Optimization Scheduling Method and System for a Preset-Time Fully Distributed Integrated Energy System

Abstract

The present invention belongs to the technical field of integrated energy systems. A method and system for optimizing the scheduling of a fully distributed integrated energy system at a preset time are provided. Taking the minimum total cost as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, and the safety operation constraints include: power balance constraints, upper and lower power limits constraints, and ramp rate constraints; assuming that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, the objective function is solved based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained. The present invention does not depend on the system initial value and parameters, can preset any time, and solve the optimization scheduling model of the integrated energy system including "source-load-storage-station" with equality and inequality constraints without using any global information, thereby improving the scheduling accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular, to a method and system for optimizing the scheduling of a preset-time fully distributed integrated energy system. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] An integrated energy system (IES) is different from traditional single-energy supply systems. It integrates various energy forms such as electricity, heat, gas, and cold inside. Through energy conversion and energy storage devices within the system, various energies are coupled, realizing the complementary and mutual assistance of different energies and meeting the multiple energy demands of users. With the development of communication technology and big data technology, the connections among the generation, conversion, storage, and utilization of energy in the IES are further strengthened. Through intelligent control and collaborative management of each link, the overall efficient and safe management and control of the system can be achieved. Against the background of the higher requirements for energy utilization in the rapid development of today's economic society, it is of great strategic significance to establish an IES with multi-energy collaboration, economic efficiency, low carbon, and environmental protection.

[0004] In the prior art, for the optimization problem of systems such as integrated energy systems, centralized optimization scheduling methods are mostly used. All subjects within the system need to master global information to obtain the optimization scheduling strategy. Moreover, the centralized optimization method has a large amount of communication and poor robustness of the communication network, and can no longer meet the current development needs of integrated energy systems. And existing distributed optimization methods either require partial global information or directly apply the distributed optimization methods of traditional power systems, and cannot dynamically meet the scheduling time scales of different energies. Summary of the Invention

[0005] To solve the deficiencies of the prior art, the present invention provides a method and system for optimizing the scheduling of a preset-time fully distributed integrated energy system. A preset-time fully distributed optimization algorithm based on a time-based generator (TBG) is designed, and the Lyapunov theory is used to prove that the algorithm converges within a preset time, does not depend on the initial values and parameters of the system, can be preset arbitrarily, and can solve the optimization scheduling model of a thermoelectric coupling integrated energy system including "source-load-storage-station" with equality and inequality constraints without using any global information, greatly improving the scheduling accuracy.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides a method for optimizing the scheduling of a preset-time fully distributed integrated energy system.

[0008] A method for optimizing the scheduling of a preset-time fully distributed integrated energy system includes the following processes:

[0009] Taking the minimum total cost as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, including: power balance constraint, power upper and lower limit constraints, and ramp rate constraint;

[0010] Assuming that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, solving the objective function based on the preset-time fully distributed optimization algorithm of the time-base generator, so that the integrated energy system reaches a convergence state within the preset time, and obtaining the optimal output strategy of the integrated energy system.

[0011] In a second aspect, the present invention provides a preset-time fully distributed integrated energy system optimization scheduling system.

[0012] A preset-time fully distributed integrated energy system optimization scheduling system includes:

[0013] A scheduling target setting unit configured to: take the minimum total cost as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, including: power balance constraint, power upper and lower limit constraints, and ramp rate constraint;

[0014] An optimal scheduling control unit configured to: assume that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, solve the objective function based on the preset-time fully distributed optimization algorithm of the time-base generator, so that the integrated energy system reaches a convergence state within the preset time, and obtaining the optimal output strategy of the integrated energy system.

[0015] In a third aspect, the present invention provides a computer device, including: a processor and a computer-readable storage medium;

[0016] A processor suitable for executing a computer program;

[0017] A computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by the processor, it realizes the preset-time fully distributed integrated energy system optimization scheduling method as described in the first aspect of the present invention.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded and executed by a processor to perform the preset-time fully distributed integrated energy system optimization scheduling method as described in the first aspect of the present invention.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] The present invention divides the thermoelectric coupling integrated energy system into four parts, namely "source - load - storage - station", according to functions, establishes its low - carbon economic dispatch model, designs a preset - time fully distributed optimization algorithm based on a time - base generator (TBG), and uses Lyapunov theory to prove that the algorithm converges within the preset time, does not depend on the initial values and parameters of the system, can be preset arbitrarily, and can solve the optimal dispatch model of the thermoelectric coupling integrated energy system including "source - load - storage - station" with equality and inequality constraints without using any global information, greatly improving the dispatch accuracy.

[0021] Advantages of additional aspects of the present invention will be given in part in the following description, will become apparent in part from the following description, or will be understood through the practice of the present invention. Brief Description of the Drawings

[0022] The specification drawings forming a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention.

[0023] Figure 1 It is the structural diagram of the integrated energy system provided for Embodiment 1 of the present invention;

[0024] Figure 2 It is the structural diagram and communication network topology diagram in the example analysis provided for Embodiment 1 of the present invention;

[0025] Figure 3 It is the thermal power response curve when the preset time is 40 s in the example analysis provided for Embodiment 1 of the present invention;

[0026] Figure 4 It is the electric power response curve when the preset time is 10 s in the example analysis provided for Embodiment 1 of the present invention;

[0027] Figure 5 It is the thermal power supply - demand balance response curve when the preset time is 40 s in the example analysis provided for Embodiment 1 of the present invention;

[0028] Figure 6 It is the electric power response curve when the preset time is 6 s in the example analysis provided for Embodiment 1 of the present invention;

[0029] Figure 7 It is the thermal power supply - demand balance response curve from 0 to 125 s in the example analysis provided for Embodiment 1 of the present invention;

[0030] Figure 8 It is the thermal power response curve from 0 to 125 s in the example analysis provided for Embodiment 1 of the present invention;

[0031] Figure 9 The simulation results of other similar algorithms in the case analysis provided in Embodiment 1 of the present invention;

[0032] Figure 10 The simulation results of the algorithm proposed in the present invention in the case analysis provided in Embodiment 1 of the present invention;

[0033] Figure 11 A schematic diagram of an optimized scheduling system for a preset-time fully distributed integrated energy system provided in Embodiment 2 of the present invention;

[0034] Figure 12 A schematic diagram of a computer device provided in Embodiment 3 of the present invention. Detailed implementation manners

[0035] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0036] It should be noted that the following detailed descriptions are all exemplary and are intended to provide further explanations of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0037] In the case of no conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0038] Embodiment 1:

[0039] As described in the background art, traditional methods cannot be applied to the low-carbon economic optimal scheduling problem of an integrated energy system that includes energy sources with different scheduling time scales. In view of this, this implementation manner proposes an optimized scheduling method for a preset-time fully distributed integrated energy system, including the following contents:

[0040] The integrated energy system can be divided into four parts: "source", "load", "storage", and "station" according to its functions. The specific structure is as Figure 1 shown. Among them, the "source" side mainly includes two parts: a power supply unit and a heat supply unit. The power sources include thermal power generation and renewable energy, and the heat sources include boilers and heat pumps, which are responsible for the supply of electric energy and thermal energy in the system; the "load" side includes an electric load and a heat load, and some electric loads can perform demand response; the "storage" side realizes the transfer of electric energy in time through a reasonable charge and discharge plan of the energy storage station; the "station" side realizes the conversion of energy forms through an energy station or a combined heat and power unit CHP, and outputs electric and heat energy in two forms at the same time.

[0041] In order to ensure the coordinated low-carbon economic operation of each part of the integrated energy system, a low-carbon economic operation model is established, and the optimization objective function can be expressed in the following form:

[0042] (1);

[0043] (2);

[0044] (3);

[0045] (4);

[0046] Wherein, are cost coefficients respectively, is the power of new energy power generation equipment i, is the power of thermal power generation equipment j, is the boiler k power.

[0047] (5);

[0048] Wherein, , is a cost coefficient, is the heat output power of the heat pump, is the carbon emission cost of the heat pump, is the electro-thermal conversion coefficient.

[0049] (6);

[0050] Wherein, is the operating cost of energy storage equipment s, is a cost coefficient, is the charge and discharge power of energy storage equipment s.

[0051] (7);

[0052] Wherein, is the power supply cost of the energy station, is the heat supply cost of the energy station, is the energy supply cost of the cogeneration unit, is the power supply power of the energy station, is the heat supply power of the energy station, is the heat supply power of the cogeneration unit.

[0053] (8);

[0054] (9);

[0055] (10);

[0056] Wherein, is a cost coefficient, , , They are respectively the carbon emission cost for power supply of the energy station, the carbon emission cost for heat supply of the energy station, and the overall carbon emission cost of the combined heat and power unit.

[0057] (11);

[0058] Where: is the cost for the load w to participate in demand response, is the demand response power;

[0059] (12);

[0060] Where: is the carbon emission intensity, is the cost coefficient.

[0061] Equipment j Carbon emission cost:

[0062] (13);

[0063] Where: is the carbon emission related coefficient of equipment j, is the carbon emission power of thermal power equipment j.

[0064] In this implementation, F represents the total cost of the IES; respectively represent the costs of the power supply side, heat supply side, "storage" side, "station" side and "load" side. respectively represent the number of new energy power generation equipment, the number of thermal power equipment, the number of boiler equipment, the number of heat pump equipment, the number of energy storage equipment, the number of energy stations, the number of combined heat and power units, and the number of loads.

[0065] The low-carbon economic optimal dispatch of the integrated energy system needs to meet the safe operation constraints of the system. Given respectively represent the sets of electric energy output equipment and heat energy output equipment, , , then the constraints can be specifically expressed as:

[0066] Power balance constraint:

[0067] (14);

[0068] (15);

[0069] Among them, respectively represent the overall electric load and heat load demand of the system, They are the power supply power, the demand response electric power, and the heating power respectively.

[0070] Power upper and lower limit constraints:

[0071] (16);

[0072] (17);

[0073] Among them, respectively represent the minimum and maximum power outputs of the electrical energy output device i , and represent the minimum and maximum demand response powers of the electrical load . w

[0074] Ramp rate constraint:

[0075] (18);

[0076] Among them, represents the ramp rate of the device i , is the output power of the device i at the previous moment are the minimum and maximum values of the output power of device i respectively.

[0077] Based on the above model, the low-carbon economic dispatch problem of the integrated energy system can be formulated as follows:

[0078] (19);

[0079] Among them, , , are the operating costs of all power supply devices, the costs of all loads participating in demand response, and the operating costs of all heating devices respectively, represents the total number of power supply devices, i represents the i th power supply device, represents the total number of loads, is the th load, represents the total number of heating devices, represents the j th heating device.

[0080] Among them, equations (14) and (15) are equality constraints, and equations (16)-(18) are inequality constraints.

[0081] It can be seen that Equation (19) is in the form of the sum of the cost functions of each device, and the constraints include equality constraints such as (14) and (15), as well as inequality constraints (16), (17), and (18). For the convenience of the subsequent proof of the algorithm, it is further expressed in the following form:

[0082] (20);

[0083] where N is the number of all nodes in the system, including the expression containing the variable , , , , , , is an n-dimensional vector space, R is the set of real numbers, and are the i th load demand and the total load demand respectively, P represents the total power of the system, represents the total cost of the system.

[0084] Note: Equation (20) is a simplified representation of Equation (19) for the convenience of subsequent proof. Equation (19) is the sum of the objective functions of all devices, N in Equation (20) is the total number of all devices, the equality constraints in Equation (20) are the simplified forms of (14) and Equation (15), and the inequality constraints are the simplified forms of (16)-(18).

[0085] For the above optimization problem, this implementation method proposes a preset time fully distributed optimization algorithm based on TBG (time base generator) to solve it. Specifically, it includes:

[0086] The network communication topology diagram of this implementation method is represented by , where the node set , the edge set is , the adjacency matrix of graph G is , if , then , otherwise . The set of adjacent nodes of node i is , the Laplacian matrix of graph G is , where is the degree matrix of the graph, represents the degree of node i .

[0087] Assumption 1: The undirected graph G is connected.

[0088] Lemma 1: Through Assumption 1, it can be obtained that:

[0089] , (21);

[0090] wherein, are respectively L the second smallest eigenvalue and the largest eigenvalue.

[0091] Assumption 2: is a -strongly convex function and continuously differentiable, and has a local -Lipschitz gradient, and are both positive constants.

[0092] Lemma 2: Through Assumption 2, it can be obtained that:

[0093] (22);

[0094] (23);

[0095] In the formula, is the gradient operator, , is a positive constant, and Q is a variable within the feasible region.

[0096] Assumption 1 ensures that the local information of each agent can be dispersed throughout the network, and Assumption 2 ensures the smoothness, strong convexity of the cost function, and the existence of the optimal point of the function.

[0097] Introduce a time-based generator that satisfies the following conditions:

[0098] , (24);

[0099] wherein, is the preset time.

[0100] Lemma 3: Consider the following time-varying system:

[0101] (25);

[0102] wherein, , , , is the initial state of the system, and the state variable will converge to the final state within the preset time , Let it be an n - dimensional vector space, and the definition of preset - time convergence is given as follows:

[0103] For any initial state When the following conditions are met, the system (25) achieves preset - time convergence:

[0104] (26);

[0105] Where, is the optimal decision value, is a very small positive constant.

[0106] To solve the problem of equation (20), using the TBG technology, a time - varying gain is introduced, and first, a preset - time fully distributed algorithm that does not depend on any initial value is proposed:

[0107] (27);

[0108] Where, is defined in equation (25), is the gradient, is the optimization variable, and is at time t ; represents the cost corresponding to the power of the i - th node at time t; is the first - order derivative of; is t the auxiliary variable at time; is the equality - constraint correction coefficient, is the auxiliary variable, is the element value of the i - th row and j - th column in the adjacency matrix A, is the set of neighbor nodes of node i; is the first - order derivative of, is the first - order derivative of.

[0109] By observing, it is found that as an auxiliary variable, its initial value can be determined in advance. Therefore, it is assumed that the initial value of this variable satisfies , and based on this, a preset - time fully distributed algorithm with less inter - agent interaction information is proposed:

[0110] (28);

[0111] Compared with equation (27), equation (28) requires ensuring that the initial value of the auxiliary variable satisfies certain conditions, and each agent does not need to interact with the information of this auxiliary variable. This algorithm reduces the communication volume, but the conditions are more stringent.

[0112] For the inequality constraint in Equation (20), the penalty function method is adopted for processing, and the penalty function is constructed as follows:

[0113] (29);

[0114] where is a very small positive constant.

[0115] And the function is constructed as: , where .

[0116] In this implementation manner, the convergence analysis of the proposed algorithm is given. For Equation (27), for the convenience of analysis, it is first rewritten in the following compact form:

[0117] (30);

[0118] where , , , , , here the algebraic expressions in the above text are transformed into matrix expressions, which is the compact form, , , are the first-order derivatives of , , respectively.

[0119] Lemma 4: When both Assumption 1 and Assumption 2 are satisfied, the distributed optimization problem in Equation (30) can be solved within the preset time , that is, it satisfies:

[0120] (31);

[0121] where represents at time t, , , , is the initial value of the Lyapunov function defined later, is the Lyapunov function, represents the optimal value of P, represents the optimal value of the transpose of represents the optimal value of the transpose of represents the optimal value of the transpose of

[0122] (32);

[0123] (33);

[0124] Among them, , , 、 and are all constants.

[0125] Proof of Lemma 4: First, we prove that there exists an optimal solution , let be the equilibrium point of (27), then it satisfies the following conditions:

[0126] (34);

[0127] When Hypothesis 1 is satisfied, we can obtain , substituting it into the first equation of (34), we can get:

[0128] (35);

[0129] Multiply the second equation in (27) on the left by , we can get:

[0130] (36);

[0131] It can be seen that the equilibrium point satisfies the KKT conditions, and is the global optimal solution.

[0132] Next, based on Lyapunov theory, analyze the convergence of the system. Let , , , then (30) can be transformed into:

[0133] (37);

[0134] Among them, , is the first derivative of , is the first derivative of , is the first derivative of .

[0135] Perform the following orthogonal transformation: , among them, , , is an orthogonal matrix, , , .

[0136] According to the properties of the Laplacian matrix in G Figure , , the system (37) can be rewritten as two subsystems:

[0137] (38);

[0138] (39);

[0139] Based on the above positive exchange edges, it is necessary to prove that can converge within a preset time. Consider the following Lyapunov function:

[0140] (40);

[0141] (41);

[0142] where .

[0143] Let , and we can get:

[0144] (42);

[0145] According to (38)-(41), we get:

[0146] (43);

[0147] According to Lemma 1 and Lemma 2, we can get:

[0148] (44);

[0149] According to Young's inequality, we can get:

[0150] (45);

[0151] Substitute Equation (44) and Equation (45) into Equation (43):

[0152] (46);

[0153] According to Equation (22) and Equation (20) respectively, we can get:

[0154] (47);

[0155] (48);

[0156] (49);

[0157] Substitute equations (47) - (49) into equation (50):

[0158] (50);

[0159] According to equations (42) and (43), we get:

[0160] (51);

[0161] where is a coefficient.

[0162] Then, invoke Lemma 1 and the comparison principle:

[0163] (52);

[0164] According to (42), we can obtain:

[0165] (53);

[0166] When at , we can get:

[0167] (54).

[0168] It can be seen that this algorithm can ensure the convergence of the system within the preset time . Q.E.D.

[0169] For equation (28), first, give its compact form:

[0170] (55);

[0171] where is in vector form.

[0172] Compared with equation (27), this algorithm only constrains the initial value of the auxiliary variable , that is . Then, multiply the left side of the third equation of equation (55) by , and we can get . Therefore, we have:

[0173] (56);

[0174] where is the value of the i-th element of the auxiliary variable at time t.

[0175] The subsequent proof is similar to the proof of Equation (27) and will not be elaborated here.

[0176] When considering inequality constraints within the system, set the penalty function through (29) and construct a new optimization function:

[0177] (57);

[0178] Equation (57) is obviously a strongly convex function. Let , represent the unique optimal solutions considering inequality constraints and without considering inequality constraints respectively. Then, we can obtain the following relationship:

[0179] (58);

[0180] where, is 's optimal value, N is the number of nodes within the system, , .

[0181] According to the KKT conditions, it can be known that:

[0182] (59);

[0183] where, is 's feasible region, , , is a variable value within the feasible region that satisfies the Slater condition. When , the optimal solution of Equation (57) can be used as the sub-optimal solution of the original problem. Therefore, replace in Equation (27) and Equation (28) with (57), and when is chosen small enough, it can be ensured that .

[0184] The structural diagram of the numerical example and the communication network topology are as Figure 2 shown. G1, G2, G3, and G4 are thermal power generation equipment respectively. L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical load equipment respectively. S1 and S2 are energy storage equipment respectively. CHP1 and CHP2 are combined heat and power units respectively. P1 and P2 are heat pump equipment respectively. B1 and B2 are boiler equipment respectively. H1, H2, H3, H4, H5, and H6 are thermal load equipment respectively. PV represents photovoltaic equipment. WT represents wind power generation equipment. The numbers 1 - 30 on the left represent 30 system nodes, and the numbers 1 - 14 on the right represent 14 system nodes.

[0185] Since the system contains both thermal and electric energy, and the time scales of the dispatching of the two types of energy are different, in order to preset different convergence times respectively, it is necessary to decouple the coupling entities. There are two types of coupling entities in the system: the energy station and the CHP. Among them, the energy station contains various energy conversion devices, and the output electric power and thermal power can be directly decoupled; for the CHP unit, its output is decoupled according to the current thermoelectric ratio, and it is assumed that the unit operates in the state of heat-determined power generation.

[0186] Algorithm-related parameters , , , construct the TBG as shown in (60).

[0187] (60);

[0188] The physical parameter settings of each entity are shown in Table 1 and Table 2. Carbon emission-related coefficients , initial values of auxiliary variables , , , total thermal energy load , total electric energy load .

[0189] Table 1: Parameters of the energy supply entity, where, Pi (0) is the power of the initial node i

[0190]

[0191] Table 2: Parameters of the load entity

[0192]

[0193] The integrated energy system includes four parts: "source-load-storage-station", contains two heterogeneous energies of heat and electricity, and the "station" among them is the entity that couples the two energies of heat and electricity. According to the idea in the previous text, the two energies have been decoupled. For the thermal energy with a larger dispatching time scale, assume its preset time , while the dispatching time scale of electric energy is smaller, assume its preset time . The power response curves are as shown in Figure 3 and Figure 4 . Figure 3 Among them, CHP1 and CHP2 are combined heat and power generation units respectively, P1 and P2 are heat pump devices respectively, B1 and B2 are boiler devices respectively, and EH represents the energy station; Figure 4Among them, G1, G2, G3, and G4 are thermal power generation equipment respectively, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical load equipment respectively, S1 and S2 are energy storage equipment respectively, CHP1 and CHP2 are combined heat and power units respectively, and EH represents the energy station.

[0194] As can be seen from the figure, after fluctuations over different preset times, the generation / consumption power of each entity in the system can tend to the optimal value over time. Figure 5 The convergence curve of the supply-demand balance of thermal energy is given. As can be seen from the figure, within the preset time, it can be ensured that the thermal energy power supplied by each entity is balanced with the thermal energy power required by the system, and the scheduling results of each entity are as follows:

[0195] (61);

[0196] All are within the physical constraints, proving the feasibility of the proposed method.

[0197] To further verify that the preset time is adjustable, reduce the preset time of the electrical energy-related entities , and its efficiency response curve is as Figure 6 shown. G1, G2, G3, and G4 are thermal power generation equipment respectively, L1, L2, L3, L4, L5, L6, L7, L8, L9, L10, L11, L12, L13, and L14 are electrical load equipment respectively, S1 and S2 are energy storage equipment respectively, CHP1 and CHP2 are combined heat and power units respectively, and EH represents the energy station. As can be seen from the figure, the generation / consumption power of each entity tends to the optimal solution within 6 s.

[0198] Next, the "plug and play" characteristic of the algorithm is verified by simulation, and the simulation results are as Figure 7 , Figure 8 shown. Figure 8 Among them, CHP1 and CHP2 are combined heat and power units respectively, P1 and P2 are heat pump equipment respectively, B1 and B2 are boiler equipment respectively, and EH represents the energy station. As can be seen from the figure, within 0 - 25 s, the generation / consumption power of each entity tends to the optimal value within the preset time of 10 s. When the load is cut off or reconnected, each entity can converge from the original state to the new optimal value according to the preset time. And the preset time can be reset according to actual needs. When the energy supply equipment is cut off or reconnected, each entity can also converge from the original state to the new optimal value. In the system converges to the same state, but the initial values of its convergence are different. The above analysis shows that the algorithm has good "plug and play" characteristics and can ensure that it tends to the optimal value within the preset time under different initial values of the optimization variables.

[0199] The same example is analyzed using the existing preset time algorithm and the formula (28) proposed in the present invention. , and the initial values of each variable are respectively , , , , , , , . The simulation results are as shown in Figure 9 and Figure 10 . P1, P2, P3, and P4 are power generation units respectively. It can be seen from the two figures that both the algorithm proposed in this implementation method and the existing algorithm can achieve convergence. However, by adjusting the introduced correction coefficient , the error of the convergence result can be made smaller, and the accuracy of the optimal solution can be improved.

[0200] Example 2:

[0201] As shown in Figure 11 , this implementation method provides a preset time fully distributed integrated energy system optimal scheduling system, including:

[0202] A scheduling target setting unit, configured to: take the minimum total cost as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, and the safety operation constraints include: power balance constraints, power upper and lower limit constraints, and ramp rate constraints;

[0203] An optimal scheduling control unit, configured to: assume that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, and solve the objective function based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within a preset time, and obtain the optimal output strategy of the integrated energy system.

[0204] It can be understood that the above-mentioned each unit can be respectively or all combined into one or several other units to form, or some of them can be further split into multiple smaller units with functional division to form, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above units are divided based on logical functions. In actual applications, the function of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present application, the system can also include other units. In actual applications, these functions can also be assisted by other units and can be realized by the cooperation of multiple units.

[0205] According to another embodiment of the present application, the system described in this embodiment can be constructed and the method of Embodiment 1 of the present application can be implemented by running a computer program (including program code) capable of executing the respective steps involved in the corresponding method described in Embodiment 1 on a general computing device such as a computer including processing elements and storage elements such as a central processing unit (CPU), a random access memory (RAM), and a read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, loaded into the above computing device through the computer-readable recording medium, and run therein.

[0206] Embodiment 3:

[0207] As Figure 12 shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. Among them, the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 can be connected through a bus or other means.

[0208] Among them, the communication interface 1002 is used to receive and send data. The computer-readable storage medium 1003 can be stored in the memory of the electronic device. The computer-readable storage medium 1003 is used to store a computer program, and the computer program includes program instructions. The processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0209] The processor 1001 (or CPU (Central Processing Unit, central processor)) is the computing core and control core of the electronic device, and is adapted to implement one or more instructions. Specifically, it is adapted to load and execute one or more instructions to implement the corresponding method flow or corresponding function.

[0210] The processor 1001 is configured to execute the following process:

[0211] Taking the total cost minimum as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, and the safety operation constraints include: power balance constraint, power upper and lower limit constraint, and ramp rate constraint;

[0212] Assuming that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, the objective function is solved based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and the optimal output strategy of the integrated energy system is obtained.

[0213] For the detailed process, please refer to the introduction in Embodiment 1 and will not be elaborated here.

[0214] Embodiment 4:

[0215] This implementation provides a computer-readable storage medium (Memory). A computer-readable storage medium is a memory device in an electronic device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the electronic device and, of course, the extended storage medium supported by the electronic device. The computer-readable storage medium provides a storage space, and this storage space stores the processing system of the electronic device.

[0216] Moreover, one or more instructions suitable for being loaded and executed by a processor are also stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; optionally, it can also be at least one computer-readable storage medium located far from the aforementioned processor.

[0217] In one embodiment, one or more instructions are stored in the computer-readable storage medium; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the following process:

[0218] Taking the minimum total cost as the objective function for the integrated energy system scheduling problem, and the objective function is configured with safety operation constraints, and the safety operation constraints include: power balance constraint, power upper and lower limit constraints, and ramp rate constraint;

[0219] Assume that the communication topologies of the source side, load side, storage side, and station side are undirected connected graphs, and solve the objective function based on the preset time fully distributed optimization algorithm of TBG, so that the integrated energy system reaches a convergence state within the preset time, and obtain the optimal output strategy of the integrated energy system.

[0220] For the detailed process, please refer to the introduction in Embodiment 1 and will not be elaborated here.

[0221] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing and scheduling a fully distributed integrated energy system with a preset time, characterized in that: The process includes: The total cost minimization is used as the objective function as the integrated energy system scheduling problem, and the objective function is configured with a safe operation constraint, the safe operation constraint includes: a power balance constraint, a power upper and lower limit constraint, and a running slope constraint, wherein the power balance constraint is an equality constraint, and the power upper and lower limit constraint and the running slope constraint are inequality constraints; Assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, the objective function is solved by a preset time fully distributed optimization algorithm based on a time base generator, so that the integrated energy system reaches a convergence state within a preset time, and the optimal output strategy of the integrated energy system is obtained; Introducing time-varying gain , solving the objective function by a preset-time fully distributed algorithm that does not depend on any initial value, assuming ,include: ； in, is the gradient, To optimize the variable, represents the power of the i-th node at time t. All power supply equipment, loads and heating equipment constitute each node, and the total number of nodes is N; represents the cost corresponding to the power of the i-th node at time t; for The first derivative of ; for t Auxiliary variables at time, is the correction coefficient of the equality constraint, and the network communication topology is Represents a node set , N represents the number of all nodes, and the set of edges is , the adjacency matrix , R represents the field of real numbers, is the element value of the i-th row and j-th column in the adjacency matrix A, is the set of neighbor nodes of node i; for The first derivative of for The first derivative of ; The correction factor introduced by adjusting To reduce the error of the convergence result; The inequality constraint is processed by the penalty function method: , is a very small positive number, To include variables The expression of Representative The power of each node is calculated, and a new optimization function is constructed based on the penalty function. represents the penalty function.

2. The preset time fully distributed integrated energy system optimization scheduling method according to claim 1, characterized in that: The objective function is: minimizing the sum of the operating costs of all power supply equipment, the costs of all loads participating in demand response, and the operating costs of all heating equipment.

3. The preset time fully distributed integrated energy system optimization scheduling method according to claim 1 or 2, characterized in that: Consider the following time-varying system: ,in, , , , is the initial state of the system, and the state variable Set the preset time Converges to the final state , , is the time base generator, for The first derivative of for The first derivative of ; For any initial state , the preset time converges when the following conditions are met, including: ,in, is the optimal decision value, is a normal number, Represents time.

4. The preset time fully distributed integrated energy system optimization scheduling method according to claim 3, characterized in that: Time base generator The following conditions must be met: , ,in, For preset time, Represents time.

5. A preset time fully distributed integrated energy system optimization scheduling system, characterized in that: The method for optimizing the scheduling of a fully distributed integrated energy system with a preset time according to any one of claims 1 to 4 comprises: The scheduling target setting unit is configured to: take the minimum total cost as the objective function as the comprehensive energy system scheduling problem, and the objective function is configured with a safe operation constraint, and the safe operation constraint includes: a power balance constraint, a power upper and lower limit constraint, and a running slope constraint; The optimal scheduling control unit is configured as follows: assuming that the communication topology of the source side, load side, storage side and station side is an undirected connected graph, solving the objective function based on a preset time fully distributed optimization algorithm of a time base generator, so that the integrated energy system reaches a convergence state within a preset time, and obtains the optimal output strategy of the integrated energy system.

6. A computer device, characterized in that: include: a processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the preset time fully distributed integrated energy system optimization scheduling method as described in any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the preset time fully distributed integrated energy system optimization scheduling method as described in any one of claims 1 to 4.

Images