Multi-energy network double-layer optimization method and system based on privacy-preserving blockchain technology

Abstract

A multi-energy network double-layer optimization method and system based on privacy-preserving blockchain technology. The method comprises: constructing a microgrid lower-layer optimization model that takes the sum of an electricity purchasing cost and a gas purchasing cost as an optimization objective; collecting information of all microgrids in a cluster, converting an optimization problem of a lower-layer autonomous optimization model, and further establishing a microgrid collaborative optimization upper-layer model; while balancing supply and demand and limiting a transformer capacity within each time period, enabling the microgrids to establish, within remaining time period, an objective function with the objective of minimizing a total cost; and integrating a distributed system manager inside a blockchain system to manage a blockchain network, and finally establishing an interconnected multi-energy microgrid double-layer optimization model based on privacy-preserving blockchain technology. The energy optimization problem is modeled into a double-layer optimization problem based on blockchain technology, so that the decision-making mechanism is simpler, the optimization performance is better, the transaction process is safer, and the transaction cost is lower.

Description

Multi-energy network double-layer optimization method and system based on privacy protection blockchain technology TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid energy optimization, in particular to a multi-energy network double-layer optimization method and system based on privacy protection blockchain technology. BACKGROUND

[0002] The continuous deterioration of the environment and the increasing depletion of energy make energy management increasingly complex. In order to improve the comprehensive utilization efficiency of energy in a multi-energy system, various forms of energy networks need to be optimized in collaboration, so that energy management is more economical. Researching collaborative optimization management of a multi-energy system can start with a micro-grid, which is a special case of a multi-energy system. As a small power supply network, it has many advantages, such as distributed energy management, which can more flexibly manage energy generation and consumption, and can also effectively manage the use of renewable resources. A micro-grid can further optimize energy utilization by managing the demand side and energy storage devices, so that various energies can be fully utilized. Limited by the capacity of a single micro-grid, its power supply capacity is weak and energy utilization efficiency is low. In order to solve this problem, optimizing the energy management of a micro-grid cannot stop at a single micro-grid, but should combine multiple interconnected micro-grids to form a micro-grid cluster, and then perform collaborative optimization management on the micro-grid cluster.

[0003] Current research on micro-grid energy optimization and collaborative management technology mainly focuses on energy management strategies, energy optimization algorithms, micro-grid energy collaborative optimization strategies, and energy market transaction mechanisms. Domestic and foreign technologies and methods mainly focus on research on distributed energy management systems, intelligent energy management systems, virtual power platforms, and blockchain technologies, with insufficient research on micro-grid energy optimization targeting internal autonomy and collaborative optimization between clusters. Through internal autonomous optimization of a micro-grid, its operating costs can be minimized. Through collaborative optimization of a micro-grid cluster, each micro-grid in the cluster can achieve optimal energy trading and energy scheduling according to actual demand, which can significantly reduce energy trading costs and maximize energy utilization efficiency.

[0004] At present, in the domestic micro-grid energy management, there is a networked micro-grid distributed energy management method, which integrates distributed generators and intelligent devices into the power distribution network, transitions from a hierarchical structure to a distributed structure, proposes to coordinate multiple distributed power generation systems in a power market environment, uses a second-order conic programming model and an alternating direction multiplier to solve the energy management problem in a distributed manner, but it is not suitable for network structures with more complex configurations; there is a method of solving the uncertainty problem of renewable energy and load through multi-scenario stochastic programming and model predictive control, thereby establishing a multi-time scale coordinated optimization model, effectively reducing uncertainty and volatility, and realizing the coordinated optimization operation of the multi-energy system, but the model practicability and generalization are weak, and it is not close to the actual application scene; there is a method of establishing a multi-energy micro-grid double-layer optimization model to cope with the uncertainty of renewable energy and load, introducing an opportunity constraint in the inner model, replacing the traditional deterministic constraint in the form of a probability constraint, but the calculation complexity is high, more calculation time and resources are needed, and the model parameter selection is sensitive.

[0005] In view of this, it is necessary to improve the micro-grid energy optimization technology. SUMMARY

[0006] In view of the above problems, the present application is proposed.

[0007] Therefore, the problem to be solved by the present application is that the existing micro-grid energy management method has the problems of not being suitable for network structures with more complex configurations, weak model practicability and generalization, and the need for more calculation time and resources, and the model parameter selection is sensitive.

[0008] To solve the above technical problems, the application provides the following technical scheme: a multi-energy network double-layer optimization method based on a privacy protection blockchain technology, which comprises the following steps: constructing a microgrid lower-layer optimization model with power capacity constraints, combined heat and power (CHP) equipment capacity constraints, CHP and electric boiler temperature rising constraints, maximum charge and discharge constraints of energy storage devices, movable load related constraints, state of charge upper and lower bound constraints, target state of charge constraints, and microgrid power balance constraints as constraint conditions, and with the sum of power purchase cost and gas purchase cost as an optimization target; collecting information of all microgrids in a cluster, converting an optimization problem of the lower-layer autonomous optimization model, and further establishing a microgrid collaborative optimization upper-layer model based on a Lagrange dual decomposition method and distributed transactions; establishing a superior entity for coordinating interconnected microgrids and managing interconnected network operation, so that the overall balance is achieved in supply and demand, the supply and demand and transformer capacity are balanced in each time period, and a target function is established with the minimum total cost as the target in the remaining time period; using a distributed system manager to perform transactions, integrating the distributed system manager into a blockchain system to manage the blockchain network, and finally establishing an interconnected multi-energy microgrid double-layer optimization model based on the privacy protection blockchain technology.

[0009] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, the conversion of the optimization problem of the lower-layer autonomous optimization model comprises converting the microgrid lower-layer optimization problem P1 into a convex optimization problem P2, specifically comprising proving the equivalence of the optimal solutions of the problems P1 and P2, converting the non-convex problem P1 caused by the mutual exclusivity of the charge and discharge power of the electric energy storage and the thermal energy storage into the convex optimization problem P2, and then converting the solution of the problem P1 into the solution of the relaxed problem P2.

[0010] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, the objective function of the lower-layer optimization model is the comprehensive minimum of the microgrid power purchase cost and gas purchase cost, and is expressed by the formula,

[0011]

[0012] wherein μ e,t represents the public electricity price, μ g,t represents the natural gas price, P e,t represents the electric quantity input from the main network by the microgrid at time t, and respectively represent the natural gas consumed by the CHP and the GF, t c represents the current time, t e represents the end time.

[0013] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, the objective function of the upper-layer optimization model is the comprehensive minimum of the purchase cost and purchase gas cost of all microgrids, and the formula is expressed as,

[0014]

[0015] Wherein, n represents a microgrid, and N represents the total number of microgrids.

[0016] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, the distributed transaction is a coexistence mode of energy transaction and coordinated control between microgrids and main grids, and between microgrids, including selling the excess energy of the microgrid to other microgrids in need through the distributed system manager, and optimizing the energy distribution of the entire microgrid cluster according to the energy demand and supply of each microgrid.

[0017] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, the distributed system manager registers and authenticates the participants by managing and coordinating the energy transaction participants; the distributed system manager coordinates the data transmission and communication by processing the data exchange and updating of the energy transaction, ensures the real-time and accuracy of the transaction, makes the microgrid interact with the distributed system manager by sending the transaction, the distributed system manager accepts the transaction and performs the corresponding operation to verify the validity of the transaction, ensures the execution of the transaction rules, and triggers the corresponding payment and settlement operation; the distributed system manager is also used for coordinating the fault detection and recovery mechanism.

[0018] As a preferred scheme of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology, wherein the privacy protection blockchain technology comprises: when the micro-grid needs to sell the surplus power, the micro-grid sends a transaction request as a transaction initiator, the transaction starts, when the seller and the buyer send their digital signature proposals to the transaction system, the distributed system manager starts, when the distributed system manager receives the proposal, the optimization algorithm starts, and a solution is obtained; the purchase rule of the buyer is not dependent on a single power source, but is based on the solution to select the seller that best meets the demand, and directly trades with the seller to generate order information; the distributed system manager verifies the order information, finds a solution with minimum energy loss, and executes the transaction after confirming that the result is valid and legal; the distributed system manager transfers the transaction money to the seller and the purchased energy to the buyer, if the verification fails, the original transaction is cancelled, the distributed system manager will match the buyer again, and the new verification is performed again; after the energy transaction is completed, the distributed system manager packages and records all the transaction information into the blockchain; after the transaction is completed, if there is a dispute between the seller and the buyer, arbitration is applied, the original ciphertext is updated after the arbitration result is generated, and a new transaction record is formed.

[0019] Another object of the present application is to provide a multi-energy network double-layer optimization system based on privacy protection blockchain technology, which can plan resource scheduling between micro-grids.

[0020] To solve the above technical problems, the application provides the following technical scheme: a system of a multi-energy network double-layer optimization method based on a privacy protection blockchain technology, comprising: a lower-layer optimization model establishing module, an upper-layer optimization model establishing module, a target function establishing module, and a micro-grid double-layer optimization model establishing module; the lower-layer optimization model establishing module is used for constructing a micro-grid lower-layer optimization model with power capacity constraints, combined heat and power equipment capacity constraints, combined heat and power and electric boiler temperature rising constraints, energy storage device maximum charging and discharging constraints, movable load related constraints, state of charge upper and lower bound constraints, target state of charge constraints, and micro-grid power balance constraints as constraint conditions, and with the sum of power purchase cost and gas purchase cost as an optimization target; the upper-layer optimization model establishing module collects information of all micro-grids in a cluster, converts the optimization problem of the lower-layer autonomous optimization model, and further establishes a micro-grid collaborative optimization upper-layer model based on a Lagrange dual decomposition method and distributed transactions; the target function establishing module is used for establishing an upper-level entity for coordinating interconnected micro-grids and managing interconnected network operation, so that the overall balance is achieved in supply and demand, the supply and demand and transformer capacity are balanced in each time period, and the micro-grid is established to minimize the total cost in the remaining time period; and the micro-grid double-layer optimization model establishing module uses a distributed system manager to perform transactions, integrates the distributed system manager inside a blockchain system to manage the blockchain network, and finally establishes an interconnected multi-energy micro-grid double-layer optimization model based on the privacy protection blockchain technology.

[0021] A computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology when executing the computer program.

[0022] A computer readable storage medium stores a computer program, and the computer program implements the steps of the multi-energy network double-layer optimization method based on the privacy protection blockchain technology when executed by a processor.

[0023] The application has the following beneficial effects: the application establishes an interconnected multi-energy micro-grid double-layer optimization model based on the privacy protection blockchain technology for micro-grid energy optimization problems. In the lower layer, the random characteristics of the rolling optimization load and renewable energy of each micro-grid are used to realize autonomous optimization of the micro-grid; in the upper layer, distributed transactions are used to realize distributed optimization, and a distributed system manager is used to solve the cooperation and interaction problem. The optimization model meets the time requirement of real-time operation, and at the same time, the scalability, information privacy and operation authority of each micro-grid in the cluster are retained, the energy transaction and data are protected, and the security of the micro-grid cluster is improved.

[0024] Compared with the existing micro-grid energy optimization method, the lower layer model solves the internal optimization demand, the upper layer optimization solves the global optimization demand, and the distributed system manager coordinates and manages the participants and components of energy transaction to realize transaction optimization. The energy optimization problem is modeled as a double-layer optimization problem based on blockchain technology, the decision mechanism is simpler, the optimization performance is better, the transaction process is safer, the transaction cost is lower, and it can be used as a supplement to the existing micro-grid energy transaction management optimization method. BRIEF DESCRIPTION OF DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work. Among them:

[0026] Figure 1 is a step diagram of the multi-energy network double-layer optimization method based on privacy protection blockchain technology in embodiment 1.

[0027] Figure 2 is a double-layer structure diagram of the multi-energy network double-layer optimization method based on privacy protection blockchain technology in embodiment 1.

[0028] Figure 3 is an energy transaction diagram of the multi-energy network double-layer optimization method based on privacy protection blockchain technology in embodiment 1.

[0029] Figure 4 is a flowchart of the energy transaction mechanism of the multi-energy network double-layer optimization method based on privacy protection blockchain technology in embodiment 1. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0031] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0032] Embodiment 1

[0033] Referring to Figures 1-4, the first embodiment of the present application provides a multi-energy network double-layer optimization method based on privacy protection blockchain technology, which includes a kind of interconnected multi-energy micro-grid double-layer optimization method based on privacy protection blockchain technology of the present application, as shown in Figure 1, including the following steps:

[0034] Step S1, obtain the output data of distributed photovoltaic and wind turbine in the micro-grid from historical data. According to the parameters of each device in the micro-grid, the future power generation, load demand and other parameters of the internal devices of the micro-grid are predicted and analyzed, which lays a data foundation for the establishment of the model and improves the prediction effect of the model.

[0035] Step S2, construct a micro-grid lower optimization model with power capacity constraints, capacity constraints of combined heat and power devices, temperature constraints of combined heat and power and electric boiler, maximum charging and discharging constraints of energy storage devices, movable load related constraints, upper and lower bound constraints of state of charge, target state of charge constraints, micro-grid power balance constraints, etc. as constraint conditions, and the sum of power purchase cost and gas purchase cost as the optimization target. As shown in Figure 2.

[0036] Step S3, collect the information of all micro-grids in the cluster, and on the basis of the lower autonomous optimization model, further establish a micro-grid collaborative optimization upper model based on Lagrange dual decomposition method and distributed transaction. Establish the upper entity of system coordinator to coordinate interconnected micro-grids and manage the operation of interconnected network, so as to achieve overall balance between supply and demand. Balance the supply and demand and limit the transformer capacity in each time period, and minimize the total cost in the remaining time period.

[0037] Step S4, the micro-grid interacts with the distributed system manager through sending transaction, the distributed system manager accepts the transaction and performs corresponding operation to verify the validity of the transaction, ensures the execution of transaction rules, and triggers corresponding payment and settlement operation. Through coordinating fault detection and recovery mechanism, it ensures the continuity and reliability of transaction. Through the distributed system manager, the transaction energy can realize more efficient, safe and reliable transaction process, so that these data changes are permanently saved on the block chain and visible to all participants.

[0038] Step S5, as shown in Fig. 3, the privacy protection blockchain technology is applied to the energy transaction, when the micro-grid needs to sell the surplus energy, the transaction request can be sent as a transaction initiator, and the transaction starts. As shown in Fig. 4, when the seller and the buyer send their digital signature proposals to the transaction system, the distributed system manager starts. When the distributed system manager receives the proposal, the optimization algorithm starts to obtain the solution. The buyer will not need to rely on a single energy source, but will choose the seller who best meets their needs and directly trade with them. The distributed system manager will verify the order information, find the solution with the minimum energy loss, and execute the transaction after confirming that the result is valid and legal. The distributed system manager will transfer the transaction money to the seller and the purchased energy to the buyer. If the verification fails, the original transaction is cancelled, and the distributed system manager will perform a new matching and again perform a new verification. After the energy transaction is completed, the distributed system manager will package and record all transaction information into the blockchain. After the transaction is completed, if there is a dispute between the two parties, arbitration can be applied. After the arbitration result is generated, the original ciphertext is updated to form a new transaction record to ensure the effectiveness of the arbitration.

[0039] Step S6, based on the above steps, a double-layer optimization model of interconnected multi-energy micro-grid based on privacy protection blockchain technology is established, the double-layer optimization model of interconnected multi-energy micro-grid based on privacy protection blockchain technology is solved, and the optimal control of micro-grid cluster energy transaction management is obtained.

[0040] Embodiment 2

[0041] The second embodiment of the present application is different from the first embodiment in that the double-layer optimization method of multi-energy grid based on privacy protection blockchain technology further comprises, for the purpose of verifying the technical effects adopted in the method, the test results are obtained by scientific demonstration means to verify the real effects of the method.

[0042] I. Assumed test conditions

[0043] In order to verify the effectiveness of the double-layer optimization model of interconnected multi-energy micro-grid based on privacy protection blockchain technology, the present application is programmed and simulated on the basis of a micro-grid cluster composed of three micro-grids. Micro-grid 1 and micro-grid 2 are residential areas, and micro-grid 3 is a commercial area. The power limit of the main transformer is 2.25MW. The connection line limits of micro-grid 1, micro-grid 2 and micro-grid 3 are 1.1MW, 2.25MW and 1.2MW respectively, and the control period is 1h. The specific parameters of each micro-grid are shown in Table 1, Table 2 and Table 3.

[0044] Table 1 Parameters of micro-grid 1

[0045]

[0046] Table 2 Microgrid 2 parameters

[0047]

[0048]

[0049] Table 3 Microgrid 3 parameters

[0050]

[0051] The renewable energy output curve, the electrical load curve and the thermal load curve of each microgrid in the cluster in a typical day, wherein the benchmark wind power output value is 600MW, and the benchmark photovoltaic power output value is 600MW.

[0052] In order to verify the effectiveness of the interconnection multi-energy microgrid double-layer optimization model based on the privacy protection blockchain technology, the present application is programmed in the MATLAB environment, the computer environment is Windows 11, the memory RAM is 16GB, the processor CPU is Inter(R) Core(TM) i7, and the main frequency is 1.8GHz.

[0053] II. Model establishment and solution

[0054] Step 1, establish the lower layer model:

[0055] (1) the lower layer model including combined heat and power (CHP) device, natural gas furnace (GF), electrical energy storage (EES) and thermal energy storage (TES) is established, and is expressed by a formula as follows:

[0056]

[0057] Wherein, P e,t represents the electrical quantity input from the main grid to the microgrid at t moment, P e,t <0 represents the remaining electrical quantity sold by the microgrid to the grid. and respectively represent the natural gas consumed by CHP and GF. ee represents the transmission efficiency. and respectively represent the gas-electricity efficiency and gas-heat efficiency of the combined heat and power CHP. represents the natural gas boiler GF efficiency, represents the electric boiler EB efficiency. represents the electric boiler power consumption. represents the energy generated by the renewable energy in the maximum power point tracking mode. and respectively represent the charging power and discharging power of EES. and Qin and Qout represent the charge and discharge quantities of TES, respectively. and Lmov and Lth represent the mobile electrical and thermal loads, respectively. e,t and L th,t represent the corresponding non-mobile loads. and Preg and Pth represent the reduced renewable energy power and thermal energy, respectively.

[0058] (2) Explicit each constraint condition:

[0059] Power capacity constraints:

[0060]

[0061] where, and Pmax and Pmin represent the maximum and minimum exchange power of the connection line, respectively.

[0062] Cogeneration, natural gas boiler, electric boiler constraints:

[0063]

[0064]

[0065]

[0066] where, Pcog, Pgas, and Pelec represent the installed capacity of cogeneration, natural gas boiler, and electric boiler, respectively; P CHP , P GF , P EB represent the corresponding power lower limits, respectively.

[0067] Cogeneration and electric boiler temperature rise constraints:

[0068]

[0069]

[0070] where, ΔP CHP and ΔP EB represent the hourly ramp rate of cogeneration and electric boiler, respectively.

[0071] Maximum charge / discharge power constraints:

[0072]

[0073]

[0074] where, and Pmax and Pmin represent the maximum and minimum charge / discharge power of EES, respectively. and denote the maximum charge and discharge capacity of TES, respectively.

[0075] EES and TES charge and discharge power constraints:

[0076]

[0077]

[0078] Constraints related to movable load:

[0079]

[0080]

[0081] where t c is the current time, and t e is the end time.

[0082] Reduced RES power upper limit and thermal energy upper limit constraints:

[0083]

[0084]

[0085] Upper and lower bounds of state of charge constraints:

[0086]

[0087]

[0088] where η ch and η dch are charge and discharge efficiencies; S max and S min are upper and lower bounds of SOC.

[0089] Target SOC constraints:

[0090]

[0091]

[0092] where t c is the current time, and t e is the end time; denotes the current SOC; S target denotes the target SOC.

[0093] (3) The objective function of the lower-level optimization model is constructed as the minimum value of the comprehensive purchase cost of electricity and gas in the microgrid, which is expressed as:

[0094]

[0095] The lower-level problem P1 is constructed by the objective function and constraints above. Since problem P1 does not satisfy the Slater condition, the distributed mechanism cannot be applied to the joint optimization of each of the multiple interconnected microgrids described in this non-convex form. Therefore, a method is proposed to relax these nonlinear constraints so that P1 can be convexly optimized as P2, i.e., P2 does not consider the constraints of EES and TES charging and discharging power, and the relationship between the optimal solutions of P1 and P2 is equivalent.

[0096] Step 2, establish the upper model:

[0097] (1) The objective of the upper model is to balance supply and demand and limit transformer capacity while minimizing the total cost in the remaining time period. The optimization modeling is as follows:

[0098]

[0099]

[0100]

[0101] where n is the number of microgrids. is the amount of electricity input from the main grid to the transformer. and represent the maximum power traded with the main grid.

[0102] (2) The Lagrange dual decomposition method and distributed trading are used to solve the upper optimization model. The Lagrange relaxation dual problem is as follows:

[0103]

[0104]

[0105] where L is the Lagrange multiplier introduced after the power balance constraint Lagrangian relaxation function:

[0106]

[0107] Since the original upper model is linear, the strong duality theorem holds, and the optimal value of the improved upper model is equivalent to the original upper model. For Define the local electricity price λ e,t = μ e,t + λ t , the improved upper model is decomposed into a master problem and N+1 sub-problems. The master problem is the microgrid agent adjusting the local electricity price vector Achieve overall supply and demand balance. N+1 sub-problems are to minimize the cost of each micro-grid at the local electricity price The transformer maximizes its profit by exchanging power with the main grid.

[0108] Step 3, establish a micro-grid energy trading model based on privacy protection blockchain technology:

[0109] (1) When the energy selling micro-grid needs to sell the remaining energy, it can act as a transaction initiator to issue a transaction request, and the transaction begins. The energy buying micro-grid obtains the current market price from the distributed system manager, and then establishes a buying order according to its own energy usage.

[0110] (2) When the seller and the buyer send their digital signature proposals to the distributed system manager, the distributed system manager will start.

[0111] (3) When the distributed system manager receives the proposal, the optimization algorithm will start, and the solution will be obtained through the matching algorithm. The distributed system manager will verify the matched order information to find the optimal transaction scheme. Then the distributed system manager will confirm that the result is valid and legal before executing the transaction.

[0112] (4) The distributed system manager transfers the transaction money to the seller and the purchased energy to the buyer. If the verification fails, the original transaction is cancelled, and the distributed system manager will perform a new matching and again perform a new verification.

[0113] (5) After the completion of the energy transaction, the distributed system manager will package and record all transaction information into the blockchain, and at the same time can query all transaction information in the blockchain, providing a fair and transparent transaction environment for further transaction strategies of micro-grids.

[0114] (6) After the completion of the transaction, if there is a dispute between the two parties, arbitration can be applied. After the arbitration result is generated, the original ciphertext will be updated to form a new transaction record to ensure the effectiveness and legality of the arbitration. At the same time, the new transaction record will also be sent to the blockchain. Each micro-grid can complete the transaction record and income distribution based on the distributed system manager, and at the same time each micro-grid can query and browse the information of its own account and all transaction information in the whole process of transaction, ensuring the fairness of the transaction.

[0115] Example 3

[0116] The third embodiment of the present application, which is different from the first two embodiments, is a multi-energy network double-layer optimization method system based on privacy protection blockchain technology, comprising a lower layer optimization model establishment module, an upper layer optimization model establishment module, a target function establishment module and a micro-grid double-layer optimization model establishment module; the lower layer optimization model establishment module is used to construct a micro-grid lower layer optimization model with power capacity constraints, combined heat and power equipment capacity constraints, combined heat and power and electric boiler temperature rising constraints, maximum charge and discharge constraints of energy storage devices, movable load related constraints, state of charge upper and lower bound constraints, target state of charge constraints, micro-grid power balance constraints as constraint conditions, and the sum of power purchase cost and gas purchase cost as the optimization target; the upper layer optimization model establishment module collects information of all micro-grids in the cluster, converts the optimization problem of the lower layer autonomous optimization model, and further establishes a micro-grid collaborative optimization upper layer model based on the Lagrange dual decomposition method and distributed transactions; the target function establishment module is used to establish an upper entity for coordinating interconnected micro-grids and managing interconnected network operation, so that the overall balance is achieved in supply and demand, the supply and demand and transformer capacity are balanced in each time period, and the micro-grid minimizes the total cost in the remaining time period; the micro-grid double-layer optimization model establishment module uses a distributed system manager to conduct transactions, integrates the distributed system manager inside the blockchain system to manage the blockchain network, and finally establishes an interconnected multi-energy micro-grid double-layer optimization model based on privacy protection blockchain technology.

[0117] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the prior art that contribute essentially or the parts of the technical solutions can be embodied in the form of software products, which are stored in a storage medium and include instructions for making a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in various embodiments of the present application. The foregoing storage medium includes U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and various program code storage media.

[0118] The logic and / or steps represented in the flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0119] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that can be later executed by a computer.

[0120] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, known in the art, or combinations thereof, can be used: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), and / or the like.

[0121] It should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application but not limit the present application, and although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A multi-energy grid double-layer optimization method based on privacy protection blockchain technology, characterized in that: The micro-grid lower-layer optimization model includes the following constraints: power capacity constraints, combined heat and power (CHP) capacity constraints, heating and power temperature rise constraints, maximum charge and discharge constraints of energy storage devices, movable load-related constraints, state of charge (SOC) upper and lower bound constraints, target SOC constraints, and micro-grid power balance constraints, and the optimization objective is the sum of the purchase cost of electricity and the purchase cost of gas. The information of all micro-grids in the cluster is collected, the optimization problem of the lower-layer autonomous optimization model is converted, and a micro-grid collaborative optimization upper-layer model is further established based on the Lagrange dual decomposition method and distributed transactions. The upper-level entity is established to coordinate interconnected micro-grids and manage the operation of the interconnected network, so that the overall balance between supply and demand is achieved at the time of supply and demand, and the target function is established with the minimum total cost as the objective in the remaining time period while balancing the supply and demand and limiting the transformer capacity in each time period. The distributed system manager is used for transaction management, and the distributed system manager is integrated in the blockchain system to manage the blockchain network, and finally an interconnected multi-energy micro-grid double-layer optimization model based on a privacy protection blockchain technology is established. The conversion of the optimization problem of the lower-layer autonomous optimization model includes converting the micro-grid lower-layer optimization problem P1 into a convex optimization problem P2, which specifically includes proving the equivalence of the optimal solutions of problems P1 and P2, and converting the non-convex problem P1 caused by the mutual exclusion of the charge and discharge power of the electric energy storage and the thermal energy storage into the convex optimization problem P2, so that solving the problem P1 is converted into solving the problem P2 by solving the relaxed problem P2.

2. The multi-energy grid bi-level optimization method based on privacy-protecting blockchain technology according to claim 1, wherein: Wherein, n represents a micro-grid, and N represents the total number of micro-grids.

3. The multi-energy grid bi-level optimization method based on privacy-protected blockchain technology according to claim 2, wherein: The objective function of the lower layer optimization model is the minimum value of the micro-grid electricity and gas purchase cost, which is expressed as, where μ e,t is expressed as the public electricity price, μ g,t is expressed as the natural gas price, P e,t is expressed as the amount of electricity imported from the main grid at time t by the microgrid, and Natural gas expressed as CHP and GF consumption, t c Expressed as current time, t e Expressed as end time.

4. The multi-energy grid bi-level optimization method based on privacy-protected blockchain technology according to claim 3, characterized in that: The objective function of the upper optimization model is the comprehensive minimum of the purchase power cost and purchase gas cost in all microgrids, which is expressed by a formula, The distributed transaction is a coexistence mode of energy transaction and coordination control between micro-grids and main grids, and between micro-grids, which includes selling excess energy of micro-grids to other micro-grids in need through the distributed system manager, and optimizing the energy distribution of the entire micro-grid cluster according to the energy demand and supply of each micro-grid.

5. The multi-energy grid bi-level optimization method based on privacy-protecting blockchain technology according to claim 4, wherein: The distributed system manager registers and authenticates participants by managing and coordinating energy transaction participants; 6. The multi-energy grid bi-level optimization method based on privacy-protecting blockchain technology according to claim 5, wherein: The distributed system manager coordinates data transmission and communication by processing data exchange and updating of energy transactions, ensures the real-time and accuracy of transactions, and enables micro-grids to interact with the distributed system manager by sending transactions, the distributed system manager accepts transactions and performs corresponding operations to verify the validity of transactions, ensures the execution of transaction rules, and triggers corresponding payment and settlement operations; The distributed system manager is also used for coordinating fault detection and recovery mechanism. The privacy protection blockchain technology includes that when a micro-grid needs to sell excess electricity, the micro-grid initiates a transaction request as a transaction initiator, the transaction starts, and when the seller and the buyer send their digital signature proposals to the transaction system, the distributed system manager starts, and when the distributed system manager receives the proposal, the optimization algorithm starts to obtain a solution; 7. The multi-energy grid bi-level optimization method based on privacy-protected blockchain technology according to claim 6, wherein: The purchase rules of the buyer are not dependent on a single power source, but based on the solution to select the seller that best meets the demand and directly transact with the seller to generate order information. ​ The distributed system manager verifies order information, finds a solution with minimum energy loss, and executes the transaction after confirming that the result is valid and legal; The distributed system manager transfers the transaction money to the seller and the purchased energy to the buyer, and if the verification fails, the original transaction is cancelled, the distributed system manager will match the buyer again, and the new verification is performed again; After the energy transaction is completed, the distributed system manager packages and records all transaction information into the blockchain; After the transaction is completed, if the seller and the buyer have a dispute, arbitration is applied, and after the arbitration result is generated, the original ciphertext is updated to form a new transaction record. 8.A system employing the multi-energy grid bi-level optimization method based on the privacy protection blockchain technology according to any one of claims 1 to 7, characterized in that: The lower optimization model establishment module, the upper optimization model establishment module, the objective function establishment module, and the micro-grid double-layer optimization model establishment module are included. The lower optimization model establishment module is configured to construct a micro-grid lower optimization model with power capacity constraints, combined heat and power equipment capacity constraints, combined heat and power and electric boiler temperature constraints, maximum charge and discharge constraints of energy storage devices, movable load related constraints, state of charge upper and lower bound constraints, target state of charge constraints, and micro-grid power balance constraints as constraint conditions, and with the sum of power purchase cost and gas purchase cost as an optimization target. The upper optimization model establishment module collects information of all micro-grids in the cluster, converts the optimization problem of the lower autonomous optimization model, and further establishes a micro-grid collaborative optimization upper model based on the Lagrange dual decomposition method and distributed transaction. The objective function establishment module is configured to establish an upper entity for coordinating interconnected micro-grids and managing interconnected network operation, so that the overall balance is achieved in supply and demand, the supply and demand and transformer capacity are balanced in each time period, and the micro-grid is established to minimize the total cost in the remaining time period. The micro-grid double-layer optimization model establishment module uses the distributed system manager for transaction, integrates the distributed system manager inside the blockchain system to manage the blockchain network, and finally establishes an interconnected multi-energy micro-grid double-layer optimization model based on privacy protection blockchain technology. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the multi-energy grid double-layer optimization method based on privacy protection blockchain technology in any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the multi-energy grid double-layer optimization method based on privacy protection blockchain technology in any one of claims 1 to 7.

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