Construction method for constructing time sequence dynamic staging regional integrated energy system

Abstract

The invention relates to the field of regional integrated energy system planning, in particular to a construction method for constructing a time sequence dynamic staging regional integrated energy system. According to the method, the staging model based on time sequence feature point extraction is adopted, the feature point with the maximum sum of the distances is recognized by comparing the sum of the orthogonal distances of the two-point connecting lines from each group of feature points to the end points in the sequence set, and compared with a conventional method in which extreme points and inflection points are selected as the feature points, key changes of a load curve can be captured more accurately, and the accuracy of the load curve is improved. According to the method, more reliable data support is provided, resource waste caused by advanced construction is avoided through construction time sequence dynamic staging evolution planning of the RIES long-term load dynamic growth characteristics, load demand changes increasing year by year are met, and therefore the whole life cycle cost is saved; and the proposed dynamic staging evolution planning can dynamically adjust the staging strategy and the equipment configuration scheme according to the future load change condition, so that the double improvement of the system performance and the economy is realized.

Description

Technical Field

[0001] The present invention relates to the field of regional integrated energy system planning, and in particular to a construction method for constructing a regional integrated energy system with dynamic time series phases. Background Art

[0002] As the environmental problems caused by energy consumption become increasingly prominent, the existing energy utilization and production methods can no longer meet the demand. As a multi-energy coupling and conversion energy supply system, Regional Integrated Energy Systems (RIES) can maximize the use of various renewable and non-renewable energy sources, thereby increasing the reliability and stability of energy supply. Since the number of building users served by RIES is large and its construction cycle is usually as long as 10 years or even longer, the long-term growth characteristics of users must be considered throughout the life cycle during the planning stage. On the one hand, it is necessary to ensure that the system can continue to meet the growing energy needs of users. On the other hand, it is necessary to dynamically adjust the energy supply plan according to the changes in energy demand caused by various uncertain factors. At the same time, it is necessary to take into account the efficient and economical operation of the system from the beginning of construction to the production.

[0003] At present, in RIES planning, static methods are often used to first divide the construction sequence into phases, and then optimize the system configuration. Although this method has certain practicality, it is highly subjective and difficult to adapt to changes caused by future uncertainties.

[0004] Therefore, it is urgent to provide a method for constructing a regional integrated energy system with dynamic phased construction sequence to solve the technical problem of uncertainty in static construction sequence. Summary of the invention

[0005] The present invention provides a construction method for constructing a regional integrated energy system with dynamic time-series phase division to solve the technical problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides a construction method for constructing a regional integrated energy system with dynamic time sequence phase division, comprising:

[0007] Step S1, analyzing the long-term load growth characteristics of the planning area according to the building construction plan of the planning area;

[0008] Step S2, extracting key characteristic points in the long-term load growth characteristics of the planning area, and using them as standard samples for RIES construction phases;

[0009] Step S3, build a phased evolution planning model based on the standard sample of RIES construction phases; according to the phased evolution planning model, use the Gurobi optimization solver in Python language to calculate the optimal solution of the model, obtain the best equipment combination, capacity configuration plan and system operation strategy for each phase; when the load demand changes due to regional uncertain conditions, re-extract key feature points for construction phases, build a phased evolution model and obtain the optimal solution, adjust the phase strategy and the new equipment configuration and system operation strategy.

[0010] Furthermore, the method of analyzing the long-term load growth characteristics of the planning area according to the building construction plan of the planning area in step S1 includes:

[0011] The long-term load growth characteristics of the planning area show both a periodic fluctuation pattern and a pattern of increasing year by year.

[0012] Furthermore, the method of extracting key characteristic points in the long-term load growth characteristics of the planning area in step S2 and using them as standard samples for RIES construction phases includes:

[0013] Step S21, determine the regional load growth change, that is,

[0014]

[0015] Q i =Q Heat (m)+Q Cool (m)

[0016] Where n represents different building types; A n (m) represents the planned area of ​​the nth type of building in the mth year, m 2 ;q Cool,n,t ,q Heat,n,t Respectively represent the cold and heat load index of the nth type of building at the tth moment, W / m 2 ;Q Heat (i) Q Cool (m) represents the cooling and heating loads of the regional building complex in the mth year, respectively, with the unit of W, and Qm represents the total load of the regional building complex in the mth year.

[0017] Step S22, establish a sequence set of load and time, that is

[0018]

[0019] Where: x1 = (T1, Q1) represents the load value Q1 corresponding to the time series in year T1, x m =(T m ,Q m ) indicates that the time series is in T mThe corresponding load value Q m ;

[0020] Step S23, determine the key feature points according to the sequence set, that is,

[0021]

[0022]

[0023] Where: d m Indicates that the mth point in the planned time period is related to the time series endpoints (T1, Q1), (T P ,Q P ), Q1 represents the load value corresponding to the first year, Q p It represents the load value corresponding to the endpoint P of the time series; ε is the sum of the distances from each point in the period to the endpoint of the time series. When ε is the maximum value, the corresponding point m is the key characteristic point of the operation period, thereby dividing the time period into different stages, which serves as a standard sample for the phased construction of RIES.

[0024] Furthermore, the method of constructing a phased evolution planning model according to the standard sample of RIES construction phases in step S3 includes:

[0025] According to the key characteristic points, standard samples are divided and the regional building load within each standard sample period is calculated, that is,

[0026]

[0027] In the formula, Q k Represents the total building load in the kth standard sample area, in W; COP k-1,σ represents the coefficient of performance (COP) of the σth type of equipment in the k-1th standard sample; P k-1,σ It represents the input power of the σth type of equipment in the k-1th standard sample, in W.

[0028] The model is constructed with the minimum cost of various energy system projects in the kth standard sample as the target, that is,

[0029]

[0030] In the formula, It represents the minimum value of the sum of construction cost data, operation cost data, maintenance cost data and carbon emission cost data in the kth standard sample of the energy system; represents the construction cost data within the kth standard sample of the system; Represents the operating cost data within the kth standard sample of the system; Represents the carbon emission cost data within the kth standard sample of the system; Represents the maintenance cost data within the kth standard sample of the system.

[0031] Furthermore, the calculation method of the system construction cost data is:

[0032] In the formula

[0033] It represents the unit capacity construction investment of the σth type of equipment in the kth standard sample, in yuan

[0034] / MW; represents the capacity configuration of the σth type of equipment in the standard sample of the kth planning stage, in MW; γ represents the discount rate, and m represents the mth year in the standard sample.

[0035] Furthermore, the system's operating cost data The calculation method is:

[0036]

[0037] Where: m, s, t represent the mth year, the sth energy supply scenario, and the tth energy supply period respectively; Q s Indicates the proportion of the selected typical energy supply day in the total life cycle energy supply day; represents the electricity price of the power grid at time t;

[0038] It represents the electric power consumed by all devices except energy storage devices in the kth standard sample at its i-th capacity in the tth period of the sth typical energy supply day; represents the natural gas price at time t; It represents the natural gas power consumed by all equipment in the kth standard sample of the system at its i-th capacity on the sth typical energy supply day; represents the electricity price of energy storage equipment at time t; It represents the electric power consumed by the energy storage device of the kth standard sample in the tth period of the sth typical energy supply day when storing energy at its i-th capacity; It represents the sum of the power generated by the gas internal combustion engine and the flue gas hot water lithium bromide heat pump at the i-th capacity in the t-th period of the k-th standard sample on the s-th typical energy supply day. Δt represents the time interval. ES refers to the energy storage device, grid refers to the power grid, Vele refers to valley electricity, ICE refers to the gas internal combustion engine, and AHP refers to the flue gas hot water lithium bromide heat pump.

[0039] Furthermore, the system's carbon emission cost data The calculation method is:

[0040]

[0041] Where: grid Indicates the unit emission coefficient of carbon dioxide produced by consuming electricity, in kg / kW; ω gas It represents the unit emission coefficient of carbon dioxide produced by consuming natural gas, and its unit is kg / kW; θ represents the emission cost of carbon dioxide, and its unit is yuan / kg.

[0042] Furthermore, the system maintenance cost data The calculation method is:

[0043]

[0044] In the formula, c σ It represents the unit power maintenance cost of the σth type of equipment, in yuan / kW·year; It is the actual class C input power of the i-th capacity of the σ-th type equipment at time t on the k-th standard sample and the s-th typical energy supply day, in kW.

[0045] Furthermore, the method for constructing a regional integrated energy system with dynamic phased construction also includes: adding constraints to the phased evolution planning model, namely

[0046] Add construction logic constraints:

[0047] In the formula, is the actual class C input power of the i-th capacity of the σ-th type of equipment at time t on the k-th standard sample and the s-th typical energy supply day, in kW; is the maximum input power of the ith capacity of the σth type of equipment, in kW. And,

[0048] Add energy storage equipment operation constraints:

[0049]

[0050] Where: It represents the electric power consumed by the energy storage device when storing energy in the kth standard sample and the sth typical energy supply day. ES (Energy Storage) here refers to the energy storage device, and Ele (electrical) refers to the power consumption. The energy storage device supplies energy in the time period except 0:00-8:00, and D represents the sequence number of the selected typical energy supply day.

[0051] Add heat load supply and demand balance constraint:

[0052]

[0053] In the formula, Q t,Heatrepresents the heat load demand at the end of time t; C σ,Heat Indicates the efficiency with which the equipment uses its heat load; and

[0054] Add cooling load supply and demand balance constraint:

[0055]

[0056] In the formula, Q t,Cool represents the cooling load demand at the end of time t; C m,Cool Indicates the efficiency of equipment cooling load utilization.

[0057] Furthermore, the method for adding construction logic constraints also includes:

[0058] For any type of equipment only one capacity is selected, namely:

[0059]

[0060] Where Y σ,i It is a 0-1 variable. When it is 0, it means that the i-th capacity of the σ-th type of equipment is not selected. When it is 1, it means that the i-th capacity of the σ-th type of equipment is selected.

[0061] Furthermore, the method for obtaining the optimal equipment combination, capacity configuration scheme and system operation strategy for each phase according to the phased evolution planning model in step S4 includes:

[0062] The Gurobi optimization solver in Python is used to obtain the optimal equipment configuration and operation strategy for each planning period.

[0063] Furthermore, the method of adjusting the phase strategy and the new equipment configuration and system operation strategy according to the load demand change caused by the regional uncertain conditions in step S3 includes:

[0064] Obtain the current development trend of the region; and

[0065] If the current development trend is better than expected, the load growth rate will be accelerated;

[0066] If the current development trend is worse than expected, the load growth rate will be slowed down.

[0067] The beneficial effect of the present invention is that the construction method of the regional integrated energy system for construction of dynamic phased time series adopts a phased model based on the extraction of time series feature points, and identifies the feature points with the largest sum of distances by comparing the sum of the orthogonal distances from each group of feature points in the sequence set to the lines connecting the two points between the endpoints. Compared with the conventional method of selecting extreme points and inflection points as feature points, it can more accurately capture the key changes of the load curve and provide more reliable data support. Through the construction of the dynamic phased evolution planning of the long-term load dynamic growth characteristics of RIES, it not only avoids the waste of resources caused by advanced construction, but also meets the changes in load demand that increase year by year, thereby saving the full life cycle cost. The proposed dynamic phased evolution planning can dynamically adjust the phased strategy and equipment configuration plan according to future load changes, so as to achieve a dual improvement in system performance and economy.

[0068] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 A schematic flow chart of a construction method for constructing a regional integrated energy system with dynamic time-series phase division according to some embodiments is shown;

[0072] Figure 2 A schematic diagram showing the structure of an electronic device / computer device / computer equipment / computer system involved in some embodiments is shown; Figure 3 A regional integrated energy system model involved in some embodiments is shown. Figure 4 The costs of three phased evolution planning schemes involved in some embodiments are shown. Figure 5 The cost of the new and old plans under dynamic phased planning based on time series feature point extraction involved in some embodiments is shown. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Glossary:

[0075] ES (Energy Storage) here refers to energy storage equipment;

[0076] grid refers to the power grid;

[0077] Vele (valley electrical) refers to valley electricity;

[0078] ICE (Gas Internal Combustion Engine) refers to gas internal combustion engine;

[0079] AHP (Absorption Heat Pump) refers to a flue gas hot water type lithium bromide heat pump.

[0080] To facilitate understanding of this embodiment, a model building method disclosed in the embodiment of the present disclosure is first introduced in detail. The execution subject of the XXXXXX method provided in the embodiment of the present disclosure is generally a computer device with certain computing capabilities, and the computer device includes, for example: a terminal device or a server or other processing device, and the terminal device can be a user equipment (User Equipment, UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementations, the model building method can be implemented by a processor calling a computer-readable instruction stored in a memory.

[0081] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0082] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0083] like Figure 1As shown, at least one embodiment provides a construction method for a regional integrated energy system with dynamic phased construction, including: step S1, analyzing the long-term load growth characteristics of the planning area according to the building construction plan of the planning area; step S2, extracting key feature points in the long-term load growth characteristics of the planning area, and using them as standard samples for RIES construction phases; step S3, constructing a phased evolution planning model according to the standard samples of RIES construction phases; according to the phased evolution planning model, using the Gurobi optimization solver in Python language to calculate the optimal solution of the model, and obtain the best equipment combination, capacity configuration plan and system operation strategy for each phase; when the load demand changes due to regional uncertain conditions, re-extracting key feature points for construction phases, constructing a phased evolution model and obtaining the optimal solution, adjusting the phase strategy and the new equipment configuration and system operation strategy.

[0084] In some embodiments, a phased model based on time series feature point extraction is adopted. By comparing the sum of the orthogonal distances from each group of feature points in the sequence set to the lines connecting the two points of the endpoints, the feature points with the largest sum of distances are identified. Compared with the conventional method of selecting extreme points and inflection points as feature points, this model can more accurately capture the key changes of the load curve and provide more reliable data support. By dynamically planning the construction sequence of the long-term dynamic growth characteristics of RIES load by phased evolution, it not only avoids the waste of resources caused by advanced construction, but also meets the increasing changes in load demand year by year, thereby saving the full life cycle cost. The proposed dynamic phased evolution plan can dynamically adjust the phased strategy and equipment configuration plan according to future load changes, so as to achieve a dual improvement in system performance and economy.

[0085] In some embodiments, the method of analyzing the long-term load growth characteristics of the planning area according to the building construction plan of the planning area in step S1 includes: the long-term load growth characteristics of the planning area show both periodic fluctuation patterns and year-on-year growth patterns.

[0086] In some embodiments, the method of extracting key feature points in the long-term load growth characteristics of the planning area in step S2 and using them as standard samples for RIES construction phases includes:

[0087] Step S21, determine the regional load growth change, that is,

[0088]

[0089] Q i =Q Heat (m)+Q Cool (m)

[0090] Where n represents different building types; A n(m) represents the planned area of ​​the nth type of building in the mth year,

[0091] Unit: m 2 ;

[0092] q Cool,n,t ,q Heat,n,t They represent the cold and heat load indexes of the nth type of building at the tth moment, in W / m 2 ;

[0093] Q Heat (m), Q Cool (m) represents the cooling and heating loads of the regional building complex in the mth year, respectively, in W; Qm represents the load of the regional building complex in the mth year.

[0094] Step S22, establish a sequence set of load and time, that is

[0095]

[0096] Where: x1 = (T1, Q1) represents the load value Q1 corresponding to the time series in year T1, x m =(T m ,Q m ) indicates that the time series is in T m The corresponding load value Q m ;

[0097] Step S23, determining key feature points based on the sequence set, and using them as a standard sample for RIES construction phases, that is,

[0098]

[0099] Where: d m Indicates that the mth point in the planned time period is related to the time series endpoints (T1, Q1), (T P ,Q P ), Q1 represents the load value corresponding to the first year, Q p It represents the load value corresponding to the endpoint P of the time series; ε is the sum of the distances from each point in the period to the endpoint of the time series. When ε is the maximum value, the corresponding point m is the key characteristic point of the operation period, thereby dividing the time period into different stages, which serves as a standard sample for the phased construction of RIES.

[0100] In some embodiments, at each planning stage, the cost incurred within the planning period is converted by the present value coefficient with the goal of minimizing the construction cost data, operation cost data, maintenance cost data and carbon emission cost data of the energy system within the operation cycle. The equipment construction cost is incurred in the first year of each planning stage, and the sum of the remaining operation and maintenance costs and carbon emission cost data is calculated at the end of each year, that is, the method of constructing a phased evolution planning model according to the standard sample of RIES construction phases in step S3 includes: dividing the standard samples according to the key feature points, and calculating the regional building load within each standard sample year, that is,

[0101]

[0102] In the formula, Q k Represents the total building load in the kth standard sample area, in W; COP k-1,σ express

[0103] The performance coefficient (COP) of the σth type of equipment in the k-1th standard sample; P k-1,σ It represents the input power of the σth type of equipment in the k-1th standard sample, in W.

[0104] The model is constructed with the minimum cost of various energy system projects in the kth standard sample as the target, that is,

[0105]

[0106] In the formula, It represents the minimum value of the sum of construction cost data, operation cost data, maintenance cost data and carbon emission cost data in the kth standard sample of the energy system; represents the construction cost data within the kth standard sample of the system; Represents the operating cost data within the kth standard sample of the system; Represents the carbon emission cost data within the kth standard sample of the system; Represents the maintenance cost data within the kth standard sample of the system.

[0107] In some embodiments, the calculation method of the construction cost data of the system is:

[0108] In the formula

[0109] It represents the unit capacity construction investment of the σth type of equipment in the kth standard sample, in Yuan / MW; represents the capacity configuration of the σth type of equipment in the kth standard sample, in MW; γ represents the discount rate, and m represents the mth year in the standard sample.

[0110] In some embodiments, the operating cost data of the system is calculated as follows:

[0111]

[0112] Where: m, s, and t represent the mth year, the sth energy supply scenario, and the tth energy supply period, respectively; D s Indicates the proportion of the selected typical energy supply day in the total life cycle energy supply day; represents the electricity price of the power grid at time t;

[0113] It represents the electric power consumed by all devices except energy storage devices in the kth standard sample at its i-th capacity in the tth period of the sth typical energy supply day; represents the natural gas price at time t; It represents the natural gas power consumed by all equipment in the kth standard sample of the system at its i-th capacity on the sth typical energy supply day; represents the electricity price of energy storage equipment at time t; It represents the electric power consumed by the energy storage device of the kth standard sample in the tth period of the sth typical energy supply day when storing energy at its i-th capacity; It represents the sum of the power generated by the gas internal combustion engine and the flue gas hot water lithium bromide heat pump at the i-th capacity in the t-th period of the k-th standard sample on the s-th typical energy supply day. Δt represents the time interval. ES refers to the energy storage device, grid refers to the power grid, Vele refers to valley electricity, ICE refers to the gas internal combustion engine, and AHP refers to the flue gas hot water lithium bromide heat pump.

[0114] In some embodiments, the carbon emission cost data of the system is calculated by:

[0115]

[0116] Where: grid Indicates the unit emission coefficient of carbon dioxide produced by consuming electricity, kg / kW; ω gas It represents the unit emission coefficient of carbon dioxide produced by consuming natural gas, kg / kW; θ represents the emission cost of carbon dioxide, yuan / kg.

[0117] In some embodiments, the calculation method of the maintenance cost data of the system is:

[0118]

[0119] In the formula, c σ It represents the maintenance cost per unit power of the σ-type equipment, RMB / (kW·year); is the actual class C input power of the i-th capacity of the σ-th type of equipment at the k-th stage and the s-th typical energy supply day at time t, in kW; It is the maximum input power of the ith capacity of the σth type equipment, in kW.

[0120] In some embodiments, the method for constructing a regional integrated energy system with dynamic phased construction sequence further includes: adding constraints to the phased evolution planning model, that is,

[0121] Add construction logic constraints:

[0122] In the formula, is the actual class C input power of the i-th capacity of the σ-th type of equipment at time t on the k-th stage and the s-th typical energy supply day, kW; is the maximum input power of the i-th capacity of the σ-th type of equipment, kW; and the energy storage equipment operation constraints are added:

[0123]

[0124] Where: It represents the electric power consumed by the energy storage device when storing energy in the kth stage and the sth typical energy supply day. ES (Energy Storage) here refers to the energy storage device, and Ele (electrical) refers to the power consumption. The energy storage device supplies energy in the time period except 0:00-8:00, and D represents the sequence number of the selected typical energy supply day.

[0125] Add heat load supply and demand balance constraint:

[0126]

[0127] In the formula, Q t,Heat represents the heat load demand at the end of time t; C σ,Heat Indicates the efficiency of equipment heat load utilization. represents the energy actually input into the i-th capacity of the σ-th equipment at time t; and

[0128] Add cooling load supply and demand balance constraint:

[0129]

[0130] In the formula, Q t,Cool represents the cooling load demand at the end of time t; C m,Cool Indicates the efficiency of equipment cooling load. It represents the energy actually input by the i-th capacity of the σ-th type of equipment at time t.

[0131] In some embodiments, the method of adding construction logic constraints further includes: selecting a capacity for any type of equipment, namely:

[0132]

[0133] Where Y σ,i It is a 0-1 variable. When it is 0, it means that the i-th capacity of the σ-th type of equipment is not selected. When it is 1, it means that the i-th capacity of the σ-th type of equipment is selected.

[0134] In some embodiments, the method of obtaining the optimal equipment combination, capacity configuration scheme and system operation strategy for each phase according to the phased evolution planning model in step S3 includes:

[0135] The Gurobi optimization solver in Python is used to obtain the optimal equipment configuration and operation strategy for each planning period.

[0136] In some embodiments, the method of adjusting the phased strategy and the new equipment configuration and system operation strategy according to the load demand changes caused by regional uncertain conditions in step S3 includes: obtaining the current development trend of the region; and if the current development trend is better than the expected trend, accelerating the load growth rate; if the current development trend is worse than the expected trend, slowing down the load growth rate.

[0137] In some embodiments, "device" and "energy storage device" and "device other than energy storage device" can all be understood as devices. In this method, they can all be substituted into corresponding formulas to obtain corresponding results.

[0138] In order to explain the specific implementation method of the present invention in detail, City A is selected as the application location of the regional building complex for analysis, and the engineering experience phased planning, uniform phased planning and the dynamic phased planning method based on time series feature point extraction in the present invention are compared to reflect the superiority of the present invention, as follows:

[0139] The RIES project in City A covers an area of ​​about 6 square kilometers and provides cooling and heating services for surrounding buildings. The planning period is from 2017 to 2031, and it is planned to be planned and constructed in three phases. This paper takes this project as the research object and conducts regional comprehensive energy evolution planning based on the dynamic phased construction sequence. Figure 3 This is the system schematic diagram of the project.

[0140] System energy input: electricity (0:00-8:00: 0.32 yuan / kWh; 8:00-24:00: 0.64 yuan / kWh), gas (3.79 yuan / m 3); System energy output: electrical load, heat load and cooling load; Terminal load: Based on the annual planned construction area of ​​the region, Trnsys software is used to simulate the annual cooling and heating load demand of the region during the planning period. This paper selects 100%, 75%, 50% and 25% of the typical design days of cooling and heating load intensity for calculation. Other calculation parameters: The time interval L is between 4 and 10 years; the planning period T = 15a; the annual discount rate of funds e = 8%; the effective area available for photovoltaic layout is 21,900 m 2 The conversion coefficients of natural gas and electricity into carbon dioxide are 2.1kg / m3 and 0.58kg / kWh respectively, and the emission cost is 70 yuan / t.

[0141] Table 1 shows the building energy area planning data and load growth forecast data of the RIES project in City A from 2017 to 2031.

[0142] Table 1A Planned building energy use area and load in urban area from 2017 to 2031

[0143]

[0144] Using Python language, the long-term load growth time series of the system in Table 1 is used as the input data set to extract the time series feature points. The RIES project with a planning period of 15 years is constructed in three phases, with a minimum interval of 4 years and a maximum interval of 10 years. The long-term load time series feature points are extracted. The first phase is from 2017 to 2021, with cold and hot load demands of 30MW and 16MW respectively; the second phase is from 2022 to 2025, with cold and hot load demands of 155MW and 81MW respectively; the third phase is from 2026 to 2031, with cold and hot load demands of 158MW and 115MW respectively.

[0145] According to the time series partitioning results, the optimization goal is to minimize the construction cost data, operation cost data, maintenance cost data and carbon emission cost data in the whole life cycle, and the equipment configuration of the next stage should be based on the equipment configuration of the previous stage, and a mixed integer programming model is constructed. In order to verify the effectiveness of the method proposed in this paper, taking the division of three phases as an example, the following article analyzes and compares three phased evolution planning schemes.

[0146] Method I: Phased planning based on engineering experience. Using engineering experience, the construction schedule is divided into three phases, each lasting 3 years + 5 years + 7 years;

[0147] Method II: Evenly divided into three phases: The construction schedule is evenly divided into three phases, each lasting 5 years + 5 years + 5 years.

[0148] Method III: Dynamic phased planning based on time series feature point extraction. The construction schedule is divided into three phases using the time series feature point extraction method, with each phase lasting 5 years + 4 years + 6 years.

[0149] Table 2 shows the equipment type and capacity configuration of the three methods under the expected load. As can be seen from Table 2, under the optimal equipment construction, operation, maintenance and carbon emission cost data throughout the life cycle, the three methods have the same equipment configuration type, all of which are equipped with gas boilers, heat pumps and chillers, and none of which are equipped with ICE-AHP. This is mainly because the equipment construction of "gas internal combustion engine + flue gas hot water type lithium bromide heat pump" is relatively high, and the cost of gas consumption during operation is relatively expensive. When solving the phased optimal solution, its addition will reduce the overall economy. By the end of the planning period, the capacity configuration of the three phased planning methods is different. The heat pump configuration capacity of method I is the highest, which is 47.50MW; method III is second, which is 40.00MW; and method II is the lowest, which is 32.50MW. According to the law of heat conservation, method II has the highest boiler configuration, method III is second, and method I is the lowest. Similarly, method II has the highest chiller configuration, method III is second, and method I is the lowest.

[0150] Table 2 Equipment types and capacity configurations of the three methods under expected load

[0151]

[0152] Figure 4 The figure shows the cost comparison of three phased evolution planning schemes. As can be seen from the figure, the equipment construction cost data of method I for the whole life cycle is the highest, which is 177 million yuan, and the equipment construction cost data of method III is the lowest, which is 171 million yuan. This is mainly because the equipment configuration of the third phase of method I is too early, while the equipment configuration of the third phase of method III is relatively late. The operating cost data of method II is the lowest, which is 144 million yuan, and the operating cost data of method I is the highest, which is 150 million yuan. This is because the chiller capacity configuration of method II is the highest and its efficiency is higher than other equipment. Due to the difference in boiler configuration, the carbon emission cost data of method II is the highest, which is 13.141 million yuan, and the carbon emission cost data of method I is the lowest, which is 12.924 million yuan. In comprehensive comparison, the total cost of method III for the whole life cycle is the lowest, which is 339 million yuan, which saves 3.41% and 0.21% respectively compared with method I and method II.

[0153] The project has invested and constructed the first phase of equipment in 2017 as planned. The overall construction pace of the project's service building complex has slowed down since 2022, and the subsequent planned building construction schedule has been adjusted, which has a great impact on the future load growth pattern. Table 3 shows the newly planned building energy area and load of the adjusted project from 2017 to 2031, of which 2017-2021 is the actual situation, which is consistent with the original plan.

[0154] Table 3 New planned building energy use area and load from 2017 to 2031

[0155]

[0156] As shown in Table 3, the maximum cooling and heating loads under the new plan are reduced by about 29% compared with the original plan. According to the principle of dynamic phased construction sequence, the actual load development curve of the area lags behind the expected load development curve. Therefore, the characteristic points of the load time series have changed. If the construction is carried out according to the original plan, it may lead to redundant configuration of system equipment. To avoid this situation, it is necessary to dynamically adjust the phased strategy according to the new plan. The time series segmentation result changes from the original "4 years + 4 years + 6 years" to "6 years + 5 years + 4 years". Since the first phase of equipment has been built, the new phased strategy should be adjusted to "5 years + 6 years + 4 years". Based on this new phased strategy and the equipment that has been built in the first phase, the equipment type, capacity configuration and life cycle cost of the system under the new planning situation are calculated as shown in Table 4.

[0157] Table 4 Equipment type and capacity configuration of method 3 under the new plan

[0158]

[0159] As shown in Table 4, since the load under the new plan lags behind the original plan, the equipment configuration of the second and third phases under the new plan is reduced compared with the original plan, and the total configuration is reduced by about 48%. Figure 5 It can be seen that after dynamic staging, the construction cost data, operation cost data, carbon emission cost data and maintenance cost data of the system's entire life cycle under the new plan are 125 million yuan, 92 million yuan, 86 million yuan and 63 million yuan respectively, which are approximately 27%, 36%, 34% and 25% lower than the original plan, respectively. The total cost is reduced by 106 million yuan, a decrease of approximately 31%, and the super-cold and hot load reduction rate is 2% (the maximum cold and hot loads are both reduced by 29%).

[0160] Some embodiments further provide a computer device, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the construction method.

[0161] Some embodiments further provide a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the construction method when the computer program / instruction is executed by a processor. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0162] Some embodiments further provide a computer program product, including a computer program / instruction, which implements the steps of the construction method when executed by a processor. The computer program product can be implemented in hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.

[0163] In summary, Method III has better life cycle economy. By dynamically planning the construction sequence of the dynamic growth characteristics of RIES long-term load, it not only avoids the waste of resources caused by advanced construction, but also meets the year-on-year increase in load demand changes, thereby saving the life cycle cost. At the same time, for the fixed phase planning of Method I and Method II, the dynamic phase evolution planning of Method III can dynamically adjust the phase strategy and equipment configuration plan according to future load changes, thereby achieving a dual improvement in system performance and economy.

[0164] In summary, the construction method of the regional integrated energy system for construction of dynamic phased construction of the present invention adopts a phased model based on the extraction of time series feature points. By comparing the sum of the orthogonal distances between each group of feature points in the sequence set and the lines connecting the two points between the endpoints, the feature points with the largest sum of distances are identified. Compared with the conventional method of selecting extreme points and inflection points as feature points, it can more accurately capture the key changes of the load curve and provide more reliable data support. Through the construction of the dynamic phased evolution planning of the long-term load dynamic growth characteristics of RIES, it not only avoids the waste of resources caused by advanced construction, but also meets the changes in load demand that increase year by year, thereby saving the full life cycle cost. The proposed dynamic phased evolution planning can dynamically adjust the phased strategy and equipment configuration plan according to future load changes, so as to achieve a dual improvement in system performance and economy.

[0165] The disclosure and other solutions, examples, embodiments, modules and functional operations described in this document can be implemented in digital electronic circuits, or computer software, firmware or hardware, including the structures disclosed in this document and their structural equivalents, or a combination of one or more thereof. The disclosure and other embodiments can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-volatile computer-readable medium for execution by a data processing device or to control the operation of the data processing device. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a storage device, a material composition that affects a machine-readable propagation signal, or a combination of one or more thereof. The term "data processing unit" or "data processing device" includes all devices, equipment and machines for processing data, including, for example, a programmable processor, a computer or a multiprocessor or a computer group. In addition to hardware, the device may also include code that creates an execution environment for a computer program, for example, code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more thereof. A propagated signal is an artificially generated signal, for example, a machine-generated electrical, optical or electromagnetic signal, which is generated to encode information for transmission to a suitable receiver device.

[0166] A computer program (also referred to as a program, software, software application, script, or code) may be written in any form of programming language (including compiled or interpreted languages) and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, subroutines, or portions of code). A computer program may be deployed for execution on one or more computers that are located at one site or distributed across multiple sites and interconnected by a communications network.

[0167] The processes and logic flows described in this document may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by, and the apparatus may also be implemented as, special purpose logic circuits, such as an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0168] For example, processors suitable for executing computer programs include general and special purpose microprocessors, and any one or more of any type of digital computer. Typically, the processor will receive instructions and data from a read-only memory or a random access memory or both. The basic components of a computer are a processor that executes instructions and one or more storage devices that store instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or operably coupled to receive data from a mass storage device or transfer data to a mass storage device, or both. However, a computer does not necessarily have such a device. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and compact disk read-only memory (CD ROM) and digital versatile disk read-only memory (DVD-ROM) disks. The processor and memory can be supplemented by dedicated logic circuits, or incorporated in dedicated logic circuits.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Inspired by the above-mentioned ideal embodiments of the present invention and through the above-mentioned description, relevant staff can make various changes and modifications without departing from the technical idea of ​​the present invention. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for constructing a regional integrated energy system with dynamic phased construction, characterized in that: include: Step S1, analyzing the long-term load growth characteristics of the planning area according to the building construction plan of the planning area; Step S2, extracting key characteristic points in the long-term load growth characteristics of the planning area, and using them as standard samples for RIES construction phases; Step S3, construct a phased evolution planning model according to the standard sample of RIES construction phases; according to the phased evolution planning model, use the Gurobi optimization solver in Python language to calculate the optimal solution of the model, and obtain the best equipment combination, capacity configuration plan and system operation strategy for each phase; when the load demand changes due to regional uncertain conditions, re-extract key feature points for construction phases, build a phased evolution model and obtain the optimal solution, adjust the phase strategy and the new equipment configuration and system operation strategy.

2. The construction method according to claim 1, characterized in that: The method of extracting key characteristic points in the long-term load growth characteristics of the planning area in step S2 and using them as standard samples for RIES construction phases includes: Step S21, determine the regional load growth change, that is, Q i =Q Heat (m)+Q Cool (m) Where n represents different building types; A c (m) represents the planned area of ​​the nth type of building in the mth year, in m 2 ; q Cool,n,t ,q Heat,n,t They represent the cold and heat load indexes of the nth type of building at the tth moment, in W / m 2 ; Q Heat (m), Q Cool (m) represents the cooling and heating loads of the regional building complex in the mth year, in W; Qm represents the load of the regional building complex in the mth year; Step S22, establish a sequence set of load and time, that is Where: x1 = (T1, Q1) represents the load value Q1 corresponding to the time series in year T1, x m =(T m ,Q m ) indicates that the time series is in T m The corresponding load value Q m ; Step S23, determining key feature points based on the sequence set, and using them as a standard sample for RIES construction phases, that is, Where: d m Indicates that the mth point in the planned time period is consistent with the time series endpoints (T1, Q1), (T P ,Q P ), Q1 represents the load value corresponding to the first year, Q p It represents the load value corresponding to the endpoint P of the time series; ε is the sum of the distances from each point in the period to the endpoint of the time series. When ε is the maximum value, the corresponding point m is the key characteristic point of the operation period, thereby dividing the time period into different stages, which serves as a standard sample for the phased construction of RIES.

3. The construction method according to claim 2, characterized in that: The method for constructing a phased evolution planning model according to the standard sample of RIES construction phases in step S3 includes: According to the key characteristic points, standard samples are divided and the regional building load within each standard sample period is calculated, that is, In the formula, Q k represents the total building load in the kth standard sample area, W; COP k-1,σ represents the performance coefficient of the σth type of equipment in the k-1th standard sample; P k-1,σ It represents the input power of the σth type of equipment in the k-1th standard sample, in W; The model is constructed with the minimum cost of various energy system projects in the kth standard sample as the target, that is, In the formula, It represents the minimum value of the sum of construction cost data, operation cost data, maintenance cost data and carbon emission cost data in the kth standard sample of the energy system; represents the construction cost data within the kth standard sample of the system; Represents the operating cost data within the kth standard sample of the system; Represents the carbon emission cost data within the kth standard sample of the system; Represents the maintenance cost data within the kth standard sample of the system.

4. The construction method according to claim 3, characterized in that: The calculation method of the system construction cost data is: In the formula It represents the unit capacity construction investment of the σth type of equipment in the kth standard sample, in Yuan / MW; represents the capacity configuration of the σth type of equipment in the kth standard sample, in MW; γ represents the discount rate, and m represents the mth year in the standard sample.

5. The construction method according to claim 4, characterized in that: System operating cost data The calculation method is: Where: m, s, and t represent the mth year, the sth energy supply scenario, and the tth energy supply period, respectively; D s Indicates the proportion of the selected typical energy supply day in the total life cycle energy supply day; represents the electricity price of the power grid at time t; It represents the electric power consumed by all devices except energy storage devices in the kth standard sample at its i-th capacity in the tth period of the sth typical energy supply day; represents the natural gas price at time t; It represents the natural gas power consumed by all equipment in the kth standard sample of the system at its i-th capacity on the sth typical energy supply day; represents the electricity price of energy storage equipment at time t; It represents the electric power consumed by the energy storage device of the kth standard sample in the tth period of the sth typical energy supply day when storing energy at its i-th capacity; It represents the sum of the power generated by the gas internal combustion engine and the flue gas hot water lithium bromide heat pump at the i-th capacity in the t-th period of the k-th standard sample on the s-th typical energy supply day. Δt represents the time interval. ES refers to the energy storage device, grid refers to the power grid, Vele refers to valley electricity, ICE refers to the gas internal combustion engine, and AHP refers to the flue gas hot water lithium bromide heat pump.

6. The construction method according to claim 5, characterized in that: Systematic carbon emission cost data The calculation method is: Where: grid Indicates the unit emission coefficient of carbon dioxide produced by consuming electricity, in kg / kW; ω gas It represents the unit emission coefficient of carbon dioxide produced by consuming natural gas, and its unit is kg / kW; θ represents the emission cost of carbon dioxide, and its unit is yuan / kg.

7. A method for constructing the method according to claim 6, characterized in that: System maintenance cost data The calculation method is: In the formula, c σ It represents the unit power maintenance cost of the σth type of equipment, in yuan / kW·year; is the actual class C input power of the i-th capacity of the σ-th type of equipment at time t on the k-th standard sample and the s-th typical energy supply day, kW.

8. The construction method according to claim 7, characterized in that: Also includes: Add constraints to the phased evolution planning model, namely Add construction logic constraints: In the formula, is the actual class C input power of the i-th capacity of the σ-th type of equipment in the k-th standard sample and the s-th typical energy supply day during the t-th period, in kW; It is the maximum input power of the ith capacity of the σth type equipment, in kW. is the maximum input power of the i-th capacity of the σ-th category equipment, kW; as well as Add energy storage equipment operation constraints: In the formula, It represents the electric power consumed by the energy storage device during energy storage in the kth standard sample and the tth period of the sth typical energy supply day. Ele refers to the power consumption. The energy storage device supplies energy in the time period except 0:00-8:

00. D represents the sequence number of the selected typical energy supply day. Add heat load supply and demand balance constraint: In the formula, Q t,Heat represents the heat load demand at the end of period t; C σ,Heat Indicates the efficiency of equipment heat load utilization; as well as Add cooling load supply and demand balance constraint: In the formula, Q t,Cool represents the cooling load demand at the end of period t; C m,Cool Indicates the efficiency of equipment cooling load. It represents the energy actually input by the i-th capacity of the σ-th type of equipment at time t.

9. The construction method according to claim 8, characterized in that: The method for adding construction logic constraints also includes: For any type of equipment, select a capacity, namely: Where Y σ,i It is a 0-1 variable. When it is 0, it means that the i-th capacity of the σ-th type of equipment is not selected. When it is 1, it means that the i-th capacity of the σ-th type of equipment is selected.

10. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the construction method according to claim 1.

11. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the construction method according to claim 1 are implemented.

12. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the construction method according to claim 1 are implemented.

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