Optimal distributed energy scheduling method and system for electric-digital interconnected multi-region energy system

Abstract

The invention provides an optimal distributed energy scheduling method and system for a power-data interconnected multi-regional energy system, and the method and system achieve the deep fusion of a power network and a data network, and achieve the information sharing, collaborative optimization and intelligent scheduling among a plurality of regional energy systems. According to the method, the energy scheduling problem of a multi-region energy system can be effectively solved, and an energy consumption evaluation model considering the influence of data transmission delay is established for a multi-region computing power system. For a multi-region power system, minimization of the operation cost of the power system and the operation cost of a data center is taken as a target function, and an electricity-data interconnection multi-region energy system optimal distributed energy scheduling model is constructed; based on dual decomposition mixed integer linear programming, a distributed energy optimization strategy is designed; and finally, an optimal energy scheduling scheme is solved, so that the optimal distributed energy scheduling of the power-digital interconnected multi-region energy system is realized.

Description

Technical Field

[0001] The present invention relates to the field of new energy and energy-saving technology / multi-regional energy system energy dispatching technology, and in particular to an optimal distributed energy dispatching method and system for a multi-regional energy system interconnected by electricity and data. Background Art

[0002] In modern society, global energy demand continues to grow, and the rapid development of renewable energy makes it difficult for traditional centralized energy management models to meet the requirements of efficient utilization, economy and environmental protection. Especially in multi-regional energy systems, due to the wide geographical distribution, diverse energy types and complex supply and demand relationships, information sharing, collaborative optimization and intelligent scheduling of cross-regional energy systems have become urgent issues to be solved. These systems usually involve the integration and management of multiple energy forms (such as electrical energy, thermal energy, etc.) to achieve efficient utilization and optimal configuration of energy.

[0003] At present, the existing research on energy management issues of multi-regional energy systems mainly focuses on integrated energy systems with electric and thermal interconnections. In terms of computing power systems, existing research often ignores the computing power resource bottleneck that a single data center may face. In addition, for distributed energy optimization strategies for multi-regional energy systems, existing research mostly takes the operating cost of energy supply equipment as the optimization target.

[0004] However, existing technologies have some obvious defects. First, distributed energy optimization strategies focus on the optimization of continuous decision variables between devices in multiple regions, and are highly dependent on real-time and continuous communication between devices, which is difficult to achieve in complex and changeable multi-energy systems. Secondly, in response to the surge in computing power demand, existing research often lacks effective strategies for allocating and supplementing computing power resources, resulting in the risk of performance bottlenecks in data centers. In addition, existing technologies are too single-minded in their optimization goals, focusing mainly on the operating costs of energy supply equipment, while ignoring the additional costs brought about by the surge in computing power demand and the impact on data processing efficiency, thus failing to fully reflect the true operating costs of the system.

[0005] Therefore, in view of the shortcomings of existing technologies, it is necessary to design a distributed energy scheduling strategy that can comprehensively consider the characteristics of multi-regional energy systems, computing resource bottlenecks, and actual operating costs, so as to achieve efficient utilization and optimal configuration of energy. Summary of the invention

[0006] In view of this, the present invention provides an optimal distributed energy scheduling method and system for an electrically-digitally interconnected multi-regional energy system to solve the problems existing in the prior art of high energy consumption and low efficiency in data centers (traditional data centers often have problems of high energy consumption and low energy efficiency, which increases operating costs and aggravates the environmental burden), the inability to ensure real-time performance of delay-sensitive loads (how to ensure the real-time requirements of delay-sensitive loads in data centers while reducing energy consumption is a challenging technical problem), and the collaborative optimization problem of multi-regional energy systems (multi-regional energy systems involve multiple energy networks and multiple user groups. How to achieve collaborative optimization among regions and improve overall energy utilization efficiency is a complex system engineering problem) to achieve efficient operation of multi-regional energy systems.

[0007] To this end, the present invention provides the following technical solutions:

[0008] On the one hand, the present invention provides an optimal distributed energy scheduling method for a multi-regional energy system with electrical-digital interconnection, comprising the following steps:

[0009] The first objective function is to minimize the energy consumption of the multi-regional energy system data center; based on the delay-sensitive load, the delay constraint condition is constructed, and the energy consumption evaluation model of the multi-regional energy system data center is established;

[0010] Taking the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the second objective function, considering the operating characteristics of the multi-regional energy system, constructing physical constraints, and establishing an optimal distributed energy scheduling model for the multi-regional energy system with electrical-digital interconnection including the energy consumption evaluation model;

[0011] Based on mixed integer linear programming, a distributed energy optimization algorithm is designed, in which a distributed iterative scheme based on dual decomposition and approximate minimization is used to solve the inequality coupling problem;

[0012] Based on the optimal distributed energy scheduling model of the multi-regional energy system interconnected by electricity and digital and the distributed energy optimization algorithm, the optimal energy scheduling plan is calculated and executed.

[0013] Furthermore, the first objective function is:

[0014]

[0015] Among them, Q is a set of data centers distributed in different regional energy systems; is the power consumption of data center j; m j is the number of active servers in data center j; θ j is the power consumption of an active server; γ j is the fixed power consumption during operation of data center j;

[0016] The delay constraints include: server quantity constraints, data load processing delay constraints and data load balancing constraints;

[0017] The server quantity constraint is:

[0018]

[0019] Among them, M j is the total number of servers in data center j;

[0020] The data load processing delay constraint is:

[0021]

[0022] Among them, κ j is the data load received by data center j; is the average service rate of each active server in data center j; W is the delay bound for data load processing;

[0023] The data load balancing constraints are:

[0024]

[0025] Where L is the total amount of data load that the data center needs to process; θ is the packet loss rate of the data center;

[0026] The energy consumption evaluation model of the multi-region energy data center is expressed as:

[0027]

[0028] (1-θ)L≤Σκ j ≤L

[0029]

[0030] Furthermore, the second objective function includes:

[0031] Based on the cost functions of energy supply equipment operation and data center operation, an economic benefit objective function is established, including:

[0032] F=min{F 1 +F 2};

[0033] Where F is the total cost, F 1 is the operating cost of the energy supply equipment, F 2 It is the cost of running the data center;

[0034]

[0035] Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, K IDC is the data center power consumption cost factor;

[0036] The physical constraints include: power balance constraints, power supply equipment capacity constraints and data center operation constraints;

[0037] The power balance constraint is:

[0038]

[0039] Among them, P load is the load demand of the multi-regional energy system;

[0040] The power supply equipment capacity constraint is:

[0041]

[0042] in, They are the upper and lower limits of the power of each power supply device;

[0043] The data center operation constraints are:

[0044]

[0045] The optimal distributed energy dispatch model for the multi-regional energy system with electrical and digital interconnection is integrated into a form that includes all coupling constraints:

[0046]

[0047] Among them, V d， V D are the number of power nodes and data center nodes, respectively, j It is a set of nodes of energy systems in different regions.

[0048] Furthermore, based on mixed integer linear programming, a distributed energy optimization algorithm is designed, including:

[0049] The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows:

[0050]

[0051] Where V is the number of nodes; Q i is the decision variable;

[0052] Design the Lagrangian function, which is expressed as follows:

[0053]

[0054] in, It is represented as a vector of Lagrange multipliers;

[0055] Design the dual problem, which is expressed as follows:

[0056]

[0057] At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, Calculate l i (k); by minimizing the i (k) calculated on L i , for the local original vector Q i (k+1) is updated:

[0058]

[0059] The dual vector is updated as follows:

[0060]

[0061] Auxiliary iterative calculation is as follows:

[0062]

[0063] Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

[0064]

[0065] In another aspect, the present invention further provides an optimal distributed energy dispatching system for a multi-regional energy system interconnected by electricity and digital means, comprising the following steps:

[0066] The energy consumption evaluation model establishment module takes minimizing the energy consumption of the multi-regional energy system data center as the first objective function; constructs delay constraints based on delay-sensitive loads and establishes an energy consumption evaluation model for the multi-regional energy system data center;

[0067] A distributed energy scheduling model establishment module takes the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the second objective function, considers the operating characteristics of the multi-regional energy system, constructs physical constraints, and establishes an optimal distributed energy scheduling model for the multi-regional energy system with electrical-digital interconnection including the energy consumption evaluation model;

[0068] The energy optimization algorithm design module designs a distributed energy optimization algorithm based on mixed integer linear programming. In the distributed energy optimization algorithm, a distributed iterative scheme based on dual decomposition and approximate minimization is used to solve the inequality coupling problem.

[0069] The energy scheduling module calculates and executes the optimal energy scheduling plan based on the optimal distributed energy scheduling model of the multi-regional energy system interconnected by electricity and digital and the distributed energy optimization algorithm.

[0070] Furthermore, the first objective function is:

[0071]

[0072] Among them, Q is a set of data centers distributed in different regional energy systems; is the power consumption of data center j; m j is the number of active servers in data center j; θ j is the power consumption of an active server; γ j is the fixed power consumption during operation of data center j;

[0073] The delay constraints include: server quantity constraints, data load processing delay constraints and data load balancing constraints;

[0074] The server quantity constraint is:

[0075]

[0076] Among them, M j is the total number of servers in data center j;

[0077] The data load processing delay constraint is:

[0078]

[0079] Among them, κ j is the data load received by data center j; is the average service rate of each active server in data center j; W is the delay bound for data load processing;

[0080] The data load balancing constraints are:

[0081]

[0082] Where L is the total amount of data load that the data center needs to process; θ is the packet loss rate of the data center;

[0083] The energy consumption evaluation model of the multi-region energy data center is expressed as:

[0084]

[0085] Furthermore, the second objective function includes:

[0086] Based on the cost functions of energy supply equipment operation and data center operation, an economic benefit objective function is established, including:

[0087] F=min{F 1 +F 2};

[0088] Where F is the total cost, F 1 is the operating cost of the energy supply equipment, F 2 It is the cost of running the data center;

[0089]

[0090] Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, K IDC is the data center power consumption cost coefficient;

[0091] The physical constraints include: power balance constraints, power supply equipment capacity constraints and data center operation constraints;

[0092] The power balance constraint is:

[0093]

[0094] Among them, P load is the load demand of the multi-regional energy system;

[0095] The power supply equipment capacity constraint is:

[0096]

[0097] in, They are the upper and lower limits of the power of each power supply device;

[0098] The data center operation constraints are:

[0099]

[0100] The optimal distributed energy dispatch model for the multi-regional energy system with electrical and digital interconnection is integrated into a form that includes all coupling constraints:

[0101]

[0102] Among them, Vd ,V D are the number of power nodes and data center nodes, respectively, j It is a set of nodes of energy systems in different regions.

[0103] Furthermore, based on mixed integer linear programming, a distributed energy optimization algorithm is designed, including:

[0104] The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows:

[0105]

[0106] Where V is the number of nodes; Q i is the decision variable;

[0107] Design the Lagrangian function, which is expressed as follows:

[0108]

[0109] in, Expressed as is a vector of Lagrange multipliers;

[0110] Design the dual problem, which is expressed as follows:

[0111]

[0112] At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, Calculate l i (k); by minimizing the i (k) calculated on L i , for the local original vector Q i (k+1) is updated:

[0113]

[0114] The dual vector is updated as follows:

[0115]

[0116] Auxiliary iterative calculation is as follows:

[0117]

[0118] Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

[0119]

[0120] Advantages and positive effects of the present invention:

[0121] The present invention provides an optimal distributed energy scheduling method for an electrically-digitally interconnected multi-regional energy system, which deeply integrates the power network and the data network to realize information sharing, collaborative optimization and intelligent scheduling among multiple regional energy systems. The present invention can effectively handle the energy scheduling problem of a multi-regional energy system. For a multi-regional computing system, the present invention establishes an energy consumption evaluation model that considers the impact of data transmission delay. For a multi-regional power system, an optimal distributed energy scheduling model for an electrically-digitally interconnected multi-regional energy system is constructed with the minimization of the operating cost of the power system and the operating cost of the data center as the objective function; and a distributed energy optimization strategy is designed based on a mixed integer linear programming of dual decomposition; and finally the optimal energy scheduling scheme is solved, thereby realizing the optimal distributed energy scheduling of an electrically-digitally interconnected multi-regional energy system. The proposal of the present invention can achieve the optimal allocation of resources and friendly protection of the environment while reducing the operating cost of the multi-regional energy system, laying a solid foundation for building a future green energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0122] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0123] Figure 1 It is a flow chart of an optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital networks in an embodiment of the present invention;

[0124] Figure 2 A schematic diagram of the power supply situation of the power supply device in an embodiment of the present invention;

[0125] Figure 3 This is a schematic diagram of data load processing in a data center according to an embodiment of the present invention;

[0126] Figure 4 Schematic diagram of dual variable iteration in an embodiment of the present invention. DETAILED DESCRIPTION

[0127] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0128] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0129] This invention takes the multi-regional energy system as the research object, and proposes an energy scheduling model and distributed energy optimization strategy for the data center and energy supply equipment of the multi-regional energy system. The core of this invention focuses on promoting the journey of the multi-regional energy system towards comprehensive electrification transformation and deep integration of green energy. The goal is to lead the system into a green, low-carbon, and sustainable development track, and provide a complete set of optimal distributed energy scheduling methods for multi-regional energy systems with electrical-digital interconnection, thereby ensuring the efficient operation of the multi-regional energy system.

[0130] Distributed energy scheduling in multi-regional energy systems relies on information interaction between various devices. The current and data flow transmission lines between devices are used as research carriers. According to different energy flows, they can be divided into two parts: energy supply equipment and data centers. Among them, as an information hub, the data center integrates information technology (IT) equipment, cooling systems to maintain environmental stability, and a series of auxiliary facilities such as lighting, security monitoring and fire protection systems to jointly support efficient data processing and storage. In view of the high requirements of data processing on time sensitivity, a multi-regional data center energy consumption evaluation model is constructed considering the data processing delay constraint. With the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the objective function, an energy scheduling model is established. The energy supply side covers traditional fuel equipment, new energy photovoltaic equipment and wind turbine equipment. For the energy scheduling model of the multi-regional energy system, a mixed integer linear programming distributed optimization algorithm based on dual decomposition is used to solve it, and then the optimal energy scheduling solution is obtained.

[0131] In view of the technical problems existing in the prior art, the present invention provides the following solutions:

[0132] Construction of energy consumption evaluation model: Through in-depth analysis of the energy consumption characteristics of the multi-region energy system data center, an energy consumption evaluation model with energy consumption as the objective function is established. At the same time, the delay constraint conditions of delay-sensitive loads are introduced to ensure energy consumption optimization while meeting business needs.

[0133] Establishment of distributed energy scheduling model: Taking the minimization of data center operating costs and energy supply equipment operating costs as the objective function, considering the operating characteristics of multi-regional energy systems (such as energy supply and demand conditions in different regions, etc.), a distributed energy scheduling model with physical constraints is constructed. This model can comprehensively consider the energy supply and demand balance and coordinated optimization among regions to maximize the overall energy utilization efficiency.

[0134] Application of mixed integer linear programming algorithm: Mixed integer linear programming algorithm is used to solve complex optimization problems in distributed energy scheduling models. This algorithm continuously approaches the optimal solution through iterative calculations and can minimize the objective function under the premise of satisfying all constraints. At the same time, since the MILP algorithm has good convergence and stability, it can provide reliable optimization results in practical applications.

[0135] like Figure 1 As shown, an optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital means in an embodiment of the present invention comprises the following steps:

[0136] Step 1: Design a multi-regional energy system data center with the energy consumption minimization as the objective function; construct delay constraints based on delay-sensitive loads, and then establish an energy consumption evaluation model for the multi-regional energy system data center through conversion.

[0137] Among them, the power consumption of data center equipment can be expressed as:

[0138]

[0139] Among them, Q is a set of data centers distributed in different regional energy systems; is the power consumption of data center j; m j is the number of active servers in data center j; θ j is the power consumption of an active server; γ j is the fixed power consumption of data center j during operation.

[0140] Constraints include: server quantity constraint, data load processing delay constraint and data load balance constraint; among which:

[0141] The server quantity constraint can be specifically expressed as:

[0142]

[0143] Among them, M j is the total number of servers in data center j.

[0144] The data load processing delay constraint can be specifically expressed as:

[0145]

[0146] Among them, κ j is the data load received by data center j; is the average service rate of each active server in data center j; W is the delay bound for data load processing.

[0147] The data load balancing constraint can be specifically expressed as:

[0148]

[0149] Among them, L is the total data load that the data center needs to process; θ is the packet loss rate of the data center.

[0150] In the specific implementation, the model is established, including:

[0151] Step 1.1: The energy consumption evaluation model of the data center is summarized, which can be specifically expressed as:

[0152]

[0153] Step 1.2: The decision variables are transformed to obtain the energy consumption assessment model of the data center, which can be specifically expressed as:

[0154]

[0155] Step 2: Taking the minimization of data center operating costs and energy supply equipment operating costs as the objective function, considering the operating characteristics of multi-regional energy systems, constructing physical constraints, and establishing an optimal distributed energy scheduling model for multi-regional energy systems with electrical and digital interconnection.

[0156] The energy consumption evaluation model of the multi-regional energy system data center obtained in step 1 is an important part of the optimal distributed energy scheduling model of the multi-regional energy system with electrical-digital interconnection. The two work together to promote energy efficiency improvement and cost reduction of the energy system through data sharing and optimization target collaboration. The energy consumption evaluation model provides key input for the scheduling model, and the optimization results of the scheduling model further guide the optimization of data center energy consumption, forming a virtuous circle.

[0157] Among them, the objective function includes:

[0158] Based on the cost functions of energy supply equipment operation and data center operation, an economic benefit objective function is established, including:

[0159] F=min{F 1 +F 2};

[0160] Where F is the total cost, F 1 is the operating cost of the energy supply equipment, F 2 It is the cost of running a data center.

[0161] The operating costs of energy supply equipment and data center can be expressed as:

[0162]

[0163] Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, K IDC is the data center power consumption cost factor.

[0164] Physical constraints, including power balance constraints, power supply equipment capacity constraints, and data center operation constraints; among which:

[0165] The power balance constraints are as follows:

[0166]

[0167] Among them, P load It is the load demand of multi-regional energy system.

[0168] The capacity constraints of power supply equipment are as follows:

[0169]

[0170] in, They are the upper and lower power limits of each power supply device.

[0171] The data center operation constraints are as follows:

[0172]

[0173] The energy management model is integrated into a form that includes all coupling constraints, as shown below:

[0174]

[0175] Among them, V d ,V D are the number of power nodes and data center nodes, respectively, jIt is a set of nodes of energy systems in different regions.

[0176] At this point, the optimal distributed energy scheduling model for the multi-regional energy system with electrical and digital interconnection in step 2 has been established.

[0177] The present invention introduces a multi-dimensional objective function in the energy consumption assessment and energy scheduling model. First, the energy consumption of the data center of the multi-regional energy system is taken as the primary objective function, which reflects the ultimate pursuit of data center energy efficiency; secondly, the minimization of the operating cost of the data center and the operating cost of the energy supply equipment is taken as the second objective function, which comprehensively considers the economy and energy efficiency, and achieves dual optimization.

[0178] For common delay-sensitive loads in data centers (such as real-time data processing, online services, etc.), the present invention constructs delay constraints to ensure that the real-time requirements of these key businesses are met while reducing energy consumption, thereby improving the overall service quality of the data center.

[0179] This invention builds a multi-region energy system model with electric-digital interconnection, realizing the deep integration and coordinated optimization of the power system and data center. This innovation not only improves the flexibility and response speed of the energy system, but also promotes the efficient configuration and utilization of energy between different regions.

[0180] Step 3: Based on mixed integer linear programming, design a distributed energy optimization algorithm and formulate an optimal energy scheduling plan.

[0181] In order to solve the complex energy scheduling problem, the present invention adopts the mixed integer linear programming (MILP) algorithm to solve it. The algorithm combines the advantages of integer programming and linear programming and can efficiently handle optimization problems involving discrete variables and continuous variables.

[0182] Among them, the design idea of ​​the distributed energy optimization algorithm is: a distributed iterative scheme based on the combination of dual decomposition and approximate minimization is proposed to solve the inequality coupling problem. Under the convexity assumption and appropriate connectivity of the communication network, the agents reach a consensus on the dual variables, do not disclose information about their optimal decisions, local objectives and constraint functions, and do not disclose information encoding their contributions to the coupling constraints. The algorithm converges to an optimal dual solution to the problem.

[0183] In specific implementation, the distributed energy optimization algorithm and optimal energy scheduling solution include:

[0184] Step 3.1: The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows:

[0185]

[0186] Where V is the number of nodes; Qi is the decision variable.

[0187] Step 3.2: Design the Lagrangian function, which can be expressed as follows:

[0188]

[0189] in, is represented as a vector of Lagrange multipliers.

[0190] Step 3.3: Design the dual problem, which can be expressed as follows:

[0191]

[0192] Step 3.4: At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, Calculate l i (k) is the value of. By minimizing i (k) calculated on L i , for the local original vector Q i (k+1) is updated as follows:

[0193]

[0194] Step 3.5: Update the dual vector as follows:

[0195]

[0196] Step 3.6: In order to narrow the gap with the optimal solution, Auxiliary iterative calculation is as follows:

[0197]

[0198] Step 3.7: Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

[0199]

[0200] Step 3.8: Based on the optimal distributed energy scheduling model of the multi-regional energy system with electrical-digital interconnection in step 2 and the distributed energy optimization algorithm in step 3, calculate and execute the optimal energy scheduling plan.

[0201] At this point, the optimal distributed energy dispatching of a multi-regional energy system with electrical-digital interconnection has been completed. During this period, the power supply of the power supply equipment of the multi-regional energy system, the load processing of the data center and the iteration of the dual variables are shown in the figure below. Figure 2-4 Specifically, the energy scheduling strategy effectively responds to the high demand challenge of computing resources in the multi-regional energy system, ensures the balanced allocation and efficient use of computing resources among regions, and improves the operational reliability and stability of the system. According to the above simulation diagram, it can be seen that the energy scheduling method proposed in the present invention is effective.

[0202] In another embodiment, the present invention further provides an optimal distributed energy dispatching system for a multi-regional energy system interconnected by electricity and digital means, comprising the following steps:

[0203] The energy consumption evaluation model establishment module takes minimizing the energy consumption of the multi-regional energy system data center as the first objective function; constructs delay constraints based on delay-sensitive loads and establishes an energy consumption evaluation model for the multi-regional energy system data center;

[0204] The distributed energy scheduling model establishment module takes the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the second objective function, considers the operating characteristics of the multi-regional energy system, constructs physical constraints, and establishes the optimal distributed energy scheduling model for the multi-regional energy system interconnected by electricity and digital;

[0205] Energy optimization algorithm design module, based on mixed integer linear programming, designs distributed energy optimization algorithms;

[0206] The energy scheduling module calculates and executes the optimal energy scheduling plan based on the optimal distributed energy scheduling model of the multi-regional energy system interconnected by electricity and digital and the distributed energy optimization algorithm.

[0207] Among them, the energy consumption evaluation model of multi-region energy data center is expressed as:

[0208]

[0209] The optimal distributed energy dispatch model for multi-regional energy systems with electrical and digital interconnection is integrated into a form that includes all coupling constraints:

[0210]

[0211] Among them, V d ,V D are the number of power nodes and data center nodes, respectively, j It is a set of nodes of energy systems in different regions.

[0212] Furthermore, based on mixed integer linear programming, a distributed energy optimization algorithm is designed, including:

[0213] The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows:

[0214]

[0215] Where V is the number of nodes; Q i is the decision variable;

[0216] Design the Lagrangian function, which is expressed as follows:

[0217]

[0218] in, It is represented as a vector of Lagrange multipliers;

[0219] Design the dual problem, which is expressed as follows:

[0220]

[0221] At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, Calculate l i (k); by minimizing the i (k) calculated on L i , for the local original vector Q i (k+1) is updated:

[0222]

[0223] The dual vector is updated as follows:

[0224]

[0225] Auxiliary iterative calculation is as follows:

[0226]

[0227] Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

[0228]

[0229] The present invention helps to promote the green and low-carbon transformation of the social economy by increasing the proportion of green energy in the energy system. The present invention can effectively deal with the energy scheduling problem of multi-regional energy systems. For multi-regional computing systems, the present invention establishes an energy consumption evaluation model that considers the impact of data transmission delays. For multi-regional power systems, the objective function is to minimize the operating costs of the power system and the operating costs of the data center, and construct an optimal distributed energy scheduling model for multi-regional energy systems that are interconnected by electricity and data. And based on the mixed integer linear programming of dual decomposition, a distributed energy optimization strategy is designed; finally, the optimal energy scheduling scheme is solved, thereby realizing the optimal distributed energy scheduling of multi-regional energy systems that are interconnected by electricity and data.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimal distributed energy scheduling method for a multi-regional energy system with electrical and digital interconnection, characterized in that: The following steps are involved: The first objective function is to minimize the energy consumption of the multi-regional energy system data center; based on the delay-sensitive load, the delay constraint condition is constructed, and the energy consumption evaluation model of the multi-regional energy system data center is established; Taking the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the second objective function, considering the operating characteristics of the multi-regional energy system, constructing physical constraints, and establishing an optimal distributed energy scheduling model for the multi-regional energy system with electrical-digital interconnection including the energy consumption evaluation model; Based on mixed integer linear programming, a distributed energy optimization algorithm is designed, in which a distributed iterative scheme based on dual decomposition and approximate minimization is used to solve the inequality coupling problem; Based on the optimal distributed energy scheduling model of the multi-regional energy system interconnected by electricity and digital and the distributed energy optimization algorithm, the optimal energy scheduling plan is calculated and executed.

2. The optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital networks according to claim 1 is characterized in that: The first objective function is: Among them, Q is a set of data centers distributed in different regional energy systems; is the power consumption of data center j; m j is the number of active servers in data center j; θ j is the power consumption of an active server; γ j is the fixed power consumption during operation of data center j; The delay constraints include: server quantity constraints, data load processing delay constraints and data load balancing constraints; The server quantity constraint is: Among them, M j is the total number of servers in data center j; The data load processing delay constraint is: Among them, κ j is the data load received by data center j; is the average service rate of each active server in data center j; W is the delay bound for data load processing; The data load balancing constraints are: Where L is the total amount of data load that the data center needs to process; is the packet loss rate of the data center; The energy consumption evaluation model of the multi-region energy data center is expressed as:

3. The optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital networks according to claim 2 is characterized in that: The second objective function comprises: Based on the cost functions of energy supply equipment operation and data center operation, an economic benefit objective function is established, including: F = min{F1+F2}; Among them, F is the total cost, F1 is the operating cost of the energy supply equipment, and F2 is the operating cost of the data center; Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, K IDC is the data center power consumption cost coefficient; The physical constraints include: power balance constraints, power supply equipment capacity constraints and data center operation constraints; The power balance constraint is: Among them, P load is the load demand of the multi-regional energy system; The power supply equipment capacity constraint is: in, They are the upper and lower limits of the power of each power supply device; The data center operation constraints are: The optimal distributed energy dispatch model for the multi-regional energy system with electrical and digital interconnection is integrated into a form that includes all coupling constraints: Among them, V d ,V D are the number of power nodes and data center nodes, respectively, j It is a set of nodes of energy systems in different regions.

4. The optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital networks according to claim 1 is characterized in that: Based on mixed integer linear programming, design distributed energy optimization algorithm, including: The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows: Where V is the number of nodes; Q i is the decision variable; Design the Lagrangian function, which is expressed as follows: in, Expressed as is a vector of Lagrange multipliers; Design the dual problem, which is expressed as follows: At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, calculate By minimizing the value of The calculated L i , for the local original vector Q i (k+1) is updated: The dual vector is updated as follows: Auxiliary iterative calculation is as follows: Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

5. An optimal distributed energy dispatching system for multi-regional energy systems with electrical and digital interconnection, characterized in that: The following steps are involved: The energy consumption evaluation model establishment module takes minimizing the energy consumption of the multi-regional energy system data center as the first objective function; constructs delay constraints based on delay-sensitive loads and establishes an energy consumption evaluation model for the multi-regional energy system data center; A distributed energy scheduling model establishment module takes the minimization of the operating cost of the data center and the operating cost of the energy supply equipment as the second objective function, considers the operating characteristics of the multi-regional energy system, constructs physical constraints, and establishes an optimal distributed energy scheduling model for the multi-regional energy system with electrical-digital interconnection including the energy consumption evaluation model; The energy optimization algorithm design module designs a distributed energy optimization algorithm based on mixed integer linear programming. In the distributed energy optimization algorithm, a distributed iterative scheme based on dual decomposition and approximate minimization is used to solve the inequality coupling problem. The energy scheduling module calculates and executes the optimal energy scheduling plan based on the optimal distributed energy scheduling model of the multi-regional energy system interconnected by electricity and digital and the distributed energy optimization algorithm.

6. The optimal distributed energy dispatching system for multi-regional energy systems interconnected by electricity and digital networks according to claim 5 is characterized in that: The first objective function is: Among them, Q is a set of data centers distributed in different regional energy systems; is the power consumption of data center j; m j is the number of active servers in data center j; θ j is the power consumption of an active server; γ j is the fixed power consumption during operation of data center j; The delay constraints include: server quantity constraints, data load processing delay constraints and data load balancing constraints; The server quantity constraint is: Among them, M j is the total number of servers in data center j; The data load processing delay constraint is: Among them, κ j is the data load received by data center j; is the average service rate of each active server in data center j; W is the delay bound for data load processing; The data load balancing constraints are: Where L is the total amount of data load that the data center needs to process; is the packet loss rate of the data center; The energy consumption evaluation model of the multi-region energy data center is expressed as:

7. The optimal distributed energy scheduling method for a multi-regional energy system interconnected by electricity and digital networks according to claim 5 is characterized in that: The second objective function comprises: Based on the cost functions of energy supply equipment operation and data center operation, an economic benefit objective function is established, including: F = min{F1+F2}; Among them, F is the total cost, F1 is the operating cost of the energy supply equipment, and F2 is the operating cost of the data center; Among them, F w ,F pv ,F fu is the cost of wind power generation, photovoltaic power generation and fossil fuel power generation, K IDC is the data center power consumption cost coefficient; The physical constraints include: power balance constraints, power supply equipment capacity constraints and data center operation constraints; The power balance constraint is: Among them, P load is the load demand of the multi-regional energy system; The power supply equipment capacity constraint is: in, They are the upper and lower limits of the power of each power supply device; The data center operation constraints are: The optimal distributed energy dispatch model for the multi-regional energy system with electrical and digital interconnection is integrated into a form that includes all coupling constraints: Among them, V d ,V D are the number of power nodes and data center nodes, respectively, j It is a set of nodes of energy systems in different regions.

8. The optimal distributed energy dispatching system for multi-regional energy systems interconnected by electricity and digital networks according to claim 5 is characterized in that: Based on mixed integer linear programming, design distributed energy optimization algorithm, including: The energy dispatch problem of multi-regional energy system is a mixed integer linear programming problem, which can be expressed as follows: Where V is the number of nodes; Q i is the decision variable; Design the Lagrangian function, which is expressed as follows: in, It is represented as a vector of Lagrange multipliers; Design the dual problem, which is expressed as follows: At each iteration, each agent i estimates the dual variable λ j (k),j=1,...,m, calculate By minimizing the value of The calculated L i , for the local original vector Q i (k+1) is updated: The dual vector is updated as follows: Auxiliary iterative calculation is as follows: Using two different sequences Q i (k) Correction, Q i (k) is the weighted average and optimal solution of the energy dispatch problem of the multi-regional energy system, expressed as follows:

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