Scheduling method, device and equipment of electric heating comprehensive energy system and medium

Abstract

The invention discloses a scheduling method, device and equipment of an electric heating integrated energy system and a medium, and relates to the technical field of energy system scheduling, and the method comprises the steps: obtaining the total measurement information of the electric heating integrated energy system; obtaining a scheduling scheme of the electric heating integrated energy system based on the total measurement information by using a constructed scheduling model of the electric heating integrated energy system; wherein the scheduling scheme refers to a scheduling scheme used for scheduling each component in the electric heating comprehensive energy system, and the operation cost and the load shedding cost of the electric heating comprehensive energy system operated based on the scheduling scheme are optimal solutions. The method is suitable for the scheduling scheme optimization scene of the electric heating integrated energy system.

Description

Technical Field

[0001] The present application relates to the technical field of energy system scheduling, and particularly to a scheduling method, device, equipment and medium for an integrated electric-thermal energy system. Background Art

[0002] With the rapid development of information technologies such as big data and the Internet of Things, the energy flow and information flow in the integrated energy system are closely related. The integrated energy system is an important channel for the development of the energy Internet, and deep integration and multi-energy complementarity can significantly improve the economy and environmental sustainability of the system. However, with the continuous improvement of the automation level of the energy system, the network scale and the number of measuring devices are also increasing significantly. The control decisions of the energy system are directly or indirectly affected by more and more external information, which will bring new security risks and cause unpredictable damage to the system. The main deficiencies of the existing solutions mainly include:

[0003] 1. There is a certain one-sidedness in modeling and analyzing only from the component level in the cyber-physical system;

[0004] 2. The modeling and analysis of the energy and information interaction in the integrated electric-thermal energy system have not been fully studied, especially the supporting role of quantitative information in the system state perception. Summary of the Invention

[0005] In view of this, the present application provides a scheduling method, device, equipment and medium for an integrated electric-thermal energy system, mainly aiming to solve the technical problem that in the existing cyber-physical system, there is data loss, resulting in a certain one-sidedness in modeling and analysis, and further resulting in a low accuracy of quantitative information in the modeling and analysis of the energy and information interaction in the integrated electric-thermal energy system for system state perception.

[0006] According to one aspect of the present application, a scheduling method for an integrated electric-thermal energy system is provided. The method includes:

[0007] Obtain the full-scale measurement information of the integrated electric-thermal energy system;

[0008] Using the constructed scheduling model of the integrated electric-thermal energy system, obtain the scheduling plan of the integrated electric-thermal energy system based on the full-scale measurement information;

[0009] Wherein, the scheduling plan refers to the scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operation cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions.

[0010] According to another aspect of the present application, a scheduling device for an integrated electric-thermal energy system is provided. The device includes:

[0011] An acquisition module, configured to acquire all measurement information of the integrated electric-thermal energy system;

[0012] A scheduling module, configured to utilize the scheduling model of the integrated electric-thermal energy system constructed, and obtain a scheduling plan for the integrated electric-thermal energy system based on the all measurement information;

[0013] Wherein, the scheduling plan refers to a scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions.

[0014] According to another aspect of the present application, there is provided a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, the scheduling method of the above integrated electric-thermal energy system is implemented.

[0015] According to still another aspect of the present application, there is provided a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, and when the processor executes the program, the scheduling method of the above integrated electric-thermal energy system is implemented.

[0016] By means of the above technical solutions, the scheduling method, device, equipment and medium of the integrated electric-thermal energy system provided by the present application, compared with the prior art in which there is data loss in the cyber-physical system, resulting in certain one-sidedness in modeling and analysis, and further resulting in relatively low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the integrated electric-thermal energy system, the present application acquires all measurement information of the integrated electric-thermal energy system; utilizes the scheduling model of the integrated electric-thermal energy system constructed, and obtains a scheduling plan for the integrated electric-thermal energy system based on the all measurement information; the scheduling plan refers to a scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions. It can be seen that by acquiring all measurement information of the integrated electric-thermal energy system, the comprehensiveness of data during modeling and analysis is guaranteed, the deep integration of the energy cyber-physical system is realized, and then based on the operating cost and load shedding cost of the integrated electric-thermal energy system, a scheduling plan for the integrated electric-thermal energy system for all measurement information is obtained, effectively improving the accuracy of quantitative information for system state perception.

[0017] The above description is only an overview of the technical solutions of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the description, and in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the specific embodiments of the present application are specifically listed below. Description of the Drawings

[0018] The accompanying drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation of the present application. In the drawings:

[0019] Figure 1 A schematic flowchart of a scheduling method for an electric-thermal integrated energy system provided by an embodiment of the present application is shown;

[0020] Figure 2 A schematic information-physical interaction diagram of a scheduling method for an electric-thermal integrated energy system provided by an embodiment of the present application is shown;

[0021] Figure 3 A schematic flowchart of another scheduling method for an electric-thermal integrated energy system provided by an embodiment of the present application is shown;

[0022] Figure 4 A schematic structural diagram of a scheduling device for an electric-thermal integrated energy system provided by an embodiment of the present application is shown;

[0023] Figure 5 A schematic structural diagram of another scheduling device for an electric-thermal integrated energy system provided by an embodiment of the present application is shown. Detailed implementation manners

[0024] The present application will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0025] Aiming at the technical problem that in the existing information-physical system, data loss exists, resulting in certain one-sidedness in modeling and analysis, and further resulting in low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the electric-thermal integrated energy system. This embodiment provides a scheduling method for an electric-thermal integrated energy system. By obtaining the full measurement information of the electric-thermal integrated energy system, the comprehensiveness of data during modeling and analysis is ensured, the deep integration of the energy information-physical system is realized, and then based on the operating cost and load shedding cost of the electric-thermal integrated energy system, a scheduling scheme for the electric-thermal integrated energy system for the full measurement information is obtained, effectively improving the accuracy of quantitative information for system state perception. As Figure 1 shown, the above method includes the following steps:

[0026] Step 101, obtain the full measurement information of the electric-thermal integrated energy system.

[0027] In this embodiment, when there is unobservable measurement information in the power system and the thermal system, state estimation technology is used to estimate the system operating state, so as to supplement the measurement information in the case of insufficient measurement equipment or missing measurement information. Specifically, the redundant information associated with the missing measurement information in other regions is used to estimate the missing measurement information in the unobservable region, so as to achieve the deep integration of the energy information physical system, thereby ensuring the accuracy of the subsequent optimal scheduling of the energy system. As Figure 2 shown, using the obtained initial measurement information, such as bus 4 and bus 5, the missing measurement information in the unobservable region is supplemented through state estimation, so that through global perception, potential load reduction can be prevented and the normal operation of the energy system can be guaranteed. It can be seen that by introducing the energy-information interaction mechanism between the measurement equipment, state estimation and the dispatching of the integrated electric-thermal energy system, the comprehensiveness of the measurement information at the sensing terminal layer can be realized to ensure the subsequent support for the perception ability of the integrated electric-thermal energy system.

[0028] Step 102: Using the established dispatching model of the integrated electric-thermal energy system, obtain the dispatching plan of the integrated electric-thermal energy system based on the full measurement information.

[0029] In this embodiment, the dispatching plan refers to the dispatching plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the dispatching plan are the optimal solutions. The dispatching model of the integrated electric-thermal energy system is an optimal dispatching model of the integrated electric-thermal energy system based on cyber-physical interaction. By state estimation, the measurement information (bus / node / branch) is supplemented, and then the dispatching model of the integrated electric-thermal energy system is corrected to quantitatively consider the impact of missing measurement information on the operation of the energy system. Specifically, based on the research requirements and problems brought about by the deep association of the cyber-physical system, a cyber-physical interaction dispatching model of the integrated electric-thermal energy system is established, where the objective function of the dispatching problem of the integrated electric-thermal energy system is to minimize the system operating cost and load shedding cost.

[0030] For this embodiment, the full measurement information of the electro-thermal integrated energy system can be obtained according to the above solution; by using the established scheduling model of the electro-thermal integrated energy system, the scheduling plan of the electro-thermal integrated energy system is obtained based on the full measurement information; the scheduling plan refers to the scheduling plan for scheduling each component in the electro-thermal integrated energy system, and the operating cost and load shedding cost of the electro-thermal integrated energy system operating based on the scheduling plan are the optimal solutions. Compared with the prior art in which there is data loss in the cyber-physical system, resulting in certain one-sidedness in modeling and analysis, and further resulting in relatively low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the electro-thermal integrated energy system, it can be seen that in this embodiment, by obtaining the full measurement information of the electro-thermal integrated energy system, the comprehensiveness of data during modeling and analysis is guaranteed, the deep integration of the energy cyber-physical system is realized, and then based on the operating cost and load shedding cost of the electro-thermal integrated energy system, the scheduling plan of the electro-thermal integrated energy system for the full measurement information is obtained, effectively improving the accuracy of quantitative information for system state perception.

[0031] Further, as a refinement and extension of the specific implementation manner of the above embodiment, in order to fully illustrate the specific implementation process of this embodiment, another scheduling method for the electro-thermal integrated energy system is provided, which deeply integrates the measurement information into the regulation and control of the system physical process, and establishes a scheduling model of the electro-thermal integrated energy system considering cyber-physical interaction to ensure the safe and stable operation of the system. Therefore, this embodiment can solve the technical problems in the prior art, such as data loss in the cyber-physical system, resulting in certain one-sidedness in modeling and analysis, further resulting in relatively low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the electro-thermal integrated energy system, and relatively large difficulties in network optimization and supervision.

[0032] In the actual operation of the energy system, the operating personnel need to optimize the scheduling of the energy system according to the obtained measurement information. When the measurement information of some nodes in the energy system is lost or the topological structure cannot be observed, it can be inferred that the control center cannot adjust the corresponding components. Therefore, the control center will not be able to accurately perceive a specific area, making it difficult to detect faults in a timely manner, and even leading to inappropriate decisions, ultimately affecting the reliability of the energy system. For example Figure 2As shown in the parameters, due to the failure of the measurement equipment in the power systems of Bus 2, Bus 3, Lines 1-4, Lines 1-2, Lines 3-6, and the heating systems of Nodes 3 and 4, the observability of the system is reduced. It can be seen that the specific impacts of the reduced observability on the scheduling model include: 1) When the thermal power of the power system bus or heat source node / load node is unobservable, the power load, generator output, heat load, and heat unit output cannot be measured, and the control center cannot adjust the output or reduce the load. Therefore, the generator output, power load, heat unit output, and heat load are all fixed at the initial values; 2) When the power flow of the power system line or the pipeline flow of the thermal system is unobservable, the control center cannot monitor the line flow or pipeline flow, and the corresponding line capacity or pipeline flow constraints are no longer effective; 3) When the node temperature of the thermal system is unobservable, the heating temperature and regenerative temperature of the heat network node cannot be detected, and the corresponding node temperature constraints are no longer effective. Based on this, as Figure 3 shown, the scheduling method for the integrated electric-thermal energy system considering cyber-physical integration includes:

[0033] Step 301: Determine the missing measurement information according to the initial measurement information of the power system and the thermal system in the integrated electric-thermal energy system.

[0034] Step 302: Perform state estimation on the missing measurement information according to the redundant information associated with the missing measurement information to obtain the estimated value of the missing measurement information.

[0035] Step 303: Based on the initial measurement information and the estimated value, obtain the full measurement information of the integrated electric-thermal energy system.

[0036] In implementation, when the output and load of the units on the unobservable buses and nodes cannot be measured, the operators cannot change the output or cut the load accordingly. When the power flow of the line cannot be monitored, overloading of the line may occur when increasing the line load, which may further expand the scope of the system failure and cause serious economic losses. Therefore, in this embodiment, the cyber-physical interaction model is used to use the measurement information of the observable area nodes around the unobservable area nodes corresponding to the missing measurement information as the redundant information of the missing measurement information to perform state estimation on the unobservable area nodes, and obtain the full measurement information of the integrated electric-thermal energy system, that is, to deeply depict the energy information interaction process from the network level, and through multi-level comprehensive state perception and energy information interaction, realize the deep integration of the energy cyber-physical system, thereby improving the reliability of subsequent scheduling decisions.

[0037] In implementation, the objective function of the scheduling model of the integrated electric-thermal energy system is to minimize the operating cost and load shedding cost of the integrated electric-thermal energy system, and the operating cost includes the operating cost of non-cogeneration units, the operating cost of cogeneration units, and the operating cost of electric boilers.

[0038] Furthermore, based on the research requirements and problems brought about by the deep correlation of the cyber-physical system, a scheduling model for the cyber-physical interaction of the integrated energy system is established, and the objective function of the scheduling problem of the electric-thermal integrated energy system is set as (1), that is, to minimize the system operation cost and the load shedding cost.

[0039]

[0040] The operation cost can be expressed as:

[0041]

[0042]

[0043] Among them, f i TU , f i CHP , f i EB are the cost functions of the non-combined heat and power unit (TU), the combined heat and power unit (CHP), and the electric boiler (EB) respectively; and are the power generation cost coefficients of TU, CHP, and EB respectively; and are the active power outputs of TU and CHP respectively; and are the heat powers of CHP and EB; f i Cut is the load shedding cost function of the electrical load P i Cut and the heat load ; is the cost coefficient of load shedding.

[0044] In implementation, the scheduling model includes a power grid model, and the constraint conditions of the power grid model include power balance constraint, line power flow limit constraint, load shedding constraint, and generator output power constraint; when the demand of the load is the estimated demand, the output power of the generator is the estimated output power, and the power flow of the power line is the estimated power flow, based on one or more of the error coefficients of the estimated demand, the error coefficient of the estimated output power, and the error coefficient of the estimated power flow, the power balance constraint, the line power flow limit constraint, the load shedding constraint, and the generator output power constraint are determined respectively.

[0045] Furthermore, for the power system, in order to consider the influence of measurement information, information constraints (6)-(8) are added, and auxiliary variables are introduced to reconstruct the power grid model.

[0046]

[0047] Among them, S F , S PD , S G are error coefficients corresponding to the estimated power flow of the power line ij, the estimated output power of the generator i, and the estimated demand of the load i respectively; P F ′, P D ′, P G ′ are the available estimated values of the branch flow, the generator output power, and the load demand respectively; are the true values corresponding to each estimated electrical variable; P F,ij is the active power flow through the power line ij; the load shedding amount at node i; P i Cut is the load shedding amount at node i; P G.i is the active output power of the generator i. Among them, dividing the true value by the available estimated value gives the error coefficients S F , S PD , S G .

[0048] When the demand of the load is the estimated demand and the output power of the generator is the estimated output power, based on the error coefficient of the estimated demand of the load and the error coefficient of the estimated output power of the generator, determine the power balance constraint; when the power flow of the power line is the estimated power flow, based on the error coefficient of the estimated power flow of the power line, determine the line flow limit constraint; when the output power of the generator is the estimated output power, based on the error coefficient of the estimated output power of the generator, determine the load shedding constraint; when the demand of the load is the estimated demand, based on the error coefficient of the estimated demand of the load, determine the generator output power constraint.

[0049] According to the requirements of the actual application scenario, the power grid adopts a DC current model, and the power balance constraint (12), the line flow limit constraints (13)-(14), the load shedding constraint (15), and the generator output power constraint (16) are considered in the power grid model.

[0050]

[0051] In the formula: and are the buses and lines of the power system; B ij is the element of the nodal conductance matrix corresponding to the line ij; θ ij is the phase angle difference between bus i and bus j; P i D is the load of bus i; P F,ij is the active power flow through the power line ij; P G.i,max / PG,i,min They are the upper / lower limits of the active output of the generator respectively. After introducing the information constraints (6)-(8), the missing measurement information can be completed through state estimation and applied to economic dispatch, thereby improving the reliability of dispatch decisions.

[0052] In implementation, the dispatch model includes a heat network model, and the constraint conditions of the heat network model include heat power balance constraint, mixed temperature equation, coupled equipment constraint, pipeline flow constraint, supply and return water temperature constraint, and load shedding constraint; when the output of the heat source node is the estimated output, the demand of the heat load is the estimated demand, the heating temperature of the node is the estimated heating temperature, the return heating temperature of the node is the estimated return heating temperature, and the flow of the node is the estimated flow, based on one or more of the error coefficients of the estimated output, the error coefficient of the estimated demand, the error coefficient of the estimated heating temperature, the error coefficient of the estimated return heating temperature, and the error coefficient of the estimated flow, the heat power balance constraint, the mixed temperature equation, the coupled equipment constraint, the pipeline flow constraint, the supply and return water temperature constraint, and the load shedding constraint are respectively determined.

[0053] Furthermore, for the thermal system, add the information constraint conditions (17)-(21), and introduce auxiliary variables and to reconstruct the heat network model.

[0054]

[0055] S Ts = Ts real . / Ts′(19)

[0056] S Tr = Tr real . / Tr′(20)

[0057] S m = m real . / m′(21)

[0058]

[0059] Among them, S HS , S HD , S Ts , S Tr and S m are the error coefficients corresponding to the estimated output of the heat source node i, the estimated demand of the heat load i, the estimated supply water temperature of the node i, the estimated return water temperature of the node i, and the estimated flow of the node i respectively; H′ S , H′ D , Ts′, Tr′, m′ are the available estimated values of the heat output, load demand, supply water temperature, return water temperature, and pipeline flow respectively; Trreal , m real are the true values corresponding to each estimated heat variable; is the output heat power of heat source node i; is the load shedding amount at node i of the thermal system; Ts i , Tr i , m i are the supply water temperature, return water temperature, and node flow rate of node i, respectively. Among them, dividing the true value by the available estimated value gives the error coefficient vector S HS , S HD , S Ts , S Tr , S m .

[0060] According to the requirements of the actual application scenario, a mass-regulation type heat network model is adopted for the heat network. The heat network model considers the heat power balance constraints (27)-(28), the mixed temperature equation constraints (29)-(30), the coupled equipment constraints (31)-(33), the pipeline flow constraint (34), the supply and return water temperature constraints (35)-(36), and the load shedding constraint (37).

[0061]

[0062]

[0063] Among them, c is the specific heat capacity of water; is the heat power injected by the heat source node into the heat network; and are the heat load and load shedding of the heat load node, respectively; and are the water flow rate and supply water temperature of the heat source node; and are the water flow rate and return water temperature of the heat load node; and are the supply water temperature and return water temperature of node i, respectively; is the water flow rate of branch ij; ψ ij is the heat loss coefficient of branch ij. e(i) represents the set of starting node of the branches with node i as the end node, and s(i) represents the set of end nodes of the branches with node i as the starting node; η CHP is the heat-electricity ratio of the combined heat and power unit; η EB is the efficiency of EB.

[0064] In implementation, the adaptive piecewise McCormick relaxation algorithm is used to relax the constraint conditions in the heat network model, obtaining the relaxed constraint conditions of the heat network model; among them, the constraint conditions to be convexified at least include the heat power balance constraint and the mixed temperature equation.

[0065] Furthermore, since the heat network model contains bilinear terms (the product of mass flow rate and temperature in the thermal system), which is a non-linear problem and cannot be directly processed by the solver, an adaptive piecewise McCormick relaxation algorithm is introduced to relax it. Further model reconstruction is carried out on the heat network model, that is, the bilinear terms are mainly reflected in equations (27)-(30), and they are convexified through piecewise McCormick envelopes and segmented by selecting node temperatures, expressed as:

[0066]

[0067]

[0068] where W ij denotes the auxiliary variable; τ i denotes the supply / return water temperature of the heating node i; and denote the lower and upper limits of the τ i variable in partition s. Each partition has a binary variable. If the value of τ i belongs to this partition, then y i,s = 1; otherwise, y i,s = 0. Another variable m ij of the bilinear term is decomposed into m ij,s , where S is the index set of s.

[0069] Step 304. Reshape the objective function of the scheduling model into a relaxation problem.

[0070] Step 305. According to the full measurement information, obtain the scheduling plan of the integrated electric and heat energy system based on the constraint conditions of the power grid model, the relaxed constraint conditions of the heat network model, and the constraint conditions of the heat network model.

[0071] In implementation, based on the partition width tolerance parameter, adaptively partition the partition where the lower bound solution of the relaxation problem is located to obtain the optimal solution of the relaxation problem, and use the optimal solution as the scheduling plan of the integrated electric and heat energy system obtained according to the full measurement information.

[0072] In implementation, reshape the original problem solved by the objective function into a relaxation problem

[0073] min(1)

[0074] s.t., Eqs. (6)-(26), (27)*-(30)*, (31)-(37) (44)

[0075] Among them, ()* represents the constraint condition after convexification of the corresponding constraint condition. Adding a large number of binary variables to the convexification model can improve the accuracy, but it will increase the computational burden. Therefore, to solve this problem, an adaptive piecewise McCormick algorithm is proposed. The main improvement of this algorithm lies in the non-uniform and adaptive partitioning of the variable domain during each iteration, that is, dynamically adding partitions around the optimal solution of the relaxation problem and continuing until the relative gap between the lower bound and the upper bound reaches the specified standard.

[0076] As shown in the following table, Algorithm 1 is the pseudocode of the adaptive piecewise McCormick relaxation algorithm. The symbol represents the result of the original problem , represents 's function value, σ and f( σ ) represent the solution and function value of the relaxed problem respectively. Among them, lines 2-3 are used to estimate the solution of the original problem . According to the solution of , a set of initial partitions are created (line 4), and then the problem is solved to obtain the initial lower bound (lines 5-6); lines 7-13 form the main loop, which iteratively updates the lower bound and the upper bound until their difference is within the given tolerance range or the calculation time exceeds the limit, and the variable domain undergoes adaptive non-uniform refinement during each iteration (line 8), which can generate a new piecewise convex relaxation and calculate a new lower bound (lines 9 and 10). At the same time, a new local solution is obtained by solving (line 11). If is less than the current value, the upper bound is modified (line 12). Finally, Algorithm 1 obtains the optimal solution of .

[0077]

[0078] Furthermore, line 8 of Algorithm 1 is the process of "adaptive segmentation". As shown in the following table, Algorithm 2 is the pseudocode of the adaptive variable domain partitioning algorithm. It starts from inputting the current variable partition and the partition width tolerance parameters ∈ p and Δ (line 1 in Algorithm 2). Δ is used to control the number and size of the partitions, thereby affecting the convergence speed of the entire algorithm. Among them, lines 3-4 determine the effective partitions of the variables and the effective partition where the current lower bound solution is located, and divide it into three new partitions, which are determined by Δ and the effective upper / lower boundaries (lines 5-11 in Algorithm 2); lines 13-14 of Algorithm 2 ensure that when the size of the partition is less than ∈p The active partitions outside the current partition will be further redefined to prevent variables from getting stuck in specific regions. It can be seen that compared with the traditional unified partitioning method, the adaptive partitioning strategy proposed in this embodiment greatly improves the computational efficiency.

[0079]

[0080]

[0081] It can be seen that in order to accelerate the solution speed, by using the above-mentioned adaptive piecewise McCormick algorithm, the original problem can be relaxed, and at the same time, it can be used to solve the bilinear terms existing in the scheduling model for the heat network model, balance the solution accuracy and the solution speed, so as to efficiently and accurately generate the optimal scheduling decision, that is, the optimal operation strategy of the integrated electro-thermal energy system considering cyber-physical interaction.

[0082] By applying the technical solution of this embodiment, compared with the technical solution in the prior art where there is data loss in the cyber-physical system, resulting in certain one-sidedness in modeling and analysis, and further resulting in relatively low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the integrated electro-thermal energy system, the scheduling framework of the integrated electro-thermal energy system based on the energy interconnection and highly integrated network technology constructed according to this embodiment can achieve optimized and reliable economic scheduling. That is, based on the existing measurement information, the state estimation is used to perceive the entire energy system, including load shedding and node operation information, so as to integrate the full measurement information into the scheduling process to generate reliable and practical scheduling decisions for the integrated electro-thermal energy system. Further, due to the existence of bilinear terms in the established scheduling model, the adaptive piecewise McCormick algorithm is introduced for model reconstruction, and in each iteration, the newly generated partition is always a subset of the previous partition and within the neighborhood of the optimal solution, and through the non-uniform and adaptive partitioning algorithm, the non-convex scheduling model with bilinear terms is relaxed into a convex scheduling model, so as to effectively solve the optimal scheduling solution of the original non-convex model and obtain reliable and practical scheduling decisions.

[0083] Further, as Figure 1 a specific implementation of the method, the embodiment of the present application provides a scheduling device for an integrated electro-thermal energy system, as Figure 4 shown. The device includes: an acquisition module 41 and a scheduling module 42.

[0084] The acquisition module 41 is used to acquire the full measurement information of the integrated electro-thermal energy system.

[0085] A scheduling module 42, configured to obtain a scheduling plan for the integrated electric-thermal energy system by using the scheduling model of the integrated electric-thermal energy system constructed based on the full measurement information; wherein, the scheduling plan refers to a scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions.

[0086] In a specific application scenario, such as Figure 5 shown, the obtaining module 41 includes: a determining sub-module 411, an estimating sub-module 412, and a complementing sub-module 413.

[0087] The determining sub-module 411 is configured to determine the missing measurement information according to the initial measurement information of the power system and the thermal system in the integrated electric-thermal energy system.

[0088] The estimating sub-module 412 is configured to perform state estimation on the missing measurement information according to the redundant information associated with the missing measurement information to obtain an estimated value of the missing measurement information.

[0089] The complementing sub-module 413 is configured to obtain the full measurement information of the integrated electric-thermal energy system based on the initial measurement information and the estimated value.

[0090] In a specific application scenario, the objective function of the scheduling model of the integrated electric-thermal energy system is to minimize the operating cost and load shedding cost of the integrated electric-thermal energy system, and the operating cost includes the operating cost of non-cogeneration units, the operating cost of cogeneration units, and the operating cost of electric boilers.

[0091] In a specific application scenario, the scheduling model includes a power grid model, and the constraint conditions of the power grid model include power balance constraint, line power flow limit constraint, load shedding constraint, and generator output power constraint; when the demand of the load is the estimated demand, the output power of the generator is the estimated output power, and the power flow of the power line is the estimated power flow, the power balance constraint, the line power flow limit constraint, the load shedding constraint, and the generator output power constraint are respectively determined based on one or more of the error coefficients of the estimated demand, the error coefficients of the estimated output power, and the error coefficients of the estimated power flow.

[0092] In a specific application scenario, the scheduling model includes a heat network model, and the constraint conditions of the heat network model include heat power balance constraint, mixed temperature equation, coupled equipment constraint, pipeline flow constraint, supply and return water temperature constraint, and load shedding constraint; when the output of the heat source node is an estimated output, the demand of the heat load is an estimated demand, the heating temperature of the node is an estimated heating temperature, the return heating temperature of the node is an estimated return heating temperature, and the flow of the node is an estimated flow, based on one or more of the error coefficients of the estimated output, the error coefficients of the estimated demand, the error coefficients of the estimated heating temperature, the error coefficients of the estimated return heating temperature, and the error coefficients of the estimated flow, the heat power balance constraint, the mixed temperature equation, the coupled equipment constraint, the pipeline flow constraint, the supply and return water temperature constraint, and the load shedding constraint are respectively determined.

[0093] In a specific application scenario, the adaptive piecewise McCormick relaxation algorithm is used to relax the constraint conditions in the heat network model to obtain the relaxed constraint conditions of the heat network model; wherein, the constraint conditions to be convexified at least include the heat power balance constraint and the mixed temperature equation.

[0094] In a specific application scenario, the scheduling module 42 includes: a reshaping sub-module 421 and a solving sub-module 422.

[0095] The reshaping sub-module 421 is used to reshape the objective function of the scheduling model into a relaxation problem.

[0096] The solving sub-module 422 is used to obtain the scheduling scheme of the integrated electric and heat energy system based on the full measurement information, the constraint conditions of the power grid model, the relaxed constraint conditions of the heat network model, and the constraint conditions of the heat network model; wherein, based on the partition width tolerance parameter, the partition where the lower bound solution of the relaxation problem is located is adaptively partitioned to obtain the optimal solution of the relaxation problem, and the optimal solution is used as the scheduling scheme of the integrated electric and heat energy system obtained according to the full measurement information.

[0097] It should be noted that for other corresponding descriptions of each functional unit involved in the scheduling device of the integrated electric and heat energy system provided in the embodiments of the present application, reference can be made to Figure 1 and Figure 3 the corresponding descriptions therein, which will not be elaborated here.

[0098] Based on the above methods as Figure 1 and Figure 3 shown, correspondingly, the embodiments of the present application also provide a computer storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the scheduling method of the integrated electric and heat energy system as Figure 1 and Figure 3 shown.

[0099] Based on such understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium (such as a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions for causing a computer device (such as a personal computer, a server, or a network device, etc.) to execute the methods described in various implementation scenarios of the present application.

[0100] Based on the above-mentioned Figure 1 、 Figure 3 shown method, and Figure 4 、 Figure 5 shown virtual device embodiments, in order to achieve the above object, the embodiments of the present application also provide a computer device, specifically a personal computer, a server, a network device, etc. The entity device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the scheduling method of the integrated electro-thermal energy system as shown in Figure 1 and Figure 3 shown.

[0101] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, sensors, an audio circuit, a WI-FI module, etc. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), etc. Optionally, the user interface may further include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0102] Those skilled in the art can understand that the structure of a computer device provided in this embodiment does not constitute a limitation on the entity device, and may include more or fewer components, or combine some components, or have different component arrangements.

[0103] The storage medium may further include an operating system and a network communication module. The operating system is a program for managing the hardware and software resources of a computer device, and supports the operation of an information processing program and other software and / or programs. The network communication module is used to implement communication between components inside the storage medium, as well as communication between other hardware and software in the entity device.

[0104] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or can also be implemented by hardware. By applying the technical solution of the present application, compared with the technical solution in the prior art where there is data loss in the cyber-physical system, resulting in certain one-sidedness in modeling and analysis, and further resulting in a relatively low accuracy of quantitative information for system state perception in the modeling and analysis of energy and information interaction in the electro-thermal integrated energy system, the electro-thermal integrated energy system scheduling framework based on the energy interconnection and highly integrated network technology constructed according to this embodiment can achieve optimized and reliable economic scheduling. That is, on the basis of the existing measurement information, the state estimation is used to perceive the entire energy system, including load shedding and node operation information, so as to integrate the full amount of measurement information into the scheduling process to generate reliable and practical electro-thermal integrated energy system scheduling decisions. Further, due to the existence of bilinear terms in the established scheduling model, the adaptive piecewise McCormick algorithm is introduced for model reconstruction, and in each iteration, the newly generated partition is always a subset of the previous partition and within the neighborhood of the optimal solution, and through the non-uniform and adaptive partitioning algorithm, the non-convex scheduling model with bilinear terms is relaxed into a convex scheduling model, so as to effectively solve the optimal scheduling solution of the original non-convex model and obtain reliable and practical scheduling decisions.

[0105] Those skilled in the art can understand that the drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the drawings are not necessarily essential for implementing the present application. Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from the present implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0106] The above serial numbers of the present application are only for description and do not represent the advantages or disadvantages of the implementation scenarios. The above disclosure is only several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present application.

Claims

1. A scheduling method for an electric-thermal integrated energy system, characterized in that Including: Obtaining all measurement information of the integrated electric-thermal energy system; Using the constructed scheduling model of the integrated electric-thermal energy system, obtaining the scheduling plan of the integrated electric-thermal energy system based on the all measurement information; Wherein, the scheduling plan refers to the scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions.

2. The method according to claim 1, characterized in that The step of obtaining all measurement information of the integrated electric-thermal energy system includes: Determining the missing measurement information according to the initial measurement information of the power system and the thermal system in the integrated electric-thermal energy system; Performing state estimation on the missing measurement information according to the redundant information associated with the missing measurement information to obtain the estimated value of the missing measurement information; Based on the initial measurement information and the estimated value, obtaining all measurement information of the integrated electric-thermal energy system.

3. The method according to claim 1 or 2, wherein The objective function of the scheduling model of the integrated electric-thermal energy system is to minimize the operating cost and load shedding cost of the integrated electric-thermal energy system, and the operating cost includes the operating cost of non-cogeneration units, the operating cost of cogeneration units, and the operating cost of electric boilers.

4. The method according to claim 1, wherein The scheduling model includes a power grid model, and the constraint conditions of the power grid model include power balance constraint, line power flow limit constraint, load shedding constraint, and generator output power constraint; When the demand of the load is the estimated demand, the output power of the generator is the estimated output power, and the power flow of the power line is the estimated power flow, respectively determining the power balance constraint, the line power flow limit constraint, the load shedding constraint, and the generator output power constraint based on one or more of the error coefficients of the estimated demand, the error coefficients of the estimated output power, and the error coefficients of the estimated power flow.

5. The method according to claim 1 or 4, characterized in that, The scheduling model includes a heat network model, and the constraint conditions of the heat network model include heat power balance constraint, mixed temperature equation, coupling equipment constraint, pipeline flow constraint, supply and return water temperature constraint, and load shedding constraint; When the output of the heat source node is the estimated output, the demand of the heat load is the estimated demand, the heating temperature of the node is the estimated heating temperature, the return heating temperature of the node is the estimated return heating temperature, and the flow of the node is the estimated flow, respectively determining the heat power balance constraint, the mixed temperature equation, the coupling equipment constraint, the pipeline flow constraint, the supply and return water temperature constraint, and the load shedding constraint based on one or more of the error coefficients of the estimated output, the error coefficients of the estimated demand, the error coefficients of the estimated heating temperature, the error coefficients of the estimated return heating temperature, and the error coefficients of the estimated flow.

6. The method according to claim 5, wherein Using the adaptive piecewise McCormick relaxation algorithm to perform relaxation processing on the constraint conditions in the heat network model to obtain the relaxed constraint conditions of the heat network model; Wherein, the constraint conditions to be convexified at least include the heat power balance constraint and the mixed temperature equation.

7. The method according to claim 6, characterized in that, The step of using the constructed scheduling model of the integrated electric-thermal energy system to obtain the scheduling plan of the integrated electric-thermal energy system based on the all measurement information includes: Remodeling the objective function of the scheduling model into a relaxation problem; Based on the full measurement information, a scheduling plan for the integrated electric-thermal energy system is obtained based on the constraint conditions of the power grid model, the relaxed constraint conditions of the heat network model, and the constraint conditions of the heat network model; Among them, based on the partition width tolerance parameter, the partition where the lower bound solution of the relaxation problem is located is adaptively partitioned to obtain the optimal solution of the relaxation problem, and the optimal solution is used as the scheduling plan of the integrated electric-thermal energy system obtained according to the full measurement information.

8. A scheduling device for an electric-thermal integrated energy system, characterized in that, It includes: An acquisition module for acquiring the full measurement information of the integrated electric-thermal energy system; A scheduling module for using the constructed scheduling model of the integrated electric-thermal energy system to obtain the scheduling plan of the integrated electric-thermal energy system based on the full measurement information; Among them, the scheduling plan refers to the scheduling plan for scheduling each component in the integrated electric-thermal energy system, and the operating cost and load shedding cost of the integrated electric-thermal energy system operating based on the scheduling plan are the optimal solutions.

9. A computer storage medium, on which a computer program is stored, characterized in that, When the program is executed by a processor, it implements the scheduling method of the integrated electric-thermal energy system according to any one of claims 1 to 7.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the program, it implements the scheduling method of the integrated electric-thermal energy system according to any one of claims 1 to 7.

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