Multi-path optimization method and device for carbon emission reduction of power utilization area

Abstract

The invention provides a multipath optimization method and device for carbon emission reduction in an electricity consumption area, and belongs to the field of carbon emission. The method comprises the following steps: acquiring an electric quantity parameter of a target power utilization area; based on the electric quantity parameters and an electricity utilization carbon emission calculation model of the electricity utilization area, the carbon emission of the target electricity utilization area is determined; determining an optimized path of the target power utilization area based on the carbon emission and the target emission reduction of the target power utilization area and a pre-constructed multi-path optimization model; the multi-path optimization model comprises a cost minimum objective function and a carbon emission constraint condition, and the cost minimum objective function is constructed based on the minimum sum of thermal power to new energy installation cost, fuel automobile to electric automobile cost, thermal power generating unit low-carbon transformation cost and energy storage capacity increasing cost; the carbon emission constraint condition is constructed based on the condition that the sum of the discharge capacities is not smaller than the target emission reduction. According to the method, the optimization path of carbon emission reduction can be quickly determined.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission technology, and in particular to a multi-path optimization method and device for carbon emission reduction in an electricity consumption area. Background Art

[0002] With the process of industrial development, global greenhouse gas emissions have increased dramatically, leading to global warming and even threatening the future sustainable development of the earth.

[0003] The power industry is the largest single industry in carbon emissions, accounting for more than 40% of total carbon emissions. In order to achieve the goal of controlling emissions and reducing carbon emissions, the power system urgently needs to transform to a green and low-carbon one. Based on the characteristics of real-time balance of "power generation-power consumption", in order to achieve the carbon reduction target in the power system, it is not only about carbon reduction on the power generation side and dispatching on the grid side, but also the implementation of carbon emission reduction measures in the power consumption area is equally important.

[0004] In the process of carbon emission and carbon management, how to determine the path of energy conservation and emission reduction is one of the current problems. How to choose a reasonable emission reduction path for different enterprise development situations so as to quickly achieve the dual carbon goals is a link lacking in existing technologies. In addition, in the process of achieving the dual carbon goals, it is necessary to consider not only the economic cost but also the stability of the power system, because electricity is the basic industry of the national economy. It is necessary to achieve the dual carbon goals while fully considering the characteristics of the power industry and economic development, and then choose a reasonable energy conservation and emission reduction path. Summary of the invention

[0005] The embodiments of the present invention provide a multi-path optimization method and device for carbon emission reduction in a power consumption area to solve the current problem that a reasonable emission reduction path cannot be determined for a power consumption area.

[0006] In a first aspect, an embodiment of the present invention provides a multi-path optimization method for carbon emission reduction in an electricity consumption area, comprising:

[0007] Obtain electricity consumption data of the target electricity consumption area;

[0008] Determine the carbon emissions of the target electricity consumption area based on the electricity consumption data and the electricity consumption carbon emissions calculation model of the electricity consumption area;

[0009] Based on the carbon emissions of the target power consumption area, the target emission reduction and the pre-built multi-path optimization model, the optimization path of the target power consumption area is determined; the multi-path optimization model includes the cost minimum objective function and carbon emission constraints. The cost minimum objective function is based on the minimum sum of the cost of thermal power conversion to new energy installation, the cost of fuel vehicle conversion to electric vehicle, the cost of low-carbon transformation of thermal power units and the cost of increasing energy storage capacity. The carbon emission constraint is based on the carbon emission reduction of thermal power conversion to new energy installation, the carbon emission reduction of fuel vehicle conversion to electric vehicle, the carbon emission reduction of low-carbonization of thermal power units and the carbon emission reduction of increasing energy storage capacity. The sum is not less than the target emission reduction. .

[0010] In a possible implementation, based on the electricity data and the electricity carbon emission calculation model of the electricity consumption area, the carbon emission of the target electricity consumption area is determined, including:

[0011] Based on the electricity data and the calculation model of the node electricity carbon emission factor, determine the electricity carbon emission factor of the target node in the target period;

[0012] Based on the connection order of all target nodes and the calculation model of the electricity carbon emission factor on the regional user side, determine the electricity carbon emission factor of the target electricity consumption area;

[0013] The carbon emissions of the target power consumption area are determined based on the electricity consumption carbon emission factor of the target power consumption area, the electricity consumption power of users in the target power consumption area during the target period, and the network loss power in the target power consumption area during the target period.

[0014] In a possible implementation, the calculation model of the node electricity carbon emission factor is:

[0015]

[0016] The calculation model of carbon emission factor of electricity consumption is:

[0017]

[0018] Among them, E j,t and ρ j,t are respectively the electricity carbon emission factor and outflow carbon flow density of target node j in period t, and are the set of generators connected to the target node j and the set of branches that inject active power into the target node j, P g,t and P l,t are the power generation of generator set g in period t and the active power injection of branch l in period t, e g and ρ l,t are the carbon emission intensity of generator set g and the outflow carbon flow density of branch l in period t, E m,n,tis the regional electricity carbon emission factor of the region m in period t, N is the set of non-suspended nodes within the region, p j,t is the load power of node j in period t.

[0019] In a possible implementation, the carbon emissions CE of the target electricity consumption area m,t for:

[0020] CE m,t =E m,n,t (P c,m,t +αP m,l,t )Δt;

[0021] Among them, P c,m,t is the power consumption of users in the target power consumption area during period t, P m,l,t is the network loss power in the target power consumption area during period t, α is the correlation coefficient of network loss allocation to the user side, and takes 0≤α≤1.

[0022] In a possible implementation, based on the carbon emissions of the target power consumption area, the target emission reduction amount and a pre-built multi-path optimization model, an optimization path of the target power consumption area is determined, including:

[0023] The multi-path optimization model is solved to determine the carbon emission reduction path with the lowest cost that meets the target emission reduction, and the carbon emission reduction of each emission reduction path is calculated respectively.

[0024] In one possible implementation, the multi-path optimization model includes a carbon emission reduction model for converting thermal power to new energy installed capacity, a carbon emission reduction model for converting fuel vehicles to electric vehicles, a carbon emission reduction model for low-carbon transformation of thermal power units, and a carbon emission reduction model for increasing energy storage capacity. The carbon emission reduction model for converting thermal power to new energy installed capacity is used to determine the cost of converting thermal power to new energy installed capacity and the carbon emission reduction amount of converting thermal power to new energy installed capacity. The carbon emission reduction model for converting fuel vehicles to electric vehicles is used to determine the cost of converting fuel vehicles to electric vehicles and the carbon emission reduction amount of converting fuel vehicles to electric vehicles. The carbon emission reduction model for low-carbon transformation of thermal power units is used to determine the cost of low-carbon transformation of thermal power units and the carbon emission reduction amount of low-carbonization of thermal power units. The carbon emission reduction model for increasing energy storage capacity is used to determine the cost of increasing energy storage capacity and the carbon emission reduction amount of increasing energy storage capacity.

[0025] In a possible implementation, the multi-path optimization model is:

[0026]

[0027] The carbon emission reduction model for replacing thermal power with new energy installed capacity is:

[0028]

[0029] The carbon emission reduction model for converting fuel vehicles to electric vehicles is:

[0030]

[0031] The carbon emission reduction model for low-carbon transformation of thermal power units is:

[0032]

[0033] The carbon emission reduction model by increasing energy storage capacity is:

[0034]

[0035] Among them, Q is the objective function, c i Expressed as the cost of carbon reduction measure i, s i is the carbon emission reduction of the i-th carbon emission reduction measure, s G is the target emission reduction; b is the newly constructed new energy installed capacity; a and c are the original new energy installed capacity and the total regional installed capacity respectively; c t 、c o are the investment cost and operating cost per unit capacity, ele is the electricity consumption, f1 and k1 are the standard coal consumption and standard coal carbon emission coefficient of thermal power generation in the region, d1 is the number of new electric vehicles, d T d T,ele is the total number of cars in the region and the number of existing electric vehicles, m is the annual mileage, A gas , A ele is the average fuel consumption and power consumption per mileage, Cal gas is the calorific value of gasoline, k 2,gas is the carbon emission factor of gasoline calorific value, k 2,ele is the carbon emission factor of regional electricity consumption; c p , ele 、c s , gas are the average purchase cost of electric vehicles and the average cost recovery of fuel vehicles sold, p ele 、p gas is the average electricity price and oil price in the region; ccus 、s bio 、s chl Represents the carbon capture amount of carbon capture technology, carbon emissions of coal replaced by biomass and green ammonia, g bio , g chl , g coal is the amount of biomass, green ammonia blended and coal fired in the unit, Cal bio , Cal chl , Cal coal is the unit calorific value of biomass, green ammonia and standard coal, c ccus 、c bio 、c chl are three costs, c m 、ck is the unit capacity transformation cost and unit capacity total maintenance cost of CCUS technology, h is the transformed unit capacity, p bio 、p chl is the unit purchase price of biomass and green ammonia; c e 、c p 、c m The unit capacity cost, unit power construction cost and unit power maintenance cost of energy storage; y 、e max 、p max It is the original energy storage capacity of the region, the maximum capacity and rated power of the newly constructed energy storage.

[0036] In a possible implementation, after obtaining the power consumption data of the target power consumption area, the following is further included:

[0037] The electricity data is queried, used and preprocessed based on authority management; the preprocessing includes cleaning, conversion and normalization.

[0038] In one possible implementation, the electricity data includes the active output and carbon emission intensity of the generator sets connected to the target node in the target power consumption area, the active injection and carbon flow density of other branches injected into the target node, and at least one or more of the user power consumption in the target power consumption area, regional network loss power, regional power supply structure and regional electricity consumption.

[0039] In a second aspect, an embodiment of the present invention provides a multi-path optimization device for carbon emission reduction in an electricity consumption area, comprising:

[0040] An acquisition module, used to acquire power consumption data of a target power consumption area;

[0041] An emission determination module is used to determine the carbon emissions of a target electricity consumption area based on electricity data and a calculation model for electricity carbon emissions in the electricity consumption area;

[0042] The path determination module is used to determine the optimal path of the target power consumption area based on the carbon emissions of the target power consumption area, the target emission reduction and the pre-built multi-path optimization model; the multi-path optimization model includes the cost minimum objective function and carbon emission constraints. The cost minimum objective function is based on the minimum sum of the cost of thermal power conversion to new energy installation, the cost of fuel vehicle conversion to electric vehicle, the cost of low-carbon transformation of thermal power units and the cost of increasing energy storage capacity. The carbon emission constraint is based on the carbon emission reduction of thermal power conversion to new energy installation, the carbon emission reduction of fuel vehicle conversion to electric vehicle, the carbon emission reduction of low-carbonization of thermal power units and the carbon emission reduction of increasing energy storage capacity. The sum is not less than the target emission reduction. .

[0043] In order to determine the optimal emission reduction path for carbon emission reduction in a power consumption area, an embodiment of the present invention provides a multi-path optimization method for carbon emission reduction in a power consumption area. First, the electricity data of the target power consumption area is obtained. Then, based on the electricity data and the electricity carbon emission calculation model of the power consumption area, the carbon emissions of the target power consumption area are determined. Finally, based on the carbon emissions of the target power consumption area, the target emission reduction and the pre-built multi-path optimization model, the optimized path of the target power consumption area is determined. Taking into account the characteristics of the power industry and the basis of economic development, the present invention makes full use of the multi-path optimization model provided by the present invention to determine the optimized path of excessive carbon emissions in order to reduce carbon emissions in the power consumption area, thereby quickly determining the optimal carbon emission reduction path for the target area. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 It is a flow chart for implementing a multi-path optimization method for carbon emission reduction in a power consumption area provided by an embodiment of the present invention;

[0046] Figure 2 is a flow chart for determining carbon emissions in a target electricity consumption area provided by an embodiment of the present invention;

[0047] Figure 3 It is a structural schematic diagram of a multi-path optimization device for carbon emission reduction in a power consumption area provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0050] Figure 1 The implementation flow chart of the multi-path optimization method for carbon emission reduction in power consumption areas provided by the embodiment of the present invention is described in detail as follows:

[0051] S110: Obtain power parameters of the target power consumption area.

[0052] Among them, the power parameters include the active output of the generator set connected to the target node in the target power consumption area, the carbon emission intensity of the generator set connected to the target node in the target power consumption area, the active power injection of other branches injected into the target node, the carbon flow density of other branches injected into the target node, the user power consumption in the target power consumption area, the regional network loss power in the target power consumption area, the regional power supply structure in the target power consumption area, and the regional power consumption in the target power consumption area. There are multiple target nodes in the target power consumption area, and the spatial ownership of all target nodes in the target power consumption area is determined.

[0053] In some embodiments, after the power parameters of the target power consumption area are acquired, these power parameters need to be preprocessed.

[0054] In this embodiment, the preprocessing includes cleaning, conversion and normalization.

[0055] In some embodiments, in order to ensure the security of data acquisition and pre-processing, permission management is implemented for querying, using and modifying data during data acquisition and data processing, and data backup is performed, data recovery plans are formulated, and physical security measures are implemented. The details are as follows:

[0056] Implement permission management for data query, use and modification: Permission management should include identity authentication and permission classification, confirm the identity and permission level of data users through username and password, and two-factor authentication, to ensure that data users can only perform the specified reading, writing, modification, deletion and other operations.

[0057] Physically secure data: Ensure the physical security of data storage devices to prevent unauthorized physical access, and develop and maintain an emergency response plan to take prompt action in the event of a security incident.

[0058] In addition, it is necessary to back up data and develop a data recovery plan: regularly back up data and develop a recovery plan to ensure that subsequent steps can proceed, regularly update the database system, and patch system security vulnerabilities.

[0059] S120: Determine the carbon emissions of the target power consumption area based on the power consumption parameter and the power consumption carbon emissions calculation model of the power consumption area.

[0060] In some embodiments, first, based on the electricity data and the node electricity carbon emission factor calculation model, the electricity carbon emission factor of the target node in the target period can be determined.

[0061] Then, based on the connection order of all target nodes and the electricity carbon emission factor calculation model on the regional user side, the electricity carbon emission factor of the target electricity consumption area is determined.

[0062] Finally, the carbon emissions of the target power consumption area are determined based on the electricity consumption carbon emission factor of the target power consumption area, the electricity consumption power of users in the target power consumption area during the target period, and the network loss power in the target power consumption area during the target period.

[0063] In this embodiment, to calculate the carbon emissions of the target power consumption area, all nodes in the target power consumption area should be determined, and any target node that is only connected to the generator set should be used as the starting node to solve the outflow carbon flow density of the node, that is, the carbon emission factor of the electricity consumption of the node. And in turn, according to the order of node connection, after multiple recursions, the carbon emission factors of electricity consumption of all nodes in the target power consumption area are solved, and the user-side regional electricity carbon emission factors of the target power consumption area are obtained according to the spatial attribution characteristics of the nodes, and the network loss carbon emissions of the grid side of the target power consumption area are calculated. According to the network loss allocation coefficient, part of the regional network loss is allocated to the user side, and the carbon emissions of the target power consumption area are obtained by combining the user electricity consumption data and time of the target power consumption area.

[0064] like Figure 2 As shown, the calculation process of carbon emissions in the target electricity consumption area is as follows:

[0065] The first step is to determine the set N of all non-suspended nodes in the target power consumption area, and the number of suspended nodes is n.

[0066] The second step is to determine the outflow carbon flow density ρ of the selected target node i connected only to the generator set in period t i,t for:

[0067]

[0068] in, is the set of generators connected to the target node i, P g,t is the power generated by generator set g during period t, e g is the carbon emission intensity of the generator set g, where thermal power units are mainly considered, and the carbon emission intensity of new energy units such as wind and solar power are regarded as zero carbon emissions.

[0069] It should be noted that the carbon emission intensity of a generator set can be defined as the carbon emission value generated by the generator set per unit of electricity and defined to remain unchanged during the period.

[0070] The third step is to make the carbon flow density of all outflows from the target node equal to the carbon emission factor of electricity consumption of the target node. According to the adjacency characteristics of carbon emission flows in the power system, multiple recursive methods are used starting from node i to solve the outflow carbon flow density and electricity carbon emission factor of other non-suspended nodes in the area in time period t in turn.

[0071] Solve the formula for the node's electricity carbon emission factor:

[0072]

[0073] in, and are the set of generators connected to the target node j and the set of branches that inject active power into the target node j, P g,t and P l,t are respectively the power generation of generator set g during period t and the active power injection of branch l during period t, e g and ρ l,t They are respectively the carbon emission intensity of generator set g and the outflow carbon flow density of branch l in period t.

[0074] Step 4: For nodes not connected to generators, there are and situation.

[0075] It can be seen from the above calculation formula that if the power generation power and carbon emission intensity of all the generator sets injected into a certain node in period t and the branch power injected and branch outflow carbon flow density in period t are known, the carbon emission factor of electricity consumption of the node in period t can be solved.

[0076] Step 5: Start the first recursion from node i. Each recursion can obtain the outflow carbon flow density of several nodes.

[0077] If the outflow carbon flux density of all non-suspended nodes cannot be obtained, the recursion is repeated multiple times, and in each subsequent recursion, the known nodes obtained by the previous recursion should be used as the boundary conditions of this recursion, and the nodes with unknown outflow carbon flux density are calculated in turn.

[0078] It can be foreseen that, through a finite number of recursions, the electricity carbon emission factors of all non-suspended nodes N in the desired electricity consumption area can be finally obtained.

[0079] The obtained electricity carbon emission factors of all n non-suspended nodes in the required area m are used to define the spatial relationship of the nodes and calculate the user-side area electricity carbon emission factor in period t:

[0080]

[0081] Among them, E m,n,t is the user-side regional electricity carbon emission factor of the region m in period t, N is the set of non-suspended nodes within the region, P j,t is the load power of node j in period t.

[0082] Step 6. Solve the carbon emissions of the power consumption area. Generally, the carbon emissions caused by network losses are allocated to one of the user side and the power supply side. However, this is contrary to the fact that both parties actually have an impact on the network loss carbon emissions, which in turn affects the fair sharing of carbon emissions responsibilities. When calculating the carbon emissions of the power consumption area, the part of the carbon emissions that should be allocated to the user side should be considered in calculating the network loss carbon emissions on the power grid side of the power consumption area.

[0083] Calculate carbon emissions by electricity consumption area:

[0084] CE m,t =E m,n,t (P c,m,t +αP m,l,t )Δt

[0085] Among them, CE m,t is the carbon emissions of electricity consumption area m in period t, P c,m,t is the power consumption of users in the area during period t, P m,l,t is the network loss power in area m during period t, α is the correlation coefficient of network loss distribution to the user side, and takes 0≤α≤1.

[0086] Among them, P c,m,t According to the needs of data analysis in step S3, it can be replaced with the power consumption of each industry in the required area during period t, so as to obtain the carbon emissions ICRE of industry b in the required power consumption area m during period t. b,m,t The distribution of grid loss carbon emissions is based on the proportion of electricity consumption in each industry during period t. m,l,t E m,n,t Δt is decomposed.

[0087] S130: Determine an optimization path for the target power consumption area based on the carbon emissions of the target power consumption area, the target emission reduction, and a pre-built multi-path optimization model.

[0088] In some embodiments, a multi-path optimization model can be constructed based on multiple carbon emission reduction measures such as promoting new energy installation, promoting electric vehicle substitution, low-carbon transformation of thermal power units, and accelerating energy storage construction in the target power consumption area to achieve path optimization of carbon emission reduction measures in the target power consumption area. The multi-path optimization model includes a cost minimization objective function and carbon emission constraints. The cost minimization objective function is constructed based on the minimum sum of the cost of thermal power conversion to new energy installation, the cost of fuel vehicle conversion to electric vehicle, the cost of low-carbon transformation of thermal power units, and the cost of increasing energy storage capacity. The carbon emission constraint is constructed based on the sum of the carbon emission reduction of thermal power conversion to new energy installation, the carbon emission reduction of fuel vehicle conversion to electric vehicle, the carbon emission reduction of low-carbonization of thermal power units, and the carbon emission reduction of increasing energy storage capacity is not less than the target emission reduction.

[0089] In this embodiment, the multi-path optimization model is solved to determine the carbon emission reduction path with the lowest cost that meets the target emission reduction amount, and the carbon emission reduction amount of each emission reduction path is calculated respectively.

[0090] In this embodiment, the multi-path optimization model includes a carbon emission reduction model for converting thermal power to new energy installed capacity, a carbon emission reduction model for converting fuel vehicles to electric vehicles, a carbon emission reduction model for low-carbon transformation of thermal power units, and a carbon emission reduction model for increasing energy storage capacity. The carbon emission reduction model for converting thermal power to new energy installed capacity is used to determine the cost of converting thermal power to new energy installed capacity and the carbon emission reduction amount of converting thermal power to new energy installed capacity. The carbon emission reduction model for converting fuel vehicles to electric vehicles is used to determine the cost of converting fuel vehicles to electric vehicles and the carbon emission reduction amount of converting fuel vehicles to electric vehicles. The carbon emission reduction model for low-carbon transformation of thermal power units is used to determine the cost of low-carbon transformation of thermal power units and the carbon emission reduction amount of low-carbonization of thermal power units. The carbon emission reduction model for increasing energy storage capacity is used to determine the cost of increasing energy storage capacity and the carbon emission reduction amount of increasing energy storage capacity.

[0091] Among them, the target emission reduction can refer to the carbon emissions without implementing carbon emission reduction measures, the emission reduction stipulated by relevant policy standards, or the standards reached by the expert group through discussion.

[0092] In this embodiment, the multi-path optimization model is:

[0093]

[0094] Among them, Q is the objective function, c i Expressed as the cost of measure i, s i is the carbon emission reduction of measure i, s G To achieve the target emission reduction.

[0095] In order to promote the construction and installation of new energy, a carbon emission reduction model for replacing thermal power with new energy was constructed. Assuming that the power consumption in the power consumption area remains unchanged, since carbon emissions in the power generation process basically come from thermal power, new energy such as wind and solar power are regarded as zero-carbon, and carbon emissions can be reduced by changing the power structure.

[0096] The carbon emission reduction model for replacing thermal power with new energy installed capacity is:

[0097]

[0098] Among them, b is the newly constructed new energy installed capacity, a and c are the original new energy installed capacity and the total installed capacity of the region, and c t 、c o are the investment cost and operating cost per unit capacity, ele is the electricity consumption, f1 and k1 are the standard coal consumption and standard coal carbon emission coefficient of thermal power generation in the region.

[0099] In order to promote the replacement of electric vehicles, a carbon emission reduction model for replacing fuel vehicles with electric vehicles was constructed. Assuming that the number of cars in the region remains unchanged, the average annual mileage, average oil price and electricity price are set as fixed constants, the purchase of an electric vehicle is equivalent to the sale of a fuel vehicle out of the region, and regional carbon emissions can be reduced by increasing the number of electric vehicles to replace fuel vehicles.

[0100] The carbon emission reduction model for converting fuel vehicles to electric vehicles is:

[0101]

[0102] Among them, d1 is the number of new electric vehicles, d T d T,ele is the total number of cars in the region and the number of existing electric vehicles, m is the annual mileage, A gas , A ele is the average fuel consumption and power consumption per mileage, Cal gas The calorific value of gasoline is 4.4*107kJ / t, k 2,gas The carbon emission factor of gasoline calorific value is 67910kgco2 / TJ to convert carbon emissions, k 2,ele is the carbon emission factor of regional electricity consumption; c p , ele 、c s , gas are the average purchase cost of electric vehicles and the average cost recovery of fuel vehicles sold, p ele 、p gas is the average electricity and oil price in the region.

[0103] In order to promote the low-carbon transformation of thermal power units, a carbon emission reduction model for low-carbon transformation of thermal power units was constructed. Biomass, green ammonia blending and CCUS technology transformation methods are usually adopted. Assuming that the power generation before and after the transformation remains unchanged,

[0104]

[0105] In order to accelerate the construction of energy storage, a carbon emission reduction model with increased energy storage capacity was constructed to smooth out fluctuations in new energy power generation and promote new energy consumption. By storing excess renewable energy, it can reduce energy waste caused by inability to consume and reduce carbon emissions. It is assumed that the invested energy storage capacity is equivalent to replacing the capacity of thermal power units and the regional electricity consumption remains unchanged.

[0106]

[0107] Among them, c e 、c p 、c m The unit capacity cost, unit power construction cost and unit power maintenance cost of energy storage; y 、emax 、p max It is the original energy storage capacity of the region, the maximum capacity and rated power of the newly constructed energy storage.

[0108] Based on the multi-path optimization model, the carbon emission reduction path with the lowest cost in the power consumption area under the condition of meeting the emission reduction target is obtained, and the carbon emission reduction s under this path is calculated. T =s1+s2+s3+s4.

[0109] In order to determine the optimal emission reduction path for carbon emission reduction in a power consumption area, an embodiment of the present invention provides a multi-path optimization method for carbon emission reduction in a power consumption area. First, the electricity data of the target power consumption area is obtained. Then, based on the electricity data and the electricity carbon emission calculation model of the power consumption area, the carbon emissions of the target power consumption area are determined. Finally, based on the carbon emissions of the target power consumption area, the target emission reduction and the pre-built multi-path optimization model, the optimized path of the target power consumption area is determined. Taking into account the characteristics of the power industry and the basis of economic development, the present invention makes full use of the multi-path optimization model provided by the present invention to determine the optimized path of excessive carbon emissions in order to reduce carbon emissions in the power consumption area, thereby quickly determining the optimal carbon emission reduction path for the target area.

[0110] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0111] The following is an embodiment of the device of the present invention. For details not described in detail, reference may be made to the corresponding method embodiments described above.

[0112] Figure 3 The schematic diagram of the structure of the multi-path optimization device for carbon emission reduction in the power consumption area provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0113] like Figure 3 As shown, the multi-path optimization device 300 for carbon emission reduction in the power consumption area includes:

[0114] An acquisition module 310 is used to acquire power consumption data of a target power consumption area;

[0115] The emission determination module 320 is used to determine the carbon emission of the target power consumption area based on the power data and the power consumption carbon emission calculation model of the power consumption area;

[0116] The path determination module 330 is used to determine the optimization path of the target power consumption area based on the carbon emissions of the target power consumption area, the target emission reduction and the pre-built multi-path optimization model; the multi-path optimization model includes a cost minimum objective function and a carbon emission constraint condition, the cost minimum objective function is based on the minimum sum of the cost of thermal power conversion to new energy installation, the cost of fuel vehicle conversion to electric vehicle, the cost of low-carbon transformation of thermal power units and the cost of increasing energy storage capacity, and the carbon emission constraint condition is based on the sum of the carbon emission reduction of thermal power conversion to new energy installation, the carbon emission reduction of fuel vehicle conversion to electric vehicle, the carbon emission reduction of low-carbonization of thermal power units and the carbon emission reduction of increasing energy storage capacity is not less than the target emission reduction. .

[0117] In a possible implementation, the emission determination module 320 is used to determine the carbon emission factor of electricity consumption of the target node in the target period based on the electricity consumption data and the node electricity consumption carbon emission factor calculation model;

[0118] Based on the connection order of all target nodes and the calculation model of the electricity carbon emission factor on the regional user side, determine the electricity carbon emission factor of the target electricity consumption area;

[0119] The carbon emissions of the target power consumption area are determined based on the electricity consumption carbon emission factor of the target power consumption area, the electricity consumption power of users in the target power consumption area during the target period, and the network loss power in the target power consumption area during the target period.

[0120] In a possible implementation, the calculation model of the node electricity carbon emission factor is:

[0121]

[0122] The calculation model of carbon emission factor of electricity consumption is:

[0123]

[0124] Among them, E j,t and ρ j,t are respectively the electricity carbon emission factor and outflow carbon flow density of target node j in period t, and are the set of generators connected to the target node j and the set of branches that inject active power into the target node j, P g,t and P l,t are the power generation of generator set g in period t and the active power injection of branch l in period t, e g and ρ l,t are the carbon emission intensity of generator set g and the outflow carbon flow density of branch l in period t, E m,n,t is the regional electricity carbon emission factor of the region m in period t, N is the set of non-suspended nodes within the region, p j,tis the load power of node j in period t.

[0125] In a possible implementation, the carbon emissions CE of the target electricity consumption area m,t for:

[0126] CE m,t =E m,n,t (P c,m,t +αP m,l,t )Δt;

[0127] Among them, P c,m,t is the power consumption of users in the target power consumption area during period t, P m,l,t is the network loss power in the target power consumption area during period t, α is the correlation coefficient of network loss allocation to the user side, and takes 0≤α≤1.

[0128] In a possible implementation, the path determination module 330 is used to solve the multi-path optimization model, determine the carbon emission reduction path with the lowest cost that meets the target emission reduction, and calculate the carbon emission reduction of each emission reduction path respectively.

[0129] In one possible implementation, the multi-path optimization model includes a carbon emission reduction model for converting thermal power to new energy installed capacity, a carbon emission reduction model for converting fuel vehicles to electric vehicles, a carbon emission reduction model for low-carbon transformation of thermal power units, and a carbon emission reduction model for increasing energy storage capacity. The constraints of the multi-path optimization model are that the sum of the carbon emission reductions from converting thermal power to new energy installed capacity, the carbon emission reductions from converting fuel vehicles to electric vehicles, the carbon emission reductions from low-carbon transformation of thermal power units, and the carbon emission reductions from increasing energy storage capacity is greater than or equal to the target emission reduction.

[0130] In a possible implementation, the multi-path optimization model is:

[0131]

[0132] The carbon emission reduction model for replacing thermal power with new energy installed capacity is:

[0133]

[0134] The carbon emission reduction model for converting fuel vehicles to electric vehicles is:

[0135]

[0136] The carbon emission reduction model for low-carbon transformation of thermal power units is:

[0137]

[0138] The carbon emission reduction model by increasing energy storage capacity is:

[0139]

[0140] Among them, Q is the objective function, c i Expressed as the cost of carbon reduction measure i, s i is the carbon emission reduction of the i-th carbon emission reduction measure, s G is the target emission reduction; b is the newly constructed new energy installed capacity; a and c are the original new energy installed capacity and the total regional installed capacity respectively; c t 、c o are the investment cost and operating cost per unit capacity, ele is the electricity consumption, f1 and k1 are the standard coal consumption and standard coal carbon emission coefficient of thermal power generation in the region, d1 is the number of new electric vehicles, d T d T,ele is the total number of cars in the region and the number of existing electric vehicles, m is the annual mileage, A gas , A eke is the average fuel consumption and power consumption per mileage, Cal gas is the calorific value of gasoline, k 2,gas is the carbon emission factor of gasoline calorific value, k 2,ele is the carbon emission factor of regional electricity consumption; c p , ele 、c s , gas are the average purchase cost of electric vehicles and the average cost recovery of fuel vehicles sold, p ele 、p gas is the average electricity price and oil price in the region; ccus 、s bio 、s chl Represents the carbon capture amount of carbon capture technology, carbon emissions of coal replaced by biomass and green ammonia, g bio , g chl , g coal is the amount of biomass, green ammonia blended and coal fired in the unit, Cal bio , Cal chl , Cal coal is the unit calorific value of biomass, green ammonia and standard coal, c ccus 、c bio 、c chl are three costs, c m 、c k is the unit capacity transformation cost and unit capacity total maintenance cost of CCUS technology, h is the transformed unit capacity, p bio 、p chl is the unit purchase price of biomass and green ammonia; c e 、c p 、c m The unit capacity cost, unit power construction cost and unit power maintenance cost of energy storage; y 、e max 、pmax It is the original energy storage capacity of the region, the maximum capacity and rated power of the newly constructed energy storage.

[0141] In a possible implementation, the electricity data is queried, used and preprocessed based on authority management; wherein the preprocessing includes cleaning, conversion and normalization.

[0142] In one possible implementation, the electricity data includes the active output and carbon emission intensity of the generator sets connected to the target node in the target power consumption area, the active injection and carbon flow density of other branches injected into the target node, and at least one or more of the user power consumption in the target power consumption area, regional network loss power, regional power supply structure and regional electricity consumption.

[0143] In order to determine the optimal emission reduction path for carbon emission reduction in a power consumption area, an embodiment of the present invention provides a multi-path optimization device for carbon emission reduction in a power consumption area. First, the electricity data of the target power consumption area is obtained. Then, based on the electricity data and the electricity carbon emission calculation model of the power consumption area, the carbon emissions of the target power consumption area are determined. Finally, based on the carbon emissions of the target power consumption area, the target emission reduction amount and the pre-built multi-path optimization model, the optimized path of the target power consumption area is determined. Taking into account the characteristics of the power industry and the basis of economic development, the present invention can fully utilize the multi-path optimization model provided by the present invention to determine the optimized path of excessive carbon emissions in order to reduce carbon emissions in the power consumption area, thereby quickly determining the optimal carbon emission reduction path for the target area.

[0144] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] Those of ordinary skill in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0146] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned multi-path optimization method for carbon emission reduction in each power consumption area can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0147] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A multi-path optimization method for carbon emission reduction in power consumption areas, characterized in that: include: Obtain power parameters of the target power consumption area; Determine the carbon emissions of the target power consumption area based on the power consumption parameter and the power consumption carbon emissions calculation model of the power consumption area; Based on the carbon emissions of the target power consumption area, the target emission reduction and a pre-constructed multi-path optimization model, the optimization path of the target power consumption area is determined; the multi-path optimization model includes a cost minimization objective function and a carbon emission constraint. The cost minimization objective function is constructed based on the minimum sum of the cost of converting thermal power to new energy installed capacity, the cost of converting fuel vehicles to electric vehicles, the cost of low-carbon transformation of thermal power units and the cost of increasing energy storage capacity. The carbon emission constraint is constructed based on the sum of the carbon emission reductions of thermal power to new energy installed capacity, the carbon emission reductions of converting fuel vehicles to electric vehicles, the carbon emission reductions of low-carbonization of thermal power units and the cost of increasing energy storage capacity being not less than the target emission reduction.

2. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 1 is characterized in that: The determining the carbon emissions of the target power consumption area based on the power parameter and the power consumption carbon emissions calculation model of the power consumption area includes: Based on the electricity quantity parameter and the node electricity carbon emission factor calculation model, determine the electricity carbon emission factor of the target node in the target period; Determine the electricity carbon emission factor of the target electricity consumption area based on the connection order of all the target nodes and the electricity carbon emission factor calculation model on the regional user side; The carbon emissions of the target power consumption area are determined based on the electricity consumption carbon emission factor of the target power consumption area, the power consumption of users in the target power consumption area during the target period, and the network loss power in the target power consumption area during the target period.

3. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 2 is characterized in that: The calculation model of the node electricity carbon emission factor is: The calculation model of carbon emission factor of electricity consumption is: Among them, E j,t and ρ j,t are respectively the electricity carbon emission factor and outflow carbon flow density of target node j in period t, and are the set of generators connected to the target node j and the set of branches that inject active power into the target node j, P g,t and P l,t are the power generation of generator set g in period t and the active power injection of branch l in period t, e g and ρ l,t are the carbon emission intensity of generator set g and the outflow carbon flow density of branch l in period t, E m,n,t is the regional electricity carbon emission factor of the region m in period t, N is the set of non-suspended nodes within the region, P j,t is the load power of node j in period t.

4. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 2 is characterized in that: The carbon emissions CE of the target electricity consumption area m,t for: CE m,t =E m,n,t (P c,m,t +αP m,l,t )Δt; Among them, P c,m,t is the power consumption of users in the target power consumption area during period t, P m,l,t is the network loss power in the target power consumption area during period t, α is the correlation coefficient of network loss allocation to the user side, and takes 0≤α≤1.

5. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 1 is characterized in that: The multi-path optimization model also includes a carbon emission reduction model for converting thermal power to new energy installed capacity, a carbon emission reduction model for converting fuel vehicles to electric vehicles, a carbon emission reduction model for low-carbon transformation of thermal power units, and a carbon emission reduction model for increasing energy storage capacity. The carbon emission reduction model for converting thermal power to new energy installed capacity is used to determine the cost of converting thermal power to new energy installed capacity and the carbon emission reduction amount of converting thermal power to new energy installed capacity. The carbon emission reduction model for converting fuel vehicles to electric vehicles is used to determine the cost of converting fuel vehicles to electric vehicles and the carbon emission reduction amount of converting fuel vehicles to electric vehicles. The carbon emission reduction model for low-carbon transformation of thermal power units is used to determine the cost of low-carbon transformation of thermal power units and the carbon emission reduction amount of low-carbonization of thermal power units. The carbon emission reduction model for increasing energy storage capacity is used to determine the cost of increasing energy storage capacity and the carbon emission reduction amount of increasing energy storage capacity.

6. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 1 or 5, characterized in that: The step of determining the optimization path of the target power consumption area based on the carbon emissions of the target power consumption area, the target emission reduction amount and a pre-built multi-path optimization model includes: The multi-path optimization model is solved to determine the carbon emission reduction path with the lowest cost that meets the target emission reduction amount, and the carbon emission reduction amount of each emission reduction path is calculated respectively.

7. The multi-path optimization method for carbon emission reduction in power consumption areas according to claim 5 is characterized in that: The multi-path optimization model is: The carbon emission reduction model for replacing thermal power with new energy installed capacity is: The carbon emission reduction model for converting fuel vehicles to electric vehicles is: The carbon emission reduction model for low-carbon transformation of thermal power units is: The carbon emission reduction model of increasing energy storage capacity is: Among them, Q is the objective function, c i Expressed as the cost of carbon reduction measure i, s i is the carbon emission reduction of the i-th carbon emission reduction measure, s G is the target emission reduction; b is the newly constructed new energy installed capacity; a and c are the original new energy installed capacity and the total regional installed capacity respectively; c t 、c o are the investment cost and operating cost per unit capacity, ele is the electricity consumption, f1 and k1 are the standard coal consumption and standard coal carbon emission coefficient of thermal power generation in the region, d1 is the number of new electric vehicles, d T ,d T,ele is the total number of cars in the region and the number of existing electric vehicles, m is the annual mileage, A gas , A ele is the average fuel consumption and power consumption per mileage, Cal gas is the calorific value of gasoline, k 2,gas is the carbon emission factor of gasoline calorific value, k 2,ele is the carbon emission factor of regional electricity consumption; c p , ele 、c s , gas are the average purchase cost of electric vehicles and the average cost recovery of fuel vehicles sold, p ele 、p gas is the average electricity price and oil price in the region; ccus 、s bio 、s chl Represents the carbon capture amount of carbon capture technology, carbon emissions of coal replaced by biomass and green ammonia, g bio , g chl , g coal is the amount of biomass, green ammonia blended and coal fired in the unit, Cal bio , Cal chl , Cal coal is the unit calorific value of biomass, green ammonia and standard coal, c ccus 、c bio 、c chl are three costs, c m 、c k is the unit capacity transformation cost and unit capacity total maintenance cost of CCUS technology, h is the transformed unit capacity, p bio 、p chl is the unit purchase price of biomass and green ammonia; c e 、c p 、c m The unit capacity cost, unit power construction cost and unit power maintenance cost of energy storage; y 、e max 、p max It is the original energy storage capacity of the region, the maximum capacity and rated power of the newly constructed energy storage.

8. The multi-path optimization method for carbon emission reduction in power consumption areas according to any one of claims 1 to 7, characterized in that: After obtaining the power parameters of the target power consumption area, the method further includes: The electricity data is queried, used and preprocessed based on authority management; the preprocessing includes cleaning, conversion and normalization.

9. The multi-path optimization method for carbon emission reduction in power consumption areas according to any one of claims 1 to 7, characterized in that: The electricity parameters include the active output and carbon emission intensity of the generator sets connected to the target node in the target electricity consumption area, the active power injection and carbon flow density of other branches injected into the target node, and at least one or more of the user power consumption, regional network loss power, regional power supply structure and regional electricity consumption in the target electricity consumption area.

10. A multi-path optimization device for carbon emission reduction in power consumption areas, characterized in that: include: An acquisition module is used to obtain power parameters of a target power consumption area; An emission determination module, used to determine the carbon emission of the target power consumption area based on the power parameter and the power consumption carbon emission calculation model of the power consumption area; A path determination module is used to determine the optimal path of the target power consumption area based on the carbon emissions of the target power consumption area, the target emission reduction and a pre-constructed multi-path optimization model; the multi-path optimization model includes a cost minimization objective function and a carbon emission constraint condition, the cost minimization objective function is constructed based on the minimum sum of the cost of converting thermal power to new energy installed capacity, the cost of converting fuel vehicles to electric vehicles, the cost of low-carbon transformation of thermal power units and the cost of increasing energy storage capacity, and the carbon emission constraint condition is constructed based on the sum of the carbon emission reduction of thermal power to new energy installed capacity, the carbon emission reduction of fuel vehicles to electric vehicles, the carbon emission reduction of low-carbonization of thermal power units and the carbon emission reduction of increasing energy storage capacity is not less than the target emission reduction.

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