Power system source network load Pareto optimal carbon responsibility calculation method based on carbon emission flow

Abstract

An electric power system source network load Pareto optimal carbon responsibility calculation method based on carbon emission flow belongs to the technical field of carbon emission, and comprises the following steps: analyzing a topological structure in an electric power network, establishing an electric power system load flow calculation model to solve a load flow distribution condition in the electric power system, obtaining load flow data, and substituting the load flow data into carbon flow calculation to obtain a load flow calculation model; and obtaining the carbon flow intensity, the carbon flow rate and the carbon potential of each node, and introducing a carbon date to solve the carbon responsibility distribution of each node. The method comprises the following steps: analyzing the carbon emission condition of a system, considering the geographic position of each node and other factors, introducing two sections of objective functions of minimum power generation cost and minimum carbon date, carrying out multi-objective function optimization solution, and constructing an evaluation function by adopting a Pareto optimal method to obtain an optimal solution. According to the method, a feasible scheme reference can be provided for carbon responsibility distribution of the power system under the low-carbon background.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission, and in particular relates to a Pareto optimal carbon responsibility calculation method for a power system source network load based on a carbon emission flow. Background Art

[0002] With the deepening of globalization and the growth of population, economy and other factors, energy demand continues to rise, and energy utilization and environmental crises continue to intensify. In order to cope with the increasingly severe challenges of climate change, countries have formulated energy policies and actively promoted renewable energy such as clean energy to reduce excessive carbon emissions. In the power industry, source grid load is the most important link, and today's power system has become a key area in energy governance. However, while meeting the demand for electricity, the power system also generates a large amount of carbon emissions. Therefore, reducing carbon emissions from the power system has become the only way to achieve sustainable energy governance and development.

[0003] In order to reduce carbon emissions from the power system and improve the sustainable development of the power industry, it is necessary to reduce the energy consumption demand of end users such as data centers, enterprises, and households, while optimizing the operation control and power supply structure of the power grid. In this process, low-carbon dispatching of the power system has become a key link.

[0004] Therefore, existing technologies urgently need a new technical solution to provide new ideas for carbon emission management in the current power system. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a Pareto optimal carbon responsibility calculation method for power system source network load based on carbon emission flow, so as to make the power system operation mode meet low-carbon operation as much as possible while ensuring the stable operation of the power system.

[0006] A Pareto optimal carbon responsibility calculation method for power system source grid load based on carbon emission flow includes the following steps:

[0007] Step 1: Introduce branch carbon flow density and node carbon potential to measure the carbon consumption of each branch in the power system and analyze the carbon emissions and network structure of the power system;

[0008] Step 2: Combine the power system flow with the power system carbon flow and calculate the power system flow by using the Newton method;

[0009] Step 3: Determine the network accuracy of the power system. If the accuracy does not reach the preset value, the modified Jacobian matrix is ​​iterated again. If the accuracy reaches the preset value, the carbon flow calculation of the power system is performed.

[0010] Step 4: Apply the carbon emission flow theoretical calculation method to calculate the carbon flow density and carbon flow intensity of each node;

[0011] Step 5: Calculate the carbon responsibility of the power system based on the carbon flow intensity of each node;

[0012] Step 6: Considering the influence of factors such as the geographical location of each node, a multi-objective function and constraints are constructed with minimum power generation cost and minimum carbon date;

[0013] Step 7: Construct an evaluation function of the multi-objective function and solve it to obtain the carbon responsibility distribution that takes into account the minimum power generation cost and the minimum carbon date.

[0014] The formula for calculating the power system flow using the Newton method in step 2 is:

[0015]

[0016] Where P i , Q i are the active power and reactive power injected into the node i in the power grid respectively; θ i ,θ j are the voltage phase angles of node i and node j respectively; U i , U j are the voltage amplitudes of nodes and nodes respectively, G ij and B ij are the real and imaginary parts of the elements in the ith row and jth column of the grid node admittance matrix, respectively.

[0017] The Jacobian matrix correction equation in step 3 is:

[0018]

[0019] In the formula, ΔW n , ΔV n , J nn are power correction, voltage correction, and Jacobian matrix element.

[0020] The carbon flow intensity of each node in step 4 is determined by the active power flow injection of the transmission line and generator connected to the node, and the calculation formula is:

[0021]

[0022] In the formula, ρ a is the carbon flow density of a node a in the transmission line branch l, G h is the output energy of the generator h, is the carbon flow density of generator h, P l is the electric power released by branch l;

[0023] R l is the carbon emission allocated to branch l

[0024]

[0025] In the formula, ρ l is the carbon flow density of a branch l of the transmission line, P l Loss is the power loss of the transmission line branch l, The power loss of the transmission line branch l has been included in the part on the load side.

[0026] The calculation formula for the carbon responsibility of the power system in step 5 is:

[0027] E n =R n β n

[0028] In the formula, β n is the carbon date corresponding to the node, R n is the node carbon flow intensity, and the calculation formula is:

[0029] R n =P n e n

[0030] Where P n is the node active power, e n is the nodal carbon potential of the n-node.

[0031] The minimum power generation cost objective function in step 6 is:

[0032]

[0033] In the formula, ε i , ε j , ε k They represent the power generation costs of thermal power unit i, hydropower unit j and other units k, respectively, P i,t , P j,t , P k,t They represent the active output of thermal power units, hydropower units and other units at time t respectively;

[0034] The minimum carbon date objective function is:

[0035]

[0036] In the formula, λ D,t is the carbon emission base of load node D at time t, γ D is the unit power load cost, P D,t is the load demand of load node z at time t.

[0037] The constraints in step 6 are:

[0038]

[0039] Where P G,t is the active power output of each generator set at time t, and the constraints are that the total active power of the generator set is equal to the sum of the active power demands of each load node, the generator output constraint, the load node power constraint, and the carbon emission constraint.

[0040] The evaluation function solution formula of the multi-objective function described in step 7 is:

[0041] minF=[f1+f2]

[0042] The rectangular coordinate system is constructed with two objective functions, and the optimal solution values ​​of the two random variable objective functions f1 and f2 are solved, and the optimal point Xr* and the corresponding optimal value fr(Xr*) of each corresponding objective function are obtained; the ideal point evaluation function method is adopted, and the Euclidean distance algorithm X is used to determine the theoretical optimal value, that is, the distance between each feasible solution x and the ideal point Xr*, and the evaluation function for multi-objective function optimization is constructed as follows:

[0043]

[0044] Through the above-mentioned design scheme, the present invention can bring the following beneficial effects: a Pareto optimal carbon responsibility calculation method for the source-grid load of the power system based on the carbon emission flow, which comprehensively considers the changing trend of the carbon emission flow in the specific power industry, and provides a more feasible solution reference for the carbon responsibility distribution of the power system under a low-carbon background. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0046] Figure 1 The present invention is a flow chart of a Pareto optimal carbon responsibility calculation method for power system source-grid load based on carbon emission flow. DETAILED DESCRIPTION

[0047] A Pareto optimal carbon responsibility calculation method for power system source grid load based on carbon emission flow, such as Figure 1 As shown, branch carbon flow density and node carbon potential are introduced to measure the carbon consumption of each branch in the power system and analyze the carbon emission of the system; the network structure of the power system is analyzed; the power system flow calculation is performed; the accuracy of the power system network is judged. If the accuracy does not meet the requirements, the Jacobian matrix is ​​modified and iterated again. If the accuracy meets the requirements, the carbon flow calculation is performed; the carbon emission flow theoretical calculation method is applied to calculate the carbon flow intensity, carbon flow rate and carbon potential of each node; the carbon responsibility of the power system is calculated; considering the influence of factors such as the geographical location of each node, a multi-objective function optimization solution is introduced; the evaluation function of the multi-objective function is constructed for solution; and the carbon responsibility distribution considering the minimum power generation cost and the minimum carbon date is obtained.

[0048] Specifically, since the production of carbon emissions in the power industry is real-time, continuous and changing, it is necessary to define the carbon emissions within a very small period of time. Therefore, the branch carbon flow rate R is defined to represent the carbon emission flow through the branch per unit time, which is numerically equal to the derivative of the branch carbon emission flow F with respect to time t, as shown in Formula 1:

[0049]

[0050] The branch carbon emission flow is the integral value of the branch carbon rate within a given time. It can also be seen that the branch carbon rate is not a ratio in the general sense and cannot reflect the changing trend of the carbon emission flow.

[0051] Since the carbon emission flow of the power system is attached to the active power flow, combining the power flow in the power system with the carbon flow in the power system reflects the relevant characteristics of the power system and makes it closer. Therefore, the branch carbon flow density ρ is used to express the combined relationship between the two: the carbon emission value on the power generation side caused by the unit power consumption of the branch transmission is numerically equal to the ratio of the carbon flow rate of any branch in the power system to the active power flow, as shown in Formula 2:

[0052]

[0053] If the branch receiving node is only connected to a generator set, the carbon flow density of the branch is equal to the carbon emission density of the generator set.

[0054] Definition i It is used to represent the carbon potential of each node. It is numerically equal to the carbon emissions on the power generation side corresponding to the unit electricity consumption at node i, as shown in Formula 3:

[0055]

[0056] Where Plinel is the active power flow of branch l, N+ is the set of all branches connected to node i with power flowing into the node; l is the branch number.

[0057] The power system flow is calculated using the polar coordinate form of the Newton method. The calculation formula is shown in formula (4):

[0058]

[0059] Where P i , Q i are the active power and reactive power injected into the node i in the power grid respectively; θ i ,θ j are the voltage phase angles of node i and node j respectively; U i , U j are the voltage amplitudes of nodes and nodes respectively, Gij and B ij are the real and imaginary parts of the elements in the ith row and jth column of the grid node admittance matrix, respectively.

[0060] By substituting the known node data into the admittance matrix, the initial voltage value is selected and substituted into the node voltage equation, the Jacobian matrix elements are calculated, and the unknown parameters of the PV node and PQ node are calculated; the settlement results are analyzed for errors, and when the error is not satisfied, the node voltage is corrected and substituted into the Jacobian matrix to calculate the error again until the error meets the requirements.

[0061] The calculation formula of the correction equation is shown in formula (5):

[0062]

[0063] In the formula, ΔW n , ΔV n , J nn are power correction, voltage correction, and Jacobian matrix elements.

[0064] Power System Carbon Emission Flow Model

[0065] 1) According to the energy scheduling principle, the carbon flow density of each branch in the transmission line is equal to the carbon flow density of the branch flowing into the node as shown in formula (6):

[0066] ρ l =ρ a (6)

[0067] Where: l is a branch of the transmission line, ρ l is the carbon flow density of the branch; a is an inflow node of branch l, ρ a is the carbon flow density flowing into node a.

[0068] 2) If the carbon emissions generated by the losses during the transmission of the transmission line are allocated to the network, the carbon emissions allocated to line l are as shown in formula (7):

[0069]

[0070] Joint formula

[0071]

[0072] If the carbon emissions generated by transmission losses have been allocated to the load side, the carbon emissions accounted for on the load side must be added to the total carbon emissions.

[0073] Where: is the carbon emission of branch l during transmission, ρ l is the carbon flow density of a branch l of the transmission line, P lLoss is the power loss of the transmission line branch l, The power loss of the transmission line branch l has been included in the part on the load side.

[0074] 3) The carbon flow intensity of each node is jointly determined by the active power flow injection of the transmission line and generator connected to the node. It follows the principle of energy merging, and the calculation formula is shown in formula (9):

[0075]

[0076] Where: a is the carbon flow density of a node a in the transmission line branch l, G h is the output energy of the generator h, is the carbon flow density of generator h, P l Loss is the electric power lost during the transmission process of branch l, ρ l is the carbon flow density of branch l, is the carbon emission already included in the load side, P l is the electric power released by branch l.

[0077] The calculation formula for calculating carbon emission responsibility based on the carbon emission intensity of each node is shown in formula (10):

[0078] E n =R n β n (10)

[0079] In the formula, β n is the carbon date corresponding to the node, R n is the node carbon flow intensity, and the calculation formula is shown in formula (11):

[0080] R n =P n e n (11)

[0081] Where P n is the node active power, e n is the nodal carbon potential of the n-node.

[0082] Taking into account the geographical location, load size, line loss and other issues of each node, the carbon responsibility distribution of each node is different. A multi-objective optimization algorithm is used to construct the objective function, constraints and evaluation function to obtain the Pareto optimal solution to determine the corrected value of the carbon responsibility of each node.

[0083] The objective function 1 is constructed, that is, the calculation formula for minimizing the power generation cost is shown in formula (12):

[0084]

[0085] In the formula, ε i , ε j , ε k They represent the power generation costs of thermal power unit i, hydropower unit j and other units k, respectively, P i,t , P j,t , P k,t They respectively represent the active output of thermal power units, hydropower units and other units at time t.

[0086] The objective function 2, i.e. the calculation formula for minimizing the carbon date, is constructed as shown in formula (13):

[0087]

[0088] In the formula, λ D,t is the carbon emission base of load node D at time t, γ D is the unit power load cost, P D,t is the load demand of load node z at time t.

[0089] The calculation formula for constructing the constraint condition is shown in formula (14):

[0090]

[0091] Where P G,t is the active power output of each generator set at time t, and the constraints are that the total active power of the generator set is equal to the sum of the active power demands of each load node, the generator output constraint, the load node power constraint, and the carbon emission constraint.

[0092] The calculation formula for multi-objective optimization solution of objective function 1 and objective function 2 is shown in formula (15):

[0093] min F=[f1+f2] (15)

[0094] The rectangular coordinate system is constructed with two objective functions, and the optimal solution values ​​of the two random variable objective functions 1 and 2 are solved, and the optimal point Xr* and the corresponding optimal value fr(Xr*) of each corresponding objective function are obtained. Due to the nonlinear characteristics of the actual power system, the ideal point evaluation function method is used, and the Euclidean distance algorithm X is used to determine the theoretical optimal value, that is, the distance between each feasible solution x and the ideal point Xr*, and the evaluation function of multi-objective function optimization is constructed as shown in formula (16):

[0095]

[0096] The embodiments described above are only some embodiments of the present invention, not all 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.

Claims

1. A Pareto optimal carbon responsibility calculation method for power system source grid load based on carbon emission flow, characterized by: The following steps are included: Step 1: Introduce branch carbon flow density and node carbon potential to measure the carbon consumption of each branch in the power system and analyze the carbon emissions and network structure of the power system; Step 2: Combine the power system flow with the power system carbon flow and calculate the power system flow by using the Newton method; Step 3: Determine the network accuracy of the power system. If the accuracy does not reach the preset value, the modified Jacobian matrix is ​​iterated again. If the accuracy reaches the preset value, the carbon flow calculation of the power system is performed. Step 4: Apply the carbon emission flow theoretical calculation method to calculate the carbon flow density and carbon flow intensity of each node; Step 5: Calculate the carbon responsibility of the power system based on the carbon flow intensity of each node; Step 6: Considering the influence of factors such as the geographical location of each node, a multi-objective function and constraints are constructed with minimum power generation cost and minimum carbon date; Step 7: Construct an evaluation function of the multi-objective function and solve it to obtain the carbon responsibility distribution that takes into account the minimum power generation cost and the minimum carbon date.

2. According to claim 1, a Pareto optimal carbon responsibility calculation method for power system source grid load based on carbon emission flow is characterized by: The formula for calculating the power system flow using the Newton method in step 2 is: Where P i , Q i are the active power and reactive power injected into the node i in the power grid respectively; θ i ,θ j are the voltage phase angles of node i and node j respectively; U i , U j are the voltage amplitudes of nodes and nodes respectively, G ij and B ij are the real and imaginary parts of the elements in the ith row and jth column of the grid node admittance matrix, respectively.

3. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The Jacobian matrix correction equation in step 3 is: In the formula, ΔW n , ΔV n , J nn are power correction, voltage correction, and Jacobian matrix element.

4. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The carbon flow intensity of each node in step 4 is determined by the active power flow injection of the transmission line and generator connected to the node, and the calculation formula is: In the formula, ρ a is the carbon flow density of a node a in the transmission line branch l, G h is the output energy of the generator h, is the carbon flow density of generator h, P l is the electric power released by branch l; R l is the carbon emission allocated to branch l In the formula, ρ l is the carbon flow density of a branch l of the transmission line, P l Loss is the power loss of the transmission line branch l, The power loss of the transmission line branch l has been included in the part on the load side.

5. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The calculation formula for the carbon responsibility of the power system in step 5 is: E n =R n b n In the formula, β n is the carbon date corresponding to the node, R n is the node carbon flow intensity, and the calculation formula is: R n =P n e n Where P n is the node active power, e n is the nodal carbon potential of the n-node.

6. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The minimum power generation cost objective function in step 6 is: In the formula, ε i , ε j , ε k They represent the power generation costs of thermal power unit i, hydropower unit j and other units k, respectively, P i,t , P j,t , P k,t They represent the active output of thermal power units, hydropower units and other units at time t respectively; The minimum carbon date objective function is: In the formula, λ D,t is the carbon emission base of load node D at time t, γ D is the unit power load cost, P D,t is the load demand of load node z at time t.

7. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The constraints in step 6 are: Where P G,t is the active power output of each generator set at time t, and the constraints are that the total active power of the generator set is equal to the sum of the active power demands of each load node, the generator output constraint, the load node power constraint, and the carbon emission constraint.

8. The method for calculating the Pareto optimal carbon responsibility of the power system source grid load based on the carbon emission flow according to claim 1 is characterized by: The evaluation function solution formula of the multi-objective function described in step 7 is: minF=[f1+f2] The rectangular coordinate system is constructed with two objective functions, and the optimal solution values ​​of the two random variable objective functions f1 and f2 are solved, and the optimal point Xr* and the corresponding optimal value fr(Xr*) of each corresponding objective function are obtained; the ideal point evaluation function method is adopted, and the Euclidean distance algorithm X is used to determine the theoretical optimal value, that is, the distance between each feasible solution x and the ideal point Xr*, and the evaluation function for multi-objective function optimization is constructed as follows:

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