Distributed power supply optimal configuration method, device, equipment and medium

Abstract

The invention relates to the technical field of distributed power supply planning, in particular to a distributed power supply optimal configuration method and device, equipment and a medium. Calculating a first net income of a distributed power supply operator according to the initial configuration power of each power supply, obtaining a second selling income of power selling of the power distribution network and a second cost of power purchasing of the power distribution network, calculating a second net income of the power distribution network, constructing a first objective function according to the first net income and the second net income, and obtaining a second objective function according to the first cost and the second cost; and constructing a second objective function, and when a preset constraint condition is met, optimizing the initial configuration power of each power supply by taking the maximum value of the first objective function and the minimum value of the second objective function as targets to obtain the configuration quantity of each power supply after optimization. According to the method and the device, the green certificate-carbon transaction is considered, the distributed power supply configuration is optimized by taking the maximum benefit of the distributed power supply operator and the power distribution network and the corresponding minimum cost as targets, and the benefits of the distributed power supply operator and the power distribution network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed power supply planning, and in particular to a distributed power supply optimization configuration method, device, equipment and medium. Background Art

[0002] The power system can be summarized into six links: "generation, transmission, distribution, transformation, use, and dispatching". Among them, "distribution" refers to the distribution network, which is a bridge connecting the main grid and the load. The distribution network before the access to the distributed power source only passively receives the power of the main grid and distributes it to the user. It is a passive network. When the distributed power source is connected to the distribution network, the distribution of the distribution network flow can be improved, but at the same time, it will cause the distribution network flow to flow in both directions, turning it into an active distribution network. This will cause some nodes in the distribution network to exceed the voltage limit, the relay protection system is complex, the short-circuit current increases, and the harmonic content increases. If the planning and operation of the distributed power source are not managed and controlled, the proportion of the distribution network connected to the distributed power source will be greatly reduced. In order to increase the consumption of distributed power sources, the configuration of distributed power sources can be optimized with the help of market regulation capabilities. Therefore, when studying the problem of optimal configuration of distributed power sources in the distribution network, the introduction of the green certificate-carbon trading mechanism takes into account the interests of multiple subjects, which is of great significance to improving the capacity of distributed power sources connected to the system, optimizing the resource allocation method, balancing the economic benefits of various subjects, and mobilizing the enthusiasm of various subjects to participate in the power market. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide a distributed power supply optimization configuration method, apparatus, device and medium to solve the problem of low efficiency in the distributed power supply configuration process.

[0004] In a first aspect, an embodiment of the present invention provides a distributed power supply optimization configuration method, the distributed power supply optimization configuration method comprising:

[0005] Obtaining the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculating the first sales income of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, and calculating the first net income of the distributed power source operator according to the first sales income and the first cost;

[0006] Obtaining a second sales revenue from electricity sold by the distribution network and a second cost for purchasing electricity by the distribution network, and calculating a second net revenue of the distribution network based on the second sales revenue and the second cost;

[0007] Constructing a first objective function according to the first net income and the second net income, and constructing a second objective function according to the first cost and the second cost;

[0008] When the preset constraints are met, the initial configuration power of each power supply is optimized with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function to obtain the optimized configuration amount of each power supply.

[0009] In a second aspect, an embodiment of the present invention provides a distributed power supply optimization configuration method, characterized in that the distributed power supply optimization configuration method includes:

[0010] An acquisition module is used to obtain the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculate the first sales income of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, and calculate the first net income of the distributed power source operator according to the first sales income and the first cost;

[0011] a calculation module, configured to obtain a second sales revenue of electricity sold by the distribution network and a second cost of electricity purchased by the distribution network, and calculate a second net income of the distribution network according to the second sales revenue and the second cost;

[0012] A construction module, configured to construct a first objective function according to the first net income and the second net income, and to construct a second objective function according to the first cost and the second cost;

[0013] The optimization module is used to optimize the initial configuration power of each power supply with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function when the preset constraints are met, so as to obtain the optimized configuration amount of each power supply.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer device is used to execute the distributed power supply optimization configuration method described in any one of the first aspects.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the distributed power supply optimization configuration method described in any one of the first aspects.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] Obtain the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculate the first sales revenue of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, calculate the first net income of the distributed power source operator according to the first sales revenue and the first cost, obtain the second sales revenue of the distribution network selling electricity and the second cost of the distribution network purchasing electricity, calculate the second net income of the distribution network according to the second sales revenue and the second cost, construct the first objective function according to the first net income and the second net income, construct the second objective function according to the first cost and the second cost, and when the preset constraints are met, optimize the initial configuration power of each power source with the maximum value of the first objective function and the minimum value of the second objective function as the goal, and obtain the optimized configuration amount of each power source. In this application, green certificate-carbon trading is taken into account, and the distributed power configuration is optimized with the maximum benefit of the distributed power source operator and the distribution network and the corresponding minimum cost as the goal, so as to improve the benefits of the distributed operator and the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0019] Figure 1 , is a flow chart of a distributed power supply optimization configuration method provided by an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of output power and power generation probability of a wind turbine in different scenarios provided by an embodiment of the present invention;

[0021] Figure 3 This is a schematic diagram of output power and power generation probability of photovoltaic power generation in different scenarios provided by an embodiment of the present invention;

[0022] Figure 4 It is a structural block diagram of a distributed power supply optimization configuration device provided by an embodiment of the present invention;

[0023] Figure 5 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] 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.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0029] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence 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 embodiments of the present invention.

[0032] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0033] See also Figure 1 , is a flow chart of a distributed power supply optimization configuration method provided by an embodiment of the present invention, such as Figure 1 As shown, the distributed power supply optimization configuration method may include the following steps.

[0034] S101: Obtain the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculate the first sales revenue of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, and calculate the first net revenue of the distributed power source operator according to the first sales revenue and the first cost.

[0035] In step S101, the initial configuration power of each power supply in the distributed power supply is obtained by the distributed power supply operator, wherein the distributed power supply includes multiple power supplies with output power, including green power generation power supplies and energy power generation power supplies, etc. The initial configuration power is the rated output power configured by the distributed power supply operator for each power supply, that is, the rated output of each power supply. According to the initial configuration power of each power supply, the first sales income of the distributed power supply operator and the first cost required to be invested by the distributed power supply operator are calculated, wherein the first sales income is the income obtained by the distributed power supply operator from selling electricity, and the first cost is the cost incurred in the process of the distributed power supply outputting the corresponding power. According to the first sales income and the first cost, the first net income of the distributed power supply operator, that is, the net profit of the distributed operator, is calculated.

[0036] In this embodiment, the initial configuration power of each power supply in the distributed power supply is obtained by the distributed power supply operator, and the first sales income of the distributed power supply operator and the first cost of the distributed power supply operator to invest are calculated according to the initial configuration power of each power supply. According to the first sales income and the first cost, the first net income of the distributed power supply operator is calculated, wherein the distributed power supply includes new energy power generation power supply and fuel power generation power supply, the new energy power generation power supply includes wind turbine power generation and photovoltaic power generation, etc., and the fuel power generation power supply includes micro gas turbine power generation. The initial configuration power of each power supply is the corresponding initial value, which can be configured according to the operator's experience or according to the actual output power of each power supply last time. In this embodiment, it is configured according to the actual output power of each power supply last time, that is, the initial configuration power of each power supply is the actual output power last time.

[0037] According to the initial configuration power of each power source, the first sales revenue of the distributed power source operator and the first cost that the distributed power source operator needs to invest are calculated. That is, according to the initial configuration power of each power source, the actual output power of each power source is determined, and then according to the corresponding selling unit price, the first sales revenue of the distributed power source operator is calculated, and according to the initial configuration power of each power source, the investment in each power source is determined, and the first cost that the distributed power source operator needs to invest is calculated. The first net revenue of the distributed power source operator is calculated by subtracting the first cost from the first sales revenue.

[0038] It should be noted that the first cost may include the investment cost, operation and maintenance cost, fuel cost and environmental governance cost of the distributed power generation operator, and may also include other costs, which are not limited in this embodiment.

[0039] Optionally, according to the initial configuration power of each power source, the first sales income of the distributed power source operator and the first cost that the distributed power source operator needs to invest are calculated, including:

[0040] According to the initial configuration power of each power source, calculate the actual output power of each power source and the actual proportion of the output power of new energy sources to the total output power;

[0041] Obtain the new energy quota ratio of distributed power generation operators, and calculate the third-party sales income of green energy transactions based on the quota ratio and the actual ratio;

[0042] According to the actual output power of each power source, the total output power is calculated. According to the total output power, the carbon quota and the actual carbon emissions are calculated. According to the carbon quota and the actual carbon emissions, the fourth sales income of carbon trading is calculated;

[0043] The first sales revenue of the distributed power generation operator is calculated based on the third sales revenue, the fourth sales revenue and the total output power.

[0044] In this embodiment, the actual output power of each power supply and the actual proportion of the new energy output power to the total output power are calculated based on the initial configuration power of each power supply, so as to calculate the sales power according to the actual output power of each power supply, and calculate the power sales revenue of the distributed operator based on the sales power and the corresponding sales unit price.

[0045] According to the initial configuration power of the wind turbine, the actual output power of the wind turbine is calculated. The calculation formula is as follows:

[0046]

[0047] Among them, P WTG is the actual output power of the wind turbine, P WTG,N is the initial configuration power of the wind turbine, V is the actual wind speed, V n is the rated wind speed, V in is the cut-in wind speed, V out To cut out wind speed.

[0048] According to the initial configuration power of photovoltaic power generation, the actual output power of photovoltaic power generation is calculated. The calculation formula is as follows:

[0049]

[0050] Among them, P PVG is the actual output power of photovoltaic power generation, P PVG,N is the initial configuration power of photovoltaic power generation, S is the actual light intensity, S n is the rated light intensity.

[0051] According to the initial configuration power of the micro gas turbine, the actual output power of the micro gas turbine is calculated. The calculation formula is as follows:

[0052] P MTG =H f η f m f

[0053] Among them, P MTG is the actual output power of the micro gas turbine, H f is the fuel calorific value, i.e. the initial configuration power, η f is the power generation efficiency of the micro gas turbine, m f is the fuel mass flow rate.

[0054] Obtain the new energy quota ratio of the distributed power supply operator. The quota ratio is the ratio of the rated output power of wind turbine power generation and photovoltaic power generation to the total rated output power. The actual ratio of new energy output power to total output power is the ratio of the actual output power of wind turbine power generation and photovoltaic power generation to the actual total output power. According to the quota ratio and the actual ratio, determine whether the power output of the distributed operator's new energy can be exchanged for green certificates for sale, so as to obtain green certificate income. If the actual ratio is greater than the quota ratio, the power that exceeds the quota ratio can be exchanged for green certificates for sale to generate corresponding income. If the actual ratio is greater than the quota ratio, it is necessary to purchase green certificates and generate corresponding expenses. Therefore, when calculating the first sales income, the green certificate income is taken into account, and the third sales income of green energy transactions is calculated to improve the accuracy of calculating the first sales income.

[0055] According to the actual output power of each power supply, the total output power is calculated, and the carbon quota and actual carbon emissions are calculated according to the total output power. According to the carbon quota and actual carbon emissions, the fourth sales income of carbon trading is calculated. Among them, the actual output power of each power supply is added to obtain the corresponding total output power, and the carbon quota and actual carbon emissions are calculated according to the total output power. The calculation formula of carbon quota is as follows:

[0056]

[0057] Among them, E0 is the carbon quota, N S is the total number of wind and solar power generation scenarios, N t is the number of time periods, p S For scene N S The probability of occurrence, λ is the carbon emission quota coefficient, N S Scene, N t The total output power in the time period is the total power sales. S Take 4, N t Take 24.

[0058] The calculation formula for actual carbon emissions is as follows:

[0059]

[0060] Among them, E g is the actual carbon emission, N S is the total number of wind and solar power generation scenarios, N t is the number of time periods, p S For scene N s The probability of occurrence, N i is the total number of system nodes, a, b, c are carbon emission coefficients, P g,i,t,s N s Scene, N t Time period, N iOutput power of the micro gas turbine at the system node.

[0061] The fourth sale income of carbon trading is calculated based on the carbon quota and the actual carbon emissions, that is, based on the carbon quota and the actual carbon emissions, it is determined whether the actual carbon emissions exceed the carbon quota. If it exceeds the carbon quota, it is necessary to purchase carbon emission rights. If it does not exceed the carbon quota, the carbon emissions corresponding to the excess carbon quota can be sold to obtain the corresponding income. The calculation formula for the fourth sale income is as follows:

[0062]

[0063] in, For the fourth sale proceeds, C c is the transaction price of unit carbon quota, E g E0 is the carbon quota, E g The actual carbon emissions.

[0064] The first sales revenue of the distributed power generation operator is calculated based on the third sales revenue, the fourth sales revenue and the total output power, wherein the first sales revenue is equal to the sum of the power sales revenue corresponding to the total output power, the third sales revenue and the fourth sales revenue.

[0065] It should be noted that if there is subsidy income, the subsidy income will be added to the corresponding first sales income.

[0066] Optionally, the third sale proceeds of green energy trading are calculated based on the quota ratio and the actual ratio, including:

[0067] Obtain the number of wind and solar power generation scenarios and the power generation curve of each power generation scenario;

[0068] According to the power generation curve, determine the power generation probability of each scenario at each moment;

[0069] The third sales revenue of green energy trading is calculated based on the power generation probability of each scenario at each moment, the actual output power of each power source, the quota ratio and the actual ratio.

[0070] In this embodiment, the number of wind and solar power generation scenarios and the power generation curve of each power generation scenario are obtained, wherein the final number of wind and solar power generation scenarios can be determined by a curve clustering method. In this example, the number of scenes is 4 times, and it can also be other times, which is not limited in this embodiment.

[0071] The power generation curve of each power generation scenario is the output power of each power generation scenario at the corresponding time and the probability of each scenario occurring. According to the power generation curve, the power generation probability of each scenario at each time is determined. Figure 2, is a schematic diagram of the output power and power generation probability of a wind turbine in different scenarios provided by an embodiment of the present invention, where the horizontal axis is the corresponding time and the vertical axis is the output power. Figure 3 , is a schematic diagram of output power and power generation probability of photovoltaic power generation in different scenarios provided by an embodiment of the present invention, where the horizontal axis is the corresponding time and the vertical axis is the output power.

[0072] The third-party sales revenue of green energy trading is calculated based on the power generation probability of each scenario at each moment, the actual output power of each power source, the quota ratio and the actual ratio. The calculation formula of the third-party revenue is as follows:

[0073]

[0074] in, is the third sale proceeds, N S is the total number of wind and solar power generation scenarios, N t is the number of time periods, p S For scene N S The probability of occurrence, C l is the unit green certificate transaction price, P new,s,t N S Scene, N t The green energy output power in the time period is the sum of the wind turbine power generation and the photovoltaic power generation. N S Scene, N t The total output power in the time period is the total electricity sales, σ is the quota ratio, and μ is the actual ratio.

[0075] Optionally, the scene number of wind and solar power generation scenes and the power generation curve of each power generation scene are obtained, including:

[0076] For any power generation scenario, historical data of wind turbine power generation and historical data of photovoltaic power generation within a preset time period are obtained, and based on the historical data of wind turbine power generation and historical data of photovoltaic power generation, probability density functions of wind turbine power generation and photovoltaic power generation within the preset time period are estimated;

[0077] Calculate the joint distribution function based on the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation;

[0078] The joint distribution function is randomly sampled to obtain the marginal distribution function value, and the power generation curve of the wind turbine power generation and the power generation curve of the photovoltaic power generation are obtained according to the marginal distribution function value.

[0079] In this embodiment, for any power generation scenario, the historical data of wind turbine power generation and the historical data of photovoltaic power generation within a preset time period are obtained, and the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation within the preset time period are estimated based on the historical data of wind turbine power generation and the historical data of photovoltaic power generation. In this embodiment, n days of historical data are used, and the historical data of each day is 24 hours of data. The non-parametric kernel density estimation method is used to select the Gaussian kernel function to generate the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation every hour within 24 hours. The calculation formula is as follows:

[0080]

[0081] in, is the probability density function of wind turbine power generation in period t, is the probability density function of photovoltaic power generation in the t period, t=1,2,3....24, x t is the average power generated by the wind turbine during period t, y t is the average photovoltaic power generation power in period t, n is the number of days, is the wind turbine power generation at the tth day and tth period, is the photovoltaic power generation in the tth day and tth period. K is the kernel function (non-negative, integral is 1, conforms to the probability density property, and the mean is 0). There are many kinds of kernel functions, uniform, triangular, biweight, triweight, Epanechnikov, normal, etc. h>0 is a smoothing parameter, called bandwidth.

[0082] According to the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation, the joint distribution function is calculated. In this embodiment, the corresponding cumulative distribution is obtained according to the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation per hour, that is, the cumulative distribution of wind turbine power generation and the cumulative distribution of photovoltaic power generation. According to the cumulative distribution of wind turbine power generation and the cumulative distribution of photovoltaic power generation, the Copula function is used to generate the corresponding joint distribution function, wherein the calculation formula of the joint distribution function is as follows:

[0083]

[0084] in, is the joint distribution function, is the cumulative distribution of wind turbine power generation, is the cumulative distribution of photovoltaic power generation, C(·) is the Copula function, t=1,2,3....24,.

[0085] The joint distribution function is randomly sampled to obtain the marginal distribution function value. According to the marginal distribution function value, the cumulative distribution of wind turbine power generation and the cumulative distribution of photovoltaic power generation are inversely transformed to obtain the power generation curve of wind turbine power generation and the power generation curve of photovoltaic power generation.

[0086] S102: Obtain a second sales revenue from electricity sales of the distribution network and a second cost for electricity purchases by the distribution network, and calculate a second net revenue of the distribution network based on the second sales revenue and the second cost.

[0087] In step S102, a second sales revenue from electricity sales of the distribution network and a second cost of electricity purchase by the distribution network are obtained, wherein the second sales revenue is the revenue obtained from electricity sales of the distribution network, and the second cost is the input cost in the distribution process of the distribution network.

[0088] In this embodiment, when obtaining the second sales revenue of the distribution network's electricity sales, it can be calculated based on the amount of electricity sold by the distribution network and the unit price of the sales. The second cost can be the cost of purchasing electricity in the distribution process of the distribution network, including the cost of purchasing electricity from distributed power supply operators, the cost of purchasing electricity from the superior power grid, the network power loss cost, the subsidy cost for interruptible loads, etc. The second sales revenue is deducted from the second cost to obtain the corresponding second net income.

[0089] S103: constructing a first objective function according to the first net income and the second net income, and constructing a second objective function according to the first cost and the second cost.

[0090] In step S103, a first objective function is constructed based on the first net income and the second net income, wherein the first objective function is a maximum value function, and a second objective function is constructed based on the first cost and the second cost, wherein the second objective function is a minimum value function. The calculation formula of the first objective function is as follows:

[0091] F1=max(E DG +E NEF )

[0092] Among them, F1 is the first objective function, max(·) is the maximum value function, and E DG is the first net income, E NEF The second net income.

[0093] The calculation formula of the second objective function is as follows:

[0094] F2=min(C DG +C NEF )

[0095] Among them, F2 is the second objective function, min(·) is the minimum function, C DG is the first cost, C NEF The second cost.

[0096] S104: When the preset constraint conditions are met, the initial configuration power of each power supply is optimized with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function to obtain the optimized configuration amount of each power supply.

[0097] In step S104, corresponding constraints are obtained. When preset constraints are met, the initial configuration power of each power supply is optimized with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function. When optimizing the initial configuration power of each power supply, the initial configuration power of each power supply is adjusted, that is, the configuration power of each power supply is changed while the total configuration power remains unchanged to obtain the optimized configuration amount of each power supply.

[0098] In this embodiment, the corresponding constraint conditions are obtained, and the preset constraint conditions include a first constraint condition constraining the first objective function and a second constraint condition constraining the second objective function, that is, different objective functions correspond to different constraint conditions. With the maximum value of the first objective function and the minimum value of the second objective function as the goal, the initial configuration power of each power supply is optimized to obtain the optimized configuration amount of each power supply.

[0099] In this embodiment, the scheme configured based on the first objective function is passed to the second objective function, the second objective function is converted into a second-order cone model, and CPLEX is called to solve the optimal power flow to obtain the distributed power supply optimization configuration scheme.

[0100] The first constraint condition includes the maximum capacity constraint of the distribution network and the constraint that the output power of the distributed power source is equal to the rated capacity of the corresponding power source. The formula corresponding to the maximum capacity constraint of the distribution network is as follows:

[0101]

[0102] in, is the maximum capacity of the distributed generation that can be connected to the grid at node i, is the maximum capacity of the micro gas turbine generator in the distributed power source that can be connected to the grid at the i node, is the maximum capacity of wind turbine generator in distributed power generation that can be connected to the grid at node i, is the maximum capacity of photovoltaic power generation in distributed power sources that can be connected to the grid at node i, is the actual capacity of the micro gas turbine generator in the distributed power source that can be connected to the grid at the i node, is the actual capacity of wind turbines in distributed power sources that can be connected to the grid at node i. is the actual capacity of photovoltaic power generation in the distributed power source that can be connected to the grid at node i. That is, the actual capacity of the grid that can be connected to the grid at node i is less than the maximum capacity.

[0103] The output power of the distributed power source is equal to the discrete constraint corresponding to the constraint of the rated capacity of the corresponding power source, that is, the actual output power of the micro gas turbine at the i node in the distributed power source is the sum of the output power of all micro gas turbines at the i node, the actual output power of the wind turbine power generation at the i node in the distributed power source is the sum of the output power of all wind turbines at the i node, and the actual output power of the photovoltaic power generation at the i node in the distributed power source is the sum of the output power of all photovoltaics at the i node.

[0104] The second constraint condition includes power balance constraint, voltage constraint and capacity constraint, wherein the formula of power balance constraint is as follows:

[0105]

[0106] Among them, P ij is the active power flowing from node i to node j, Q ij is the reactive power flowing from node i to node j, I ij is the current between branches ij, P j,k is the active power flowing from node j to node k, Q j,k is the reactive power flowing from node j to node k, P 1,j is the active power of the load carried by node j, Q j,k is the reactive power of the load carried by node j, R DG,j is the active power injected into node j by the distributed generation, Q dG,j is the reactive power injected into node j by the distributed generation, R ij is the resistance between branches ij, U i is the voltage at node i, U j is the voltage at node j, X ij is the reactance between branches ij.

[0107] The formula corresponding to the voltage constraint is as follows:

[0108] U min ≤U i ≤U max

[0109] Among them, U i The actual voltage at node i, U min is the minimum voltage of node i, U max is the maximum voltage at node i.

[0110] The capacity constraint is that the power between branches ij is less than the maximum value allowed to flow between branches ij.

[0111] It should be noted that the second constraint condition may also include a reverse power flow constraint, a distributed power generation active power removal constraint, an on-load tap-changing transformer constraint, an interruptible load interruption constraint, etc. Among them, the reverse power flow constraint is that the sum of the distributed power generation active power is less than the sum of the system load active power. The distributed power generation active power removal constraint is that in the corresponding scenario, the sum of the wind and solar power generation removal at the i node is less than the maximum wind and solar power generation removal at the i node in the corresponding scenario. The on-load tap-changing transformer constraint is that in the corresponding scenario, the secondary side voltage value of the i on-load tap-changing transformer is between the upper and lower limits of the secondary side voltage of the on-load tap-changing transformer. The interruptible load interruption constraint is that the load active power interruption at the i-th node in the corresponding scenario is between the upper and lower limits of the load active power interruption at the i-th node in the corresponding scenario.

[0112] Obtain the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculate the first sales revenue of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, calculate the first net income of the distributed power source operator according to the first sales revenue and the first cost, obtain the second sales revenue of the distribution network selling electricity and the second cost of the distribution network purchasing electricity, calculate the second net income of the distribution network according to the second sales revenue and the second cost, construct the first objective function according to the first net income and the second net income, construct the second objective function according to the first cost and the second cost, and when the preset constraints are met, optimize the initial configuration power of each power source with the maximum value of the first objective function and the minimum value of the second objective function as the goal, and obtain the optimized configuration amount of each power source. In this application, green certificate-carbon trading is taken into account, and the distributed power configuration is optimized with the maximum benefit of the distributed power source operator and the distribution network and the corresponding minimum cost as the goal, so as to improve the benefits of the distributed operator and the distribution network.

[0113] See also Figure 4 , Figure 4 1 is a block diagram of a distributed power supply optimization configuration device provided by an embodiment of the present invention. For the sake of convenience, only the parts related to the embodiment of the present application are shown. Figure 4 The distributed power supply optimization configuration device 40 includes an acquisition module 41, a calculation module 42, a construction module 43, and an optimization module 44.

[0114] The acquisition module 41 is used to obtain the initial configuration power of each power supply in the distributed power supply by the distributed power supply operator, calculate the first sales revenue of the distributed power supply operator and the first cost that the distributed power supply operator needs to invest according to the initial configuration power of each power supply, and calculate the first net revenue of the distributed power supply operator according to the first sales revenue and the first cost.

[0115] The calculation module 42 is used to obtain the second sales revenue of the distribution network selling electricity and the second cost of the distribution network purchasing electricity, and calculate the second net income of the distribution network based on the second sales revenue and the second cost.

[0116] The construction module 43 is used to construct a first objective function according to the first net income and the second net income, and to construct a second objective function according to the first cost and the second cost.

[0117] The optimization module 44 is used to optimize the initial configuration power of each power supply with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function when the preset constraints are met, so as to obtain the optimized configuration amount of each power supply.

[0118] Optionally, the acquisition module 41 includes:

[0119] The first calculation unit is used to calculate the actual output power of each power supply and the actual proportion of the new energy output power to the total output power according to the initial configuration power of each power supply.

[0120] The second calculation unit is used to obtain the new energy quota ratio of the distributed power supply operator, and calculate the third sales income of the green energy transaction based on the quota ratio and the actual ratio.

[0121] The third calculation unit is used to calculate the total output power according to the actual output power of each power supply, calculate the carbon quota and the actual carbon emissions according to the total output power, and calculate the fourth sales income of the carbon trading according to the carbon quota and the actual carbon emissions.

[0122] The fourth calculation unit is used to calculate the first sales income of the distributed power supply operator according to the third sales income, the fourth sales income and the total output power.

[0123] Optionally, the second calculation unit includes:

[0124] The acquisition subunit is used to obtain the scene number of wind and solar power generation scenes and the power generation curve of each power generation scene.

[0125] The determination subunit is used to determine the power generation probability of each scenario at each moment according to the power generation curve.

[0126] The calculation subunit is used to calculate the third sale income of the green energy transaction based on the power generation probability of each scenario at each moment, the actual output power of each power source, the quota ratio and the actual ratio.

[0127] Optionally, the acquisition subunit includes:

[0128] The estimation submodule is used to obtain the historical data of wind turbine power generation and photovoltaic power generation within a preset time period for any power generation scenario, and estimate the probability density function of wind turbine power generation and photovoltaic power generation within the preset time period based on the historical data of wind turbine power generation and photovoltaic power generation.

[0129] The calculation submodule is used to calculate the joint distribution function according to the probability density function of wind turbine power generation and the probability density function of photovoltaic power generation.

[0130] A submodule is obtained, which is used to randomly sample the joint distribution function to obtain the marginal distribution function value, and according to the marginal distribution function value, a power generation curve of wind turbine power generation and a power generation curve of photovoltaic power generation are obtained.

[0131] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0132] Figure 5 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 5 As shown, the computer device of this embodiment includes: at least one processor ( Figure 5 Only one is shown), a memory, and a computer program stored in the memory and executable on at least one processor, and when the processor executes the computer program, the steps in any of the above-mentioned distributed power supply optimization configuration method embodiments are implemented.

[0133] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 5 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0134] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0136] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated 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 such understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiment when executed by the processor. 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 at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0137] The present application implements all or part of the processes in the above-mentioned embodiment method, and may also be completed through a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing the computer program product.

[0138] 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.

[0139] Those of ordinary skill in the art will appreciate that the 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 this application.

[0140] In the embodiments provided in the present application, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0141] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0142] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These 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 application, and should all be included in the protection scope of the present application.

Claims

1. A distributed power supply optimization configuration method, characterized in that: The distributed power supply optimization configuration method comprises: Obtaining the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculating the first sales income of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, and calculating the first net income of the distributed power source operator according to the first sales income and the first cost; Obtaining a second sales revenue from electricity sold by the distribution network and a second cost for purchasing electricity by the distribution network, and calculating a second net revenue of the distribution network based on the second sales revenue and the second cost; Constructing a first objective function according to the first net income and the second net income, and constructing a second objective function according to the first cost and the second cost; When the preset constraints are met, the initial configuration power of each power supply is optimized with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function to obtain the optimized configuration amount of each power supply.

2. The distributed power optimization configuration method according to claim 1, characterized in that: The calculating, according to the initial configuration power of each power source, the first sale income of the distributed power source operator and the first cost required to be invested by the distributed power source operator comprises: Calculate the actual output power of each power source and the actual proportion of the new energy output power to the total output power according to the initial configuration power of each power source; Obtaining the new energy quota ratio of the distributed power supply operator, and calculating the third sale income of the green energy transaction according to the quota ratio and the actual ratio; Calculate the total output power according to the actual output power of each power source, calculate the carbon quota and the actual carbon emissions according to the total output power, and calculate the fourth sale income of the carbon trading according to the carbon quota and the actual carbon emissions; The first sales revenue of the distributed power source operator is calculated according to the third sales revenue, the fourth sales revenue and the total output power.

3. The distributed power optimization configuration method according to claim 2, characterized in that: The calculating of the third sale proceeds of the green energy transaction according to the quota ratio and the actual ratio includes: Obtain the number of wind and solar power generation scenarios and the power generation curve of each power generation scenario; According to the power generation curve, determining the power generation probability of each scenario at each moment; The third sales income of the green energy transaction is calculated based on the power generation probability of each scenario at each moment, the actual output power of each power source, the quota ratio and the actual ratio.

4. The distributed power optimization configuration method according to claim 3, characterized in that: The obtaining of the scene times of wind and solar power generation scenes and the power generation curve of each power generation scene includes: For any power generation scenario, historical data of wind turbine power generation and historical data of photovoltaic power generation within a preset time period are obtained, and based on the historical data of wind turbine power generation and historical data of photovoltaic power generation, probability density functions of wind turbine power generation and photovoltaic power generation within the preset time period are estimated; Calculating a joint distribution function based on the wind turbine power generation probability density function and the photovoltaic power generation probability density function; The joint distribution function is randomly sampled to obtain a marginal distribution function value, and a power generation curve of wind turbine power generation and a power generation curve of photovoltaic power generation are obtained according to the marginal distribution function value.

5. The distributed power optimization configuration method according to claim 1, characterized in that: The first cost includes the investment cost, operation and maintenance cost, fuel cost and environmental governance cost of the distributed power operator.

6. The distributed power optimization configuration method according to claim 1, characterized in that: The preset constraint conditions include a first constraint condition constraining the first objective function and a second constraint condition constraining the second objective function.

7. The distributed power supply optimization configuration method according to claim 6, characterized in that: The first constraint condition includes a maximum capacity constraint of the distribution network and a constraint that the output power of the distributed power source is equal to the rated capacity of the corresponding power source; The second constraint condition includes a power balance constraint, a voltage constraint and a capacity constraint.

8. A distributed power supply optimization configuration method, characterized in that: The distributed power supply optimization configuration method comprises: An acquisition module is used to obtain the initial configuration power of each power source in the distributed power source by the distributed power source operator, calculate the first sales income of the distributed power source operator and the first cost that the distributed power source operator needs to invest according to the initial configuration power of each power source, and calculate the first net income of the distributed power source operator according to the first sales income and the first cost; a calculation module, configured to obtain a second sales revenue of electricity sold by the distribution network and a second cost of electricity purchased by the distribution network, and calculate a second net income of the distribution network according to the second sales revenue and the second cost; A construction module, configured to construct a first objective function according to the first net income and the second net income, and to construct a second objective function according to the first cost and the second cost; The optimization module is used to optimize the initial configuration power of each power supply with the goal of maximizing the value of the first objective function and minimizing the value of the second objective function when the preset constraints are met, so as to obtain the optimized configuration amount of each power supply.

9. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the distributed power supply optimization configuration method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the distributed power supply optimization configuration method according to any one of claims 1 to 7 is implemented.

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