Low-carbon optimal scheduling method for integrated energy system

Abstract

The invention discloses a low-carbon optimal scheduling method for an integrated energy system, and relates to the technical field of energy systems. Comprising four steps, by considering a multi-source energy storage device, the comprehensive energy system can store energy in a load valley period and discharge energy in a load peak period through the multi-source energy storage device, so that the electricity purchase quantity of the comprehensive energy system from an external power grid and the energy supply pressure of equipment can be effectively reduced, and the method has the effect of peak load shifting. According to the comprehensive energy system low-carbon optimization scheduling method, a stepped carbon transaction cost model is considered. Compared with a traditional carbon transaction cost model, the stepped carbon transaction has stricter carbon emission to the system, and due to the fact that the total carbon emission of the comprehensive energy system is lower than the carbon quota distributed freely, the comprehensive energy system can obtain certain rewards according to the stepped carbon transaction cost model, and the carbon emission of the comprehensive energy system is reduced. And the output of the cleaning unit in the comprehensive energy system is further increased.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a low-carbon optimization scheduling method for an integrated energy system. Background Art

[0002] The Integrated Energy System (IES) can plan and dispatch the output of various energy equipment on the basis of meeting the demand-side loads such as electricity, heat, and gas, thereby reducing the system construction and operation costs and improving the utilization rate of clean energy. However, the lack of wind power absorption capacity has brought challenges to IES dispatching. The existing method to improve wind power absorption is mainly power-to-gas (P2G) technology. P2G can convert high-generation wind power at night into hydrogen or natural gas storage to alleviate the load during high-cost periods. However, the efficiency of power-to-gas in the traditional P2G mode is low, and the energy supply mode is single, which leads to energy waste and increased wind abandonment costs.

[0003] The invention with publication number CN115809787A discloses a low-carbon optimization scheduling method for an integrated energy system, including: constructing a two-stage P2G model; constructing a demand response model; introducing a step-by-step carbon trading mechanism, and constructing an integrated energy system scheduling model based on the two-stage power-to-gas P2G model and the step-by-step carbon trading mechanism; solving the optimized electricity and heat load curves using a pre-constructed demand response model based on electricity and heat load data; solving the optimized electricity and heat load curves, wind power output forecast data, gas load forecast data, and a step-by-step carbon trading mechanism using a pre-constructed integrated energy system scheduling model to obtain N integrated energy system scheduling schemes, and calculating the operating costs and carbon emissions of the N integrated energy system scheduling schemes; and determining the final integrated energy system scheduling plan.

[0004] As shown in the above invention, the existing low-carbon optimization scheduling method of the integrated energy system is generally based on the basic concept of P2G technology, and finely constructs a P2G two-stage operation model to calculate the operating costs and carbon emissions of N integrated energy system scheduling schemes; and determines the final integrated energy system scheduling plan. However, this method only targets the day-ahead scheduling stage when optimizing demand-side resources, and does not fully consider the response capabilities of demand response resources at different time scales. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a low-carbon optimization scheduling method for an integrated energy system, which solves the existing problems.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a low-carbon optimization scheduling method for an integrated energy system, comprising the following steps:

[0007] Step 1: Obtain data on clean energy power plants, cogeneration units, gas boilers and energy storage equipment within at least one historical period, and establish a dispatching model for cogeneration units, gas boilers and energy storage equipment included in the integrated energy system;

[0008] Step 2: Based on the dispatchable characteristics of multiple loads and the response capabilities at different time scales, a demand response model based on day-ahead price is constructed;

[0009] Step 3: Based on the price-based demand response model and the step-by-step carbon trading mechanism, establish a step-by-step carbon trading mechanism model for the integrated energy system with compensation coefficients;

[0010] Step 4: Use the constructed integrated energy system scheduling model to solve the problem, adjust the carbon trading cost through the step-by-step carbon trading mechanism model, and obtain the integrated energy system scheduling plan.

[0011] Preferably, the historical period is at least one month, and the clean energy power plants include wind power plants, photovoltaic power plants and hydropower plants. When acquiring the clean energy point data, weather condition data of the corresponding power plants are also included.

[0012] Preferably, the cogeneration unit data includes unit start and stop constraints, ramp constraints, power limit constraints, spinning reserve constraints, node power balance constraints, and basic power generation cost data.

[0013] Preferably, the carbon trading price data includes the carbon emission quota required for the power system and the cost of one unit of carbon emission quota. The carbon trading data also includes carbon tax data for the power system. The electricity demand data includes demand data for different time periods within a historical cycle.

[0014] Preferably, the demand response model is a price-based demand response model.

[0015] Preferably, the price-based demand response model includes:

[0016]

[0017] Where: e i,j is the cross elasticity coefficient, which indicates the user's response to changes in electricity prices in other periods and is a non-negative value; e i,i is the self-elasticity coefficient, which indicates the user's response to the electricity price in the current period and is a negative value; is the load change at time i after demand response; is the initial load at time i; Δρ j , Δρ i are the changes in electricity prices at time j and time i respectively; are the initial electricity prices at time j and time i respectively;

[0018] The price-based load demand response model for time periods t is:

[0019]

[0020] Where: E is the price elasticity matrix.

[0021] Preferably, the tiered carbon trading cost is:

[0022]

[0023] E c =E Z -E P2G ;

[0024]

[0025] In the formula, C co2 is the carbon trading cost in the comprehensive energy system, E c is the actual total carbon emissions of the comprehensive energy system, c is the carbon trading price; h represents the length of the carbon emission interval, μ and λ are the reward coefficient and penalty coefficient respectively, E Z and E p Represents the actual carbon emissions and free carbon emission quotas of thermal power units, gas boilers and combined heat and power systems; E P2G represents the amount of carbon dioxide absorbed by the power-to-gas equipment, and η is the carbon capture coefficient.

[0026] Preferably, on the basis of the existing carbon trading mechanism, in order to further constrain the carbon emissions of the industrial park, a compensation coefficient α is introduced, that is, when the actual carbon emissions are lower than the initial quota, the market gives a certain subsidy as compensation. Based on this, a step-by-step carbon trading mechanism model with a compensation coefficient is constructed:

[0027]

[0028] E IES =E P -E a ;

[0029] Where: E IES is the actual carbon emission of the system E P With the initial carbon quota E a The difference, is the carbon trading cost of the system at that moment, a positive value indicates the purchase of quotas, and a negative value indicates the use of the remaining quotas to obtain subsidies; k is the carbon trading base price of the day, α is the compensation coefficient; β is the price increase; d is the length of the carbon emission interval corresponding to each step.

[0030] Beneficial Effects

[0031] The present invention provides a low-carbon optimization scheduling method for an integrated energy system. Compared with the prior art, it has the following beneficial effects:

[0032] 1. The low-carbon optimization scheduling method of the integrated energy system takes into account multi-source energy storage devices. The integrated energy system can store energy during low-load periods and release energy during peak load periods through multi-source energy storage devices. Therefore, it can effectively reduce the integrated energy system's power purchase from the external power grid and the energy supply pressure of equipment, and has the effect of peak shaving and valley filling. At the same time, the day-ahead optimization scheduling model that considers the price-based demand response strategy and the ladder-type carbon trading mechanism can improve the economy of the integrated energy system.

[0033] 2. The low-carbon optimization scheduling method of the integrated energy system takes into account the ladder-type carbon trading cost model. Compared with the traditional carbon trading cost model, the ladder-type carbon trading has stricter carbon emissions for the system. Since the total carbon emissions of the integrated energy system are lower than the free carbon quota, according to the ladder-type carbon trading cost model, the integrated energy system can obtain certain rewards, thereby further increasing the output of clean units in the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See Figure 1 , the present invention provides the following three technical solutions:

[0037] The first implementation method: a low-carbon optimization scheduling method for an integrated energy system, comprising the following steps:

[0038] Step 1: Obtain data on clean energy power plants, cogeneration units, gas boilers and energy storage equipment within at least one historical period, and establish a dispatching model for cogeneration units, gas boilers and energy storage equipment included in the integrated energy system;

[0039] Step 2: Based on the dispatchable characteristics of multiple loads and the response capabilities at different time scales, a demand response model based on day-ahead price is constructed;

[0040] Step 3: Based on the price-based demand response model and the step-by-step carbon trading mechanism, establish a step-by-step carbon trading mechanism model for the integrated energy system with compensation coefficients;

[0041] Step 4: Use the constructed integrated energy system scheduling model to solve the problem, adjust the carbon trading cost through the step-by-step carbon trading mechanism model, and obtain the integrated energy system scheduling plan.

[0042] Among them, a historical cycle is at least one month. Clean energy power plants include wind power plants, photovoltaic power plants and hydropower plants. When obtaining clean energy point data, it also includes weather condition data of the corresponding power plant. The cogeneration unit data includes unit start and stop constraints, climbing constraints, power limit constraints, rotating reserve constraints, node power balance constraints, and basic power generation cost data. The carbon trading price data includes the carbon emission quota required for the power system and the cost of a unit of carbon emission quota. At the same time, the carbon trading data also includes the carbon tax data of the power system. The power demand data includes the demand data in different time periods within a historical cycle.

[0043] The present invention takes into account multi-source energy storage devices. The integrated energy system can store energy during low-load periods and release energy during peak load periods through the multi-source energy storage devices. Therefore, it can effectively reduce the integrated energy system's power purchase volume from the external power grid and the energy supply pressure of equipment, and has the effect of shaving peaks and filling valleys. At the same time, the day-ahead optimization scheduling model that considers the price-based demand response strategy and the ladder-type carbon trading mechanism can improve the economy of the integrated energy system.

[0044] Second implementation method: the demand response model is a price-based demand response model, which includes:

[0045]

[0046] Where: e i,j is the cross elasticity coefficient, which indicates the user's response to changes in electricity prices in other periods and is a non-negative value; e i,i is the self-elasticity coefficient, which indicates the user's response to the electricity price in the current period and is a negative value; is the load change at time i after demand response; is the initial load at time i; Δρ i , Δρ i are the changes in electricity prices at time j and time i respectively; are the initial electricity prices at time j and time i respectively;

[0047] The price-based load demand response model for time periods t is:

[0048]

[0049] Where: E is the price elasticity matrix.

[0050] The cost of tiered carbon trading is:

[0051]

[0052] E c =E Z -E P2G ;

[0053]

[0054] In the formula, C co2 is the carbon trading cost in the comprehensive energy system, E c is the actual total carbon emissions of the comprehensive energy system, c is the carbon trading price; h represents the length of the carbon emission interval, μ and λ are the reward coefficient and penalty coefficient respectively, E Z and E p Represents the actual carbon emissions and free carbon emission quotas of thermal power units, gas boilers and combined heat and power systems; E P2G represents the amount of carbon dioxide absorbed by the power-to-gas equipment, and η is the carbon capture coefficient.

[0055] On the basis of the existing carbon trading mechanism, in order to further constrain the carbon emissions of the park, the compensation coefficient α is introduced, that is, when the actual carbon emissions are lower than the initial quota, the market will give a certain subsidy as compensation. Based on this, a step-by-step carbon trading mechanism model with a compensation coefficient is constructed:

[0056]

[0057] E IES =E P -E a ;

[0058] Where: E IES is the actual carbon emission of the system E P With the initial carbon quota E a The difference, is the carbon trading cost of the system at that moment, a positive value indicates the purchase of quotas, and a negative value indicates the use of the remaining quotas to obtain subsidies; k is the carbon trading base price of the day, α is the compensation coefficient; β is the price increase; d is the length of the carbon emission interval corresponding to each step.

[0059] The present invention considers the ladder-type carbon trading cost model. Compared with the traditional carbon trading cost model, the ladder-type carbon trading has stricter carbon emissions for the system. Since the total carbon emissions of the integrated energy system are lower than the free allocated carbon quota, according to the ladder-type carbon trading cost model, the integrated energy system can obtain certain rewards, thereby further increasing the output of clean units in the integrated energy system.

[0060] A third embodiment: The present invention provides a low-carbon optimization scheduling device for an integrated energy system, including a processor and a storage medium;

[0061] The storage medium is used to store instructions, and a computer program is stored thereon. When the computer program is executed by the processor, the steps of the method of embodiment 1 are implemented.

[0062] The processor is used to operate according to the instructions to execute the steps of the method according to embodiment 1.

[0063] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0064] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0065] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-carbon optimization scheduling method for an integrated energy system, characterized in that: The following steps are involved: Step 1: Obtain data on clean energy power plants, cogeneration units, gas boilers and energy storage equipment within at least one historical period, and establish a scheduling model for the cogeneration units, gas boilers and energy storage equipment included in the integrated energy system; Step 2: Based on the dispatchable characteristics of multiple loads and the response capabilities at different time scales, a demand response model based on day-ahead price is constructed; Step 3: Based on the price-based demand response model and the step-by-step carbon trading mechanism, establish a step-by-step carbon trading mechanism model for the integrated energy system with compensation coefficients; Step 4: Use the constructed integrated energy system scheduling model to solve the problem, adjust the carbon trading cost through the step-by-step carbon trading mechanism model, and obtain the integrated energy system scheduling plan.

2. A low-carbon optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The historical period is at least one month, and the clean energy power plants include wind power plants, photovoltaic power plants and hydroelectric power plants. When acquiring the clean energy point data, the weather condition data of the corresponding power plants is also included.

3. A low-carbon optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The cogeneration unit data includes unit start and stop constraints, ramp constraints, power limit constraints, spinning reserve constraints, node power balance constraints, and basic power generation cost data.

4. A low-carbon optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The carbon trading price data includes the carbon emission quota required by the power system and the cost of one unit of carbon emission quota. The carbon trading data also includes the carbon tax data of the power system. The power demand data includes the demand data in different time periods within a historical cycle.

5. The low-carbon optimization scheduling method for an integrated energy system according to claim 1 is characterized in that: The demand response model is a price-based demand response model.

6. A low-carbon optimization scheduling method for an integrated energy system according to claim 1, characterized in that: The price-based demand response model includes:

7. The low-carbon optimization scheduling method for an integrated energy system according to claim 1 is characterized by:

8. The low-carbon optimization scheduling method for an integrated energy system according to claim 1, characterized in that:

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