Multi-energy system scheduling method, system and device and storage medium

Abstract

The invention discloses a multi-energy system scheduling method, system and device and a storage medium, and the method comprises the steps: constructing a multi-target optimization model according to the characteristics of each energy conversion device of a target multi-energy system, the multi-objective optimization model comprises a first objective function for maximizing the net revenue of the system, a second objective function for minimizing the energy abandoning rate of the system and corresponding constraint conditions; and solving the multi-objective optimization model through a multi-objective particle swarm algorithm to obtain an optimal solution, and performing multi-energy multi-stage scheduling on the target multi-energy system according to the optimal solution. The method improves the accuracy and reliability of multi-energy system scheduling, thereby improving the stability of the multi-energy system, and can be widely applied to the technical field of power grid data scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid data dispatching, and in particular to a multi-energy system dispatching method, system, device and storage medium. Background Art

[0002] In the process of accelerating the construction of new power systems, the complexity of the grid structure continues to increase, which puts forward more stringent requirements for the safe operation and power supply reliability of the grid. As a key link in the production and operation of the grid, grid dispatching involves the coordinated participation of multi-level dispatching agencies. Dispatching agencies at all levels need to obtain and process power system operation data covering all aspects such as generation, transmission, distribution, and use in real time and accurately in order to make quick and accurate decisions. With the increasing proportion of renewable energy and the increasingly complex and changeable grid operation environment, the traditional power system dispatching mode has been unable to meet the needs of real-time regulation. The large amount of data accumulated over a long period of operation makes the data management and analysis of the power dispatching system more complicated. These data are not only of various types and complex structures, but may also be affected by various factors such as communication failures and equipment defects, resulting in uneven data quality, which affects the accuracy and reliability of multi-energy system dispatching. Summary of the invention

[0003] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0004] To this end, an object of an embodiment of the present invention is to provide a multi-energy system scheduling method, which improves the accuracy and reliability of the multi-energy system scheduling, thereby improving the stability of the multi-energy system.

[0005] Another object of an embodiment of the present invention is to provide a multi-energy system scheduling system.

[0006] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0007] In a first aspect, an embodiment of the present invention provides a multi-energy system scheduling method, comprising the following steps:

[0008] Constructing a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system, wherein the multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints;

[0009] The multi-objective optimization model is solved by a multi-objective particle swarm algorithm to obtain an optimal solution, and multi-energy multi-level scheduling is performed on the target multi-energy system according to the optimal solution.

[0010] Furthermore, in one embodiment of the present invention, the first objective function is:

[0011] MAX(F1=π PIES -C PIES )

[0012]

[0013]

[0014] Among them, F1 represents the net profit of the system, π PIES and C PIES Represent system benefits and system costs respectively, pc e (t), pc h (t) and pc c (t) represent the internal electricity price, internal heating price and internal cooling price at time t, respectively. e,in (t), Q h,in (t) and Q c,in (t) represent the electric load demand, heat load demand and cooling load demand satisfied by the internal units at time t, T represents the operation cycle, C O Represents the unit operating cost, C i represents the depreciation cost of unit i, N represents the set of units, Φ represents the set of all equipment in the system, μ φ represents the unit operating cost of the equipment φ, represents the operating power of the device φ at time t, represents the total investment cost of unit i, represents the remaining cost of unit i, Day i Indicates the service life of unit i.

[0015] Further, in one embodiment of the present invention, the second objective function is:

[0016]

[0017] Among them, F2 represents the system energy abandonment rate, and They represent the wind curtailment and solar curtailment at time t, respectively. and They represent the actual available wind power and actual total photovoltaic power at time t respectively.

[0018] Furthermore, in one embodiment of the present invention, the constraints include electric energy balance constraints, thermal energy balance constraints, gas energy balance constraints, cooling energy balance constraints, energy transmission constraints, unit output constraints and logical constraints.

[0019] Further, in one embodiment of the present invention, solving the multi-objective optimization model by a multi-objective particle swarm algorithm to obtain an optimal solution specifically includes:

[0020] Initializing the population and the external archive according to the multi-objective optimization model;

[0021] Calculate fitness, update particle speed and position, and select the global optimal particle and the local optimal particle;

[0022] The external archive is updated according to the global optimal particle and the local optimal particle, and the fitness is calculated again until a preset number of iterations is reached to obtain an optimal solution set;

[0023] The solution with the highest satisfaction is selected as the optimal solution according to the optimal solution set.

[0024] Further, in one embodiment of the present invention, the updating of the external archive according to the global optimal particle and the local optimal particle specifically includes:

[0025] Determining the dominant solution and the dominated solution in the external archive, and deleting the dominated solution;

[0026] Determine an updated solution according to the global optimal particle and the local optimal particle;

[0027] The particle crowding distances of the dominant solution and the updated solution are calculated respectively, and the solution with the larger particle crowding distance is selected for retention to obtain the updated external archive.

[0028] Furthermore, in one embodiment of the present invention, the satisfaction is calculated by the following formula:

[0029]

[0030] Among them, U represents satisfaction, μ j represents the satisfaction score of the jth objective function, J represents the objective function set, and f j represents the function value of the jth objective function, and They represent the minimum and maximum values ​​of the j-th objective function respectively.

[0031] In a second aspect, an embodiment of the present invention provides a multi-energy system scheduling system, including:

[0032] An optimization model building module, used to build a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system, wherein the multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints;

[0033] The optimization model solving module is used to solve the multi-objective optimization model through a multi-objective particle swarm algorithm to obtain an optimal solution, and perform multi-energy multi-level scheduling on the target multi-energy system according to the optimal solution.

[0034] In a third aspect, an embodiment of the present invention provides a multi-energy system scheduling device, including:

[0035] at least one processor;

[0036] at least one memory for storing at least one program;

[0037] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned multi-energy system scheduling method.

[0038] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to execute the above-mentioned multi-energy system scheduling method when executed by the processor.

[0039] The advantages and beneficial effects of the present invention will be partly given in the following description, partly become apparent from the following description, or be understood through the practice of the present invention:

[0040] The embodiment of the present invention constructs a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system. The multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints. The multi-objective optimization model is solved by a multi-objective particle swarm algorithm to obtain an optimal solution, and the target multi-energy system is scheduled according to the optimal solution. The embodiment of the present invention takes into account the characteristics of each energy conversion device of the multi-energy system, establishes a multi-objective optimization model with the objectives of maximizing the net benefit of the system and minimizing the energy abandonment rate, and obtains an optimal solution by solving the multi-objective particle swarm algorithm. According to the optimal solution, the multi-energy multi-level scheduling of the multi-energy system can be completed, thereby improving the accuracy and reliability of the scheduling of the multi-energy system, thereby improving the stability of the multi-energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solution in the embodiments of the present invention, the following introduction is made to the drawings required for use in the embodiments of the present invention. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solution of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1A flowchart of a multi-energy system scheduling method provided by an embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the conversion process of the CCHP combined power generation unit;

[0044] Figure 3 This is a schematic diagram of the conversion process of the power-to-gas equipment;

[0045] Figure 4 A structural block diagram of a multi-energy system scheduling system provided by an embodiment of the present invention;

[0046] Figure 5 A structural block diagram of a multi-energy system scheduling device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0048] In the description of the present invention, the meaning of "a plurality" is two or more than two. If there is a description of "a first" or "a second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In addition, unless otherwise defined, all technical and scientific terms used in this document have the same meaning as those commonly understood by those skilled in the art.

[0049] The embodiment of the present invention takes into account P2G equipment, CCHP cogeneration equipment, electric refrigeration machines, etc., designs a multi-energy multi-level scheduling operation structure, takes maximizing the system net profit and minimizing the energy abandonment rate as the goal, establishes a multi-energy multi-level scheduling operation multi-objective optimization model, and proposes a corresponding multi-objective particle swarm solution algorithm;

[0050] Reference Figure 1 The embodiment of the present invention provides a multi-energy system scheduling method, which specifically includes the following steps:

[0051] S101. Construct a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system. The multi-objective optimization model includes a first objective function for maximizing the net benefit of the system, a second objective function for minimizing the energy abandonment rate of the system, and corresponding constraints.

[0052] Specifically, the characteristics of the energy conversion equipment involved in the conversion are analyzed, which are specifically divided into CCHP combined power generation unit characteristics and P2G equipment characteristics.

[0053] The CCHP combined power generation unit uses natural gas to supply the gas turbine equipment to operate, meeting the end-user's electricity load demand, and the waste heat discharged by the system is further passed through the waste heat boiler and absorption chiller to meet the end-user's cooling / heating load demand. It is currently a widely used energy conversion technology. When the CCHP combined power generation unit participates in the coordinated operation of the integrated energy system, it can not only improve the primary energy utilization efficiency, with its utilization rate reaching 60%-70%, but also fully develop the energy cascade utilization, and improve the stability and safety of the system. The specific energy conversion process is as follows Figure 2 shown.

[0054] Power to Gas (P2G) technology is an energy conversion device that converts renewable energy such as wind power and photovoltaic power on the power supply side, as well as the surplus electricity of the external power grid, into natural gas or hydrogen. When the P2G energy conversion equipment participates in the coordinated operation of the integrated energy system, the energy utilization efficiency can reach 60%-70%, playing a valley-filling role, and further strengthening the coupling relationship between the power subsystem and the natural gas subsystem, which is conducive to the consumption of abandoned energy. The specific energy conversion process is as follows Figure 3 shown.

[0055] As an optional implementation, the first objective function is:

[0056] MAX(F1=π PIES -C PIES )

[0057]

[0058] Among them, F1 represents the net profit of the system, π PIES and C PIES Represent system benefits and system costs respectively, pc e (t), pc h (t) and pc c (t) represent the internal electricity price, internal heating price and internal cooling price at time t, respectively. e,in (t), Q h,in (t) and Q c,in (t) represent the electric load demand, heat load demand and cooling load demand satisfied by the internal units at time t, T represents the operation cycle, C O Represents the unit operating cost, C i represents the depreciation cost of unit i, N represents the set of units, Φ represents the set of all equipment in the system, μ φrepresents the unit operating cost of the equipment φ, represents the operating power of the device φ at time t, represents the total investment cost of unit i, represents the remaining cost of unit i, Day i Indicates the service life of unit i.

[0059] As an optional implementation, the second objective function is:

[0060]

[0061] Among them, F2 represents the system energy abandonment rate, and They represent the wind curtailment and solar curtailment at time t, respectively. and They represent the actual available wind power and actual total photovoltaic power at time t respectively.

[0062] As a further optional implementation, the constraints include electric energy balance constraints, thermal energy balance constraints, gas energy balance constraints, cooling energy balance constraints, energy transmission constraints, unit output constraints and logical constraints.

[0063] Specifically, according to the characteristics of multi-energy and multi-level dispatching operation system, each energy conversion equipment is involved in the system operation structure, and a multi-objective optimization model with the goal of maximizing the system net profit and minimizing the energy abandonment rate is proposed. The details are as follows:

[0064] Objective function 1: Net benefit maximization (F1) consists of system benefits and costs, namely:

[0065] MAX(F1=π PIES -C PIES )

[0066] In the formula, F1 is the net profit of the system, π PIES and C PIES They are system benefits and costs, respectively, and their mathematical models are:

[0067]

[0068] In the formula, pc e (t), pc h (t) and pc c (t) are the internal electricity, heating and cooling prices respectively; Q e,in (t), Q h,in (t) and Q c,in (t) are the electricity, heating and cooling load demands satisfied by the internal units respectively.

[0069]

[0070] In the formula, C O is the operating cost; C i is the depreciation cost of the unit.

[0071]

[0072] Where Φ is the set of all devices in the system; μ φ is the unit operating cost of equipment type φ; is the operating power of equipment type φ at time t.

[0073]

[0074] In the formula, C i ii is the total investment cost of unit i; C i re is the remaining cost of unit i, Day i is the service life of unit i.

[0075] Objective function 2: Minimization of the energy abandonment rate (F2) is the ratio of the amount of wind and solar power abandoned by the system to the actual amount of available new energy, that is:

[0076]

[0077] In the formula, They are wind abandonment and solar abandonment respectively; They represent the total amount of actual available wind power and photovoltaic power respectively.

[0078] In order to ensure that the conversion equipment participates in the stable operation of the system, the corresponding constraints need to be considered, as follows:

[0079] (1) Power balance constraints

[0080] The power consumption and power generation in the system need to maintain an instantaneous balance, that is:

[0081]

[0082] In the formula, is the power purchased from the large power grid at time t; is the output power of photovoltaic and wind turbine at time t; is the output power of the gas turbine at time t; is the charging and discharging power of the energy storage at time t; is the electric power sold to the large power grid at time t; is the output power of the air source heat pump in period t; is the output power of the electric boiler at time t; is the output power of P2G at time t; is the power output of the electric refrigerator at time t; L e,t is the output power of photovoltaic power abandonment, wind power abandonment and other power consumption at time t;

[0083] (2) Thermal energy balance constraints

[0084] The system is connected to the external heat network, so it is necessary to consider the sale and purchase of heat energy, namely:

[0085]

[0086] In the formula, The thermal power purchased for the external supply system at time t; is the corresponding heat supply of the air source heat pump in period t; is the heat supply of the electric boiler in period t; is the heating power of the gas boiler at time t; is the heat generation power of the gas turbine at time t; The thermal power sold to the external supply system at time t; is the heat power absorbed by the absorption refrigerator at time t; and is the charging and discharging power of thermal energy storage at time t; L h,t is the other heat energy consumed at time t.

[0087] (3) Gas energy balance constraints

[0088] In the system, in addition to the external gas market, there is only P2G energy conversion equipment inside the system to supply gas energy, namely:

[0089]

[0090] In the formula, is the natural gas power purchased from the natural gas system at time t; is the gas production power of P2G at time t; is the natural gas power sold to the natural gas system at time t; is the gas consumption power of the gas boiler at time t; is the gas consumption power of the gas turbine at time t; L g,t is the other natural gas power consumed at time t.

[0091] (4) Cold energy balance constraint

[0092] In the system, cooling is mainly provided by two energy conversion devices: electric refrigerator and absorption refrigerator, namely:

[0093]

[0094] In the formula, is the cooling power output of the absorption refrigerator at time t; is the cooling power output of the electric refrigerator at time t; L c,t It is the sum of cooling loads of each unit at time t.

[0095] (5) Energy transmission constraints

[0096] Since the design system is connected to the external power grid, heat network and gas network, it realizes the exchange with the external energy market. That is to say, when the internal energy supply of the system is greater than the internal energy demand, it can be exported to the external energy market. When the internal energy supply of the system is less than the internal energy demand, it needs to be purchased from the external energy market. However, considering the actual situation, the transmission of energy at any time is limited to a certain range, as shown in the following formula:

[0097]

[0098] In the formula, The minimum and maximum values ​​of the system output electric power; The power purchased and sold at time t; The minimum and maximum values ​​of the system output electric power; The thermal power purchased and sold at time t; The minimum and maximum values ​​of natural gas power output for the system; The natural gas power purchased and sold at time t;

[0099] At the same time, the supply and demand of cold energy generally only needs to consider internal conditions.

[0100] (6) Unit output constraints

[0101] In the system operation structure, the output constraints of each unit also need to be considered, including wind power, photovoltaic, air source heat pump, P2G equipment, gas boiler, CCHP unit, electric refrigerator and electric / thermal energy storage device, as described below:

[0102] a) Wind and photovoltaic units

[0103]

[0104] In the formula, and It is the upper limit of the output of wind power and photovoltaic units.

[0105] b) Air source heat pump

[0106]

[0107] In the formula, and are the upper and lower limits of the air source heat pump climbing. and is the output range of the air source heat pump at time t.

[0108] c) P2G energy conversion equipment

[0109]

[0110] In the formula, and The upper and lower limits of the P2G device’s output are and express.

[0111] d) Gas boiler equipment

[0112]

[0113] In the formula, and They represent the upper and lower limits of the climbing slope and the output range of the gas boiler respectively.

[0114] e) Gas turbine equipment

[0115]

[0116] In the formula, and and They represent the upper and lower limits of the gas turbine's climbing grade and the output range respectively.

[0117] f) Waste heat boiler equipment

[0118]

[0119] In the formula, and They are the upper and lower limits of the waste heat boiler output respectively.

[0120] g) Absorption Refrigerator

[0121]

[0122] In the formula, and They are the upper and lower limits of the output of the absorption chiller respectively.

[0123] h) Electric Refrigerator

[0124]

[0125] In the formula, and They are the upper and lower limits of output respectively.

[0126] i) Electrical energy storage

[0127]

[0128] In the formula, and is the charging and discharging power of the energy storage at time t; and The upper and lower limits of the energy storage capacity of the electric energy storage; and They are the upper and lower limits of charge and discharge of electric energy storage respectively; and They are the energy storage capacity at the beginning and end of the operation of the electric energy storage system respectively.

[0129] j) Thermal energy storage

[0130]

[0131] In the formula, and represents the upper and lower limits of the energy storage capacity of thermal energy storage; and They are the upper and lower limits of heat charging and discharging of thermal energy storage respectively; and They are the energy storage capacity at the beginning and end of the operation of the thermal energy storage system respectively.

[0132] (7) Logical constraints

[0133] In addition to the above constraints, some logical constraints of system components need to be considered, such as each energy storage unit cannot charge and discharge at the same time, and energy transmission cannot buy and sell energy at the same time. In other words, there will only be one certain logical state at the same time. Taking the electric energy storage component as an example, the logical constraints of charging and discharging are:

[0134]

[0135] In the formula, and are two auxiliary 0-1 variables, The energy storage component is in a charging state, otherwise it is not in a charging state. When the electric energy storage component is in the discharge state, otherwise it is not in the discharge state. This ensures that at the same time, the electric energy storage component can only choose one of the three states of discharge, charge and non-operation. Similar to the above electric energy storage components, the logical constraints of thermal energy storage and energy transmission are also represented by the above mathematical model.

[0136] S102. Solve the multi-objective optimization model through a multi-objective particle swarm algorithm to obtain an optimal solution, and perform multi-energy multi-level scheduling on the target multi-energy system according to the optimal solution.

[0137] As an optional implementation, the multi-objective optimization model is solved by a multi-objective particle swarm algorithm to obtain an optimal solution, which specifically includes:

[0138] S1021. Initialize the population and external archive according to the multi-objective optimization model;

[0139] S1022, calculate fitness, update the speed and position of particles, and select the global optimal particle and the local optimal particle;

[0140] S1023, updating the external archive according to the global optimal particle and the local optimal particle, and returning to calculate the fitness until the preset number of iterations is reached to obtain the optimal solution set;

[0141] S1024. Select the solution with the highest satisfaction as the optimal solution based on the optimal solution set.

[0142] As an optional implementation, updating the external archive according to the global optimal particle and the local optimal particle specifically includes:

[0143] S10231. Determine the dominant solution and dominated solution in the external archive, and delete the dominated solution;

[0144] S10232. Determine an updated solution based on the global optimal particle and the local optimal particle;

[0145] S10233. Calculate the particle crowding distances of the dominant solution and the updated solution respectively, and select the solution with the larger particle crowding distance to retain, so as to obtain the updated external archive.

[0146] Specifically, the particle swarm optimization (PSO) is a new type of swarm intelligence random search method with the characteristics of fast convergence speed, high efficiency and wide application range. When solving the source-source collaborative multi-objective optimization problem proposed in this paper, the basic multi-objective particle swarm optimization (MOPSO) has certain shortcomings in the convergence and diversity distribution of solutions. On this basis, in order to ensure that the solution of the source-source collaborative multi-objective optimization problem can converge to the Pareto optimal frontier, obtain a Pareto optimal solution set consisting of non-inferior solutions, and achieve a uniform distribution effect, it is necessary to improve the basic multi-objective particle swarm algorithm. The specific approach is as follows:

[0147] (1) When selecting the global optimal particle and the local optimal particle in the model population, the particle speed and position need to be continuously updated.

[0148] (2) When searching for the optimal solution of the model, the existing dominant solutions and updated solutions in the external archive are compared, the dominated solutions are deleted, and the optimal solution set and operation strategy in the external archive are continuously updated on the basis of maintaining the original size of the external archive. At the same time, in the process of updating the optimal solution set strategy, the particles are screened according to the crowding distance between them. The specific mathematical expression is as follows:

[0149]

[0150] Where P t is the crowding distance of the tth particle, are the maximum and minimum values ​​of the jth objective function in the model, respectively, where j is 1 or 2. In addition, when P t The larger it is, the sparser the particles near particle t will become. This is used to determine the particles that can remain in the external archive, which is the so-called dominant solution. This not only avoids the phenomenon of falling into local optimality when establishing the optimization model, but also ensures the diversity effect of the external archive.

[0151] (3) In the process of searching for the optimal solution, in order to avoid falling into the local optimal situation, the mutation operator is introduced and the disturbance factor is set to maintain the diversity within the population.

[0152] As an optional implementation, the satisfaction is calculated by the following formula:

[0153]

[0154] Among them, U represents satisfaction, μ j represents the satisfaction score of the jth objective function, J represents the objective function set, and f j represents the function value of the jth objective function, and They represent the minimum and maximum values ​​of the j-th objective function respectively.

[0155] From the above, it can be seen that when using the improved multi-objective particle swarm algorithm to solve the model, only the Pareto optimal solution set can be obtained. Therefore, in the multi-objective optimization problem of maximizing the system net benefit and minimizing the energy abandonment rate, the idea of ​​fuzzy evaluation method is introduced to select the solution with the highest satisfaction as the compromise solution of the model. The specific expression is as follows:

[0156]

[0157] In the formula, U represents satisfaction, f j is the jth objective function value, are the minimum and maximum values ​​of the j-th objective function of the model, respectively.

[0158] Based on the above formula, the satisfaction of each solution in the optimal solution set is solved, and then the solution with the greatest satisfaction is selected as the optimal solution, so as to perform multi-energy multi-level scheduling on the target multi-energy system according to the optimal solution.

[0159] The above is an explanation of the method steps of the embodiment of the present invention. It can be recognized that the embodiment of the present invention takes into account the characteristics of each energy conversion device in the multi-energy system, establishes a multi-objective optimization model with the goal of maximizing the net benefit of the system and minimizing the energy abandonment rate, and obtains the optimal solution through the multi-objective particle swarm algorithm. According to the optimal solution, the multi-energy multi-level scheduling of the multi-energy system can be completed, which improves the accuracy and reliability of the scheduling of the multi-energy system, thereby improving the stability of the multi-energy system.

[0160] Reference Figure 4 , an embodiment of the present invention provides a multi-energy system scheduling system, including:

[0161] An optimization model building module is used to build a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system. The multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints;

[0162] The optimization model solving module is used to solve the multi-objective optimization model through the multi-objective particle swarm algorithm to obtain the optimal solution, and perform multi-energy and multi-level scheduling of the target multi-energy system according to the optimal solution.

[0163] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0164] Reference Figure 5 , an embodiment of the present invention provides a multi-energy system scheduling device, comprising:

[0165] at least one processor;

[0166] at least one memory for storing at least one program;

[0167] When the at least one program is executed by the at least one processor, the at least one processor implements the multi-energy system scheduling method.

[0168] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0169] An embodiment of the present invention further provides a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the above-mentioned multi-energy system scheduling method.

[0170] A computer-readable storage medium according to an embodiment of the present invention can execute a multi-energy system scheduling method provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0171] The embodiment of the present invention also discloses a computer program product or a computer program, wherein the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes Figure 1 The method shown.

[0172] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

[0173] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise specified to the contrary, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present invention. More specifically, in view of the properties, functions and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional skills of the engineer. Therefore, those skilled in the art can implement the present invention set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0174] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0175] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0176] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and editable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the above-mentioned program is printed, since the above-mentioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or processing in other suitable ways as necessary, and then stored in a computer memory.

[0177] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0178] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0179] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

[0180] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A multi-energy system scheduling method, characterized in that: The following steps are involved: Constructing a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system, wherein the multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints; The multi-objective optimization model is solved by a multi-objective particle swarm algorithm to obtain an optimal solution, and multi-energy multi-level scheduling is performed on the target multi-energy system according to the optimal solution.

2. A multi-energy system scheduling method according to claim 1, characterized in that: The first objective function is: MAX(F1=π PIES -C PIES ) Among them, F1 represents the net profit of the system, π PIES and C PIES Represent system benefits and system costs respectively, pc e (t), pc h (t) and pc c (t) represent the internal electricity price, internal heating price and internal cooling price at time t, respectively. e,in (t), Q h,in (t) and Q c,in (t) represent the electric load demand, heat load demand and cooling load demand satisfied by the internal units at time t, T represents the operation cycle, C O Represents the unit operating cost, C i represents the depreciation cost of unit i, N represents the set of units, Φ represents the set of all equipment in the system, μ φ represents the unit operating cost of equipment φ, P t φ represents the operating power of the device φ at time t, C i ii represents the total investment cost of unit i, C i re represents the remaining cost of unit i, Day i Indicates the service life of unit i.

3. A multi-energy system scheduling method according to claim 1, characterized in that: The second objective function is: Among them, F2 represents the system energy abandonment rate, and They represent the wind curtailment and solar curtailment at time t, respectively. t WT and P t PV They represent the actual available wind power and actual total photovoltaic power at time t respectively.

4. A multi-energy system scheduling method according to claim 1, characterized in that: The constraints include electric energy balance constraints, thermal energy balance constraints, gas energy balance constraints, cooling energy balance constraints, energy transmission constraints, unit output constraints and logical constraints.

5. A multi-energy system scheduling method according to claim 1, characterized in that: The method of solving the multi-objective optimization model by using a multi-objective particle swarm algorithm to obtain an optimal solution specifically includes: Initializing the population and the external archive according to the multi-objective optimization model; Calculate fitness, update particle speed and position, and select the global optimal particle and the local optimal particle; The external archive is updated according to the global optimal particle and the local optimal particle, and the fitness is calculated again until a preset number of iterations is reached to obtain an optimal solution set; The solution with the highest satisfaction is selected as the optimal solution according to the optimal solution set.

6. A multi-energy system scheduling method according to claim 5, characterized in that: The updating of the external archive according to the global optimal particle and the local optimal particle specifically includes: Determining the dominant solution and the dominated solution in the external archive, and deleting the dominated solution; Determine an updated solution according to the global optimal particle and the local optimal particle; The particle crowding distances of the dominant solution and the updated solution are calculated respectively, and the solution with the larger particle crowding distance is selected for retention to obtain the updated external archive.

7. A multi-energy system scheduling method according to claim 5, characterized in that: The satisfaction is calculated by the following formula: Among them, U represents satisfaction, μ j represents the satisfaction score of the jth objective function, J represents the objective function set, and f j represents the function value of the jth objective function, and They represent the minimum and maximum values ​​of the j-th objective function respectively.

8. A multi-energy system dispatching system, characterized in that: include: An optimization model building module, used to build a multi-objective optimization model according to the characteristics of each energy conversion device of the target multi-energy system, wherein the multi-objective optimization model includes a first objective function of maximizing the net benefit of the system, a second objective function of minimizing the energy abandonment rate of the system, and corresponding constraints; The optimization model solving module is used to solve the multi-objective optimization model through a multi-objective particle swarm algorithm to obtain an optimal solution, and perform multi-energy multi-level scheduling on the target multi-energy system according to the optimal solution.

9. A multi-energy system scheduling device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a multi-energy system scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The processor-executable program is used to execute a multi-energy system scheduling method as described in any one of claims 1 to 7 when executed by the processor.

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