Multi-energy micro-grid group economic dispatching method, system and device considering energy supply network, and storage medium

Abstract

The invention discloses a multi-energy micro-grid group economic dispatching method, system and device considering an energy supply network and a storage medium, and the method comprises the steps: building a regional integrated energy system comprising a power network, a heat supply network and at least one multi-energy micro-grid, and obtaining a multi-energy micro-grid model, a power network model and a heat supply network model; the method comprises the following steps: acquiring predicted time-sharing prices of transactions between a power network and a heat supply network and each multi-energy micro-grid, comparing marginal costs and time-sharing prices of output of equipment in the multi-energy micro-grid, selecting an optimal transaction chain, taking other transactions as alternative transaction chains, and after the transaction chains are determined, fixing a regional integrated energy system in a single step length; and establishing a target function by taking the minimum operation cost of the regional integrated energy system as a target, and solving the target function by adopting an ADMM algorithm to obtain the integrated energy scheduling method with the minimum operation cost. Not only can the flexibility of global energy scheduling be improved, but also error estimation of purchasing energy from the outside of the multi-energy micro-grid can be effectively avoided.

Description

Technical Field

[0001] The present invention belongs to the field of energy management and relates to a method, system, device and storage medium for economic dispatching of a multi-energy microgrid group considering an energy supply network. Background Art

[0002] The integrated energy system (IES) coordinates and optimizes the production, conversion, distribution and use of multiple energy sources to achieve efficient flow and sharing of energy between different regions, reduce the contradiction between supply and demand in a single region, and thus improve the overall consumption of renewable energy across the country. However, on the one hand, the power generation of renewable energy such as wind and solar is affected by climate and geographical location and has high volatility. On the other hand, the energy transmission between regions is restricted by the network. Therefore, in order to improve the flexibility of energy production and consumption, the scheduling strategy of energy load in each region can be further explored. With the rapid development of technologies such as 5G, big data, and the Internet of Things, the integrated energy system is also gradually integrating and adapting to the digital information age. As an important infrastructure for data storage, computing and optimization, data centers have gradually become the physical support for IES information interconnection, digital monitoring and lean management. At the same time, it provides data services for the development of IES and brings new opportunities for expanding business boundaries. Unlike other flexible loads, data centers have extremely high requirements for the stability of energy supply and have high energy consumption. At the same time, the large amount of heat generated by the operation of servers can be considered to be supplied to nearby users through waste heat recovery. By scheduling user computing request tasks, the transfer of loads between regions can be achieved, and the energy consumption ratio of each region can be further coordinated.

[0003] IES with data centers has received extensive attention at home and abroad. According to the scale of the integrated energy system, data flow-information flow modeling can be divided into three levels: cross-regional level, regional level and user level. The cross-regional model focuses on considering the impact of geographical differences, and pays more attention to the site selection of data centers and the sizing of energy supply equipment; the regional model uses the flexibility of the spatiotemporal scheduling of data flows to achieve coordination with the energy supply network; the user-level model focuses on improving the energy utilization efficiency of distributed energy. However, after overemphasizing the differences between the levels, the correlation between different models is easily overlooked. For small and medium-sized parks or microgrid groups, each sub-region can be regarded as a producer and consumer, and energy interaction can be achieved through the energy supply network to improve global energy efficiency. Therefore, it is necessary to comprehensively consider network connectivity and regional production and consumption structure. In the prior art, a new type of cooperative microgrid composed of multiple local data centers and shared energy storage is studied, which reduces the operating cost and carbon emissions of the data center by coordinating online energy scheduling and real-time business. There is also research on the water-energy coupling and coordinated operation between the Internet data center with a water cooling system and the regional integrated energy system formed by the coupling of the distribution network and the water network, making the model more flexible. However, most existing studies only consider the network connection of the power system and lack network transmission models of other energy sources, which is different from actual energy scheduling. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to provide a method, system, device and storage medium for economic dispatching of a multi-energy microgrid group taking into account the energy supply network, which can not only improve the flexibility of global energy dispatching, but also effectively avoid the wrong estimation of the energy purchased by the multi-energy microgrid from the outside.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A multi-energy microgrid economic dispatch method considering energy supply network includes the following processes: S1, establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model; S2, obtain the predicted time-of-use prices of transactions between the power network and the heating network and each multi-energy microgrid, and select the preferred transaction chain by comparing the marginal cost and time-of-use price of the equipment output in the multi-energy microgrid. The others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length; S3, based on this transaction chain, takes the minimization of the operating cost of the regional integrated energy system as the goal, establishes the objective function, uses the ADMM algorithm to solve the objective function, and obtains the integrated energy scheduling method with the minimum operating cost.

[0006] Preferably, the multi-energy microgrid model includes a data center model and an energy storage device model, and the data center model includes a data center information model, a data center IT equipment energy consumption model, and a data center refrigeration equipment energy consumption model.

[0007] Furthermore, the data center information model uses the relationship between storage queues and computing loads to represent:

[0008] in, express Time Microgrid The amount of batch processing load stored, express Time Microgrid The amount of batch load to be processed; Set limits on data center storage capacity, information processing task interactions between different microgrids at different time periods, and the maximum tolerable delay time for batch processing loads; The energy consumption model of IT equipment in a data center is expressed as:

[0009] Represents microgrid The number of servers in and are the rated power and no-load power of the server respectively; The total load limit for each microgrid is handled; The energy consumption model of data center cooling equipment is expressed as:

[0010] It represents the energy consumption index of the data center, which corresponds to the ratio of the total power consumption of the data center to the power consumption of IT equipment, and is always greater than 1. is the cooling coefficient of the cooling system.

[0011] Furthermore, the energy storage device model is expressed as:

[0012] , Indicates energy storage level and upper limit; , , are the charging and discharging power and upper limit of the energy storage system respectively; and To determine the energy storage device A 0-1 variable for charging and discharging the time period.

[0013] Preferably, the power network model is expressed as:

[0014] in, , Transmit power and capping for the network; , is the generator power and upper limit; is the line transmission reactance; , are the node phase angle and the reference node phase angle; , , , It is the traded power with the grid, photovoltaic power generation, load power consumption other than data centers, and power exchanged between multi-energy microgrids; , Corresponding to buying and selling electricity respectively.

[0015] Preferably, the heating network model is expressed as:

[0016] in, is the heating power of the equipment at the node; is the heat sold to the microgrid at the node; is the heat produced by the heat pump in the microgrid; is the specific heat capacity of the water in the pipe; For Node Flow to Node water flow; , Node To Node Pipe outlet temperatures and nodes The outlet temperature; , It is the collection of inflow and outflow nodes of the heating pipeline outside the microgrid; Produce heat for the heat pump in the microgrid; is the waste heat recovery coefficient of the data center; is the heat load in the microgrid; Exchange heat between microgrids; is the ambient temperature; is the heat transfer coefficient per unit length of pipeline; For Node To Node The length of the pipe.

[0017] Preferably, the process of solving the objective function using the ADMM algorithm is as follows: the power network, the heating network and multiple multi-energy microgrids are divided into n sub-areas, each of which contains its own decision variables, which are local variables. The decision variables within the power network include the output of the power generation equipment / the phase angle of each node and the transaction power with the multi-energy microgrid; the decision variables of the heating network include the heating power of the heating equipment, the temperature of each node and the heat sold to the multi-energy microgrid; the decision variables of each multi-energy microgrid include the processing capacity of the calculated load, the output of various energy supply equipment and the transaction volume between microgrid groups; additionally set the variables on the transaction chain established by energy or information as global variables; Each region updates other local variables within the region according to the initial value of the global variable. After all sub-regions have updated the optimization results, the global variables are updated and it is determined whether the convergence conditions are met. If not, the updated global variables are sent to each associated sub-region to replace the initial value. This process is repeated until convergence, and a comprehensive energy scheduling method with the lowest operating cost is obtained.

[0018] A multi-energy microgrid group economic dispatch system considering energy supply network, comprising: A system establishment module, used to establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model; The transaction chain determination module is used to obtain the predicted time-sharing prices of transactions between the power network and the heating network and each multi-energy microgrid. By comparing the marginal cost and time-sharing price of the equipment output in the multi-energy microgrid, the preferred transaction chain is selected, and the others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length; The scheduling method solving module is used to establish an objective function based on the transaction chain with the goal of minimizing the operating cost of the regional integrated energy system, and use the ADMM algorithm to solve the objective function to obtain the integrated energy scheduling method with the minimum operating cost.

[0019] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method for economic dispatching a multi-energy microgrid group considering an energy supply network are implemented.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the multi-energy microgrid economic dispatch method considering the energy supply network.

[0021] Compared with the prior art, the present invention has the following beneficial effects: The present invention fully considers the coupling between different energy sources and the potential of data centers as producers and consumers, and establishes a network model of the distribution network and the regional heating network as well as a physical model of energy conversion of equipment in the micro-energy network. Compared with the traditional hierarchical optimization model or the park microgrid model that only considers transactions with the power grid, this model can not only improve the flexibility of global energy scheduling, but also effectively avoid the misestimation of the energy purchased from the outside by the multi-energy microgrid. Compared with the traditional ADMM method, the present invention considers the selection of energy trading chains under different step lengths based on the time-of-use price and the marginal cost changes in the microgrid, and is not limited to a fixed network structure. This method can further reduce the global energy cost of the park and improve the absorption rate of distributed energy. At the same time, a central coordinator is used to receive and transmit part of the information of each sub-area to meet the requirements for device privacy protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the internal physical structure of a multi-energy microgrid in an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of a regional integrated energy system including a multi-energy microgrid in an embodiment of the present invention; Figure 3 This is a schematic diagram of information transmission in each sub-area in an embodiment of the present invention; Figure 4 The figure is a calculation flow chart of the whole process of algorithm application in the embodiment of the present invention. DETAILED DESCRIPTION

[0023] The embodiments of the present invention are described in detail below, examples of which 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 cannot be understood as limiting the present invention.

[0024] The present invention proposes an economic dispatch model for a micro-energy grid cluster including a data center, which includes the following processes: S1, establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model.

[0025] S2, obtains the predicted time-of-use prices of transactions between the power network and the heating network and each multi-energy microgrid. By comparing the marginal cost and time-of-use price of the equipment output in the multi-energy microgrid, the preferred transaction chain is selected, and the others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length.

[0026] S3, based on this transaction chain, takes the minimization of the operating cost of the regional integrated energy system as the goal, establishes the objective function, uses the ADMM algorithm to solve the objective function, and obtains the integrated energy scheduling method with the minimum operating cost.

[0027] The model takes into account the connection relationship between the micro-energy grid group and the distribution network and the regional heating network, as well as the information and energy interaction between the micro-energy grid groups, in order to improve the flexibility of energy utilization in each microgrid. In each step of the scheduling cycle, each microgrid preferentially compares the predicted time-of-use electricity price, heat price and marginal cost of the energy supply equipment in the microgrid, selects the energy trading party, and determines the energy transmission relationship. Subsequently, the distribution network, regional heating network and each micro-energy grid are divided into sub-regions, and the ADMM algorithm is used to optimize the computing task exchange between microgrid data centers and the electricity and heat exchange between regions with the goal of minimizing the global cost.

[0028] like Figure 1 As shown, it is a schematic diagram of the internal physical structure of the multi-energy microgrid in an embodiment of the present invention, which mainly includes a data center and energy storage equipment, corresponding to the following model: 1. Data Center Model The data center as a whole is a flexible power load, and the power consumption is mainly affected by the amount of computing tasks. Therefore, we first need to establish an information queue model for the data center. In terms of energy-consuming equipment, the core of the data center is IT equipment, which is used for data processing, storage and communication. With the continuous operation of IT equipment, a large amount of heat will be generated, requiring a cooling system to continuously cool down. In addition, the data center is also equipped with lighting equipment and other auxiliary service equipment, but the power consumption accounts for a small proportion. Therefore, we mainly consider the energy consumption model of IT equipment and cooling equipment.

[0029] A. Data Center Information Model The data center receives computing requests from users and sends them to the server for storage and processing. We call such requests computing loads. Usually, computing loads are divided into interactive loads and batch loads according to the delay sensitivity of computing processing, corresponding to non-delayed and delayable respectively. Suppose the task processing volume of the two types of loads arriving at each moment is and ,in, , represents the multi-energy microgrid set, , represents a time period set. The arriving interactive load is processed immediately, and the arriving batch load can be added to the storage queue and processed later. Therefore, the relationship between the storage queue and the computing load can be expressed as:

[0030] in, express Multi-energy microgrid The amount of batch processing load stored, express The processing volume of batch load tasks during the period, express Multi-energy microgrid The amount of batch load that is ready to be processed.

[0031] Considering the storage capacity limitations of the data center, the following requirements must also be met:

[0032] in, Refers to the last moment, To ensure that all task requests are completed at the last moment. The upper limit of the storage capacity of the data center.

[0033] Different microgrids need to realize information processing task interaction in different time periods:

[0034] ,The positive and negative values ​​of each variable are related to the direction of task transfer among microgrids.

[0035] After task interaction, each microgrid The load handled by a time period is expressed as:

[0036] Considering the user's requirements for data center service quality, all batch processing loads must be processed within a tolerable delay time, corresponding to the following constraints:

[0037] is the maximum tolerable delay time.

[0038] B. Data Center IT Equipment Energy Consumption Model The energy consumption of IT equipment in a data center is approximately linearly related to the amount of tasks processed, which can be expressed as:

[0039] Represents microgrid The number of servers in and are the rated power and no-load power of the server respectively; The upper limit of the total load that each microgrid can handle.

[0040] C. Data Center Cooling Equipment Energy Consumption Model In order to simplify the model, the energy consumption of refrigeration equipment is often calculated by ratio conversion:

[0041] It represents the energy consumption index of the data center, which corresponds to the ratio of the total power consumption of the data center to the power consumption of IT equipment, and is always greater than 1. is the cooling coefficient of the cooling system. Assuming that the indoor temperature of the data center is always kept stable, the waste heat power is equal to the cooling power, corresponding to formula (13).

[0042] 2. Energy Storage Equipment Model Energy storage equipment cannot be charged and discharged at the same time, corresponding to the following model:

[0043] , Indicates energy storage level and upper limit; , , are the charging and discharging power and upper limit of the energy storage system respectively; and To determine the energy storage device A 0-1 variable for charging and discharging the time period.

[0044] like Figure 2 As shown, it is a schematic diagram of the structure of the regional integrated energy system containing multi-energy microgrids in an embodiment of the present invention, including a power network, a heating network and at least one multi-energy microgrid. The power network and the heating network are bidirectionally connected to each multi-energy microgrid, and there is also bidirectional interaction of electric energy, thermal energy and computing load between each multi-energy microgrid.

[0045] The corresponding network models are as follows: 1. Power Network Model For the transmission of electric energy in medium and low voltage distribution networks, a DC power flow model can be used:

[0046] in, , Transmit power and capping for the network; , is the generator power and upper limit; is the line transmission reactance; , are the node phase angle and the reference node phase angle; , , , It is the traded power with the grid, photovoltaic power generation, load power consumption other than data centers, and power exchanged between multi-energy microgrids; , Corresponding to buying and selling electricity respectively.

[0047] 2. Heating network model Assume that each multi-energy microgrid is only connected to one heat node, and ignore the heat loss of the internal pipeline transmission of the multi-energy microgrid. Because the waste heat quality of the data center in the microgrid is low, it needs to be improved by a heat pump. At the same time, the heat pump also acts as a heat source in the multi-energy microgrid, and the heat exchange station is used to realize the heat interaction between the multi-energy microgrid and the external heating pipeline. The heating network adopts a quality regulation model, the flow of the heating pipeline is constant, and the temperature is variable. The corresponding model is as follows:

[0048] in, is the heating power of the equipment at the node (if any); is the heat sold to the microgrid at the node; is the heat produced by the heat pump in the microgrid; is the specific heat capacity of the water in the pipe; For Node Flow to Node water flow; , Node To Node Pipe outlet temperatures and nodes The outlet temperature; , It is the collection of inflow and outflow nodes of the heating pipeline outside the microgrid; Produce heat for the heat pump in the microgrid; is the waste heat recovery coefficient of the data center; is the heat load in the microgrid; Exchange heat between microgrids; is the ambient temperature; is the heat transfer coefficient per unit length of pipeline; For Node To Node The length of the pipe.

[0049] 3. Electric-heat interaction between multi-energy microgrids Microgrids can realize point-to-point transactions, and the balance of the total amount of exchange must be ensured:

[0050] IV. Objective Function With the goal of minimizing the overall operating cost of the regional energy supply network and multi-energy microgrid group, the following objective function is established:

[0051] in, The operating costs of power generation equipment at distribution network nodes; The transaction income between the distribution network and the multi-energy microgrid; The operating costs of the heating network node equipment; The trading income of heating network and multi-energy microgrid; The operating cost of small diesel units in multi-energy microgrids; is the maintenance cost of energy storage equipment in the multi-energy microgrid; is the operating cost of the heat pump in the multi-energy microgrid; The revenue is from point-to-point transactions within the multi-energy microgrid.

[0052] like Figure 3 As shown in FIG. 1 , a schematic diagram of information transmission of each sub-region in an embodiment of the present invention is shown. First, the algorithm implementation process is introduced, and then, how each region implements the interaction of scheduling information is explained: 1. ADMM algorithm ADMM handles the problem in the following form:

[0053] in, and They are and As a convex function, (34) and (35) can be written in augmented Lagrangian form:

[0054] is the Lagrange multiplier, is the set parameter. Further, the solution is iterated in the following way:

[0055] Each iteration needs to use the information from the previous iteration until the following convergence conditions are met and the distributed algorithm is solved:

[0056] (40) and (41) correspond to the primal residual and the dual residual, respectively. and They are all very small constants.

[0057] In the entire model, we divide the two networks and multiple multi-energy microgrids into n sub-areas, each of which contains its own decision variables, which we call local variables. Represented by . The decision variables within the power grid include the output of the power generation equipment / phase angle of each node and the amount of electricity traded with the multi-energy microgrid; the decision variables of the heating network include the heating power of the heating equipment, the temperature of each node and the amount of heat sold to the multi-energy microgrid; the decision variables of each multi-energy microgrid include the processing capacity of the computing load, the output of various energy supply equipment and the transaction volume between microgrid groups (electricity and heat interaction between multi-energy microgrids). In addition, we additionally set the variables on the transaction chain established by energy or information as global variables, using Because each sub-area needs to establish connections with other areas through transaction chains, the variables on the transaction chain will be used in at least two areas.

[0058] Based on the above analysis, the ADMM model is rewritten into a form suitable for existing models:

[0059] It should be noted that in (43) It only refers to the decision variables on the transaction chain. Here The update rule of is the same as (37), and It needs to be updated according to the number of overlaps between global variables and local variables. The process is as follows:

[0060] (45) It only represents the updated decision variables on the transaction chain. is the number of overlaps between global variables and local variables.

[0061] 2. Scheduling Information Interaction Process like Figure 3 As shown in Figure 1, each region first updates other local variables in the region according to the initial value of the global variable. This is a parallel optimization process. After all sub-regions have updated the optimization results, some information related to the global variables is transmitted to the central coordinator. The central coordinator updates the global variables according to (45) and determines whether the convergence conditions are met. If not, the updated global variables are sent to each associated sub-region to replace the initial value, and this process is repeated until convergence. During the whole process, each sub-region always exchanges only part of the information, which effectively protects the privacy of internal device information.

[0062] like Figure 4 As shown in FIG. 1 , it is a calculation flow chart of the whole process of applying the algorithm in an embodiment of the present invention, which includes the following steps: Step 1: Select the transaction chain First, the predicted time-of-use prices of transactions between the two energy supply networks and each multi-energy microgrid are obtained. Then, by comparing the marginal cost of the output of the equipment in the multi-energy microgrid and the time-of-use price, the preferred transaction chain is selected, and the others are used as alternative transaction chains. For example, when the time-of-use electricity price of the power grid is low, the multi-energy microgrid Priority is given to trading with the power grid, that is, to establish a trading chain. However, the trading chain may not be limited to this one. Prioritize trading with other multi-energy microgrids, but the first multi-energy microgrid that may be preferred can only promise a small amount of trading volume, which cannot meet the needs of the multi-energy microgrid. If there is demand, then it is necessary to continue to establish a transaction chain with the second multi-energy microgrid, and so on.

[0063] Step 2: Distributed method calculation After all transaction chains are determined, the regional integrated energy system is fixed within a single step. The two networks and each multi-energy microgrid are divided into multiple sub-areas. First, the central coordinator gives the initial value of the global variable based on past experience and sends it to the associated sub-areas. Each sub-area is optimized in parallel and the local variables are updated. Subsequently, the local variables related to the global variables are uploaded to the central coordinator, which determines whether they have converged and updates the global variables. This process is repeated until convergence, and finally a comprehensive energy scheduling method with the lowest operating cost is obtained. The specific calculation process is as follows: Figure 3 Introduce the algorithmic process in the content.

[0064] Step 3: Scheduling execution After the central coordinator determines that convergence has been achieved, it will transmit the convergence signal to all sub-areas. After receiving the signal, each sub-area will immediately collect the optimization results of the last time within its own area, list the planned output of each device and the planned transaction volume of information and electricity and heat, and execute according to the plan.

[0065] The following are device embodiments of the present invention, which can be used to perform method embodiments of the present invention. For details not disclosed in the device embodiments, please refer to the method embodiments of the present invention.

[0066] In another embodiment of the present invention, there is provided a multi-energy microgrid economic dispatching system considering the energy supply network. The multi-energy microgrid economic dispatching system considering the energy supply network can be used to implement the above-mentioned multi-energy microgrid economic dispatching method considering the energy supply network. Specifically, the multi-energy microgrid economic dispatching system considering the energy supply network includes a system establishment module, a transaction chain determination module and a dispatching method solving module.

[0067] Among them, the system establishment module is used to establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model.

[0068] The transaction chain determination module is used to obtain the predicted time-of-use prices of transactions between the power network and the heating network and each multi-energy microgrid. By comparing the marginal cost and time-of-use price of the equipment output in the multi-energy microgrid, the preferred transaction chain is selected, and the others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length.

[0069] The scheduling method solving module is used to establish an objective function based on the transaction chain with the goal of minimizing the operating cost of the regional integrated energy system, and use the ADMM algorithm to solve the objective function to obtain the integrated energy scheduling method with the minimum operating cost.

[0070] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGAs), or other processors. GateArray, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the multi-energy microgrid group economic dispatch method considering the energy supply network, including: S1, establishing a regional comprehensive energy system including a power network, a heating network and at least one multi-energy microgrid, and obtaining a multi-energy microgrid model, a power network model and a heating network model; S2, obtaining the predicted time-sharing price of the transaction between the power network and the heating network and each multi-energy microgrid, and selecting the preferred transaction chain by comparing the marginal cost and time-sharing price of the output of the equipment in the multi-energy microgrid, and the others as alternative transaction chains. After the transaction chain is determined, the regional comprehensive energy system is fixed within a single step; S3, based on the transaction chain, with the minimum operating cost of the regional comprehensive energy system as the goal, an objective function is established, and the objective function is solved by the ADMM algorithm to obtain a comprehensive energy dispatch method with the minimum operating cost.

[0071] In another embodiment, the present invention further provides a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and 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.

[0072] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the economic dispatch method of a multi-energy microgrid group considering the energy supply network in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded by the processor and the following steps are executed: S1, establish a regional integrated energy system including an electric power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, an electric power network model and a heating network model; S2, obtain the predicted time-sharing price of transactions between the electric power network and the heating network and each multi-energy microgrid, and select the preferred transaction chain by comparing the marginal cost and time-sharing price of the output of equipment in the multi-energy microgrid, and the others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length; S3, on the basis of the transaction chain, with the goal of minimizing the operating cost of the regional integrated energy system, establish an objective function, use the ADMM algorithm to solve the objective function, and obtain a comprehensive energy dispatch method with the minimum operating cost.

[0073] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0074] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0078] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0079] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0080] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0081] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0082] It should be understood that the above description is for illustration and not for limitation. Many embodiments and many applications beyond the examples provided will be apparent to those skilled in the art upon reading the above description. Therefore, the scope of this patent should not be determined with reference to the above description, but rather with reference to the preceding claims and the full scope of equivalents to which such claims are entitled. For the purpose of comprehensiveness, all articles and references, including disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein in the preceding claims is not intended to be a waiver of such subject matter, nor should it be considered that the applicant has not considered such subject matter to be part of the disclosed inventive subject matter.

Claims

1. A method for economic dispatching of a multi-energy microgrid group considering an energy supply network, characterized in that: The process includes: S1, establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model; S2, obtain the predicted time-of-use prices of transactions between the power network and the heating network and each multi-energy microgrid, and select the preferred transaction chain by comparing the marginal cost and time-of-use price of the equipment output in the multi-energy microgrid. The others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length; S3, based on this transaction chain, takes the minimization of the operating cost of the regional integrated energy system as the goal, establishes the objective function, uses the ADMM algorithm to solve the objective function, and obtains the integrated energy scheduling method with the minimum operating cost.

2. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 1 is characterized in that: The multi-energy microgrid model includes a data center model and an energy storage device model. The data center model includes a data center information model, a data center IT equipment energy consumption model, and a data center refrigeration equipment energy consumption model.

3. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 2 is characterized in that: The data center information model uses the relationship between storage queues and computing loads to represent: in, express Time Microgrid The amount of batch processing load stored, express Time Microgrid The amount of batch load to be processed; Set limits on data center storage capacity, information processing task interactions between different microgrids at different time periods, and the maximum tolerable delay time for batch processing loads; The energy consumption model of IT equipment in a data center is expressed as: Represents microgrid The number of servers in and are the rated power and no-load power of the server respectively; The total load limit for each microgrid is handled; The energy consumption model of data center cooling equipment is expressed as: It represents the energy consumption index of the data center, which corresponds to the ratio of the total power consumption of the data center to the power consumption of IT equipment, and is always greater than 1. is the cooling coefficient of the cooling system.

4. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 2 is characterized in that: The energy storage device model is expressed as: , Indicates energy storage level and upper limit; , , are the charging and discharging power and upper limit of the energy storage system respectively; and To determine the energy storage device A 0-1 variable for charging and discharging the time period.

5. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 1, characterized in that: The power network model is expressed as: in, , Transmit power and capping for the network; , is the generator power and upper limit; is the line transmission reactance; , are the node phase angle and the reference node phase angle; , , , It is the traded power with the grid, photovoltaic power generation, load power consumption other than data centers, and power exchanged between multi-energy microgrids; , Corresponding to buying and selling electricity respectively.

6. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 1, characterized in that: The heating network model is expressed as: in, is the heating power of the equipment at the node; is the heat sold to the microgrid at the node; is the heat produced by the heat pump in the microgrid; is the specific heat capacity of the water in the pipe; For Node Flow to Node water flow; , Node To Node Pipe outlet temperatures and nodes The outlet temperature; , It is the collection of inflow and outflow nodes of the heating pipeline outside the microgrid; Produce heat for the heat pump in the microgrid; is the waste heat recovery coefficient of the data center; is the heat load in the microgrid; Exchange heat between microgrids; is the ambient temperature; is the heat transfer coefficient per unit length of pipeline; For Node To Node The length of the pipe.

7. The method for economic dispatching of a multi-energy microgrid group considering the energy supply network according to claim 1, characterized in that: The process of solving the objective function using the ADMM algorithm is as follows: the power network, heating network and multiple multi-energy microgrids are divided into n sub-areas, each of which contains its own decision variables, which are local variables. The decision variables within the power network include the output of the power generation equipment / phase angle of each node and the amount of electricity traded with the multi-energy microgrid; the decision variables of the heating network include the heating power of the heating equipment, the temperature of each node and the amount of heat sold to the multi-energy microgrid; the decision variables of each multi-energy microgrid include the processing capacity of the calculated load, the output of various energy supply equipment and the transaction volume between microgrid groups; additional variables on the transaction chain established by energy or information are set as global variables; Each region updates other local variables within the region according to the initial value of the global variable. After all sub-regions have updated the optimization results, the global variables are updated and it is determined whether the convergence conditions are met. If not, the updated global variables are sent to each associated sub-region to replace the initial value. This process is repeated until convergence, and a comprehensive energy scheduling method with the lowest operating cost is obtained.

8. A multi-energy microgrid economic dispatch system considering energy supply network, characterized in that: include: A system establishment module, used to establish a regional integrated energy system including a power network, a heating network and at least one multi-energy microgrid, and obtain a multi-energy microgrid model, a power network model and a heating network model; The transaction chain determination module is used to obtain the predicted time-sharing prices of transactions between the power network and the heating network and each multi-energy microgrid. By comparing the marginal cost and time-sharing price of the equipment output in the multi-energy microgrid, the preferred transaction chain is selected, and the others are used as alternative transaction chains. After the transaction chain is determined, the regional integrated energy system is fixed within a single step length; The scheduling method solving module is used to establish an objective function based on the transaction chain with the goal of minimizing the operating cost of the regional integrated energy system, and use the ADMM algorithm to solve the objective function to obtain the integrated energy scheduling method with the minimum operating cost.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the economic dispatch method of a multi-energy microgrid group considering the energy supply network as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for economic dispatch of a multi-energy microgrid group considering the energy supply network as described in any one of claims 1 to 7 are implemented.

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