Comprehensive energy system planning method based on multiple scenes

Abstract

The invention provides an integrated energy system planning method based on multiple scenes. The method comprises the following steps: respectively collecting energy consumption data of each user in a conventional weather scene, and performing clustering based on the energy consumption data to obtain typical energy consumption data of different user types in the conventional weather scene with different seasonal characteristics; aiming at the typical energy consumption data of each user type under the conventional weather scene of each seasonal feature, establishing and solving a target function to obtain a basic capacity configuration scheme of the integrated energy system; determining the occurrence probability of different extreme weathers in the extreme weather scene and the equipment output influence volatility under different extreme weathers; determining a standby capacity configuration scheme of the integrated energy system based on the occurrence probability and the equipment output influence fluctuation ratio; and determining a final capacity configuration scheme of the integrated energy system based on the basic capacity configuration scheme of the integrated energy system and the standby capacity configuration scheme of the integrated energy system. According to the invention, the optimal capacity configuration of the integrated energy system can be realized.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy technology, and in particular to a multi-scenario based integrated energy system planning method. Background Art

[0002] With the continuous development and widespread application of energy technology, rural energy consumption has gradually shifted from extensive and highly polluting energy consumption to refined and low-carbon energy consumption. Solving rural regional energy supply problems based on an integrated energy system has become one of the development trends of the energy Internet.

[0003] Related technologies for optimizing the capacity of rural integrated energy systems have mostly focused on fixed scenarios. However, energy usage varies across seasons and weather conditions, and optimizing the capacity of rural integrated energy systems based solely on a single, fixed scenario clearly cannot yield an optimal planning solution. Summary of the Invention

[0004] The embodiment of the present invention provides a multi-scenario based integrated energy system planning method to solve the problem in related technologies that the optimal planning scheme for the integrated energy system cannot be effectively determined.

[0005] In a first aspect, an embodiment of the present invention provides a multi-scenario integrated energy system planning method, comprising:

[0006] Collecting energy consumption data of each user under normal weather conditions, clustering the energy consumption data, and obtaining typical energy consumption data of different user types under normal weather conditions with different seasonal characteristics;

[0007] Based on the typical energy consumption data of each user type under normal weather scenarios with seasonal characteristics, an objective function is established and solved to obtain the basic capacity configuration plan of the integrated energy system corresponding to each user type under normal weather scenarios with seasonal characteristics;

[0008] Determine the probability of occurrence of different extreme weather conditions in extreme weather scenarios based on Monte Carlo simulation, and determine the fluctuation rate of the output of each device in the integrated energy system under different extreme weather conditions in extreme weather scenarios;

[0009] Determine a corresponding integrated energy system backup capacity configuration plan under extreme weather scenarios based on the occurrence probability and the fluctuation rate of the equipment output impact;

[0010] Based on the basic capacity configuration plan of the integrated energy system corresponding to each user type in conventional weather scenarios under different seasonal characteristics, and the backup capacity configuration plan of the integrated energy system, the final capacity configuration plan of the integrated energy system corresponding to each user type under different seasonal characteristics is determined.

[0011] In a second aspect, an embodiment of the present invention provides a multi-scenario integrated energy system planning device, comprising:

[0012] A classification module is used to collect energy consumption data of each user under normal weather scenarios, and cluster the energy consumption data to obtain typical energy consumption data of different user types under normal weather scenarios with different seasonal characteristics;

[0013] A basic configuration module is used to establish an objective function based on the typical energy consumption data of each user type under normal weather scenarios with seasonal characteristics, and solve the objective function to obtain the basic capacity configuration plan of the integrated energy system corresponding to each user type under normal weather scenarios with seasonal characteristics;

[0014] The standby configuration module is used to determine the probability of occurrence of different extreme weather conditions in extreme weather scenarios based on Monte Carlo simulation, and to determine the fluctuation rate of the output of each device in the integrated energy system under different extreme weather conditions in extreme weather scenarios;

[0015] The backup configuration module is further configured to determine a corresponding integrated energy system backup capacity configuration plan under extreme weather scenarios based on the occurrence probability and the fluctuation rate of the equipment output impact;

[0016] The comprehensive configuration module is used to determine the final capacity configuration plan of the integrated energy system corresponding to each user type under different seasonal characteristics based on the basic capacity configuration plan of the integrated energy system corresponding to the conventional weather scenarios under different seasonal characteristics of each user type, and the backup capacity configuration plan of the integrated energy system.

[0017] In a third aspect, an embodiment of the present invention provides an electronic device 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.

[0018] In a fourth aspect, an embodiment of the present invention 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.

[0019] Compared with the prior art, the embodiment of the present invention takes into account the differences in energy consumption characteristics of different user types, and the energy consumption characteristics of the same user type in different seasons are also different. First, by clustering the energy consumption data, typical energy consumption data of different user types in normal weather scenarios with different seasonal characteristics can be extracted. Then, these typical energy consumption data are used as the data basis to solve the objective function, so that the basic capacity configuration plan of the integrated energy system in normal weather scenarios with different seasonal characteristics for different user types can be obtained. On this basis, the embodiment of the present invention further considers the probability of occurrence of extreme weather scenarios and the fluctuation rate of the equipment output influence of each device in extreme weather scenarios. Based on the probability of occurrence of extreme weather scenarios and the fluctuation rate of the equipment output influence, the backup energy configuration plan of the integrated energy system to deal with extreme weather scenarios is determined. Finally, based on the basic capacity configuration plan and the backup capacity configuration plan, the final capacity configuration plan of the integrated energy system is determined, thereby achieving the optimal capacity configuration of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a flowchart of an implementation method of a multi-scenario integrated energy system planning method provided by an embodiment of the present invention;

[0022] FIG2( a ) is a schematic diagram of clustering results provided by an embodiment of the present invention;

[0023] FIG2( b ) is a schematic diagram of a typical electric load data curve corresponding to the cluster center of each cluster in the clustering result provided by an embodiment of the present invention;

[0024] FIG2( c ) is a schematic diagram of a typical heat load data curve corresponding to the cluster center of each cluster in the clustering result provided by an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of solving the objective function provided by an embodiment of the present invention;

[0026] Figure 4 Schematic diagram of the structure of a multi-scenario integrated energy system planning device provided by an embodiment of the present invention;

[0027] Figure 5 is a schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0029] Related technologies for optimizing the capacity of rural integrated energy systems have mostly focused on fixed scenarios. However, energy usage varies across seasons and weather conditions, and optimizing the capacity of rural integrated energy systems based solely on a single, fixed scenario clearly cannot yield an optimal planning solution.

[0030] In order to achieve optimal planning for the integrated energy system, in the implementation of the present application, by clustering the energy consumption data, typical energy consumption data of different user types in regular weather scenarios with different seasonal characteristics can be extracted, and then the objective function is solved based on the typical energy consumption data to obtain the basic capacity configuration plan of the integrated energy system under regular weather scenarios with different seasonal characteristics for different user types. On this basis, the embodiment of the present invention further considers the probability of occurrence of extreme weather scenarios and the fluctuation rate of the output of each device in extreme weather scenarios to determine the backup energy configuration plan of the integrated energy system to deal with extreme weather scenarios. Finally, based on the basic capacity configuration plan and the backup capacity configuration plan, the final capacity configuration plan of the integrated energy system is determined, thereby achieving the optimal capacity configuration of the integrated energy system.

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0032] Figure 1 The following is a flowchart of the implementation of the multi-scenario integrated energy system planning method provided in an embodiment of the present invention:

[0033] Step 101 : Collect energy consumption data of each user in normal weather scenarios, cluster the energy consumption data, and obtain typical energy consumption data of different user types in normal weather scenarios with different seasonal characteristics.

[0034] The integrated energy system in the embodiment of the present invention mainly includes: a solar photovoltaic / thermal system (Photovoltaic / Thermal System, PV / T) and an electric-thermal hybrid energy storage system. Among them, the PV / T system couples the heat exchange structure with the photovoltaic module and removes the working waste heat of the photovoltaic module through the heat collecting medium. The photovoltaic / thermal comprehensive efficiency of the PV / T system can reach 60% to 80%, which is significantly higher than that of a separate photovoltaic-electric system or photovoltaic-thermal system. The PV / T system is mainly reflected in its ability to convert solar energy into electrical energy and thermal energy, including the efficiency of photovoltaic conversion and photothermal conversion.

[0035] The mathematical model of the PV / T system can be expressed as:

[0036]

[0037] Where, P pvt_h and P pvt_e Represent the heating power and power supply power of the PV / T system respectively, β represents the solar cell coverage rate, A represents the area of ​​the PV / T system, α represents the PV / T efficiency factor of the covered solar cells, E represents the solar irradiance, (τa) e Represents the effective product of the transmittance of the transparent cover and the absorption ratio of the heat absorbing plate, U L Represents the heat loss coefficient of the PV / T system, t m represents the average temperature of PV / T, t a represents the ambient temperature, α′ represents the PV / T efficiency factor of uncovered solar cells, η h and η e Represent the photothermal efficiency and photoelectric efficiency of the PV / T system, K pvt represents the solar cell conversion efficiency coefficient, η0 represents the photoelectric conversion efficiency of the solar cell under standard conditions, t pv Represents the solar cell temperature of the PV / T system.

[0038] The structure of the electric-thermal hybrid energy storage system is divided into four parts, including an air source heat pump, an electric boiler, an energy storage battery and a hot water storage tank. It can improve energy utilization through the electric-thermal coupling mechanism and achieve energy time complementarity, economic complementarity and stability complementarity.

[0039] (1) When the power supply of the PV / T system is too large, the energy storage battery works in the charging state, absorbing excess photovoltaic power, while the air source heat pump and electric boiler consume part of the photovoltaic power; when the power generation power of the PV / T system is too low, the energy storage battery works in the discharging state to supply power to users, while the air source heat pump and electric boiler will purchase electricity from the grid to meet the energy supply.

[0040]

[0041] (2) When the heating power of the PV / T system is too large, the water storage tank works in the heat storage state and absorbs excess solar heat; when the heating power of the PV / T system is too low, the water storage tank works in the heat release state to provide heat to users. At the same time, the air source heat pump and electric boiler will purchase electricity from the power grid to meet the energy supply.

[0042]

[0043] Where, P pvt_e (t) and P pvt_h (t) represent the power supply and heating power of the PV / T system at time t, P eload (t) and P hload (t) represent the electrical load and thermal load at time t, P ebs (t) represents the output power of the energy storage battery at time t, ESOC(t+1) and ESOC(t) represent the states of the energy storage battery at time (t+1) and time t, respectively. ebs Indicates the rated power of the energy storage battery, P grid_buy (t) represents the amount of electricity purchased from the grid at time t, P hst (t) represents the output power of the hot water storage tank at time t, HSOC(t+1) and HSOC(t) represent the status of the hot water storage tank at time (t+1) and time t, P hst Indicates the rated power of the hot water tank, P ashp (t) represents the heating power of the air source heat pump at time t, P eb (t) represents the heating power of the electric boiler at time t, and 0.9 and 0.1 represent the upper and lower limits of the SOC of the energy storage device, respectively.

[0044] Taking into account that extreme weather can easily affect the output of equipment in an integrated energy system, the embodiments of the present invention mainly design a basic capacity configuration plan for each device in the integrated energy system for conventional weather scenarios, and set a backup capacity configuration plan for each device in the integrated energy system for extreme weather scenarios, thereby combining the basic capacity configuration plan and the backup capacity configuration plan to determine the final capacity configuration plan for each device in the integrated energy system.

[0045] Among them, for normal weather scenarios, the embodiment of the present invention takes into account that the energy consumption characteristics of different users are different, and the energy consumption characteristics of the same user in different seasons are also slightly different. Therefore, the embodiment of the present invention clusters the energy consumption data of each user in normal weather scenarios, thereby obtaining typical energy consumption data of different user types under different seasonal characteristics.

[0046] Since the energy supply characteristics of PV / T systems and electric-thermal hybrid energy storage systems are closely related to seasons and user needs, the embodiment of the present invention extracts energy consumption characteristic indicators based on the energy consumption data of different users under normal weather scenarios, and uses an improved K-Means clustering algorithm to cluster and divide the energy consumption characteristic indicators.

[0047] In the embodiment of the present invention, three types of energy consumption characteristic indicators, namely, energy consumption of each user, power consumption during peak hours, and load fluctuation rate, are used to reflect the energy consumption level and energy consumption characteristics of the user. The specific formula is as follows:

[0048]

[0049] In the formula, C1 represents the user's energy consumption, Q eload (t) represents the user’s electric energy at time t, Q hload (t) represents the thermal energy of the user at time t, C2 represents the proportion of power consumption during the peak period, P represents the total duration of the peak period, Q peak (t1) represents the power consumption of the user during the peak period t1, C3 represents the user load fluctuation rate, P eload (t) represents the user's electricity load at time t, μ1 represents the user's average daily electricity load, P eload (t) represents the user's electricity load at time T.

[0050] On the basis of determining the energy consumption characteristic index of each user, the embodiment of the present invention utilizes an improved K-means clustering algorithm to cluster the energy consumption characteristic index.

[0051] Conventional K-means clustering algorithms mostly randomly select K values. However, the embodiment of the present invention calculates a clustering effect evaluation index based on the following formula and determines the optimal K value through the clustering effect evaluation index.

[0052] The calculation formula of clustering effect evaluation index is:

[0053]

[0054] Where, I DBI It is a clustering effect evaluation index. The better the clustering effect, the smaller its value. K represents the number of clusters, and D k,h is the centroid of the indicator, indicating the center position of the cluster, and are the average distances between each data point in the kth cluster and the cluster center, and the average distances between each data point in the hth cluster and the cluster center, respectively. k,h is the distance between the cluster center of the kth cluster and the cluster center of the hth cluster.

[0055] Based on the aforementioned clustering effect evaluation index, a K value that minimizes the clustering effect evaluation index can be determined, and clustering can be performed based on the K value to obtain a clustering result. In this embodiment of the present invention, the energy usage data corresponding to the cluster center of each cluster in the clustering result is determined as typical energy usage data for different user types under normal weather scenarios with different seasonal characteristics.

[0056] For example, the embodiment of the present invention extracts energy consumption characteristic indicators from the energy consumption data of 35 users in conventional scenarios and clusters the energy consumption characteristic indicators. The clustering results are shown in Figures 2(a) to 2(c). The 35 users can be clustered into 9 categories using the improved K-means algorithm. The electricity load curves corresponding to the 9 cluster centers can be roughly divided into three types of users according to the user type: stable users, single-peak users, and double-peak users. Among them, the electricity consumption trend of stable users is relatively stable, with no obvious peaks. Single-peak users mostly experience peak electricity consumption between 18:00 and 22:00 in the evening, showing a single-peak trend. Double-peak users mostly experience peak electricity consumption at noon and in the evening, showing a double-peak trend. For stable users, their energy consumption characteristic indicators in winter, summer, and transition seasons (i.e., spring and autumn) are divided into different clusters. For example, the Class I-III curves shown in Figures 2(b) and 2(C) represent winter, Class II represents summer, and Class III represents the transition season (i.e., spring and autumn). Similarly, for unimodal users, their energy consumption characteristics in winter, summer, and transition seasons are divided into different clusters. For example, the Class IV-VI curves shown in Figures 2(b) and 2(C) represent winter, Class V represents summer, and Class VI represents the transition season. For bimodal users, their energy consumption characteristics in winter, summer, and transition seasons are divided into different clusters. For example, the Class VII-IX users shown in Figures 2(b) and 2(C) represent winter, Class VIII represents summer, and Class IX represents the transition season.

[0057] As can be seen from the above examples, the embodiments of the present invention cluster users' energy usage data under normal weather scenarios to obtain typical energy usage data for different user types under normal weather scenarios with different seasonal characteristics. Here, typical energy usage data includes typical electrical load data and typical thermal load data.

[0058] Step 102 : For each user type's typical energy consumption data under a regular weather scenario with each seasonal characteristic, establish an objective function, and solve the objective function to obtain a basic capacity configuration plan for the integrated energy system corresponding to each user type under a regular weather scenario with each seasonal characteristic.

[0059] Based on the typical energy consumption data of each user type determined in step 101 under normal weather scenarios with seasonal characteristics, an objective function is established and solved to obtain the basic capacity configuration plan of the integrated energy system corresponding to the user type under normal weather scenarios with seasonal characteristics.

[0060] The embodiment of the present invention can obtain the basic capacity configuration plan of the integrated energy system corresponding to each user type in the normal weather scenario under the characteristics of each season by solving the objective function corresponding to each user type in the normal weather scenario under the characteristics of each season.

[0061] Here, the basic capacity configuration plan of the integrated energy system may include the basic capacity corresponding to each device in the integrated energy system.

[0062] Step 103 , determining the occurrence probability of different extreme weather conditions in the extreme weather scenario based on Monte Carlo simulation, and determining the fluctuation rate of the output impact of each device in the integrated energy system under different extreme weather conditions in the extreme weather scenario.

[0063] This embodiment of the present invention can identify extreme weather conditions as rainstorms with a precipitation of 50 mm or more, temperatures below -10°C, and temperatures above 40°C. This embodiment of the present invention can use Monte Carlo simulations based on historical weather data to estimate the probability of occurrence of extreme weather conditions such as rainstorms, low temperatures, and high temperatures. The specific formula is as follows:

[0064]

[0065] Where, P s is the probability of occurrence of the sth extreme weather in the T time period, N MC is the number of simulations performed in the Monte Carlo simulation, N mc,s For Nth mc The number of times the sth extreme weather event occurs in the T time period in the simulation;

[0066] In some embodiments, a specific implementation method for determining the fluctuation rate of device output impact under different extreme weather conditions in an extreme weather scenario for each device in the integrated energy system is as follows:

[0067] First, the first output curve of each device in the integrated energy system under normal weather scenarios and the second output curves under different extreme weather conditions in extreme weather scenarios are obtained respectively; secondly, for each extreme weather in the extreme weather scenarios, based on the second output curve corresponding to the extreme weather and the first output curve, the output difference of each device in the integrated energy system under the extreme weather and normal weather scenarios at different times is calculated respectively; then, for each device in the integrated energy system, the average value corresponding to the output difference of the device under the extreme weather and normal weather scenarios at different times is calculated, and the average value is determined as the device output influence volatility of the device under the extreme weather.

[0068] Considering that rainstorm weather mainly affects the output of the PV / T system. Low temperature weather mainly affects the output of the PV / T system and the air heat source pump. High temperature weather mainly affects the output of the PV / T system and the energy storage battery. Accordingly, when calculating the output difference between each device in the integrated energy system under normal weather scenarios and under extreme weather at time t, the embodiment of the present invention mainly calculates the output difference of the PV / T system, the air heat source pump and the energy storage battery. The PV / T system mainly includes: PV / T electrical system and PV / T thermal system. The specific calculation formula is as follows:

[0069]

[0070] in, and D EBS (t) is the output difference of PV / T electric system, PV / T thermal system, air source heat pump and energy storage battery, A is the area of ​​PV / T system, E is the area of ​​PV / T system, ex (t) and E av are the solar irradiance under extreme weather conditions and the average solar irradiance, η h and η e Represent the photothermal efficiency and photoelectric efficiency of the PV / T system respectively, α′ represents the PV / T efficiency factor of uncovered solar cells, η e is the photovoltaic efficiency of the PV / T system, T ac (t) is the actual temperature at time t, T av is the average operating temperature of the air heat source pump, COP re is the power performance coefficient of the air source heat pump under normal circumstances, Pro ashp is the probability of occurrence of air source heat pump in extreme scenarios, C rate is the rated capacity of the energy storage battery, T st is the acceptable standard temperature of the energy storage battery, T c is the temperature correction factor.

[0071] Therefore, for any device easily affected by extreme weather, such as a PV / T system, an air heat source pump, or an energy storage battery, the present invention can calculate the output difference corresponding to each device at different times according to the above formula under each extreme weather condition. Based on this, the present invention can calculate the average of the output differences corresponding to each device at different times and determine this average as the device output fluctuation impact rate under that extreme weather condition.

[0072] For example, the embodiment of the present invention may calculate the average value of the output differences of each device at different times on a typical day in extreme weather, and determine the average value as the device output impact fluctuation rate of the device in extreme weather.

[0073] Step 104 : Determine a corresponding integrated energy system backup capacity configuration plan under extreme weather scenarios based on the occurrence probability and the fluctuation rate of equipment output.

[0074] Here, the backup capacity configuration plan of the integrated energy system includes: the backup capacity of each device in the integrated energy system.

[0075] In some embodiments, the Determine the spare capacity of the i-th equipment in the integrated energy system, and thus determine the spare capacity configuration plan of the integrated energy system.

[0076] Where, is the spare capacity of the i-th equipment in the integrated energy system, S is the number of extreme weather types included in the extreme weather scenario, and P s is the probability of occurrence of the sth extreme weather, is the fluctuation rate of the output of the i-th device under the s-th extreme weather condition, C i The basic capacity set for the i-th device in normal weather scenarios.

[0077] It should be noted that the basic capacity C of the i-th device set in normal weather scenarios i It can include: the basic capacity of the i-th device under normal weather conditions for different user types and different seasonal characteristics. Correspondingly, the spare capacity of the i-th device in the integrated energy system It may include: the backup capacity set for the i-th device under extreme weather scenarios for different user types and different seasonal characteristics.

[0078] Step 105, based on the basic capacity configuration plan of the integrated energy system corresponding to each user type in the conventional weather scenario under different seasonal characteristics, and the backup capacity configuration plan of the integrated energy system, determine the final capacity configuration plan of the integrated energy system corresponding to each user type under different seasonal characteristics.

[0079] Here, the integrated energy system basic capacity configuration scheme may include: the basic capacity of each device in the integrated energy system. In embodiments of the present invention, the backup capacity of each device under extreme weather scenarios for each user type under different seasonal characteristics can be added to the basic capacity of each device under normal weather scenarios for each user type under different seasonal characteristics, thereby obtaining the final capacity of each device for each user under different seasonal characteristics.

[0080] Compared with the prior art, the embodiment of the present invention takes into account the differences in energy consumption characteristics of different user types, and the energy consumption characteristics of the same user type in different seasons are also different. First, by clustering the energy consumption data, typical energy consumption data of different user types in normal weather scenarios with different seasonal characteristics can be extracted. Then, these typical energy consumption data are used as the data basis to solve the objective function, so that the basic capacity configuration plan of the integrated energy system in normal weather scenarios with different seasonal characteristics for different user types can be obtained. On this basis, the embodiment of the present invention further considers the probability of occurrence of extreme weather scenarios and the fluctuation rate of the equipment output influence of each device in extreme weather scenarios. Based on the probability of occurrence of extreme weather scenarios and the fluctuation rate of the equipment output influence, the backup energy configuration plan of the integrated energy system to deal with extreme weather scenarios is determined. Finally, based on the basic capacity configuration plan and the backup capacity configuration plan, the final capacity configuration plan of the integrated energy system is determined, thereby achieving the optimal capacity configuration of the integrated energy system.

[0081] The following is a detailed introduction to the implementation of establishing the objective function and solving the objective function.

[0082] In the embodiment of the present invention, the objective function may include: a first objective function with the minimum economic cost as the goal, a second objective function with the minimum carbon emission as the goal, and a second objective function with the maximum The third objective function takes efficiency as the goal.

[0083] In some embodiments, the Establish the first objective function with the minimum economic cost as the goal;

[0084] Where f1 represents the first objective function, C inv Represents the initial investment cost of the system, C os Represents the cost of purchasing energy, C mt represents the system maintenance cost, I represents the set of all equipment in the integrated energy system, r represents the discount rate, and n represents the equipment life span. Represents the unit capacity investment cost of equipment i, M i represents the total configuration capacity of equipment i, T represents the operating period of the integrated energy system, and E grid,buy (t) represents the system's purchased electricity at time t, C e(t) represents the price of electricity sold by the grid at time t, Represents the annual operation and maintenance cost per unit capacity of equipment i.

[0085] According to f2=minC E =∑ t∈T E grid,buy (t)μ e Establish a second objective function with the goal of minimizing carbon emissions;

[0086] Where f2 represents the second objective function, C E Represents the system carbon emissions, μ e Represents the carbon emission coefficient of grid electricity.

[0087] according to

[0088] Build with maximum The third objective function with efficiency as the goal;

[0089] Wherein, f3 represents the third objective function, η e 'represent efficiency, and Represent the system output at time t and system input P eload (t) and P hload (t) are the electrical load and thermal load input at time t, λ e and λ h Represent the electrical energy quality factor and thermal energy quality factor, P ebs (t) is the input power of the energy storage battery at time t, P hst (t) is the input power of the hot water tank at time t, P ashp (t) is the input power of the air source heat pump at time t, P eb (t) is the input power of the electric boiler at time t, P pvt (t) is the input power of the solar photovoltaic / thermal system at time t, λ rn is the thermal energy quality factor, T0 is the suitable temperature, T g is the temperature before heat release, T h is the temperature after exotherm.

[0090] Here, the equipment in the integrated energy system includes: energy storage batteries, hot water storage tanks, air source heat pumps, electric boilers and solar photovoltaic / thermal systems.

[0091] In the process of solving the above objective function, it is necessary to proceed under the premise of satisfying the constraints. The constraints in the embodiment of the present invention mainly include energy balance constraints, system capacity constraints, PV / T system operation constraints, electric thermal hybrid energy storage system operation constraints and regional restriction constraints.

[0092] The most basic requirement of an integrated energy system is to achieve a balance between energy supply and demand, which is also a condition that must be considered during planning. The energy balance constraints are as follows:

[0093] Electric balance equation: P pvt_e (t)+P ebs_dis (t) = P eload (t)+P ebs_c (t);

[0094] Heat balance equation: P pvt_h (t)+P eb (t)+P ashp (t)+P HST_dis (t) = P hload (t)+P HST_C (t);

[0095] Where, P pvt_e (t) represents the power generation of the PV / T system at time t, P ebs_c (t) and P ebs_dis (t) represent the charging power and discharging power of the energy storage battery at time t, P eload (t) represents the user’s electricity load at time t, P pvt_h (t) represents the heating power of the PV / T system at time t, P eb (t) represents the power of the electric boiler at time t, P ashp (t) represents the power of the air source heat pump at time t, P HST_dis (t) and P HST_C (t) represent the heat release power and heating power of the hot water tank at time t, P hload (t) represents the heat load of the user at time t.

[0096] During the capacity planning of the integrated energy system, the equipment capacity should be limited within a certain range to avoid extreme and abnormal capacity configuration of the equipment in the system. The specific expression of the system capacity constraint is as follows:

[0097] C i,min ≤C i ≤C i,max ;

[0098] Where C i,min and C i,max Represent the minimum capacity limit and maximum capacity limit of the i-th device, C iRepresents the capacity of the i-th device to be planned.

[0099] The electrical and thermal output of the PV / T system must be within a certain range. Excessive or insufficient output will affect the efficiency of the system. The specific expressions for the PV / T system operation constraints are as follows:

[0100]

[0101] Where, P pvt_e,min and P pvt_e,max Respectively represent the lower limit and upper limit of the PV / T system power supply, △P pvt_e,down and △P pvt_e,up Respectively represent the lower limit and upper limit of the PV / T system power supply climbing capability, P pvt_e (t-1) represents the power generation of the PV / T system at time t-1, P pvt_h,min and P pvt_h,max Respectively represent the lower limit and upper limit of PV / T system heating, △P pvt_h,down and △P pvt_h,up Represent the lower and upper limits of the heating ramping capability of the PV / T system, P pvt_h (t-1) represents the heating power of the PV / T system at time t-1.

[0102] In the electric-thermal hybrid energy storage system, the operating constraints of the air source heat pump and electric boiler mainly include energy supply power constraints and ramp constraints. The operating constraints of the hot water tank and energy storage battery mainly include state of charge constraints and charge and discharge power constraints. The specific expressions of the operating constraints of the electric-thermal hybrid energy storage system are as follows:

[0103]

[0104] Where, P ashp,min and P ashp,max Respectively represent the lower and upper limits of the air source heat pump output, △P ashp,down and △P ashp,up Respectively represent the lower limit and upper limit of the climbing ability of the air source heat pump, P ashp (t-1) represents the power of the air source heat pump at time t-1, P eb,min and P eb,max Respectively represent the lower limit and upper limit of the electric boiler output, △P eb,down and △P eb,up Respectively represent the lower limit and upper limit of the electric boiler's climbing ability, P eb (t-1) represents the power of the electric boiler at time t-1, ESOC(t) represents the state of charge of the energy storage battery at time t; ESOC min and ESOC max Represent the minimum and maximum values ​​of the state of charge of the energy storage battery, P ebs(t) represents the charge and discharge power of the energy storage battery at time t, P ebs_c_max and P ebs_dis_max Represent the maximum value of the energy storage battery charging power and the maximum value of the discharge power, η ebs_c_max and η ebs_dis_max Represent the charging efficiency and discharging efficiency of the energy storage battery respectively, HSOC(t) represents the capacity state of the hot water storage tank at time t, and HSOC min and HSOC max Represent the minimum safety margin and maximum safety margin of the remaining capacity of the hot water tank, P hst_c_max and P hst_dis_max Represent the maximum value of thermal energy storage power and thermal energy release power of the hot water storage tank, η hst_c_max and η hst_dis_max They represent the efficiency of storing heat energy and the efficiency of releasing heat energy of the hot water tank respectively.

[0105] The planning of rural integrated energy systems must be within the user's available space, especially the number of PV / T systems that occupy a larger space must be controlled within a certain range.

[0106] The specific expression of the area restriction constraint is:

[0107] Where N PVT Represents the number of PVT, A PVT Represents the total area occupied by all PVTs, A U,PVT Represents the area occupied by unit PVT.

[0108] In an embodiment of the present invention, the objective function is solved under the constraints of the above-mentioned constraints based on the typical energy consumption data of each user type in the normal weather scenario under each seasonal characteristic, thereby obtaining the basic capacity configuration of each device of each user type in the normal weather scenario under each seasonal characteristic, thereby obtaining the basic capacity configuration plan of the integrated energy system for each user type in the normal weather scenario under each seasonal characteristic.

[0109] In some embodiments, see Figure 3 , when solving the objective function, you can follow the steps below:

[0110] Step 301 : Use multiple preset optimization algorithms to solve the objective function respectively, and obtain the solution performance index of each optimization algorithm during the solution process.

[0111] Here, the plurality of preset optimization algorithms may include: genetic algorithm, particle swarm optimization algorithm, Harris hawk algorithm, squirrel search algorithm, sparrow search algorithm and simulated annealing algorithm.

[0112] Solution performance indicators may include: convergence time, solution speed, objective function value, and stability. Convergence time refers to the time required for the algorithm to reach the preset convergence standard. Solution speed refers to the total time consumed by the algorithm from the start of execution to the result. The objective function value can characterize the performance level achieved by the objective function value output by the algorithm. Stability refers to the characteristic that the output result of the algorithm can remain relatively stable without significant fluctuations or deviations in response to changes such as changes in input data and parameter fine-tuning. The embodiment of the present invention can characterize the stability indicator by utilizing the variance of the objective function value.

[0113] Step 302 : assign weights to the respective solution performance indicators, and determine the comprehensive solution performance evaluation value corresponding to each optimization solution algorithm based on the weighted performance indicators.

[0114] The embodiment of the present invention takes into account both subjective and objective factors in the index evaluation process, adopts the hierarchical analysis method and entropy weight method to perform subjective and objective weighting, and determines the index combination weight by weighted average. The specific weighting formula is as follows:

[0115] B g′ =ρw g′ +(1-ρ)v g′

[0116] Where B g′ is the combined weight of the g'th indicator, w g′ is the initial weight obtained by the hierarchical analysis method, ρ is the correction coefficient of the initial weight, and v g′ is the weight corrected by the entropy weight method.

[0117] In the capacity planning process of the integrated energy system for each user type under each seasonal characteristic, the comprehensive solution performance evaluation value of different algorithms in the process of optimizing the objective function can be calculated according to the weight of each indicator, and the algorithm with the highest comprehensive solution performance evaluation value is used as the solution algorithm in the capacity planning process of the integrated energy system for this user type under the normal weather scenario with the seasonal characteristics.

[0118] Step 303: Improve the algorithm with the highest comprehensive solution performance evaluation value, and solve the objective function based on the improved algorithm.

[0119] Genetic algorithm, particle swarm algorithm, Harris hawk algorithm, squirrel search algorithm, sparrow search algorithm and simulated annealing algorithm all achieve optimization solution of multi-objective functions through steps such as initializing population, differential mutation and multi-objective solution, and output the optimal solution set that satisfies multiple objective functions at the same time.

[0120] When improving the algorithm with the highest comprehensive solution performance evaluation value, the embodiment of the present invention can use cubic chaos mapping and elite reverse learning strategy to improve the population initialization step.

[0121] Here, a cubic sequence can be generated using a cubic chaos map, which is then transformed to obtain the initial partial population. Then, elite individuals are selected from this initial partial population based on fitness evaluation, and their reverse solutions are calculated. These reverse solutions, along with the initial partial population, form the complete initialized population.

[0122] Specifically, according to Execute the initialization population step in the algorithm;

[0123] Where y j+1 is the j+1th cubic sequence, y j is the jth cubic sequence, N is the number of individuals in the population, y is the set of cubic sequences, X j+1 is the population individual j+1, lb and ub are the upper and lower bounds of the search space respectively, OP j is the population individual X j The reverse solution, k∈(0,1) random number, X j is the individual j in the population.

[0124] Here, X j+1 Used to characterize the initial part of the population individuals, OP j The reverse solution used to characterize the elite individuals. The above formula can be used to determine the initial partial population individuals generated by the cubic sequence, as well as the reverse solution of the elite individuals in the initial partial population individuals, thereby determining the initial population.

[0125] The population initialization step is used to generate multiple individuals, each representing a set of solutions to the objective function. Chaotic operators offer the advantages of randomness and regularity, allowing them to traverse all states within a certain range without repetition. The elite reverse learning strategy uses a lens imaging reverse learning method to calculate the reverse solution to lens imaging, expanding the range of possible solutions and increasing the probability of selecting a superior solution. Combining these two strategies expands the global exploration range while avoiding algorithmic uncertainty caused by the randomness of the initial population, improving the algorithm's ability to avoid local extremes and its convergence speed.

[0126] To avoid falling into a local optimum mid-way through the algorithm, the embodiment of the present invention can also improve the differential mutation step when improving the algorithm with the highest comprehensive solution performance evaluation value. The differential mutation step is used to generate new population individuals based on population individuals in the current population.

[0127] according to Execute the differential mutation step in the algorithm;

[0128] Where, For the new population individuals generated, is the global optimal individual; and are two randomly selected individuals from the population, λ sf is the scaling factor.

[0129] The embodiment of the present invention randomly selects two population individuals to calculate the difference, and mutates them with the global optimal individual to generate a new individual, thereby retaining the population individuals with better fitness to prevent the algorithm from falling into a local optimum.

[0130] The multi-objective solution step in the genetic algorithm, particle swarm optimization algorithm, Harris Hawk algorithm, squirrel search algorithm, sparrow search algorithm, and simulated annealing algorithm is used to determine a set of Pareto optimal solutions based on multiple objective functions. Embodiments of the present invention can also improve this objective solution step using the VIKOR method based on adaptive gradient improvement.

[0131] Specifically, according to Execute the multi-objective solution step in the algorithm;

[0132] Where w m is the weight of the mth objective function, N represents the number of individuals in the population, is the positive ideal solution of the mth objective function, is the negative ideal solution of the mth objective function, f′ jm Represents the element value of the jth row and mth column in the normalized matrix of the decision matrix, M represents the number of objective functions, r′1 is the cumulative square of the gradient value, r1 is the gradient value, its initial value is 0, g is the gradient value calculated by a single weight, w′ m is the relative weight of the optimized objective function, α is the minimum value set to avoid the denominator being 0, μ is the global learning rate, S j is the group effect of the jth individual in the population, R j is the individual regret of the jth individual in the population, f jm is the element value of the jth row and mth column in the decision matrix, Q j represents the compromise index of the jth individual in the population, v represents the decision-making mechanism coefficient, S min and S max represent the minimum and maximum values ​​of the group effect, R min and R max They represent the minimum and maximum values ​​of individual regret respectively. In this embodiment, v is set to 0.5, indicating that the group effect is equally important as the individual regret.

[0133] This embodiment of the present invention uses an adaptive gradient-modified VIKOR method to determine the weight of the objective function. Based on this, the objective function weight is optimized to determine the optimized relative weight of the objective function. Finally, based on the optimized relative weight of the objective function, the individual with the lowest trade-off index is selected from the Pareto solution set (i.e., all individuals in the population), i.e., the optimal equilibrium solution.

[0134] The adaptive gradient improvement algorithm dynamically adjusts the learning rate based on gradient information from different parameters or time points, improving the efficiency and stability of the optimization process. VIKOR is a multi-criteria decision analysis method that integrates multiple evaluation criteria to find a compromise solution that is as close to the ideal as possible. The combination of the two allows for dynamic adjustment of the weights of the evaluation criteria, comprehensively considering the interactions and conflicts between the various evaluation criteria, and ultimately finding a more reasonable compromise solution.

[0135] 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 invention.

[0136] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0137] Figure 4 The following is a schematic diagram of the structure of a multi-scenario integrated energy system planning device provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0138] like Figure 4 As shown, the multi-scenario based integrated energy system planning device 4 includes: a classification module 41, a basic configuration module 42, a backup configuration module 43 and a comprehensive configuration module 44.

[0139] The classification module 41 is used to collect energy consumption data of each user under normal weather conditions, and cluster the energy consumption data to obtain typical energy consumption data of different user types under normal weather conditions with different seasonal characteristics;

[0140] The basic configuration module 42 is used to establish an objective function based on the typical energy consumption data of each user type under normal weather scenarios with seasonal characteristics, and solve the objective function to obtain the basic capacity configuration plan of the integrated energy system corresponding to each user type under normal weather scenarios with seasonal characteristics;

[0141] The standby configuration module 43 is used to:

[0142] Determine the probability of occurrence of different extreme weather conditions in extreme weather scenarios based on Monte Carlo simulation, and determine the fluctuation rate of the output of each device in the integrated energy system under different extreme weather conditions in extreme weather scenarios;

[0143] Determine the corresponding integrated energy system backup capacity configuration plan for extreme weather scenarios based on the probability of occurrence and the impact of equipment output volatility;

[0144] The comprehensive configuration module 44 is used to determine the final capacity configuration plan of the integrated energy system corresponding to each user type under different seasonal characteristics based on the basic capacity configuration plan of the integrated energy system corresponding to the conventional weather scenarios under different seasonal characteristics of each user type, and the backup capacity configuration plan of the integrated energy system.

[0145] Optionally, the integrated energy system spare capacity configuration plan includes: spare capacity of each device in the integrated energy system;

[0146] The standby configuration module 43 is specifically configured to:

[0147] according to Determine the spare capacity of the i-th equipment in the integrated energy system;

[0148] Where, is the spare capacity of the i-th equipment in the integrated energy system, S is the number of extreme weather types included in the extreme weather scenario, and P s is the probability of occurrence of the sth extreme weather, is the fluctuation rate of the output of the i-th device under the s-th extreme weather condition, c i The basic capacity set for the i-th device in normal weather scenarios.

[0149] Optionally, the standby configuration module 43 is specifically configured to:

[0150] Obtaining the first output curve of each device in the integrated energy system under normal weather scenarios, and the second output curve under different extreme weather conditions in extreme weather scenarios;

[0151] For each extreme weather scenario, based on the second output curve corresponding to the extreme weather and the first output curve, calculate the output difference of each device in the integrated energy system under the extreme weather and normal weather scenarios at different times;

[0152] For each device in the integrated energy system, the average value corresponding to the output difference between the device in the extreme weather scenario and the normal weather scenario at different times is calculated, and the average value is determined as the device output impact fluctuation rate of the device in the extreme weather scenario.

[0153] Optionally, the objective function includes a first objective function with minimum economic cost as the goal;

[0154] The basic configuration module 42 is specifically used to:

[0155] according to Establish the first objective function with the minimum economic cost as the goal;

[0156] Where f1 represents the first objective function, C inv Represents the initial investment cost of the system, C os Represents the cost of purchasing energy, C mt represents the system maintenance cost, I represents the set of all equipment in the integrated energy system, r represents the discount rate, and n represents the equipment life span. Represents the unit capacity investment cost of equipment i, M i represents the total configuration capacity of equipment i, T represents the operating period of the integrated energy system, and E grid,buy (t) represents the system's purchased electricity at time t, C e (t) represents the price of electricity sold by the grid at time t, Represents the annual operation and maintenance cost per unit capacity of equipment i.

[0157] Optionally, the objective function further includes: a second objective function with minimum carbon emissions as the goal;

[0158] The basic configuration module 42 is further used to:

[0159] According to f2=minC E =∑ t∈T E grid,buy (t)μ e Establish a second objective function with the goal of minimizing carbon emissions;

[0160] Where f2 represents the second objective function, C E Represents the system carbon emissions, μ e Represents the carbon emission coefficient of grid electricity.

[0161] Optionally, the equipment in the integrated energy system includes: energy storage batteries, hot water storage tanks, air source heat pumps, electric boilers and solar photovoltaic / thermal systems; the objective function also includes: maximizing The third objective function with efficiency as the goal;

[0162] The basic configuration module 42 is further used to:

[0163] according to

[0164] Build with maximum The third objective function with efficiency as the goal;

[0165] Wherein, f3 represents the third objective function, η e 'represent efficiency, and Represent the system output at time t and system input P eload (t) and P hload (t) are the electrical load and thermal load input at time t, λ e and λ h Represent the electrical energy quality factor and thermal energy quality factor, P ebs (t) is the input power of the energy storage battery at time t, P hst (t) is the input power of the hot water tank at time t, P ashp (t) is the input power of the air source heat pump at time t, P eb (t) is the input power of the electric boiler at time t, P pvt (t) is the input power of the solar photovoltaic / thermal system at time t, λ rn is the thermal energy quality factor, T0 is the suitable temperature, T g is the temperature before heat release, T h is the temperature after exotherm.

[0166] Optionally, the basic configuration module 42 is specifically configured to:

[0167] Use multiple preset optimization algorithms to solve the objective function respectively, and obtain the solution performance index of each optimization algorithm in the solution process;

[0168] Assign weights to each solution performance indicator respectively, and determine the comprehensive solution performance evaluation value corresponding to each optimization solution algorithm based on the weighted performance indicators;

[0169] The algorithm with the highest comprehensive solution performance evaluation value is improved, and the objective function is solved based on the improved algorithm.

[0170] Optionally, the plurality of preset optimization solution algorithms all include: a population initialization step; the population initialization step is used to initialize and generate a plurality of population individuals, each population individual representing a set of solutions corresponding to the objective function;

[0171] The basic configuration module 42 is specifically used to:

[0172] according to Execute the initialization population step in the algorithm;

[0173] Where y j+1 is the j+1th cubic sequence, y jis the jth cubic sequence, N is the number of individuals in the population, y is the set of cubic sequences, X j+1 is the population individual j+1, lb and ub are the upper and lower bounds of the search space respectively, OP j is the population individual X j The reverse solution, k∈(0,1) random number, X j is the individual j in the population.

[0174] Optionally, the plurality of preset optimization solving algorithms all include: a differential mutation step; the differential mutation step is used to generate new population individuals based on population individuals in the current population;

[0175] The basic configuration module 42 is further used to:

[0176] according to Execute the differential mutation step in the algorithm;

[0177] Where, For the new population individuals generated, is the global optimal individual; and are two randomly selected individuals from the population, λ sf is the scaling factor.

[0178] Optionally, there are multiple objective functions, and the multiple preset optimization solution algorithms all include: a multi-objective solution step; the multi-objective solution step is used to determine a set of Pareto optimal solutions based on the multiple objective functions;

[0179] The basic configuration module 42 is further used to:

[0180] according to Execute the multi-objective solution step in the algorithm;

[0181] Where w m is the weight of the mth objective function, N represents the number of individuals in the population, is the positive ideal solution of the mth objective function, is the negative ideal solution of the mth objective function, f′ jm Represents the element value of the jth row and mth column in the normalized matrix of the decision matrix, M represents the number of objective functions, r′1 is the cumulative square of the gradient value, r1 is the gradient value, its initial value is 0, g is the gradient value calculated by a single weight, w′ m is the relative weight of the optimized objective function, α is the minimum value set to avoid the denominator being 0, μ is the global learning rate, S j is the group effect of the jth individual in the population, R j is the individual regret of the jth individual in the population, f jm is the element value of the jth row and mth column in the decision matrix, Q jrepresents the compromise index of the jth individual in the population, v represents the decision-making mechanism coefficient, S min and S max represent the minimum and maximum values ​​of the group effect, R min and R max They represent the minimum and maximum values ​​of individual regret respectively.

[0182] This device embodiment can be used to implement the above method embodiment. Its technical principles and implementation effects are the same as those of the above method embodiment, and will not be repeated here.

[0183] Figure 5 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50. When the processor 50 executes the computer program 52, the steps in the above-mentioned embodiments of the multi-scenario integrated energy system planning method are implemented, for example Figure 1 Alternatively, when the processor 50 executes the computer program 52, the functions of the modules in the above-mentioned device embodiments are realized, for example, Figure 4 The functions of modules 41 to 44 are shown.

[0184] Exemplarily, the computer program 52 may be divided into one or more modules, which are stored in the memory 51 and executed by the processor 50 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program 52 in the electronic device 5. For example, the computer program 52 may be divided into Figure 4 Modules 41 to 44 are shown.

[0185] The electronic device 5 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art will understand that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0186] The processor 50 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. The general-purpose processor may be a microprocessor or any conventional processor.

[0187] The memory 51 can be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 51 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Furthermore, the memory 51 can also include both an internal storage unit of the electronic device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the electronic device. The memory 51 can also be used to temporarily store data that has been output or is about to be output.

[0188] In actual applications, the above functions can be distributed and 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 into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units 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 system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0189] Those skilled 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. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0190] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit them. Those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These 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 invention, and should all be included in the scope of protection of the present invention.

Claims

1. A multi-scenario integrated energy system planning method, characterized in that: include: Collecting energy consumption data of each user under normal weather conditions, clustering the energy consumption data, and obtaining typical energy consumption data of different user types under normal weather conditions with different seasonal characteristics; Based on the typical energy consumption data of each user type under normal weather scenarios with seasonal characteristics, an objective function is established and solved to obtain the basic capacity configuration plan of the integrated energy system corresponding to each user type under normal weather scenarios with seasonal characteristics; Determine the probability of occurrence of different extreme weather conditions in extreme weather scenarios based on Monte Carlo simulation, and determine the fluctuation rate of the output of each device in the integrated energy system under different extreme weather conditions in extreme weather scenarios; Determine a corresponding integrated energy system backup capacity configuration plan under extreme weather scenarios based on the occurrence probability and the fluctuation rate of the equipment output impact; Based on the basic capacity configuration plan of the integrated energy system corresponding to each user type in conventional weather scenarios under different seasonal characteristics, and the backup capacity configuration plan of the integrated energy system, the final capacity configuration plan of the integrated energy system corresponding to each user type under different seasonal characteristics is determined.

2. The multi-scenario integrated energy system planning method according to claim 1, characterized in that: The integrated energy system spare capacity configuration scheme includes: spare capacity of each device in the integrated energy system; The determining of a corresponding integrated energy system backup capacity configuration plan under an extreme weather scenario based on the occurrence probability and the fluctuation rate of the equipment output includes: according to Determining the spare capacity of the i-th equipment in the integrated energy system; Where, is the spare capacity of the i-th equipment in the integrated energy system, S is the number of extreme weather types included in the extreme weather scenario, and P s is the probability of occurrence of the sth extreme weather, is the fluctuation rate of the output of the i-th device under the s-th extreme weather condition, C i The basic capacity set for the i-th device in normal weather scenarios.

3. The multi-scenario integrated energy system planning method according to claim 1 or 2, characterized in that: The determination of the fluctuation rate of the output of each device in the integrated energy system under different extreme weather conditions in the extreme weather scenario includes: Obtaining respectively a first output curve of each device in the integrated energy system under a normal weather scenario and a second output curve under different extreme weather conditions in an extreme weather scenario; For each extreme weather scenario, based on the second output curve corresponding to the extreme weather and the first output curve, calculate the output difference of each device in the integrated energy system under the extreme weather scenario and the normal weather scenario at different times; For each device in the integrated energy system, the average value corresponding to the output difference between the device in the extreme weather and normal weather scenarios at different times is calculated, and the average value is determined as the device output impact fluctuation rate of the device in the extreme weather.

4. The multi-scenario integrated energy system planning method according to claim 1 or 2, characterized in that: The objective function includes a first objective function with minimum economic cost as the goal; The establishing of the objective function comprises: according to Establish the first objective function with the minimum economic cost as the goal; Where f1 represents the first objective function, C inv Represents the initial investment cost of the system, C os Represents the cost of purchasing energy, C mt represents the system maintenance cost, I represents the set of all equipment in the integrated energy system, r represents the discount rate, and n represents the equipment life span. Represents the unit capacity investment cost of equipment i, M i represents the total configuration capacity of equipment i, T represents the operating period of the integrated energy system, and E grid,buy (t) represents the system's purchased electricity at time t, C e (t) represents the price of electricity sold by the grid at time t, Represents the annual operation and maintenance cost per unit capacity of equipment i.

5. The multi-scenario integrated energy system planning method according to claim 4 is characterized in that: The objective function also includes: a second objective function with minimum carbon emissions as the goal; The establishing of the objective function further includes: According to f2=minC E =∑ t∈T E grid,buy (t)μ e Establish a second objective function with the goal of minimizing carbon emissions; Where f2 represents the second objective function, C e Represents the system carbon emissions, μ e Represents the carbon emission coefficient of grid electricity.

6. The multi-scenario integrated energy system planning method according to claim 4 or 5, characterized in that: The equipment in the integrated energy system includes: energy storage batteries, hot water storage tanks, air source heat pumps, electric boilers and solar photovoltaic / thermal systems; the objective function also includes: maximizing The third objective function with efficiency as the goal; The establishing of the objective function further includes: according to Build with maximum The third objective function with efficiency as the goal; Wherein, f3 represents the third objective function, η e 'represent efficiency, and Represent the system output at time t and system input P eload (t) and P hload (t) are the electrical load and thermal load input at time t, λ e and λ h Represent the electrical energy quality factor and thermal energy quality factor, P ebs (t) is the input power of the energy storage battery at time t, P hst (t) is the input power of the hot water tank at time t, P ashp (t) is the input power of the air source heat pump at time t, P eb (t) is the input power of the electric boiler at time t, P pvt (t) is the input power of the solar photovoltaic / thermal system at time t, λ rn is the thermal energy quality factor, T0 is the suitable temperature, T g is the temperature before heat release, T h is the temperature after exotherm.

7. The multi-scenario integrated energy system planning method according to claim 1 or 2, characterized in that: The solving of the objective function comprises: Using multiple preset optimization algorithms to solve the objective function respectively, and obtaining the solution performance index of each optimization algorithm during the solution process; Assign weights to each solution performance indicator respectively, and determine the comprehensive solution performance evaluation value corresponding to each optimization solution algorithm based on the weighted performance indicators; The algorithm with the highest comprehensive solution performance evaluation value is improved, and the objective function is solved based on the improved algorithm.

8. The multi-scenario integrated energy system planning method according to claim 7, characterized in that: The plurality of preset optimization solution algorithms all include: a population initialization step; the population initialization step is used to initialize and generate a plurality of population individuals, each population individual representing a set of solutions corresponding to the objective function; The improvement of the algorithm includes: according to Execute the population initialization step in the algorithm; Where y j+1 is the j+1th cubic sequence, y j is the jth cubic sequence, y is the set of cubic sequences, N is the number of individuals in the population, X j+1 is the population individual j+1, lb and ub are the upper and lower bounds of the search space respectively, OP j is the population individual X j The reverse solution, k∈(0,1) random number, X j is the individual j in the population.

9. The multi-scenario integrated energy system planning method according to claim 7 or 8, characterized in that: The plurality of preset optimization solution algorithms all include: a differential mutation step; the differential mutation step is used to generate new population individuals based on population individuals in the current population; The improvement of the algorithm further includes: according to Execute the differential mutation step in the algorithm; Where, For the new population individuals generated, is the global optimal individual; and are two randomly selected individuals from the population, λ sf is the scaling factor.

10. The multi-scenario integrated energy system planning method according to claim 7 or 8, characterized in that: There are multiple objective functions, and the multiple preset optimization solution algorithms all include: a multi-objective solution step; the multi-objective solution step is used to determine a set of Pareto optimal solutions based on the multiple objective functions; The improvement of the algorithm further includes: according to Execute the multi-objective solution step in the algorithm; Where w m is the weight of the mth objective function, N represents the number of individuals in the population, is the positive ideal solution of the mth objective function, is the negative ideal solution of the mth objective function, f′ jm Represents the element value of the jth row and mth column in the normalized matrix of the decision matrix, M represents the number of objective functions, r1′ is the cumulative square of the gradient value, r1 is the gradient value, its initial value is 0, g is the gradient value calculated by a single weight, w′ m is the relative weight of the optimized objective function, α is the minimum value set to avoid the denominator being 0, μ is the global learning rate, S j is the group effect of the jth individual in the population, R j is the individual regret of the jth individual in the population, f jm is the element value of the jth row and mth column in the decision matrix, Q j represents the compromise index of the jth individual in the population, v represents the decision-making mechanism coefficient, S min and S max represent the minimum and maximum values ​​of the group effect, R min and R max They represent the minimum and maximum values ​​of individual regret respectively.

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