CN113139675B - Comprehensive electrical load interval prediction method for park comprehensive energy system - Google Patents
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Abstract
The invention provides a comprehensive electric load interval prediction method for a park comprehensive energy system. The method comprises the following steps: acquiring the maximum power supply demand, the maximum heat supply demand and the maximum cooling demand of a park comprehensive energy system; extracting equipment composition information of the park comprehensive energy system; acquiring the number of contribution intervals of the energy storage equipment to the comprehensive electric load; acquiring the number of contribution intervals of the cogeneration equipment to the comprehensive electric load; acquiring the number of contribution intervals of the electric refrigeration equipment to the comprehensive electric load; acquiring the number of contribution intervals of the new energy power generation equipment to the comprehensive electric load; acquiring the number of comprehensive contribution intervals of the comprehensive energy system equipment of the park; and obtaining a comprehensive electric load prediction interval of the comprehensive energy system of the park. The method gives consideration to the influence of the coupling operation of the multi-energy conversion equipment on the comprehensive electric load, gives a prediction interval of the comprehensive electric load of the comprehensive energy system of the park, makes up the defects of the conventional prediction of the comprehensive electric load, and provides support for determining the planned load boundary of the power grid.
Description
Technical Field
The invention relates to the field of load prediction of an integrated energy system, in particular to a comprehensive electric load interval prediction method of a park integrated energy system.
Background
The power load prediction is an important component of power system planning, and various prediction methods are formed aiming at the fact that the power load is relatively mature at present, wherein the prediction methods comprise a per-capita electricity consumption method, an elasticity coefficient, a production value unit consumption method, an industry-based electricity consumption method, a load density method, a large-user installation and natural growth method, a linear regression method and the like. The park comprehensive energy system is connected as a terminal of a power distribution network, has various energy loads such as electricity, cold and hot gas and the like, and mainly realizes the coordinated operation among various energy sources of the park through energy conversion equipment (such as cogeneration equipment, electric refrigeration equipment and the like). Due to the fact that the comprehensive energy utilization characteristics of the park are changed by the introduction of the various energy coupling devices in the comprehensive energy system of the park, the prediction of the comprehensive electric load of the comprehensive energy system of the park has important guiding significance for operation planning of the power distribution network.
The method is characterized in that a complex prediction algorithm of various loads of a park is provided based on a deep learning algorithm, and the complex prediction algorithm has a good prediction effect, but the study of the loads in the park is still focused, the calculation is complex and time-consuming, and the complex prediction algorithm is not suitable for engineering popularization and application. At present, from the side of a distribution network, comprehensive electric load prediction research for a garden comprehensive energy system has no borrowable model.
Therefore, in order to better guide the application in practical engineering, a method capable of predicting the comprehensive electrical load in the park comprehensive energy system is needed, the influence of the coupling operation of the multi-energy conversion equipment on the comprehensive electrical load needs to be considered, the practicability of the engineering needs to be achieved, and support is provided for determining the planned load boundary of the power grid.
Disclosure of Invention
The invention aims to provide a comprehensive electric load interval prediction method of a park comprehensive energy system by combining the power supply, heat supply and cold supply requirements in the park comprehensive energy system.
The purpose of the invention is realized by at least one of the following technical solutions.
A comprehensive electric load interval prediction method for a park comprehensive energy system comprises the following steps:
s1, acquiring the maximum power supply requirement Q of a park comprehensive energy system from an information management database of the park E Maximum heating demand Q H And maximum cooling demand Q L ；
S2, extracting equipment composition information of the park comprehensive energy system;
s3, acquiring the contribution interval number C of the energy storage equipment to the comprehensive electric load 1 ；
S4, acquiring the number C of the contribution intervals of the cogeneration equipment to the comprehensive electric load 2 ；
S5, acquiring the number C of contribution intervals of the electric refrigeration equipment to the comprehensive electric load 3 ；
S6, acquiring the number C of contribution intervals of the new energy power generation equipment to the comprehensive electric load 4 ；
S7, to C 1 、C 2 、C 3 、C 4 Carrying out interval addition calculation to obtain the comprehensive contribution interval number C of the park comprehensive energy system equipment;
s8, to Q E Subtracting the number of intervals C to obtain a comprehensive electrical load prediction interval [ Q ] of the park comprehensive energy system LD ，Q LU ]。
Further, in step S2, the equipment composition information of the campus comprehensive energy system includes an installation state α of an energy storage device, an installation state λ of a cogeneration device, an installation state γ of an electric refrigeration device, and an installation state β of a new energy power generation device in the campus comprehensive energy system.
Further, for the installation state α of the energy storage device, the installation state λ of the cogeneration device, the installation state γ of the electric refrigeration device, and the installation state β of the new energy power generation device in the campus integrated energy system, the installation state of the device refers to the presence or absence of the corresponding device, and if the corresponding device is present, the installation state of the device takes a value of 1; if not, the installation state of the equipment takes a value of 0.
Further, in step S3, the number C of the sections of contribution of the energy storage device to the integrated electrical load 1 The reduction of the comprehensive electric load by the energy storage device represented by the closed interval is determined by the formula (1):
C 1 ＝α[K x- ,K x+ ]k R S R ； (1)
in the formula, S R Is the installation capacity of the energy storage device; k is a radical of R The ratio of the maximum charge-discharge power to the installation capacity of the energy storage device; [ K ] x- ,K x+ ]The interval number is the charging and discharging proportion of the energy storage equipment.
Furthermore, the interval number [ K ] of the charging and discharging proportion of the energy storage equipment x- ,K x+ ]And determining by combining the load factor of the electrical load in the park through the formula (2):
in the formula, P av The average value of the electric load in the garden is obtained; p max Mu is the interval scaling factor, and mu is the maximum value of the electrical load in the park.
Further, the interval scaling factor μ takes a value of 0.1.
Further, in step S4, the number of sections C of contribution of the cogeneration plant to the integrated electric load 2 The "reduction amount of the cogeneration plant to the integrated electric load" represented by the closed interval is determined by equation (3):
wherein epsilon is the heat-electricity ratio of the cogeneration equipment; [ K ] CHP- ,K CHP+ ]The probability interval that the maximum power supply demand and the maximum heat supply demand appear simultaneously in the park comprehensive energy system is provided.
Further, in step S5, the number C of sections in which the electric refrigeration equipment contributes to the integrated electric load 3 The reduction of the integrated electrical load by the electrical refrigeration apparatus represented by the closed section is determined by equation (4):
in the formula, theta is the refrigeration coefficient of the electric refrigeration equipment; [ K ] TEC- ,K TEC+ ]The probability interval of simultaneous occurrence of the maximum power supply demand and the maximum cooling demand in the park comprehensive energy system is set; because the electric refrigeration equipment has a superposition effect on the comprehensive electric load, a negative sign is added in the formula (3) to show that the calculation result has a negative effect on the reduction of the comprehensive electric load.
Further, in step S6, the number of sections C of contribution of the new energy power plant to the integrated electric load 4 The reduction amount of the new energy power generation equipment to the comprehensive electric load is represented by a closed interval, and is determined by the formula (5):
C 4 ＝βη[K TP- ,K TP+ ]P N ； (5)
in the formula, eta is the efficiency of the new energy power generation equipment; [ K ] TP- ,K TP+ ]The probability interval that the maximum power supply requirement in the park comprehensive energy system and the maximum output of the new energy power generation equipment occur simultaneously is set; p N The installation capacity of the new energy power generation equipment.
Further, the obtained comprehensive electrical load prediction interval [ Q ] of the park comprehensive energy system LD ，Q LU ]And the minimum and maximum calculation boundaries of the load in the power distribution network planning are used.
Compared with the prior art, the invention has the following advantages:
(1) The influence of the coupling operation of the multi-energy conversion equipment on the comprehensive electric load is considered, the comprehensive electric load of the park comprehensive energy system is predicted, and the defects of the conventional prediction on the comprehensive electric load are overcome;
(2) The comprehensive electric load prediction method of the park comprehensive energy system based on interval operation gives a load prediction interval, so that a load prediction result is more inclusive.
Drawings
Fig. 1 is a flowchart of a method for predicting an integrated electrical load section of a park integrated energy system according to an embodiment of the present invention.
Fig. 2 is a diagram of a computational model of an integrated energy system for a campus.
Detailed Description
The following description of the embodiments of the present invention is provided in connection with the accompanying drawings and examples, but the invention is not limited thereto.
The embodiment is as follows:
in this embodiment, taking the comprehensive energy system of a certain park in the southern area as an example, a comprehensive electrical load interval prediction method for the comprehensive energy system of the park as shown in fig. 1 includes the following steps:
s1, acquiring park comprehensive energy from an information management database of the parkMaximum power supply requirement Q of the system E Maximum heating demand Q H And maximum cooling demand Q L ；
In this embodiment, a calculation model diagram of the park integrated energy system is shown in fig. 2, and the maximum power supply requirement in the calculation model is Q E Is 600kW, the maximum heat supply demand Q H Is 150kW, the maximum cooling demand Q L Is 100kW;
s2, extracting equipment composition information of the park comprehensive energy system, wherein the equipment composition information comprises an installation state alpha of energy storage equipment, an installation state lambda of cogeneration equipment, an installation state gamma of electric refrigeration equipment and an installation state beta of new energy power generation equipment in the park comprehensive energy system;
the installation state of the equipment refers to whether corresponding equipment exists or not, and if the corresponding equipment exists, the installation state of the equipment takes a value of 1; if not, the installation state of the equipment takes a value of 0.
In this embodiment, according to the calculation model diagram of fig. 2, α, λ, γ, and β all take a value of 1.
S3, acquiring the number C of the contribution intervals of the energy storage equipment to the comprehensive electric load 1 ；
The number of intervals C of the energy storage device contributing to the integrated electrical load 1 The reduction of the comprehensive electric load by the energy storage device represented by the closed interval is determined by the formula (1):
C 1 ＝α[K x- ,K x+ ]k R S R ； (1)
in the formula, S R Is the installation capacity of the energy storage device; k is a radical of R The ratio of the maximum charge-discharge power to the installation capacity of the energy storage device; [ K ] x- ,K x+ ]The interval number is the charging and discharging proportion of the energy storage equipment.
Interval number [ K ] of charging and discharging proportion of energy storage equipment x- ,K x+ ]And determining by combining the load factor of the electrical load in the park through the formula (2):
in the formula, P av The average value of the electric load in the garden is obtained; p is max In this embodiment, the value of the interval scaling factor μ is 0.1.
In this embodiment, the installation capacity S of the energy storage device R Is 200kVA; ratio k of maximum charge-discharge power to installation capacity of energy storage device R Is 0.25; interval number [ K ] of charging and discharging proportion of energy storage equipment x- ,K x+ ]Is [0.4,0.6 ]]Calculated to obtain C 1 Is [20,30 ]]kW。
S4, acquiring the number C of contribution intervals of the cogeneration equipment to the comprehensive electric load 2 ；
The number of the sections C of the cogeneration plant contributing to the integrated electrical load 2 The reduction amount of the integrated electric load by the cogeneration plant represented by the closed section is determined by the equation (3):
wherein epsilon is the heat-electricity ratio of the cogeneration equipment; [ K ] CHP- ,K CHP+ ]The probability interval that the maximum power supply demand and the maximum heat supply demand appear simultaneously in the park comprehensive energy system is provided.
In this example, the heat-to-power ratio e of the cogeneration apparatus was 1.14; probability interval [ K ] of simultaneous occurrence of maximum power supply demand and maximum heat supply demand in park integrated energy system CHP- ,K CHP+ ]Is [0.8,1.0 ]]Calculated to obtain C 2 Is [105,132 ]]kW。
S5, acquiring the number C of contribution intervals of the electric refrigeration equipment to the comprehensive electric load 3 ；
The number of the contribution intervals C of the electric refrigeration equipment to the comprehensive electric load 3 The reduction of the integrated electrical load by the electrical refrigeration apparatus represented by the closed section is determined by equation (4):
in the formula, theta is the refrigeration coefficient of the electric refrigeration equipment; [ K ] TEC- ,K TEC+ ]The probability interval that the maximum power supply demand and the maximum cooling demand appear simultaneously in the park comprehensive energy system is set; because the electric refrigeration equipment has a superposition effect on the comprehensive electric load, a negative sign is added in the formula (3) to indicate that the calculation result has a negative effect on the reduction of the comprehensive electric load.
In this embodiment, the refrigeration coefficient θ of the electric refrigeration equipment is 3.2; probability interval [ K ] of simultaneous occurrence of maximum power supply demand and maximum cooling demand in park integrated energy system TEC- ,K TEC+ ]Is [0.8,1.0 ]]Calculated to obtain C 3 Is [ -31, -25 ]]kW。
S6, acquiring the number C of the contribution intervals of the new energy power generation equipment to the comprehensive electric load 4 ；
The number C of the contribution intervals of the new energy power generation equipment to the comprehensive electric load 4 The reduction amount of the new energy power generation equipment to the comprehensive electric load is represented by a closed interval, and is determined by the formula (5):
C 4 ＝βη[K TP- ,K TP+ ]P N ； (5)
in the formula, eta is the efficiency of the new energy power generation equipment; [ K ] TP- ,K TP+ ]The probability interval that the maximum power supply requirement in the park comprehensive energy system and the maximum output of the new energy power generation equipment occur simultaneously is set; p N The installation capacity of the new energy power generation equipment.
In this embodiment, the efficiency η of the new energy power generation equipment is 0.8; probability interval [ K ] for simultaneous occurrence of maximum power supply demand and maximum output of new energy power generation equipment in park comprehensive energy system TP- ,K TP+ ]Is [0.7,0.9 ]](ii) a Installation capacity P of new energy power generation equipment N At 70kW, calculated as C 4 Is [39,50 ]]kW。
S7, to C 1 、C 2 、C 3 、C 4 Carrying out interval addition calculation to obtain the comprehensive contribution interval number C of the park comprehensive energy system equipment;
in this example, the calculated total contribution interval number C was [133,187] kW.
S8, pair Q E Subtracting the number of intervals C to obtain a comprehensive electrical load prediction interval [ Q ] of the park comprehensive energy system LD ，Q LU ]In this embodiment, the prediction interval of the comprehensive electrical load of the calculated park comprehensive energy system is [413,467 [ ], 467 ]]kW。
The obtained comprehensive electrical load prediction interval [ Q ] of the park comprehensive energy system LD ，Q LU ]And the minimum and maximum calculation boundaries of the load in the power distribution network planning are used.
It should be noted that, for the sake of simplicity, the comprehensive electrical load interval prediction method of the park comprehensive energy system is described as a series of steps or operation combinations, but those skilled in the art should understand that the present application is not limited by the described sequence of actions, as some steps or operations may be performed in other sequences or simultaneously according to the present application.
The preferred embodiments of the present application disclosed above are intended only to facilitate the understanding of the present invention and the core idea. For those skilled in the art, there may be variations in the specific application scenarios and implementation operations based on the concepts of the present invention, and the description should not be taken as a limitation of the present invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A comprehensive electric load interval prediction method for a park comprehensive energy system is characterized by comprising the following steps:
s1, acquiring the maximum power supply requirement Q of a park comprehensive energy system from an information management database of the park E Maximum heat supply demand Q H And maximum cooling demand Q L ；
S2, extracting equipment composition information of the park comprehensive energy system; the equipment composition information of the park comprehensive energy system comprises an installation state alpha of energy storage equipment in the park comprehensive energy system, an installation state lambda of cogeneration equipment, an installation state gamma of electric refrigeration equipment and an installation state beta of new energy power generation equipment;
s3, acquiring the contribution interval number C of the energy storage equipment to the comprehensive electric load 1 (ii) a The number of the contributing intervals C of the energy storage device to the comprehensive electrical load 1 The reduction of the comprehensive electric load by the energy storage device represented by the closed interval is determined by the formula (1):
C 1 ＝α[K x- ,K x+ ]k R S R ； (1)
in the formula, S R Is the installation capacity of the energy storage device; k is a radical of formula R The ratio of the maximum charge-discharge power to the installation capacity of the energy storage device; [ K ] x- ,K x+ ]The interval number is the charging and discharging proportion of the energy storage equipment;
s4, acquiring the number C of the contribution intervals of the cogeneration equipment to the comprehensive electric load 2 (ii) a Interval number [ K ] of charging and discharging proportion of energy storage equipment x- ,K x+ ]And determining by combining the load factor of the electrical load in the park through the formula (2):
in the formula, P av The average value of the electric load in the park is obtained; p is max The maximum value of the electrical load in the garden is shown, and mu is an interval scaling factor; number of intervals C of contribution of the cogeneration plant to the integrated electrical load 2 The "reduction amount of the cogeneration plant to the integrated electric load" represented by the closed interval is determined by equation (3):
wherein epsilon is the heat-electricity ratio of the cogeneration equipment; [ K ] CHP- ,K CHP+ ]The probability interval that the maximum power supply demand and the maximum heat supply demand appear in the park comprehensive energy system at the same time is set;
s5, acquiring the number C of the contribution intervals of the electric refrigeration equipment to the comprehensive electric load 3 ；
S6, acquiring the number C of contribution intervals of the new energy power generation equipment to the comprehensive electric load 4 ；
S7, to C 1 、C 2 、C 3 、C 4 Performing interval addition calculation to obtain the comprehensive contribution interval number C of the comprehensive energy system equipment of the park;
s8, pair Q E Subtracting the number of intervals C to obtain a comprehensive electrical load prediction interval [ Q ] of the park comprehensive energy system LD ，Q LU ]。
2. The method for predicting the comprehensive electrical load interval of the integrated energy system of the park as claimed in claim 1, wherein in step S2, for the installation state α of the energy storage device, the installation state λ of the cogeneration device, the installation state γ of the electric refrigeration device, and the installation state β of the new energy power generation device in the integrated energy system of the park, the installation state of the device refers to the presence or absence of the corresponding device, and if the corresponding device is present, the installation state of the device takes a value of 1; if not, the installation state of the equipment takes a value of 0.
3. The method for predicting the comprehensive electrical load interval of the integrated energy system of the park according to claim 1, wherein the value of the interval scaling factor μ is 0.1.
4. The method according to claim 1, wherein the section prediction method for the integrated electrical load of the park integrated energy system,
in step S5, the number C of the sections of the electric refrigeration equipment contributing to the comprehensive electric load 3 The reduction of the integrated electrical load by the electrical refrigeration apparatus represented by the closed section is determined by equation (4):
in the formula (I), the compound is shown in the specification,
the refrigeration coefficient of the electric refrigeration equipment; [ K ] TEC- ,K TEC+ ]For the maximum power supply requirement and maximum power supply in the comprehensive energy system of the parkProbability interval of simultaneous occurrence of large cooling demand.
5. The method for predicting the integrated electrical load section of the integrated energy system for a park according to claim 4, wherein the number C of the sections of the new energy power plant contributing to the integrated electrical load in step S6 4 The reduction amount of the new energy power generation equipment to the comprehensive electric load is represented by a closed interval, and is determined by the formula (5):
C 4 ＝βη[K TP- ,K TP+ ]P N ； (5)
in the formula, eta is the efficiency of the new energy power generation equipment; [ K ] TP- ,K TP+ ]The probability interval that the maximum power supply requirement in the comprehensive energy system of the park and the maximum output of the new energy power generation equipment occur simultaneously is set; p is N The installation capacity of the new energy power generation equipment.
6. The method according to any one of claims 1 to 5, wherein the prediction section [ Q ] of the integrated electrical load of the park energy system is obtained LD ，Q LU ]And the minimum and maximum calculation boundaries of the load in the power distribution network planning are used.
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| CN115983430B (en) * | 2022-12-02 | 2023-12-29 | 成都市迈德物联网技术有限公司 | Comprehensive energy system management optimization method and system |
| CN116703135B (en) * | 2023-08-10 | 2023-10-20 | 安徽博诺思信息科技有限公司 | Power line construction planning analysis and evaluation method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109002947A (en) * | 2018-10-29 | 2018-12-14 | 哈尔滨工业大学 | A kind of region multi-energy system thermoelectricity schedule model method |
| CN109670694A (en) * | 2018-12-10 | 2019-04-23 | 国网河南省电力公司经济技术研究院 | A kind of multipotency source supply system load forecasting method |
| CN111614121A (en) * | 2020-06-04 | 2020-09-01 | 四川大学 | Multi-energy park day-ahead economic dispatching method considering demand response and comprising electric automobile |
| CN111709554A (en) * | 2020-05-22 | 2020-09-25 | 广西电网有限责任公司 | Method and system for joint prediction of net loads of power distribution network |
| CN111899121A (en) * | 2020-06-23 | 2020-11-06 | 深圳职业技术学院 | Simple source-load coordinated operation method for regional energy system based on electric-to-gas equipment |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105140967B (en) * | 2015-10-16 | 2017-07-18 | 华中科技大学 | A kind of appraisal procedure of the demand of peak regulation containing New-energy power system |
| CN109256799B (en) * | 2018-09-17 | 2021-07-16 | 大连理工大学 | New energy power system optimal scheduling method based on sample entropy |
| CN110611336B (en) * | 2019-10-10 | 2021-01-19 | 国网(苏州)城市能源研究院有限责任公司 | Optimal operation method of park multi-energy system with double-stage demand side response |
-
2021
- 2021-03-08 CN CN202110251776.0A patent/CN113139675B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109002947A (en) * | 2018-10-29 | 2018-12-14 | 哈尔滨工业大学 | A kind of region multi-energy system thermoelectricity schedule model method |
| CN109670694A (en) * | 2018-12-10 | 2019-04-23 | 国网河南省电力公司经济技术研究院 | A kind of multipotency source supply system load forecasting method |
| CN111709554A (en) * | 2020-05-22 | 2020-09-25 | 广西电网有限责任公司 | Method and system for joint prediction of net loads of power distribution network |
| CN111614121A (en) * | 2020-06-04 | 2020-09-01 | 四川大学 | Multi-energy park day-ahead economic dispatching method considering demand response and comprising electric automobile |
| CN111899121A (en) * | 2020-06-23 | 2020-11-06 | 深圳职业技术学院 | Simple source-load coordinated operation method for regional energy system based on electric-to-gas equipment |
Non-Patent Citations (3)
| Title |
|---|
| Stochastic dynamic solution for off-design operation optimization of combined cooling, heating, and power systems with energy storage;Kuang Jiyuan 等;《Applied Thermal Engineering》;20190909;第1-12页 * |
| 冷热电联供型微电网优化调度的研究;孙龙印;《中国优秀硕士学位论文全文数据库 工程科技辑》;20190615(第06期);第C042-408页 * |
| 基于区域能源中心的居民电-热用能多目标优化;匡萃浙 等;《电力建设》;20210228;第42卷(第2期);第58-67页 * |
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