Integrated energy system scheduling method based on improved multi-target dung beetle optimization algorithm

Abstract

The invention discloses an integrated energy system scheduling method based on an improved multi-target dung beetle optimization algorithm, and the method comprises the steps: constructing an integrated energy system model, mining low-carbon resources from the two sides of a source load, and analyzing the low-carbon characteristics; secondly, establishing a demand response model on an energy consumption side; and finally, an improved multi-target dung beetle optimization algorithm is adopted to solve the integrated energy system optimization scheduling model, and a self-adaptive weight coefficient is introduced into a dung beetle rolling ball position updating formula by introducing rapid non-dominated sorting and crowding degree calculation. A spiral bubble net optimization mode is fused in a dung beetle breeding and dung beetle foraging formula, and a Levy flight strategy is introduced in a dung beetle stealing position updating formula to improve the convergence precision and search speed of a multi-target dung beetle optimization algorithm. According to the invention, the consumption of new energy output is obviously improved, the emission of CO2 is reduced, and the energy consumption benefit of a user side is improved while the operation cost of the comprehensive energy system is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-energy optimization scheduling of integrated energy systems, and in particular relates to an integrated energy system scheduling method based on an improved multi-objective dung beetle optimization algorithm. Background Art

[0002] The coupling degree between distributed energy and renewable energy will gradually increase. How to reasonably dispatch multiple energy sources in the integrated energy system and improve the consumption of new energy while reducing the operating cost and carbon emissions of the integrated energy system has become a research hotspot in this field.

[0003] The day-ahead optimization scheduling problem of the integrated energy system mainly takes the daily minimum operating cost as the optimization objective function. However, the actual problem involves multi-objective optimization problems that require consideration of multiple objective functions. These objective functions may be contradictory and have characteristics such as nonlinearity, complex constraints, and high solution dimensions, which are difficult to solve using mathematical methods. In order to solve these problems, researchers have widely applied heuristic optimization algorithms such as evolutionary algorithms, particle swarm algorithms, and genetic algorithms to the multi-objective optimization scheduling problem of the integrated energy system. These algorithms search for the optimal solution set through continuous evolution, crossover, and mutation. Compared with the above-mentioned traditional optimization algorithms, the multi-objective dung beetle optimization intelligent algorithm has strong versatility and a simple process. It can solve the problems of poor convergence, slow convergence, and easy to fall into local optimal solutions of traditional optimization algorithms. Summary of the invention

[0004] The purpose of the present invention is to provide an optimized scheduling method for an integrated energy system based on an improved multi-objective dung beetle optimization algorithm in view of the deficiencies in the prior art.

[0005] To achieve the above purpose, the embodiment of the present invention provides the following solution: A comprehensive energy system scheduling method based on an improved multi-objective dung beetle optimization algorithm, comprising: Build an integrated energy system model, explore low-carbon resources and analyze their low-carbon characteristics from both the source and load sides of the integrated energy system, and establish a demand response model on the energy consumption side of the integrated energy system; The output of each device in each period of the integrated energy system is set as the optimization variable, and the output power of each device in each period of time is obtained according to the constraints, with the daily operating income and total carbon emissions as the optimization objective function; The Pareto frontier is found by improving the multi-objective dung beetle optimization algorithm, where the improved multi-objective dung beetle optimization algorithm is as follows: a. Initialize the dung beetle population, number of iterations, and external archive size parameters, and randomly generate each dung beetle position; b. Calculate the fitness value of each dung beetle, and perform non-dominated sorting and crowding calculations. Select the top N dung beetles based on the Pareto sorting and crowding calculations. c. The positions of each dung beetle are updated according to the formulas of dung beetle rolling, dancing, reproduction, foraging and stealing; d. Select the first N dung beetles to form a new population based on Pareto sorting and crowding calculation; e. Determine whether the maximum number of iterations has been reached. If so, the optimal parameters are outputted. Otherwise, return to step c. TOPSIS is used to perform secondary optimization on the Pareto frontier, select the optimal solution and the worst solution after the positive value of the scheduling strategy objective function, calculate the distance between each scheduling strategy and the two solutions, calculate the comprehensive score of the plan based on the distance, and rank all the plans to determine the optimal scheduling strategy.

[0006] Furthermore, the integrated energy system includes new energy power generation devices, energy conversion devices, energy storage devices and upstream power grids, among which the energy conversion devices include carbon capture devices, power-to-gas devices, cogeneration units, electric refrigerators, absorption refrigerators, electric heat pumps, and various devices in fuel cells.

[0007] Furthermore, the demand side of the integrated energy system includes electrical load and at least one of thermal load, cooling load and gas load.

[0008] Furthermore, the demand response model uses the electricity price demand elasticity matrix to describe the load response behavior caused by electricity price changes, and establishes the transferable load and curtailable load model of the electricity load: ; Where: , They are t The amount of electricity load that can be transferred and reduced after momentary demand response; is the initial electrical load before response; , They are the price demand elasticity matrices for transferable and curtailable loads respectively; , are the electricity prices before and after the response respectively.

[0009] Furthermore, during the low electricity price period, part of the thermal load is converted into electrical load through alternative demand response, and during the peak period, part of the electrical load is converted into thermal load, thus realizing the mutual substitution of electrical energy and thermal energy: ; Where: is the replaceable electrical load, is the amount of heat load that is replaced; is the electric heat substitution coefficient.

[0010] Furthermore, an adaptive weight coefficient is introduced into the dung beetle rolling ball position update formula, and the multi-target dung beetle rolling ball and dancing position update formula is improved as follows: ; Where: Shidi i Dung beetle t The position of the iteration, Indicates i The dancing dung beetle t The position of the iteration, w is the adaptive weight coefficient, a is a natural number with a value equal to 1 or -1, b is a constant with a value range of (0,1), To simulate changes in light intensity, is the deflection angle, Indicates the maximum number of iterations of the population.

[0011] Furthermore, the spiral bubble net optimization method of the whale optimization algorithm is integrated into the dung beetle reproduction and foraging formulas, and the multi-objective dung beetle reproduction and foraging position update formulas are improved as follows: ; Where: Indicates i Only breeding dung beetles t The position of the iteration, Indicates i Only foraging dung beetles t The position of the iteration, is the local optimal position, k is 5, l is a random number in [-1,1], b 1 , b 2 is a 1×D random vector, Lf * , Uf * Respectively represent the upper and lower limits of dung beetle reproduction, C 1 is a random number that follows a normal distribution, C 2 is a random number from (0,1), Lf l and Uf l They are the upper and lower boundaries of the dung beetle's foraging area.

[0012] Furthermore, the dung beetle stealing position update formula is introduced Levy The flight strategy and the improved multi-target stealing dung beetle position update formula are as follows: ; In the formula, Indicates i Only the thieving dung beetle is t The position of the iteration, is the global optimal position, for Levy Flight coefficient; S is a constant with a value of 0.5, g is a random vector that follows a normal distribution of 1×D, r 1 , r 2 , r 3 A random number in the range (0,1).

[0013] Furthermore, the optimal scheduling strategy is selected as follows: ; Where: , They are the optimal solution and the worst solution after the objective function value of the scheduling strategy is forward-oriented; for i The first scheduling strategy j evaluation indicators; For the i The distance between the objective function value of each scheduling scheme and the optimal solution is For the i The distance between the objective function value of each scheduling scheme and the worst solution; is the optimal scheduling strategy.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm provided by the present invention can solve the problems of poor economic efficiency of the integrated energy system, excessive carbon emissions, slow convergence speed of the traditional optimization algorithm, and easy to fall into the local optimal solution. The four strategies of the improved dung beetle optimization algorithm work together to significantly improve the performance of the algorithm in multi-objective optimization problems. Specifically: Introducing fast non-dominated sorting and crowding calculation: This improved strategy reduces the computational complexity of the algorithm through fast non-dominated sorting, and uses crowding calculation to maintain population diversity, so that individuals in the Pareto solvable domain can be extended to the entire Pareto domain and evenly distributed; Introducing adaptive weight coefficients in the dung beetle rolling ball position update formula: The adaptive weight method can adaptively adjust parameters according to the results of the search process, thereby improving the robustness and performance of the algorithm. Integrating the spiral bubble net optimization method of the whale optimization algorithm into the dung beetle breeding and dung beetle foraging formulas: The spiral bubble net optimization method enables individuals to explore in the solution space along a spiral path, effectively broadening the search field of view and increasing the possibility of finding a better solution. The Lévy flight strategy is introduced into the dung beetle stealing position update formula: the Lévy flight strategy provides a random walk method with a step size that conforms to the Levy distribution, which helps the algorithm to jump out of the local optimum and increase the diversity of the population. The improved dung beetle optimization algorithm provided by the present invention not only improves the global search capability of the algorithm, but also enhances its efficiency in local search, accelerates the convergence process of the algorithm, and improves the distribution uniformity of the solution, so that the improved dung beetle optimization algorithm shows higher convergence accuracy, faster convergence speed and stronger robustness when solving multi-objective optimization problems.

[0015] Finally, TOPSIS was used to perform secondary optimization on the Pareto frontier, and various IES energy sources were reasonably scheduled in an economical and efficient manner according to the scheduling strategy. The proposed multi-objective optimal scheduling of the integrated energy system taking into account power-to-gas and demand response significantly improved the consumption of new energy output and reduced CO2 emissions, thereby reducing the operating cost of the integrated energy system and improving the energy efficiency on the user side. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart for solving the optimal scheduling of the integrated energy system according to an embodiment of the present invention; Figure 2 A collaborative operation framework diagram of the integrated energy system optimization scheduling according to an embodiment of the present invention; Figure 3 A comparison diagram of load optimization curves before and after demand response according to an embodiment of the present invention; Figure 4 A diagram showing the optimization results of the multi-objective dung beetle optimization algorithm provided by an embodiment of the present invention; Figure 5 A diagram of the results of the optimal dispatch of electric loads provided by an embodiment of the present invention; Figure 6 A heat load optimization scheduling result diagram provided by an embodiment of the present invention; Figure 7 This is a diagram of cooling load optimization scheduling results provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to be able to fully convey the scope of the present invention to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0018] The purpose of the present invention is to provide an optimization scheduling method for an integrated energy system based on a multi-objective dung beetle optimization algorithm to solve the problem of dynamic scheduling of existing integrated energy systems with complex constraints and high solution dimensions.

[0019] like Figure 1 As shown in FIG. 1 , a comprehensive energy system scheduling solution process based on an improved multi-objective dung beetle optimization algorithm under the dual carbon background is shown in FIG. The overall framework of the comprehensive energy system model for the coordinated operation of power-to-gas equipment (Power-to-Gas, P2G), carbon capture system and new energy power generation mentioned in the embodiment of the present invention is as follows: Figure 2 As shown, it includes carbon capture power plants, cogeneration units, power-to-gas equipment, electric refrigerators, absorption refrigerators, electric heat pumps, fuel cells, photovoltaics, wind power and energy storage devices. The supply side includes wind turbines and photovoltaic arrays, upper-level power grids and natural gas grids. The demand side includes electrical loads, cooling loads and heating loads.

[0020] The following is a detailed introduction to the integrated energy system scheduling method process based on the improved multi-objective dung beetle optimization algorithm.

[0021] The embodiment of the present invention fully considers the coupling relationship of each device in the IES, including the carbon capture power plant, cogeneration unit, power-to-gas equipment, electric refrigerator, absorption refrigerator, electric heat pump, fuel cell, photovoltaic, wind power and energy storage device, and at the same time considers the problems of poor economic efficiency and excessive carbon emissions of the integrated energy system, and constructs a multi-energy collaborative scheduling model with daily operating income and total carbon emissions as optimization targets. The output of each device in the integrated energy system in each period is set as the optimization variable, and the "electricity determines heat" method is adopted to obtain the output power of the device in each period according to the constraint conditions, and the daily operating income and total carbon emissions are used as the optimization objective function. Finally, the Pareto frontier is found through the multi-objective dung beetle optimization algorithm. TOPSIS is used to perform secondary optimization on the Pareto frontier, select the optimal solution and the worst solution after the objective function value of the scheduling strategy is forwarded, and then calculate the distance between each scheduling strategy and the two solutions, calculate the comprehensive score of the scheme according to the distance, and rank all schemes to determine the optimal scheduling strategy.

[0022] Step 1: Explore low-carbon resources from both the source and load sides of the integrated energy system and analyze their low-carbon characteristics. In this embodiment, power-to-gas equipment is installed on the supply side to form a carbon capture power plant and power-to-gas, which are then coordinated and operated with new energy power generation; Step 2: A demand response model is established on the energy consumption side of the integrated energy system. The demand response model is used to guide users to adjust the energy demand of electricity, cooling and heating loads. The optimized system electricity and heating load curves are obtained according to the demand response model, and the obtained load curves are combined with IES. This embodiment guides the user demand side to adjust the electricity and heat energy in the form of time-of-use electricity prices, and realizes the optimization and regulation of electricity, cooling and heating loads in demand response through the coupling complementarity of multiple energies, and divides DR into price-based demand response and substitution-based demand response. Considering the optimization curve of electricity and heat loads in demand response, Figure 3 As shown in the figure, it can be seen that the peak-to-valley difference of the electric load curve on the demand side after demand response is relatively reduced compared with that before demand response, which can effectively smooth the energy consumption curve on the demand side. When the electricity price is at the peak period, PBDR will reduce part of the electric load and transfer part of the electric load to the valley period. The alternative demand response will replace part of the thermal load with the electric load, reducing the electric load during the peak period; when the electricity price is at the valley period, part of the electric load will replace the thermal load, increasing the electric load during the valley period, thereby achieving peak shaving and valley filling.

[0023] 1) Use price-based demand response (PBDR) to adjust the electric load curve, thereby realizing the transfer and reduction of electric load, and ultimately achieving the purpose of improving the utilization rate of electric energy. This embodiment uses the electricity price demand elasticity matrix to describe the load response behavior caused by changes in electricity prices, and then establishes the transferable load and reducible load model of the electric load: ; Where: , They are t The amount of electricity load that can be transferred and reduced after momentary demand response; is the initial electrical load before response; , They are the price demand elasticity matrices for transferable and curtailable loads respectively; , are the electricity prices before and after the response respectively.

[0024] 2) Substitution demand response: During the period when electricity prices are at a low point, part of the thermal load is converted into electrical load through substitution demand response, and during the peak period, part of the electrical load is converted into thermal load, thereby achieving mutual substitution between electrical energy and thermal energy.

[0025] ; Where: is the replaceable electrical load, is the amount of heat load corresponding to the one being replaced; is the electric heat substitution coefficient.

[0026] Step 3: Obtain the unit's output power in each period according to the constraints, and use the daily operating income and total carbon emissions as the optimization objective function; The power, cooling and heat balance demand constraints of the integrated energy system are as follows: Electrical load balance equation:

[0027] Heat load balance equation:

[0028] Cooling load balance equation:

[0029] Where: They are t The electricity, heating and cooling loads at all times, They are t The amount of electricity purchased and sold at any given moment; is the output power of the photovoltaic device at time t; is the output power of the wind power device at time t; is the output power of the cogeneration unit at time t; is the output power of the carbon capture power plant at time t; is the output power of the fuel cell at time t; is the output electric power of the energy storage device at time t; is the input electric power of the electric heat pump at time t; is the input power of the electric refrigerator at time t; is the input electric power of the energy storage device at time t; Output thermal power for the combined heat and power unit; is the output thermal power of the electric heat pump at time t; is the input heat power of the absorption refrigerator; is the cooling power output of the electric refrigerator at time t; is the cooling power output of the absorption chiller.

[0030] Equipment operation constraints 1) Output constraints of each IES conversion device

[0031] Where: For IES i The output of the equipment; They are respectively i The upper and lower limits of the output of the equipment; N is the number of IES equipment.

[0032] 2) Energy storage equipment constraints

[0033] Where: and Respectively represent the upper and lower limits of the energy storage device capacity; is the charging and discharging state variable of the energy storage device. The energy storage device is in a discharging state. In charging state.

[0034] This paper constructs a multi-objective optimization scheduling model for the integrated energy system with daily operating costs and CO2 emissions as targets.

[0035] 1) Daily operating cost:

[0036] Where: represents the daily operating cost; Cost of purchasing natural gas; The cost of purchasing coal; The cost of purchasing electricity; The cost of selling electricity; Indicates the IES operation and maintenance cost.

[0037] 2) CO2 emissions:

[0038] Where: Indicates CO2 emissions; CO2 produced by thermal power units; CO2 produced by gas turbines; CO2 consumed by the carbon capture device.

[0039] Step 4: Solve the multi-energy coordinated scheduling model of the integrated energy system according to the improved multi-objective dung beetle optimization algorithm, calculate the daily operating cost and carbon dioxide emissions, and obtain the Pareto frontier, that is, the optimization strategy of the integrated energy system.

[0040] The dung beetle optimization algorithm introduces a multi-objective algorithm framework of fast non-dominated sorting and crowding calculation, and improves the ball rolling, dancing, reproduction, foraging and stealing formulas of dung beetles. An improved multi-objective dung beetle optimization algorithm is proposed. The optimization contents of the improved multi-objective dung beetle optimization algorithm include: first, introducing fast non-dominated sorting and crowding calculation on the basis of the dung beetle optimization algorithm; second, using the adaptive weight method, introducing adaptive weight coefficients in the dung beetle rolling position update formula; third, incorporating the spiral bubble net optimization method of the whale optimization algorithm into the dung beetle reproduction and dung beetle foraging formulas; fourth, introducing the Lévy flight strategy in the dung beetle stealing position update formula. The above four improvement strategies improve the convergence accuracy and search speed of the multi-objective dung beetle optimization algorithm.

[0041] 1) The improved multi-target dung beetle rolling ball and dancing position update formula is as follows:

[0042] Where: Indicates i Dung beetle t The position of the iteration, Indicates i The dancing dung beetle t The position of the iteration, w is the adaptive weight coefficient, a is a natural number with a value equal to 1 or -1, b is a constant with a value range of (0,1), To simulate changes in light intensity, is the deflection angle, Indicates the maximum number of iterations of the population.

[0043] 2) The improved multi-target dung beetle breeding and foraging position update formula is as follows:

[0044] Where: Indicates i Only breeding dung beetles t The position of the iteration, Indicates i Only foraging dung beetles t The position of the iteration, is the local optimal position, k is 5, l is a random number in [-1,1], b 1 , b 2 is a 1×D random vector, Lf * , Uf * Respectively represent the upper and lower limits of dung beetle reproduction, C 1 is a random number that follows a normal distribution, C 2 is a random number from (0,1), Lf l and Uf l They are the upper and lower boundaries of the dung beetle's foraging area.

[0045] 3) The improved multi-target stealing dung beetle position update formula is as follows:

[0046] Where: Indicates i Only the thieving dung beetle is t The position of the iteration, is the global optimal position, for Levy Flight coefficient; S is a constant with a value of 0.5. g is a random vector that follows a normal distribution of 1×D, r 1 , r 2 , r 3 A random number in the range (0,1).

[0047] The steps of improving the multi-objective dung beetle optimization algorithm are as follows: a. Initialize the dung beetle population, number of iterations, and external archive size parameters, and randomly generate each dung beetle position; b. Calculate the fitness value of each dung beetle, and perform non-dominated sorting and crowding calculations. Select the top N dung beetles based on the Pareto sorting and crowding calculations. c. Each dung beetle position is updated according to the improved dung beetle rolling, dancing, breeding, foraging and stealing formulas.

[0048] d. Select the first N dung beetles to form a new population based on Pareto sorting and crowding calculation; e. Determine whether the maximum number of iterations has been reached. If so, end the process of outputting the optimal parameters; otherwise, return to step c.

[0049] The optimization results of the multi-objective dung beetle optimization algorithm provided in this embodiment are as follows: Figure 4 shown.

[0050] Step 5: Use TOPSIS to perform secondary optimization on the Pareto frontier, select the optimal solution and the worst solution after the dispatch strategy objective function value is positive, and then calculate the distance between each dispatch strategy and these two solutions. According to the comprehensive score of the distance calculation scheme, the optimal compromise solution is obtained. The operation results of the carbon capture power plant, cogeneration unit, power-to-gas equipment, electric refrigerator, absorption refrigerator, electric heat pump, fuel cell and energy storage device can be obtained, such as Figure 5 , Figure 6 , Figure 7 shown.

[0051] TOPSIS selects the optimal solution and the worst solution after the objective function value of the scheduling strategy is positively transformed, and then calculates the distance between each scheduling strategy and these two solutions, calculates the comprehensive score of the plan based on the distance, and ranks all plans to determine the optimal scheduling strategy.

[0052]

[0053] Where: , They are the optimal solution and the worst solution after the objective function value of the scheduling strategy is forward-oriented; for i The first scheduling strategy j evaluation indicators; For the i The distance between the objective function value of each scheduling scheme and the optimal solution is For the i The distance between the objective function value of each scheduling scheme and the worst solution; is the ideal scheduling strategy.

[0054] The present invention discloses a method for scheduling an integrated energy system based on an improved multi-objective dung beetle optimization algorithm, in order to improve the short-term scheduling level of the integrated energy system, and at the same time solve the problems of poor economic efficiency, excessive carbon emissions, slow convergence speed of traditional optimization algorithms, and easy to fall into local optimal solutions. This embodiment establishes an integrated energy system model containing power-to-gas and carbon capture systems, and proposes an optimization scheduling method based on an improved multi-objective dung beetle optimization algorithm. The specific steps include: first, mining low-carbon resources and analyzing low-carbon characteristics from both sides of the source and load of the integrated energy system, installing power-to-gas equipment on the supply side, forming a carbon capture power plant, power-to-gas, and then coordinating and operating with new energy power generation; secondly, establishing a demand response model on the energy consumption side; finally, using the improved multi-objective dung beetle optimization algorithm to solve the optimization scheduling model of the integrated energy system. The multi-objective optimization scheduling of the integrated energy system taking into account power-to-gas and demand response proposed in this embodiment significantly improves the consumption of new energy output, reduces CO2 emissions, and improves the energy efficiency of the user side while reducing the operating cost of the integrated energy system.

[0055] In summary, in this embodiment, the integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm can solve the problems of poor economic efficiency of the integrated energy system, excessive carbon emissions, slow convergence speed of the traditional optimization algorithm, and easy to fall into the local optimal solution. The proposed multi-objective optimization scheduling of the integrated energy system taking into account power-to-gas and demand response significantly improves the consumption of new energy output, reduces CO2 emissions, and improves the energy efficiency of the user side while reducing the operating cost of the integrated energy system.

[0056] The present invention has been described in detail above through embodiments, but the contents described are only exemplary embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. The protection scope of the present invention is defined by the claims. Anyone who utilizes the technical solution described in the present invention, or a technician in the field, inspired by the technical solution of the present invention, designs a similar technical solution within the essence and protection scope of the present invention to achieve the above technical effects, or makes equal changes and improvements to the scope of application, shall still belong to the scope of protection covered by the patent of the present invention. It should be noted that in order to make a clear statement, the description of some components and processes that have no direct and obvious connection with the scope of protection of the present invention but are known to those skilled in the art are omitted in the description of the present invention.

Claims

1. A comprehensive energy system scheduling method based on an improved multi-objective dung beetle optimization algorithm, characterized in that: include: Build an integrated energy system model, explore low-carbon resources and analyze their low-carbon characteristics from both the source and load sides of the integrated energy system, and establish a demand response model on the energy consumption side of the integrated energy system; The output of each device in each period of the integrated energy system is set as the optimization variable, and the output power of each device in each period of time is obtained according to the constraints, with the daily operating income and total carbon emissions as the optimization objective function; The Pareto frontier is found by improving the multi-objective dung beetle optimization algorithm, where the improved multi-objective dung beetle optimization algorithm is as follows: a. Initialize the dung beetle population, number of iterations, and external archive size parameters, and randomly generate each dung beetle position; b. Calculate the fitness value of each dung beetle, and perform non-dominated sorting and crowding calculations. Select the top N dung beetles based on the Pareto sorting and crowding calculations. c. The positions of each dung beetle are updated according to the formulas of dung beetle rolling, dancing, reproduction, foraging and stealing; d. Select the first N dung beetles to form a new population based on Pareto sorting and crowding calculation; e. Determine whether the maximum number of iterations has been reached. If so, the optimal parameters are outputted. Otherwise, return to step c. TOPSIS is used to perform secondary optimization on the Pareto frontier, select the optimal solution and the worst solution after the positive value of the scheduling strategy objective function, calculate the distance between each scheduling strategy and the two solutions, calculate the comprehensive score of the plan based on the distance, and rank all the plans to determine the optimal scheduling strategy.

2. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: The integrated energy system includes new energy power generation devices, energy conversion devices, energy storage devices and upstream power grids. The energy conversion devices include carbon capture devices, power-to-gas devices, cogeneration units, electric refrigerators, absorption refrigerators, electric heat pumps and various devices in the fuel cell category.

3. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: The demand side of the integrated energy system includes electrical load and at least one of heat load, cooling load and gas load.

4. The method for optimizing and dispatching a comprehensive energy system based on a multi-objective dung beetle optimization algorithm according to claim 3 is characterized in that: The demand response model uses the electricity price demand elasticity matrix to describe the load response behavior caused by electricity price changes, and establishes the transferable load and curtailable load model of the electricity load: ； Where: , They are t The amount of electricity load that can be transferred and reduced after momentary demand response; is the initial electrical load before response; , They are the price demand elasticity matrices for transferable and curtailable loads respectively; , are the electricity prices before and after the response respectively.

5. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 4 is characterized in that: During the low electricity price period, part of the thermal load is converted into electrical load through alternative demand response, and during the peak period, part of the electrical load is converted into thermal load, so as to realize the mutual substitution of electrical energy and thermal energy: ； Where: is the replaceable electrical load, is the amount of heat load that is replaced; is the electric heat substitution coefficient.

6. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: An adaptive weight coefficient is introduced into the dung beetle rolling ball position update formula, and the multi-target dung beetle rolling ball and dancing position update formula is improved as follows: ； Where: Indicates i Dung beetle t The position of the iteration, Indicates i The dancing dung beetle t The position of the iteration, w is the adaptive weight coefficient, a is a natural number with a value equal to 1 or -1, b is a constant with a value range of (0,1), To simulate changes in light intensity, is the deflection angle, Indicates the maximum number of iterations of the population.

7. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: The spiral bubble net optimization method of the whale optimization algorithm is integrated into the dung beetle reproduction and foraging formulas, and the multi-objective dung beetle reproduction and foraging position update formulas are improved as follows: ； Where: Indicates i Only breeding dung beetles t The position of the iteration, Indicates i Only foraging dung beetles t The position of the iteration, is the local optimal position, k is 5, l is a random number in [-1,1], b 1 , b 2 is a 1×D random vector, Lf * , Uf * Respectively represent the upper and lower limits of dung beetle reproduction, C 1 is a random number that follows a normal distribution, C 2 is a random number from (0,1), Lf l and Uf l They are the upper and lower boundaries of the dung beetle's foraging area.

8. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: Introduced in the dung beetle stealing position update formula Levy The flight strategy and the improved multi-target stealing dung beetle position update formula are as follows: ； In the formula, Indicates i Only the thieving dung beetle is t The position of the iteration, is the global optimal position, for L évy Flight coefficient; S is a constant with a value of 0.5, g is a random vector that follows a normal distribution of 1×D, r 1 , r 2 , r 3 A random number in the range (0,1).

9. The integrated energy system scheduling method based on the improved multi-objective dung beetle optimization algorithm according to claim 1 is characterized in that: The optimal scheduling strategy is selected as follows: ； Where: , They are the optimal solution and the worst solution after the objective function value of the scheduling strategy is forward-oriented; for i The scheduling strategy j evaluation indicators; For the i The distance between the objective function value of each scheduling scheme and the optimal solution is For the i The distance between the objective function value of each scheduling scheme and the worst solution; is the optimal scheduling strategy.

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