Comprehensive energy system optimal configuration method and device, terminal and storage medium

Abstract

The invention provides an integrated energy system optimal configuration method and device, a terminal and a storage medium, and relates to the field of integrated energy system application. The method comprises the steps that under the constraint conditions of power balance constraint, equipment operation constraint and equipment capacity constraint, a region-level integrated energy system is constructed, and the region-level integrated energy system takes the minimum total cost and the minimum carbon emission as optimization targets; and solving an optimal configuration scheme of the regional-level integrated energy system by using a firefly algorithm, the optimal configuration scheme including equipment output data and energy demand of the regional-level integrated energy system. According to the invention, the system optimization rate can be accelerated.

Description

Technical Field

[0001] The present application relates to the application field of integrated energy systems, and in particular to an integrated energy system optimization configuration method, device, terminal and storage medium. Background Art

[0002] In the rapid development of modern society, efficient use and sustainable supply of energy are the key to energy issues. Integrated energy systems achieve optimal distribution and efficient use of energy by integrating different energy forms and using intelligent technologies, meeting society's demand for diversified energy services. Such systems are the core of realizing the vision of energy internet. At present, several demonstration projects have been established to demonstrate the potential of integrated energy systems.

[0003] However, with development, the energy system has become increasingly complex, resulting in the inability of the energy system built in the early stage to meet actual needs. In addition, according to the configuration of the early energy system, the energy utilization rate is greatly reduced, thereby reducing the overall operating cost. Summary of the invention

[0004] The present application provides a method, device, terminal and storage medium for optimizing configuration of an integrated energy system to solve the problem of low energy utilization rate of the integrated energy system in the prior art.

[0005] In a first aspect, the present application provides a method for optimizing configuration of an integrated energy system, comprising:

[0006] Under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, a regional integrated energy system is constructed, wherein the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization goals;

[0007] The firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system.

[0008] In a second aspect, the present application provides a comprehensive energy system optimization configuration device, comprising:

[0009] A construction module is used to construct a regional integrated energy system under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, wherein the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization objectives;

[0010] The solution module is used to use the firefly algorithm to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system.

[0011] In a third aspect, the present application provides a terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0012] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0013] The present application provides a method, device, terminal and storage medium for optimizing the configuration of an integrated energy system. The method constructs a regional integrated energy system under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints. The regional integrated energy system takes the minimum total cost and the minimum carbon emissions as optimization goals. The firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system. The optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system. The present application constructs a regional integrated energy system with the minimum total cost and the minimum carbon emissions as optimization goals. While meeting energy needs, it achieves efficient energy utilization and environmental sustainability. The firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, which can speed up the rate of system optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a flowchart of the implementation of the comprehensive energy system optimization configuration method provided in the embodiment of the present application;

[0016] Figure 2 is a flow chart of the implementation of the firefly algorithm provided in the embodiment of the present application;

[0017] Figure 3 It is a secondary indicator distribution diagram of the comprehensive energy system evaluation indicator system provided in the embodiment of the present application;

[0018] Figure 4 It is a specific implementation flow chart of the comprehensive evaluation model of regional comprehensive energy system based on the extension cloud model provided in the embodiment of the present application;

[0019] Figure 5It is a structural schematic diagram of a comprehensive energy system optimization configuration device provided in an embodiment of the present application;

[0020] Figure 6 It is a schematic diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] In order to make the purpose, technical solutions and advantages of the present application clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0023] Under the current trend of global energy structure transformation and green development, it is particularly important to build a low-carbon, clean and sustainable integrated energy system. This system not only needs to optimize the configuration and utilization of energy to reduce the overall operating cost, but also needs to reduce carbon emissions as much as possible to meet the challenges of climate change. Therefore, this application proposes a method for optimizing the configuration of an integrated energy system, which takes the minimum total cost and the minimum carbon emissions as the optimization goals, comprehensively considers various constraints, constructs a regional integrated energy system, and uses the firefly algorithm to solve the regional integrated energy system. The regional integrated energy system is designed with full consideration of cost control and environmental impact in the process of energy conversion and utilization, and strives to achieve efficient energy utilization and environmental sustainability while meeting energy needs.

[0024] Figure 1 The implementation flow chart of the integrated energy system optimization configuration method provided in the embodiment of the present application is detailed as follows:

[0025] In step 101, under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, a regional integrated energy system is constructed, and the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization goals.

[0026] In the embodiment of the present application, the constraints including power balance constraint, equipment operation constraint and equipment capacity constraint are comprehensively considered, and the regional integrated energy system is constructed with the minimum total cost and the minimum carbon emission as the optimization goal. The regional integrated energy system can be an electric cooling and heating system.

[0027] In a possible implementation, the objective function of the regional integrated energy system may include a first sub-objective function with the minimum total cost, and the first sub-objective function is:

[0028]

[0029] Where C is the total cost of the regional integrated energy system, C E is the cost of electricity purchase and sale, C G is the cost of purchasing and selling gas, C O is the operating cost of the regional integrated energy system, C P To increase the penalty costs for abandoning wind and solar power, is the carbon trading cost of the regional integrated energy system, is the electricity price of the power grid at time t, is the gas price of the natural gas network at time t, is the unit price of gas turbine operation at time t, is the unit price of wind and solar power at time t, is the interaction power with the grid at time t, is the interaction power with the natural gas network at time t, is the power consumption of the refrigerator at time t, is the P2G power consumption at time t, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the predicted wind power at time t, is the predicted photovoltaic power at time t, T is the total operation cycle of the regional integrated energy system, t is the operation time of the regional integrated energy system, and min is the minimum function.

[0030] Optionally, the embodiment of the present application further considers the economy and environmental protection of the regional integrated energy system, with minimizing the total cost as the optimization goal.

[0031] The factors that affect the total cost of regional integrated energy systems mainly include: the cost of purchasing and selling electricity C E , Gas purchase and sale cost C G , the operating cost of the regional integrated energy system C O In order to reflect the wind and solar power consumption capacity of the regional integrated energy system, this embodiment increases the penalty cost C caused by abandoning wind and solar power. P In order to reduce the carbon emissions of the regional integrated energy system and enhance environmental protection, this embodiment increases the carbon trading cost of the regional integrated energy system. The first sub-objective function of optimizing the regional integrated energy system with the minimum total cost is obtained:

[0032] Among them, the cost of purchasing and selling electricity C E The calculation formula is: Gas purchase and sale cost C G The calculation formula is: Operation cost of regional integrated energy system C O The calculation formula is: And increase the penalty cost of abandoning wind and solar power C P The calculation formula is:

[0033] In a possible implementation, the objective function of the regional integrated energy system may include a second sub-objective function with the minimum carbon emissions, and the second sub-objective function is:

[0034]

[0035] in, is the carbon emissions of the regional integrated energy system, is the interaction power with the grid at time t, is the amount of natural gas consumed by the CCHP unit at time t, is the power of CCHP unit at time t, δ E is the carbon emission coefficient of power grid electricity, δ NG is the carbon emission coefficient of natural gas, θ NG is the natural gas calorific value conversion coefficient, T is the total operation cycle of the regional integrated energy system, t is the operation time of the regional integrated energy system, and min is the minimum value function.

[0036] Optionally, the regional integrated energy system of this embodiment also takes the minimum carbon emission as the optimization goal, and the carbon emission mainly comes from the power purchase from the power grid and the natural gas consumed by the combined cooling, heating and power (CCHP) unit. Then the second sub-objective function of the regional integrated energy system to minimize carbon emissions is: in, The unit is kg; The unit is kg / kWh; δ NG The unit is kg / m 3 .

[0037] For the constraints in this embodiment, among them:

[0038] The power balance constraint may include electric power balance constraint, thermal power balance constraint and cooling power balance constraint. The calculation formulas of each constraint are as follows:

[0039] The electric power balance constraint is:

[0040]

[0041] in, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the external power purchase amount at time t, is the output power of the energy storage battery at time t, is the input power of the energy storage battery at time t, is the load of the regional integrated energy system at time t, ε e is the battery energy storage state parameter, and t is the operating time of the regional integrated energy system.

[0042] The thermal power balance constraint is:

[0043]

[0044] in, is the heat network output power, Output thermal power for the gas turbine, Output heat power for electric boiler, Output thermal power for gas boiler, is the output power of the heat storage tank, Input power to the heat storage tank, is the system heat load, ε h is the energy storage state parameter of the heat storage tank.

[0045] The cooling power balance constraint is:

[0046]

[0047] in, is the CCHP cooling power at time t (kW), is the cooling power of the heat pump at time t (kW), is the cooling power of the electric refrigeration mechanism at time t, is the cooling power of the ice storage tank at time t (kW), is the cooling power of the ice storage tank at time t (kW), is the cooling load demand at time t (kW).

[0048] Equipment operation constraints may include electric boiler power constraints, gas boiler constraints, gas turbine constraints, and energy storage battery charging and discharging power constraints. The calculation formulas for each constraint are as follows:

[0049] The power constraint of the electric boiler is:

[0050]

[0051] in, is the lower limit of electric boiler power consumption, It is the upper limit of the power consumption of electric boiler.

[0052] The gas boiler constraints are:

[0053]

[0054] in, It is the upper limit of the thermal power output of the gas boiler.

[0055] The gas turbine constraints are:

[0056]

[0057] in, is the lower limit of gas turbine operating power, The upper limit of gas turbine operating power.

[0058] The charging and discharging power constraints of the energy storage battery are:

[0059]

[0060] in, is the charging and discharging power of the energy storage battery at time t, is the lower limit of the charging and discharging power of the energy storage battery, It is the upper limit of charging and discharging power of energy storage battery.

[0061] The equipment capacity constraint is:

[0062]

[0063] Among them, SOC bat is the capacity of the energy storage battery, is the minimum capacity constraint of the energy storage battery, is the maximum capacity constraint of the energy storage battery, SOC tes is the capacity of the heat storage tank, is the minimum capacity constraint of the heat storage tank, is the maximum capacity constraint of the heat storage tank, SOC ces is the ice storage tank capacity, is the minimum capacity constraint of the ice storage tank, It is the maximum capacity constraint of the ice storage tank.

[0064] In step 102, the firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system.

[0065] Among them, the solution algorithm is an important support for the optimization of the operation of the integrated energy system. The optimization of the operation of the integrated energy system is a typical nonlinear optimization problem, involving a large amount of data and parameters. It is difficult to solve with traditional precise algorithms, and it is necessary to use intelligent algorithms to find the optimal solution to the problem.

[0066] In the firefly algorithm, the position of each firefly represents a feasible solution to the problem to be solved, and the brightness of the firefly represents the fitness of the firefly position. The brighter the firefly individual, the better the position in the solution space. Among individual fireflies, high-brightness fireflies will attract low-brightness fireflies. In the solution space, each firefly will fly towards fireflies with higher brightness than itself to search for a better position.

[0067] The firefly population can be regarded as a search solution space, but each feasible solution in the solution space corresponds to each individual firefly. Therefore, the algorithm's solution process is like the change process between individual fireflies. There are two key points: firefly brightness and attractiveness. The higher the brightness, the better the target, the closer the target function is to the expected value, and the stronger the attraction, so other individuals in the field will be more willing to move closer to it.

[0068] in accordance with Figure 2 , the specific solution process of the firefly algorithm is as follows:

[0069] (1) Initialize algorithm parameters: set the number of fireflies, maximum attraction, light absorption factor, step size factor, and maximum number of iterations or search accuracy.

[0070] (2) Population initialization: Randomly initialize the positions of the fireflies and calculate the objective function value of each firefly as its initial brightness.

[0071] (3) Iteration process: Enter the loop until the termination condition is met. The iterative process is as follows:

[0072] 1) Calculate the relative brightness and attraction between fireflies;

[0073] 2) The direction of firefly movement is determined by relative brightness, with brighter fireflies attracting dimmer ones;

[0074] 3) Update the spatial position of the fireflies, including random movement and moving towards brighter fireflies.

[0075] 4) Recalculate the brightness of the fireflies after the updated position.

[0076] (4) Update the global optimal solution: If a better solution is found during the iteration process, update the global optimal solution record.

[0077] (5) Output results: After the termination condition is met, the global optimal solution and the corresponding solution value are output.

[0078] In the embodiment of the present application, the firefly algorithm is used to solve the regional integrated energy system, and the optimal configuration scheme including equipment output data and energy demand of the regional integrated energy system is obtained. The equipment output data is the output of each operating equipment in the regional integrated energy system, and the energy demand includes the energy demand of electricity, heat, cold and gas in the regional integrated energy system.

[0079] In a possible implementation, the firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, which may include:

[0080] Initialize multiple configuration schemes and iteration times of regional integrated energy systems, and determine the total cost and carbon emissions corresponding to various configuration schemes;

[0081] For each configuration scheme, the configuration scheme is regarded as a firefly, the total cost of the configuration scheme is used as the horizontal coordinate, and the carbon emission of the configuration scheme is used as the vertical coordinate to construct the current position coordinates of the firefly;

[0082] For each firefly, the current position coordinates of all fireflies are used to calculate the attraction between the firefly and other fireflies, and determine the moving direction of the firefly, which is the direction in which the firefly moves towards the firefly corresponding to the maximum attraction;

[0083] For each firefly, the relative brightness of the firefly and each firefly in its moving direction is calculated using the current position coordinates, and the firefly with the largest relative brightness to the firefly in its moving direction is taken as the target firefly corresponding to the firefly; based on the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly, the current moving distance of the firefly is calculated, and the firefly is moved the current moving distance in its moving direction to obtain the next position coordinates of the firefly;

[0084] Determine whether the current number of iterations has reached the maximum number of iterations;

[0085] If the current number of iterations does not reach the maximum number of iterations, the next position coordinates of each firefly are used to update the current position coordinates of the corresponding firefly, the current number of iterations is increased by 1, and the step of calculating the attraction between each firefly and other fireflies using the current position coordinates of all fireflies is returned to continue execution;

[0086] If the current number of iterations reaches the maximum number of iterations, the brightness of the corresponding fireflies is calculated using the next position coordinates of each firefly, and the next position coordinates corresponding to the firefly with the maximum brightness are selected as the optimal position coordinates;

[0087] According to the optimal position coordinates, the optimal configuration scheme corresponding to the optimal position coordinates is determined.

[0088] Optionally, the process of solving the optimal configuration scheme of the regional integrated energy system using the firefly algorithm in this embodiment is as follows:

[0089] Initialize multiple configuration schemes and iteration times of the regional integrated energy system, and determine the total cost and carbon emissions corresponding to various configuration schemes. Then, take each configuration scheme as a firefly, and use the total cost of the configuration scheme as the horizontal coordinate and the carbon emissions of the configuration scheme as the vertical coordinate to determine the current position coordinates of the firefly corresponding to the configuration scheme in a two-dimensional table.

[0090] Initialize the parameters, including the number of fireflies, maximum attraction, light absorption factor, step factor, and maximum number of iterations. Then, for each firefly, use the current position coordinates of all fireflies to calculate the attraction between the firefly and other fireflies. The attraction calculation formula is:

[0091]

[0092] Among them, β ij is the attraction of the i-th firefly to the j-th firefly, β0 is the maximum attraction of the firefly, γ is the light absorption factor, indicating that the brightness will decrease with the absorption of the propagation medium. The size of the light absorption factor will affect the convergence speed of the firefly algorithm and the final optimization result, and is usually taken as a constant; r ij is the distance between the i-th firefly and the j-th firefly, usually expressed as Euclidean distance, and its calculation formula is:

[0093]

[0094] Among them, x i is the position coordinate of the i-th firefly, x j is the position coordinate of the jth firefly, x i,k is the kth component of the position coordinates of the i-th firefly, x j,k is the kth component of the position coordinates of the jth firefly, d is the dimension of the search area, and k is the total cost and carbon emissions in the position coordinates.

[0095] Since a firefly will be attracted to a brighter firefly, and the attraction is proportional to the brightness of the firefly, the firefly corresponding to the maximum attraction is selected from the attraction between the firefly and other fireflies, and the direction in which the firefly moves toward the firefly corresponding to the maximum attraction is used as the moving direction of the firefly.

[0096] After determining the moving direction of each firefly at the current iteration, for each firefly:

[0097] Using the current position coordinates of the firefly, the current position coordinates of the firefly and each firefly in its moving direction are input into the relative brightness function to obtain the relative brightness of the firefly and each firefly in its moving direction. The relative brightness function is:

[0098]

[0099] Among them, I is the relative brightness and I0 is the maximum brightness of the firefly.

[0100] The firefly with the largest relative brightness among the relative brightness of the firefly and the fireflies in the moving direction is taken as the target firefly corresponding to the firefly;

[0101] The current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly are input into the distance function to obtain the current moving distance of the firefly. The distance function is:

[0102] X j (t+1)=X j (t)+β ij (X i (t)-X j (t))+α(rand-0.5)

[0103] Among them, X j (t+1) is the position coordinate of the jth firefly at time t+1, X j (t) is the position coordinate of the jth firefly at time t, X i (t) is the position coordinate of the t-th firefly at time t, β ij is the attraction of the i-th firefly to the j-th firefly, rand is a random step size that obeys a uniform distribution in the interval [0, 1], α is a constant in the interval [0, 1], and t is the number of iterations.

[0104] The calculation formula of the constant α between the interval [0, 1] is: α(t) = α t .

[0105] Move the firefly in its moving direction by the current moving distance to obtain the next position coordinates of the firefly.

[0106] Determine whether the current number of iterations has reached the maximum number of iterations. If the current number of iterations has not reached the maximum number of iterations, use the next position coordinates of each firefly to update the current position coordinates of the firefly, add 1 to the current number of iterations, and return to the attraction calculation step to continue execution; if the current number of iterations has reached the maximum number of iterations, use the next position coordinates of each firefly to calculate the brightness of the firefly, and select the next position coordinates corresponding to the firefly corresponding to the maximum brightness from all brightness as the optimal position coordinates.

[0107] Then, the optimal configuration scheme of the corresponding regional integrated energy system is determined according to the optimal position coordinates, that is, the optimal total cost and carbon emissions of the regional integrated energy system.

[0108] In addition, in order to improve the performance and benefits of the integrated energy system, the existing evaluation system needs to be studied and improved in depth. A more accurate and comprehensive evaluation system will help to better understand the operating status of the system, guide the optimization and upgrading of the system, and provide a scientific basis for policy making.

[0109] The evaluation research of integrated energy system aims to comprehensively measure the performance of the system in key dimensions such as technology maturity, environmental sustainability, economic rationality and social impact by constructing a scientific and rigorous multi-dimensional evaluation index system. The weight determination target mostly adopts the method of combining the analytic hierarchy process and the entropy method to ensure the objectivity and scientificity of the evaluation. The application of fuzzy comprehensive evaluation method provides an effective mathematical tool for dealing with uncertainty and ambiguity in evaluation. With the innovation of energy technology and changes in the market environment, the evaluation system and methods are also constantly innovating and optimizing to adapt to new technologies and market requirements, providing a solid theoretical basis and decision-making support for the scientific planning, reasonable design, efficient operation and continuous optimization of the energy system, and promoting scientific and technological progress and industrial upgrading in the energy field.

[0110] Based on the above research, indicator selection boundaries and requirements, the embodiment of the present application can construct a regional comprehensive energy evaluation index system from four dimensions: technical benefits, economic benefits, environmental benefits and social benefits. For each dimension, the most representative and practical secondary indicators are selected, with quantitative indicators as the main and qualitative indicators as the auxiliary. Quantitative indicators should give indicator definitions and calculation formulas, and qualitative indicators should be given in detail to facilitate subsequent evaluations, and then combined with the material element theory to generate a variable weight extension cloud model. Finally, calculate the cloud correlation between the corresponding eigenvalues ​​of each indicator and the cloud model of the comprehensive evaluation level boundary of the regional comprehensive energy system to determine the pros and cons of the solution.

[0111] After the regional integrated energy system constructed as above and the optimal solution of the regional integrated energy system is solved, in order to verify the practicality and effectiveness of the regional integrated energy system, this application further constructs a set of regional integrated energy system evaluation index system. The core goal of this evaluation index system is to achieve an accurate quantitative evaluation of the operating efficiency of the regional integrated energy system, comprehensively consider the technical, economic, social and environmental factors of the regional integrated energy system, objectively measure the flexibility and reliability of the regional integrated energy system, and conduct systematic monitoring of environmental impacts. Through a series of scientific indicators and methods, decision support can be provided for policy makers and system designers to ensure the sustainable optimization of the regional integrated energy system. Through the regional integrated energy system evaluation index system, the performance of the regional integrated energy system can be more comprehensively understood, potential problems can be discovered and solved in a timely manner, and the regional integrated energy system can be promoted to develop in a more efficient, environmentally friendly and reliable direction.

[0112] Specifically, after determining the four dimensions of technical benefits, economic benefits, environmental benefits, and social benefits, the embodiment of the present application analyzes and determines each quantitative and qualitative evaluation indicator layer by layer, and preliminarily establishes a regional comprehensive energy system evaluation indicator system including 12 indicators, ensuring the comprehensiveness and accuracy of the evaluation results.

[0113] (1) Technical benefit indicators include primary energy utilization rate, wind-solar matching degree and maintainability of equipment and facilities.

[0114] 1) Primary energy utilization rate: The primary energy utilization rate in the embodiment of the present application is the ratio of the output electricity, heat and gas energy of the regional comprehensive energy system to the primary energy consumption, that is:

[0115]

[0116] Among them, PEA is the primary energy utilization rate, T is the total operation cycle of the regional comprehensive energy system, t is the operation time of the regional comprehensive energy system, ε is the power generation efficiency of the coal-fired unit, is the transmission loss rate, L e (t) is the basic load, L h (t) is the transferable load, L g (t) is the replaceable load, λ e is the unified conversion parameter of basic load, λ h is the unified conversion parameter of transferable load, λ g Unify the conversion parameters for replaceable loads.

[0117] 2) Scenery-solar matching:

[0118] The Pearson correlation coefficient, also known as the Pearson correlation coefficient, is a statistic that measures the degree of linear correlation between two variables. It is usually represented by the letter r and is defined between -1 and 1.

[0119] The embodiment of the present application uses the Pearson correlation coefficient to evaluate the wind-solar matching degree. The wind-solar matching degree refers to the quantification of the relationship between the wind-solar combined output fluctuation and the load fluctuation. The closer the value is to 1, the higher the matching degree between the wind-solar combined output and the load. The calculation formula of the Pearson correlation coefficient is:

[0120]

[0121] Among them, r(X,Y) represents the Pearson correlation coefficient of two time series. The coefficient is between 0 and 1. When the coefficient is 1, it means that the two time series are "completely correlated". The closer it is to 1, the higher the correlation between the two time series. are the average values ​​of two time series respectively; when calculating the wind-solar matching degree, X(t) is the sum of wind-solar output, and Y(t) is the sum of load.

[0122] 3) Maintainability of equipment and facilities:

[0123] As a complex system integrating multiple energy forms, the regional integrated energy system is equipped with many cutting-edge and efficient equipment. These devices have different operating modes, and the system's operating strategy is relatively complex. Therefore, the regional integrated energy system places higher requirements on the professional level of the operation and maintenance team.

[0124] (2) Economic benefit indicators include construction cost, operating cost, investment payback period and internal rate of return.

[0125] 1) Construction cost:

[0126] The construction cost of regional integrated energy system mainly includes: energy supply system equipment purchase cost, station infrastructure cost, equipment assembly and commissioning cost and other related supporting costs. The calculation formula of construction cost is:

[0127]

[0128] Among them, C st is the construction cost, k i is the number of the i-th type of equipment (units); c i is the unit cost of the i-th equipment (10,000 yuan); n is the number of equipment types, i = 1, 2, 3,…

[0129] 2) The calculation formula for operating cost is:

[0130]

[0131] Among them, C op is the system operating cost, For other operating expenses.

[0132] 3) Payback period:

[0133] The dynamic payback period is an indicator of how long it will take to recover the cost, taking into account the benchmark rate of return. The calculation formula is:

[0134]

[0135] Where T is the dynamic investment payback period, CI t is the amount of capital inflow in year t, CO t is the amount of capital outflow in year t, i c is the benchmark rate of return, t is the number of years, and T is the total number of years.

[0136] 4) Internal Rate of Return:

[0137] The internal rate of return refers to the discount rate when the present value of the inflow amount and the outflow amount are equal and the net present value is equal to 0. The calculation formula is as follows:

[0138]

[0139] Among them, IRR is the internal rate of return of the system.

[0140] (3) Environmental benefit indicators include CO2 emission reduction rate, clean energy supply rate and gas pollutant emissions.

[0141] 1)CO2 emission reduction rate:

[0142] Carbon dioxide is different from polluting gases. It is a greenhouse gas. Excessive carbon dioxide is not good for the environment. Carbon dioxide emissions refer to the total amount of carbon dioxide emitted during the system's primary energy consumption process. The CO2 emission reduction rate calculation formula is:

[0143]

[0144] in, The system is the CO2 emission reduction rate compared with the traditional distribution system. is the CO2 emission of the traditional distribution system, is the current system CO2 emissions, The amount of coal used for power generation in the system, The amount of natural gas used for power generation in the system, is the CO2 emission coefficient of coal-fired power, is the CO2 emission coefficient for natural gas power generation.

[0145] 2) Clean energy supply rate:

[0146] Clean energy has lower pollutant emissions in energy supply, and increasing its proportion in the energy structure is a key strategy to achieve low-carbon development. By increasing the use of clean energy, not only can carbon emissions be significantly reduced, but also dependence on traditional fossil fuels can be reduced, thus bringing positive impacts on economic benefits. The embodiment of this application defines the functional efficiency of clean energy as the ratio of the actual contribution of wind and solar energy to the total energy supply of the entire system. The specific formula is as follows:

[0147]

[0148] Among them, CER is the clean energy supply rate, β ge The efficiency of natural gas to electricity conversion.

[0149] 3) Emissions of gaseous pollutants:

[0150] The gas pollutant emission reduction rate is the ratio of the gas pollutant emissions reduced by the use of clean energy to the total gas pollutant emissions in the region. Common gas pollutants include sulfur dioxide SO2, nitrogen oxides CO x , the calculation formula for gas pollutant emissions is:

[0151]

[0152] in, is sulfur dioxide emissions, is the nitrogen oxide emission, c is the nitrogen oxide emission factor of coal combustion (g / kWh), and d is the nitrogen oxide emission factor of natural gas combustion (g / Nm 3 ), e is the sulfur dioxide emission factor for coal combustion (g / kWh), and f is the sulfur dioxide emission factor for natural gas combustion (g / Nm 3 ), Q coal is the amount of coal burned, Q gas is the amount of natural gas burned.

[0153] (4) Social benefit indicators include industrial benefits and employment benefits.

[0154] 1) Industrial benefits:

[0155] The regional integrated energy system integrates and utilizes energy technologies such as ground source heat pumps, distributed photovoltaic power generation systems, distributed wind power and energy storage systems. After the project is completed and put into operation, it will play a positive demonstration effect, drive the same industry to adopt similar clean functional energy systems, and will inevitably generate huge demand for application equipment such as heat pump equipment, photovoltaics, wind power, and energy storage, effectively promote the progress of related energy utilization technologies in China, and drive the development of related industries such as core equipment manufacturing, Internet equipment, energy conservation and environmental protection.

[0156] 2) Employment benefits:

[0157] The regional integrated energy system will drive local employment and the development of the service industry, while the development of related industrial chains such as equipment research and development and manufacturing will certainly promote the employment of related engineering and technical personnel.

[0158] Based on the above content, the regional comprehensive energy system multi-dimensional evaluation index system comprehensively considers the four dimensions of technology, economy, environment and society, and comprehensively selects 12 secondary indicators to build the evaluation index system. The specific distribution refers to Figure 3 .

[0159] The matter-element theory in topology uses matter-element as the basic element to describe things. It comprehensively considers high-dimensional incompatibility problems and can objectively describe the development process and changing laws of things. In the comprehensive evaluation of regional integrated energy systems, the operating state of the system is uncertain, and the deterministic evaluation indicators calculated using sampling data obviously cannot fully reflect it. Correspondingly, the deterministic values ​​that describe the characteristics of things in the traditional matter-element model are no longer applicable at this time, and it is necessary to improve it in combination with the unique advantages of the cloud model in dealing with uncertainty problems. This application combines cloud theory with matter-element theory, and proposes a comprehensive evaluation model for regional integrated energy systems based on the topological cloud model, which improves the objectivity and adaptability of the evaluation process. The specific implementation process of the comprehensive evaluation model for regional integrated energy systems based on the topological cloud model is as follows. Figure 4 shown.

[0160] The specific implementation steps of the regional comprehensive energy system comprehensive evaluation model based on the Extension Cloud model are as follows:

[0161] Step 1: Collect evaluation data, formulate evaluation standards, and construct a matrix of the comprehensive evaluation system by calculating the key values ​​of each indicator. Among them, quantitative indicators are directly calculated based on the basic values ​​provided in the plan; while qualitative indicators need to be scored by experts based on the existing system operation status, the feasibility requirements of similar comprehensive evaluation systems, current national policies and the latest relevant technical information, combined with personal professional judgment, and according to the scoring criteria to give a percentage score for each indicator.

[0162] Step 2: Divide the comprehensive evaluation level and determine the classic domain, that is, the boundary range of each level. Given that the diversity of factors such as system scale, energy type diversity, and user group composition may have a significant impact on the evaluation criteria of decision makers, a "cure the disease with the right medicine" strategy must be adopted. This means that it is necessary to invite professionals to determine the level division of indicators based on the specific circumstances of the evaluation objectives, rather than simply applying a universal indicator level standard.

[0163] Step 3: After determining the level boundaries, first use the level boundaries of the evaluation indicators as a double constraint space [C min ,C max ], and use the following transformation relationship equations to moderately expand the level limit cloud model (E x ,E n ,H e ).

[0164]

[0165] Among them, E x For expectation, E n is entropy, H e is the super entropy, C max is the upper limit of the level, C min is the lower limit of the level, s is a constant, which can be adjusted based on the fuzziness, discreteness, randomness and actual situation of the indicator.

[0166] Secondly, the normal cloud model (E x ,E n ,H e ) replaces the characteristic value in the traditional matter-element, and the expected E x 、Entropy E n and super entropy H e Converted into m cloud droplets (x1, x2, ..., x n ), the transformation from quantitative values ​​to qualitative concepts is realized through the probability that a single cloud droplet can represent the entire extension cloud model. The extension cloud model is as follows:

[0167]

[0168] Among them, R j The planned level for the comprehensive evaluation of regional integrated energy systems (E x1 ,E n1 ,H e1 )、(E x2 ,E n2 ,H e2 ),...,(E xn ,E nn ,H en ) are the transport volume values, and are the evaluation indicators C1, C2, ..., C n About R-level j The cloud description of n is about level R j Description of the cloud.

[0169] Step 4: According to the characteristics of the forward normal cloud model, each index value x to be evaluated is regarded as a single cloud droplet; secondly, E x As expected, H eAs the super entropy, a normally distributed random number E′ is generated. n Finally, the cloud correlation k between the corresponding characteristic values ​​of each indicator and the boundary cloud model of the comprehensive evaluation level of the regional integrated energy system is calculated, and the calculation formula is:

[0170]

[0171] The above formula is introduced to transform the sampling randomness problem in reality into the index value x unchanged, and the level limit E′ n The relative randomness of fluctuations, where It is consistent with the fluctuation caused by state uncertainty, and the size of random fluctuation is determined by the super entropy H e The cloud correlation between the index values ​​of the object element to be evaluated and the extension cloud model of the comprehensive evaluation level of each evaluation index is calculated to obtain the evaluation matrix K of the comprehensive evaluation level of the regional comprehensive energy system.

[0172]

[0173] Among them, k ij C is the evaluation index of the matter-element to be evaluated i and the correlation between the boundary cloud model of comprehensive evaluation level j, m is the number of rating indicators, and n is the comprehensive evaluation level.

[0174] Step 5: Get the final evaluation vector:

[0175] B=diag(W′K)

[0176] Among them, B is the evaluation vector, W′ is the weight coefficient of the indicator system, and K is the evaluation matrix.

[0177] Based on the evaluation vector, the comprehensive evaluation level of all evaluation schemes is obtained by optimizing the comprehensive cloud correlation, and then the pros and cons of the schemes are determined according to their evaluation levels.

[0178] The present application provides a method for optimizing the configuration of an integrated energy system. By constructing a regional integrated energy system under the constraints of power balance constraints, equipment operation constraints, and equipment capacity constraints, the regional integrated energy system takes the minimum total cost and the minimum carbon emissions as optimization goals; the firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system. The present application constructs a regional integrated energy system with the minimum total cost and the minimum carbon emissions as optimization goals, and achieves efficient energy utilization and environmental sustainability while meeting energy needs; and the firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, which can speed up the rate of system optimization.

[0179] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0180] The following is an embodiment of the device of the present application. For details not described in detail, please refer to the corresponding method embodiment described above.

[0181] Figure 5 The schematic diagram of the structure of the integrated energy system optimization configuration device provided in the embodiment of the present application is shown. For the convenience of explanation, only the part related to the embodiment of the present application is shown, which is described in detail as follows:

[0182] like Figure 5 As shown, the comprehensive energy system optimization configuration device 5 includes:

[0183] A construction module 51 is used to construct a regional integrated energy system under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, wherein the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization objectives;

[0184] The solution module 52 is used to use the firefly algorithm to solve the optimal configuration plan of the regional integrated energy system. The optimal configuration plan includes the equipment output data and energy demand of the regional integrated energy system.

[0185] The present application provides a comprehensive energy system optimization configuration device, which constructs a regional comprehensive energy system under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints. The regional comprehensive energy system takes the minimum total cost and the minimum carbon emissions as optimization goals; the firefly algorithm is used to solve the optimal configuration scheme of the regional comprehensive energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional comprehensive energy system. The present application constructs a regional comprehensive energy system with the minimum total cost and the minimum carbon emissions as optimization goals, and realizes the efficient use of energy and environmental sustainability while meeting energy needs; and the firefly algorithm is used to solve the optimal configuration scheme of the regional comprehensive energy system, which can speed up the rate of system optimization.

[0186] In a possible implementation, the objective function of the regional integrated energy system may include a first sub-objective function with the minimum total cost, and the first sub-objective function is:

[0187]

[0188] Where C is the total cost of the regional integrated energy system, C E is the cost of electricity purchase and sale, C G is the cost of purchasing and selling gas, C Ois the operating cost of the regional integrated energy system, C P To increase the penalty costs for abandoning wind and solar power, is the carbon trading cost of the regional integrated energy system, is the electricity price of the power grid at time t, is the gas price of the natural gas network at time t, is the unit price of gas turbine operation at time t, is the unit price of wind and solar power at time t, is the interaction power with the grid at time t, is the interaction power with the natural gas network at time t, is the power consumption of the refrigerator at time t, is the P2G power consumption at time t, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the predicted wind power at time t, is the predicted photovoltaic power at time t, T is the total operation cycle of the regional integrated energy system, t is the operation time of the regional integrated energy system, and min is the minimum function.

[0189] In a possible implementation, the objective function of the regional integrated energy system may include a second sub-objective function with the minimum carbon emissions, and the second sub-objective function is:

[0190]

[0191] in, is the carbon emissions of the regional integrated energy system, is the interaction power with the grid at time t, is the amount of natural gas consumed by the CCHP unit at time t, is the power of CCHP unit at time t, δ E is the carbon emission coefficient of power grid electricity, δ NG is the carbon emission coefficient of natural gas, θ NG is the natural gas calorific value conversion coefficient, T is the total operation cycle of the regional integrated energy system, t is the operation time of the regional integrated energy system, and min is the minimum value function.

[0192] In a possible implementation, the power balance constraint may include an electric power balance constraint, a thermal power balance constraint, and a cold power balance constraint, and the equipment operation constraint may include an electric boiler power constraint, a gas boiler constraint, a gas turbine constraint, and an energy storage battery charging and discharging power constraint;

[0193] The electric power balance constraint is:

[0194]

[0195] in, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the external power purchase amount at time t, is the output power of the energy storage battery at time t, is the input power of the energy storage battery at time t, is the load of the regional integrated energy system at time t, ε e is the battery energy storage state parameter, t is the operating time of the regional integrated energy system;

[0196] The thermal power balance constraint is:

[0197]

[0198] in, is the heat network output power, Output thermal power for the gas turbine, Output heat power for electric boiler, Output thermal power for gas boiler, is the output power of the heat storage tank, Input power to the heat storage tank, is the system heat load, ε h is the energy storage state parameter of the heat storage tank;

[0199] The cooling power balance constraint is:

[0200]

[0201] in, is the CCHP cooling power at time t, is the cooling power of the heat pump at time t, is the cooling power of the electric refrigeration mechanism at time t, is the cooling power of the ice storage tank at time t, is the cooling power of the ice storage tank at time t, is the cooling load demand at time t;

[0202] The power constraint of the electric boiler is:

[0203]

[0204] in, is the lower limit of electric boiler power consumption, The upper limit of the power consumption of electric boilers;

[0205] The gas boiler constraints are:

[0206]

[0207] in, It is the upper limit of the thermal power output of the gas boiler;

[0208] The gas turbine constraints are:

[0209]

[0210] in, is the lower limit of gas turbine operating power, The upper limit of gas turbine operating power;

[0211] The charging and discharging power constraints of the energy storage battery are:

[0212]

[0213] in, is the charging and discharging power of the energy storage battery at time t, is the lower limit of the charging and discharging power of the energy storage battery, The upper limit of charging and discharging power of the energy storage battery;

[0214] The equipment capacity constraint is:

[0215]

[0216] Among them, SOC bat is the capacity of the energy storage battery, is the minimum capacity constraint of the energy storage battery, is the maximum capacity constraint of the energy storage battery, SOC tes is the capacity of the heat storage tank, is the minimum capacity constraint of the heat storage tank, is the maximum capacity constraint of the heat storage tank, SOC ces is the ice storage tank capacity, is the minimum capacity constraint of the ice storage tank, It is the maximum capacity constraint of the ice storage tank.

[0217] In one possible implementation, the solution module can be used to:

[0218] Initialize multiple configuration schemes and iteration times of regional integrated energy systems, and determine the total cost and carbon emissions corresponding to various configuration schemes;

[0219] For each configuration scheme, the configuration scheme is regarded as a firefly, the total cost of the configuration scheme is used as the horizontal coordinate, and the carbon emission of the configuration scheme is used as the vertical coordinate to construct the current position coordinates of the firefly;

[0220] For each firefly, the current position coordinates of all fireflies are used to calculate the attraction between the firefly and other fireflies, and determine the moving direction of the firefly, which is the direction in which the firefly moves towards the firefly corresponding to the maximum attraction;

[0221] For each firefly, the relative brightness of the firefly and each firefly in its moving direction is calculated using the current position coordinates, and the firefly with the largest relative brightness to the firefly in its moving direction is taken as the target firefly corresponding to the firefly; based on the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly, the current moving distance of the firefly is calculated, and the firefly is moved the current moving distance in its moving direction to obtain the next position coordinates of the firefly;

[0222] Determine whether the current number of iterations has reached the maximum number of iterations;

[0223] If the current number of iterations does not reach the maximum number of iterations, the next position coordinates of each firefly are used to update the current position coordinates of the corresponding firefly, the current number of iterations is increased by 1, and the step of calculating the attraction between each firefly and other fireflies using the current position coordinates of all fireflies is returned to continue execution;

[0224] If the current number of iterations reaches the maximum number of iterations, the brightness of the corresponding fireflies is calculated using the next position coordinates of each firefly, and the next position coordinates corresponding to the firefly with the maximum brightness are selected as the optimal position coordinates;

[0225] According to the optimal position coordinates, the optimal configuration scheme corresponding to the optimal position coordinates is determined.

[0226] In a possible implementation, the solution module can also be used to:

[0227] For each firefly, the current position coordinates of the firefly are input into the relative brightness function, and the current position of each firefly in the moving direction of the firefly is input into the relative brightness function respectively to obtain the relative brightness of the firefly and each firefly in its moving direction. The relative brightness function is:

[0228]

[0229] Among them, I is the relative brightness, I0 is the maximum brightness of the firefly, γ is the light intensity absorption factor, r ij is the distance between the i-th firefly and the j-th firefly, x i is the position coordinate of the i-th firefly, x j is the position coordinate of the jth firefly, x i,kis the kth component of the position coordinates of the i-th firefly, x j,k is the kth component of the position coordinates of the jth firefly, d is the dimension of the search area, and k is the total cost and carbon emissions in the position coordinates.

[0230] In a possible implementation, the solution module can also be used to:

[0231] For each firefly, the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly are input into the distance function to obtain the current moving distance of the firefly. The distance function is:

[0232] X j (t+1)=X j (t)+β ij (X i (t)-X j (t))+α(rand-0.5)

[0233] Among them, X j (t+1) is the position coordinate of the jth firefly at time t+1, X j (t) is the position coordinate of the jth firefly at time t, X i (t) is the position coordinate of the i-th firefly at time t, β ij is the attraction of the i-th firefly to the j-th firefly, rand is a random step size that obeys a uniform distribution in the interval [0, 1], α is a constant in the interval [0, 1], and t is the number of iterations.

[0234] Figure 6 is a schematic diagram of a terminal provided in an embodiment of the present application. Figure 6 As shown, the terminal 6 of this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, the steps in the above-mentioned database management method embodiments are implemented, for example Figure 1 Alternatively, when the processor 60 executes the computer program 62, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 5 The functions of each module are shown.

[0235] Exemplarily, the computer program 62 may be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the terminal 6. For example, the computer program 62 may be divided into Figure 5 The modules shown.

[0236] The terminal 6 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will appreciate that Figure 6 It is only an example of terminal 6 and does not constitute a limitation on terminal 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0237] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0238] The memory 61 may be an internal storage unit of the terminal 6, such as a hard disk or memory of the terminal 6. The memory 61 may also be an external storage device of the terminal 6, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 6. Further, the memory 61 may also include both an internal storage unit of the terminal 6 and an external storage device. The memory 61 is used to store the computer program and other programs and data required by the terminal. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0239] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0240] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0241] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0242] In the embodiments provided in the present application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0243] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0245] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various database management method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0246] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing the configuration of a comprehensive energy system, characterized in that: include: Under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, a regional integrated energy system is constructed, wherein the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization goals; The firefly algorithm is used to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system.

2. The comprehensive energy system optimization configuration method according to claim 1, characterized in that: The objective function of the regional integrated energy system includes a first sub-objective function with the minimum total cost, and the first sub-objective function is: Where C is the total cost of the regional integrated energy system, C E is the cost of electricity purchase and sale, C G is the cost of purchasing and selling gas, C O is the operating cost of the regional integrated energy system, C P To increase the penalty costs for abandoning wind and solar power, is the carbon trading cost of the regional integrated energy system, is the electricity price of the power grid at time t, is the gas price of the natural gas network at time t, is the unit price of gas turbine operation at time t, is the wind and solar power unit price at time t, is the interaction power with the grid at time t, is the interaction power with the natural gas network at time t, is the power consumption of the refrigerator at time t, is the P2G power consumption at time t, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the predicted wind power at time t, is the predicted photovoltaic power at time t, T is the total operation cycle of the regional integrated energy system, t is the operation time of the regional integrated energy system, and min is the minimum function.

3. The method for optimizing the configuration of an integrated energy system according to claim 1, characterized in that: The objective function of the regional integrated energy system includes a second sub-objective function with the minimum carbon emissions, and the second sub-objective function is: in, is the carbon emissions of the regional integrated energy system, is the interaction power with the grid at time t, is the amount of natural gas consumed by the CCHP unit at time t, is the power of CCHP unit at time t, δ E is the carbon emission coefficient of power grid electricity, δ NG is the carbon emission coefficient of natural gas, θ NG is the natural gas calorific value conversion coefficient, T is the total operation cycle of the regional comprehensive energy system, t is the operation time of the regional comprehensive energy system, and min is the minimum value function.

4. The method for optimizing the configuration of an integrated energy system according to claim 1, characterized in that: The power balance constraints include electric power balance constraints, thermal power balance constraints and cold power balance constraints, and the equipment operation constraints include electric boiler power constraints, gas boiler constraints, gas turbine constraints and energy storage battery charging and discharging power constraints; The electric power balance constraint is: in, is the gas turbine operating power at time t, is the photovoltaic power generation power at time t, is the wind turbine power generation at time t, is the external power purchase amount at time t, is the output power of the energy storage battery at time t, is the input power of the energy storage battery at time t, is the load of the regional integrated energy system at time t, ε e is the battery energy storage state parameter, and t is the operating time of the regional integrated energy system; The thermal power balance constraint is: in, is the heat network output power, Output thermal power for the gas turbine, Output heat power for electric boiler, Output thermal power for gas boiler, is the output power of the heat storage tank, Input power to the heat storage tank, is the system heat load, ε h is the energy storage state parameter of the heat storage tank; The cooling power balance constraint is: in, is the CCHP cooling power at time t, is the cooling power of the heat pump at time t, is the cooling power of the electric refrigeration mechanism at time t, is the cooling power of the ice storage tank at time t, is the cooling power of the ice storage tank at time t, is the cooling load demand at time t; The electric boiler power constraint is: in, is the lower limit of electric boiler power consumption, The upper limit of the power consumption of electric boilers; The gas boiler constraints are: in, It is the upper limit of the thermal power output of the gas boiler; The gas turbine constraints are: in, is the lower limit of gas turbine operating power, The upper limit of gas turbine operating power; The energy storage battery charging and discharging power constraints are: in, is the charging and discharging power of the energy storage battery at time t, is the lower limit of the charging and discharging power of the energy storage battery, The upper limit of charging and discharging power of the energy storage battery; The equipment capacity constraint is: Among them, SOC bat is the capacity of the energy storage battery, is the minimum capacity constraint of the energy storage battery, is the maximum capacity constraint of the energy storage battery, SOC tes is the capacity of the heat storage tank, is the minimum capacity constraint of the heat storage tank, is the maximum capacity constraint of the heat storage tank, SOC ces is the ice storage tank capacity, is the minimum capacity constraint of the ice storage tank, It is the maximum capacity constraint of the ice storage tank.

5. The comprehensive energy system optimization configuration method according to claim 1, characterized in that: The method of using the firefly algorithm to solve the optimal configuration scheme of the regional integrated energy system includes: Initializing multiple configuration schemes and iteration times of the regional integrated energy system, and determining the total cost and carbon emissions corresponding to various configuration schemes; For each configuration scheme, the configuration scheme is regarded as a firefly, the total cost of the configuration scheme is used as the horizontal coordinate, and the carbon emission of the configuration scheme is used as the vertical coordinate to construct the current position coordinates of the firefly; For each firefly, the current position coordinates of all fireflies are used to calculate the attraction between the firefly and other fireflies, and determine the moving direction of the firefly, where the moving direction is the direction in which the firefly moves toward the firefly corresponding to the maximum attraction; For each firefly, the relative brightness of the firefly and each firefly in its moving direction is calculated using the current position coordinates, and the firefly with the largest relative brightness to the firefly in its moving direction is taken as the target firefly corresponding to the firefly; based on the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly, the current moving distance of the firefly is calculated, and the firefly is moved the current moving distance in its moving direction to obtain the next position coordinates of the firefly; Determine whether the current number of iterations reaches the maximum number of iterations; If the current number of iterations does not reach the maximum number of iterations, the next position coordinates of each firefly are used to update the current position coordinates of the corresponding firefly, the current number of iterations is increased by 1, and the step of calculating the attraction between each firefly and other fireflies using the current position coordinates of all fireflies is returned to continue execution; If the current number of iterations reaches the maximum number of iterations, the brightness of the corresponding fireflies is calculated using the next position coordinates of each firefly, and the next position coordinates corresponding to the firefly with the maximum brightness are selected as the optimal position coordinates; According to the optimal position coordinates, an optimal configuration scheme corresponding to the optimal position coordinates is determined.

6. The method for optimizing the configuration of the integrated energy system according to claim 5, characterized in that: The method of calculating the relative brightness of each firefly and each firefly in its moving direction by using the current position coordinates of each firefly includes: For each firefly, the current position coordinates of the firefly are input into the relative brightness function, and the current position of each firefly in the moving direction of the firefly is input into the relative brightness function respectively to obtain the relative brightness of the firefly and each firefly in its moving direction. The relative brightness function is: Where I is the relative brightness, I0 is the maximum brightness of the firefly, γ is the light intensity absorption factor, r ij is the distance between the i-th firefly and the j-th firefly, x i is the position coordinate of the i-th firefly, x j is the position coordinate of the jth firefly, x i,k is the kth component of the position coordinates of the i-th firefly, x j,k is the kth component of the position coordinates of the jth firefly, d is the dimension of the search area, and k is the total cost and carbon emissions in the position coordinates.

7. The method for optimizing the configuration of an integrated energy system according to claim 5, characterized in that: The method of calculating the current moving distance of the firefly based on the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly includes: For each firefly, the current position coordinates of the firefly, the current position coordinates of the target firefly corresponding to the firefly, and the relative brightness between the firefly and the corresponding target firefly are input into the distance function to obtain the current moving distance of the firefly. The distance function is: X j (t+1)=X j (t)+β ij (X i (t)-X j (t))+α(rand-0.5) Among them, X j (t+1) is the position coordinate of the jth firefly at time t+1, X j (t) is the position coordinate of the jth firefly at time t, X i (t) is the position coordinate of the i-th firefly at time t, β ij is the attraction of the i-th firefly to the j-th firefly, rand is a random step size that obeys a uniform distribution in the interval [0, 1], α is a constant in the interval [0, 1], and t is the number of iterations.

8. An integrated energy system optimization configuration device, characterized in that: include: A construction module is used to construct a regional integrated energy system under the constraints of power balance constraints, equipment operation constraints and equipment capacity constraints, wherein the regional integrated energy system takes minimizing total cost and minimizing carbon emissions as optimization objectives; The solution module is used to use the firefly algorithm to solve the optimal configuration scheme of the regional integrated energy system, and the optimal configuration scheme includes the equipment output data and energy demand of the regional integrated energy system.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the integrated energy system optimization configuration method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the configuration of an integrated energy system as described in any one of claims 1 to 7 are implemented.

Images