Comprehensive energy system-communication system optimization design method and device

Abstract

The invention belongs to the field of park integrated energy planning and communication network planning, and relates to an integrated energy system-communication system optimization design method and device. An integrated energy system model based on communication network reliability is constructed, the integrated energy system and the communication network are optimized at the same time, and the target is to minimize the planning cost of the integrated energy system and the operation cost of the communication network; constructing a two-stage integrated energy-communication system collaborative optimization model for minimizing the comprehensive cost of the whole integrated energy system and the communication system in the scheduling and operation process; and solving the joint optimization model of the communication and energy system based on a heuristic monkey group algorithm. According to the invention, the communication network and the integrated energy system are closely combined to realize collaborative optimization of the two. The robustness of the integrated energy system under the condition that a communication network is unreliable is improved, the energy scheduling and system operation efficiency is optimized, and the solving speed and efficiency in a large-scale complex scene are improved, so that the stability and the real-time performance of the system are ensured.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy planning and communication network planning of a park, and more specifically, relates to an integrated energy system-communication system optimization design method and device. Background Art

[0002] In the planning process of integrated energy systems, the reliability of communication networks has become a key factor. Modern integrated energy systems are highly dependent on the coordinated operation and information exchange between various energy devices, and all of this needs to be achieved through a stable communication network. If communication is unreliable, information transmission is delayed or lost, the real-time and accuracy of energy scheduling and optimization will be greatly reduced, which will in turn affect the overall operating efficiency and economy of the system. Therefore, in the planning of future integrated energy systems, more attention must be paid to the robustness and availability of communication networks to ensure that efficient optimization and coordinated scheduling can be maintained in various emergencies.

[0003] Integrated energy system planning technologies mainly focus on balancing energy supply and demand, optimizing system costs, and improving energy efficiency. In these solutions, information exchange between energy devices usually relies on communication networks, assuming that the communication system is reliable and stable. However, with the expansion of the scale and complexity of integrated energy systems, the role of communication networks in ensuring information transmission becomes increasingly critical. Communication networks support real-time optimization and scheduling of energy systems by enabling coordination and information sharing between devices. In addition, the nonlinear behavior of communication networks, such as dynamic changes in network topology, fluctuations in channel bandwidth, and node communication delays, further increases the difficulty of optimization. Therefore, the performance of the communication network will directly affect the performance optimization of the integrated energy system.

[0004] The nonlinear model of communication networks is difficult to integrate effectively with integrated energy systems. The behavior of communication networks is complex and dynamic, especially due to uncertainties such as network topology, bandwidth changes, and congestion, which makes it difficult to match the linear characteristics of integrated energy systems with the network model. Although there are some attempts to combine communication network models with energy system models for joint optimization, the different characteristics and dynamic behaviors of the two make integration a huge challenge.

[0005] Most existing integrated energy planning technologies assume that the communication network is always reliable, but do not fully consider the instability of the communication network, such as delays, packet loss, or communication interruptions. When the communication network fails, information transmission is blocked, which will cause energy scheduling and optimization to be unable to proceed in real time, affecting the overall operating efficiency and stability of the system. Therefore, existing technologies are insufficient in dealing with communication problems in practical applications and it is difficult to ensure the robustness and efficient operation of the system.

[0006] Due to the highly nonlinear characteristics of communication networks, existing technologies have difficulty integrating them into integrated energy systems. The complexity of communication network models, such as dynamic network topology, channel bandwidth fluctuations, delays, and congestion, increases the difficulty of jointly optimizing them with energy systems. Most existing energy system planning tools use linear or quasi-linear models, while the nonlinear behavior of communication networks is incompatible with these models, making it difficult to achieve effective model integration and collaborative optimization.

[0007] Even if communication networks and energy systems can be jointly modeled, existing technologies still face the challenge of low solution efficiency when solving such large-scale nonlinear optimization problems. Joint optimization models usually involve a large number of nonlinear constraints and equations, which leads to the slow solution speed and high computational complexity of traditional optimization algorithms (such as linear programming, dynamic programming, etc.) when dealing with such problems. When faced with dynamic changes and uncertainties in practical applications, existing optimization methods are difficult to obtain effective solutions within a reasonable time, thus affecting the real-time performance and optimization effect of the system. Summary of the invention

[0008] The purpose of the present invention is to provide an optimization design method and device for an integrated energy system-communication system. In view of the problems in the prior art that the communication network is not fully considered, the communication network model is complex and difficult to be effectively integrated with the integrated energy system, and the joint optimization model has low solution efficiency, the communication network and the integrated energy system are closely integrated to achieve the coordinated optimization of the two. The method aims to improve the robustness of the integrated energy system in the case of unreliable communication networks, optimize energy scheduling and system operation efficiency, and improve the solution speed and efficiency in large-scale complex scenarios, thereby ensuring the stability and real-time performance of the system.

[0009] The technical solution of the present invention is as follows:

[0010] The first aspect of the present invention is to provide an optimization design method for an integrated energy system-communication system, aiming to improve the overall performance and stability of the integrated energy system under unreliable communication conditions. The technical solution is divided into three main steps:

[0011] Construction of an integrated energy system model based on the reliability of the communication network. The integrated energy system and the communication network are optimized at the same time, with the goal of minimizing the planning cost of the integrated energy system and the operating cost of the communication network. A model of an integrated energy system that integrates the communication network is constructed, which takes into account the unreliability of the communication network to ensure that the energy system can effectively respond and continue to operate in failure scenarios such as communication delays, data loss, or network interruptions. By introducing the dynamic behavior of the communication network and its impact on the energy system, the system's responsiveness to emergencies and overall reliability are improved.

[0012] A two-level integrated energy-communication system collaborative optimization model is used to minimize the comprehensive cost of the entire integrated energy system and communication system during scheduling and operation. A two-level collaborative optimization strategy is designed, covering communication network status detection and multi-hop operation strategy. This optimization model ensures that the communication network and energy system work together at all stages to achieve efficient resource allocation and intelligent scheduling of energy flow, thereby improving the system's operating efficiency and stability.

[0013] A fast solution method for joint optimization of communication and energy systems. To ensure the real-time and effectiveness of the model in large-scale applications, this paper proposes a heuristic monkey swarm algorithm to solve the joint optimization model of communication and energy systems. By reducing the computational complexity and improving the solution speed, the algorithm can quickly respond to changes in the state of the communication system and the needs of the energy system, realize the joint optimization of large-scale integrated energy systems and communication networks, and ensure the efficient operation of the system in complex environments.

[0014] Furthermore, the overall objective function of the integrated energy system model based on communication network reliability is expressed as:

[0015] min(C com +C en )

[0016] Where: C com represents the operation and planning cost of the communication network; C en Represents the planning and operating costs of an integrated energy system.

[0017] Furthermore, the operation and planning costs of the communication network include: the deployment, maintenance and data transmission costs of communication equipment during operation, the expansion and upgrade costs of the communication network and the operation risk costs that may be caused by the unreliability of the communication network.

[0018] Furthermore, the planning and operating costs of the integrated energy system include the installation, maintenance, and operation costs of energy equipment, as well as the operating costs generated during energy transmission and scheduling.

[0019] Furthermore, the integrated energy system model based on communication network reliability includes the scale constraints of the communication network and the scale constraints of the integrated energy system.

[0020] The scale constraints of the communication network include: the number of communication nodes, bandwidth, channel capacity physical limitations, and the design constraints of the network topology, expressed as:

[0021]

[0022] Among them: i and j represent nodes, SBend i,j represents the planned bandwidth between nodes i and j, Bendmax The maximum planned bandwidth limit for the communication network.

[0023] The scale constraints of the integrated energy system include: capacity limits of power generation and energy storage equipment, and transmission capacity constraints of each energy flow, ensuring that the system does not exceed the physical limits of the equipment during scheduling, expressed as:

[0024]

[0025] Among them: SP gen represents the planned capacity of power generation equipment, SH gen Indicates the planned capacity of the heating equipment, SQ stor represents the planned capacity of the energy storage equipment, and are the minimum and maximum planned capacities of power generation equipment, and are the minimum and maximum planned capacities of the heating equipment, and Plan the minimum and maximum capacity for energy storage equipment.

[0026] Furthermore, the objective function of the two-stage integrated energy-communication system collaborative optimization model is:

[0027] Min(C run +C repair +C env +C loan )

[0028] Among them, C rum is the operating cost, C repair is the maintenance cost, C env is the environmental cost, C loan is the bandwidth cost;

[0029]

[0030] Where: C fuel represents the fuel cost of CHP units and other fuel equipment, C elec is the electricity consumption cost, C om is the fixed operation and maintenance cost of the equipment, C comm Charges for bandwidth usage of communications systems;

[0031]

[0032] Where: C maint Cost of equipment maintenance and repair;

[0033]

[0034] Where: Cco2 is the cost of carbon dioxide emissions, C emissions Costs of emitting other pollutants;

[0035]

[0036] Among them: α is the linear cost coefficient, which represents the basic cost per unit bandwidth; β is the quadratic cost coefficient, which reflects the increasing marginal cost of bandwidth usage when bandwidth resources are scarce; B ij For bandwidth.

[0037] Furthermore, the operation constraints of the two-stage integrated energy-communication system collaborative optimization model include: power generation constraints, energy balance constraints, energy storage constraints, bandwidth constraints, bandwidth allocation constraints, delay constraints, node load constraints and link cascading effect constraints of the integrated energy system;

[0038] The bandwidth constraint is expressed as:

[0039]

[0040] Among them, C ij is the communication capacity between nodes i and j, B ij is the bandwidth, S ij is the signal power, N ij is the noise power;

[0041] The bandwidth allocation constraint is expressed as:

[0042]

[0043] Among them: B ij,k is the bandwidth allocated to the kth link, is the total bandwidth available;

[0044] The delay constraint is expressed as:

[0045]

[0046] D max D ij is the transmission delay from link i to j, L ij is the amount of data transferred, D max is the maximum delay allowed by the system;

[0047] The node load constraint is expressed as:

[0048]

[0049] Where: P ij is the load from node i to node j, P max is the maximum load of node i;

[0050] The link cascading effect constraint is expressed as:

[0051] C path =min(C 12 ,C 23 ,...,C n-1,n )

[0052] Among them C path is the bandwidth of the transmission path from node 1 to node n, and each link C in the path n-1,n The bandwidth of each link may be different, affected by the physical conditions of the link itself, the capabilities of the communication equipment, and interference conditions.

[0053] Furthermore, the joint optimization model of the communication and energy systems is written as the following unified expression:

[0054]

[0055] in: For equipment planning cost vector, vector b m represents the maximum capacity of equipment planning, T0 is the unit matrix, and the cost coefficient vector Coefficient matrix T s , W s ,x,y are the b of the planning layer and the communication layer respectively s Variable; is the right-hand side vector, Γ(x,y) represents the nonlinear constraint based on Shannon's formula.

[0056] Furthermore, the steps of the heuristic-based monkey colony algorithm are as follows:

[0057] Initialize the monkey group: randomly generate a certain number of monkey individuals in the search space, each of which represents a potential solution, which is the configuration scheme of the communication and energy systems;

[0058] Fitness evaluation: Each monkey performs fitness evaluation based on the value of the objective function, and evaluates the effectiveness of each solution by calculating the operating cost, maintenance cost, environmental cost, and bandwidth cost;

[0059] Information sharing and local search: Individuals in the monkey group communicate their fitness values ​​with each other through the information sharing mechanism, and select individuals with higher fitness for local search;

[0060] Global search and exploration: After the fitness is updated, the monkey group will conduct a global search and randomly select a certain proportion of individuals to change their positions to avoid falling into the local optimal solution. At this time, the monkey group will mutate and crossover according to the pros and cons of the current solution to generate a new potential solution;

[0061] Termination condition: The algorithm continues to execute within the preset maximum number of iterations, or stops when the fitness reaches the set threshold. At termination, the individual with the best fitness is recorded as the final solution.

[0062] The second aspect of the present invention is to provide an integrated energy system-communication system optimization design device, comprising:

[0063] The integrated energy system model generation module based on the reliability of the communication network is used to optimize the integrated energy system and the communication network at the same time, with the goal of minimizing the planning cost of the integrated energy system and the operating cost of the communication network;

[0064] A level-1 integrated energy-communication system collaborative optimization model generation module is used to minimize the comprehensive cost of the entire integrated energy system and communication system during the scheduling and operation process;

[0065] A fast solution module for the joint optimization of communication and energy systems, which solves the joint optimization model of communication and energy systems based on the heuristic monkey swarm algorithm.

[0066] Advantages and beneficial effects of the present invention:

[0067] 1. Improve communication reliability and system scheduling efficiency: The device significantly reduces the packet loss rate in the communication process of the integrated energy system from 7.3% to 2.5% by considering the optimized design of communication. This improvement in communication reliability ensures the accurate transmission of information, thereby optimizing the scheduling efficiency and overall performance of the integrated energy system.

[0068] 2. Improve the solution efficiency and apply to scenarios with real-time requirements: Solution S3 has a significant advantage in computing time and can find a solution within 100 seconds, which is more than 50% higher than the existing technical solutions and improves the solution efficiency. While maintaining the accuracy of the optimal solution (99.99%), the device is more suitable for the integrated energy system-communication system optimization design scenario with high real-time requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 This is a diagram of the optimized design device for the integrated energy system-communication system of the present invention. DETAILED DESCRIPTION

[0070] The present invention is further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the protection scope of the present invention cannot be limited thereto.

[0071] The integrated energy system-communication system optimization design method of the present invention includes three parts: constructing an integrated energy system model based on the reliability of the communication network, constructing a two-level integrated energy-communication system collaborative optimization model and a fast solution method for the joint optimization of the communication and energy systems.

[0072] Integrated energy system model based on communication network reliability

[0073] The core of this model is to optimize the integrated energy system and the communication network at the same time, taking into account the impact of the unreliability of the communication network on the scheduling of the energy system. Its goal is to ensure that the system can achieve efficient and stable operation while meeting various operating constraints by minimizing the planning cost of the integrated energy system and the operating cost of the communication network.

[0074] The objective function consists of two parts:

[0075] Operation and planning costs of communication networks: This part covers the deployment, maintenance and data transmission costs of communication equipment during operation. The expansion and upgrade of the communication network will also bring corresponding costs. In addition, the operational risk costs that may be caused by the unreliability of the communication network must also be considered.

[0076] Comprehensive energy system planning costs: Comprehensive energy system planning costs include the installation, maintenance, and operation costs of energy equipment (such as generators, energy storage devices, thermal systems, etc.), as well as the operating costs incurred during energy transmission and dispatch. The goal is to reduce the cost of energy supply under various load conditions by optimizing the dispatch strategy.

[0077] Therefore, the overall objective function of the model can be expressed as:

[0078] min(C com +C en )

[0079] Where: C com represents the operation and planning cost of the communication network; C en Represents the planning and operating costs of an integrated energy system.

[0080] Constraints

[0081] The scale constraints of the communication network include physical constraints such as the number of communication nodes, bandwidth, channel capacity, and the design constraints of the network topology. Specifically, the communication network must meet the real-time scheduling requirements of the energy system under the condition of limited resources and ensure the timely transmission of information. Its mathematical form can be expressed as:

[0082]

[0083] Where: i and j represent nodes. i,j represents the planned bandwidth between nodes i and j, Bend max The maximum planned bandwidth limit for the communication network.

[0084] Scale constraints of integrated energy systems: Scale constraints of integrated energy systems include capacity constraints of power generation and energy storage equipment, as well as transmission capacity constraints of various energy flows, to ensure that the system does not exceed the physical limits of the equipment during scheduling. Its mathematical form is:

[0085]

[0086] Among them: SP gen represents the planned capacity of power generation equipment, SH gen Indicates the planned capacity of the heating equipment, SQ stor represents the planned capacity of the energy storage equipment, and are the minimum and maximum planned capacities of power generation equipment, and are the minimum and maximum planned capacities of the heating equipment, and Plan the minimum and maximum capacity for energy storage equipment.

[0087] A two-level integrated energy-communication system collaborative optimization model

[0088] The core of the model is to achieve the coordinated optimization of the integrated energy system and the communication network. The model also takes into account the operating constraints of energy storage equipment, cogeneration units, electric boilers, electric air conditioners, and the bandwidth limitations of the communication system to ensure efficient scheduling and optimization of the system at all stages.

[0089] The objective function consists of three parts: operating cost C rum , Maintenance cost C repair and environmental cost C env , which aims to minimize the comprehensive costs of the entire integrated energy system and communication system during the scheduling and operation process.

[0090] Operating costs: including fuel costs of energy equipment, electricity consumption costs, fixed equipment operation and maintenance costs, and bandwidth usage costs of communication networks, etc.

[0091]

[0092] Where: C fuel represents the fuel cost of CHP units and other fuel equipment, C elec is the electricity consumption cost, C om is the fixed operation and maintenance cost of the equipment, C comm Charges for bandwidth usage of communication systems.

[0093] Maintenance cost: Taking into account possible failures and maintenance needs during equipment operation, maintenance cost reflects the expenses of fault repair, parts replacement, etc.

[0094]

[0095] Where: C maint Cost of equipment maintenance and repair.

[0096] Environmental costs: including the costs of environmental impacts such as carbon emissions caused by energy consumption, especially considering the emissions of equipment such as cogeneration units.

[0097]

[0098] Where: C co2 is the cost of carbon dioxide emissions, C emissions The cost of emitting other pollutants.

[0099] The final objective function is:

[0100] min(C rum +C repair +C env )

[0101] In addition to the power generation constraints, energy balance constraints, and energy storage constraints of the conventional integrated energy system, the operating constraints of the two-level integrated energy-communication system collaborative optimization model also further consider bandwidth constraints and optimal bandwidth ratio constraints.

[0102] In the collaborative optimization model of communication systems, bandwidth constraints are used to describe the maximum capacity of communication links through the Shannon formula. In the bandwidth optimization part, we not only consider the link capacity of the communication network, but also introduce the allocation and cost optimization of bandwidth resources. To further complicate the model, bandwidth constraints can involve multiple levels of transmission links and consider communication performance under different channel conditions. In addition, the allocation of bandwidth resources on the link can also be modeled to ensure efficient coordination between communication traffic and energy flow of the integrated energy system.

[0103] The bandwidth of the communication system is limited by Shannon's formula, that is, the channel capacity is related to the signal-to-noise ratio, and the usage of the communication bandwidth must be within the capacity range.

[0104]

[0105] Among them, C ij is the communication capacity between nodes i and j, B ij is the bandwidth, S ij is the signal power, N ij is the noise power. This formula shows that the maximum transmission capacity of the link is proportional to the bandwidth and the signal-to-noise ratio, ensuring that the communication network can meet the scheduling needs of the integrated energy system when the data traffic is large.

[0106] Bandwidth allocation constraint: In order to achieve effective communication, the system needs to allocate bandwidth for different data flows. For K communication links, the bandwidth allocation constraint can be expressed as:

[0107]

[0108] Among them: B ij,k is the bandwidth allocated to the kth link, is the total available bandwidth. This constraint ensures that all communication needs within the system can be met while avoiding communication delays caused by insufficient bandwidth, which can affect the real-time scheduling of the integrated energy system.

[0109] Delay constraint: In an integrated energy system, communication delay will affect the real-time nature of information, and thus affect the optimal scheduling of energy. The delay constraint can be expressed as:

[0110]

[0111] D max D ij is the transmission delay from link i to j, L ij is the amount of data transferred, D max is the maximum delay allowed by the system. This constraint ensures that in the communication network, information can be transmitted to the control node within an acceptable time, thereby maintaining the efficient operation of the integrated energy system.

[0112] Node load constraint: The load of each communication node must be controlled within a certain range to ensure the stability of the communication system. The constraint can be expressed as:

[0113]

[0114] Where: P ij is the load from node i to node j, P max is the maximum load of node i. This constraint ensures that the node can still operate normally under high load conditions, avoiding communication interruption caused by overload, which affects the optimization of the integrated energy system.

[0115] Link cascading effect constraints. When multi-hop communication is involved, each link in the transmission path has its bandwidth limitation, and the final available bandwidth is limited by the weakest link in the link. This makes the bandwidth of the entire transmission path:

[0116] C path =min(C 12 ,C 23 ,...,C n-1,n )

[0117] Among them C pathis the bandwidth of the transmission path from node 1 to node n, and each link C in the path n-1,n The bandwidth of each link may be different, affected by the physical conditions of the link itself, the capabilities of the communication equipment, and interference conditions.

[0118] Bandwidth cost function

[0119] The use of bandwidth will also incur corresponding economic costs, especially when communication resources are scarce or high-quality channel resources are limited. The bandwidth cost will increase nonlinearly with the increase in its usage. In this case, the bandwidth cost can be expressed as:

[0120]

[0121] Among them: α is the linear cost coefficient, which represents the basic cost per unit bandwidth; β is the quadratic cost coefficient, which reflects the increasing marginal cost of bandwidth usage when bandwidth resources are scarce.

[0122] The above bandwidth usage cost is added to the overall optimization objective function of the integrated energy system, and the objective function is:

[0123] Min(C run +C repair +C env +C loan )

[0124] A fast solution method for joint optimization of communication and energy systems

[0125] In the joint optimization model of communication and energy systems, the solution process often faces the problems of low solution efficiency and slow convergence speed due to the complexity of the model and its high-dimensional nonlinear characteristics. In order to solve this problem, the present invention proposes a heuristic monkey swarm algorithm to quickly solve the joint optimization model of communication and energy systems. First, the above model is written as the following unified expression:

[0126]

[0127] in: For equipment planning cost vector, vector b m represents the maximum capacity of equipment planning, T0 is the unit matrix. Cost coefficient vector Coefficient matrix T s , W s ,x,y are the b of the planning layer and the communication layer respectively s Variable; is the right-hand side vector. Γ(x,y) represents the nonlinear constraint based on Shannon's formula.

[0128] Solving steps of the optimization model

[0129] Initialize the monkey group: randomly generate a certain number of monkey individuals in the search space, each of which represents a potential solution (i.e., the configuration scheme of the communication and energy systems). The initialization stage includes setting the size of the monkey group, the individual positions, and the optimization parameters.

[0130] Fitness evaluation: Each monkey performs fitness evaluation based on the value of the objective function. The fitness value reflects the degree to which the individual solution satisfies the joint optimization goal of the communication and energy systems. The effectiveness of each solution is evaluated by calculating the operating cost, maintenance cost, environmental cost, and bandwidth cost.

[0131] Information sharing and local search: The individuals in the monkey group communicate their fitness values ​​with each other through the information sharing mechanism, and select individuals with higher fitness for local search. This process can help the monkey group quickly adjust the search direction and migrate to the area with better solutions.

[0132] Global search and exploration: After the fitness is updated, the monkey group will conduct a global search and randomly select a certain proportion of individuals to change their positions to avoid falling into the local optimal solution. At this time, the monkey group will mutate and crossover according to the pros and cons of the current solution to generate a new potential solution.

[0133] Termination condition: The algorithm continues to execute within the preset maximum number of iterations, or stops when the fitness reaches the set threshold. At termination, the individual with the best fitness is recorded as the final solution.

[0134] In order to verify the effectiveness of the integrated energy system-communication system optimization design method described in the present invention, a case analysis is carried out by taking a certain integrated energy system to be planned as an example. This method constructs a multi-level collaborative optimization model by introducing the reliability consideration of the communication network.

[0135] In order to evaluate the performance of the optimization design method, the following two sets of comparisons were conducted:

[0136] 1. Considering the communication scheme: The integrated energy system-communication system optimization design method (S1) described in the present invention is adopted to fully consider the reliability of the communication network and its impact on the energy system.

[0137] 2. Solution without considering communication: The traditional integrated energy system optimization method (S2) is adopted, in which the influence of the communication network is not considered.

[0138] In addition, in order to further verify the effectiveness of the algorithm, the solving efficiency of the monkey swarm algorithm is compared with the performance of traditional optimization algorithms (such as genetic algorithm, particle swarm algorithm, etc.):

[0139] 3. Monkey Colony Algorithm: Monkey Colony Algorithm is applied to optimize the scheme considering communication (S3).

[0140] 4. Conventional algorithm: Under the same circumstances, the traditional numerical optimization algorithm is used to solve (S4).

[0141] According to the comparison results, the packet loss rate of Scheme S1 considering communication is 2.5%, which is significantly lower than the 7.3% of Scheme S2 not considering communication. This shows that Scheme S1 has a significant advantage in communication reliability and can effectively reduce the number of packet losses from 73 to 25. This effect of reducing the packet loss rate will improve the dispatch efficiency and overall performance of the integrated energy system, ensure that information can be transmitted more accurately, and thus achieve better energy resource allocation and utilization.

[0142] When comparing the computational performance of Schemes S3 and S4, the computational time of Scheme S3 is 90.2 seconds, which is significantly lower than Scheme S4's 156.7 seconds. This shows that S3 has a clear advantage in solution efficiency and can find solutions faster. At the same time, both schemes achieved 99.99% in terms of optimal solution accuracy, showing their consistency in result accuracy. In summary, although the two schemes have the same solution accuracy, Scheme S3 has more advantages and can achieve efficient optimization results in a shorter time. It is suitable for integrated energy system-communication system optimization design scenarios with high real-time requirements.

[0143] Table 1 Packet loss rate comparison

[0144]

[0145]

[0146] Table 2 Comparison of calculation time

[0147]

[0148] like Figure 1 As shown, the second aspect of the present invention provides an integrated energy system-communication system optimization design device, comprising:

[0149] The integrated energy system model generation module based on the reliability of the communication network is used to optimize the integrated energy system and the communication network at the same time, with the goal of minimizing the planning cost of the integrated energy system and the operating cost of the communication network;

[0150] A level-1 integrated energy-communication system collaborative optimization model generation module is used to minimize the comprehensive cost of the entire integrated energy system and communication system during the scheduling and operation process;

[0151] A fast solution module for the joint optimization of communication and energy systems, which solves the joint optimization model of communication and energy systems based on the heuristic monkey swarm algorithm.

[0152] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept, which all belong to the protection scope of the present invention.

Claims

1. A comprehensive energy system-communication system optimization design method, characterized in that: include: Construct an integrated energy system model based on the reliability of the communication network, and optimize the integrated energy system and the communication network at the same time, with the goal of minimizing the planning cost of the integrated energy system and the operating cost of the communication network; Construct a two-level integrated energy-communication system collaborative optimization model to minimize the comprehensive cost of the entire integrated energy system and communication system during scheduling and operation; A heuristic monkey swarm algorithm is used to solve the joint optimization model of communication and energy systems.

2. The integrated energy system-communication system optimization design method according to claim 1, characterized in that: The overall objective function of the integrated energy system model based on communication network reliability is expressed as: min(C com +C en ) Where: C com represents the operation and planning cost of the communication network; C en Represents the planning and operating costs of an integrated energy system.

3. The integrated energy system-communication system optimization design method according to claim 2 is characterized in that: The operation and planning costs of the communication network include: the deployment, maintenance and data transmission costs of communication equipment during operation, the expansion and upgrade costs of the communication network and the operation risk costs that may be caused by the unreliability of the communication network.

4. The integrated energy system-communication system optimization design method according to claim 2, characterized in that: The planning and operating costs of the integrated energy system include the installation, maintenance, and operation costs of energy equipment, as well as the operating costs generated during energy transmission and scheduling.

5. The integrated energy system-communication system optimization design method according to claim 2, characterized in that: The integrated energy system model based on communication network reliability includes the scale constraints of the communication network and the scale constraints of the integrated energy system. The scale constraints of the communication network include: the number of communication nodes, bandwidth, channel capacity physical limitations, and the design constraints of the network topology, expressed as: Among them: i and j represent nodes, SBend i,j represents the planned bandwidth between nodes i and j, Bend max The maximum planned bandwidth limit for the communication network. The scale constraints of the integrated energy system include: capacity limits of power generation and energy storage equipment, and transmission capacity constraints of each energy flow, ensuring that the system does not exceed the physical limits of the equipment during scheduling, expressed as: Among them: SP gen represents the planned capacity of power generation equipment, SH gen Indicates the planned capacity of the heating equipment, SQ stor represents the planned capacity of the energy storage equipment, and are the minimum and maximum planned capacities of power generation equipment, and are the minimum and maximum planned capacities of the heating equipment, and Plan the minimum and maximum capacity for energy storage equipment.

6. The integrated energy system-communication system optimization design method according to claim 1, characterized in that: The objective function of the two-level integrated energy-communication system collaborative optimization model is: Min(C run +C repair +C env +C loan ) Among them, C rum is the operating cost, C repair is the maintenance cost, C env is the environmental cost, C loan For bandwidth cost; Where: C fuel represents the fuel cost of CHP units and other fuel equipment, C elec is the electricity consumption cost, C om is the fixed operation and maintenance cost of the equipment, C comm Charges for bandwidth usage of communications systems; Where: C maint Cost of equipment maintenance and repair; Where: C co2 is the cost of carbon dioxide emissions, C emissions Costs of emitting other pollutants; Among them: α is the linear cost coefficient, which represents the basic cost per unit bandwidth; β is the quadratic cost coefficient, which reflects the increasing marginal cost of bandwidth usage when bandwidth resources are scarce; B ij For bandwidth.

7. The integrated energy system-communication system optimization design method according to claim 6, characterized in that: The operation constraints of the two-level integrated energy-communication system collaborative optimization model include: power generation constraints, energy balance constraints, energy storage constraints, bandwidth constraints, bandwidth allocation constraints, delay constraints, node load constraints and link cascade effect constraints of the integrated energy system; The bandwidth constraint is expressed as: Among them, C ij is the communication capacity between nodes i and j, B ij is the bandwidth, S ij is the signal power, N ij is the noise power; The bandwidth allocation constraint is expressed as: Among them: B ij,k is the bandwidth allocated to the kth link, is the total bandwidth available; The delay constraint is expressed as: D max D ij is the transmission delay from link i to j, L ij is the amount of data transferred, D max is the maximum delay allowed by the system; The node load constraint is expressed as: Where: P ij is the load from node i to node j, P max is the maximum load of node i; The link cascading effect constraint is expressed as: C path =min(C 12 ,C 23 ,...,C n-1,n ) Among them C path is the bandwidth of the transmission path from node 1 to node n, and each link C in the path n-1,n The bandwidth of each link may be different, affected by the physical conditions of the link itself, the capabilities of the communication equipment, and interference conditions.

8. The integrated energy system-communication system optimization design method according to claim 1, characterized in that: The joint optimization model of the communication and energy systems is written as follows: in: For equipment planning cost vector, vector b m represents the maximum capacity of equipment planning, T0 is the unit matrix, and the cost coefficient vector Coefficient matrix T s , W s ,x,y are the b of the planning layer and the communication layer respectively s Variable; is the right-hand side vector, Γ(x,y) represents the nonlinear constraint based on Shannon's formula.

9. The integrated energy system-communication system optimization design method according to claim 8, characterized in that: The steps of the heuristic-based monkey swarm algorithm are as follows: Initialize the monkey group: randomly generate a certain number of monkey individuals in the search space, each of which represents a potential solution, which is the configuration scheme of the communication and energy systems; Fitness evaluation: Each monkey performs fitness evaluation based on the value of the objective function, and evaluates the effectiveness of each solution by calculating the operating cost, maintenance cost, environmental cost, and bandwidth cost; Information sharing and local search: Individuals in the monkey group communicate their fitness values ​​with each other through the information sharing mechanism, and select individuals with higher fitness for local search; Global search and exploration: After the fitness is updated, the monkey group will conduct a global search and randomly select a certain proportion of individuals to change their positions to avoid falling into the local optimal solution. At this time, the monkey group will mutate and crossover according to the pros and cons of the current solution to generate a new potential solution; Termination condition: The algorithm continues to execute within the preset maximum number of iterations, or stops when the fitness reaches the set threshold. At termination, the individual with the best fitness is recorded as the final solution.

10. An integrated energy system-communication system optimization design device, characterized in that: include: The integrated energy system model generation module based on the reliability of the communication network is used to optimize the integrated energy system and the communication network at the same time, with the goal of minimizing the planning cost of the integrated energy system and the operating cost of the communication network; A level-1 integrated energy-communication system collaborative optimization model generation module is used to minimize the comprehensive cost of the entire integrated energy system and communication system during the scheduling and operation process; A fast solution module for the joint optimization of communication and energy systems, which solves the joint optimization model of communication and energy systems based on the heuristic monkey swarm algorithm.

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