained from the electricity market price.

[0089] S32. Determine the constraints for the steady-state operation model of the electrothermal coupling system:

[0090] Determine the active power constraints of non-cogeneration units, wind turbine units, operating characteristic equation constraints of cogeneration units, operating characteristic equation constraints of electric boilers, ramp-up constraints of active power for both non-cogeneration units and cogeneration units, power system constraints, heating system constraints, and heating network topology constraints of the electrothermal coupling system.

[0091] Specifically, the constraints for determining the scheduling model of the electro-thermal coupled system include:

[0092] Active power constraints of non-CHP units in electro-thermal coupled systems:

[0093]

[0094] In the formula, tg represents the set of all non-CHP units; This represents the lower limit of the active power of the i-th non-CHP unit. This represents the upper limit of active power for the i-th non-CHP unit;

[0095] Active power constraints of wind turbines in electro-thermal coupling systems:

[0096] In the power system, the active power of the i-th wind turbine unit during time period t does not exceed the upper limit of the predicted wind power capacity.

[0097]

[0098]

[0099] In the formula, w represents the set of all wind turbine units; w C This represents the set of candidate wind turbine units. This indicates the planning status of the i-th candidate wind turbine at the a-th candidate installation location in year y.

[0100] Operating characteristic equation constraints for the coupling element in an electro-thermal coupling system—a combined heat and power (CHP) unit:

[0101]

[0102]

[0103]

[0104] In the formula, cg represents the set of all CHP units, cg C Represents the set of candidate CHP units; This represents the active power of the i-th CHP unit during time period t. This represents the thermal power of the i-th CHP unit during time period t. Let x represent the x-coordinate of the k-th vertex of the approximate polygon of the feasible region for the i-th CHP unit. Let represent the ordinate of the k-th vertex of the approximate polygon of the feasible region for the i-th CHP unit. NK represents the combination coefficient of the i-th CHP unit during time period t. i This represents the number of vertices in the approximate polygon of the feasible operating region of the i-th CHP unit. The approximate polygon of the feasible operating region of the CHP unit is obtained from the manufacturer's manual for the CHP unit. The value ranges from 0 to 1, representing the planning status of the i-th candidate CHP unit at the a-th candidate installation location in year y.

[0105] Operating characteristic equation constraints for the coupling element—electric boiler—in an electro-thermal coupling system:

[0106]

[0107]

[0108]

[0109] In the formula, ε represents the set of all electric boilers, ε C This represents the set of candidate electric boilers. This represents the electrical power consumption of the i-th electric boiler during time period t. This represents the thermal output of the i-th electric boiler during time period t. This indicates that the rated power of the i-th electric boiler is obtained from the manufacturer's instruction manual for the electric boiler unit. This indicates the heat-to-power ratio of the i-th unit. The rated power and heat-to-power ratio are obtained from the manufacturer's instruction manual for the electric boiler. The value ranges from 0 to 1, representing the planning status of the i-th candidate electric boiler at the a-th candidate installation location in year y.

[0110] Ramp-up constraints on active power of non-CHP units in electro-thermal coupling systems:

[0111]

[0112]

[0113]

[0114]

[0115] In the formula, and Let represent the upward and downward ramp rates of the active power of the i-th non-CHP generator set during time period t, respectively. and These represent the maximum permissible upward ramp rate and the maximum permissible downward ramp rate of the active power of the i-th non-CHP generator set, respectively, which can be obtained from the manufacturer's manual for the non-CHP generator set. Δt is the time interval between two adjacent dispatch periods. and Let represent the active power of the i-th non-CHP generator set at time t and the active power at time t-1, respectively.

[0116] Ramp-up constraints of active power of CHP units in electro-thermal coupling systems:

[0117]

[0118]

[0119]

[0120]

[0121] In the formula, and Let represent the upward and downward ramp rates of the active power of the i-th CHP generator set during time period t, respectively. and These represent the maximum permissible upward ramp rate and the maximum permissible downward ramp rate of the i-th CHP generator set, respectively, which can be obtained from the manufacturer's manual for the CHP generator set. Δt represents the time interval between two adjacent dispatch periods. and Let represent the active power of the i-th CHP generator set at time t and at time t-1, respectively.

[0122] The power balance equation constraints for an electro-thermal coupled system are expressed as follows:

[0123]

[0124]

[0125] In the formula, This represents the set of non-CHP generator sets connected to the power grid bus b. This represents the set of CHP generator sets connected to the power grid bus b. This represents the set of wind turbine units connected to the power grid bus b. This represents the set of electric boilers connected to the power grid bus b. Represents the set of power system lines. This represents the electrical output of the i-th non-CHP unit during time period t. This represents the electrical output of the i-th CHP unit during time period t. This represents the electrical output of the i-th wind turbine during time period t. f represents the electrical energy consumption of the i-th electric boiler unit during time period t. l,t For the power flow through line l, D b,t Let B be the load of the power grid bus b during time period t, bs(l) represent the sending bus of line l, br(l) represent the receiving bus of line l, and B l Let θ be the admittance of line l. b,t The phase angle of the power grid bus b during time period t;

[0126] The power flow constraints of an electro-thermal coupled system are expressed as follows:

[0127]

[0128] In the formula, F l This indicates the upper limit of power for line l;

[0129] Power system backup constraints in electro-thermal coupling systems:

[0130]

[0131] In the formula, SRU and SRD represent the top and bottom standby capacities required in the electro-thermal coupling system to ensure the safe operation of the power system, respectively, which can be obtained from the electricity market;

[0132] Output constraints of heating stations in electro-thermal coupled systems:

[0133] The heating system uses CHP units, electric boilers, etc. as heat sources to provide heating.

[0134]

[0135] In the formula, This represents the set of CHP units in heat station j. Let H represent the set of electric boilers in heat station j, and let H be the set of heat stations. This represents the heat output of heat station j during time period t.

[0136] Thermal power balance constraints of heating system nodes in electro-thermal coupled systems:

[0137]

[0138] In the formula, This represents the set of heat stations connected to heating node n. This represents the set of pipes from which heat energy flows out of node n. This represents the set of pipes through which heat energy flows into node n. Let n represent the set of heat exchange stations connected to heating node n. This represents the heat required by the i-th heat exchange station during time period t. and These represent the heat flowing into and out of the p-th pipe during time period t, respectively.

[0139] Heat transfer constraints in the heating system piping of the electro-thermal coupling system:

[0140]

[0141]

[0142] In the formula, u p,t This indicates the on / off state of pipe p during time period t, with 0 indicating disconnection and 1 indicating connection. This indicates the maximum amount of heat that pipe p is allowed to carry;

[0143] Heat supply system network loss constraints of electro-thermal coupled systems:

[0144]

[0145]

[0146] In the formula, λ represents the heat loss of pipe p during time period t. p The heat transfer coefficient per unit length of pipe p can be obtained from the energy management system of the electro-thermal coupling system, where the length of pipe p is A. p The cross-sectional area of ​​pipe p, L p A p It can be obtained through measurement; ρ represents the density of water, and its value is taken as 1000 kg / m³. 3 , The ambient temperature at time t is obtained through measurement. and Let be the temperatures of the water flowing into the supply pipe p and the return pipe p respectively during time period t. To reduce heat loss... and It should be as small as possible, therefore its lower bound is used. and (Approximate substitutions can be made from heating codes):

[0147]

[0148] Thermal network topology constraints of electro-thermal coupled systems:

[0149]

[0150]

[0151]

[0152] In the formula, N p Let N represent the set of heating nodes connected to pipe p. HS Let z represent the set of heating nodes connected to the heating station, N(i) represent the set of nodes connected to node i, and z ij,t The direction of heat flow in the pipe is represented by 1, which indicates that the heat in the pipe flows from node i to node j, and 0 indicates that the heat in the pipe does not flow from node i to node j.

[0153] S4. Establish the objective function for the collaborative planning of the electrothermal coupling system by combining the investment model and the steady-state operation model, and solve the collaborative planning scheme of the electrothermal coupling system based on the objective function for the collaborative planning of the electrothermal coupling system, the result of the wind turbine output prediction, and the constraints of the steady-state operation model.

[0154] Establish the objective function for the collaborative planning of the electrothermal coupling system:

[0155]

[0156] In the formula, S y φ represents the set of typical days selected in year y. s This represents the number of days in a typical scenario s.

[0157] Using the branch and bound method, based on the objective function of the electrothermal coupling system collaborative planning, and according to the results of the wind turbine output prediction and the constraints of the steady-state operation model, the collaborative planning scheme of the electrothermal coupling system is obtained.

[0158] The collaborative planning scheme for the electrothermal coupling system includes the planned models, locations, and timing of the fans, cogeneration units, and electric boilers within the electrothermal coupling system.

[0159] Example 2

[0160] Please refer to Figure 2 A collaborative planning terminal 1 for an electrothermal coupling system includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements the various steps of the collaborative planning method for an electrothermal coupling system according to Embodiment 1.

[0161] In summary, the present invention provides a collaborative planning method and terminal for an electrothermal coupling system. It establishes an investment model for the electrothermal coupling system by minimizing investment data as the objective function and constraining the state and quantity of candidate equipment. Historical load data and historical wind turbine data of the electrothermal coupling system are clustered to obtain clustering scenarios, and wind turbine output is predicted based on these scenarios. A steady-state operation model for the electrothermal coupling system is established with minimizing operating costs as the objective function. An objective function for collaborative planning of the electrothermal coupling system is established by combining the investment model and the steady-state operation model. Based on the objective function, the prediction results, and the constraints of the steady-state operation model, the collaborative planning scheme for the electrothermal coupling system is solved. Therefore, by optimizing the heating structure, the system's wind power absorption can be promoted, and the grid connection capacity of wind turbines in the electrothermal coupling system can be enhanced, providing support for the construction of a new power system based on new energy sources. Furthermore, heat network reconfiguration can provide additional flexibility to support the supply and demand balance of heat load, while assisting the grid side in peak shaving through multi-energy complementarity, thereby avoiding unnecessary equipment investment during the planning period and contributing to improving efficiency, saving energy, and reducing emissions.

[0162] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A collaborative planning method for an electrothermal coupling system, characterized in that, Including the following steps: The investment data of the electrothermal coupling system is obtained, and an objective function is established with the goal of minimizing the investment data. The state and quantity of candidate devices are constrained as constraints to establish an investment model for the electrothermal coupling system. The objective function established with the goal of minimizing the aforementioned investment data includes: Establishing the objective function for the investment model of the electrothermal coupling system: ； In the formula, C inv This indicates the investment cost over the entire planning period. This represents the investment cost of the wind turbine in year y. This represents the investment cost of the combined heat and power unit in year y. δ represents the investment cost of the electric boiler in year y, NY represents the planning period, and δ represents the investment cost of the boiler in year y. y This represents the discount factor for year y. The historical load data and historical fan data of the electrothermal coupling system are clustered to obtain clustering scenarios, and the fan output is predicted based on the clustering scenarios. With the objective function of minimizing operating cost, a steady-state operation model for the electrothermal coupling system is established, including: The objective function for establishing the operating model of the electrothermal coupling system is: In the formula, This represents the system's operating cost during time period t. Let be the power generation cost of the i-th cogeneration generator unit during time period t. Let be the power generation cost of the i-th non-cogeneration generator unit during time period t. Let be the power generation cost of the i-th wind turbine during time period t. Let represent the active power of the i-th combined heat and power unit during time period t. This represents the thermal power of the i-th combined heat and power unit during time period t. This represents the electrical output of the i-th wind turbine during time period t. This represents the electrical output of the i-th non-cogeneration unit during time period t; Determine the constraints of the steady-state operation model of the electrothermal coupling system: determine the active power constraints of the non-cogeneration units, the active power constraints of the wind turbine units, the operating characteristic equation constraints of the cogeneration units, the operating characteristic equation constraints of the electric boilers, the ramp-up constraints of the active power of the non-cogeneration units and the cogeneration units, the power system constraints, the heating system constraints, and the heating network topology constraints of the electrothermal coupling system. Based on the investment model and the steady-state operation model, establish the objective function for the collaborative planning of the electrothermal coupling system: ； In the formula, S y This represents the set of typical days selected in year y. C represents the number of days in a typical scenario s. inv This indicates the investment cost over the entire planning period. δ represents the system's operating cost during time period t. y This represents the discount factor for year y. Using the branch and bound method, based on the objective function of the electrothermal coupling system collaborative planning, and according to the results of the fan output prediction and the constraints of the steady-state operation model, the electrothermal coupling system collaborative planning scheme is obtained. The electrothermal coupling system collaborative planning scheme includes the planning model, planning location, and planning time of the fan, cogeneration unit, and electric boiler in the electrothermal coupling system.

2. The collaborative planning method for an electrothermal coupling system according to claim 1, characterized in that, The constraints on the state and number of candidate devices include: The state constraints of the candidate devices are: ； ； ； In the formula, This represents the planning status of the i-th candidate wind turbine at the a-th candidate installation location in year y. This indicates the planning status of the i-th candidate cogeneration unit at the a-th candidate installation location in year y. w represents the planning status of the i-th candidate electric boiler at the a-th candidate installation location in year y. C cg represents the set of candidate wind turbine units. C ε represents the set of candidate cogeneration units. C Let A represent the set of candidate electric boilers. i This represents the set of candidate planning positions for the i-th candidate device; The constraint on the number of candidate devices is: ； ； 。 3. A collaborative planning terminal for an electrothermal coupling system, 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, it implements each step of the collaborative planning method for an electrothermal coupling system as described in any one of claims 1 to 2.

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