A Multi-agent Joint Planning Method and System for Regional Integrated Energy Systems

Abstract

The present invention discloses a multi-agent joint planning method and system for a regional integrated energy system, constructs a joint planning problem with the lowest total planning and operation cost as the goal for multiple stakeholders within the RIES, uses enhanced Benders decomposition to achieve distributed solution of the problem, and formulates an optimal joint planning scheme for the whole system. The distributed solution method adopted by the present invention is applicable to a variety of complex non-convex models, and can better achieve privacy protection of each agent in joint planning. Through simulation, it is shown that the distributed solution method has good convergence characteristics, the joint planning scheme effectively reduces the economic cost of the multi-energy coupling RIES with multiple agents, effectively improves its energy utilization efficiency, while avoiding excessive configuration of equipment capacity, and promotes the development of renewable energy within the region.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system planning, and particularly relates to a multi-agent joint planning method and system for a regional integrated energy system. Background Art

[0002] The integrated energy system can organically coordinate the production, supply, storage, sales and other links of multiple types of energy, realize the transformation of the energy structure, and improve the energy utilization efficiency. It is the only way for China to achieve low-carbon and green development. In recent years, with the proposal of concepts such as the energy Internet, the regional integrated energy system (RIES) has developed rapidly, and a large number of engineering practices have been carried out at home and abroad. At present, the theoretical research on RIES by scholars at home and abroad is also very rich, but there are still many challenges in issues such as planning, operation, and operation.

[0003] With the gradual opening of the energy market, there are also multiple types of market players such as integrated energy service providers (IESPs) and user aggregators (UAs) within the RIES. IESPs and UAs belong to the same RIES but are managed by different entities. IESPs are responsible for the production and sales of energy in the region and make profits therefrom, and UAs can be responsible for integrating user needs and unified management. Since each entity has different economic interests, the independent planning methods of IESPs and each UA directly affect the planning operation cost and energy efficiency of the RIES. Considering the realistic conditions such as the privacy protection and information sharing of each entity, the cooperation planning between IESPs and UAs is often restricted.

[0004] In the prior art, it is impossible to achieve the distributed solution of the multi-agent joint planning problem of the RIES while considering the independence and privacy protection of each entity. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a multi-agent joint planning method and system for a regional integrated energy system to solve the technical problem that the integrated energy system with multi-agent cooperation cannot be distributedly planned in view of the above deficiencies in the prior art.

[0006] The present invention adopts the following technical solutions:

[0007] A multi-agent joint planning method for a regional integrated energy system of the present invention includes the following steps:

[0008] S1. Construct a joint planning problem with the lowest total planning operation cost as the goal based on multiple entities within the regional integrated energy system;

[0009] S2. Use the enhanced Benders decomposition method to decompose the joint planning problem established in step S1 into one master problem and N sets of sub-problems. The integrated energy service provider and user aggregator in the regional integrated energy system iteratively solve the master problem and N sets of sub-problems, and based on the obtained optimal joint planning solution of the regional integrated energy system, achieve the optimal joint planning of the regional integrated energy system.

[0010] Specifically, in step S1, the regional integrated energy system includes an integrated energy service provider and multiple user aggregators. Taking the minimization of the annual total cost of the regional integrated energy system as the objective function and the integrated energy service provider, user aggregator, and multi-energy pipe network as the constraints, the regional integrated energy system is modeled to obtain the multi-agent joint planning model of the regional integrated energy system, and the joint planning problem is determined according to the multi-agent joint planning model of the regional integrated energy system.

[0011] Furthermore, the joint planning problem is specifically:

[0012]

[0013] s.t.

[0014] Ax + By + Cm ≥ e

[0015]

[0016]

[0017] where x, x i ' are the variables related to the integrated energy service provider and user aggregator i respectively; y, y i ' are the continuous variables related to the integrated energy service provider and user aggregator i respectively; m is the coupling variable between the integrated energy service provider and each user aggregator; gy is the cost related to the integrated energy service provider; h i y i ' represents the cost related to user aggregator i; Ax + By + Cm ≥ e represents the constraint related to the integrated energy service provider; D i m + E i x i ' + F i y i ' ≥ f represents the constraint related to user aggregator i; A, B, C, D i , E i , F i are coefficient matrices respectively; e, f i , g, h i are coefficient vectors respectively.

[0018] Furthermore, the objective function is:

[0019]

[0020]

[0021]

[0022]

[0023] Among them, N represents the number of user aggregators; S represents the number of scenarios; represents the equivalent annual investment cost of the integrated energy service provider; represents the equivalent annual investment cost of user aggregator i; represents the operating cost of scenario s; κ s represents the occurrence probability of scenario s; X IESP ,X UA ,X Grid respectively represent the integrated energy service provider, the equipment related to user aggregators, and various types of pipe networks; D represents the total number of days in a year; μ x represents the unit investment cost of equipment of type x; represents the rated capacity of the kth piece of equipment of type x; T represents the number of time periods; represents the relevant operating expenses of the integrated energy service provider; represents the equipment operating cost function of the user aggregator; represents the demand response penalty function of the user aggregator.

[0024] Specifically, in step S2, the main problem is specifically:

[0025]

[0026] s.t.

[0027] Ax + By + Cm ≥ e

[0028]

[0029] x ∈ {0, 1}

[0030] Benders Cuts (If any)

[0031] Among them, θ i is the lower limit of the cost related to user aggregator i determined by the main problem, A, B, and C are coefficient matrices respectively, x is a variable related to the integrated energy service provider, gy is the cost related to the integrated energy service provider, y is a continuous variable related to the integrated energy service provider, m is a coupling variable between the integrated energy service provider and each user aggregator, and N represents the number of user aggregators.

[0032] Specifically, in step S2, the sub-problems include the unified dual sub-problem and the feasibility restoration sub-problem. The unified dual sub-problem is specifically:

[0033]

[0034] Among them, J i and L i are dual variables respectively; L i is an auxiliary 0-1 variable; N i is a set of constraints related to dual variables; M i is a set of constraints related to the auxiliary 0-1 variable; In the current solution of the master problem and form a vector, indicating that when J i and L i have optimal solutions and at this time the corresponding objective function value, f' i is a coefficient vector, D' i is a coefficient matrix;

[0035] The feasibility restoration sub-problem is specifically:

[0036]

[0037] s.t.

[0038] D' i m'+E' i x i '+F' i y i '≥f i '

[0039] x i '∈{0,1}

[0040] Among them, represents the objective function value; represents the feasibility deviation from any solution m' within the feasible region, In the current solution of the master problem and form a vector, x i ' is a variable related to user aggregator i; y i ' is a continuous variable related to user aggregator i, D' i ,E' i ,F' i ,f' are the corresponding coefficient matrices or coefficient vectors respectively.

[0041] Furthermore, the unified dual sub-problem is determined by approximately dualizing the original sub-problem with the help of auxiliary 0-1 variables and the min-max inequality. The original sub-problem includes a feasibility sub-problem and an optimality sub-problem. The unified dual sub-problem is specifically as follows:

[0042]

[0043] s.t.

[0044]

[0045] x i ' ∈ {0, 1}

[0046] v' i ≥ 0

[0047] where v' i is a vector composed of slack variables.

[0048] Furthermore, the feasibility sub-problem is specifically as follows:

[0049]

[0050] s.t.

[0051]

[0052] x i ' ∈ {0, 1}

[0053] v i ≥ 0

[0054] where 1 and 0 respectively represent the 1-vector and the zero-vector; v i is a vector composed of a group of non-negative slack variables.

[0055] Furthermore, the optimality sub-problem is specifically as follows:

[0056]

[0057] s.t.

[0058]

[0059] x i ' ∈ {0, 1}

[0060] In a second aspect, an embodiment of the present invention provides a multi-agent joint planning system for a regional integrated energy system, including:

[0061] A construction module that constructs a joint planning problem with the goal of minimizing the total planning and operation cost based on multiple agents within the regional integrated energy system;

[0062] The planning module uses the enhanced Benders decomposition method to decompose the joint planning problem established by the construction module into one master problem and N groups of sub-problems. The integrated energy service provider and user aggregator in the regional integrated energy system iteratively solve the master problem and N groups of sub-problems, and based on the obtained optimal joint planning scheme of the regional integrated energy system, the optimal joint planning of the regional integrated energy system is realized.

[0063] Compared with the prior art, the present invention has at least the following beneficial effects:

[0064] A multi-agent joint planning method for a regional integrated energy system of the present invention can consider the detailed modeling of each agent and multi-energy pipe networks within the RIES, construct a joint planning problem with the goal of maximizing the economy of the whole system. Compared with the independent planning schemes of each agent, the decoupled planning schemes of each energy system, and the planning scheme of centralized energy supply, the joint planning scheme can effectively reduce the economic cost of the multi-energy coupling RIES with multiple agents, effectively improve its energy utilization efficiency, avoid excessive configuration of equipment capacity at the same time, promote the development of renewable energy in the region, and use the enhanced Benders decomposition to overcome the situation that the general Benders decomposition cannot solve non-convex sub-problems containing 0-1 variables, with good convergence performance, expand the application of distributed algorithms in the field of integrated energy system planning, so that the present invention can realize the distributed solution and independent planning decision-making of each agent, and fully ensure the privacy and independence of each agent.

[0065] Furthermore, it can consider multiple independent economic agents in the regional integrated energy system, including integrated energy service providers and multiple user aggregators, establish an objective function with the goal of minimizing the annual total cost of the regional integrated energy system, and can conduct detailed modeling of the regional integrated energy system from three aspects: integrated energy service providers, user aggregators, and multi-energy pipe networks, to obtain a multi-agent joint planning model of the regional integrated energy system that takes into account the interests of all agents.

[0066] Furthermore, it can uniformly represent the joint planning problem in a matrix form that is convenient for problem decomposition, classify various types of variables and constraints in the problem, and use x, x i ' to represent variables related to the integrated energy service provider and user aggregator i; use y, y i ' to represent continuous variables related to the integrated energy service provider and user aggregator i; use m to represent the coupling variable between the integrated energy service provider and each user aggregator; use Ax + By + Cm ≥ e to represent the constraints related to the integrated energy service provider; use D i m + E i x i '+ F i y i ' ≥ f to represent the constraints related to user aggregator i.

[0067] Furthermore, the objective function of the joint planning problem can carefully consider the equivalent annual investment cost of the integrated energy service provider, the equivalent annual investment cost of each user aggregator, and the operating cost of the system under uncertain scenarios. It can account for the investment costs of various types of equipment and pipe networks, while also taking into account the demand response penalties on the user side.

[0068] Furthermore, the enhanced Benders decomposition can be used to obtain the master problem. While reducing the scale of the problem and taking into account the privacy conditions of all parties, the relevant variables of the integrated energy service provider are solved. The specific variables include: the lower bound θ of the cost related to user aggregator i determined by the master problem i , the relevant variables x of the integrated energy service provider, the relevant continuous variables y of the integrated energy service provider, and the coupling variables m between the integrated energy service provider and each user aggregator.

[0069] Furthermore, multiple sets of sub-problems can be obtained by using the enhanced Benders decomposition. Each set of sub-problems includes a unified dual sub-problem and a feasibility recovery sub-problem. It can achieve the distributed solution of the relevant variables of each user aggregator and return the Benders cut to the master problem, effectively eliminating infeasible solutions and dual gaps.

[0070] Furthermore, with the help of auxiliary 0-1 variables and the max-min inequality, the original sub-problem can be approximately dualized to determine the unified dual sub-problem. The original sub-problem includes a feasibility sub-problem and an optimality sub-problem.

[0071] Furthermore, the feasibility of the current solution of the master problem can be verified by solving the feasibility sub-problem.

[0072] Furthermore, the optimality of the current solution of the master problem can be verified by solving the optimality sub-problem.

[0073] It can be understood that the beneficial effects of the second aspect above can be referred to the relevant descriptions in the first aspect above and will not be elaborated here.

[0074] In summary, the present invention effectively reduces the economic cost of the multi-energy coupling RIES with multiple agents, effectively improves its energy utilization efficiency, while avoiding excessive configuration of equipment capacity and promoting the development of renewable energy within the region.

[0075] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. Description of the Drawings

[0076] Figure 1 It is a schematic diagram of the general structure of the multi-energy coupling RIES with multiple agents faced by the present invention;

[0077] Figure 2 It is a flow chart of the distributed solution based on the enhanced Benders decomposition adopted by the present invention;

[0078] Figure 3 This is the electrical-gas-thermal RIES topological structure diagram for the simulation calculation of the present invention. Among them, (a) is the topological structure of the power system, (b) is the topological structure of the natural gas system, and (c) is the topological structure of the heating system;

[0079] Figure 4 This is the combined planning result diagram for the simulation calculation of the electrical-gas-thermal RIES of the present invention. Among them, (a) is the planning result of the power system, (b) is the planning result of the natural gas system, and (c) is the planning result of the heating system;

[0080] Figure 5 This is the iterative process diagram of the distributed solution based on the enhanced Benders decomposition of the present invention. Specific embodiments

[0081] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0082] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0083] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0084] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the preceding and following related objects.

[0085] It should be understood that although terms such as first, second, and third may be used in the embodiments of the present invention to describe preset ranges and the like, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0086] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0087] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. And those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0088] The present invention provides a multi-agent joint planning method for a regional integrated energy system, aiming at the multi-agent of RIES to construct a joint planning problem with the lowest total planning and operation cost, and then using enhanced Benders decomposition to achieve distributed solution of the problem to obtain an optimal joint planning scheme, that is, a multi-agent joint planning method for a regional integrated energy system based on enhanced Benders decomposition, realizing the reasonable configuration of various types of equipment, improving energy utilization efficiency, making the economic benefits reach the best, and achieving the optimal planning of the regional integrated energy system (RIES) containing multiple agents on the premise of protecting the privacy and independence of each interest subject.

[0089] A multi-agent joint planning method for a regional integrated energy system of the present invention includes the following steps:

[0090] S1. Construct a joint planning problem with the lowest total planning and operation cost for multiple interest subjects within RIES;

[0091] The joint planning problem first clarifies the structure of the RIES system, models the RIES from three aspects: IESP, UA, and multi-energy pipe network, and at the same time considers the planning costs, operation costs, and various constraints of multiple agents within the RIES, and determines the mathematical expression of the joint planning problem with the lowest equivalent annual total cost as the goal.

[0092] S101. Define that RIES is a multi - energy coupling system composed of an integrated energy service provider (IESP) and multiple user aggregators (UA).

[0093] The IESP is responsible for purchasing and producing various types of energy, such as electric energy, natural gas, thermal energy, etc., and selling and transporting them to each UA to make a profit. The IESP needs to choose to purchase energy from the energy market or invest in certain energy production equipment and energy conversion equipment, and also needs to invest in multi - energy pipe networks for transporting energy to each UA. Each UA is responsible for aggregating the energy demands of users within a certain range and managing the integrated demand response. It can invest in small - scale energy supply equipment nearby to meet flexible energy demands.

[0094] S102. Model RIES from three aspects: IESP, UA, and multi - energy pipe networks.

[0095] The IESP and UA models are used to define the operation constraints of their related equipment, and the multi - energy pipe network model is used to define the operation constraints of various energy supply pipelines and networks and the energy balance constraints at each node of the system.

[0096] S103. Establish a multi - agent joint planning model for RIES.

[0097] The multi - agent joint planning model for RIES aims to minimize the annual total cost of the whole system, including the equivalent annual investment cost and the annual operation cost. The objective function is:

[0098]

[0099]

[0100]

[0101]

[0102] Among them, N represents the number of UAs; S represents the number of scenarios; represents the equivalent annual investment cost of the IESP; represents the equivalent annual investment cost of UA i; represents the operation cost of scenario s; κs represents the occurrence probability of scenario s; X IESP ,X UA ,X Grid respectively represent the IESP, UA - related equipment, and various pipe networks; D represents the total number of days in a year; μ x represents the unit investment cost of x - type equipment; represents the rated capacity of the k - th x - type equipment; T represents the number of time periods; represents the related operation cost of the IESP; represents the UA equipment operation cost function; Represents the UA demand response penalty function;

[0103] The constraints of this model include the relevant operation constraints of IESP, UA, and multi - energy pipe networks, energy balance constraints, and investment constraints for various types of equipment;

[0104] S104. According to the RIES multi - agent joint planning model, the mathematical expression of the RIES multi - agent joint planning problem is determined as follows:

[0105]

[0106] s.t.

[0107] Ax + By + Cm ≥ e

[0108]

[0109]

[0110] Among them, x, x i ' are the 0 - 1 variables related to IESP and UA i respectively; y, y i ' are the continuous variables related to IESP and UA i respectively; m is the coupling variable between IESP and each UA; gy is the cost related to IESP; h i y i ' represents the cost related to UA i; Ax + By + Cm ≥ e represents the constraints related to IESP; D i m + E i x i '+ F i y i ' ≥ f represents the constraints related to UA i; A, B, C, D i , E i , F i are coefficient matrices respectively; e, f i , g, h i are coefficient vectors respectively.

[0111] A further improvement of the present invention is that for the modeling of IESP and UA, various non - convex models containing 0 - 1 variables can be considered, and for the modeling of multi - energy pipe networks, various simplified or refined power grid and pipeline models can be considered.

[0112] A further improvement of the present invention is that the RIES multi - agent joint planning problem is decomposed into 1 master problem and multiple groups of sub - problems by using enhanced Benders decomposition, and the IESP and each UA respectively implement distributed solutions for the master and sub - problems to formulate an optimal joint planning scheme for the entire system.

[0113] S2. Use enhanced Benders decomposition to achieve distributed solution of the problem and formulate an optimal joint plan for the entire system.

[0114] S201. Decompose the RIES multi-agent joint planning problem (denoted as the original problem) into 1 master problem and N groups of original sub-problems;

[0115] The master problem is expressed as:

[0116]

[0117] s.t.

[0118] Ax + By + Cm ≥ e

[0119]

[0120] x ∈ {0, 1}

[0121] Benders Cuts (If any)

[0122] where θ i is the lower bound of the UA i-related cost determined by the master problem and increases with the addition of Benders cuts during iteration.

[0123] Each group of sub-problems includes a unified dual sub-problem and a feasibility recovery sub-problem. The unified dual sub-problem is an approximation of the dual of the unified original sub-problem and is expressed as follows:

[0124]

[0125] where J i and L i are dual variables respectively; L i is an auxiliary 0-1 variable; N i is the set of constraints related to the dual variable; M i is the set of constraints related to the auxiliary 0-1 variable; is the vector formed by and in the current solution of the master problem; represents the objective function value corresponding to i and L i when there are optimal solutions and at ; f' i is the coefficient vector; D' i is the coefficient matrix.

[0126] The original sub-problems include: a, the feasibility sub-problem; b, the optimality sub-problem. The unified original sub-problem is obtained through the following process:

[0127] a, Feasibility sub-problem

[0128] N groups of original sub-problems are obtained by general Benders decomposition; including the feasibility sub-problem for verifying the current solution of the master problem which is expressed as follows:

[0129]

[0130] s.t.

[0131]

[0132] x i ' ∈ {0, 1}

[0133] v i ≥ 0

[0134] where 1 and 0 represent the 1-vector and the zero-vector respectively; v i is a vector composed of a group of non-negative slack variables;

[0135] b, Optimality sub-problem

[0136] The optimality sub-problem for verifying the optimality in the current solution of the master problem is expressed as follows:

[0137]

[0138] s.t.

[0139]

[0140] x i ' ∈ {0, 1}

[0141] The feasibility sub-problem and the optimality sub-problem are combined to obtain a unified original sub-problem;

[0142] Unified original sub-problem:

[0143]

[0144] s.t.

[0145]

[0146]

[0147]

[0148] x i ' ∈ {0, 1}

[0149] v i ≥ 0, ξ i≥0, η i ≥0

[0150] where ξ i , η i are all non - negative slack variables;

[0151] The unified original sub - problem is simplified and represented as follows:

[0152]

[0153] s.t.

[0154]

[0155] x i ' ∈ {0, 1}

[0156] v' i ≥0

[0157] where v' i is a vector composed of slack variables; D' i , E' i , F' i , f' are the corresponding coefficient matrices or coefficient vectors respectively; is the vector composed of and in the current solution of the master problem.

[0158] By means of auxiliary 0 - 1 variables and the max - min inequality, the unified original sub - problem is approximately dualized to obtain the unified dual sub - problem;

[0159] Introduce a vector composed of auxiliary 0 - 1 variables and let x i ' be equal to Regarding x i ' as a continuous variable, the unified original sub - problem can be transformed as follows:

[0160]

[0161] s.t.

[0162]

[0163]

[0164] v' i ≥0

[0165]

[0166] where J i and K i are vectors composed of the dual variables corresponding to two types of constraints respectively;

[0167] It is further expressed as follows:

[0168]

[0169] Among them,

[0170] The dual of the internal linear programming problem is obtained in the following form:

[0171]

[0172] Among them,

[0173] The max-min inequality is introduced to obtain the following inequality relationship:

[0174]

[0175] The enumeration method is used to list all possible 0 and 1 combinations of the 0-1 variables in the above formula, which is expressed in the following form:

[0176]

[0177] Among them, M i ={L i ≤0, L i ≤K i}; Therefore, the right-hand side term of the inequality can be expressed as:

[0178]

[0179] After further arrangement, the unified dual sub-problem is obtained:

[0180]

[0181] Among them, represents the objective function value corresponding to when J i and L i have the optimal solutions and at this time .

[0182] The feasibility restoration sub-problem is used to eliminate the duality gap, specifically:

[0183]

[0184] s.t.

[0185] D' i m'+E' i x i '+F' i y i '≥fi '

[0186] x i ' ∈ {0, 1}

[0187] where represents the objective function value; represents the feasibility deviation from any solution m' within the feasible region; E' i and F' i are coefficient matrices.

[0188] The feasibility deviation of the feasibility restoration sub - problem is calculated by the following formula:

[0189]

[0190]

[0191]

[0192] where is used to measure the deviation between n is the absolute deviation amount; w n is the normalization factor; m'n, are the specific elements of m', respectively; τ is a sufficiently small positive number.

[0193] The IESP and each UA iteratively solve the master problem and each group of sub - problems. The calculation process converges and the optimal joint planning scheme for the entire system is obtained if and only if no Benders cut is generated by each UA and the current solution of the master problem remains unchanged.

[0194] Please refer to Figure 2 , where the IESP is responsible for solving the master problem and transmitting the current solution to each UA Each UA first independently solves the unified dual sub - problem. When is greater than zero, the current solution of the master problem does not meet the feasibility and optimality requirements, and the UA adds the following Benders cut to the master problem:

[0195]

[0196] When is greater than or equal to zero, each UA needs to further solve the feasibility sub - problem. When is greater than zero, the UA adds the following Benders cut to the master problem:

[0197]

[0198] In another embodiment of the present invention, a multi-agent joint planning system for a regional integrated energy system is provided. This system can be used to implement the above-mentioned multi-agent joint planning method for a regional integrated energy system. Specifically, the multi-agent joint planning system for a regional integrated energy system includes a construction module and a planning module.

[0199] Among them, the construction module constructs a joint planning problem with the lowest total planning and operation cost as the goal based on multiple agents within the regional integrated energy system.

[0200] The planning module uses the enhanced Benders decomposition method to decompose the joint planning problem established by the construction module into 1 master problem and N groups of sub-problems. The integrated energy service provider and user aggregation method within the regional integrated energy system iteratively solves the master problem and N groups of sub-problems, and realizes the optimal joint planning of the regional integrated energy system based on the obtained optimal joint planning scheme of the regional integrated energy system.

[0201] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the present invention claimed, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0202] Select the electrical-gas-thermal coupled RIES for simulation calculation.

[0203] Please refer to Figure 3 , Figure 3 (a), Figure 3 (b) and Figure 3 (c) respectively show the topological structures of the power system, natural gas system, and heating system in the RIES, including 14 power nodes, 8 natural gas nodes, and 8 heating nodes. Four typical scenarios are selected for the simulation calculation, including the output of each wind and solar unit, the energy demand of each UA, energy prices, etc. The total number of time periods T for each scenario is 24.

[0204] Please refer to Figure 4 , Figure 4 (a), Figure 4 (b) and Figure 4(c) The combined planning results of the power system, natural gas system, and heating system within the RIES are respectively presented. Table 1 further compares the calculation results of the combined planning scheme with the independent planning schemes of each entity, the decoupled planning schemes of each system, and the centralized heating planning scheme, as follows:

[0205] Table 1 Comparison of Results in Each Scenario

[0206]

[0207] The simulation calculations show that the RIES combined planning scheme obtained by using the present invention has more advantages in reducing the total cost of system planning and operation and improving energy utilization efficiency. It better stimulates the development of renewable energy within the region, avoids excessive capacity configuration or energy purchase, reduces energy loss, and improves energy utilization efficiency.

[0208] Please refer to Figure 5 , for the iterative process of the distributed solution method based on enhanced Benders decomposition in the figure. As the iteration progresses, the total system cost monotonically increases, while the deviation oscillates and decays to a very small value, and the algorithm has good convergence characteristics.

[0209] In summary, a multi-agent combined planning method and system for a regional integrated energy system according to the present invention uses a distributed solution method applicable to various complex non-convex models, and can better achieve the privacy protection of each entity in the combined planning; through simulation, it shows that the distributed solution method has good convergence characteristics, the combined planning scheme effectively reduces the economic cost of the multi-energy coupled RIES with multiple agents, effectively improves its energy utilization efficiency, avoids excessive configuration of equipment capacity, and promotes the development of renewable energy within the region.

[0210] Those skilled in the art can clearly understand that for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be described in detail here.

[0211] In the above embodiments, the descriptions of the respective embodiments each have their own emphases. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0212] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0213] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0214] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0215] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0216] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0217] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0218] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the process Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for the functions specified in one block or a plurality of blocks.

[0220] The above is only to illustrate the technical idea of the present invention and should not be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A multi-agent joint planning method for regional integrated energy systems, characterized in that Including the following steps: S1. Based on multiple entities within the regional integrated energy system, construct a joint planning problem with the goal of minimizing the total planning and operation cost; S2. Use the enhanced Benders decomposition method to decompose the joint planning problem established in step S1 into one master problem and a group of sub-problems, and the integrated energy service providers and user aggregation methods within the regional integrated energy system iteratively solve the master problem and a group of sub-problems, and based on the obtained optimal joint planning scheme of the regional integrated energy system, realize the optimal joint planning of the regional integrated energy system; The joint planning problem is specifically: Among them, are the integrated energy service provider and the user aggregator respectively related variables; are the integrated energy service provider and the user aggregator respectively related continuous variables; is the coupling variable between the integrated energy service provider and each user aggregator; is the cost related to the integrated energy service provider; represents the user aggregator related cost; represents the constraint related to the integrated energy service provider; represents the user aggregator related constraint; are the coefficient matrices respectively; are the coefficient vectors respectively; The main problem is specifically: Among them, The user aggregator determined for the main problem The lower limit of the relevant cost Respectively, the coefficient matrix The relevant variables of the integrated energy service provider The relevant costs of the integrated energy service provider The relevant continuous variables of the integrated energy service provider The coupling variables between the integrated energy service provider and each user aggregator Indicates the number of user aggregators; The sub-problems include the unified dual sub-problem and the feasibility restoration sub-problem. The unified dual sub-problem is specifically: wherein, and are dual variables respectively; is an auxiliary 0-1 variable; is a set of constraints related to dual variables; is a set of constraints related to the auxiliary 0-1 variable; is a vector formed by and in the current solution of the master problem, represents that when and have optimal solutions and at this time the corresponding objective function value, is a coefficient vector, is a coefficient matrix; The feasibility restoration sub-problem is specifically: Among them, represents the objective function value; represents the feasibility deviation from any solution in the feasible region, is the vector formed by and in the current solution of the master problem, is a variable related to the user aggregator ; is a continuous variable related to the user aggregator ; are the corresponding coefficient matrices or coefficient vectors respectively.

2. The multi-agent joint planning method for regional integrated energy systems according to claim 1, wherein In step S1, the regional integrated energy system includes an integrated energy service provider and multiple user aggregators. Taking the minimization of the annual total cost of the regional integrated energy system as the objective function and using the integrated energy service provider, user aggregators, and multi-energy pipe networks as constraints, model the regional integrated energy system to obtain a multi-entity joint planning model of the regional integrated energy system, and determine the joint planning problem according to the multi-entity joint planning model of the regional integrated energy system.

3. The multi-agent joint planning method for regional integrated energy systems according to claim 1, characterized in that The objective function is: Among them, represents the number of user aggregators; represents the number of scenarios; represents the equivalent annual investment cost of the integrated energy service provider; represents the user aggregator equivalent annual investment cost; represents the scenario operating cost; represents the scenario occurrence probability; respectively represent the integrated energy service provider, the equipment related to the user aggregator, and various pipe networks; represents the total number of days in a year; represents the unit investment cost of the equipment of type; represents the th rated capacity of the equipment of type; represents the number of time periods; represents the relevant operating expenses of the integrated energy service provider; represents the equipment operating cost function of the user aggregator; represents the demand response penalty function of the user aggregator.

4. The multi-agent joint planning method for regional integrated energy systems according to claim 1, characterized in that The unified dual sub-problem is determined by approximately dualizing the original sub-problem with the help of auxiliary 0-1 variables and the max-min inequality. The original sub-problems include the feasibility sub-problem and the optimality sub-problem. The unified dual sub-problem is specifically: wherein, is a vector composed of slack variables.

5. The multi-agent joint planning method for regional integrated energy systems according to claim 4, characterized in that The feasibility sub-problem is specifically: Among them, respectively represent the 1 vector and the zero vector; is a vector composed of a group of non - negative slack variables.

6. The multi-agent joint planning method for regional integrated energy systems according to claim 4, wherein The optimality sub-problem is specifically: 。 7. A multi-agent joint planning system for regional integrated energy systems, characterized in that, Including: A construction module that constructs a joint planning problem with the goal of minimizing the total planning and operation cost based on multiple entities within the regional integrated energy system; The planning module uses the enhanced Benders decomposition method to decompose the joint planning problem established by the construction module into one master problem and a group of sub-problems. The integrated energy service providers and user aggregation algorithms within the regional integrated energy system iteratively solve the master problem and a group of sub-problems, and based on the obtained optimal joint planning scheme of the regional integrated energy system, the optimal joint planning of the regional integrated energy system is realized; The joint planning problem is specifically: Among them, are respectively the integrated energy service provider and the user aggregator related variables; are respectively the integrated energy service provider and the user aggregator related continuous variables; is the coupling variable between the integrated energy service provider and each user aggregator; is the cost related to the integrated energy service provider; represents the user aggregator related cost; represents the constraints related to the integrated energy service provider; represents the user aggregator related constraints; are respectively the coefficient matrices; are respectively the coefficient vectors; The main problem is specifically: Among them, the user aggregator determined for the main problem the lower limit of the relevant cost are the coefficient matrices respectively are the relevant variables of the integrated energy service provider are the relevant costs of the integrated energy service provider are the relevant continuous variables of the integrated energy service provider are the coupling variables between the integrated energy service provider and each user aggregator represents the number of user aggregators; The sub-problems include the unified dual sub-problem and the feasibility restoration sub-problem. The unified dual sub-problem is specifically: wherein, and are dual variables respectively; is an auxiliary 0-1 variable; is a set of constraints related to the dual variables; is a set of constraints related to the auxiliary 0-1 variable; in the current solution of the master problem, and form a vector, represents that when and have optimal solutions and at this time the corresponding objective function value, is a coefficient vector, is a coefficient matrix; The feasibility restoration sub-problem is specifically: Among them, represents the objective function value; represents the feasibility deviation from any solution within the feasible region ; is the vector formed by and in the current solution of the master problem, is a variable related to the user aggregator ; is a continuous variable related to the user aggregator ; are the corresponding coefficient matrices or coefficient vectors respectively.

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