A regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions

Abstract

The present invention discloses a method for regional source-load coordinated scheduling that considers the cumulative entropy of carbon emissions, and relates to the field of power system scheduling and management. A method for regional source-load coordinated scheduling that considers the cumulative entropy of carbon emissions comprises: a communication interface module, an energy management optimization module, and an algorithm solution module; the communication interface module transmits information about generator sets on the power generation side and information about the load side; the energy management optimization module establishes a two-layer optimization scheduling model for source-load coordination that considers comprehensive costs and the cumulative entropy of carbon emissions. The two-layer optimization scheduling model includes an upper-layer model and a lower-layer model. By attributing the carbon emissions generated on the power generation side to the load side, the obtained scheduling scheme can take into account the fairness of the carbon emission responsibility allocation on the load side while improving the economic efficiency of system operation. The scheduling scheme provided by the present invention can take into account the fairness of the carbon emission responsibility allocation on the load side while improving the economic efficiency of system operation.

Description

Technical Field

[0001] The present invention belongs to the field of power system dispatching and management, and specifically relates to a regional source-load coordinated dispatching method considering the cumulative entropy of carbon emissions. Background Art

[0002] Global warming, driven by excessive fossil fuel consumption, is one of the most severe challenges facing human survival and development. CO₂ is the primary cause of climate change. As the primary force in reducing CO₂ emissions, the power industry must strive for low-carbon development as a necessary measure for its sustainable development. Currently, traditional power generation still relies on non-renewable energy sources such as coal. Intuitively, only the power generation stage in the power system generates direct carbon emissions. Consequently, current research on low-carbon power systems has tended to focus on the generation side. In reality, while the load side does not directly generate carbon emissions, electricity production is required to meet the power demands of the loads, indirectly contributing to carbon emissions from the power system. From this perspective, the load side is the fundamental cause of carbon emissions in the power system. In recent years, power system carbon emission flow analysis theory, centered on "carbon flow," has been developed. Based on the carbon flow density and carbon emission density of any line and node in real-time grid power flow, it can scientifically and rationally attribute carbon emissions from the generation side to the load side. This, combined with demand response theory, allows for further exploration of coordinated source-load scheduling methods in a low-carbon context.

[0003] Research on low-carbon scheduling has yielded some results. However, most of this research focuses on the power generation side, formulating scheduling strategies by considering carbon emissions or carbon trading costs on the power generation side. In fact, energy consumption on the load side indirectly leads to carbon emissions on the power generation side. Summary of the Invention

[0004] The purpose of the present invention is to provide a regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions includes: a communication interface module, an energy management optimization module, and an algorithm solution module; the communication interface module transmits information about generator sets on the power generation side and information about the load side; the energy management optimization module establishes a two-layer optimization scheduling model for source-load coordination considering comprehensive costs and the cumulative entropy of carbon emissions, the two-layer optimization scheduling model including an upper model and a lower model;

[0007] The steps of establishing the upper-level model include: taking minimizing the comprehensive cost as a first objective function, and then determining a first constraint condition, wherein the first constraint condition includes: unit output constraint, line flow constraint, balance node constraint, and power balance constraint; the steps of establishing the lower-level model include: taking maximizing the cumulative entropy of carbon emissions as a second objective function, and then determining a second constraint condition, wherein the second constraint condition includes a load response constraint and a carbon emission constraint;

[0008] The algorithm solving module solves the mathematical model in the energy management optimization module according to a built-in solving algorithm.

[0009] Furthermore, the calculation method of the carbon emission cumulative entropy index is specifically as follows: Where N L Indicates the number of load nodes; E i represents the annual carbon emission allocation of load node i; E i,lim represents the annual carbon emission quota of load node i; Where D is the number of typical days per year; N d is the number of days corresponding to the dth typical day; T is the total number of time periods corresponding to each day, which is generally 24; E i,d,t is the carbon emission allocation of load node i per hour in each typical day;

[0010] Furthermore, the expression of the first objective function is: minC total =C1+C2+C3, where C1 represents the annual power generation cost, C2 represents the annual carbon emission cost, and C3 represents the annual demand response cost.

[0011] Furthermore, the twist power generation cost Where N g Indicates the number of generator sets; c g represents the unit power generation cost of unit g; P g,d,t represents the hourly output power of unit g in a typical day; the annual carbon emission cost Where, e g Indicates the carbon emission coefficient of unit g, E lim represents the carbon emission quota on the power generation side, σ represents the unit carbon emission cost; the corresponding cost of the annual demand Where c i represents the unit response power cost coefficient of node i; D i,d,t represents the response volume of node i per hour in each typical day.

[0012] Furthermore, the unit output constraint is: P min,g ≤P g,d,t ≤P max,g , where P max,g、P min,g They represent the upper and lower limits of the active output of unit g respectively; the line power flow constraint is: P min,l ≤P l,d,t ≤P max,l , where P l,d,t P represents the active power flow of line l per hour in each typical day. max,l 、P min,l Respectively represent the upper and lower limits of the transmission power of line l. In order to reduce the complexity of the upper model, the DC power flow method can be used to calculate P l,d,t ; The equilibrium node constraint is: θ ref,d,t =0, where θ ref,d,t is the phase angle of the balancing node every hour in each typical day; the power balance constraint is: Where D exp,i,d,t The load forecast power for node i per hour in each typical day.

[0013] Furthermore, the expression of the second objective function is: Where, E i represents the annual carbon emission allocation of load node i, E i,lim Represents the annual carbon emission quota of load node i.

[0014] Furthermore, the load response amount approximately includes: 0.8D exp,i,d,t ≤D exp,i,d,t -D i,d,t ≤1.2D exp,i,d,t , where D exp,i,d,t -D i,d,t Indicates the node load power after the response. The upper and lower limits of the load change are generally assumed to be 20% of the node load. Ensure that the total system load remains unchanged before and after the response within the dispatch cycle; the carbon emission constraint is:

[0015]

[0016] E i,d,t =e i,d,t (D exp,i,d,t -D i,d,t )

[0017]

[0018] In the above formula, e i,d,t represents the node carbon potential of node i every hour in each typical day; N is the number of all nodes in the power system including generation nodes and load nodes; E d,t represents the node carbon potential matrix for each hour of each typical day; P N,d,trepresents the active flux matrix of the node per hour in each typical day; P B,d,t represents the hourly branch power flow distribution matrix for each typical day; P G,d,t represents the unit injection distribution matrix for each hour of each typical day; E G Represents the carbon emission intensity vector of Ng generating units.

[0019] Furthermore, the algorithm solving module adopts an alternating iterative solving strategy to realize the coordinated operation of the upper and lower layer models according to their respective decision variables, thereby determining the final scheduling solution.

[0020] Beneficial effects of the present invention: The present invention proposes a regional source-load coordinated scheduling method that considers the cumulative entropy of carbon emissions, and establishes a two-layer optimized scheduling model for source-load coordination that considers both comprehensive costs and the cumulative entropy of carbon emissions. The upper-layer model aims to minimize comprehensive costs, thereby improving the economic efficiency of system operation, while the lower-layer model aims to maximize the cumulative entropy of carbon emissions, thereby improving the fairness of carbon emission responsibility sharing. A corresponding mathematical model is established and then input into the algorithm solution module to obtain the final scheduling solution. The resulting scheduling solution can improve the economic efficiency of system operation while taking into account the fairness of carbon emission responsibility sharing on the load side. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention will be further described below with reference to the accompanying drawings.

[0022] Figure 1 It is a schematic structural diagram of a calculator of the present invention;

[0023] Figure 2 It is a schematic diagram of the algorithm flow of the calculator of the present invention; DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0025] like Figure 1 As shown, a regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions includes: a communication interface module, an energy management optimization module, and an algorithm solution module; the communication interface module transmits information about the generator set on the power generation side and information about the load side; the energy management optimization module establishes a two-layer optimization scheduling model for source-load coordination considering comprehensive costs and the cumulative entropy of carbon emissions, and the two-layer optimization scheduling model includes an upper model and a lower model;

[0026] The steps of establishing the upper-level model include: taking minimizing the comprehensive cost as a first objective function, and then determining a first constraint condition, wherein the first constraint condition includes: unit output constraint, line flow constraint, balance node constraint, and power balance constraint; the steps of establishing the lower-level model include: taking maximizing the cumulative entropy of carbon emissions as a second objective function, and then determining a second constraint condition, wherein the second constraint condition includes a load response constraint and a carbon emission constraint;

[0027] The algorithm solving module solves the mathematical model in the energy management optimization module according to a built-in solving algorithm.

[0028] Furthermore, the calculation method of the carbon emission cumulative entropy index is specifically as follows: Where N L Indicates the number of load nodes; E i represents the annual carbon emission allocation of load node i; E i,lim represents the annual carbon emission quota of load node i; Where D is the number of typical days per year; N d is the number of days corresponding to the dth typical day; T is the total number of time periods corresponding to each day, which is generally 24; E i,d,t is the carbon emission allocation of load node i per hour in each typical day;

[0029] Furthermore, the expression of the first objective function is: minC total =C1+C2+C3, where C1 represents the annual power generation cost, C2 represents the annual carbon emission cost, and C3 represents the annual demand response cost.

[0030] Furthermore, the twist power generation cost Where N g Indicates the number of generator sets; c g represents the unit power generation cost of unit g; P g,d,t represents the hourly output power of unit g in a typical day; the annual carbon emission cost Where, e g Indicates the carbon emission coefficient of unit g, E lim represents the carbon emission quota on the power generation side, σ represents the unit carbon emission cost; the corresponding cost of the annual demand Where c i represents the unit response power cost coefficient of node i; D i,d,t represents the response volume of node i per hour in each typical day.

[0031] Furthermore, the unit output constraint is: P min,g ≤P g,d,t ≤P max,g , where P max,g、P min,g They represent the upper and lower limits of the active output of unit g respectively; the line power flow constraint is: P min,l ≤P l,d,t ≤P max,l , where P l,d,t P represents the active power flow of line l per hour in each typical day. max,l 、P min,l Respectively represent the upper and lower limits of the transmission power of line l. In order to reduce the complexity of the upper model, the DC power flow method can be used to calculate P l,d,t ; The equilibrium node constraint is: θ ref,d,t =0, where θ ref,d,t is the phase angle of the balancing node every hour in each typical day; the power balance constraint is: Where D exp,i,d,t The load forecast power for node i per hour in each typical day.

[0032] Furthermore, the expression of the second objective function is: Where, E i represents the annual carbon emission allocation of load node i, E i,lim Represents the annual carbon emission quota of load node i.

[0033] Furthermore, the load response amount approximately includes: 0.8D exp,i,d,t ≤D exp,i,d,t -D i,d,t ≤1.2D exp,i,d,t , where D exp,i,d,t -D i,d,t Indicates the node load power after the response. The upper and lower limits of the load change are generally assumed to be 20% of the node load. Ensure that the total system load remains unchanged before and after the response within the dispatch cycle; the carbon emission constraint is:

[0034]

[0035] E i,d,t =e i,d,t (D exp,i,d,t -D i,d,t )

[0036]

[0037] In the above formula, e i,d,t represents the node carbon potential of node i every hour in each typical day; N is the number of all nodes in the power system including generation nodes and load nodes; E d,t represents the node carbon potential matrix for each hour of each typical day; P N,d,trepresents the active flux matrix of the node per hour in each typical day; P B,d,t represents the hourly branch power flow distribution matrix for each typical day; P G,d,t represents the unit injection distribution matrix for each hour of each typical day; E G Represents the carbon emission intensity vector of Ng generating units.

[0038] like Figure 2 As shown, the algorithm solving module uses an alternating iterative solving strategy to realize the coordinated operation of the upper and lower layer models through their respective decision variables, thereby determining the final scheduling solution.

[0039] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0040] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions, characterized in that: include: Communication interface module, energy management optimization module and algorithm solution module; The communication interface module transmits information about the generator set on the power generation side and information about the load side; the energy management optimization module establishes a two-layer optimization scheduling model for source-load coordination that considers comprehensive costs and cumulative entropy of carbon emissions, and the two-layer optimization scheduling model includes an upper model and a lower model; The steps of establishing the upper-level model include: taking minimizing the comprehensive cost as a first objective function, and then determining a first constraint condition, wherein the first constraint condition includes: unit output constraint, line flow constraint, balance node constraint, and power balance constraint; the steps of establishing the lower-level model include: taking maximizing the cumulative entropy of carbon emissions as a second objective function, and then determining a second constraint condition, wherein the second constraint condition includes a load response constraint and a carbon emission constraint; The algorithm solving module solves the mathematical model in the energy management optimization module according to a built-in solving algorithm; The calculation method of the carbon emission cumulative entropy index is specifically as follows: Where N L Indicates the number of load nodes; E i represents the annual carbon emission allocation of load node i; E i,lim represents the annual carbon emission quota of load node i; Where D is the number of typical days per year; N d is the number of days corresponding to the dth typical day; T is the total number of time periods corresponding to each day, which is 24; E i,d,t is the carbon emission allocation of load node i per hour in each typical day; The expression of the second objective function is: Where, E i represents the annual carbon emission allocation of load node i, E i,lim represents the annual carbon emission quota of load node i; The load response amount includes approximately: 0.8D exp,i,d,t ≤D exp,i,d,t -D i,d,t ≤1.2D exp,i,d,t , where D exp,i,d,t -D i,d,t Indicates the node load power after response, the upper and lower limits of load change, which are 20% of the node load; Ensure that the total system load remains unchanged before and after the response within the dispatch cycle; the carbon emission constraint is: E i,d,t =e i,d,t (D exp,i,d,t -D i,d,t ) In the above formula, e i,d,t represents the node carbon potential of node i every hour in each typical day; N is the number of all nodes in the power system including generation nodes and load nodes; E d,t represents the node carbon potential matrix for each hour of each typical day; P N,d,t represents the active flux matrix of the node per hour in each typical day; P B,d,t represents the hourly branch power flow distribution matrix for each typical day; P G,d,t represents the unit injection distribution matrix for each hour of each typical day; E G Represents the carbon emission intensity vector of Ng generating units.

2. A regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions according to claim 1, characterized in that: The expression of the first objective function is: minC total =C1+C2+C3, where C1 represents the annual power generation cost, C2 represents the annual carbon emission cost, and C3 represents the annual demand response cost.

3. A regional source-load coordinated scheduling method considering the cumulative entropy of carbon emissions according to claim 2, characterized in that: Stated annual electricity generation costs Where N g Indicates the number of generator sets; c g represents the unit power generation cost of unit g; P g,d,t represents the hourly output power of unit g in a typical day; the annual carbon emission cost Where, e g Indicates the carbon emission coefficient of unit g, E lim represents the carbon emission quota on the power generation side, σ represents the unit carbon emission cost; the corresponding cost of the annual demand Where c i represents the unit response power cost coefficient of node i; D i,d,t represents the response volume of node i per hour in each typical day.

4. The method for regional source-load coordinated scheduling considering the cumulative entropy of carbon emissions according to claim 1 is characterized in that: The unit output constraint is: P min,g ≤P g,d,t ≤P max,g , where P max,g 、P min,g They represent the upper and lower limits of the active output of unit g respectively; the line power flow constraint is: P min,l ≤P l,d,t ≤P max,l , where P l,d,t P represents the active power flow of line l per hour in each typical day. max,l 、P min,l Respectively represent the upper and lower limits of the transmission power of line l. In order to reduce the complexity of the upper model, the DC power flow method can be used to calculate P l,d,t ; The equilibrium node constraint is: θ ref,d,t =0, where θ ref,d,t is the phase angle of the balancing node every hour in each typical day; the power balance constraint is: Where D exp,i,d,t The load forecast power for node i per hour in each typical day.

5. The method for regional source-load coordinated scheduling considering the cumulative entropy of carbon emissions according to claim 1 is characterized in that: The algorithm solving module uses an alternating iterative solving strategy to realize the coordinated operation of the upper and lower layer models according to their respective decision variables, thereby determining the final scheduling plan.

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