CN113947236A

CN113947236A - Integrated energy scheduling method, computing device and medium

Info

Publication number: CN113947236A
Application number: CN202111040572.9A
Authority: CN
Inventors: 袁灿; 周文闻; 邱剑; 曹天翔; 周凡珂; 谢予丛
Original assignee: Alibaba Cloud Computing Ltd
Current assignee: Alibaba Cloud Computing Ltd
Priority date: 2021-09-06
Filing date: 2021-09-06
Publication date: 2022-01-18
Anticipated expiration: 2041-09-06
Also published as: CN113947236B

Abstract

The embodiment of the specification provides a comprehensive energy scheduling method, a computing device and a medium, wherein the comprehensive energy scheduling method comprises the following steps: after acquiring resource data of the comprehensive energy, historical consumption of the energy consumption equipment in a specified region range on the comprehensive energy and building data in the specified region range, according to the acquired historical consumption and the building data, predicting the consumption of the comprehensive energy sources in the next prediction time period by the energy consumption equipment in the appointed region range to obtain the prediction data of the consumption, then according to the prediction data of the consumption and the resource data of the comprehensive energy, a target scheduling strategy aiming at the energy consumption equipment in the next prediction time period is determined, the scheduling strategy is the basis for performing the scheduling operation of the comprehensive energy, by the embodiment of the specification, the target scheduling strategy for the energy consumption equipment in the next prediction time period can be accurately predicted, the use efficiency of the comprehensive energy in the next prediction time period can be ensured, and the optimized comprehensive energy scheduling is realized.

Description

Integrated energy scheduling method, computing device and medium

Technical Field

The embodiment of the specification relates to the technical field of energy optimization, in particular to a comprehensive energy scheduling method, computing equipment and a medium.

Background

With the continuous development of society, people are gradually aware of the importance of economic and environmental collaborative development. Carbon neutralization is an important target of future social development, the core of the method is to promote low-carbon transformation and energy revolution of energy sources, and essential measures are to control and reduce consumption of fossil energy, increase power generation proportion of renewable energy sources and improve the overall energy efficiency level of the society. The new energy power generation for enterprises, parks and the like is a trend of future development. However, renewable energy power generation has strong seasonal and regional characteristics, is greatly influenced by weather factors, has output randomness and fluctuation, brings larger frequency modulation peak-to-peak pressure to the current power utilization peak-to-valley difference which rises year by year, and has abandonment of a large amount of new energy power generation in the actual operation process.

The comprehensive energy comprises various energy resources such as cold, heat, electricity, gas, photovoltaic and the like, the energy resources are integrated, the complementarity among different energy sources can be fully excavated and released, the safety, the stability and the controllability of energy supply are realized in a larger range, the fluctuation and the randomness of the energy sources are simultaneously stabilized, the utilization efficiency of the energy sources is improved, and the carbon emission brought by energy consumption is reduced. It is desirable to provide a more rational integrated energy scheduling scheme.

Disclosure of Invention

In view of this, the embodiments of the present disclosure provide an integrated energy scheduling method. One or more embodiments of the present disclosure also relate to an integrated energy scheduling apparatus, a computing device, a computer-readable storage medium, and a computer program to solve technical problems of the related art.

According to a first aspect of embodiments herein, there is provided an integrated energy scheduling method, including:

acquiring resource data of the comprehensive energy, historical consumption of energy consumption equipment in a specified region range on the comprehensive energy and building data in the specified region range, wherein the building data comprises building attribute data and geographic environment data;

according to the historical consumption and the building data, predicting the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range to obtain prediction data of the consumption;

and determining a target scheduling strategy aiming at the energy consumption equipment in the next prediction time period according to the prediction data of the consumption and the resource data of the comprehensive energy.

Optionally, the step of predicting, according to the historical consumption and the building data, the consumption of the comprehensive energy in the next prediction time period by the energy consumption device in the specified region to obtain prediction data of the consumption includes:

aiming at a first energy in the comprehensive energy, inquiring a preset corresponding table according to the building data to obtain a consumption coefficient of the first energy, wherein the preset corresponding table records the corresponding relation between the building data and the consumption coefficient, and the preset corresponding table is established in advance according to the design rule of the first energy;

and calculating the prediction data of the consumption of the first energy by the energy consumption equipment in the appointed region range according to the historical consumption and the consumption coefficient of the first energy.

Optionally, the building attribute data includes a building function type and a building area; the geographic environment data includes a geographic location and a climate environment; the consumption coefficient of the first energy comprises unit area consumption, a region consumption correction coefficient and a climate consumption correction coefficient; the preset corresponding table comprises a first corresponding table, a second corresponding table and a third corresponding table;

the method comprises the steps of inquiring a preset corresponding table according to building data aiming at a first energy in the comprehensive energy to obtain a consumption coefficient of the first energy, and comprises the following steps:

inquiring a first corresponding table according to the building function type to obtain the target unit area consumption, wherein the first corresponding table records the corresponding relation between the building function type and the unit area consumption;

inquiring a second corresponding table according to the geographic position to obtain a target area consumption correction coefficient, wherein the second corresponding table records the corresponding relation between the geographic position and the area consumption correction coefficient;

and inquiring a third corresponding table according to the climate environment to obtain a target climate consumption correction coefficient, wherein the third corresponding table records the corresponding relation between the climate environment and the climate consumption correction coefficient.

Optionally, the step of calculating prediction data of the consumption amount of the first energy by the energy consumption devices in the designated area range according to the historical consumption amount and the consumption coefficient of the first energy includes:

calculating the maximum consumption of the first energy in the designated region range according to the target unit area consumption, the target region consumption correction coefficient and the building area;

calculating the annual consumption of the first energy in the designated region according to the maximum consumption of the first energy in the designated region and the target climate consumption correction coefficient;

and determining prediction data of the consumption of the first energy by the energy consumption equipment in the specified region according to the historical consumption and the annual consumption of the first energy in the specified region.

Optionally, the step of determining a target scheduling policy for the energy consuming device in a next prediction time period according to the prediction data of the consumption and the resource data of the integrated energy includes:

and according to the prediction data of the consumption and the resource data of the comprehensive energy, under a preset constraint condition, optimally solving by taking the optimal scheduling strategy for the energy consumption equipment as a target, and determining a target scheduling strategy for the energy consumption equipment in the next prediction time period, wherein the preset constraint condition at least comprises the consumption constraint of the energy consumption equipment on the comprehensive energy and the energy supply constraint of the comprehensive energy.

Optionally, the preset constraint condition further includes: and the charging and discharging performance of the energy storage equipment in the designated region range is restricted, and the relevance restriction among the energy sources in the comprehensive energy sources is restricted.

Optionally, after the step of determining a target scheduling policy for the energy consuming device in a next prediction time period according to the prediction data of the consumption amount and the resource data of the integrated energy, the method further includes:

sending the target scheduling strategy to a management and control interface of the client for display;

and receiving a confirmed scheduling instruction sent by the client, and sending the scheduling instruction to the energy equipment indicated by the target scheduling strategy according to the target scheduling strategy when the next predicted time period is reached.

According to a second aspect of embodiments herein, there is provided an integrated energy scheduling apparatus, comprising:

the data acquisition module is configured to acquire resource data of the comprehensive energy, historical consumption of the comprehensive energy by energy consumption equipment in a specified region range and building data in the specified region range, wherein the building data comprises building attribute data and geographic environment data;

the prediction module is configured to predict the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range according to the historical consumption and the building data to obtain prediction data of the consumption;

and the scheduling planning module is configured to determine a target scheduling strategy for the energy consumption equipment in the next prediction time period according to the prediction data of the consumption and the resource data of the comprehensive energy.

Optionally, the prediction module is further configured to, for a first energy in the comprehensive energy, query a preset correspondence table according to the building data to obtain a consumption coefficient of the first energy, where the preset correspondence table records a correspondence between the building data and the consumption coefficient, and the preset correspondence table is pre-established according to a design rule of the first energy; and calculating the prediction data of the consumption of the first energy by the energy consumption equipment in the appointed region range according to the historical consumption and the consumption coefficient of the first energy.

the prediction module is further configured to query a first corresponding table according to the building function type to obtain the target unit area consumption, wherein the first corresponding table records the corresponding relation between the building function type and the unit area consumption; inquiring a second corresponding table according to the geographic position to obtain a target area consumption correction coefficient, wherein the second corresponding table records the corresponding relation between the geographic position and the area consumption correction coefficient; and inquiring a third corresponding table according to the climate environment to obtain a target climate consumption correction coefficient, wherein the third corresponding table records the corresponding relation between the climate environment and the climate consumption correction coefficient.

Optionally, the prediction module is further configured to calculate a maximum consumption of the first energy within a specified region range according to the target consumption per unit area, the target region consumption correction coefficient and the building area; calculating the annual consumption of the first energy in the designated region according to the maximum consumption of the first energy in the designated region and the target climate consumption correction coefficient; and determining prediction data of the consumption of the first energy by the energy consumption equipment in the specified region according to the historical consumption and the annual consumption of the first energy in the specified region.

Optionally, the scheduling planning module is further configured to perform optimal solution on the optimal scheduling policy for the energy consuming device as a target under a preset constraint condition according to the predicted data of the consumption and the resource data of the integrated energy, and determine a target scheduling policy for the energy consuming device in a next prediction time period, where the preset constraint condition at least includes consumption constraint of the energy consuming device on the integrated energy and energy supply constraint of the integrated energy.

Optionally, the integrated energy scheduling apparatus further includes: the system comprises a push display module and a control module;

the push display module is configured to send the target scheduling strategy to a management and control interface of the client for display;

and the control module is configured to receive the confirmed scheduling instruction sent by the client, and send the scheduling instruction to the energy equipment indicated by the target scheduling policy according to the scheduling policy with the optimal target cost in the next predicted time period when the next predicted time period is reached.

According to a third aspect of embodiments herein, there is provided a computing device comprising: a memory and a processor;

the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions are executed by the processor to execute the integrated energy scheduling method.

According to a fourth aspect of embodiments herein, there is provided a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the integrated energy scheduling method described above.

According to a fifth aspect of embodiments herein, there is provided a computer program, wherein the computer program is caused to execute the above-mentioned integrated energy scheduling method when the computer program is executed in a computer.

One embodiment of the specification realizes that after resource data of comprehensive energy, historical consumption of the comprehensive energy by energy consumption equipment in a specified region range and building data in the specified region range are obtained, according to the obtained historical consumption and the building data, the consumption of the comprehensive energy by the energy consumption equipment in the specified region range is predicted in the next prediction time period, so that prediction data of the consumption can be obtained, then according to the prediction data of the consumption and the resource data of the comprehensive energy, a target scheduling strategy for the energy consumption equipment in the next prediction time period is determined, the scheduling strategy is a basis for performing comprehensive energy scheduling operation, according to the embodiment of the specification, the target scheduling strategy for the energy consumption equipment in the next prediction time period can be accurately predicted, and the use efficiency of the comprehensive energy in the next prediction time period can be ensured, and the optimized comprehensive energy scheduling is realized.

Drawings

FIG. 1 is a flow chart of an integrated energy scheduling method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of an implementation framework of an integrated energy scheduling method according to an embodiment of the present disclosure;

FIG. 3a is a flow diagram of another method for integrated energy scheduling provided by an embodiment of the present disclosure;

FIG. 3b is a schematic diagram of an integrated energy management system according to an embodiment of the present disclosure

Fig. 4 is a schematic structural diagram of an integrated energy scheduling device according to an embodiment of the present disclosure;

fig. 5 is a block diagram of a computing device according to an embodiment of the present disclosure.

Detailed Description

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present description. This description may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, as those skilled in the art will be able to make and use the present disclosure without departing from the spirit and scope of the present disclosure.

The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used in one or more embodiments of the present specification refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It will be understood that, although the terms first, second, etc. may be used herein in one or more embodiments to describe various information, these information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, a first can also be referred to as a second and, similarly, a second can also be referred to as a first without departing from the scope of one or more embodiments of the present description. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

First, the noun terms to which one or more embodiments of the present specification relate are explained.

Optimizing: the optimization theory is about the theory and method of optimal design, optimal control and optimal management. Optimization is the process of making a device perform the desired optimal function under certain constraints. The optimal selection is made from a plurality of possible selections, so that the set objective function reaches the maximum or the minimum under the constraint condition. More and more theories and methods are used, such as linear programming, nonlinear programming, dynamic programming, queuing theory, strategy theory, decision theory, game theory and the like.

In the present specification, an integrated energy scheduling method is provided, and the present specification also relates to an integrated energy scheduling apparatus, a computing device, a computer-readable storage medium, and a computer program, which are described in detail one by one in the following examples.

The execution main body of the comprehensive energy scheduling method provided by the embodiment of the specification can be a control device, a server and the like for scheduling the comprehensive energy. The integrated energy scheduling method provided by the embodiments of the present disclosure may be implemented by at least one of software, hardware circuit, and logic circuit disposed in the execution main body.

Fig. 1 is a flowchart illustrating an integrated energy scheduling method according to an embodiment of the present disclosure, which includes the following steps.

Step 102: the method comprises the steps of obtaining resource data of the comprehensive energy, historical consumption of energy consumption equipment in a specified region range on the comprehensive energy and building data in the specified region range, wherein the building data comprise building attribute data and geographic environment data.

In this specification, the designated geographic area specifically refers to a jurisdiction area such as a specific building, a small-sized park, a factory, a large-sized park, a house, and the like, which is monitored and managed by a specific subject or person, where the specific subject or person mentioned herein may be a property, an enterprise supervisor, an administration committee, a regulatory agency, an owner, and the like, and a controller that performs management and control in this specification is a computing device used by the specific subject or person, and specifically performs management and control by operating a client on the computing device. In the specification, comprehensive energy is adopted in a designated regional scope, and the comprehensive energy comprises various energy resources such as cold, heat, electricity, gas, photovoltaic and the like. That is, the energy consumption devices in the designated area consume the various energy resources every day, and accordingly, energy consumption is generated. The resource data of the integrated energy refers to data on the reserve amount and value of the various energy resources, for example, the electricity price, the water price, the carbon transaction price, the natural gas price, or the reserve amount of the resources such as electricity, water, natural gas, photovoltaic, or the like.

Energy consumption equipment is arranged in a designated region range, for example, an elevator and a public lighting lamp are installed in a specific building (e.g., an office building), an air conditioner, a fluorescent lamp, a personal computer and the like are installed in each room, the energy consumption equipment can consume the comprehensive energy in the use process, the historical consumption of the energy consumption equipment in the designated region range on the comprehensive energy can be correspondingly obtained, and specifically, the historical consumption can comprise energy consumption monthly bills, energy consumption types, total consumed power and the like. In the embodiment of the present specification, building data in the specified geographical range needs to be obtained, and specifically, the building data includes building attribute data and geographic environment data, where the building attribute data refers to inherent attribute data of a building, such as building function type (office building, factory building, house, etc.), building area, and the geographic environment data refers to a geographic location (which may be represented by longitude and latitude) where the building is located, a climate environment (weather, cold, hot, and cold, etc.), and the like.

The resource data of the integrated energy source is changed according to the specific environment, and therefore, every time the resource data of the integrated energy source is changed, the changed resource data, such as the current electricity price, can be acquired from the relevant institution. The historical consumption and building data of the energy consumption equipment in the designated region range on the comprehensive energy can be input by a user from an interactive interface when the comprehensive energy scheduling requirement exists, and can be obtained from a fixed storage position. The manner of acquiring the data is not particularly limited in the present specification.

And step 104, predicting the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range according to the historical consumption and the building data to obtain the prediction data of the consumption.

In this description, after the historical consumption and the building data of the energy consumption devices in the specified region for the comprehensive energy are obtained, the historical consumption and the building data represent the environment in the specified region and the actually generated consumption of the comprehensive energy, and these parameters directly influence the consumption of the energy consumption devices in the specified region for the comprehensive energy in the next prediction time period. Specifically, when the consumption of the comprehensive energy is predicted by the energy consumption equipment in the specified region within the next prediction time period, different energy sources need to meet different design requirements during design, the specific design requirements are set in detail in design rules (specifically, requirements in design files such as design manuals and design specifications) of each energy source, and the design requirements are often related to building data and historical consumption, so that the consumption of the comprehensive energy by the energy consumption equipment in the specified region within the next prediction time period can be predicted based on the historical consumption and the building data of the energy consumption equipment in the specified region and the design requirements of the design rules of each energy source. Specifically, the prediction data of the consumption amount may be a total consumption amount or a consumption curve in the next prediction period.

In an implementation manner of the embodiment of the present specification, the step 104 may be specifically implemented by:

In this embodiment, for any energy (hereinafter referred to as a first energy) in the comprehensive energy (for example, cold energy, hot energy, photovoltaic energy, natural gas energy, and the like), the consumption requirement of the energy consumption device for the first energy and the building data in the specified region range meet certain design requirements, which are generally set in the design rule of the first energy, and according to the requirements in the design rule of the first energy, the corresponding relationship between the building data and the consumption coefficient can be correspondingly established to form a preset corresponding table, the consumption coefficient refers to a correlation coefficient that affects the consumption of the energy consumption device for the comprehensive energy, after the building data in the specified region range is obtained, the consumption coefficient of the first energy can be found by querying the preset corresponding table according to the building data, and further, according to the historical consumption and the consumption coefficient of the first energy, the prediction data of the consumption of the first energy by the energy consumption equipment in the specified region range can be calculated. According to the embodiment, the consumption of the energy consumption equipment in the specified region range on the comprehensive energy can be accurately predicted according to the building data.

In one implementation of an embodiment of the present description, the building attribute data includes a building function type and a building area; the geographic environment data includes a geographic location and a climate environment; the consumption coefficient of the first energy comprises unit area consumption, a region consumption correction coefficient and a climate consumption correction coefficient; the preset corresponding table comprises a first corresponding table, a second corresponding table and a third corresponding table.

Correspondingly, for a first energy in the comprehensive energy, according to the building data, the step of querying the preset correspondence table to obtain the consumption coefficient of the first energy may be specifically implemented in the following manner:

In this embodiment, the consumption coefficient of the first energy mainly refers to a relevant coefficient of influence of the building function type, the geographical location, and the climate environment on the consumption, and in this embodiment, the preset mapping table includes three mapping tables, which are: a first correspondence table, which records the correspondence between the building function type and the unit area consumption; a second correspondence table, which records the correspondence between the geographic position and the area consumption correction coefficient; third correspondence table: and recording the corresponding relation between the climate environment and the climate consumption correction coefficient. Correspondingly, the first corresponding table can be inquired according to the building function type to obtain the target unit area consumption; inquiring a second corresponding table according to the geographic position to obtain a consumption correction coefficient of the target area; and inquiring a third corresponding table according to the climate environment to obtain a target climate consumption correction coefficient. According to the embodiment, according to the acquired building data, the consumption coefficient of the energy can be quickly obtained through a table look-up mode, the execution efficiency of the comprehensive energy scheduling method is improved, the corresponding relation is pre-established based on the design rule of the energy, certain industry specifications are met, and the determined consumption coefficient is high in precision.

Next, a process of determining the consumption coefficient of cold and hot energy will be described by taking the cold and hot energy as an example.

The design rules of cold and hot energy are mainly in the technical measures of civil building engineering design across the country, heating, ventilation, air conditioning and power, and practical heatingThe second edition of air-conditioning design manual, and the design Specifications for heating, ventilation and air-conditioning of civil buildings. Wherein, the corresponding relation between the building function type and the unit area consumption is stipulated in the national civil building engineering design technical measure heating ventilation air conditioner power and the practical heat supply air conditioner design manual second edition, therefore, based on the design rules in the two files, a first corresponding table can be established in advance, and the corresponding target unit area consumption hs (target unit area heat consumption) and cs (target unit area cold consumption) can be found by inquiring the first corresponding table according to the building function type in the building data; the corresponding relation between the geographical position and the area consumption correction coefficient is specified in the second edition of the practical heat supply air conditioner design manual, so that a second corresponding table can be established in advance based on the design rule in the file, and the corresponding target area consumption correction coefficient CL can be found by inquiring the second corresponding table according to the geographical position in the building data_est(correction factor for Cold consumption in target area) and HL_est(target area heat consumption correction coefficient); the corresponding relation between the climate environment and the climate consumption coefficient is also specified in the second edition of the design manual for practical heat supply and air conditioning, so that based on the design rule in the file, a third correspondence table can be established in advance, and the corresponding target climate consumption coefficient ct can be found by inquiring the third correspondence table according to the climate environment in the building data_i(target climate Cold consumption coefficient) and ht_i(target climate heat consumption coefficient).

In an implementation manner of the embodiment of the present specification, the step of calculating the prediction data of the consumption amount of the first energy by the energy consumption device in the specified area range according to the historical consumption amount and the consumption coefficient of the first energy may be specifically implemented by the following steps:

In this embodiment, after obtaining the target consumption per unit area, the target area consumption correction coefficient, and the target climate consumption correction coefficient, the prediction data of the consumption amount of the first energy by the energy consumption device in the specified area range may be calculated based on these coefficients. The method specifically comprises the following steps: according to the target unit area consumption, the target area consumption correction coefficient and the building area, calculating the maximum consumption of the first energy in the specified area range, then according to the maximum consumption of the first energy in the specified area range and the target climate consumption correction coefficient, calculating the annual consumption of the first energy in the specified area range, and finally according to the historical consumption and the annual consumption of the first energy in the specified area range, for example, the historical consumption is the historical consumption of the energy consumption equipment to the comprehensive energy in the past 8 months, and the annual consumption of the first energy in the specified area range is calculated, so that the consumption of the energy consumption equipment to the first energy in the specified area range of the future 4 months can be correspondingly predicted. According to the method and the device, the consumption of the energy consumption equipment in the specified region range to the comprehensive energy in the next prediction time period can be estimated without depending on historical consumption only by acquiring the easily-obtained parameters of the building function type, the geographical position, the climate environment and the like.

Next, a process of calculating the prediction data of the consumption amount of the first energy by the energy consumption equipment in the specified area range will be described by taking the cold and hot energy as an example.

As described above, the target heat consumption per unit area hs, the target cold consumption per unit area cs, and the target district cold consumption correction coefficient CL are obtained_estTarget area heat consumption correction factor HL_estTarget climate cold consumption coefficient ct_iTarget climate heat consumption coefficient ht_iThen, the target heat consumption per unit area hs and the target cold consumption per unit area hs can be usedQuantity cs, target area cold consumption correction coefficient CL_estTarget area heat consumption correction factor HL_estAnd calculating the maximum consumption of cold and hot energy in a specified region by using the formulas (1) and (2).

CL_max＝CL_est*cs*cold_area (I)

HL_max＝HL_est*hs*heat_area (2)

Wherein, CL_maxFor maximum consumption of cold energy in a specified geographical region, cold _ area is the building cold area, HL_maxThe heat _ area is the building heat area for the maximum consumption of thermal energy within a specified geographical area.

Then, according to the maximum consumption of cold and heat energy in a designated region range and the target climate cold consumption coefficient ct_iTarget climate heat consumption coefficient ht_iAnd calculating the annual consumption of cold and hot energy in a specified regional range by using the formulas (3) and (4).

Wherein, CL_totalFor the annual consumption of cold energy in a given geographical area, HL_totalThe annual consumption of the thermal energy in a specified geographical range, and n is the number of energy consumption equipment in the specified geographical range.

After the annual consumption of the cold and hot energy in the designated regional range is obtained, the prediction data of the consumption of the cold and hot energy by the energy consumption equipment in the designated regional range can be further calculated through data analysis according to the historical consumption and the annual consumption of the cold and hot energy in the designated regional range.

In addition, the "national civil construction engineering design technical measure heating ventilation air conditioning power" specifies the corresponding relationship among the building function type, the building cold and heat area, and the hourly consumption coefficient, and therefore, based on the design rule in the file, the fourth correspondence table can be established in advance, and the corresponding target hourly consumption coefficient can be found by querying the fourth correspondence table according to the building function type and the building area in the building data. And calculating the maximum consumption according to the target hourly consumption coefficient according to the maximum consumption of the cold and hot energy in the specified region range and the target hourly consumption coefficient, namely multiplying the maximum consumption at the time point by the corresponding hourly consumption coefficient to obtain a daily consumption curve of the energy consumption equipment in the specified region range to the cold and hot energy.

Of course, the embodiment only provides a specific process for predicting the consumption of the cold and hot energy by the energy consumption equipment in the specified region range in the next prediction time period for the cold and hot energy, and the prediction processes of other energy sources are the same or similar. Of course, for some special energy sources, such as electric energy, the predicted consumption can be directly calculated according to the historical consumption, the historical consumption specifically includes the monthly electricity bill and the electricity utilization type, and then the historical total consumption can be calculated in an accumulated manner according to the monthly electricity bill, the electricity utilization type and the electricity price, so that the consumption in the next prediction time period is further calculated.

And step 106, determining a target scheduling strategy aiming at the energy consumption equipment in the next prediction time period according to the prediction data of the consumption and the resource data of the comprehensive energy.

In this specification, after obtaining the prediction data of the consumption amount and the resource data of the integrated energy, the target scheduling policy for the energy consumption device in the next prediction time period may be predicted according to the prediction data of the consumption amount and the resource data of the integrated energy. Specifically, the prediction data of the consumption and the resource data of the comprehensive energy are used as input, a pre-established comprehensive energy scheduling model is used for solving, different scheduling strategies for the energy consumption equipment in an appointed region are obtained, and optimal solving is performed by taking the optimal scheduling strategy for the energy consumption equipment as a target. Specifically, the comprehensive energy scheduling model is an optimization model, the optimization target can be the optimal scheduling strategy, and further can be the optimal cost, and then the specific solving process is to perform the optimal solution by using the comprehensive energy scheduling model to solve the optimal target scheduling strategy for the energy consumption equipment in the designated region range.

The comprehensive energy scheduling model is pre-established based on the use conditions of energy consumption equipment and energy equipment in a specified region range, the comprehensive energy in the embodiment of the specification comprises energy sources such as electricity, heat, cold, gas and photovoltaic, and specifically, the creation process of the comprehensive energy scheduling model can be as follows: firstly, energy utilization models, energy conversion models, cost models and loss models of energy equipment such as photovoltaic power generation, storage battery energy storage, gas turbines, waste heat recovery devices, gas boilers, absorption refrigerators, air conditioners and the like are established, and then the models of the energy equipment are integrated into a unified optimization model, wherein the optimization model is a comprehensive energy scheduling model.

In an implementation manner of the embodiment of the present specification, step 106 may be specifically implemented by:

In this embodiment, the comprehensive energy scheduling model is an optimization model obtained by integrating an energy utilization model, an energy conversion model, a cost model and a loss model of energy equipment such as photovoltaic power generation, storage battery energy storage, a gas turbine, a waste heat recovery device, a gas boiler, an absorption chiller, an air conditioner and the like. Specifically, the solution process of the comprehensive energy scheduling model is to optimally solve the pre-established comprehensive energy scheduling model, and when solving, an objective function can be established by taking the optimal scheduling strategy for the energy consumption equipment as a target, that is, when solving the comprehensive energy scheduling model, the optimal scheduling strategy is taken as a target, illustratively, the optimal scheduling strategy can be embodied by optimal cost (that is, the highest economic benefit of a user), and the optimal target scheduling strategy for the energy consumption equipment in the next prediction time period is solved under a certain constraint condition by considering resource data of the comprehensive energy, and under the target scheduling strategy, the use efficiency of the comprehensive energy in the next prediction time period is highest, and further, the economic performance of the comprehensive energy expansion or new construction is optimal. By using the embodiment, the comprehensive energy scheduling model and the solving method can accurately determine the scheduling strategy of the energy consumption equipment in the next prediction time period, ensure the highest use efficiency of the comprehensive energy, and achieve the purposes of energy conservation and emission reduction.

Taking the highest economic benefit of the user as an example, the objective function in this embodiment mainly includes: annual initial investment cost, annual maintenance cost, annual operating cost, annual loss cost and carbon trading profit. The annual initial investment cost comprises unit capacity investment cost of different types of energy equipment and target installation capacity of the different types of energy equipment, and the target installation capacity is an output result of energy scheduling; the annual maintenance cost comprises the time-of-use electricity price and the electricity selling price of the electricity market; annual operating costs include power at various times of day for different types of energy devices; the annual loss cost comprises cost data relating to discharge of the energy storage device; the carbon trading profit is created based on the carbon dioxide trading price. In summary, the objective function can be specifically shown in equation (5).

min c^sch＝c^inv+c^sus+c^op+c^loss-c^co2 (5)

Wherein, c^schTo equal annual cost, c^invTo make the initial investment cost annual, c^susFor annual maintenance costs, c^opFor annual operating costs, c^lossFor annual loss costs, c^co2Profits for carbon trading. c. C^inv、c^sus、c^op、c^lossAnd c^co2Specifically, as shown in formulas (6) to (10).

Wherein D represents the next day within the predicted time period, including different types of days, M_dNumber of d-type days; t is a time step, and is usually 24; i is^kInvestment cost per unit volume of energy equipment k, C^kA target installation capacity for the energy device k;

in order to take into account the time-of-use electricity prices of the electricity market,

for the price of electricity sold based on the electricity market,

exchanging power for the transformer substation at the time t; omega^kFor the loss cost factor of the energy device k,

the power of the energy equipment k at the time t under d types of days;

the charging and discharging states of the energy storage device at the t moment on the d-class days, c^esThe loss cost of one complete discharge of the energy storage device,

the discharge power of the energy storage equipment at the t moment on the class d day is obtained; p is a radical of^co2Trading price for carbon dioxide (dollar/t); EF_grid,CMA baseline mean emission factor for the grid (kgCO 2/kWh);

the power for the photovoltaic array is switchable at time t.

In the embodiment, the preset constraint conditions at least include consumption constraints of the energy consumption equipment on the comprehensive energy sources and energy supply constraints of the comprehensive energy sources. The energy supply constraint of the comprehensive energy is also called output constraint. Taking photovoltaic output constraints as an example, the output constraints are shown in formula (11).

Wherein the content of the first and second substances,

maximum power that the photovoltaic array can convert at time t if

This means that there is no light rejection, which is usually not done in buildings or parks for economic reasons. In this embodiment, the constraint on the consumption of the integrated energy by the energy consuming device is mainly embodied in the power balance constraint, as shown in formula (12).

Wherein the content of the first and second substances,

the battery discharge and charge powers at time t are respectively,

the total load of the comprehensive energy power utilization at the moment t. The power balance constraint may specifically be an electrical power balance constraint, as shown in equation (13).

Wherein the content of the first and second substances,

the power generated by the micro-combustion engine at the moment t,

the electric power for air conditioning at time t,

the electric power for the electric boiler is used at the moment t.

In an implementation manner of the embodiment of this specification, the presetting of the constraint condition further includes: and the charging and discharging performance of the energy storage equipment in the designated region range is restricted, and the relevance restriction among the energy sources in the comprehensive energy sources is restricted.

The charge and discharge performance constraint of the energy storage device in a specified region is also referred to as energy storage device re-emission constraint, and the association constraint between the energy sources in the comprehensive energy source is also referred to as Combined Cooling, Heating and Power (CCHP) constraint.

The energy storage device playback energy constraints are shown in equations (14) - (19), and the CCHP related constraints are shown in equations (20) - (29).

Wherein the content of the first and second substances,

and

respectively represent 0-1 variables of charge and discharge energy states,

indicating the charging, otherwise, not charging;

indicating release, otherwise, not release. Under the constraint of the formula (16), it can be ensured that the energy storage device can only be in one of charging, discharging or idle state at the same time.

Representing the energy stored by the energy storage device at time t. Constraints (19) are added for scheduling rationality.

CCHP includes little combustion engine, waste heat recovery device, heat exchange device, gas boiler, heat accumulation groove, absorption refrigerator and electric boiler, and the CCHP restraint is as follows:

wherein the content of the first and second substances,

for the micro-combustion engine consuming the natural gas volume at time t, H_fIs natural gas with low heat value;

minimum and maximum efficiencies eta of electricity and heat generation of micro-combustion engine_MTEThe combustion efficiency of the micro-combustion engine.

Electric power and thermal power generated by the micro-combustion engine at the moment t are respectively generated;

the thermal power of a preheating recovery device, a gas boiler and an electric boiler is respectively set;

the refrigeration power and the corresponding electric power and thermal power of the air conditioner and the absorption refrigerator respectively. The maximum power of each energy device should also be less than its corresponding installation capacity.

The model is a comprehensive energy scheduling model of four types of energy equipment of a photovoltaic energy storage air conditioner CCHP, each energy equipment can be combined with other energy equipment at will to form a sub-model, the sub-model only needs to fix the value of a non-existent equipment variable to 0, and the scheduling strategy with the optimal target cost can be solved by inputting the value into the model, and is not listed one by one.

By setting the constraints, the optimization problem is solved under the constraints, the solved result is more consistent with the actual situation, and the accuracy of the scheduling strategy is further improved.

Specifically, when performing optimization solution, a solver (e.g., mindopt, gurobi, etc.) may be used to perform solution to obtain the target scheduling policy, and a solution process of the solver is a manner conventionally used by those skilled in the art, and is not described here any more.

By applying the embodiment of the specification, after the resource data of the comprehensive energy, the historical consumption of the comprehensive energy by the energy consumption equipment in the specified region range and the building data in the specified region range are obtained, the consumption of the comprehensive energy by the energy consumption equipment in the specified region range in the next prediction time period is predicted according to the obtained historical consumption and the building data, then the prediction data of the consumption is obtained, the target scheduling strategy for the energy consumption equipment in the next prediction time period is determined according to the prediction data of the consumption and the resource data of the comprehensive energy, the scheduling strategy is the basis for performing the comprehensive energy scheduling operation, the target scheduling strategy for the energy consumption equipment in the next prediction time period can be accurately predicted through the embodiment of the specification, the use efficiency of the comprehensive energy in the next prediction time period can be ensured, and the optimized comprehensive energy scheduling is realized.

To sum up, the implementation framework of the integrated energy scheduling method provided in the embodiment of the present disclosure mainly includes two parts, as shown in fig. 2, fig. 2 shows an execution framework diagram of the integrated energy scheduling method provided in an embodiment of the present disclosure, and the execution framework diagram includes a prediction part and a planning and scheduling part.

The forecasting part inputs the historical consumption of the energy consumption equipment in a specified region range on the comprehensive energy and building data in the specified region range and outputs the consumption (including the annual consumption and daily consumption of each energy) of the energy consumption equipment in the specified region range on the comprehensive energy in the next forecasting time period; the input of the planning and scheduling part is the consumption of the energy consumption equipment in a specified region range to the comprehensive energy in the next prediction time period, and the resource data (mainly comprising electricity price and carbon transaction price) of the comprehensive energy, and the output is the target installation capacity of each energy equipment in the comprehensive energy as a scheduling strategy with the optimal target cost. And after the scheduling strategy is obtained, comprehensive energy scheduling is carried out on the designated region range based on the scheduling strategy, and energy conservation, emission reduction and economy analysis are carried out on the comprehensive energy scheduling in the operation process. For the implementation process of the prediction part and the planning and scheduling part, reference is made to the specific embodiment shown in fig. 1, which is not described herein again.

Based on the embodiment shown in fig. 1, fig. 3a shows a flowchart of another integrated energy scheduling method provided according to an embodiment of the present disclosure, which specifically includes the following steps.

Step 302, acquiring resource data of the comprehensive energy, historical consumption of the energy consumption equipment in a specified region range on the comprehensive energy, and building data in the specified region range, wherein the building data comprises building attribute data and geographic environment data.

And step 304, predicting the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range according to the historical consumption and the building data to obtain the prediction data of the consumption.

And step 306, determining a target scheduling strategy for the energy consumption equipment in the next prediction time period according to the prediction data of the consumption and the resource data of the comprehensive energy.

Wherein, regarding

steps

302, 304, and 306, the same as or similar to

steps

102, 104, and 106 in the embodiment shown in fig. 1 may be specifically referred to in the embodiment shown in fig. 1, and are not repeated here.

And 308, sending the target scheduling strategy to a management and control interface of the client for display.

And 310, receiving a confirmed scheduling instruction sent by the client, and sending the scheduling instruction to the energy equipment indicated by the target scheduling policy according to the scheduling policy with the optimal target cost in the next predicted time period when the next predicted time period is reached.

In this embodiment, after the target scheduling policy for the energy-consuming device in the next prediction time period is determined, the target scheduling policy may be recommended in a targeted manner, that is, the target scheduling policy is sent to the management and control interface of the client to be displayed, so that a supervisor can visually see the target scheduling policy for the energy-consuming device in the next prediction time period on the management and control interface of the client, and the purpose of accurately recommending the scheduling policy is achieved.

In the embodiment of the present specification, the scheduling policy may be continuously updated in a machine learning manner, so that when the scheduling policy is actually applied, the determined scheduling policy is more in line with an actual situation and is more accurate.

In addition, the management and control interface is provided with an interactive function, a supervisor can select whether to confirm execution of the target scheduling policy on the management and control interface, and clicks on the management and control interface to confirm execution, the client initiates a scheduling confirmation instruction, and after receiving the scheduling confirmation instruction sent by the client, the client can send the scheduling instruction to the energy equipment indicated by the target scheduling policy, specifically, the client can send an opening instruction to the energy equipment needing to be opened and send a closing instruction to the energy equipment needing to be closed. The embodiment adds an interactive function, and a supervisor can directly perform simple operation on the client, so that the dispatching management of the energy equipment can be realized.

As shown in fig. 3b, which is a schematic diagram of an architecture of an integrated energy management system according to an embodiment of the present disclosure, an integrated energy management platform 320 obtains resource data of integrated energy collected by a data collection device 341 in an integrated energy source side 340, historical consumption of integrated energy by energy consumption devices in a specified region and building data in the specified region, predicts consumption of integrated energy by the energy consumption devices in the specified region in a next prediction time period according to the historical consumption and the building data, obtains prediction data of consumption, determines a target scheduling policy for the energy consumption devices in the next prediction time period according to the prediction data of consumption and the resource data of integrated energy, sends the target scheduling policy to a client management and control interface of a management terminal 360 for display, and a supervisor selects whether to confirm execution of the target scheduling policy on the client management and control interface of the management terminal 360, if the supervisor clicks on the management and control interface to confirm execution, the client of the management terminal 360 initiates a confirmation scheduling instruction, and after receiving the confirmation scheduling instruction sent by the client of the management terminal 360, the integrated energy management platform 320 may send a scheduling instruction to the energy device 342 of the integrated energy source side 340 indicated by the target scheduling policy.

By applying the embodiment of the specification, after the resource data of the comprehensive energy, the historical consumption of the comprehensive energy by the energy consumption equipment in the specified region range and the building data in the specified region range are obtained, the consumption of the comprehensive energy by the energy consumption equipment in the specified region range in the next prediction time period is predicted according to the obtained historical consumption and the building data, then the prediction data of the consumption is obtained, the target scheduling strategy for the energy consumption equipment in the next prediction time period is determined according to the prediction data of the consumption and the resource data of the comprehensive energy, the scheduling strategy is the basis for performing the comprehensive energy scheduling operation, the target scheduling strategy for the energy consumption equipment in the next prediction time period can be accurately predicted through the embodiment of the specification, the use efficiency of the comprehensive energy in the next prediction time period can be ensured, and the optimized comprehensive energy scheduling is realized. And after the target scheduling strategy for the energy consumption equipment in the next prediction time period is determined, the target scheduling strategy is sent to the control interface of the client to be displayed, so that a supervisor can visually see the target scheduling strategy on the control interface of the client, and the aim of accurately recommending the scheduling strategy is fulfilled. In addition, the management and control interface is provided with an interactive function, a supervisor can select whether to confirm execution of the target scheduling policy on the management and control interface, the supervisor clicks on the management and control interface to confirm execution, the client side can initiate a scheduling confirmation instruction, after receiving the scheduling confirmation instruction sent by the client side, the dispatching instruction can be sent to the energy equipment indicated by the target scheduling policy, and the supervisor can directly perform simple operation on the client side, so that the scheduling management of the energy equipment can be realized.

Corresponding to the above method embodiment, the present specification further provides an embodiment of an integrated energy scheduling apparatus, and fig. 4 shows a schematic structural diagram of an integrated energy scheduling apparatus provided in an embodiment of the present specification. As shown in fig. 4, the apparatus includes:

the data acquisition module 420 is configured to acquire resource data of the comprehensive energy, historical consumption of the comprehensive energy by energy consumption equipment in a specified region range and building data in the specified region range, wherein the building data comprises building attribute data and geographic environment data;

the prediction module 440 is configured to predict the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range according to the historical consumption and the building data, so as to obtain prediction data of the consumption;

and the scheduling planning module 460 is configured to determine a target scheduling strategy for the energy consumption equipment in the next prediction time period according to the prediction data of the consumption amount and the resource data of the comprehensive energy.

Optionally, the predicting module 440 may be further configured to, for a first energy in the comprehensive energy, query a preset correspondence table according to the building data to obtain a consumption coefficient of the first energy, where the preset correspondence table records a correspondence between the building data and the consumption coefficient, and the preset correspondence table is pre-established according to a design rule of the first energy; and calculating the prediction data of the consumption of the first energy by the energy consumption equipment in the appointed region range according to the historical consumption and the consumption coefficient of the first energy.

the prediction module 440 may be further configured to query a first mapping table according to the building function type to obtain a target unit area consumption, where the first mapping table records a corresponding relationship between the building function type and the unit area consumption; inquiring a second corresponding table according to the geographic position to obtain a target area consumption correction coefficient, wherein the second corresponding table records the corresponding relation between the geographic position and the area consumption correction coefficient; and inquiring a third corresponding table according to the climate environment to obtain a target climate consumption correction coefficient, wherein the third corresponding table records the corresponding relation between the climate environment and the climate consumption correction coefficient.

Optionally, the predicting module 440 may be further configured to calculate a maximum consumption of the first energy within the designated region range according to the target consumption per unit area, the target region consumption correction coefficient, and the building area; calculating the annual consumption of the first energy in the designated region according to the maximum consumption of the first energy in the designated region and the target climate consumption correction coefficient; and determining prediction data of the consumption of the first energy by the energy consumption equipment in the specified region according to the historical consumption and the annual consumption of the first energy in the specified region.

Optionally, the scheduling planning module 460 may be further configured to perform an optimal solution with a scheduling policy for the energy consuming device as an optimal target under a preset constraint condition according to the predicted data of the consumption amount and the resource data of the integrated energy, and determine a target scheduling policy for the energy consuming device in a next prediction time period, where the preset constraint condition at least includes a consumption amount constraint of the energy consuming device on the integrated energy and an energy supply constraint of the integrated energy.

The above is a schematic solution of the integrated energy scheduling apparatus of this embodiment. It should be noted that the technical solution of the integrated energy scheduling apparatus and the technical solution of the integrated energy scheduling method belong to the same concept, and details of the technical solution of the integrated energy scheduling apparatus, which are not described in detail, can be referred to the description of the technical solution of the integrated energy scheduling method.

FIG. 5 illustrates a block diagram of a computing device 500 provided in accordance with one embodiment of the present description. The components of the computing device 500 include, but are not limited to, a memory 510 and a processor 520. Processor 520 is coupled to memory 510 via bus 530, and database 550 is used to store data.

Computing device 500 also includes access device 540, access device 540 enabling computing device 500 to communicate via one or more networks 560. Examples of such networks include a Public Switched Telephone Network (PSTN), a Local Area Network (LAN), a Wide Area Network (WAN), a Personal Area Network (PAN), or a combination of communication networks such as the internet. The Access device 440 may include one or more of any type of Network Interface (e.g., a Network Interface Card (NIC)) whether wired or Wireless, such as an IEEE802.11 Wireless Local Area Network (WLAN) Wireless Interface, a worldwide Interoperability for Microwave Access (Wi-MAX) Interface, an ethernet Interface, a Universal Serial Bus (USB) Interface, a cellular Network Interface, a bluetooth Interface, a Near Field Communication (NFC) Interface, and so forth.

In one embodiment of the present description, the above-described components of computing device 500, as well as other components not shown in FIG. 5, may also be connected to each other, such as by a bus. It should be understood that the block diagram of the computing device architecture shown in FIG. 5 is for purposes of example only and is not limiting as to the scope of the present description. Those skilled in the art may add or replace other components as desired.

Computing device 500 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., tablet, personal digital assistant, laptop, notebook, netbook, etc.), mobile phone (e.g., smartphone), wearable computing device (e.g., smartwatch, smartglasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or PC. Computing device 500 may also be a mobile or stationary server.

Wherein the processor 520 is configured to execute computer-executable instructions that, when executed by the processor, implement the steps of the above-described integrated energy scheduling method.

The above is an illustrative scheme of a computing device of the present embodiment. It should be noted that the technical solution of the computing device and the technical solution of the above-mentioned integrated energy scheduling method belong to the same concept, and details that are not described in detail in the technical solution of the computing device can be referred to the description of the technical solution of the above-mentioned integrated energy scheduling method.

An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which when executed by a processor, implement the steps of the above-mentioned integrated energy scheduling method.

The above is an illustrative scheme of a computer-readable storage medium of the present embodiment. It should be noted that the technical solution of the storage medium and the technical solution of the above-mentioned integrated energy scheduling method belong to the same concept, and details that are not described in detail in the technical solution of the storage medium can be referred to the description of the technical solution of the above-mentioned integrated energy scheduling method.

An embodiment of the present specification further provides a computer program, wherein when the computer program is executed in a computer, the computer program causes the computer to execute the steps of the integrated energy scheduling method.

The above is an illustrative scheme of a computer program of the present embodiment. It should be noted that the technical solution of the computer program and the technical solution of the above-mentioned integrated energy scheduling method belong to the same concept, and details that are not described in detail in the technical solution of the computer program can be referred to the description of the technical solution of the above-mentioned integrated energy scheduling method.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The computer instructions comprise computer program code which may be in the form of source code, object code, an executable file or some intermediate form, or the like. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

It should be noted that, for the sake of simplicity, the foregoing method embodiments are described as a series of acts, but those skilled in the art should understand that the present embodiment is not limited by the described acts, because some steps may be performed in other sequences or simultaneously according to the present embodiment. Further, those skilled in the art should also appreciate that the embodiments described in this specification are preferred embodiments and that acts and modules referred to are not necessarily required for an embodiment of the specification.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

The preferred embodiments of the present specification disclosed above are intended only to aid in the description of the specification. Alternative embodiments are not exhaustive and do not limit the invention to the precise embodiments described. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the embodiments and the practical application, to thereby enable others skilled in the art to best understand and utilize the embodiments. The specification is limited only by the claims and their full scope and equivalents.

Claims

1. An integrated energy scheduling method, comprising:

acquiring resource data of comprehensive energy, historical consumption of energy consumption equipment in a specified region for the comprehensive energy and building data in the specified region, wherein the building data comprises building attribute data and geographic environment data;

according to the historical consumption and the building data, predicting the consumption of comprehensive energy in the next prediction time period by the energy consumption equipment in the specified region range to obtain prediction data of the consumption;

2. The method of claim 1, wherein the step of predicting the consumption of the comprehensive energy in the next prediction time period by the energy consumption equipment in the designated regional area according to the historical consumption and the building data to obtain the prediction data of the consumption comprises:

and calculating the prediction data of the consumption of the first energy by the energy consumption equipment in the specified region range according to the historical consumption and the consumption coefficient of the first energy.

3. The method of claim 2, the building attribute data comprising a building function type and a building area; the geographic environment data comprises a geographic location and a climate environment; the consumption coefficient of the first energy comprises unit area consumption, a region consumption correction coefficient and a climate consumption correction coefficient; the preset corresponding table comprises a first corresponding table, a second corresponding table and a third corresponding table;

the step of inquiring a preset corresponding table according to the building data aiming at a first energy in the comprehensive energy to obtain a consumption coefficient of the first energy comprises the following steps:

inquiring the first corresponding table according to the building function type to obtain the target unit area consumption, wherein the first corresponding table records the corresponding relation between the building function type and the unit area consumption;

inquiring the second corresponding table according to the geographic position to obtain a target area consumption correction coefficient, wherein the second corresponding table records the corresponding relation between the geographic position and the area consumption correction coefficient;

and inquiring the third corresponding table according to the climate environment to obtain a target climate consumption correction coefficient, wherein the third corresponding table records the corresponding relation between the climate environment and the climate consumption correction coefficient.

4. The method according to claim 3, wherein the step of calculating the prediction data of the consumption of the first energy by the energy consumption equipment in the designated area according to the historical consumption and the consumption coefficient of the first energy comprises:

calculating the maximum consumption of the first energy in the specified region range according to the target unit area consumption, the target region consumption correction coefficient and the building area;

calculating the annual consumption of the first energy in the specified region according to the maximum consumption of the first energy in the specified region and the target climate consumption correction coefficient;

and determining prediction data of the consumption of the first energy by the energy consumption equipment in the specified region range according to the historical consumption and the annual consumption of the first energy in the specified region range.

5. The method according to any one of claims 1 to 4, wherein the step of determining a target scheduling policy for the energy consuming device in the next prediction period based on the prediction data of the consumption amount and the resource data of the integrated energy source comprises:

and according to the predicted data of the consumption and the resource data of the comprehensive energy, under a preset constraint condition, optimally solving by taking the optimal scheduling strategy of the energy consumption equipment as a target, and determining the target scheduling strategy of the energy consumption equipment in the next prediction time period, wherein the preset constraint condition at least comprises the consumption constraint of the energy consumption equipment on the comprehensive energy and the energy supply constraint of the comprehensive energy.

6. The method of claim 5, the preset constraints further comprising: and the charging and discharging performance of the energy storage equipment in the designated region range is restricted, and the relevance between the energy sources in the comprehensive energy sources is restricted.

7. The method of claim 1, further comprising, after the step of determining a target scheduling policy for the energy consuming device for the next predicted time period based on the predicted consumption data and the resource data of the integrated energy resource:

sending the target scheduling strategy to a management and control interface of a client for display;

and receiving a confirmed scheduling instruction sent by the client, and sending a scheduling instruction to the energy equipment indicated by the target scheduling policy according to the target scheduling policy when the next predicted time period is reached.

8. A computing device, comprising: a memory and a processor;

the memory is configured to store computer-executable instructions and the processor is configured to execute the computer-executable instructions, which when executed by the processor, implement the integrated energy scheduling method of any one of claims 1 to 7.

9. A computer readable storage medium storing computer executable instructions which, when executed by a processor, implement the integrated energy scheduling method of any one of claims 1 to 7.

10. A computer program which, when executed in a computer, causes the computer to carry out the integrated energy scheduling method of any one of claims 1 to 7.