Coordinated optimization strategy for electric heating hydrogen comprehensive energy system based on photo-thermal power station

Abstract

The invention relates to an electro-thermal hydrogen comprehensive energy system coordination optimization strategy based on a photo-thermal power station. The strategy specifically comprises the steps that S1, a thermoelectric coupling model is constructed; s2, establishing a hydrogen thermoelectric coupling model; s3, confirming constraint conditions of a system operation process; s4, Camp is adopted; a CG decomposition algorithm decomposes the model into a main problem and a sub-problem, and repeated iteration is carried out to obtain an optimal solution. Through the optimization planning, new energy waste can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of solar thermal power stations, and more specifically, relates to a coordinated optimization strategy for an electric, thermal, and hydrogen integrated energy system based on a solar thermal power station. Background Art

[0002] To promote resource conservation, new energy has become ubiquitous across various sectors. With the continuous optimization of the energy structure, the proportion of clean energy sources such as wind, solar, and hydrogen continues to increase. However, the traditional energy structure is characterized by a lack of coordination between different energy sources, leading to significant energy waste.

[0003] At present, almost all robots work under pre-set programs or remote control, and their intelligence level is far from that of humans. Artificial intelligence technology has not yet been popularized in conventional fields, especially related fields with strong uncertainties, and virtual simulation cannot be achieved.

[0004] The prior art CN115248979A discloses a method for collaborative optimization of a distributed energy system, comprising the following steps: obtaining the extreme values of the cooling, heating, and electricity loads in a target time period and the operating parameters of each device in the distributed energy system; establishing an objective function of the distributed energy system with the goal of minimizing the total operating cost; wherein, the objective function is constrained by the cooling, heating, and electricity loads in the target time period, the energy balance of the distributed energy system, and the extreme values of the operating parameters of each device in the distributed energy system; using a cuckoo search algorithm to solve the objective function to obtain the operating parameters of each device in the distributed energy system within the target time period; wherein, the iteration step size in the cuckoo search algorithm is the variable.

[0005] The existing technology constructs an objective function based on the cooling, heating and electricity loads of the distributed energy system in the target time period, and uses the cuckoo search algorithm to solve it. Since the iteration step size is set to the change amount when the operating parameters are updated, the algorithm can converge faster and the accuracy of the operating parameters of each device can be improved, thereby reducing the operating cost of the system. However, it cannot solve the problem of energy waste. Summary of the Invention

[0006] In response to the above-mentioned technical problems, the present invention provides a coordinated optimization strategy for an electric, thermal and hydrogen integrated energy system based on a solar thermal power station to reduce energy waste.

[0007] The present invention adopts the following specific technical solutions:

[0008] A coordinated optimization strategy for an electric-thermal-hydrogen integrated energy system based on a CSP plant includes the following steps:

[0009] S1: Build thermocouple model;

[0010] S2: Establish a hydrogen thermoelectric coupling model;

[0011] S3: Confirm the constraints of the system operation process;

[0012] S4: Use the C&CG decomposition algorithm to decompose the model into the main problem and sub-problems, and iterate repeatedly to obtain the optimal solution.

[0013] Preferably, step S1 specifically includes:

[0014] S10: Construct CSP mathematical model;

[0015] S11: Build thermal energy storage model;

[0016] S12: Construct EB mathematical model.

[0017] Preferably, in step S10, the heat energy collected by the heat collecting device is used to generate electricity when there is a load demand, and is supplied to the heat storage device for heat storage when the load is low, and is released to generate electricity during peak load periods, that is:

[0018]

[0019] Where: H csp_r (t) is the thermal power accumulated by the CSP solar collector at time t; H csp_e (t) is the thermal power directly used by the solar collector to generate electricity at time t; The thermal power supplied to the heat storage device by the heat collecting equipment at time t;

[0020] CSP thermoelectric conversion is determined by the heat energy supplied by the heat collection equipment and the heat storage device. Therefore, the power generation capacity of CSP is:

[0021]

[0022] Where: P csp_d (t) is the electric power output by CSP at time t; is the heat release power of the heat storage device used for power generation at time t; and η csp_e They are the heat release efficiency of the heat storage device and the thermoelectric conversion efficiency of the CSP power generation equipment.

[0023] Preferably, in step S11, the mathematical model of the thermal energy storage model is:

[0024]

[0025] Where: H csp_bat (t) is the thermal energy stored in the heat storage device at time t; are the total heat storage power and total heat release power of the heat storage device at time t respectively; is the maximum storage capacity of the heat storage device; is the charging efficiency of the heat storage device; S H (t), S H (t-1) are the heat storage states of the heat storage device at time t and t-1 respectively.

[0026] Preferably, in step S12, the EB is an electrothermal coupling device that converts electrical energy into thermal energy. The coordinated operation of the EB and the CSP power station can realize a bidirectional flow of heat and electricity. The model is as follows:

[0027]

[0028] Where: H eh (t) is the thermal power generated by the EB unit at time t; λ eh is the EB electrothermal conversion efficiency; P eh (t) and H eh_load (t) are the electric power obtained by EB and the heat load power directly supplied by EB at time t.

[0029] Preferably, step S2 specifically includes the following steps:

[0030] S20: Establish EL mathematical model;

[0031] S21: Establish HFC mathematical model;

[0032] S22: Establishing HT mathematical model;

[0033] S24: Establish an electric energy storage model.

[0034] Preferably, EL is used as a thermoelectric hydrogen coupling unit to convert electrical energy into hydrogen energy and thermal energy. The specific conversion relationship is as follows:

[0035]

[0036] Where: P el (t) and P el_H2 (t) are the total electric power consumed by EL and the power used for hydrogen production at time t; H el (t) is the power generated by EL for heating at time t; η el is the hydrogen production efficiency; P el_fc (t) are the power of EL hydrogen production used for hydrogen storage and the power directly consumed by HFC electricity generation at time t;

[0037] HFC is a hydrogen-heat-electricity coupling unit that converts hydrogen energy into electricity and heat. The specific conversion relationship is as follows:

[0038]

[0039] Where: P fc (t), H fc (t) are the total power obtained by HFC at time t, the power used for electricity generation, and the power used by HFC for heat generation; is the power obtained by HFC from HT equipment at time t; η fc is the HFC hydrogen-to-power efficiency;

[0040] The function of HT is similar to that of thermal energy storage device, which can support energy conversion and storage, and improve the flexibility and economy of the system. Its specific model is as follows:

[0041]

[0042] Where: is the hydrogen power stored in the HT device at time t; and They are HT charging and discharging efficiency respectively; are the heat storage states of the hydrogen storage device at time t and t-1 respectively; is the maximum storage capacity of HT;

[0043] The electric energy storage device can suppress the power output fluctuation caused by the uncertainty of wind and solar power output, ensuring the storage, flow and release of electric energy. Its model is similar to the above energy storage model, which is:

[0044]

[0045] Where: P es_bat (t) is the electric power stored in the electric energy storage device at time t; are the charging and discharging power of the storage device at time t respectively; are the charging and discharging efficiency of the energy storage device; S bat (t), S bat (t-1) are the storage states of the energy storage device at time t and t-1 respectively; The maximum storage capacity of the electrical energy storage device

[0046] Preferably, step S3 includes the following steps:

[0047] S30: confirming power balance constraints;

[0048] S31: confirm the interaction constraints with the distribution network;

[0049] S32: confirming energy storage system constraints;

[0050] S33: Confirm the constraints of each device.

[0051] Preferably, during the operation of the multi-energy coupling system, the supply and demand of electrical power and thermal power must be balanced at all times, and the constraints are as follows:

[0052]

[0053]

[0054] Where: P load (t) is the system power load demand at time t; H load (t) is the system heat load demand at time t;

[0055] The calculation of the interaction constraints with the distribution network is as follows:

[0056]

[0057] Where: is the maximum interactive power between the system and the distribution network; α buy The system's electricity purchase and sales status from the distribution network. A value of 1 indicates purchasing electricity from the distribution network, and a value of 0 indicates selling electricity to the grid.

[0058] System energy storage devices include electric energy storage, hydrogen energy storage, and thermal energy storage. Taking electric energy storage as an example, its constraints are as follows:

[0059]

[0060] Where: The energy storage device is in the charging and discharging state. When the value is 1, the energy storage device is in the charging state. When the value is 0, the energy storage device is in the discharging state. bat (1) S bat (24) are the initial and final storage states of the energy storage device; are the lower and upper limits of the power storage state respectively;

[0061] In step S33, the specific constraints of each device are as follows:

[0062] CU constraints:

[0063]

[0064] Where: P gmin 、P gmax are the lower and upper limits of CU output respectively; is the maximum landslide and climbing power of CU at time t;

[0065] WT, PV constraints:

[0066]

[0067] This formula shows that the maximum wind and solar outputs do not exceed the predicted values;

[0068] EB constraints:

[0069]

[0070] Where: The upper limit of EB heating power;

[0071] CSP constraints:

[0072]

[0073] Where: are the lower and upper limits of CSP power generation output at time t respectively; are the maximum landslide and climbing powers of CSP at time t, respectively;

[0074] EL constraints:

[0075]

[0076] Where: are the lower and upper limits of EL hydrogen production ramp at time t, respectively; are the lower and upper limits of the input EL power at time t, respectively;

[0077] HFC constraints:

[0078]

[0079] Where: are the lower and upper limits of HFC output at time t respectively; are the lower limit and upper limit of HFC output ramp at time t respectively.

[0080] Preferably, in step S4, the C&CG decomposition algorithm is used to decompose the model into a main problem MP and a subproblem SP, and repeated iterations are performed to obtain the optimal solution; MP seeks min and relaxes some constraints, thereby providing a lower bound for the optimal value; SP seeks max, providing a feasible solution for the original problem, which can be used as an upper bound for the optimal solution; MP and SP are continuously updated and converged, and finally the optimal solution is obtained;

[0081] The MP form is:

[0082]

[0083]

[0084] Where: γ is an auxiliary variable; z 2,i Returns the solution of the subproblem after the i-th iteration; i The worst scenario value returned for the i-th subproblem; i t is the current iteration number; L B is the lower bound of the optimal total cost of the system; The objective function value of the main problem obtained by returning to the worst scenario after the i-th iteration;

[0085] The SP form is:

[0086]

[0087]

[0088] Where: The first stage decision variable value obtained by solving the main problem is the determined value; U B is the upper bound of the optimal total cost of the system; Solve the subproblem for the i-th time to obtain the value of the decision variable in the second stage;

[0089] MP is a single-level problem involving only integers and is easy to solve. SP is a double-level max-min problem and is difficult to solve directly. Therefore, the duality principle is used to transform the inner min into a max and merge it with the outer max, transforming the double-level problem in the second stage into a single-level problem for easier solution. The dual form of SP is:

[0090]

[0091] Where: ε, θ, ω, μ are the dual variables corresponding to the constraints in the second stage atomic problem; a is a vector whose elements are all 0; due to the existence of bilinear terms in the objective function T μ is a non-deterministic polynomial problem and is difficult to solve directly. Therefore, SP is linearized by the big M method.

[0092]

[0093] Where: o* is the column vector consisting of the predicted values of uncertain variables;

[0094] To introduce auxiliary variables; M is a sufficiently large real number; I + =[I load ,I Hload ]; I - =[I wt ,I pv ]; b = [1,1].

[0095] The beneficial effects of the present invention are:

[0096] 1) By building a comprehensive wind, solar, thermal, and hydrogen energy system that includes a solar thermal power station, the system can better utilize clean energy, reduce energy waste, improve the absorption capacity of new energy, and enhance system flexibility. These technical benefits jointly promote the optimization and sustainable development of the energy structure. Through the application of this system and optimization strategies, it is possible to effectively improve energy utilization, reduce energy waste, promote the development and utilization of clean energy, and enhance system stability and flexibility.

[0097] 2) By comprehensively considering multiple clean energy sources such as solar thermal power plants, wind power, solar energy, and hydrogen energy, and building a multi-energy coupling system, the overall performance and economic benefits of the system can be effectively improved. The realization of these technical results provides a useful reference for research and practice in the field of clean energy, helps promote the application and promotion of clean energy technologies, and makes a positive contribution to the sustainable development of the energy sector. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 This is the structural frame of the electric, thermal and hydrogen integrated energy system of the present invention;

[0099] Figure 2 This is the algorithm flow of the electric, thermal and hydrogen integrated energy system of the present invention;

[0100] Figure 3 This is a flow chart of the coordinated optimization strategy of the electric, thermal and hydrogen integrated energy system based on the solar thermal power station of the present invention. DETAILED DESCRIPTION

[0101] In order to clearly illustrate the technical features of this solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0102] In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present application is not limited to the specific embodiments disclosed below.

[0103] In addition, in the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present application, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0104] In this application, unless otherwise expressly specified or limited, terms such as "installed," "connected," "connect," and "fixed" should be understood in a broad sense. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0105] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0106] Example 1

[0107] The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant is disclosed, which includes the following steps:

[0108] S1: Build thermocouple model;

[0109] S2: Establish a hydrogen thermoelectric coupling model;

[0110] S3: Confirm the constraints of the system operation process;

[0111] S4: Use the C&CG decomposition algorithm to decompose the model into the main problem and sub-problems, and iterate repeatedly to obtain the optimal solution.

[0112] In this embodiment, the establishment of the thermocouple model specifically includes three models, namely, a CSP mathematical model, a thermal energy storage model, and a mathematical model.

[0113] Among them, the CSP mathematical model:

[0114] The heat energy collected by the solar collector is used to generate electricity when there is a demand for it. When the load is low, it is supplied to the heat storage device for heat storage. During the peak load period, heat is released to generate electricity. That is,

[0115]

[0116] Where: H csp_r (t) is the thermal power accumulated by the CSP solar collector at time t; H csp_e (t) is the thermal power directly used by the solar collector to generate electricity at time t; The thermal power supplied to the heat storage device by the thermal collecting equipment at time t.

[0117] CSP thermoelectric conversion is determined by the heat energy supplied by the heat collection equipment and the heat storage device. Therefore, the power generation capacity of CSP is:

[0118]

[0119] Where: P csp_d (t) is the electric power output by CSP at time t; is the heat release power of the heat storage device used for power generation at time t; and η csp_e They are the heat release efficiency of the heat storage device and the thermoelectric conversion efficiency of the CSP power generation equipment.

[0120] Thermal energy storage model:

[0121] The thermal storage device of the CSP power station can buffer the fluctuation of light intensity, promote the conversion and storage of energy, and realize the peak load shifting of energy, so as to improve the economic benefits of IES and increase the flexibility of system operation. The mathematical model of the thermal energy storage model is:

[0122]

[0123] Where: H csp_bat (t) is the thermal energy stored in the heat storage device at time t; are the total heat storage power and total heat release power of the heat storage device at time t respectively; is the maximum storage capacity of the heat storage device; is the charging efficiency of the heat storage device; S H (t), S H (t-1) are the heat storage states of the heat storage device at time t and t-1 respectively.

[0124] EB mathematical model:

[0125] EB is an electrothermal coupling device that converts electrical energy into thermal energy. The coordinated operation of EB and CSP power station can realize the bidirectional flow of heat and electricity. Its model is as follows:

[0126]

[0127] Where: H eh (t) is the thermal power generated by the EB unit at time t; λ eh is the EB electrothermal conversion efficiency; P eh (t) and H eh_load (t) are the electric power obtained by EB and the heat load power directly supplied by EB at time t.

[0128] In this embodiment, by constructing a wind, solar, thermal, and hydrogen integrated energy system that includes a solar thermal power station, the system can better utilize clean energy, reduce energy waste, improve the absorption capacity of new energy, and enhance system flexibility. These technical effects jointly promote the optimization and sustainable development of the energy structure. Through the application of this system and optimization strategy, it is possible to effectively improve energy utilization, reduce energy waste, promote the development and utilization of clean energy, and enhance the stability and flexibility of the system.

[0129] Furthermore, this invention provides important reference and guidance for technological innovation and development in the energy sector. By comprehensively considering multiple clean energy sources, such as solar thermal power plants, wind energy, solar energy, and hydrogen energy, and constructing a multi-energy coupling system, the overall performance and economic benefits of the system can be effectively improved. The realization of these technical effects provides a useful reference for research and practice in the field of clean energy, helps promote the application and promotion of clean energy technologies, and makes a positive contribution to the sustainable development of the energy sector.

[0130] Example 2

[0131] A coordinated optimization strategy for an electric, thermal, and hydrogen integrated energy system based on a solar thermal power station is disclosed, comprising the following steps:

[0132] S1: Build thermocouple model;

[0133] S2: Establish a hydrogen thermoelectric coupling model;

[0134] S3: Confirm the constraints of the system operation process;

[0135] S4: Use the C&CG decomposition algorithm to decompose the model into the main problem and sub-problems, and iterate repeatedly to obtain the optimal solution.

[0136] The difference between this embodiment and embodiment 1 is that in step S2, the hydrogen thermocouple model specifically includes:

[0137] EL mathematical model:

[0138] As a thermoelectric hydrogen coupling unit, EL can convert electrical energy into hydrogen energy and thermal energy. The specific conversion relationship is as follows:

[0139]

[0140] Where: P el (t) and are the total electric power consumed by EL and the power used for hydrogen production at time t; H el (t) is the power generated by EL for heating at time t; η el is the hydrogen production efficiency; P el_fc (t) are the power used for hydrogen storage and the power directly consumed for HFC electricity generation at time t respectively.

[0141] HFC mathematical model:

[0142] HFC is a hydrogen-heat-electricity coupling unit that converts hydrogen energy into electricity and heat. The specific conversion relationship is as follows:

[0143]

[0144] Where: P fc (t), H fc (t) are the total power obtained by HFC at time t, the power used for electricity generation, and the power used by HFC for heat generation; is the power obtained by HFC from HT equipment at time t; η fc is the HFC hydrogen-to-electricity efficiency.

[0145] HT mathematical model:

[0146] The function of HT is similar to that of thermal energy storage device, which can support energy conversion and storage, and improve the flexibility and economy of the system. Its specific model is as follows:

[0147]

[0148] Where: is the hydrogen power stored in the HT device at time t; and They are HT charging and discharging efficiency respectively; are the heat storage states of the hydrogen storage device at time t and t-1 respectively; The maximum storage capacity of HT.

[0149] Electric energy storage model:

[0150] The electric energy storage device can suppress the power output fluctuation caused by the uncertainty of wind and solar power output, ensuring the storage, flow and release of electric energy. Its model is similar to the above energy storage model, which is:

[0151]

[0152] Where: P es_bat (t) is the electric power stored in the electric energy storage device at time t; are the charging and discharging power of the storage device at time t respectively; are the charging and discharging efficiency of the energy storage device; S bat (t), S bat (t-1) are the storage states of the energy storage device at time t and t-1 respectively; It is the maximum storage capacity of the electric energy storage device.

[0153] Constraints on system operation

[0154] Power balance constraints:

[0155] During the operation of the multi-energy coupling system, the supply and demand of electrical power and thermal power must be balanced at all times. The constraints are as follows:

[0156]

[0157]

[0158] Where: P load (t) is the system power load demand at time t; H load (t) is the system heat load demand at time t.

[0159] Interaction constraints with the distribution network:

[0160]

[0161] Where: is the maximum interactive power between the system and the distribution network; α buy The system's electricity purchase and sales status from the distribution network. A value of 1 indicates purchasing electricity from the distribution network, and a value of 0 indicates selling electricity to the grid.

[0162] Energy storage system constraints:

[0163] System energy storage devices include electric energy storage, hydrogen energy storage, and thermal energy storage. Because they have similar functions, their models are also roughly the same. Taking electric energy storage devices as an example, their constraints are as follows:

[0164]

[0165] Where: The energy storage device is in the charging and discharging state. When the value is 1, the energy storage device is in the charging state. When the value is 0, the energy storage device is in the discharging state. bat (1) S bat (24) are the initial and final storage states of the energy storage device; are the lower and upper limits of the power storage state respectively.

[0166] Constraints for each device:

[0167] CU constraints:

[0168]

[0169] Where: P gmin 、P gmax are the lower and upper limits of CU output respectively; is the maximum landslide and climbing power of CU at time t.

[0170] WT, PV constraints:

[0171]

[0172] This formula shows that the maximum wind and solar outputs do not exceed the predicted values.

[0173] EB constraints:

[0174]

[0175] Where: This is the upper limit of EB heating power.

[0176] CSP constraints:

[0177]

[0178] Where: are the lower and upper limits of CSP power generation output at time t respectively; are the maximum landslide and climbing powers of CSP at time t, respectively.

[0179] EL constraints:

[0180]

[0181] Where: are the lower and upper limits of EL hydrogen production ramp at time t, respectively; are the lower and upper limits of the input EL power at time t, respectively.

[0182] HFC constraints:

[0183]

[0184] Where: are the lower and upper limits of HFC output at time t respectively; are the lower limit and upper limit of HFC output ramp at time t respectively.

[0185] Example 3

[0186] A coordinated optimization strategy for an electric, thermal, and hydrogen integrated energy system based on a solar thermal power station is disclosed, comprising the following steps:

[0187] S1: Build thermocouple model;

[0188] S2: Establish a hydrogen thermoelectric coupling model;

[0189] S3: Confirm the constraints of the system operation process;

[0190] S4: Use the C&CG decomposition algorithm to decompose the model into the main problem and sub-problems, and iterate repeatedly to obtain the optimal solution.

[0191] The difference between this embodiment and embodiment 1 is that:

[0192] The C&CG decomposition algorithm is used to decompose the model into a master problem (MP) and subproblems (SP). The optimal solution is then iterated repeatedly. The MP solves for the minimum value and relaxes some constraints, providing a lower bound for the optimal value. The SP solves for the maximum value, providing a feasible solution to the original problem that serves as an upper bound for the optimal solution. The MP and SP are continuously updated and converge, ultimately achieving the optimal solution.

[0193] The MP form is:

[0194]

[0195]

[0196] Where: γ is an auxiliary variable; z 2,i Returns the solution of the subproblem after the i-th iteration; i The worst scenario value returned for the i-th subproblem; i t is the current iteration number; L B is the lower bound of the optimal total cost of the system; It is the objective function value of the main problem obtained by solving the worst scenario after the i-th iteration.

[0197] The SP form is:

[0198]

[0199]

[0200] Where: The first stage decision variable value obtained by solving the main problem is the determined value; U B is the upper bound of the optimal total cost of the system; Solve the subproblem for the i-th time and obtain the value of the decision variable in the second stage.

[0201] MP is a single-level problem involving only integers and is easy to solve; SP is a double-level max-min problem and is difficult to solve directly. Therefore, the duality principle is used to transform the inner min into a max and merge it with the outer max, transforming the double-level problem in the second stage into a single-level problem for easier solution. The dual form of SP is:

[0202]

[0203] Where: ε, θ, ω, μ are the dual variables corresponding to the constraints in the second stage atomic problem; a is a vector whose elements are all 0. Since there is a bilinear term o in the objective function T μ is a non-deterministic polynomial problem and is difficult to solve directly. Therefore, SP is linearized by the big M method.

[0204]

[0205] Where: o* is the column vector consisting of the predicted values of uncertain variables;

[0206]

[0207] To introduce auxiliary variables; M is a sufficiently large real number;

[0208] I + =[I load ,I Hload ]; I - =[I wt ,I pv ]; b = [1,1].

[0209] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A coordinated optimization strategy for an electric, thermal, and hydrogen integrated energy system based on a CSP plant, characterized by: The following steps are involved: S1: Build thermocouple model; S2: Establish a hydrogen thermoelectric coupling model; S3: Confirm the constraints of the system operation process; S4: Use the C&CG decomposition algorithm to decompose the model into the main problem and sub-problems, and iterate repeatedly to obtain the optimal solution.

2. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 1 is characterized in that: Step S1 specifically includes: S10: Construct CSP mathematical model; S11: Build thermal energy storage model; S12: Construct EB mathematical model.

3. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 2 is characterized in that: In step S10, the heat energy collected by the heat collection device is used to generate electricity when there is a load demand, and is supplied to the heat storage device for heat storage when the load is low. At the same time, heat is released to generate electricity during the peak load period, that is, Where: H csp_r (t) is the thermal power accumulated by the CSP solar collector at time t; H csp_e (t) is the thermal power directly used by the solar collector to generate electricity at time t; The thermal power supplied to the heat storage device by the heat collecting equipment at time t; CSP thermoelectric conversion is determined by the heat energy supplied by the heat collection equipment and the heat storage device. Therefore, the power generation capacity of CSP is: Where: P csp_d (t) is the electric power output by CSP at time t; is the heat release power of the heat storage device used for power generation at time t; and η csp_e They are the heat release efficiency of the heat storage device and the thermoelectric conversion efficiency of the CSP power generation equipment.

4. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 2 is characterized in that: In step S11, the mathematical model of the thermal energy storage model is: Where: H csp_bat (t) is the thermal energy stored in the heat storage device at time t; are the total heat storage power and total heat release power of the heat storage device at time t respectively; is the maximum storage capacity of the heat storage device; is the charging efficiency of the heat storage device; S H (t), S H (t-1) are the heat storage states of the heat storage device at time t and t-1 respectively.

5. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 2 is characterized in that: In step S12, EB is an electrothermal coupling device that converts electrical energy into thermal energy. The coordinated operation of EB and CSP power station can realize the bidirectional flow of heat and electricity. Its model is as follows: Where: H eh (t) is the thermal power generated by the EB unit at time t; λ eh is the EB electrothermal conversion efficiency; P eh (t) and H eh_load (t) are the electric power obtained by EB and the heat load power directly supplied by EB at time t.

6. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 1 is characterized in that: The step S2 specifically includes the following steps: S20: Establish EL mathematical model; S21: Establish HFC mathematical model; S22: Establishing HT mathematical model; S24: Establish an electric energy storage model.

7. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 6 is characterized in that: As a thermoelectric hydrogen coupling unit, EL can convert electrical energy into hydrogen energy and thermal energy. The specific conversion relationship is as follows: Where: P el (t) and are the total electric power consumed by EL and the power used for hydrogen production at time t; H el (t) is the power generated by EL for heating at time t; η el is the hydrogen production efficiency; P el_fc (t) are the power of EL hydrogen production used for hydrogen storage and the power directly consumed by HFC electricity generation at time t; HFC is a hydrogen-heat-electricity coupling unit that converts hydrogen energy into electricity and heat. The specific conversion relationship is as follows: Where: P fc (t), H fc (t) are the total power obtained by HFC at time t, the power used for electricity generation, and the power used by HFC for heat generation; is the power obtained by HFC from HT equipment at time t; η fc is the HFC hydrogen-to-power efficiency; The function of HT is similar to that of thermal energy storage device, which can support energy conversion and storage, and improve the flexibility and economy of the system. Its specific model is as follows: Where: is the hydrogen power stored in the HT device at time t; and They are HT charging and discharging efficiency respectively; are the heat storage states of the hydrogen storage device at time t and t-1 respectively; is the maximum storage capacity of HT; The electric energy storage device can suppress the power output fluctuation caused by the uncertainty of wind and solar power output, ensuring the storage, flow and release of electric energy. Its model is similar to the above energy storage model, which is: Where: P es_bat (t) is the electric power stored in the electric energy storage device at time t; are the charging and discharging power of the storage device at time t respectively; are the charging and discharging efficiency of the energy storage device; S bat (t), S bat (t-1) are the storage states of the energy storage device at time t and t-1 respectively; It is the maximum storage capacity of the electric energy storage device.

8. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 1 is characterized in that: The step S3 comprises the following steps: S30: confirming power balance constraints; S31: confirm the interaction constraints with the distribution network; S32: confirming energy storage system constraints; S33: Confirm the constraints of each device.

9. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a CSP plant according to claim 8 is characterized in that: During the operation of the multi-energy coupling system, the supply and demand of electrical power and thermal power must be balanced at all times. The constraints are as follows: Where: P load (t) is the system load demand at time t; H load (t) is the system heat load demand at time t; The calculation of the interaction constraints with the distribution network is as follows: Where: is the maximum interactive power between the system and the distribution network; α buy The system's electricity purchase and sales status from the distribution network. A value of 1 indicates purchasing electricity from the distribution network, and a value of 0 indicates selling electricity to the grid. System energy storage devices include electric energy storage, hydrogen energy storage, and thermal energy storage. Taking electric energy storage as an example, its constraints are as follows: Where: The energy storage device is in the charging and discharging state. When the value is 1, the energy storage device is in the charging state. When the value is 0, the energy storage device is in the discharging state. bat (1) S bat (24) are the initial and final storage states of the energy storage device; are the lower and upper limits of the power storage state respectively; In step S33, the specific constraints of each device are as follows: CU constraints: Where: P gmin 、P gmax are the lower and upper limits of CU output respectively; is the maximum landslide and climbing power of CU at time t; WT, PV constraints: This formula shows that the maximum wind and solar outputs do not exceed the predicted values; EB constraints: Where: The upper limit of EB heating power; CSP constraints: Where: are the lower and upper limits of CSP power generation output at time t respectively; are the maximum landslide and climbing powers of CSP at time t, respectively; EL constraints: Where: are the lower and upper limits of EL hydrogen production ramp at time t, respectively; are the lower and upper limits of the input EL power at time t, respectively; HFC constraints: Where: are the lower and upper limits of HFC output at time t respectively; are the lower limit and upper limit of HFC output ramp at time t respectively.

10. The coordinated optimization strategy for the electric, thermal and hydrogen integrated energy system based on a solar thermal power station according to claim 1, characterized in that: In step S4, the C&CG decomposition algorithm is used to decompose the model into a main problem MP and a subproblem SP, and repeated iterations are performed to obtain the optimal solution. MP calculates the min value and relaxes some constraints, thereby providing a lower bound for the optimal value. SP calculates the max value and provides a feasible solution for the original problem, which can be used as an upper bound for the optimal solution. MP and SP are continuously updated and converged, and finally the optimal solution is obtained. The MP form is: Where: γ is an auxiliary variable; Returns the solution of the subproblem after the i-th iteration; i The worst scenario value returned for the i-th subproblem; i t is the current iteration number; L B is the lower bound of the optimal total cost of the system; The objective function value of the main problem obtained by returning to the worst scenario after the i-th iteration; The SP form is: Where: The first stage decision variable value obtained by solving the main problem is the determined value; U B is the upper bound of the optimal total cost of the system; Solve the subproblem for the i-th time to obtain the value of the decision variable in the second stage; MP is a single-level problem involving only integers and is easy to solve. SP is a double-level max-min problem and is difficult to solve directly. Therefore, the duality principle is used to transform the inner min into a max and merge it with the outer max, transforming the double-level problem in the second stage into a single-level problem for easier solution. The dual form of SP is: Where: ε, θ, ω, μ are the dual variables corresponding to the constraints in the second stage atomic problem; a is a vector whose elements are all 0; due to the existence of bilinear terms in the objective function T μ is a non-deterministic polynomial problem and is difficult to solve directly. Therefore, SP is linearized by the big M method. Where: o* is the column vector consisting of the predicted values of uncertain variables; To introduce auxiliary variables; M is a sufficiently large real number; I + =[I load ,I Hload ]; I - =[I wt ,I pv ]; b = [1,1].

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