Comprehensive energy scheduling method and device, electronic equipment and medium

Abstract

The embodiment of the invention relates to the technical field of integrated energy system scheduling, and provides an integrated energy scheduling method and device, electronic equipment and a medium, and the method comprises the steps: constructing a multi-energy-flow balance model of an integrated energy system, which comprises multiple types of physical constraints, based on an energy supply and demand structure of the integrated energy system; based on the multi-energy flow balance model, determining a total operation cost objective function of the integrated energy system; according to the total operation cost objective function and the scheduling rule of the integrated energy system, determining an optimal integrated energy scheduling scheme; the optimal comprehensive energy scheduling scheme is substituted into the multi-energy-flow balance model for calculation, and an optimal scheduling instruction set is obtained; and performing comprehensive energy scheduling based on the optimal scheduling instruction set. Therefore, the accuracy, the economical efficiency and the low-carbon property of energy scheduling are improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system dispatching technology, and in particular to an integrated energy dispatching method, device, electronic equipment and medium. Background Technology

[0002] Integrated energy systems, by integrating multiple energy forms such as electricity, heat, cooling, and gas, achieve tiered utilization and complementary synergy of energy, and have become an important development direction for improving energy efficiency, ensuring energy security, and promoting the consumption of renewable energy. To comprehensively evaluate the operational performance of integrated energy systems, it is crucial to study the economy, reliability, and low-carbon characteristics of their dispatching schemes. Among these, the total operating cost and carbon emission level of the system are core indicators for measuring its overall performance.

[0003] With the deepening of energy transition, precision scheduling technology for integrated energy systems has been widely applied in several key areas such as regional energy internet, industrial park energy management, and smart cities, highlighting its increasing importance. This technology significantly improves energy utilization efficiency and economy by optimizing the coordinated scheduling of multiple energy sources, making its application more efficient and reliable in scenarios such as high-proportion renewable energy integration and precise matching of diverse loads. In regional energy internet, precision scheduling can effectively balance the supply and demand of electricity, heat, and gas, improve system operational stability, and support the large-scale consumption of distributed energy. This plays a crucial role in optimizing energy structure, reducing operating costs, and improving energy supply reliability. In industrial park energy management, this technology achieves cascaded and circular energy utilization through multi-energy flow coordinated optimization, particularly demonstrating key benefits in scenarios such as waste heat recovery and combined cooling, heating, and power (CCHP). Integrated energy precision dispatch technology, in the field of smart cities, improves the overall energy efficiency of energy systems and enhances the intelligence level of urban energy management. This includes forecast-based load control, price-signal-based demand-side response, and precise carbon footprint tracking, helping city managers achieve precise energy supply, efficient energy use, and low-carbon transformation, significantly improving the resilience and sustainability of urban energy systems. Integrated energy precision dispatch technology plays a crucial role in modern energy systems, not only improving their economic efficiency and environmental friendliness but also laying a solid foundation for the further development of smart energy in the future.

[0004] The operating cost of an integrated energy system is closely related to system size, energy prices, network topology, equipment parameters, and load characteristics. The scheduling of each energy node is typically regulated by the energy management system, while energy allocation depends on network power flow constraints. In modern energy systems, optimization algorithms are commonly used for scheduling decisions. These algorithms solve mathematical models to find the optimal scheduling scheme, but they may face convergence difficulties during the solution process. For example, a scheduling problem involving N nodes, M types of energy, and T time periods has a decision variable dimension of N×M×T, which results in a high-dimensional nonlinear optimization problem, leading to the "curse of dimensionality" challenge.

[0005] To achieve ideal economic and low-carbon goals, it is essential to precisely optimize the scheduling of various energy sources, equipment start-up and shutdown plans, and network power flow distribution within an integrated energy system. This effectively balances system operating costs and environmental benefits. This optimization process is widely known as integrated energy system optimal scheduling. Due to the complex nonlinear relationship between system parameters and operating costs, intelligent optimization methods are typically employed for scheduling decisions to ensure the discovery of a globally optimal or near-optimal configuration. Common intelligent optimization algorithms include particle swarm optimization (PSO), genetic algorithms, and ant colony optimization (ACO).

[0006] Traditional methods for optimizing scheduling typically require first establishing a complex mathematical model, and then solving it using optimization algorithms. Taking a comprehensive energy system with N nodes, M energy sources, and T time periods as an example, if the traditional particle swarm optimization algorithm is used, assuming the algorithm iterates G times and each iteration has K swarm members, then a total of G×K fitness function calculations are needed. Each fitness function calculation requires simulating the multi-energy flow state of the system throughout the entire scheduling cycle. This method involves a huge computational load, resulting in excessively long computation time and low algorithm efficiency. Due to its high dimensionality, non-convexity, and complex constraints, it has a slow convergence speed and is prone to getting trapped in local optima, making it difficult to meet the real-time and accuracy requirements of scheduling schemes in practical engineering. Summary of the Invention

[0007] This invention provides a comprehensive energy dispatching method, device, electronic device, and medium to address the shortcomings of existing comprehensive energy dispatching methods, such as large computational load, long computation time, low algorithm efficiency, slow convergence speed, and susceptibility to local optima, which make it difficult to meet the requirements of practical engineering. This invention improves the accuracy, economy, and low carbon emissions of energy dispatching.

[0008] This invention provides a comprehensive energy dispatching method, comprising: Based on the energy supply and demand structure of the integrated energy system, a multi-energy flow balance model of the integrated energy system containing multiple types of physical constraints is constructed. Based on the multi-energy flow balance model, the objective function for the total operating cost of the integrated energy system is determined; Based on the total operating cost objective function and the scheduling rules of the integrated energy system, determine the optimal integrated energy scheduling scheme; Substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set; Integrated energy scheduling is performed based on the aforementioned optimal scheduling instruction set.

[0009] In one possible implementation, the method further includes: Based on the multiple energy nodes included in the integrated energy system and the multiple time periods of the energy dispatch cycle, the constraints of the power network, the heating network, and the natural gas network are determined. Based on the energy supply and demand structure of the integrated energy system, a multi-energy flow balance model is constructed, which includes the constraints of the power network, the heat network, and the natural gas network.

[0010] In one possible implementation, the method further includes: Based on the multi-energy flow balance model, the cumulative cost of electricity, heat and natural gas is calculated for each time period and each energy node within the scheduling cycle. By combining the accumulated costs and the carbon emission costs generated by the operation of the integrated energy system, the objective function for the total operating cost of the integrated energy system is determined.

[0011] In one possible implementation, the method further includes: The total operating cost objective function is solved using the dwarf mongoose optimization algorithm based on the scheduling rules of the integrated energy system. In the solution process, the dwarf meerkat optimization algorithm is guided by the global optimal position for local development, by random reference positions for global exploration and by the population average position to maintain diversity, and by the pheromone mechanism for information exchange. When the pheromone concentration is lower than the preset threshold, the meerkat position is reinitialized, and finally converges to obtain the global optimal integrated energy scheduling scheme.

[0012] In one possible implementation, the method further includes: In the process of solving the dwarf meerkat optimization algorithm, the position vector of each meerkat is verified by multi-energy flow balance constraints. Invalid scheduling schemes that do not meet the upper limit constraints of each energy node output, network transmission capacity constraints and energy supply and demand balance constraints are removed, and valid meerkat positions that meet the constraints are regenerated. After each re-initialization of the meerkat location, verify the feasibility of the new location; If it exceeds the constraint boundary, it is corrected to the boundary value; In the event of a total supply shortage, priority will be given to increasing the dispatch capacity of low-priced power stations to ensure energy supply stability.

[0013] In one possible implementation, the method further includes: The control parameters of the dwarf mongoose optimization algorithm adopt an adaptive dynamic adjustment strategy. The pheromone concentration threshold is dynamically set based on the population fitness distribution.

[0014] In one possible implementation, the method further includes: Substituting the optimal integrated energy dispatch scheme into the multi-energy flow balance model, a structured optimal dispatch instruction set is calculated, which includes the optimal energy dispatch amount for each energy node at different time periods.

[0015] The present invention also provides an integrated energy dispatching device, comprising the following modules: The module is used to construct a multi-energy flow balance model of the integrated energy system, which includes multiple types of physical constraints, based on the energy supply and demand structure of the integrated energy system. The determination module is used to determine the total operating cost objective function of the integrated energy system based on the multi-energy flow balance model. The determination module is also used to determine the optimal integrated energy dispatch scheme based on the total operating cost objective function and the dispatch rules of the integrated energy system; The calculation module is used to substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set; The scheduling module is used to perform comprehensive energy scheduling based on the optimal scheduling instruction set.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the integrated energy dispatching method as described above.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the integrated energy scheduling method as described above.

[0018] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the integrated energy scheduling method as described above.

[0019] The integrated energy dispatching method, apparatus, electronic equipment, and medium provided by this invention construct a multi-energy flow balance model of the integrated energy system, incorporating multiple physical constraints, based on the energy supply and demand structure of the integrated energy system. Based on the multi-energy flow balance model, the total operating cost objective function of the integrated energy system is determined. According to the total operating cost objective function and the dispatching rules of the integrated energy system, the optimal integrated energy dispatching scheme is determined. The optimal integrated energy dispatching scheme is substituted into the multi-energy flow balance model for calculation to obtain the optimal dispatching instruction set. Integrated energy dispatching is then performed based on the optimal dispatching instruction set. Compared to existing integrated energy dispatching methods, which suffer from high computational load, long computation time, low algorithm efficiency, slow convergence speed, and susceptibility to local optima, making them difficult to meet the requirements of practical engineering, this scheme can maximize the reliability of integrated energy dispatching and solve the problem of optimal and precise control and dispatching of integrated energy systems under constantly changing energy dispatching environments and methods. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is one of the flowcharts of the integrated energy dispatching method provided by the present invention.

[0022] Figure 2 This is the second flowchart of the integrated energy dispatching method provided by the present invention.

[0023] Figure 3 This is a schematic diagram of the process for precise scheduling of an integrated energy system based on the dwarf mongoose algorithm provided by the present invention.

[0024] Figure 4 This is a schematic diagram comparing the simulation results of the integrated energy dispatching method provided by this invention with existing dispatching methods.

[0025] Figure 5 This is a schematic diagram of the integrated energy dispatching device provided by the present invention.

[0026] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0029] This invention provides a comprehensive energy dispatching method, comprising: obtaining the total demand for M types of electricity from N energy supply stations in a comprehensive energy system and the different electricity prices of each supply station; based on the number of supply stations N, the types of electricity M, the preset population size and parameter range, randomly initializing the population positions using the dwarf meerkat optimization algorithm; constructing an optimization model with the goal of minimizing the comprehensive energy procurement cost, and using the dwarf meerkat optimization algorithm to iteratively optimize the population, simulating the social behaviors of meerkats such as foraging, sentry warning, and caregiver behavior for global exploration and local development, and finally searching for the globally optimal solution, i.e., the optimal target electricity dispatching scheme for the N supply stations; and accurately dispatching the M types of electricity for each supply station according to the target dispatching scheme. The specific method is as follows.

[0030] Figure 1 This is one of the flowcharts illustrating the integrated energy dispatching method provided by the present invention, such as... Figure 1 As shown, the method includes the following: S11. Based on the energy supply and demand structure of the integrated energy system, construct a multi-energy flow balance model of the integrated energy system that includes multiple types of physical constraints.

[0031] Based on the energy supply and demand structure of the integrated energy system, a multi-energy flow balance model is first constructed, which includes constraints from the power network, the heat network, and the natural gas network, forming a unified framework for the coupling of multiple energy flows of electricity, heat, and gas.

[0032] S12. Based on the multi-energy flow balance model, determine the objective function for the total operating cost of the integrated energy system.

[0033] Based on the multi-energy flow balance model, the cumulative cost of electricity, heat and natural gas is calculated for each time period and each energy node within the scheduling cycle. The total operating cost objective function of the integrated energy system is determined by combining the cumulative cost with the carbon emission cost generated by the operation of the integrated energy system.

[0034] S13. Determine the optimal integrated energy scheduling scheme based on the total operating cost objective function and the scheduling rules of the integrated energy system.

[0035] Based on the total operating cost objective function and scheduling rules, the dwarf mongoose optimization algorithm is used to determine the optimal scheduling scheme. The algorithm divides the population into an Alpha group for local development, a Scout group for global exploration, and a Babysitter group to maintain diversity. Information is exchanged through the pheromone mechanism, and the positions are reinitialized when the pheromone concentration is below the threshold. Finally, the algorithm converges to obtain the global optimal solution, which is the optimal integrated energy scheduling scheme.

[0036] S14. Substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set.

[0037] The optimal integrated energy dispatch scheme is substituted into the multi-energy flow balance model for calculation, resulting in a structured optimal dispatch instruction set. This instruction set contains the optimal energy dispatch amount for each energy node at different time periods, specifying information such as dispatch power, transmission path, and execution time.

[0038] S15. Perform integrated energy scheduling based on the optimal scheduling instruction set.

[0039] Integrated energy dispatching is carried out based on the optimal dispatching instruction set. The instructions are sent to the control systems of each supply station through the energy management platform, and the actual output and load data are collected in real time to verify the execution. If deviation occurs, temporary optimization is triggered to ensure the stable operation of the system.

[0040] The integrated energy dispatching method provided by this invention constructs a multi-energy flow balance model of the integrated energy system, incorporating multiple physical constraints, based on the energy supply and demand structure of the integrated energy system. Based on the multi-energy flow balance model, the total operating cost objective function of the integrated energy system is determined. According to the total operating cost objective function and the dispatching rules of the integrated energy system, the optimal integrated energy dispatching scheme is determined. The optimal integrated energy dispatching scheme is substituted into the multi-energy flow balance model for calculation to obtain the optimal dispatching instruction set. Integrated energy dispatching is then performed based on the optimal dispatching instruction set. Compared to existing integrated energy dispatching methods, which suffer from high computational load, long computation time, low algorithm efficiency, slow convergence speed, and susceptibility to local optima, making it difficult to meet the requirements of practical engineering, this method can maximize the reliability of integrated energy supply and solve the problem of optimal and precise control and dispatching of integrated energy systems under constantly changing energy dispatching environments and methods.

[0041] Figure 2 This is the second flowchart illustrating the integrated energy dispatching method provided by the present invention, as shown below. Figure 2 As shown, the method includes the following: S21. Based on the multiple energy nodes included in the integrated energy system and the multiple time periods of the energy dispatch cycle, determine the power network constraints, heating network constraints, and natural gas network constraints.

[0042] S22. Based on the energy supply and demand structure of the integrated energy system, construct a multi-energy flow balance model that includes the constraints of the power network, the heat network, and the natural gas network.

[0043] The data acquisition of the integrated energy dispatching method provided by this invention includes two aspects: one is the number of power suppliers and the type of electrical energy in the target area; the other is the control constraints, energy transmission convenience index and energy utilization rate corresponding to each power supplier. The control risk index is used to describe the safety level of the electrical energy provided by the corresponding power supplier, the energy transmission convenience index is used to describe the convenience level of the electrical energy provided by the corresponding power supplier, and the energy utilization rate is used to describe the actual consumption of each type of electrical energy provided by the corresponding power supplier.

[0044] This method is specifically designed for complex optimization problems involving multiple energy flow couplings and multiple constraints in modern integrated energy systems, and is particularly suitable for scenarios such as regional energy internet and industrial park energy systems.

[0045] Specifically, ① First, a mathematical model of the integrated energy system (multi-energy flow balance model) with coupled electricity, heat, and gas is established. Considering the system contains N energy nodes, with a scheduling period of T time intervals, the physical constraints of each energy network are as follows: Power network constraints: The power network adopts an AC power flow model to ensure that the system operates within a safe and stable range. Among these constraints: Current equation, This equation describes the active power balance relationship at node i. Inject power into the node, and Let i and j be the voltage magnitudes at nodes i and j, respectively. and These are the line conductance and susceptance, respectively. This is the phase angle difference.

[0046] Voltage constraint, This constraint ensures that the voltage at each node remains within the allowable range, preventing equipment damage or system instability caused by voltage exceeding limits.

[0047] Line capacity constraints This constraint limits the transmission power of line ij to its thermal stability limit, ensuring the safe operation of the power grid.

[0048] Thermal network constraints: Thermal networks consider the coupling relationship between hydraulic and thermal properties, and mainly include: Hydraulic balance, This equation indicates that at node i, the sum of the mass flow rates into that node is zero, reflecting the law of conservation of mass. Let be the mass flow rate of pipe ij. Let i be the set of pipes connected to node i.

[0049] Temperature propagation, This equation describes the process of heat propagation in the pipe. Let the water supply temperature be at node j. Let be the return water temperature at node i. The heat transfer coefficient of the pipe is... For the length of the pipe, The specific heat capacity of water, The ambient temperature.

[0050] Pressure constraints, This constraint ensures that the pressure at each node in the thermal system is within a safe operating range, preventing excessively high or low pressure from affecting the normal operation of the system.

[0051] Natural gas network constraints: Natural gas networks take into account the characteristics of gas flow and equipment operating limitations, specifically including: Flow equation This equation describes the steady-state flow characteristics of natural gas in a pipeline. Let be the natural gas flow rate of pipeline ij. For pipeline flow coefficient, and These represent the pressures at the two ends of the pipeline.

[0052] Pressure constraints, This constraint ensures that the pressure at each node in the natural gas system is within the allowable range, guaranteeing gas supply safety and normal equipment operation.

[0053] Compressor constraints, This constraint limits the compressor's operating flow range, ensuring that the compressor operates under safe conditions while maintaining the required pressure levels in the pipeline network.

[0054] The aforementioned multi-energy flow constraints collectively constitute the operational boundary conditions of the integrated energy system. The optimization algorithm needs to find the optimal scheduling scheme while satisfying all these constraints. The coupling relationships between the various energy networks are realized through energy conversion devices, and the operating characteristics of these devices will also be incorporated into the optimization model as additional constraints.

[0055] S23. Based on the multi-energy flow balance model, calculate the cumulative cost of electricity, heat and natural gas for each time period and each energy node within the scheduling cycle.

[0056] S24. Combining the accumulated cost and the carbon emission cost generated by the operation of the integrated energy system, determine the objective function of the total operating cost of the integrated energy system.

[0057] Figure 3 This is a flowchart illustrating the precise scheduling of an integrated energy system based on the dwarf mongoose algorithm provided by the present invention. This embodiment combines... Figure 3 Explanation: ② Algorithm Initialization Settings: The parameter settings of the dwarf meerkat optimization algorithm have a significant impact on its performance. Considering the characteristics of integrated energy system optimization problems, the algorithm parameters need to be set according to the system scale: Let N be the total number of energy supply stations participating in the dispatch within the target area (N is a positive integer), forming a set of supply stations. Each supply station has at least one type of power supply capability. Based on the load demand of the target area (industrial power, heating, cooling, etc.), the total number of power types to be dispatched is determined to be M (M is a positive integer), forming a power type set. The scheduling cycle is set to 1 day, divided into T scheduling periods (T is a positive integer, T=24, 1 period per hour), and the set of periods is as follows. Energy prices and load demand remain stable within a single time period.

[0058] Construct an energy price parameter matrix by collecting the prices of different types of electricity from each supply station over T time periods through an energy management platform, and then constructing an (M×N×T) dimensional energy price matrix. The dimensions correspond to the type of electricity M and the number of supply stations i, respectively. ), scheduling period t ( The matrix expression is: Energy demand constraint matrix: Constructing an (M×T) dimensional total energy demand matrix The dimensions correspond to the type of electricity and the scheduling period, and the matrix expression is as follows: in, This represents the total demand for type-1 electrical energy during time period t, which must be met so that the actual supply after scheduling (including transmission losses) is not less than this value.

[0059] Output constraint matrix for a single power supply station: Considering the equipment capacity and energy characteristics of the power supply station (e.g., photovoltaic power is only generated during the day), construct an (M×N×T) dimensional output upper limit matrix. The dimensions correspond to the type of electricity, the supply station, and the dispatch period. The matrix expression is as follows: in, This indicates the maximum output of type i power at supply station during time period t; the dispatched power must be less than or equal to this value.

[0060] Transmission efficiency matrix: Construct an (M×N) dimensional transmission efficiency matrix The dimensions correspond to the type of electricity and the supply station, and the matrix expression is: in, This represents the transmission efficiency of power type i at power supply station (affected by line / network characteristics, with values ​​of (0,1)). It also indicates the efficiency when no power of this type is transmitted. .

[0061] Transmission capacity constraint matrix: Construct an (M×N) dimensional transmission capacity upper limit matrix The dimensions correspond to the type of electricity and the supply station, and the matrix expression is: in, This indicates the maximum transmission capacity of power supply station type i (limited by pipeline / line design specifications), and the dispatch power must be less than or equal to this value.

[0062] Initialization of parameters for the dwarf mongoose optimization algorithm: Population Size: Let the population size of the dwarf meerkat be K (K is a positive integer), and each meerkat corresponds to one energy scheduling scheme, with a position vector. for 3D matrix This represents the dispatch power of type i power supply station in time period t of the kth meerkat scheme, which must satisfy the following conditions: .

[0063] Population grouping: K meerkats were divided into 3 groups, with the Alpha group (leader group) accounting for a certain percentage. (Responsible for partial development), Scout group (exploration group) percentage (Responsible for overall exploration) Babysitter group (balance group) percentage (to maintain population diversity), and .

[0064] Control parameters: Setting leadership factors (Controlling the development step size of the Alpha group), Exploration factors (Control the exploration range of the Scout group), balance factor (Controlling the diversity intensity of the Babysitter group); pheromone concentration threshold (Reinitialize the mongoose position if it falls below this value); Maximum number of iterations (Ensure the algorithm converges).

[0065] ③ Objective Function Construction: The objective function for the total system operating cost adopts a multi-objective weighted aggregation method, unifying multiple optimization objectives such as economy, environmental protection, and reliability into the problem of minimizing the total operating cost. The objective function can be extended to: in, This represents the total system cost. N represents the total number of time periods in the scheduling cycle; N represents the total number of energy nodes in the system. , , Let represent the prices of electricity, heat, and natural gas for the i-th node during time period t, respectively. , , These represent the electrical energy, thermal energy, and natural gas dispatch quantities of the i-th node during time period t, respectively. The cost of carbon emissions from the system.

[0066] ④ Calculate the initial total operating cost by substituting the values ​​into the objective function. : in, The power of the initial scheduling scheme, For the initial carbon emission cost, The price of energy of type i at time t when it is dispatched at the supply station.

[0067] .

[0068] in, Carbon emission coefficient (clean energy) of type i electricity supplied by station i during time period t. ), The price of carbon emissions.

[0069] S25. Using the dwarf mongoose optimization algorithm, the total operating cost objective function is solved according to the scheduling rules of the integrated energy system to obtain the globally optimal integrated energy scheduling scheme.

[0070] ⑤ Fitness assessment of dwarf meerkats: The fitness function for the k-th meerkat is defined with the objective of minimizing the total integrated energy operating cost. in, The carbon emission cost of the kth meerkat scheme is calculated using the following formula: The position vectors of K meerkats Verify each constraint (output limit, transmission capacity, multi-energy flow balance), eliminate invalid schemes (re-randomize locations), and calculate the fitness value for valid schemes. And record the current global best fitness value. (Minimum cost) and the corresponding global optimal position .

[0071] ⑥ Alpha group leadership behavior: The Alpha group's mongooses are in the best position globally. To guide local development, the location update formula is: in, This refers to the position of a certain Mongoose in the Alpha group before the update. For leadership factors ( (The higher the value, the higher the development intensity) After the update, the feasibility of the constraints needs to be verified: if Then let If total supply is insufficient, prioritize increasing the power output of stations with lower electricity prices. Calculate the updated fitness value and retain the best locations.

[0072] ⑦ Scout group exploration behavior: The dwarf meerkat expands its search range by randomly referencing locations, conducting global exploration. The location update formula is: in, This refers to the position of a certain Mongoose in the Scout group before the update. This is a reference position randomly selected from the current population. For exploration factors ( The larger the value, the wider the exploration range.

[0073] ⑧ The Babysitter group of meerkats adjusts its position based on the population average to avoid over-concentration. The position update formula is: .

[0074] in, This is the position of a certain mongoose in the Babysitter group before the update. The average position of the population. For balance factor ( (The smaller the value, the stronger the diversity retention).

[0075] ⑨ Information exchange mechanism and location reinitialization Pheromon concentration calculation: The three groups of meerkats communicate through pheromones. The formula for updating the pheromone concentration of the kth meerkat is: in, , These are the maximum and minimum fitness values ​​of the current population, respectively. The larger the value, the better the solution.

[0076] Location reinitialization: When the pheromone concentration is below the threshold τ, the location of the mongoose is reinitialized. .

[0077] in, , The locations of two randomly selected meerkats The result is a random number, ensuring that the new position fluctuates around the global optimum.

[0078] ⑩ Let the current iteration number g = g + 1 (initially g=1) and determine whether g has reached the target number. : like Return to step ⑤, repeat fitness assessment, three sets of behavioral simulation and information exchange; like Stop iteration and output the global best position. (Optimal scheduling scheme) and minimum fitness value (Minimum operating cost).

[0079] S26. Substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model to calculate the structured optimal dispatch instruction set, which includes the optimal energy dispatch amount of each energy node at different time periods.

[0080] The generation and execution of scheduling instructions will Convert to a structured scheduling instruction set The system categorizes power sources by "supply station - time period - power type," clearly defining information such as dispatch power, transmission path, and execution time. This information is then transmitted through the communication module of the energy management platform. The commands are sent to the control systems of each supply station; real-time data on actual output and load are collected to verify command execution; if deviations occur (such as intermittent insufficient energy output), temporary optimization is triggered (by calling the algorithm). iteration < This ensures a stable supply.

[0081] Based on the integrated energy dispatching method provided by this invention, an integrated energy dispatching simulation was conducted. The simulation parameter settings and simulation results are as follows: The target area includes three types of energy: electricity, heat, and gas. It comprises 15 energy supply stations. The dwarf meerkat optimization algorithm uses a population size of K=40, dividing the population into Alpha (20%), Scout (50%), and Babysitter (30%) groups. Leadership factor Φ=0.3, exploration factor Ψ=0.5, balance factor Ω=0.2; pheromone concentration threshold τ=0.2; and a preset maximum iteration count upper limit G_max=100. The range of change for any element in the individual position vector must satisfy the energy supply station output constraints and network transmission constraints, i.e. .

[0082] In the comparison algorithm, the particle swarm optimization algorithm has a particle count of 40, a social learning factor and an individual learning factor of 1.8, and a preset maximum iteration limit G_max=100.

[0083] In the ant colony algorithm used for comparison, the population consists of 40 individuals, the pheromone evaporation factor is set to 0.89, and the maximum number of iterations is preset to G_max=100.

[0084] The Grey Wolf optimization algorithm used as a comparison algorithm has a population of 40 individuals, a convergence factor with a linear variation range of [2,0], a weight factor of 0.5, and a preset maximum number of iterations G_max=100.

[0085] In the genetic algorithm used for comparison, the population consists of 40 individuals, with a crossover probability of 0.85, a mutation probability of 0.05, and a preset maximum number of iterations G_max=100.

[0086] In the integrated energy dispatch simulation, the target area can be comprehensively dispatched based on the above comparison algorithms.

[0087] Figure 4 This diagram illustrates a comparison of simulation results between the integrated energy scheduling method provided by this invention and existing scheduling methods. The simulation comparison shows the reliability curve of the target scheduling scheme obtained by the integrated energy precision scheduling method based on the dwarf mongoose optimization algorithm provided by this invention. The algorithm evolution process demonstrates that the integrated low-carbon energy scheduling method provided by this invention significantly improves solution accuracy and convergence compared to scheduling methods based on the other four algorithms. It effectively enhances the economy and reliability of the target scheduling scheme.

[0088] The integrated energy dispatching method provided by this invention applies the dwarf mongoose algorithm to solve the problem of precise integrated energy dispatching. It can obtain the dispatching scheme with the highest reliability more accurately and efficiently. Based on the above dispatching scheme, the power energy in the target area is dispatched, which can maximize the reliability of integrated energy dispatching. Under the circumstances of constantly changing energy dispatching environment and dispatching methods, it can solve the problem of optimal precise control and dispatching of integrated energy.

[0089] The integrated energy dispatching device provided by the present invention is described below. The integrated energy dispatching device described below can be referred to in correspondence with the integrated energy dispatching method described above.

[0090] Figure 5 This is a schematic diagram of the integrated energy dispatching device provided by the present invention, specifically including: Module 501 is used to construct a multi-energy flow balance model of the integrated energy system, which includes multiple types of physical constraints, based on the energy supply and demand structure of the integrated energy system. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0091] The determination module 502 is used to determine the objective function of the total operating cost of the integrated energy system based on the multi-energy flow balance model. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0092] The determining module 502 is further configured to determine the optimal integrated energy scheduling scheme based on the total operating cost objective function and the scheduling rules of the integrated energy system. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0093] The calculation module 503 is used to substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0094] The scheduling module 504 is used for comprehensive energy scheduling based on the optimal scheduling instruction set. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0095] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6As shown, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640, wherein the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute an integrated energy dispatching method. The method includes: constructing a multi-energy flow balance model of the integrated energy system, which includes multiple types of physical constraints, based on the energy supply and demand structure of the integrated energy system; determining the total operating cost objective function of the integrated energy system based on the multi-energy flow balance model; determining the optimal integrated energy dispatching scheme according to the total operating cost objective function and the dispatching rules of the integrated energy system; substituting the optimal integrated energy dispatching scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatching instruction set; and performing integrated energy dispatching based on the optimal dispatching instruction set.

[0096] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the integrated energy dispatching method provided by the above methods. The method includes: constructing a multi-energy flow balance model of the integrated energy system that includes multiple types of physical constraints based on the energy supply and demand structure of the integrated energy system; determining the total operating cost objective function of the integrated energy system based on the multi-energy flow balance model; determining the optimal integrated energy dispatching scheme according to the total operating cost objective function and the dispatching rules of the integrated energy system; substituting the optimal integrated energy dispatching scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatching instruction set; and performing integrated energy dispatching based on the optimal dispatching instruction set.

[0098] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the integrated energy dispatching method provided by the above methods. This method includes: constructing a multi-energy flow balance model of the integrated energy system, incorporating multiple types of physical constraints, based on the energy supply and demand structure of the integrated energy system; determining the total operating cost objective function of the integrated energy system based on the multi-energy flow balance model; determining the optimal integrated energy dispatching scheme according to the total operating cost objective function and the dispatching rules of the integrated energy system; substituting the optimal integrated energy dispatching scheme into the multi-energy flow balance model for calculation to obtain an optimal dispatching instruction set; and performing integrated energy dispatching based on the optimal dispatching instruction set.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive energy dispatching method, characterized in that, include: Based on the energy supply and demand structure of the integrated energy system, a multi-energy flow balance model of the integrated energy system containing multiple types of physical constraints is constructed. Based on the multi-energy flow balance model, the objective function for the total operating cost of the integrated energy system is determined; Based on the total operating cost objective function and the scheduling rules of the integrated energy system, determine the optimal integrated energy scheduling scheme; Substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set; Integrated energy scheduling is performed based on the aforementioned optimal scheduling instruction set.

2. The method according to claim 1, characterized in that, The energy supply and demand structure based on the integrated energy system includes constructing a multi-energy flow balance model of the integrated energy system containing multiple types of physical constraints, including: Based on the multiple energy nodes included in the integrated energy system and the multiple time periods of the energy dispatch cycle, the constraints of the power network, the heating network, and the natural gas network are determined. Based on the energy supply and demand structure of the integrated energy system, a multi-energy flow balance model is constructed, which includes the constraints of the power network, the heat network, and the natural gas network.

3. The method according to claim 1, characterized in that, The objective function for determining the total operating cost of the integrated energy system based on the multi-energy flow balance model includes: Based on the multi-energy flow balance model, the cumulative cost of electricity, heat and natural gas is calculated for each time period and each energy node within the scheduling cycle. By combining the accumulated costs and the carbon emission costs generated by the operation of the integrated energy system, the objective function for the total operating cost of the integrated energy system is determined.

4. The method according to any one of claims 1-3, characterized in that, The step of determining the optimal integrated energy dispatch scheme based on the total operating cost objective function and the dispatch rules of the integrated energy system includes: The total operating cost objective function is solved using the dwarf mongoose optimization algorithm based on the scheduling rules of the integrated energy system. In the solution process, the dwarf meerkat optimization algorithm is guided by the global optimal position for local development, by random reference positions for global exploration and by the population average position to maintain diversity, and by the pheromone mechanism for information exchange. When the pheromone concentration is lower than the preset threshold, the meerkat position is reinitialized, and finally converges to obtain the global optimal integrated energy scheduling scheme.

5. The method according to claim 4, characterized in that, The method further includes: In the process of solving the dwarf meerkat optimization algorithm, the position vector of each meerkat is verified by multi-energy flow balance constraints. Invalid scheduling schemes that do not meet the upper limit constraints of each energy node output, network transmission capacity constraints and energy supply and demand balance constraints are removed, and valid meerkat positions that meet the constraints are regenerated. After each re-initialization of the meerkat location, verify the feasibility of the new location; If it exceeds the constraint boundary, it is corrected to the boundary value; In the event of a total supply shortage, priority will be given to increasing the dispatch capacity of low-priced power stations to ensure energy supply stability.

6. The method according to claim 4, characterized in that, The control parameters of the dwarf mongoose optimization algorithm adopt an adaptive dynamic adjustment strategy. The pheromone concentration threshold is dynamically set based on the population fitness distribution.

7. The method according to claim 4, characterized in that, The step of substituting the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set includes: Substituting the optimal integrated energy dispatch scheme into the multi-energy flow balance model, a structured optimal dispatch instruction set is calculated, which includes the optimal energy dispatch amount for each energy node at different time periods.

8. A comprehensive energy dispatching device, characterized in that, include: The module is used to construct a multi-energy flow balance model of the integrated energy system, which includes multiple types of physical constraints, based on the energy supply and demand structure of the integrated energy system. The determination module is used to determine the total operating cost objective function of the integrated energy system based on the multi-energy flow balance model. The determination module is also used to determine the optimal integrated energy dispatch scheme based on the total operating cost objective function and the dispatch rules of the integrated energy system; The calculation module is used to substitute the optimal integrated energy dispatch scheme into the multi-energy flow balance model for calculation to obtain the optimal dispatch instruction set; The scheduling module is used to perform comprehensive energy scheduling based on the optimal scheduling instruction set.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the integrated energy scheduling method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the integrated energy dispatching method as described in any one of claims 1 to 7.

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