Source load complementary scheduling optimization method and system for industrial load

Abstract

The invention relates to an industrial load-oriented source-load complementary scheduling optimization method and system, and the method comprises the following steps: collecting historical operation data of a source side and a load side in an industrial park, forming sample data, carrying out the preprocessing and multi-energy coupling analysis of the sample data, and constructing a source-load complementary comprehensive energy system operation architecture; a transaction mechanism is introduced, and a source-load complementary integrated energy system scheduling model is formed; and based on the real-time operation data of the source side and the load side in the industrial park, solving the scheduling model of the source-load complementary integrated energy system by taking the minimization of the operation cost of the industrial park as a target to obtain a scheduling result of the source-load complementary integrated energy system. According to the method, through collaborative optimization scheduling on the source-load side, the renewable energy consumption capacity of the industrial park is improved, carbon emission and operation cost are effectively reduced, and the greenness, economical efficiency and sustainable operation capacity of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of power system dispatch optimization and energy management, and in particular to a source-load complementary dispatch optimization method and system for industrial loads. Background Art

[0002] In recent years, the global climate change problem has become increasingly serious, mainly due to the emission of greenhouse gases such as carbon dioxide, which has led to global temperature rise. As a major industrial power and energy consumer in the world, my country faces huge challenges in energy supply and environmental protection. my country's energy consumption mainly relies on fossil fuels such as coal and oil. With the rapid development of my country's economy and the acceleration of urbanization, the demand for energy in industrial production and residents' lives has increased significantly, resulting in a sharp increase in greenhouse gas emissions. Although my country's per capita carbon emissions are still lower than those of developed countries, with continued economic growth in the future, my country's carbon emissions are expected to continue to rise, bringing greater pressure to energy conservation and emission reduction.

[0003] The energy structure of many industrial parks relies on fossil fuels with high carbon emissions. In this context, the demand for low-carbon economic operation of industrial parks is becoming increasingly prominent. Therefore, the monitoring and prediction of carbon emissions and power dispatching behavior in industrial parks have become the key to policy formulation and optimization. The existing power dispatching methods have failed to effectively control and reduce carbon emissions, lack effective market mechanisms to stimulate and regulate energy production and consumption, and only focus on a single goal, ignoring other important factors.

[0004] Therefore, in the face of the severe challenges of global climate change, the research and development and implementation of more accurate and real-time source-load complementary scheduling optimization methods are of great significance to the goal of promoting the low-carbon transformation of industrial parks. Summary of the invention

[0005] The present invention provides a source-load complementary scheduling optimization method and system for industrial loads, aiming to improve the renewable energy consumption rate and reduce carbon emissions. The method collaboratively manages the energy consumption and carbon emissions of industrial parks by introducing the green certificate (GCT) mechanism and the stepped carbon trading (CET) mechanism. The constructed source-load complementary integrated energy system model realizes the coordinated scheduling of various energy forms such as photovoltaics, wind power, and gas turbines through an energy hub to improve energy utilization efficiency; and through an incentive-based demand response mechanism, it guides users to adjust load demand according to real-time electricity prices to achieve peak shaving and valley filling and optimize supply and demand balance. Through the interaction of the GCT and CET mechanisms, enterprises achieve the goals of energy consumption and carbon emission control by purchasing green certificates and carbon emission rights. Combined with the solver, low-carbon and efficient operation of industrial parks can be achieved, which helps sustainable development.

[0006] The present invention solves the above technical problems in the following way: a source-load complementary scheduling optimization method for industrial loads, comprising the following steps:

[0007] Collect historical operation data of the source side and the load side in the industrial park to form sample data, pre-process the sample data to form a data set, perform multi-energy coupling analysis on the data set, and obtain the interaction results between the source side and the load side in the industrial park;

[0008] Based on the interaction between the source side and the load side in the industrial park, the operation framework of the source-load complementary integrated energy system is constructed;

[0009] Introduce a trading mechanism into the operation framework of the source-load complementary regional integrated energy system to form a dispatching model for the source-load complementary integrated energy system;

[0010] Based on the real-time operation data of the source side and the load side in the industrial park, the scheduling model of the source-load complementary integrated energy system is solved with the goal of minimizing the operation cost of the industrial park, and the scheduling result of the source-load complementary integrated energy system is obtained.

[0011] Preferably, the historical operation data of the source side and the load side in the industrial park are collected to form sample data, and the sample data are preprocessed to form a data set, and the data set is subjected to multi-energy coupling analysis to obtain the interaction results between the source side and the load side in the industrial park, specifically including:

[0012] Collect historical operating data on the source side and the load side in the industrial park to form sample data; the historical operating data on the source side and the load side include the power generation of renewable energy (such as photovoltaic and wind power), the consumption of traditional energy (such as gas turbines and cogeneration devices), the power and heat load demand of the industrial park, and the historical carbon emission data of enterprises;

[0013] Perform outlier correction and standardization on sample data to form a data set;

[0014] The power output form of electricity and heat, the change form of electricity and thermal energy storage, and the input form of electricity and thermal load in the dataset are analyzed to obtain the interaction results between the source side and the load side in the industrial park.

[0015] Preferably, the construction of a source-load complementary integrated energy system operation architecture based on the interaction results between the source side and the load side in the industrial park specifically includes:

[0016] Based on the interaction between the source side and the load side in the industrial park, various energy forms in the industrial park are integrated to build a source-load complementary integrated energy system operation framework. In the source-load complementary integrated energy system operation framework, the output power of various energy sources meets the requirements of the industrial park side, and the expression is as follows:

[0017] P(t)=P PV (t)+PWT (t)+P CHP (t)+P GF (t)+P Grid (t)

[0018] H(t)=H GB (t)+H GF (t)+H CHP (t)+H Grid (t)

[0019] Where P(t) and H(t) represent the total power output of electricity and heat, respectively. PV (t), P WT (t), P CHP (t), P GF (t), P Grid (t) are the power outputs of photovoltaic, wind power, cogeneration, gas turbine and grid, respectively; H GB (t), H GF (t), H CHP (t), H Grid (t) are the thermal power outputs of boilers, gas turbines, cogeneration units and heating networks, respectively.

[0020] Preferably, the trading mechanism includes a green certificate-tiered carbon trading interaction mechanism and an incentive-based demand response mechanism.

[0021] The GCT mechanism is used to increase the renewable energy consumption rate, while the CET mechanism encourages enterprises to reduce carbon emissions by setting carbon emission quotas and implementing tiered pricing for the excess, while ensuring the economic operation of the system. An incentive-based demand response mechanism is introduced on the load side, and users flexibly adjust the load according to their electricity and heat energy needs, using price incentives to reduce energy demand during peak hours, optimize the load curve, and achieve the effect of "peak shaving and valley filling".

[0022] Further preferably, the green certificate-tiered carbon trading interaction mechanism is as follows:

[0023] The amount of electricity consumed by enterprises in industrial parks from renewable energy sources is the green certificate demand. The green certificate trading market sets the target electricity consumption of renewable energy for enterprises based on the green certificate demand as the green certificate quota. When an enterprise fails to reach the target electricity consumption of renewable energy, it needs to purchase green certificates corresponding to the difference in the target electricity consumption from the green certificate trading market to complete the green certificate quota. The expression of the green certificate transaction cost is as follows:

[0024]

[0025] Where: C GCTis the transaction cost of green certificates; λ is the unit price of green certificates; ρ is the quota coefficient of green certificates that the industrial park needs to hold; δ is the quantitative coefficient of renewable energy power generation converted into green certificates; P L (t) is the electrical load at time t;

[0026] The carbon emission amount that enterprises in industrial parks should meet is the carbon emission demand. The carbon trading market sets the carbon emission quota that enterprises should meet based on the carbon emission demand. If the enterprise exceeds the established carbon emission quota, it should purchase the corresponding carbon emission rights that exceed the quota to make up for the carbon emission quota. The expression of its carbon emission cost is as follows:

[0027]

[0028] Where: C CET is the carbon emission transaction cost; σ is the unit carbon transaction price; k is the step length; γ is the unit price growth rate; B st is the carbon emission trading amount;

[0029] When consuming electricity from renewable energy, the enterprise will also receive additional carbon emission quotas from the government for renewable energy conversion. Such carbon emission quotas from renewable energy conversion can be used to offset the carbon emission quotas that the enterprise should meet.

[0030]

[0031] Among them, B green is the carbon emission quota converted from renewable energy, κ is the conversion coefficient of renewable energy into carbon emission quota, δ is the coefficient of green certificate trading, P PV (t) and P WT (t) represent the power output of photovoltaic and wind power respectively.

[0032] Further preferably, the incentive-based demand response mechanism is as follows:

[0033] By setting up incentive demand response costs, under the condition of meeting the set user satisfaction, users are encouraged to increase electricity consumption during periods of low electricity prices and reduce electricity consumption during periods of high electricity prices to obtain incentive benefits. The expression of incentive demand response costs is as follows:

[0034]

[0035] Where C IDR is the cost of incentive demand response; a i 、b i are the electrical and thermal compensation coefficients respectively; P Li , H Li are the electric and thermal loads participating in the response respectively; i is the load adjustment type, i=1 means load transfer, i=2 means load reduction; P Li (t) and HLi (t) are the electricity and heat loads participating in demand response, respectively, and i represents the type of load;

[0036] The expression of user satisfaction is as follows:

[0037]

[0038] Among them, Ω is user satisfaction, P Li (t) and H Li (t) are the power and heat loads participating in the response, P L (t) and H L (t) is the electricity and heat load under normal conditions;

[0039] The industrial park source side adjusts the energy supply strategy according to the real-time user electricity demand. If the electricity demand suddenly increases during certain periods, the industrial park may start backup energy equipment (such as cogeneration devices, gas turbines, etc.) to provide more electricity and heat. The peak-shaving benefits generated are as follows:

[0040]

[0041] Where: are the profit coefficients of thermal and electric load peak regulation respectively; j is the starting time of the load peak; ΔD is the length of the peak period.

[0042] When the source side of the industrial park cannot meet the user's electricity demand, it needs to purchase additional energy from the superior thermal power company; if the actual user satisfaction does not meet the set user satisfaction, the source side of the industrial park needs to adjust the internal energy supply strategy to meet the user. The industrial park and the thermal power company interact through "peak shaving demand" and "purchase demand" to ensure that the energy supply of the industrial park can be met under different load conditions. Changes in user satisfaction directly affect the energy supply strategy of the industrial park. If user satisfaction is low, the industrial park may adjust its electricity demand and increase peak shaving and purchase demand to ensure that user needs are met.

[0043] Preferably, based on the real-time operation data of the source side and the load side in the industrial park, the advanced scheduling model of the source-load complementary integrated energy system is solved with the goal of minimizing the operation cost of the industrial park to obtain the scheduling result of the source-load complementary integrated energy system, which specifically includes:

[0044] The objective function of minimizing the operating cost of the industrial park is established as follows;

[0045] minf=C op +C CET +C GCT +C IDR -C ps

[0046] Among them, C op is the basic operating cost of the system, C CET and C GCT are the costs of carbon trading and green certificate trading, C IDR is the demand response cost, C ps For peak load benefits;

[0047] The expression of basic operating cost is:

[0048]

[0049] Where: S gas (t) is the natural gas demand at time t; τ t is the time-sharing gas price; τ w , τ p are the electricity costs of wind and solar respectively; C pro Equipment maintenance costs;

[0050] Based on the real-time electric load and heat load in the industrial park, the real-time gas price and electricity price of the service provider, the Gurobi solver is used to solve the target of the advanced scheduling model of the source-load complementary integrated energy system to obtain the scheduling optimization plan.

[0051] The present invention also provides a source-load complementary dispatch optimization system for industrial loads, comprising:

[0052] The data analysis module is used to collect the historical operation data of the source side and the load side in the industrial park to form sample data, and pre-process the sample data to form a data set, and perform multi-energy coupling analysis on the data set to obtain the interaction results between the source side and the load side in the industrial park;

[0053] The architecture building module is used to build the source-load complementary integrated energy system operation architecture based on the interaction results between the source side and the load side in the industrial park;

[0054] Model building module, used to introduce trading mechanism into the operation framework of regional integrated energy system with complementary sources and loads, and form a dispatching model of the integrated energy system with complementary sources and loads;

[0055] The scheduling optimization module is used to solve the scheduling model of the source-load complementary integrated energy system based on the real-time operating data of the source side and the load side in the industrial park, with the goal of minimizing the operating cost of the industrial park, and obtain the scheduling results of the source-load complementary integrated energy system.

[0056] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the source-load complementary scheduling optimization method for industrial parks as described above is implemented.

[0057] The present invention also provides an electronic device, comprising a memory and a processor: the memory is used to store computer-executable instructions, the processor is used to execute the computer-executable instructions, and when the computer-executable instructions are executed by the processor, the source-load complementary scheduling optimization method for industrial parks as described above is implemented.

[0058] The beneficial effects of the present invention are as follows:

[0059] 1. Aiming at the energy supply and demand scheduling problem in industrial parks, the present invention adopts a multi-energy coupling model and an incentive-based demand response mechanism. By acquiring data on the source side (such as photovoltaics, wind power, and gas turbines) and the load side (electricity and heat demand) in real time and combining a multi-objective optimization algorithm, the energy utilization efficiency is improved and carbon emissions are effectively reduced, providing a practical solution for carbon emission reduction management in industrial parks.

[0060] 2. This invention introduces the Green Power Certificate (GCT) and the Stepped Carbon Trading (CET) mechanism to help enterprises meet the renewable energy consumption requirements through the green certificate mechanism and solve the problem of excess carbon emissions through the carbon trading market. This mechanism provides enterprises with a flexible carbon emission management method, helps reduce carbon emission costs, and promotes low-carbon economic operation.

[0061] 3. The present invention uses a solver to comprehensively consider renewable energy consumption, carbon emission management and demand response for the multi-objective optimization problem of industrial parks, and generates an optimized energy scheduling plan. This method supports the operation of industrial parks under low-carbon and high-efficiency conditions, and provides effective technical support for energy management in industrial parks.

[0062] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0064] Figure 1 A flow chart of a source-load complementary scheduling optimization method for industrial loads provided in Example 1;

[0065] Figure 2 The daily load curve of the industrial park is collected in Example 1;

[0066] Figure 3This is a schematic diagram of the operation architecture of the source-load complementary integrated energy system in Example 1;

[0067] Figure 4 This is a schematic diagram of the scheduling model of the source-load complementary integrated energy system in Example 1;

[0068] Figure 5 is the real-time gas price and electricity price in Example 1;

[0069] Figure 6 Schematic diagram of carbon emissions and carbon trading amounts generated during the scheduling process of Examples 1 to 3 and Comparative Example 1;

[0070] Figure 7 This is the real-time electric load optimization result curve during the industrial park scheduling process in Example 1;

[0071] Figure 8 This is the real-time heat load optimization result curve during the industrial park scheduling process in Example 1;

[0072] Fig. 9 This is the real-time electric load optimization result curve during the industrial park scheduling process in Example 3;

[0073] Fig.10 This is the real-time heat load optimization result curve during the industrial park scheduling process in Example 3;

[0074] Fig.11 A module diagram of a source-load complementary scheduling optimization system for industrial loads provided in Example 4. DETAILED DESCRIPTION

[0075] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0076] The following embodiments and comparative examples are all from an industrial park in a certain area of ​​Anhui Province.

[0077] Example 1

[0078] like Figure 1 As shown, this embodiment provides a source-load complementary scheduling optimization method for industrial loads, comprising the following steps:

[0079] S1. Collect historical operation data on the source side and the load side of the industrial park to form sample data, preprocess the sample data to form a data set, perform multi-energy coupling analysis on the data set, and obtain the interaction results between the source side and the load side of the industrial park, as follows:

[0080] Collect historical operating data on the source side and the load side of the industrial park to form sample data; the historical operating data on the source side and the load side include the power generation of renewable energy (such as photovoltaic and wind power), the consumption of traditional energy (such as gas turbines and cogeneration devices), the power and thermal load demand of the industrial park, and the historical carbon emission data of enterprises; the collection time is 24 hours, the step length is 1 hour, and the typical daily load curve of the industrial park in the area is as follows: Figure 2 As shown:

[0081] The sample data is corrected for outliers by using an interpolation method based on historical data to fill in the data to ensure data integrity. The specific filling formula is as follows:

[0082]

[0083] Among them, P t Represents the carbon emission value at the current moment, P t-1 and P t-12 Represents the carbon emission values ​​of the previous month and the same month of the previous year respectively.

[0084] After outlier correction, standardization is performed to form a data set;

[0085]

[0086] Among them, X is the original value in the data, X min and X max are the minimum and maximum values ​​in the data set, respectively. The standardized data range is [0,1], which is convenient for subsequent analysis.

[0087] The power output form of electricity and heat, the change form of electricity and thermal energy storage, and the input form of electricity and thermal load in the dataset are analyzed to obtain the interaction results between the source side and the load side in the industrial park.

[0088] S2. Based on the interaction between the source side and the load side in the industrial park, the operation framework of the source-load complementary integrated energy system is constructed, such as Figure 3 As shown;

[0089] Based on the interaction between the source side and the load side in the industrial park, various energy forms in the industrial park are integrated to build a source-load complementary integrated energy system operation framework. In the source-load complementary integrated energy system operation framework, the output power of various energy sources meets the requirements of the industrial park side, and the expression is as follows:

[0090] P(t)=P PV (t)+P WT (t)+P CHP (t)+P GF (t)+P Grid(t)

[0091] H(t)=H GB (t)+H GF (t)+H CHP (t)+H Grid (t)

[0092] Where P(t) and H(t) represent the total power output of electricity and heat, respectively. PV (t), P WT (t), P CHP (t), P GF (t), P Grid (t) are the power outputs of photovoltaic, wind power, cogeneration, gas turbine and grid, respectively; H GB (t), H GF (t), H CHP (t), H Grid (t) are the thermal power outputs of boilers, gas turbines, cogeneration units and heating networks, respectively.

[0093] S3. Introduce the green certificate-tiered carbon trading interaction mechanism and incentive-based demand response mechanism into the operation framework of the source-load complementary regional integrated energy system to form a source-load complementary integrated energy system scheduling model, such as Figure 4 As shown;

[0094] The green certificate-tiered carbon trading interaction mechanism is as follows:

[0095] The amount of electricity consumed by enterprises in industrial parks from renewable energy sources is the green certificate demand. The green certificate trading market sets the target electricity consumption of renewable energy for enterprises based on the green certificate demand as the green certificate quota. When an enterprise fails to reach the target electricity consumption of renewable energy, it needs to purchase green certificates corresponding to the difference in the target electricity consumption from the green certificate trading market to complete the green certificate quota. The expression of the green certificate transaction cost is as follows:

[0096]

[0097] Where: C GCT is the transaction cost of green certificates; λ is the unit price of green certificates; ρ is the quota coefficient of green certificates that the industrial park needs to hold; δ is the quantitative coefficient of renewable energy power generation converted into green certificates; P L (t) is the electrical load at time t;

[0098] The carbon emission amount that enterprises in industrial parks should meet is the carbon emission demand. The carbon trading market sets the carbon emission quota that enterprises should meet based on the carbon emission demand. If the enterprise exceeds the established carbon emission quota, it should purchase the corresponding carbon emission rights that exceed the quota to make up for the carbon emission quota. The expression of its carbon emission cost is as follows:

[0099]

[0100] Where: C CET is the carbon emission transaction cost; σ is the unit carbon transaction price; k is the step length; γ is the unit price growth rate; B st is the carbon emission trading amount;

[0101] When consuming electricity from renewable energy, the enterprise will also receive additional carbon emission quotas from the government for renewable energy conversion. Such carbon emission quotas from renewable energy conversion can be used to offset the carbon emission quotas that the enterprise should meet.

[0102]

[0103] Among them, B green is the carbon emission quota converted from renewable energy, κ is the conversion coefficient of renewable energy into carbon emission quota, δ is the coefficient of green certificate trading, P PV (t) and P WT (t) represent the power output of photovoltaic and wind power respectively.

[0104] The incentive demand response mechanism is as follows:

[0105] By setting up incentive-based demand response costs, under the condition of meeting the set user satisfaction, users are encouraged to increase electricity consumption during periods of low electricity prices and reduce electricity consumption during periods of high electricity prices to obtain incentive benefits (specific real-time prices such as Figure 5 The cost of incentive-based demand response is expressed as follows:

[0106]

[0107] Where C IDR is the cost of incentive demand response; a i 、b i are the electrical and thermal compensation coefficients respectively; P Li , H Li are the electric and thermal loads participating in the response respectively; i is the load adjustment type, i=1 means load transfer, i=2 means load reduction; P Li (t) and H Li (t) are the electricity and heat loads participating in demand response, respectively, and i represents the type of load;

[0108] The expression of user satisfaction is as follows:

[0109]

[0110] Among them, Ω is user satisfaction, P Li (t) and H Li(t) are the power and heat loads participating in the response, P L (t) and H L (t) is the electricity and heat load under normal conditions;

[0111] The industrial park source side adjusts the energy supply strategy according to the real-time user electricity demand. If the electricity demand suddenly increases during certain periods, the industrial park may start backup energy equipment (such as cogeneration devices, gas turbines, etc.) to provide more electricity and heat. The peak-shaving benefits generated are as follows:

[0112]

[0113] Where: are the profit coefficients of thermal and electric load peak regulation respectively; j is the starting time of the load peak; ΔD is the length of the peak period.

[0114] When the source side of the industrial park cannot meet the user's electricity demand, it needs to purchase additional energy from the superior thermal power company; if the actual user satisfaction does not meet the set user satisfaction, the source side of the industrial park needs to adjust the internal energy supply strategy to meet the user. The industrial park and the thermal power company interact through "peak shaving demand" and "purchase demand" to ensure that the energy supply of the industrial park can be met under different load conditions. Changes in user satisfaction directly affect the energy supply strategy of the industrial park. If user satisfaction is low, the industrial park may adjust its electricity demand and increase peak shaving and purchase demand to ensure that user needs are met.

[0115] S4. Based on the real-time operation data of the source side and the load side in the industrial park, the dispatch model of the source-load complementary integrated energy system is solved with the goal of minimizing the operation cost of the industrial park, and the dispatch results of the source-load complementary integrated energy system are obtained, which specifically include the following:

[0116] Establish an objective function to minimize the operating costs of the industrial park

[0117] minf=C op +C CET +C GCT +C IDR -C ps

[0118] Among them, C op is the basic operating cost of the system, C CET and C GCT are the costs of carbon trading and green certificate trading, C IDR is the demand response cost, C ps For peak load benefits;

[0119] The expression of basic operating cost is:

[0120]

[0121] Where: S gas (t) is the natural gas demand at time t; τ t is the time-sharing gas price; τ w , τ p are the electricity costs of wind and solar respectively; C pro Equipment maintenance costs;

[0122] Based on the real-time electric load and heat load in the industrial park, the real-time gas price and electricity price of the service provider, the Gurobi solver is used to solve the target of the advanced scheduling model of the source-load complementary integrated energy system to obtain the scheduling optimization plan.

[0123] Example 2

[0124] This embodiment provides another source-load complementary scheduling optimization method for industrial loads. The steps are basically the same as those in Example 1. The difference is that in step S3, only the incentive-based demand response mechanism is introduced into the source-load complementary regional integrated energy system operation architecture to form a source-load complementary integrated energy system scheduling model.

[0125] Example 3

[0126] This embodiment provides another source-load complementary scheduling optimization method for industrial loads, and its steps are basically the same as those in Example 1. The difference is that in step S3, only the incentive-type demand response mechanism, the green certificate mechanism, and the step-by-step carbon trading mechanism are introduced into the source-load complementary regional integrated energy system operation architecture to form a source-load complementary integrated energy system scheduling model.

[0127] The green certificate mechanism is as follows:

[0128] The amount of electricity consumed by enterprises in industrial parks from renewable energy sources is the green certificate demand. The green certificate trading market sets the target electricity consumption of renewable energy for enterprises based on the green certificate demand as the green certificate quota. When an enterprise fails to reach the target electricity consumption of renewable energy, it needs to purchase green certificates corresponding to the difference in the target electricity consumption from the green certificate trading market to complete the green certificate quota. The expression of the green certificate transaction cost is as follows:

[0129]

[0130] Where: C GCT is the transaction cost of green certificates; λ is the unit price of green certificates; ρ is the quota coefficient of green certificates that the industrial park needs to hold; δ is the quantitative coefficient of renewable energy power generation converted into green certificates; P L (t) is the electrical load at time t.

[0131] The tiered carbon trading mechanism is as follows:

[0132] The carbon emission amount that enterprises in industrial parks should meet is the carbon emission demand. The carbon trading market sets the carbon emission quota that enterprises should meet based on the carbon emission demand. If the enterprise exceeds the established carbon emission quota, it should purchase the corresponding carbon emission rights that exceed the quota to make up for the carbon emission quota. The expression of its carbon emission cost is as follows:

[0133]

[0134] Where: C CET is the carbon emission transaction cost; σ is the unit carbon transaction price; k is the step length; γ is the unit price growth rate; B st is the carbon emission trading amount;

[0135] Comparative Example 1

[0136] This comparative example provides another source-load complementary scheduling method for industrial parks, and its steps are basically the same as those in Example 1, except that S3 does not introduce any trading mechanism, and only the basic scheduling is performed by the source-load complementary regional integrated energy system operation architecture.

[0137] The transaction mechanism introduced into the scheduling model provided by each embodiment and comparative example is shown in Table 1.

[0138] Table 1 Transaction mechanism setting information of each embodiment and comparative example

[0139]

[0140] According to relevant parameters and scenario settings, the optimization results of each embodiment and comparative example are analyzed from three perspectives: required cost, carbon reduction capability, and unit output.

[0141] The relevant costs and peak-shaving benefits are shown in Table 2. Both the GCT-CET mechanism and the incentive-based demand-side response can reduce the comprehensive operating cost of RIES. Although Examples 1 to 3 generate dispatching costs for load reduction and transfer on the user side through the optimization mechanism of the incentive-based demand-side response, they bring peak-shaving benefits while reducing carbon emissions, effectively reducing the total cost of the system. By comparing Example 3 with Example 1, the total cost of Example 1 is reduced by 2,872.03 yuan compared with that of Example 1, a decrease of 3.61%. The reduction in GCT costs indicates that the output of wind and solar units has increased, thereby reducing the basic operating costs of the system.

[0142] Table 2 Optimization scheduling costs and benefits of various embodiments and comparative examples

[0143]

[0144] The carbon emissions and carbon trading amounts of each embodiment and comparative example are as follows: Figure 6As shown, both the GCT-CET mechanism and the incentive-based demand response mechanism can play a role in carbon reduction to a certain extent. Compared with Example 1, the carbon emissions generated in Example 3 decreased by 8.37%. Compared with the reduction in carbon emissions caused by the load reduction brought about by the simple demand-side response, its carbon reduction level has been improved to a certain extent. By comparing Example 3 with Example 1, it can be seen that the carbon reduction effect of the GCT-CET interactive mechanism and the incentive-based demand response proposed in the present invention has a carbon emission reduction rate of 7.71% on the basis of the original joint optimization, further improving the carbon reduction capacity of the system.

[0145] The load optimization results and output conditions of Example 1 and Example 3 are as follows: Figures 7-10 As shown. Example 1 is relative to Example 3, and introduces the CET-GCT interaction mechanism. Figure 7 and Fig. 9 For example, the maximum peak-to-valley difference of the electric load curve is reduced from 1.92MW to 1.71MW, indicating that without sacrificing economy and user satisfaction, Example 1 more effectively guides the thermal power load in the peak period (17:00-21:00) to shift to the valley period (22:00-05:00) in an orderly manner, achieving a good peak-shaving effect. This shows that the source-load complementary scheduling model of the GCT-CET interactive mechanism can better tap the peak-shaving potential of incentive-based demand response, effectively reduce the operating pressure of related units, and realize low-carbon economic scheduling of industrial parks.

[0146] Example 4

[0147] This embodiment provides a source-load complementary scheduling optimization system for industrial loads, including:

[0148] The data analysis module is used to collect historical operating data of the source side and the load side in the industrial park to form sample data, and pre-process the sample data to form a data set, and perform multi-energy coupling analysis on the data set to obtain the interaction results between the source side and the load side in the industrial park;

[0149] The architecture building module is used to build the source-load complementary integrated energy system operation architecture based on the interaction results between the source side and the load side in the industrial park;

[0150] Model building module, used to introduce trading mechanism into the operation framework of regional integrated energy system with complementary sources and loads, and form a dispatching model of the integrated energy system with complementary sources and loads;

[0151] The scheduling optimization module is used to solve the scheduling model of the source-load complementary integrated energy system based on the real-time operating data of the source side and the load side in the industrial park, with the goal of minimizing the operating cost of the industrial park, and obtain the scheduling results of the source-load complementary integrated energy system.

[0152] Example 5

[0153] The present invention also provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the source-load complementary scheduling optimization method for an industrial park as described in Example 1 is implemented.

[0154] Example 6

[0155] The present invention also provides an electronic device, comprising a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the source-load complementary scheduling optimization method for industrial parks as described in Example 1 is implemented.

[0156] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A source-load complementary scheduling optimization method for industrial loads, characterized in that: The following steps are involved: Collect historical operation data of the source side and the load side in the industrial park to form sample data, pre-process the sample data to form a data set, perform multi-energy coupling analysis on the data set, and obtain the interaction results between the source side and the load side in the industrial park; Based on the interaction between the source side and the load side in the industrial park, the operation framework of the source-load complementary integrated energy system is constructed; Introduce a trading mechanism into the operation framework of the source-load complementary regional integrated energy system to form a dispatching model for the source-load complementary integrated energy system; Based on the real-time operation data of the source side and the load side in the industrial park, the scheduling model of the source-load complementary integrated energy system is solved with the goal of minimizing the operation cost of the industrial park, and the scheduling result of the source-load complementary integrated energy system is obtained.

2. According to claim 1, a source-load complementary scheduling optimization method for industrial loads is characterized in that: The historical operation data of the source side and the load side in the industrial park are collected to form sample data, and the sample data are preprocessed to form a data set, and the data set is subjected to multi-energy coupling analysis to obtain the interaction results between the source side and the load side in the industrial park, specifically including: Collect historical operating data on the source side and the load side of the industrial park to form sample data; the historical operating data on the source side and the load side include the power generation of renewable energy photovoltaic and wind power, the energy consumption of traditional gas turbines and cogeneration devices, the power and heat load demand of the industrial park, and the historical carbon emission data of enterprises; Perform outlier correction and standardization on sample data to form a data set; The power output form of electricity and heat, the change form of electricity and thermal energy storage, and the input form of electricity and thermal load in the dataset are analyzed to obtain the interaction results between the source side and the load side in the industrial park.

3. According to the source-load complementary scheduling optimization method for industrial loads as described in claim 1, it is characterized in that: Based on the interaction between the source side and the load side in the industrial park, the operation framework of the source-load complementary integrated energy system is constructed, which specifically includes: Based on the interaction between the source side and the load side in the industrial park, various energy forms in the industrial park are integrated to build a source-load complementary integrated energy system operation framework. In the source-load complementary integrated energy system operation framework, the output power of various energy sources meets the requirements of the industrial park side, and the expression is as follows: P(t)=P PV (t)+P WT (t)+P CHP (t)+P GF (t)+P Grid (t) H(t)=H GB (t)+H GF (t)+H CHP (t)+H Grid (t) Where P(t) and H(t) represent the total power output of electricity and heat, respectively. PV (t), P WT (t), P CHP (t), P GF (t), P Grid (t) are the power outputs of photovoltaic, wind power, cogeneration, gas turbine and grid respectively; H GB (t), H GF (t), H CHP (t), H Grid (t) are the thermal power outputs of boilers, gas turbines, cogeneration units and heating networks, respectively.

4. According to claim 1, a source-load complementary scheduling optimization method for industrial loads is characterized in that: The trading mechanism includes green certificates, a tiered carbon trading interaction mechanism, and an incentive-based demand response mechanism.

5. The source-load complementary scheduling optimization method for industrial loads according to claim 4 is characterized in that: The interaction mechanism between green certificates and tiered carbon trading is as follows: The amount of electricity consumed by enterprises in industrial parks from renewable energy sources is the green certificate demand. The green certificate trading market sets the target electricity consumption of renewable energy for enterprises based on the green certificate demand as the green certificate quota. When an enterprise fails to reach the target electricity consumption of renewable energy, it needs to purchase green certificates corresponding to the difference in the target electricity consumption from the green certificate trading market to complete the green certificate quota. The expression of the green certificate transaction cost is as follows: Where: C GCT is the transaction cost of green certificates; λ is the unit price of green certificates; ρ is the green certificate quota coefficient that the industrial park needs to hold; δ is the quantitative coefficient of renewable energy power generation converted into green certificates; P L (t) is the electrical load at time t; The carbon emission amount that enterprises in industrial parks should meet is the carbon emission demand. The carbon trading market sets the carbon emission quota that enterprises should meet based on the carbon emission demand. If the enterprise exceeds the established carbon emission quota, it should purchase the corresponding carbon emission rights that exceed the quota to make up for the carbon emission quota. The expression of its carbon emission cost is as follows: Where: C CET is the carbon emission transaction cost; σ is the unit carbon transaction price; k is the step length; γ is the unit price growth rate; B st is the carbon emission trading amount; When consuming electricity from renewable energy, the enterprise will also receive additional carbon emission quotas from the government for renewable energy conversion. Such carbon emission quotas from renewable energy conversion can be used to offset the carbon emission quotas that the enterprise should meet. Among them, B green is the carbon emission quota converted from renewable energy, κ is the conversion coefficient of renewable energy into carbon emission quota, δ is the coefficient of green certificate trading, P PV (t) and P WT (t) represent the power output of photovoltaic and wind power respectively.

6. According to claim 4, a source-load complementary scheduling optimization method for industrial loads is characterized in that: The incentive demand response mechanism is as follows: By setting up incentive demand response costs, under the condition of meeting the set user satisfaction, users are encouraged to increase electricity consumption during periods of low electricity prices and reduce electricity consumption during periods of high electricity prices to obtain incentive benefits. The expression of incentive demand response costs is as follows: Where C IDR is the cost of incentive demand response; a i , b i are the electrical and thermal compensation coefficients respectively; P Li , H Li are the electric and thermal loads participating in the response respectively; i is the load adjustment type, i=1 means load transfer, i=2 means load reduction; P Li (t) and H Li (t) are the electricity and heat loads participating in demand response, respectively, and i represents the type of load; The expression of user satisfaction is as follows: Among them, Ω is user satisfaction, P Li (t) and H Li (t) are the power and heat loads participating in the response, P L (t) and H L (t) is the electricity and heat load under normal conditions; The industrial park source side adjusts the energy supply strategy according to the real-time user electricity demand, and the peak load benefits generated are as follows: Where: are the profit coefficients of thermal and electric load peak regulation respectively; j is the starting time of the load peak; ΔD is the length of the peak period; When the source side of the industrial park cannot meet the electricity demand of users, it is necessary to purchase additional energy from the superior thermal power company; if the actual user satisfaction does not meet the set user satisfaction, the source side of the industrial park needs to adjust the internal energy supply strategy to meet the user's satisfaction.

7. The source-load complementary scheduling optimization method for industrial loads according to claim 1 is characterized in that: Based on the real-time operation data of the source side and the load side in the industrial park, the advanced scheduling model of the source-load complementary integrated energy system is solved with the goal of minimizing the operation cost of the industrial park, and the scheduling results of the source-load complementary integrated energy system are obtained, which specifically include: The objective function of minimizing the operating cost of the industrial park is established as follows; minf=C op +C CET +C GCT +C IDR -C ps Among them, C op is the basic operating cost of the system, C CET and C GCT are the costs of carbon trading and green certificate trading, C IDR is the demand response cost, C ps For peak load benefits; The expression of basic operating cost is: Where: S gas (t) is the natural gas demand at time t; τ t is the time-sharing gas price; τ w , τ p are the electricity costs of wind and solar respectively; C pro Equipment maintenance costs; Based on the real-time electric load and heat load in the industrial park, the real-time gas price and electricity price of the service provider, the Gurobi solver is used to solve the target of the advanced scheduling model of the source-load complementary integrated energy system to obtain the scheduling optimization plan.

8. A source-load complementary dispatch optimization system for industrial loads, characterized in that: include: The data analysis module is used to collect the historical operation data of the source side and the load side in the industrial park to form sample data, and pre-process the sample data to form a data set, and perform multi-energy coupling analysis on the data set to obtain the interaction results between the source side and the load side in the industrial park; The architecture building module is used to build the source-load complementary integrated energy system operation architecture based on the interaction results between the source side and the load side in the industrial park; Model building module, used to introduce trading mechanism into the operation framework of regional integrated energy system with complementary sources and loads, and form a dispatching model of the integrated energy system with complementary sources and loads; The scheduling optimization module is used to solve the scheduling model of the source-load complementary integrated energy system based on the real-time operating data of the source side and the load side in the industrial park, with the goal of minimizing the operating cost of the industrial park, and obtain the scheduling results of the source-load complementary integrated energy system.

9. A computer storage medium, wherein the computer readable storage medium stores a computer program, characterized in that: When the computer program is executed by the processor, it implements the source-load complementary scheduling optimization method for industrial parks as described in any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a memory and a processor: the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the source-load complementary scheduling optimization method for industrial parks as described in any one of claims 1 to 7 is implemented.

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