A power source site selection and sizing method

Abstract

The application provides a power source site selection and capacity determination method, and relates to the technical field of power system optimization, which comprises the following steps: obtaining operation parameters of a unit, determining a first constraint set of the unit and a second constraint set of power source site selection and capacity determination according to the operation parameters; obtaining a distribution network parameter set, constructing a target function of power source site selection and capacity determination according to the distribution network parameter set and the operation parameters; determining an initial glowworm population based on a configured target rule, and performing glowworm algorithm iteration optimization based on the initial glowworm population to determine an optimal result of the target function under the condition of meeting the first constraint set and the second constraint set, so that the distribution network containing the distributed power source can be safely, reliably and economically operated.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system optimization, and in particular to a power source site selection and capacity determination method. BACKGROUND

[0002] At present, energy saving and emission reduction and low-carbon lifestyle are vigorously advocated, and various new energies become ideal alternatives to traditional fossil energies due to their environment-friendly characteristics, and gradually dominate in some areas; distributed power (Distributed Generation, DG), as one of the key links in the new energy system, not only promotes the diversification of energy structure, but also, through effective integration with traditional centralized power grids, builds a new power system operation mode which can ensure safe and stable power supply, reduce environmental pollution and realize green and sustainable development.

[0003] Distributed power refers to a power generation device with relatively small capacity (usually not more than 50 MW), adjacent to load centers or connected to distribution networks, and has the characteristics of economy, efficiency and reliability; distributed energy will gradually replace fossil fuels in certain periods and areas, thereby maximizing the energy use efficiency of the entire power grid system; when a large number of distributed power sources are randomly connected to or disconnected from the distribution network, it will affect the reliability, relay protection performance, power quality and network loss of the system, and increase the complexity of load forecasting, thereby making the distribution network planning face more challenges; therefore, determining the optimal installation location and capacity configuration of distributed power sources to ensure that the distribution network containing distributed power sources can be safely, reliably and economically operated is a major problem faced by distribution network planning. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0005] To this end, a first object of the present application is to provide a power source site selection and capacity determination method to determine the optimal deployment location and capacity of distributed power sources.

[0006] A second object of the present application is to provide a power source site selection and capacity determination device.

[0007] A third object of the present application is to provide an electronic device.

[0008] A fourth object of the present application is to provide a computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above objects, a power source site selection and capacity determination method according to a first aspect of the present application comprises:

[0011] obtaining operation parameters of the unit, and determining a first constraint set of the unit and a second constraint set of power source siting and sizing according to the operation parameters;

[0012] obtaining a power distribution network parameter set, and constructing a target function of power source siting and sizing according to the power distribution network parameter set and the operation parameters;

[0013] determining an initial glowworm population based on a configured target rule, and performing glowworm algorithm iterative optimization based on the initial glowworm population to determine an optimal result of the target function when the first constraint set and the second constraint set are satisfied; different glowworms correspond to different results of the target function.

[0014] To achieve the above object, the second aspect embodiment of the present application provides a power source siting and sizing device, comprising:

[0015] a first obtaining module configured to obtain operation parameters of the unit, and determine a first constraint set of the unit and a second constraint set of power source siting and sizing according to the operation parameters;

[0016] a second obtaining module configured to obtain a power distribution network parameter set, and construct a target function of power source siting and sizing according to the power distribution network parameter set and the operation parameters;

[0017] an optimization module configured to determine an initial glowworm population based on a configured target rule, and perform glowworm algorithm iterative optimization based on the initial glowworm population to determine an optimal result of the target function when the first constraint set and the second constraint set are satisfied; different glowworms correspond to different results of the target function.

[0018] To achieve the above object, the third aspect embodiment of the present application provides an electronic device, comprising a processor and a memory connected with the processor in communication;

[0019] the memory stores computer execution instructions;

[0020] the processor executes the computer execution instructions stored in the memory to implement the method of the first aspect embodiment.

[0021] To achieve the above object, the fourth aspect embodiment of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to implement the method of the first aspect embodiment.

[0022] To achieve the above object, the fifth aspect embodiment of the present application provides a computer program product, comprising a computer program, and the computer program is executed by a processor to implement the method of the first aspect embodiment.

[0023] The power source site selection and capacity determination method provided in the application determines a first constraint set, a second constraint set and a target function by obtaining a set of operation parameters and power distribution network parameters, determines an initial glowworm population based on a configured target rule, and performs iterative optimization based on the initial glowworm population, thereby improving the optimization efficiency of the glowworm algorithm for the target function and the accuracy of the optimization result, determining the optimal result of the target function while meeting the first constraint set and the second constraint set, and greatly improving the energy efficiency of the power grid system.

[0024] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0025] The above and / or additional aspects and advantages of the application will become apparent and be readily appreciated from the following description, including the accompanying drawings, wherein:

[0026] Figure 1 A flowchart of a power source site selection and capacity determination method provided in an embodiment of the application;

[0027] Figure 2 A flowchart of another power source site selection and capacity determination method provided in an embodiment of the application;

[0028] Figure 3 A structural diagram of a power source site selection and capacity determination device provided in an embodiment of the application. DETAILED DESCRIPTION

[0029] The embodiments of the application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are intended to explain the application, and cannot be understood as limiting the application.

[0030] A power source site selection and capacity determination method in an embodiment of the application is described below with reference to the accompanying drawings.

[0031] Figure 1 A flowchart of a power source site selection and capacity determination method provided in an embodiment of the application. As shown in Figure 1 the method includes the following steps:

[0032] S101, obtaining operation parameters of a unit, and determining a first constraint set of the unit and a second constraint set of power source site selection and capacity determination according to the operation parameters.

[0033] Optionally, the operation parameters of the generator set can include, but are not limited to, rated power, rated voltage, real-time voltage, current, power generation capacity, power generation efficiency, power demand, voltage variable range, storage capacity, storage battery life, storage efficiency, and other related operation parameters, and details are not described herein.

[0034] In some implementations, the first constraint set of the unit can include common constraints such as load constraints, power generation constraints, or total capacity constraints, to ensure that the operation capacity of the unit is not exceeded and the safe operation of the unit is ensured, and details are not described herein.

[0035] In some implementations, the second constraint set of the power source siting and sizing can include power source siting and sizing related constraints, such as Distributed Generation (DG) output constraints or node voltage constraints, to more comprehensively ensure the safe operation of the DG.

[0036] S102, obtaining a power distribution network parameter set, and constructing a target function of power source siting and sizing according to the power distribution network parameter set and the operation parameters.

[0037] In some implementations, the power distribution network parameter set is a parameter related to the power distribution network, including but not limited to power distribution network line related parameters, DG operation related parameters, power distribution network loss related parameters, and environmental pollution parameters caused by DG emission of greenhouse gases.

[0038] It can be understood that the target function is a power source siting and sizing model provided in the embodiment, and the optimal solution of the power source siting and sizing is determined by solving the target function to obtain the best layout result; in the embodiment, the minimum total power distribution network operation cost is the final result, and the result of the target function is the total power distribution network operation cost, and the optimal solution of the target function is to determine the minimum value of the total power distribution network operation cost.

[0039] Optionally, the corresponding relationship between the data of different dimensions and the cost can be determined according to the operation parameters, the power distribution network line related parameters, the DG operation related parameters, the power distribution network loss related parameters, and the environmental pollution parameters caused by the DG emission of greenhouse gases, so as to construct the target function reflecting the total power distribution network operation cost.

[0040] S103, determining an initial firefly population based on the configured target rule, and performing firefly algorithm iteration optimization based on the initial firefly population to determine the optimal result of the target function while meeting the first constraint set and the second constraint set.

[0041] It can be understood that the firefly algorithm is an intelligent optimization algorithm which simulates the behavior of fireflies attracting each other according to individual brightness, and the optimization result is subject to the initial distribution of the population to some extent, so the embodiment generates an initial firefly population based on the target rule for iterative optimization, wherein different fireflies correspond to different results of the target function, thereby improving the optimization efficiency and accuracy.

[0042] In the formula, the target rule is a rule for selecting an initial firefly population, for example, the optimal selection result of the historical power site selection capacitor is written into the target rule, so as to generate an initial firefly, and the historical result is used as an initial firefly for optimization, thereby improving the optimization efficiency compared with the existing random selection, and improving the acquisition effect of the initial firefly population.

[0043] In some implementations, the target rule can further include a pre-configured firefly generation rule, according to which the initial firefly is constantly generated to obtain the initial firefly population, thereby improving the optimization effect.

[0044] It can be understood that the attraction of firefly j to firefly i can be represented as:

[0045]

[0046] In the formula, r is a constant, and the value is [0, 1]; r is the maximum attraction, which is the same as the attraction at r=1, and the value in the embodiment is 1; is the light absorption coefficient, and the value is [0.01, 100]; is the Euclidean distance between firefly i and firefly j.

[0047] In the iteration process, if there is a pareto dominance of firefly j to firefly i in the current environment, the next iteration firefly is:

[0048]

[0049] In the formula, t is the current iteration number; and represent firefly i and firefly j, respectively; is the attraction of firefly j to firefly i; is a constant, and the value is [0, 1]; is a random number vector, which can be obtained through Gaussian distribution, uniform distribution or other distribution.

[0050] In the iteration process, if there is no pareto dominance of firefly j to firefly i in the current environment, the next iteration firefly is:

[0051]

[0052] wherein, is an average value obtained by a random weight coefficient weighted average of the fireflies.

[0053] It can be understood that if the current environment exists the pareto dominance of firefly j to firefly i, it indicates that the current environment exists the firefly j which is better than firefly i, and it indicates that firefly i is not the optimal result, then continue to update the next iteration, if the current environment does not exist the pareto dominance of firefly j to firefly i, then firefly i is the local optimal result, and the optimal result of the final iteration is determined by the weighted average of the fireflies.

[0054] It can be understood that each firefly can be understood as an m-dimensional feature vector, m is the number of independent variables in the objective function, that is, each independent variable is taken as the dimension of the feature vector, and the feature vector corresponding to each firefly is the value of the independent variable in the objective function, so that the result of the objective function can be determined according to each firefly; assuming that the initial firefly population is 10 initial fireflies, then according to the 10 initial fireflies, the next iteration firefly corresponding to each initial firefly is obtained, at this time the environment includes 10 initial fireflies and 10 iteration obtained fireflies, the first 10 fireflies with smaller objective function are selected from the 20 fireflies, and the selected 10 fireflies continue to be iterated to obtain new 20 fireflies, and the selected 20 fireflies continue to be iterated until the preset iteration number is reached, so that the firefly corresponding to the minimum value of the objective function is determined as the optimal firefly, the feature vector of the optimal firefly includes the optimal value of each independent variable in the objective function, so as to realize the best optimization.

[0055] It can be understood that when the first constraint set and the second constraint set are satisfied, the optimal result of the objective function determined is also the minimum value of the total cost of the power distribution network, and the power source site selection and capacity determination can save the cost to the maximum extent and greatly improve the energy efficiency of the power grid system under the condition of stable and safe operation.

[0056] In this embodiment, the first constraint set of the unit and the second constraint set of the power source siting and sizing are determined according to the operation parameters; the power source siting and sizing is constrained under more comprehensive constraint conditions to ensure that the unit can operate safely; the objective function of the power source siting and sizing is constructed according to the power distribution network parameter set and the operation parameters; the initial glowworm population is determined based on the configured target rules, and the iterative optimization is performed based on the initial glowworm population to determine the optimal result of the objective function under the conditions of meeting the first constraint set and the second constraint set; the more optimized initial glowworm population is used as the optimization starting point to improve the optimization efficiency of the glowworm algorithm on the objective function and the accuracy of the optimization result, and the power source siting and sizing under the current optimal result can save the cost to the maximum extent and greatly improve the energy efficiency of the power grid system under the condition of stable and safe operation.

[0057] On the basis of the above-mentioned embodiments, Figure 2 The flowchart of another power source siting and sizing method provided by the embodiments of the present application is shown in Figure 6. Figure 2 As shown in the figure, the method comprises the following steps:

[0058] S201, obtaining the operation parameters of the unit and determining the first constraint set of the unit and the second constraint set of the power source siting and sizing according to the operation parameters.

[0059] In some implementations, the first constraint set comprises at least one of the following: a net load balance constraint, a minimum start-stop time constraint of the thermal power unit, a thermal power unit output constraint, a thermal power unit ramping constraint, a water power unit output constraint, and a water power unit total power constraint.

[0060] For example, the net load balance constraint can be expressed as:

[0061]

[0062] wherein, is the output of the thermal power unit i at time t; is the output of the water power unit k at time t; is the net load at time t.

[0063] For example, the minimum start-stop time constraint of the thermal power unit can be expressed as:

[0064]

[0065] wherein, and respectively represent the minimum start-up period number and the minimum shutdown period number of the thermal power unit; and respectively represent the number of continuous start-up periods and the number of continuous shutdown periods of the thermal power unit i at time t-1; and denotes the on-off state of thermal generating unit i at time period t-1 and t, where 1 means on and 0 means off.

[0066] For example, the thermal generating unit output constraint can be expressed as:

[0067]

[0068] wherein, and denote the lower and upper output limits of thermal generating unit i, respectively.

[0069] For example, the thermal generating unit ramping constraint can be expressed as:

[0070]

[0071] wherein, and denote the upward and downward ramping rates of thermal generating unit i, respectively; is the maximum output of thermal generating unit i.

[0072] For example, the hydro generating unit output constraint can be expressed as:

[0073]

[0074] wherein, and denote the lower and upper output limits of hydro generating unit k, respectively.

[0075] For example, the hydro generating unit total energy constraint can be expressed as:

[0076]

[0077] wherein, denotes the output of hydro generating unit, denotes the total energy of hydro generating unit.

[0078] In some implementations, the second set of constraints includes at least one of a node voltage constraint, a conductor current inequality constraint, and a DG output constraint.

[0079] In some implementations, the upper limit and the lower limit of the node voltage, the upper limit and the lower limit of the node current, and the maximum load of the unit can be obtained based on the operation parameters; the node voltage constraint can be determined according to the upper limit and the lower limit of the node voltage, so that the node voltage is between the upper limit and the lower limit of the node voltage; the conductor current inequality constraint can be determined according to the upper limit and the lower limit of the branch current, so that the branch current is between the upper limit and the lower limit of the branch current; the load upper boundary value and the load lower boundary value can be determined according to the maximum load of the unit, and the DG output constraint can be determined so that the total capacity of the DG connected to the power grid is between the load lower boundary value and the load upper boundary value.

[0080] Optionally, the node voltage constraint can be expressed as:

[0081]

[0082] wherein, and respectively represent the lower limit and the upper limit of the node voltage, and N is a node set.

[0083] Optionally, the conductor current inequality constraint can be expressed as:

[0084]

[0085] wherein, and respectively represent the lower limit and the upper limit of the branch current, and L is a branch set.

[0086] Optionally, the DG output constraint can be expressed as:

[0087]

[0088] wherein, is the total capacity of the DG connected to the power grid; and respectively represent 10% of the maximum load of the unit and 25% of the maximum load of the unit, wherein 10% of the maximum load of the unit is the load lower boundary value, and 25% of the maximum load of the unit is the load upper boundary value.

[0089] In the embodiments of the present application, the implementation method of step S201 can be implemented by any one of the embodiments of the present disclosure, and here it is not limited, and will not be repeated.

[0090] S202, a power distribution network parameter set is obtained, and a target function of power source site selection and capacity determination is constructed according to the power distribution network parameter set and the operation parameters.

[0091] In some implementations, the first cost of distribution network line upgrading and grid maintenance can be determined according to the time period from line commissioning to line upgrading, the time period from line commissioning to the end of planning, the fixed annual interest rate, the line fixed investment cost, the annual fixed management cost of the line, and the upgrading indication information of the line in the set of distribution network parameters; the second cost of DG operation can be determined according to the maximum generation hours of DG, the total number of DGs, the power factor of each DG, the capacity of each DG, and the generation and operation cost of each DG in the set of distribution network parameters; the third cost of network loss of the distribution network can be determined according to the unit price, the annual maximum load loss hours of the branch, and the active network loss of the branch in the set of distribution network parameters; and the fourth cost of environmental pollution compensation can be determined according to the greenhouse gas emission intensity per ton, the annual power generation per ton at each selected node, the greenhouse gas environmental value discount standard, and the greenhouse gas emission collection price in the set of distribution network parameters.

[0092] Preferably, the line equivalent payment capital investment recovery coefficient can be determined according to the time period from line commissioning to line upgrading, the time period from line commissioning to the end of planning, and the fixed annual interest rate; and the first cost can be determined according to the line fixed investment cost, the annual fixed management cost of the line, the upgrading indication information of the line, and the line equivalent payment capital investment recovery coefficient.

[0093] Optionally, the first cost can be represented as:

[0094]

[0095] wherein, the first cost is represented by C1; the fixed annual interest rate is represented by r; the time period from line commissioning to line upgrading is represented by T; the time period from line commissioning to the end of planning is represented by Tp; the line fixed investment cost is represented by C; the annual fixed management cost of the line is represented by M; the upgrading indication information of the line is represented by L, and L is 0 or 1, wherein 1 represents that the line needs to be upgraded, and 0 represents that the line does not need to be upgraded; and L represents the total number of grid branches; and the line equivalent payment capital investment recovery coefficient is represented by a and b respectively.

[0096] Preferably, the product of the power factor, the capacity, and the generation and operation cost of each DG can be obtained to obtain a first result; the sum of all the first results based on the total number of DGs can be determined to obtain a second result; and the product of the second result and the maximum hours of DG generation can be obtained to obtain the second cost.

[0097] Optionally, the second cost can be represented as:

[0098]

[0099] in, Indicates the second expense; Indicates the maximum number of hours that DG power generation is utilized; Indicates the first The power factor of each DG is taken as a uniform value to avoid tedious calculations. Indicates the first The capacity of one DG; Indicates the first The power generation and operating cost per DG is expressed in yuan / kWh; n represents the total number of DG units. The first result; This is the second result.

[0100] Preferably, the product of the annual maximum load loss hours, active power loss and unit electricity price for each branch can be obtained as the third result for each branch; the third results of all branches are summed to obtain the third cost.

[0101] Alternatively, the third fee can be expressed as:

[0102]

[0103] in, Indicates third-party expenses; This indicates the unit electricity price (yuan / kWh); branch road The number of hours of maximum annual load loss; Indicates the active power loss of the branch circuit. This represents the power factor of branch j. This represents the resistance of branch j. Indicates the rated voltage. This represents the active power of branch j.

[0104] Preferably, the sum of the greenhouse gas environmental value discount standard and the greenhouse gas emission levy price can be obtained as the fourth result; the fifth result can be determined based on the fourth result and the annual power generation per metric ton at each candidate node; the sum of the fifth results of all candidate nodes can be obtained as the sixth result, and the fourth cost can be obtained based on the product of the sixth result and the greenhouse gas emission intensity generated per metric ton.

[0105] Alternatively, the fourth fee can be expressed as:

[0106]

[0107] in, The fourth expense is the compensation that DG (Dry Gross Foodstuff) is required to pay for greenhouse gas emissions. emission intensity of greenhouse gas generated per tonne; the first annual power generation per tonne at the first candidate node; represents the greenhouse gas environmental value discount standard; represents the greenhouse gas emission collection price; is the fourth result; is the number of all candidate nodes; is the sixth result.

[0108] Further, the first cost, the second cost, the third cost and the fourth cost can be weighted and summed to construct an objective function; the objective function can be represented as:

[0109]

[0110] wherein, is the objective function, representing the total cost of the power distribution network operation; , , and are weight factors of the first cost, the second cost, the third cost and the fourth cost respectively, taking values between 0-1 and adding up to 1.

[0111] S203, randomly initializing the multi-dimensional data of the firefly to obtain a first initial firefly.

[0112] Optionally, the data dimension of the firefly is the same as the number of independent variables in the objective function, for example, the objective function has m independent variables, the data dimension of the firefly is m, each firefly can be regarded as a vector with a dimension of m, and the data of each dimension in the vector is randomly initialized to obtain the first initial firefly. In this embodiment, the value of each dimension in the vector is normalized, that is, the value of each dimension is in the interval of 0-1.

[0113] S204, iteratively updating based on the first initial firefly and the target rule to obtain an initial firefly population.

[0114] Optionally, in the target rule, the first mapping range of each dimension data in the first initial firefly can be queried, and the update value of the corresponding dimension can be generated according to the first mapping range; the second initial firefly is determined based on the update value of each dimension, and in the target rule, the second mapping range of each dimension data in the second initial firefly is queried; the update value of the corresponding dimension is generated according to the second mapping range, and the third initial firefly is determined based on the update value of each dimension. In this way, a preset number of initial fireflies are generated to obtain an initial firefly population.

[0115] In some implementations, the chaos mapping improved algorithm can be used to improve the poor uniformity of the initialization process. The target rule is determined based on a piecewise linear mapping, and different transformation formulas are set for initial values distributed in different positions to obtain the target rule.

[0116] Optionally, the target rule can be expressed as:

[0117]

[0118] wherein, is the data of the n-th dimension in the first initial firefly; is the data of the n-th dimension in the second initial firefly; d is a control parameter for determining the mapping range and there is no overlapping part between the mapping ranges, and can take a random value between 0.35 and 0.45, and in the embodiment, d is 0.4.

[0119] For example, the first initial firefly is randomly generated in the range of 0-1 in each dimension, and according to the specific value of each dimension in the first initial firefly, the corresponding first mapping range is matched in the target rule, for example, the first mapping range is , then according to the update value of the corresponding dimension in the second initial firefly is determined, and so on. The mapping range of the data of each dimension in the first initial firefly is identified in the target rule, so as to determine the update value of each dimension according to the corresponding first mapping range, and the second initial firefly is obtained according to the update value. Further, the mapping range of the data of each dimension in the second initial firefly is identified in the target rule, so as to determine the update value of each dimension according to the corresponding second mapping range, and the third initial firefly is obtained according to the update value. In this way, the initial firefly population is obtained by iterating a predetermined number of initial fireflies.

[0120] S205, based on the initial firefly population, the firefly algorithm is iterated and optimized to determine the optimal result of the target function when the first constraint set and the second constraint set are satisfied.

[0121] In the embodiment of the application, the implementation method of step S205 can be implemented by any one of the embodiments of the present disclosure, and this is not limited herein, and will not be repeated.

[0122] In the embodiment, the first constraint set and the second constraint set are acquired based on the operation parameters of the unit, and the safe operation of the unit is ensured through the multiple constraints in the first constraint set and the second constraint set; different types of costs are acquired according to the parameter set of the power distribution network and the operation parameters, and the objective function is determined according to the costs, the initial firefly population is iteratively selected according to the target rule, the optimization efficiency is improved compared with the existing random selection, the acquisition effect of the initial firefly population is improved, the iterative optimization of the objective function is performed based on the initial firefly population, and the optimal result of the objective function, that is, the minimum value of the total cost of the power distribution network operation, is obtained. The power source positioning and capacity determination based on the current optimal result can save the cost to the maximum extent and greatly improve the energy efficiency of the power grid system under the condition of stable and safe operation.

[0123] To achieve the above embodiment, the application further provides a power source positioning and capacity determination device.

[0124] Figure 3 A structural schematic diagram of a power source positioning and capacity determination device provided by the embodiment of the application is shown in FIG. 3. Figure 3 As shown in the figure, the power source positioning and capacity determination device 300 includes:

[0125] The first acquisition module 301 is configured to acquire the operation parameters of the unit, and determine the first constraint set of the unit and the second constraint set of the power source positioning and capacity determination according to the operation parameters.

[0126] The second acquisition module 302 is configured to acquire the parameter set of the power distribution network, and construct the objective function of the power source positioning and capacity determination according to the parameter set of the power distribution network and the operation parameters.

[0127] The optimization module 303 is configured to determine the initial firefly population based on the configured target rule, and perform iterative optimization of the firefly algorithm based on the initial firefly population, to determine the optimal result of the objective function when the first constraint set and the second constraint set are satisfied; wherein different fireflies correspond to different results of the objective function.

[0128] Further, in a possible implementation manner of the embodiment of the application, the optimization module 303 includes:

[0129] The multi-dimensional data of the firefly is randomly initialized to obtain a first initial firefly;

[0130] In the target rule, the first mapping range of each dimension data in the first initial firefly is queried, and the update value of the corresponding dimension is generated according to the first mapping range;

[0131] The second initial firefly is determined based on the update value of each dimension, and in the target rule, the second mapping range of each dimension data in the second initial firefly is queried;

[0132] According to the second mapping range, an updated value of a corresponding dimension is generated, and a third initial firefly is determined based on the updated value of each dimension, and the like, so as to generate a preset number of initial fireflies, and obtain an initial firefly population.

[0133] Further, in a possible implementation manner of the embodiment of the application, the first constraint set includes at least one of a net load balance constraint, a minimum start-stop time constraint of a thermal power unit, a thermal power unit output constraint, a thermal power unit ramping constraint, a hydropower unit output constraint, and a total hydropower unit power constraint.

[0134] Further, in a possible implementation manner of the embodiment of the application, the second constraint set includes at least one of a node voltage constraint, a branch current inequality constraint, and a distributed power source DG output constraint.

[0135] Further, in a possible implementation manner of the embodiment of the application, the first obtaining module includes:

[0136] Based on the operation parameter, an upper limit and a lower limit of the node voltage, an upper limit and a lower limit of the node current, and a maximum load of the unit are obtained;

[0137] According to the upper limit and the lower limit of the node voltage, the node voltage constraint is determined to be that the node voltage is between the upper limit and the lower limit of the node voltage;

[0138] According to the upper limit and the lower limit of the branch current, the branch current inequality constraint is determined to be that the branch current is between the upper limit and the lower limit of the branch current;

[0139] According to the maximum load of the unit, a load upper boundary value and a load lower boundary value are determined, and the DG output constraint is determined to be that a total capacity of the DG accessing the power grid is between the load lower boundary value and the load upper boundary value.

[0140] Further, in a possible implementation manner of the embodiment of the application, the second obtaining module includes:

[0141] According to a time period from line enabling to line upgrading, a time period from line enabling to a planning end period, a fixed annual interest rate, a line fixed investment cost, an annual fixed management cost of the line, and line upgrading indication information in the distribution network parameter set, a first cost of distribution network line upgrading and power grid maintenance is determined;

[0142] According to a maximum generation hour number of distributed power source DG power generation, a total number of DGs, a power factor of each DG, a capacity of each DG, and a generation and operation cost of each DG in the distribution network parameter set, a second cost of DG operation is determined;

[0143] According to a unit price, an annual maximum load loss hour number of a branch, and an active power loss of the branch in the distribution network parameter set, a third cost of distribution network loss is determined.

[0144] determine a fourth cost of environmental pollution compensation according to the greenhouse gas emission intensity per ton produced in the power distribution network parameter set, the annual power generation per ton at each candidate node, the greenhouse gas environmental value discount standard, and the greenhouse gas emission collection price;

[0145] weight and sum the first cost, the second cost, the third cost, and the fourth cost to construct a target function.

[0146] Further, in a possible implementation manner of the embodiment of the present application, the second obtaining module comprises:

[0147] determine a line equivalent capital investment recovery coefficient according to a time period from line enabling to line upgrading, a time period from line enabling to the end of planning, and a fixed annual interest rate;

[0148] determine the first cost according to the line fixed investment cost, the annual fixed management cost of the line, the upgrading indication information of the line, and the line equivalent capital investment recovery coefficient.

[0149] Further, in a possible implementation manner of the embodiment of the present application, the second obtaining module comprises:

[0150] obtain a first result by multiplying the power factor, the capacity, and the power generation and operation cost of each DG;

[0151] determine a second result by summing all the first results based on the total number of DGs;

[0152] obtain a second cost by multiplying the second result and the maximum utilization hour of DG power generation;

[0153] Further, in a possible implementation manner of the embodiment of the present application, the second obtaining module comprises:

[0154] obtain a third result of each branch by multiplying the annual maximum load loss hour, the active power loss, and the unit price of each branch;

[0155] determine a third cost by summing the third results of all the branches.

[0156] Further, in a possible implementation manner of the embodiment of the present application, the second obtaining module comprises:

[0157] obtain a fourth result by summing the greenhouse gas environmental value discount standard and the greenhouse gas emission collection price;

[0158] determine a fifth result according to the fourth result and the annual power generation per ton at each candidate node;

[0159] Sum up the fifth results of all candidate nodes as a sixth result, and obtain a fourth cost based on a product of the sixth result and an emission intensity of greenhouse gases per ton of generated.

[0160] It should be noted that the aforementioned explanation of the power source site selection and capacity determination method embodiment is also applicable to the power source site selection and capacity determination device of the embodiment, which will not be described here again.

[0161] In the embodiment of the application, the first constraint set and the second constraint set are acquired based on the operating parameters of the unit, and the safe operation of the unit is ensured through the multiple constraint conditions in the first constraint set and the second constraint set; different types of costs are acquired according to the power distribution network parameter set and the operating parameters, and the target function is determined according to the costs, the initial firefly population is iteratively selected according to the target rule, the optimization efficiency is improved compared to the existing random selection, the acquisition effect of the initial firefly population is improved, the iterative optimization of the target function is performed based on the initial firefly population, the optimal result of the target function, that is, the minimum value of the total cost of the power distribution network operation, is obtained, and the power source site selection and capacity determination based on the current optimal result can maximize the cost saving and greatly improve the energy efficiency of the power grid system under the condition of stable and safe operation.

[0162] To achieve the above-mentioned embodiments, the application further provides an electronic device, comprising a processor and a memory in communication connection with the processor; the memory stores computer execution instructions; and the processor executes the computer execution instructions stored in the memory to realize the method provided in the foregoing embodiments.

[0163] To achieve the above-mentioned embodiments, the application further provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by a processor to realize the method provided in the foregoing embodiments.

[0164] To achieve the above-mentioned embodiments, the application further provides a computer program product, comprising a computer program, and the computer program is executed by a processor to realize the method provided in the foregoing embodiments.

[0165] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in the application comply with relevant laws and regulations and do not violate public order and good customs.

[0166] It is important to note that user's personal information shall be collected for legitimate and reasonable uses of the service and not shared or sold outside of those legitimate uses. Further, such collection / sharing shall occur after receiving the consent of the users, including but not limited to informing the users to read the user agreement / user notice before using the function, and signing the agreement / authorization including authorization of relevant user information. In addition, any necessary steps shall be taken to protect and secure access to such personal information data, and ensure that other individuals with access to the personal information data follow their privacy policies and procedures.

[0167] The present application contemplates providing an implementation in which the user has the option to opt in or opt out of allowing the use or access of personal information data. That is, the present disclosure contemplates providing users with control to permit, deny, or limit how their information is shared with other entities, including providers of social media services. The present disclosure further contemplates providing users with control over what information is collected about the user's online activities as well as how it is collected and used by a social media service.

[0168] In the foregoing detailed description of embodiments, reference is made to descriptive terms such as "one embodiment", "some embodiments", "an example", "a specific example" or "some examples" etc. which indicate that the particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The illustrative examples set forth in the description above are not intended to be exhaustive or to be construed as indicative of one or more embodiments or examples of the application. Furthermore, the description infra sometimes uses terms like "above" or "below" which are used for descriptive purposes only and not intended to be construed as indicating relative importance or implying the presence of certain features, structures, materials, or characteristics of the application. Thus, a feature described as "above" or "below" can be included in any embodiment or example of the application. Moreover, the description of a particular feature or aspect of the application should not be construed as an indication that it is essential to the application or that it will be limiting on the overall scope of the application. The description of a particular feature or aspect of the application should not be construed as an indication that it is essential to the application or that it will be limiting on the overall scope of the application.

[0169] Furthermore, the terms "first", "second", "third", "fourth", etc. are used herein for descriptive purposes only and should not be construed as indicating or implying relative importance or an ordered sequence. Thus, features defined with "first", "second" or "third" can also appear or not appear, respectively, in any combination, in other embodiments or examples of the application. The meaning of "a", "an", and "the" includes singular and plural referents unless the context clearly dictates otherwise. Thus, e.g., reference to "a feature" can include multiple features unless the context clearly indicates otherwise.

[0170] Any processes or methods described in the flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) of a larger group of executable instructions. It should also be understood that the preferred embodiments of the application can include many other where the steps are carried out out of the order given or simultaneously.

[0171] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of the manufacturing and / or processing. The computer-readable medium can include, but is not limited to, the following: an electronic connection (an electronic device having one or more wires), a portable computer diskette (a magnetic device), a RAM (random access memory), a ROM (read-only memory), an EPROM (erasable programmable ROM) or Flash memory, an optical fiber device, and a portable CD ROM. Additionally, the computer-readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for example, an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in order to be executed.

[0172] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. As such, in some embodiments, specifically configured hardware can be used to implement at least some of the functionality described herein. For example, if implemented in hardware, the hardware can include any or a combination of the following: a discrete logic circuit having logic gates for implementing logic functions upon data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0173] Those of skill in the art would understand that information and signals can be represented using any of a variety of technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0174] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0175] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A power supply addressing and capacity determination method, characterized in that, The method includes: The unit's operating parameters are obtained, and a first set of constraints and a second set of constraints for power source location and capacity determination are determined based on the operating parameters. The first set of constraints includes at least one of the following: net load balance constraints, minimum start-up and shutdown time constraints for thermal power units, output constraints for thermal power units, ramp-up constraints for thermal power units, output constraints for hydropower units, and total power generation constraints for hydropower units. The second set of constraints includes at least one of the following: node voltage constraints, conductor current inequality constraints, and distributed generation (DG) output constraints. Obtain the distribution network parameter set, and construct the objective function for power source location and capacity determination based on the distribution network parameter set and the operating parameters; An initial firefly population is determined based on the configured target rules, and the firefly algorithm is iteratively optimized based on the initial firefly population to determine the optimal result of the objective function when the first set of constraints and the second set of constraints are satisfied; wherein different fireflies correspond to different results of the objective function; The step of constructing the objective function for power source location and capacity determination based on the distribution network parameter set and the operating parameters includes: Based on the time period from line activation to line upgrade, the time period from line activation to the end of the planning period, the fixed annual interest rate, the fixed investment fee of the line, the annual fixed management fee of the line, and the line upgrade indication information in the distribution network parameter set, the first cost of distribution network line upgrade and power grid maintenance is determined. The second cost of DG operation is determined based on the maximum number of generating hours of distributed generation (DG), the total number of DGs, the power factor of each DG, the capacity of each DG, and the generation and operation costs of each DG in the distribution network parameter set. The third cost of distribution network losses is determined based on the unit electricity price, the annual maximum load loss hours of the branch, and the active power loss of the branch in the set of distribution network parameters. The fourth expense for environmental pollution compensation is determined based on the greenhouse gas emission intensity per metric ton, the annual power generation per metric ton at each candidate node, the greenhouse gas environmental value discount standard, and the greenhouse gas emission collection price in the aforementioned power distribution network parameter set. The objective function is constructed by weighted summing of the first cost, the second cost, the third cost, and the fourth cost; The initial firefly population is determined based on the configured target rules, including: The multi-dimensional data of fireflies is randomly initialized to obtain the first initial firefly; In the target rule, the first mapping range of each dimension of data in the first initial firefly is queried, and the updated value of the corresponding dimension is generated according to the first mapping range; The second initial firefly is determined based on the updated value of each dimension, and the second mapping range of each dimension data in the second initial firefly is queried in the target rule. The update values ​​for the corresponding dimensions are generated according to the second mapping range, and the third initial firefly is determined based on the update values ​​of each dimension. This process is repeated to generate a preset number of initial fireflies, thus obtaining the initial firefly population.

2. The method according to claim 1, characterized in that, The method for determining the second set of constraints includes: Based on the operating parameters, the upper and lower limits of node voltage, the upper and lower limits of node current, and the maximum load of the unit are obtained. Based on the upper and lower limits of the node voltage, the node voltage constraint is determined to be that the node voltage is between the upper and lower limits of the node voltage. Based on the upper and lower limits of the branch current, the conductor current inequality constraint is determined to be that the branch current is between the upper and lower limits of the branch current. Based on the unit's maximum load, determine the upper and lower load boundary values, and determine that the DG output constraint is that the total DG grid connection capacity is between the lower and upper load boundary values.

3. The method according to claim 1, characterized in that, The method for obtaining the first fee includes: The capital investment recovery factor for the line is determined based on the time period from line activation to line upgrade, the time period from line activation to the end of the planning period, and the fixed annual interest rate. The first cost is determined based on the fixed investment cost of the line, the annual fixed management cost of the line, the upgrade instruction information of the line, and the equal-amount capital investment recovery coefficient of the line.

4. The method according to claim 1, characterized in that, The method for obtaining the second fee includes: The first result is obtained by multiplying the power factor, capacity, and power generation and operating costs of each DG. Based on the total number of DG, determine all sums of the first result to obtain the second result; The second cost is obtained by multiplying the second result by the maximum number of hours the DG power generation is utilized.

5. The method according to claim 1, characterized in that, The method for obtaining the third fee includes: The product of the annual maximum load loss hours, the active power loss, and the unit electricity price for each branch is obtained as the third result for each branch. The third cost is obtained by summing the third results for all branches.

6. The method according to claim 1, characterized in that, The method for obtaining the fourth fee includes: The sum of the greenhouse gas environmental value discount standard and the greenhouse gas emission levy price is obtained as the fourth result; The fifth result is determined based on the fourth result and the annual power generation per metric ton at each candidate node; The sum of the fifth results of all candidate nodes is taken as the sixth result, and the fourth cost is obtained based on the product of the sixth result and the greenhouse gas emission intensity per metric ton.

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