Comprehensive energy scheduling model construction method based on alternating direction multiplier method

Abstract

The invention discloses a comprehensive energy scheduling model construction method and system based on an alternating direction multiplier method, and relates to the technical field of power systems. A comprehensive energy load comprehensive demand response model, an electric vehicle ordered charging model and a virtual heat storage heat supply network energy flow calculation model are combined to construct a comprehensive energy system optimization scheduling model; carrying out distributed solution on the interconnected integrated energy system by adopting an alternating direction multiplier method to obtain a multi-region distributed self-collaborative optimization scheduling strategy; and dividing the integrated energy system into a plurality of main bodies, and carrying out distributed solution by adopting an alternating direction multiplier method so as to realize a global target of lowest operation and maintenance cost of each main body and obtain a distributed self-collaborative optimization scheduling strategy among the multiple resource main bodies. According to the method, an optimal scheduling model is solved by adopting an alternating direction multiplier method, so that similar effects can be realized through interaction of necessary boundary variables (boundary power) among main bodies.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for constructing a comprehensive energy dispatching model based on an alternating direction multiplier method. Background Art

[0002] The participation of the integrated energy system in the flexible dispatching of the distribution network level can more fully tap the flexibility and adjustable potential of the power grid. At the same time, through the mutual conversion and utilization of energy, it can make resource allocation more reasonable, improve the renewable energy consumption rate, and generate good social value and environmental benefits. By adopting reasonable control strategies, the operating costs of integrated energy operators can be reduced, thereby generating good corporate economic benefits.

[0003] Considering the situation where there are divisible resource entities when solving the distribution network dispatch optimization problem involving the integrated energy system, the distributed optimization algorithm designed using the alternating direction multiplier method only needs to exchange information related to "exchanging power at the boundary". If the global overall optimization method is adopted, each subject needs to provide all variable and parameter information when solving, resulting in the privacy leakage of the subject; the advantage of using the alternating direction multiplier method is that each subject does not need to provide all variable and parameter information when solving. This method can avoid the privacy leakage of the subject and ensure good privacy. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is: How to provide a method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method?

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a method for constructing an integrated energy scheduling model based on the alternating direction multiplier method, comprising: constructing an optimized scheduling model for the integrated energy system based on the operation model of the working equipment of the integrated energy system, and combining the integrated energy load integrated demand response model, the orderly charging model of electric vehicles and the heat network energy flow calculation model of virtual heat storage; using the alternating direction multiplier method to perform distributed solution on the interconnected integrated energy system to obtain a multi-region distributed self-cooperative optimization scheduling strategy; dividing the integrated energy system into multiple subjects, and using the alternating direction multiplier method to perform distributed solution to achieve the global goal of minimizing the operation and maintenance cost of each subject, and obtaining a distributed self-cooperative optimization scheduling strategy among multiple resource subjects.

[0007] As a preferred scheme of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method described in the present invention, wherein: the comprehensive energy system working equipment operation model includes a fan model, a photovoltaic power generation unit model, a gas boiler model, an electric boiler model, a power-to-gas equipment model, a battery model, and a heat storage device model; the comprehensive energy load comprehensive demand response model refers to the classification of loads participating in the comprehensive demand response into reducible loads, transferable loads, and alternative loads according to the comprehensive demand response participation of the three loads of electricity, heat, and gas; the electric vehicle orderly charging model refers to the range of net charging required for the electric vehicle after arriving at the charging pile at the urban complex station, which is determined by the grid-connected charge state, the user-specified minimum off-grid charge state, and the battery capacity; the thermal network energy flow calculation model of the virtual heat storage refers to the description of the relationship between the inlet and outlet hot water temperatures of a pipeline based on the node mass method according to the heat exchange first station and the heat load power constraints.

[0008] As a preferred scheme of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method described in the present invention, wherein: the construction of the comprehensive energy system optimization scheduling model refers to minimizing the total operating cost of the comprehensive energy operator as the objective function, and comprehensively considering the flow constraints of the distribution network, heat network, and gas network of the comprehensive energy system, to construct an optimization scheduling model of the comprehensive energy system considering virtual heat storage; dividing the comprehensive energy system into multiple entities includes dividing the comprehensive energy system into three main parts: comprehensive energy operators, electric vehicle operators, and energy storage operators.

[0009] As a preferred solution of the method for constructing a comprehensive energy dispatch model based on the alternating direction multiplier method described in the present invention, the alternating direction multiplier method is used to perform distributed solution on the interconnected comprehensive energy system to obtain a multi-region distributed self-cooperative optimization dispatch strategy, including the following steps:

[0010] Connect two systems 1 and 2 with consistent optimization objective functions through a tie line, and interconnect the two related systems at the distribution network node;

[0011] After adding the penalty term, the objective functions of system 1 and system 2 are:

[0012]

[0013] Where F1 k and is the objective function of each region after adding the penalty term, f1 k and is the original objective function of each region, and the two are unrelated; and They are the Lagrange multiplier and penalty factor corresponding to time t in the k-th iteration; and They are the decision variables for each region to solve the contact power from its own perspective; is the average communication power, which is a constant that needs to be continuously updated during the iteration process. The value of each iteration is as follows:

[0014] In the process of iterative solution, the convergence criteria are also selected as the original residual and the dual residual, and the calculation formula is as follows:

[0015]

[0016] in, is the original residual; is the dual residual;

[0017] Through the adaptive step size adjustment mechanism ADMM, the penalty factor is dynamically adjusted according to the relationship between the original residual and the dual residual of each iteration. The adjusted expression is as follows:

[0018]

[0019] Where ξ1 and ξ2 are the acceleration factor and deceleration factor respectively, and ζ is the proportional coefficient between the original residual and the dual residual.

[0020] As a preferred solution of the method for constructing a comprehensive energy dispatch model based on the alternating direction multiplier method described in the present invention, after adding the penalty term, the objective functions of the comprehensive energy operator IES, the electric vehicle operator EV, and the energy storage operator ESS are as follows:

[0021]

[0022] In the formula, f k is the objective function value at the kth iteration, f MTESD,ESS,k is the operation and maintenance cost of the ESS part of the multi-type energy storage system, and the calculation formula is as follows:

[0023] Among them, c ESS is the unit power operation and maintenance cost of the battery;

[0024] and The calculation formula is as follows:

[0025]

[0026] As a preferred solution of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method described in the present invention, the original residual and the dual residual are calculated, and their expressions are respectively:

[0027]

[0028] Determine whether the convergence condition is met, that is:

[0029]

[0030] As a preferred solution of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method described in the present invention, the penalty factor and the Lagrange multiplier are updated by an adaptive step size method, that is:

[0031]

[0032]

[0033] Then, the solution is iterated repeatedly until the convergence condition is met and the optimization result is output.

[0034] To further solve the above technical problems, the present invention provides the following technical solutions: a comprehensive energy scheduling model construction system based on the alternating direction multiplier method, comprising: a comprehensive energy system modeling module, which is used to construct an optimization scheduling model of the comprehensive energy system based on the operating characteristics of each working equipment in the comprehensive energy system and the comprehensive demand response characteristics of the comprehensive energy load, the orderly charging characteristics of electric vehicles and the energy flow calculation characteristics of the thermal network with virtual heat storage; a multi-region distributed optimization solution module, which is used to use the ADMM algorithm to perform distributed solution on the interconnected comprehensive energy system, so as to obtain a distributed self-collaborative optimization scheduling strategy among multiple regions, ensuring that each region can achieve the optimal operating state while cooperating with each other; a multi-resource subject collaborative optimization module, which is used to divide the entire comprehensive energy system into multiple independent subjects, and perform distributed solution on these subjects through the ADMM algorithm to achieve the goal of minimizing the operation and maintenance cost of each subject, and finally obtain a distributed self-collaborative optimization scheduling strategy among multiple resource subjects.

[0035] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method as described above are implemented.

[0036] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method as described above are implemented.

[0037] Beneficial effects of the present invention: The present invention adopts the alternating direction multiplier method to solve the optimization scheduling model, so that similar effects can be achieved between each subject by interacting with necessary boundary variables (boundary power). This method breaks down the original global optimization problem into several sub-problems, optimizes and solves each sub-problem separately, and obtains the optimal scheduling strategy. The problem can be solved by decoupling and transforming the Lagrangian augmented function of the original problem, alternately solving the minimum of different resource subjects under a given reference communication power and repeatedly iterating the reference communication power until convergence. The new optimization problem constructed by the alternating direction multiplier method consists of several sub-optimization problems, and the sub-problems are equivalent to the global problem. In the solution process, each resource subject only provides boundary variable information related to the adjacent subject, and uses this information to optimize the sub-problem, and finally achieves the global overall optimization goal; therefore, the alternating direction multiplier method not only protects information privacy, but also realizes the optimal scheduling of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 It is an overall flow chart of a method for constructing a comprehensive energy scheduling model based on an alternating direction multiplier method provided by an embodiment of the present invention;

[0040] Figure 2 It is a schematic diagram of the communication relationship structure of an interconnected system provided by an embodiment of the present invention;

[0041] Figure 3 It is a flow chart of distributed self-cooperative optimization solution for a multi-region system provided by an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of a framework of relationships between multiple operators provided by an embodiment of the present invention;

[0043] Figure 5 It is a result of optimizing the power dispatching of optimized tie lines in two regions provided by an embodiment of the present invention;

[0044] Figure 6 It is a diagram of the convergence process of solving the operation and maintenance cost of interconnecting two systems using an alternating direction multiplier method provided by an embodiment of the present invention;

[0045] Figure 7 It is a residual convergence curve of two systems interconnected to solve the alternating direction multiplier method provided by an embodiment of the present invention;

[0046] Figure 8 It is the first schematic diagram of the interaction power between adjacent subjects of two systems;

[0047] Fig. 9 This is the second schematic diagram of the interaction power between adjacent entities of two systems. DETAILED DESCRIPTION

[0048] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0049] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0050] Example 1

[0051] Reference Figure 1 to Figure 4 , which is an embodiment of the present invention, provides a method for constructing a comprehensive energy scheduling model based on an alternating direction multiplier method, comprising:

[0052] S1: Based on the operation model of the working equipment of the integrated energy system, and combined with the comprehensive energy load comprehensive demand response model, the electric vehicle orderly charging model and the heat network energy flow calculation model with virtual heat storage, an optimal scheduling model of the integrated energy system is constructed.

[0053] Specifically, the construction of the integrated energy system optimization scheduling model is based on the mathematical model of the integrated energy system working equipment, while taking into account the integrated demand response, orderly charging of electric vehicles and virtual heat storage of heat network pipelines. Among them, the integrated demand response on the demand side refers to the extension of the demand response of general electric loads to other types of loads in the integrated energy system such as electricity, gas, and heat. According to the participation of the three types of loads in the comprehensive demand response of electricity, heat, and gas, the present invention divides the loads participating in the comprehensive demand response into reducible loads, transferable loads, and alternative loads, among which the transferable loads are only allowed to participate in the regulation in part of the 24h period.

[0054] The operating model of the working equipment of the integrated energy system includes the wind turbine model, photovoltaic generator model, gas boiler model, electric boiler model, power-to-gas equipment model, battery model, and heat storage device model.

[0055] The electric vehicles considered in the present invention are all considered to be in the urban complex station. The net charging amount range required by the electric vehicle after arriving at the charging pile of the urban complex station is determined by the grid-connected charge state, the user-specified minimum off-grid charge state and the battery capacity.

[0056] A heat network energy flow calculation model considering virtual heat storage is established, including the first heat exchange station, heat load power constraints, and the relationship between the inlet and outlet hot water temperatures of a pipeline is described based on the node mass method.

[0057] S2: The alternating direction multiplier method is used to perform distributed solution on the interconnected integrated energy system. Each integrated energy system has its own independent objective function. When solving, they all hope that their respective objective functions are minimized, that is, the cost is lowest, and a multi-region distributed self-cooperative optimization scheduling strategy is obtained.

[0058] The alternating direction multiplier method is a computational framework for solving convex optimization problems in separable regions or bodies. It combines the augmented Lagrangian algorithm and further develops it. Due to its good convergence performance and fast solution speed, the alternating direction multiplier method is widely used in solving distributed problems in convex optimization. For some complex frameworks, there may be non-convex and non-smooth optimization problems. The alternating direction multiplier method provides a strategy with a wide range of applications, simple solution, practicality and reliability.

[0059] The standard form of the alternating direction multiplier method to solve the problem is as follows:

[0060] Where x∈R p ,y∈R q ,A∈R m×p ,B∈R m×q ,c∈R m , function f:R p →R and g:R q →R is a convex function.

[0061] In order to make the problem easier to solve, the above formula is transformed into the following Lagrangian function structure:

[0062] Where λ is the Lagrange multiplier and ρ is the penalty factor.

[0063] The specific method is to fix two variables, update one at a time, get the new variable, repeat the iteration, and alternately seek the minimum until convergence. This is the alternating direction multiplier method. Therefore, the iterative format of the alternating direction multiplier method is as follows:

[0064]

[0065] λ k+1 =λ k +ρ(Ax k+1+By k+1 -c); where k is the number of iterations.

[0066] When solving iteratively, the convergence criterion is determined by the primal residual and the dual residual, that is:

[0067] M k =||Ax k +By k -c||2;D k =||ρA T B(y k -y k-1 )||2; the convergence condition is:

[0068] M k ≤ε M ;D k ≤ε D ; In the formula, ε M , ε D are two sufficiently small positive numbers.

[0069] When both convergence conditions are met at the same time, the problem is considered to have converged.

[0070] The present invention takes the collaborative optimization of two regions as an example. Two systems 1 and 2 with consistent optimization objective functions are connected through a tie line, and the two related systems are interconnected at the distribution network node, such as Figure 2 shown.

[0071] After adding the penalty term, the objective functions of system 1 and system 2 are:

[0072]

[0073] Where F1 k and is the objective function of each region after adding the penalty term, f1 k and It is the original objective function of each region, and the two are unrelated. and They are the Lagrange multiplier and penalty factor corresponding to time t in the k-th iteration; and They are the decision variables for each region to solve the contact power from its own perspective; is the average communication power, which is a constant that needs to be continuously updated during the iteration process. The value of each iteration is as follows:

[0074] In the iterative solution process, the convergence criteria are also selected as the original residual and the dual residual. The calculation formula is as follows:

[0075]

[0076] In general, the penalty factor can remain unchanged during each iteration. However, in order to speed up the convergence speed, an adaptive step size adjustment mechanism is introduced to improve the convergence speed of the algorithm while reducing the impact of the initial value selection of the penalty factor ρ on the performance and results. The basic idea of ​​the adaptive step size ADMM is to dynamically adjust the penalty factor by a certain proportion according to the relationship between the original residual and the dual residual of each iteration during the solution iteration process. The adjusted expression is as follows:

[0077]

[0078] Where ξ1 and ξ2 are the acceleration factor and deceleration factor respectively, ζ is the proportional coefficient between the original residual and the dual residual, and all these quantities are constants.

[0079] Therefore, we can get the flow chart of distributed self-cooperative optimization solution for multi-region system as shown in the attached figure. Figure 2 shown.

[0080] S3: The integrated energy system is divided into three main parts: integrated energy operators, electric vehicle operators, and energy storage (battery) operators. Each subject has its own independent objective function. When solving, they all hope that their respective objective functions are minimized, that is, the cost is the lowest. The alternating direction multiplier method is used for distributed solution to achieve the global goal of the lowest operation and maintenance cost of each operator, and obtain a distributed self-cooperative optimization scheduling strategy among multiple resource subjects.

[0081] After adding the penalty term, the objective functions of IES operators, EV operators, and ESS operators are as follows:

[0082]

[0083] In the formula, f k is the objective function value at the kth iteration, f MTESD,ESS,k is the operation and maintenance cost of the ESS part of the multi-type energy storage system, and the calculation formula is as follows:

[0084] In the formula, c ESS It is the unit power operation and maintenance cost of the battery.

[0085] and The calculation formula is as follows:

[0086]

[0087] Calculate the original residual and dual residual, and their expressions are:

[0088]

[0089] Determine whether the convergence condition is met, that is:

[0090]

[0091] The adaptive step size method is used to update the penalty factor and Lagrange multiplier, namely:

[0092]

[0093]

[0094] Then, the solution is iterated repeatedly until the convergence condition is met and the optimization result is output.

[0095] The relationship framework between the operators is as follows: Figure 4 shown.

[0096] Therefore, the present application proposes a method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method. By establishing a comprehensive energy system optimization operation model and a distributed self-cooperative optimization scheduling model, it completes the establishment of an optimization scheduling strategy on the basis of reasonably protecting the privacy of regional or distributed resource entities.

[0097] Example 2

[0098] As an embodiment of the present invention, a comprehensive energy scheduling model construction system based on the alternating direction multiplier method is provided, including: a comprehensive energy system modeling module, which is used to construct an optimization scheduling model of the comprehensive energy system based on the operating characteristics of each working equipment in the comprehensive energy system and the comprehensive demand response characteristics of the comprehensive energy load, the orderly charging characteristics of electric vehicles and the energy flow calculation characteristics of the thermal network with virtual heat storage; a multi-region distributed optimization solution module, which is used to use the ADMM algorithm to perform distributed solution on the interconnected comprehensive energy system, so as to obtain a distributed self-cooperative optimization scheduling strategy among multiple regions, ensuring that each region can achieve the optimal operating state while cooperating with each other; a multi-resource subject collaborative optimization module, which is used to divide the entire comprehensive energy system into multiple independent subjects, and perform distributed solution on these subjects through the ADMM algorithm to achieve the goal of minimizing the operation and maintenance cost of each subject, and finally obtain a distributed self-cooperative optimization scheduling strategy among multiple resource subjects.

[0099] Example 3

[0100] It is an embodiment of the present invention, which is different from the previous embodiment in that:

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Example 4

[0106] like Figure 5 ~ is an embodiment of the present invention, which provides a method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0107] In order to verify the effectiveness of the proposed scheduling method, two urban integrated energy systems are used for testing. Each integrated energy network consists of an IEEE 33-node distribution network, a 20-node Belgian natural gas network and a 44-node heating network.

[0108] The initial values ​​of the Lagrange multiplier coefficients are all set to 0, the initial values ​​of the penalty factors are all set to 1, the acceleration factor, deceleration factor, and the proportional coefficient of the original residual to the dual residual of the adaptive step size adjustment penalty factor are all set to 1.5, and the precision coefficients of the dual residual and the original residual are all set to 0.001. The contact power of the optimized scheduling result is as follows: Figure 5 shown.

[0109] The convergence process is analyzed below. The convergence curve of the objective function is as follows: Figure 6 shown.

[0110] Figure 7 The logarithmic curves of the primal residual and the dual residual as the number of iterations changes are shown. After 20 iterations, both residuals reach the preset threshold, which shows that the solution of the alternating direction multiplication method meets the expected conditions. It can be seen from the figure that although the dual residual has reached the allowable error range after the 7th iteration, it is still necessary to continue iterating to make the primal residual close to the convergence accuracy threshold. Only when both residuals reach the allowable error range, the solution meets the convergence condition.

[0111] Based on the above communication results, the communication power is used as the power supply or load of the subsystem, and each subsystem is divided into three entities: comprehensive energy operators, energy storage (battery) operators, and electric vehicle operators. The distributed solution is performed with the minimum operating cost of each entity as the objective function to obtain the distributed optimization scheduling result. The interactive power between the entities is as follows Figure 8 and Fig. 9 shown.

[0112] A single integrated energy network is selected, and the operation and maintenance costs of each subject, the number of iterations of each method, and the time consumption are calculated by centralized solution, distributed solution of alternating direction multiplier method (non-adaptive step size), and distributed solution of alternating direction multiplier method (adaptive step size) as shown in Table 1. The results show that the calculation results of these methods are basically consistent, which means that the autonomy of subject decision-making can be achieved through the alternating direction multiplier method, and the results of distributed optimization and centralized optimization are almost the same. In distributed solution, the integrated energy operator and the electric vehicle operator only exchange the boundary information of electric vehicle power, and the integrated energy operator and the energy storage (battery) operator only exchange the boundary information of the net exchanged electric power in each scheduling period. All equipment parameter information within each subject does not need to participate in the global level scheduling, which well protects the privacy of the subject. Compared with centralized optimization, distributed optimization only exchanges boundary power information, requires continuous iteration, and takes a long time to solve. However, parallel solution can reduce the computational burden, so it is believed that it still has high computational efficiency. After the adaptive step size is adopted, the penalty factor is automatically adjusted, the number of iterations and the total time required for solving the problem are reduced, but the cost of each subject remains unchanged, which shows the superiority of the adaptive step size alternating direction multiplier method. Based on the above analysis, it can be seen that the algorithm framework for solving the multi-regional and multi-operator distributed optimization scheduling model based on the alternating direction multiplier method can effectively realize the distributed optimization of the integrated energy system.

[0113] Table 1 Comparison of calculation results of different algorithms

[0114] method Centralized solution ADMM (non-adaptive ρ) ADMM (Adaptive Step Size ρ) IES Cost 11184 yuan 11242.8 yuan 11242.8 yuan EV Costs 2121 yuan 2158 yuan 2158 yuan ESS Cost 25.5 yuan 27.1 yuan 27.1 yuan Iterations × 251 times 22 times Total time to solve 45.58s 5680.96s 517.46s

[0115] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method, characterized in that: include: Based on the operation model of working equipment in the integrated energy system, combined with the integrated energy load integrated demand response model, the electric vehicle orderly charging model and the heat network energy flow calculation model with virtual heat storage, an integrated energy system optimization scheduling model is constructed; The alternating direction multiplier method is used to perform distributed solution on the interconnected integrated energy system, and a multi-region distributed self-cooperative optimization scheduling strategy is obtained; The integrated energy system is divided into multiple entities, and the alternating direction multiplier method is used for distributed solution to achieve the global goal of minimizing the operation and maintenance cost of each entity, and obtain a distributed self-cooperative optimization scheduling strategy among multiple resource entities.

2. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 1, characterized in that: The integrated energy system working equipment operation model includes a wind turbine model, a photovoltaic generator model, a gas boiler model, an electric boiler model, a power-to-gas equipment model, a battery model, and a heat storage device model; The comprehensive energy load comprehensive demand response model refers to dividing the loads participating in the comprehensive demand response into curtailable loads, transferable loads and alternative loads according to the comprehensive demand response participation of the three loads of electricity, heat and gas; The electric vehicle orderly charging model means that the range of net charge required by the electric vehicle after arriving at the charging pile of the urban complex station is determined by the grid-connected state of charge, the minimum off-grid state of charge specified by the user, and the battery capacity; The heat network energy flow calculation model of the virtual heat storage is to describe the relationship between the inlet and outlet hot water temperatures of a pipeline based on the node mass method according to the heat exchange first station and the heat load power constraint.

3. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 2, characterized in that: The construction of the integrated energy system optimization dispatching model refers to taking minimization of the total operating cost of the integrated energy operator as the objective function, and comprehensively considering the flow constraints of the integrated energy system distribution network, heat network, and gas network, to construct an integrated energy system optimization dispatching model considering virtual heat storage; Dividing the integrated energy system into multiple entities includes dividing the integrated energy system into three main parts: integrated energy operators, electric vehicle operators, and energy storage operators.

4. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 3, characterized in that: The alternating direction multiplier method is used to perform distributed solution on the interconnected integrated energy system, and a multi-region distributed self-cooperative optimization scheduling strategy is obtained, which includes the following steps: Connect two systems 1 and 2 with consistent optimization objective functions through a tie line, and interconnect the two related systems at the distribution network node; After adding the penalty term, the objective functions of system 1 and system 2 are: Where F1 k and is the objective function of each region after adding the penalty term, f1 k and is the original objective function of each region, and the two are unrelated; and They are the Lagrange multiplier and penalty factor corresponding to time t in the k-th iteration; and They are the decision variables for each region to solve the contact power from its own perspective; is the average communication power, which is a constant that needs to be continuously updated during the iteration process. The value of each iteration is as follows: In the process of iterative solution, the convergence criteria are also selected as the original residual and the dual residual, and the calculation formula is as follows: in, is the original residual; is the dual residual; Through the adaptive step size adjustment mechanism ADMM, the penalty factor is dynamically adjusted to a preset ratio according to the relationship between the original residual and the dual residual of each iteration. The adjusted expression is as follows: Where ξ1 and ξ2 are the acceleration factor and deceleration factor respectively, and ζ is the proportional coefficient between the original residual and the dual residual.

5. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 4, characterized in that: After adding the penalty term, the objective functions of the integrated energy operator IES, the electric vehicle operator EV, and the energy storage operator ESS are as follows: In the formula, f k is the objective function value at the kth iteration, f MTESD,ESS,k is the operation and maintenance cost of the ESS part of the multi-type energy storage system, and the calculation formula is as follows: Among them, c ESS is the unit power operation and maintenance cost of the battery; and The calculation formula is as follows:

6. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 5, characterized in that: Calculate the original residual and dual residual, the expressions are: Determine whether the convergence condition is met, that is:

7. The method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to claim 6, characterized in that: The adaptive step size method is used to update the penalty factor and Lagrange multiplier, namely: Then, the solution is iterated repeatedly until the convergence condition is met and the optimization result is output.

8. A system using the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method as described in any one of claims 1 to 7, characterized in that: include: The integrated energy system modeling module is used to build an optimal scheduling model for the integrated energy system based on the operating characteristics of each working equipment in the integrated energy system, the integrated demand response characteristics of the integrated energy load, the orderly charging characteristics of electric vehicles, and the energy flow calculation characteristics of the heat network with virtual heat storage; The multi-region distributed optimization solution module is used to perform distributed solution on the interconnected integrated energy system using the ADMM algorithm, thereby obtaining a distributed self-cooperative optimization scheduling strategy among multiple regions, ensuring that each region can achieve the optimal operating state while cooperating with each other; The multi-resource subject collaborative optimization module is used to divide the entire integrated energy system into multiple independent subjects, and perform distributed solutions on these subjects through the ADMM algorithm to achieve the goal of minimizing the operation and maintenance cost of each subject, and finally obtain a distributed self-collaborative optimization scheduling strategy among multiple resource subjects.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for constructing a comprehensive energy scheduling model based on the alternating direction multiplier method according to any one of claims 1 to 7 are implemented.

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