Power supply locating and sizing method

Abstract

The invention provides a power supply locating and sizing method, and relates to the technical field of power system optimization, and the power supply locating and sizing method comprises the steps: obtaining the operation parameters of a unit, and determining a first constraint set of the unit and a second constraint set of power supply locating and sizing according to the operation parameters; obtaining a power distribution network parameter set, and constructing a power supply locating and sizing objective function according to the power distribution network parameter set and the operation parameters; and determining an initial firefly population based on the configured target rule, performing firefly algorithm iterative optimization based on the initial firefly population, determining an optimal result of the target function when the first constraint set and the second constraint set are satisfied, and ensuring that the power distribution network including the distributed power supply can operate safely, reliably and economically.

Description

Technical Field

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

[0002] At present, energy conservation, emission reduction and low-carbon lifestyle are being vigorously advocated. Various new energy sources have become ideal alternatives to traditional fossil energy due to their environmentally friendly characteristics, and are gradually becoming dominant in some regions. Distributed Generation (DG), or distributed power generation system, 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 that can ensure safe and stable power supply, reduce environmental pollution and achieve green and sustainable development.

[0003] Distributed power sources refer to power generation devices with relatively small capacity (usually not exceeding 50 megawatts), located near load centers or connected to distribution networks, and are economical, efficient and reliable. Distributed energy will gradually replace fossil fuels in specific periods and regions, thereby maximizing the energy efficiency of the entire power grid system. When a large number of distributed power sources are randomly connected to or exit the distribution network, it will affect the system's reliability, relay protection performance, power quality and network losses, and increase the complexity of load forecasting, which in turn makes distribution network planning face more challenges. Therefore, determining the optimal installation location and capacity configuration of distributed power sources and ensuring that the distribution network containing distributed power sources can operate safely, reliably and economically are the main problems faced by distribution network planning. Summary of the invention

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

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

[0006] The second objective of the present application is to provide a power supply site selection and capacity determination device.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective 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-mentioned purpose, the first embodiment of the present application proposes a method for selecting a power supply site and determining the capacity, comprising:

[0011] Acquire operating parameters of the unit, and determine a first constraint set of the unit and a second constraint set for power site selection and capacity determination according to the operating parameters;

[0012] Acquire a distribution network parameter set, and construct an objective function for power source site selection and capacity determination according to the distribution network parameter set and the operating parameters;

[0013] An initial firefly population is determined based on the configured target rule, and a firefly algorithm is iteratively optimized 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; different fireflies correspond to different results of the objective function.

[0014] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a power supply site selection and capacity determination device, including:

[0015] A first acquisition module is used to acquire the operating parameters of the unit, and determine a first constraint set of the unit and a second constraint set of power source site selection and capacity determination according to the operating parameters;

[0016] A second acquisition module is used to acquire a distribution network parameter set, and construct an objective function for power source site selection and capacity determination according to the distribution network parameter set and the operating parameters;

[0017] An optimization module is used to determine an initial firefly population based on a configured target rule, and to perform iterative optimization of a 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.

[0018] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0019] The memory stores computer-executable instructions;

[0020] The processor executes the computer-executable instructions stored in the memory to implement the method described in the embodiment of the first aspect.

[0021] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method described in the first aspect embodiment.

[0022] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, including a computer program, which implements the method described in the first aspect when executed by a processor.

[0023] The present application provides a method for site selection and sizing of power sources, which obtains operating parameters and distribution network parameter sets, determines a first constraint set, a second constraint set and an objective function, determines an initial firefly population based on configured objective rules, and performs iterative optimization based on the initial firefly population, thereby improving the optimization efficiency of the firefly algorithm for the objective function and the accuracy of the optimization results, and determines the optimal result of the objective function when the first constraint set and the second constraint set are met, thereby greatly improving the energy efficiency of the power grid system.

[0024] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0026] Figure 1 A schematic diagram of a flow chart of a method for selecting a power supply site and determining the capacity provided in an embodiment of the present application;

[0027] Figure 2 A flow chart of another method for selecting a power source site and determining its capacity provided in an embodiment of the present application;

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

[0029] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0030] A method for selecting a power supply site and determining the capacity of a power supply according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0031] Figure 1 The following is a flow chart of a method for selecting a power supply site and determining the capacity provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0032] S101, obtaining operating parameters of the unit, and determining a first constraint set of the unit and a second constraint set for power site selection and sizing according to the operating parameters.

[0033] Optionally, the operating parameters of the generator set may 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, battery life, storage efficiency and other related operating parameters, which are not described in detail.

[0034] In some implementations, the first constraint set of the unit may include common constraints such as load constraints, power generation constraints, or total capacity constraints, which are used to ensure that the operating capacity of the unit is not exceeded and to ensure the safe operation of the unit. The details are not elaborated here.

[0035] In some implementations, the second constraint set for power site selection and sizing may include constraints related to power site selection and sizing, such as output constraints or node voltage constraints of distributed generation (DG), to more comprehensively ensure the safe operation of DG.

[0036] S102, obtaining a distribution network parameter set, and constructing an objective function for power source site selection and sizing according to the distribution network parameter set and operating parameters.

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

[0038] It can be understood that the objective function is the power site selection and sizing model provided in this embodiment. The optimal solution for power site selection and sizing is determined by solving the objective function to obtain the best layout result. In this embodiment, the final result is to minimize the total operating cost of the distribution network. The result of the objective function is the total operating cost of the distribution network. Finding the optimal solution to the objective function is to determine the minimum value of the total operating cost of the distribution network.

[0039] Optionally, the corresponding relationship between the data and the cost can be determined based on the data of different dimensions such as operating parameters, distribution network line related parameters, DG operation related parameters, distribution network loss related parameters and environmental pollution parameters caused by DG greenhouse gas emissions, so as to construct an objective function reflecting the total cost of distribution network operation.

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

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

[0042] Among them, the target rule is the rule used to select the initial firefly population. For example, the optimal selection results of the historical power supply site capacitor are written into the target rule to generate the initial fireflies. The historical results are used as the initial fireflies for optimization. Compared with the existing random selection, the optimization efficiency is improved to improve the acquisition effect of the initial firefly population.

[0043] In some implementations, the target rules may also include pre-configured firefly generation rules, according to which initial fireflies are continuously generated to obtain an initial firefly population, thereby improving the optimization effect.

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

[0045]

[0046] Among them, β 0 is the maximum attraction, which is the same as the attraction at r = 1, and in this embodiment is set to 1; γ is the light absorption coefficient, and is set to [0.01, 100]; r ij is the Euclidean distance between firefly i and firefly j.

[0047] During the iteration process, if there is a Pareto dominance of firefly j over firefly i in the current environment, the fireflies in the next iteration are:

[0048]

[0049] Among them, t is the current iteration number; x i (t) and x j (t) represents firefly i and firefly j respectively; β ij (r ij ) is the attraction of firefly j to firefly i; α t is a constant, with a value of [0,1]; is a random number vector, which can be obtained through Gaussian distribution, uniform distribution or other distributions.

[0050] During the iteration process, if there is no Pareto domination of firefly j over firefly i in the current environment, the fireflies in the next iteration are:

[0051]

[0052] in, It is the average value obtained by weighted averaging of multiple fireflies with random weight coefficients.

[0053] It can be understood that if there is a Pareto dominance of firefly j over firefly i in the current environment, it indicates that there is a firefly j that is better than firefly i in the current environment, which means that firefly i is not the optimal result, and then continue to the next iterative update; if there is no Pareto dominance of firefly j over firefly i in the current environment, then firefly i is the local optimal result, and the weighted average of the fireflies is used to determine the optimal result of the final iteration.

[0054] As an example, each firefly can be understood as an m-dimensional feature vector, where m is the number of independent variables in the objective function, that is, each independent variable is used as the dimension of the feature vector. The feature vector corresponding to each firefly is also a single value of the independent variable in the objective function, so that the result of the objective function can be determined based on each firefly. Assuming that the initial firefly population is 10 initial fireflies, iterative analysis is performed on the 10 initial fireflies to obtain the next iterative firefly corresponding to each initial firefly. At this time, the environment includes 10 initial fireflies and 10 iterative fireflies. The first 10 fireflies with the smaller objective function are selected from the 20 fireflies, and the iteration is continued with the 10 selected fireflies to obtain 20 new fireflies. The 20 fireflies are continued to be selected and iterated until the preset number of iterations is reached, so as to determine that the firefly corresponding to the minimum objective function is the optimal firefly. The feature vector of the optimal firefly includes the optimal value of each variable in the objective function, so as to achieve the best optimization.

[0055] It can be understood that when the first set of constraints and the second set of constraints are met, the optimal result of the objective function is the minimum value of the total operating cost of the distribution network. Using the current optimal result to select the site and size of the power source can maximize cost savings and greatly improve the energy efficiency of the power grid system while ensuring stable and safe operation.

[0056] In this embodiment, a first constraint set of the unit and a second constraint set of the power site selection and sizing are determined according to the operating parameters; the power site selection and sizing are constrained with more comprehensive constraints to ensure that the unit can operate safely; the objective function of the power site selection and sizing is constructed according to the distribution network parameter set and the operating parameters; the initial firefly population is determined based on the configured objective rules, and iterative optimization is performed 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 met; a more optimized initial firefly population is used as the starting point for optimization, so as to improve the optimization efficiency of the firefly algorithm for the objective function and the accuracy of the optimization results, and the power site selection and sizing based on the current optimal result can maximize cost savings and greatly improve the energy efficiency of the power grid system while operating stably and safely.

[0057] Based on the above embodiments, Figure 2 A flow chart of another power site selection and capacity determination method provided in an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0058] S201, obtaining operating parameters of the unit, and determining a first constraint set of the unit and a second constraint set for power site selection and sizing according to the operating parameters.

[0059] In some implementations, the first constraint set includes at least one of: a net load balance constraint, a minimum start and stop time constraint of a thermal power unit, a thermal power unit output constraint, a thermal power unit ramp constraint, a hydropower unit output constraint, and a hydropower unit total power constraint.

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

[0061]

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

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

[0064]

[0065] Among them, T i,on and T i,off They represent the minimum start-up period and minimum shutdown period of thermal power units respectively; and They represent the number of consecutive startup periods and consecutive shutdown periods of thermal power unit i in period t-1 respectively; and It indicates the start and stop status of thermal power unit i in period t-1 and period t, where start is 1 and stop is 0.

[0066] For example, the output constraint of thermal power units can be expressed as:

[0067]

[0068] Among them, P G,i,min and P G,i,max They represent the lower and upper output limits of thermal power unit i respectively.

[0069] For example, the ramp constraint of a thermal power unit can be expressed as:

[0070]

[0071] in, and They represent the upward climbing rate and downward climbing rate of thermal power unit i respectively; P G,i,max It is the maximum output of thermal power unit.

[0072] For example, the output constraint of a hydropower unit can be expressed as:

[0073]

[0074] Among them, P H,k,min and P H,k,max They respectively represent the lower and upper output limits of hydropower unit k.

[0075] For example, the total power constraint of the hydropower unit can be expressed as:

[0076] HE hs,k =ΣP H,k

[0077] Among them, P H,k Indicates the output of the hydropower unit, HE hs,k Indicates the total power of the hydropower unit.

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

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

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

[0081] U i,min ≤U i ≤U i,max , i∈N

[0082] Among them, U i,min and U i,max They represent the lower limit and upper limit of the node voltage respectively, and N is the node set.

[0083] Alternatively, the wire current inequality constraint can be expressed as:

[0084] I j,min ≤I j ≤I j,max , j∈L

[0085] Among them, I j,min and I j,max They represent the lower limit and upper limit of branch current respectively, and L is the branch set.

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

[0087] S L,min ≤S ∑DG ≤S L,max

[0088] Among them, S ∑DG is the total capacity of DG connected to the grid; S L,min and S L,max They are 10% and 25% of the maximum load of the unit respectively, among which 10% of the maximum load of the unit is the lower load boundary value, and 25% of the maximum load of the unit is the upper load boundary value.

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

[0090] S202, obtaining a distribution network parameter set, and constructing an objective function for power source site selection and sizing according to the distribution network parameter set and operating parameters.

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

[0092] Preferably, the line equal installment capital investment recovery coefficient can be determined based on the time period from line activation to line upgrade, the time period from line activation to the end of planning, and the fixed annual interest rate; the first fee can be determined based on the line fixed investment tariff, the line's annual fixed management fee, the line's upgrade indication information, and the line equal installment capital investment recovery coefficient.

[0093] Alternatively, the first fee can be expressed as:

[0094]

[0095] Among them, G 1 represents the first fee; r represents the fixed annual interest rate; b i Indicates the time period from line activation to line upgrade; p i Indicates the time period from the line opening to the end of planning; M jl Indicates the fixed investment fee of the line; M mj represents the annual fixed management cost of the line; β j Indicates the upgrade indication information of the line, with a value of 0 or 1, 1 indicates that the line needs to be upgraded, and 0 indicates that the line does not need to be upgraded; L indicates the total number of power grid branches; and They respectively represent the equal installment capital investment recovery coefficient of the line.

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

[0097] Alternatively, the second fee can be expressed as:

[0098]

[0099] Among them, G 2 Indicates the second fee; T max Indicates the maximum number of hours of DG power generation utilization; δ i Indicates the power factor of the ith DG. To avoid cumbersome calculation, the power factor takes a uniform value; S DGi represents the capacity of the ith DG; M cDGi represents the power generation and operation cost of the i-th DG, in units of RMB / kWh; n represents the total number of DGs; δ i S DGi M cDGi is the first result; For the second result.

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

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

[0102]

[0103] Among them, G 3 Indicates the third fee; M e Indicates the unit electricity price (yuan / kWh); γ jmax is the annual maximum load loss hours of branch j; Indicates the active network loss of the branch, δ j Represents the power factor of branch j, R j represents the resistance of branch j, U N Indicates rated voltage, W j 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 is determined based on the fourth result and the annual power generation per metric ton at each candidate node; the sum of the fifth nodes of all candidate nodes is obtained as the sixth result, and the fourth fee is obtained based on the product of the sixth result and the greenhouse gas emission intensity per metric ton generated.

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

[0106]

[0107] Among them, G 4 H represents the fourth fee, which is the compensation fee that DG needs to pay for emitting greenhouse gases; mt E is the greenhouse gas emission intensity per metric ton produced; mt,i The annual power generation per metric ton at the i-th candidate node; V CO2 Represents the discount standard of greenhouse gas environmental value; R CO2 represents the price of greenhouse gas emissions; (V CO2 +R CO2 ) is the fourth result; N mt is the number of all nodes to be selected; This is the sixth result.

[0108] Furthermore, the objective function can be constructed by weighted summing the first fee, the second fee, the third fee and the fourth fee; then the objective function can be expressed as:

[0109] G=α 1 G 1 +α 2 G 2 +α 3 G 3 +α 4 G 4

[0110] Among them, G is the objective function, which represents the total cost of distribution network operation; α 0 , α 2 , α 3 and α 4 They are weight factors of the first fee, the second fee, the third fee, and the fourth fee, respectively. Their values ​​are between 0 and 1 and their sum is 1.

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

[0112] Optionally, the data dimension of the firefly is the same as the number of independent variables in the objective function. For example, if the objective function has m independent variables, the data dimension of the firefly is m. Each firefly can be regarded as a vector of dimension m. 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 range of 0-1.

[0113] S204, performing iterative update based on the first initial fireflies and the target rule to obtain an initial firefly population.

[0114] Optionally, in the target rule, the first mapping range of the data of each dimension 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 the data of each dimension 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, and so on, to generate a preset number of initial fireflies to obtain the initial firefly population.

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

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

[0117]

[0118] where x n is the data of the nth dimension in the first initial firefly; x+1 n is the data of the nth dimension in the second initial firefly; d is a control parameter used to determine the mapping range and there is no overlapping part between the mapping ranges, and it can take a random value between 0.35 - 0.45. In this embodiment, d is 0.4.

[0119] Exemplarily, the first initial firefly with each dimension in the range of 0 - 1 is randomly generated. 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, if the first mapping range is 0 ≤ x n < d, then according to the update value of the corresponding dimension in the second initial firefly is determined, and so on. The mapping range identification of the data of each dimension in the first initial firefly is performed 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 identification of the data of each dimension in the second initial firefly is performed 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, and so on, until a preset number of initial fireflies are iteratively obtained to obtain the initial firefly population.

[0120] S205, perform firefly algorithm iterative optimization based on the initial firefly population, and determine the optimal result of the objective function when satisfying the first constraint set and the second constraint set.

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

[0122] In this embodiment, the first constraint set and the second constraint set are obtained based on the operating parameters of the unit, and the safe operation of the unit is ensured by various constraints in the first constraint set and the second constraint set; different types of costs are obtained according to the distribution network parameter set and the operating parameters, and the objective function is determined according to the costs, and the initial firefly population is iteratively selected according to the target rule, which improves the optimization efficiency compared with the existing random selection, so as to improve the acquisition effect of the initial firefly population, and the objective function is iteratively optimized based on the initial firefly population to obtain the optimal result of the objective function, that is, the minimum value of the total operating cost of the distribution network. The power site selection and sizing based on the current optimal result can maximize cost savings and greatly improve the energy efficiency of the power grid system while ensuring stable and safe operation.

[0123] In order to implement the above-mentioned embodiment, the present application also proposes a power supply site selection and capacity determination device.

[0124] Figure 3 This is a schematic diagram of the structure of a power site selection and capacity determination device provided in an embodiment of the present application. Figure 3 As shown, the power supply site selection and capacity determination device 300 includes:

[0125] The first acquisition module 301 is used to acquire the operating parameters of the unit, and determine the first constraint set of the unit and the second constraint set for power site selection and capacity determination according to the operating parameters.

[0126] The second acquisition module 302 is used to acquire a distribution network parameter set, and construct an objective function for power source site selection and capacity determination according to the distribution network parameter set and operating parameters.

[0127] The optimization module 303 is used to determine the initial firefly population based on the configured target rules, 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 met; different fireflies correspond to different results of the objective function.

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

[0129] Randomly initialize the multi-dimensional data of fireflies to obtain the first initial fireflies;

[0130] In the target rule, query the first mapping range of each dimension data in the first initial firefly, and generate an updated value of the corresponding dimension according to the first mapping range;

[0131] Determine a second initial firefly based on the updated value of each dimension, and query a second mapping range of each dimension data in the second initial firefly in the target rule;

[0132] Generate an updated value of the corresponding dimension according to the second mapping range, and determine a third initial firefly based on the updated value of each dimension, and so on, generate a preset number of initial fireflies to obtain an initial firefly population.

[0133] Furthermore, in a possible implementation of an embodiment of the present application, the first constraint set includes: at least one of: a net load balance constraint, a minimum start and stop time constraint of a thermal power unit, an output constraint of a thermal power unit, a climbing constraint of a thermal power unit, an output constraint of a hydropower unit, and a total power constraint of a hydropower unit.

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

[0135] Furthermore, in a possible implementation of the embodiment of the present application, the first acquisition module includes:

[0136] Based on the operating parameters, obtain the upper and lower limits of the node voltage, the upper and lower limits of the node current, and the maximum load of the unit;

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

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

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

[0140] Furthermore, in a possible implementation of the embodiment of the present application, the second acquisition module includes:

[0141] Determine the first cost of the distribution network line upgrade and grid maintenance according to the time period from the line activation to the line upgrade, the time period from the line activation to the end of the planning, the fixed annual interest rate, the line fixed investment fee, the annual fixed management fee of the line and the upgrade indication information of the line in the distribution network parameter set;

[0142] Determine the second cost of DG operation according to the maximum generation hours of distributed generation DG generation utilization in the distribution network parameter set, 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;

[0143] Determine the third cost of the distribution network loss according to the unit electricity price in the distribution network parameter set, the annual maximum load loss hours of the branch and the active network loss of the branch;

[0144] Determine the fourth fee for environmental pollution compensation based on the greenhouse gas emission intensity per metric ton generated in the distribution network parameter set, the annual power generation per metric ton at each candidate node, the greenhouse gas environmental value discount standard and the greenhouse gas emission levy price;

[0145] The objective function is constructed by taking the weighted sum of the first cost, the second cost, the third cost and the fourth cost.

[0146] Furthermore, in a possible implementation of the embodiment of the present application, the second acquisition module includes:

[0147] Determine the capital investment recovery factor for the line equal installment based on the time period from line commissioning to line upgrade, the time period from line commissioning to the end of planning, and a fixed annual interest rate;

[0148] The first fee is determined based on the line fixed investment tariff, the annual fixed management fee of the line, the upgrade instruction information of the line, and the line equal installment capital investment recovery coefficient.

[0149] Furthermore, in a possible implementation of the embodiment of the present application, the second acquisition module includes:

[0150] The first result is obtained by obtaining the product of the power factor, capacity, and generation and operation costs of each DG;

[0151] Determine all sums of the first results based on the total number of DGs to obtain a second result;

[0152] The second cost is obtained by multiplying the second result by the maximum number of hours of DG power generation utilization.

[0153] Furthermore, in a possible implementation of the embodiment of the present application, the second acquisition module includes:

[0154] Obtain the product of the annual maximum load loss hours, active network loss and unit electricity price of each branch as the third result of each branch;

[0155] The third cost is obtained by summing the third results of all branches.

[0156] Furthermore, in a possible implementation of the embodiment of the present application, the second acquisition module includes:

[0157] Obtaining the sum of the greenhouse gas environmental value discount standard and the greenhouse gas emission levy price as the fourth result;

[0158] Determine a fifth result based on the fourth result and the annual power generation per metric ton at each candidate node;

[0159] The sum of the fifth nodes of all the nodes to be selected is obtained as a sixth result, and a fourth fee is obtained based on the product of the sixth result and the greenhouse gas emission intensity per metric ton generated.

[0160] It should be noted that the above explanation of the embodiment of the power site selection and capacity determination method is also applicable to the power site selection and capacity determination device of this embodiment, and will not be repeated here.

[0161] In an embodiment of the present application, a first constraint set and a second constraint set are obtained based on the operating parameters of the unit, and the safe operation of the unit is ensured by various constraints in the first constraint set and the second constraint set; different types of costs are obtained according to the distribution network parameter set and the operating parameters, and the objective function is determined according to the costs, and the initial firefly population is iteratively selected according to the target rule, which improves the optimization efficiency compared to the existing random selection, so as to improve the acquisition effect of the initial firefly population, and the objective function is iteratively optimized based on the initial firefly population to obtain the optimal result of the objective function, that is, the minimum value of the total operating cost of the distribution network, and the power site selection and sizing based on the current optimal result can maximize cost savings and greatly improve the energy efficiency of the power grid system while ensuring stable and safe operation.

[0162] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0163] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0164] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

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

[0166] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0167] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0168] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0169] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0170] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0171] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0172] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0173] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0174] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0175] The storage medium mentioned above may 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for selecting a power source site and determining its capacity, characterized in that: The method comprises: Acquire operating parameters of the unit, and determine a first constraint set of the unit and a second constraint set for power site selection and capacity determination according to the operating parameters; Acquire a distribution network parameter set, and construct an objective function for power source site selection and capacity determination according to the distribution network parameter set and the operating parameters; An initial firefly population is determined based on the configured target rule, and a firefly algorithm is iteratively optimized 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; different fireflies correspond to different results of the objective function.

2. The method according to claim 1, characterized in that The configuration-based target rule determines the initial firefly population, including: Randomly initialize the multi-dimensional data of fireflies to obtain the first initial fireflies; In the target rule, query the first mapping range of each dimension data in the first initial firefly, and generate an updated value of the corresponding dimension according to the first mapping range; Determine a second initial firefly based on the updated value of each dimension, and query a second mapping range of each dimension data in the second initial firefly in the target rule; Generate an updated value of the corresponding dimension according to the second mapping range, and determine a third initial firefly based on the updated value of each dimension, and so on, generate a preset number of initial fireflies to obtain an initial firefly population.

3. The method according to claim 1, characterized in that The first constraint set includes: at least one of: a net load balance constraint, a minimum start and stop time constraint of a thermal power unit, an output constraint of a thermal power unit, a ramp constraint of a thermal power unit, an output constraint of a hydropower unit, and a total power constraint of a hydropower unit.

4. The method according to any one of claims 1 to 3, characterized in that The second constraint set includes: at least one of a node voltage constraint, a wire current inequality constraint, and a distributed power source DG output constraint.

5. The method according to claim 4, characterized in that The method for determining the second constraint set includes: Based on the operating parameters, obtaining the upper and lower limits of the node voltage, the upper and lower limits of the node current, and the maximum load of the unit; According to the upper limit and the lower limit of the node voltage, determining the node voltage constraint that the node voltage is between the upper limit and the lower limit of the node voltage; According to the upper limit and the lower limit of the branch current, determining the conductor current inequality constraint that the branch current is between the upper limit and the lower limit of the branch current; According to the maximum load of the unit, an upper load boundary value and a lower load boundary value are determined, and the DG output constraint is determined so that the total capacity of the DG connected to the power grid is between the lower load boundary value and the upper load boundary value.

6. The method according to claim 1, characterized in that The constructing an objective function for power source site selection and capacity determination according to the distribution network parameter set and the operating parameters includes: Determine the first cost of the distribution network line upgrade and grid maintenance according to the time period from the line activation to the line upgrade, the time period from the line activation to the end of the planning, the fixed annual interest rate, the line fixed investment fee, the annual fixed management fee of the line and the upgrade indication information of the line in the distribution network parameter set; Determine the second cost of DG operation according to the maximum generation hours of distributed generation DG power generation utilization, 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 distribution network parameter set; Determine the third cost of the distribution network loss according to the unit electricity price, the annual maximum load loss hours of the branch and the active network loss of the branch in the distribution network parameter set; Determine the fourth fee of environmental pollution compensation according to the greenhouse gas emission intensity per metric ton generated in the distribution network parameter set, the annual power generation per metric ton at each candidate node, the greenhouse gas environmental value discount standard and the greenhouse gas emission levy price; The objective function is constructed by taking a weighted sum of the first fee, the second fee, the third fee and the fourth fee.

7. The method according to claim 6, characterized in that The method for obtaining the first fee includes: Determine the line equal installment capital investment recovery factor based on the time period from the line opening to the line upgrade, the time period from the line opening to the end of the planning, and the fixed annual interest rate; The first fee is determined based on the fixed investment rate of the line, the annual fixed management fee of the line, the upgrade indication information of the line, and the equal installment capital investment recovery coefficient of the line.

8. The method according to claim 6, characterized in that The method for obtaining the second fee includes: Obtaining a first result by multiplying the power factor, the capacity, and the power generation and operation cost of each DG; Determine all sums of the first results based on the total number of DGs to obtain a second result; The second fee is obtained by multiplying the second result by the maximum number of hours of DG power generation utilization.

9. The method according to claim 6, characterized in that The method for obtaining the third fee includes: Obtaining the product of the annual maximum load loss hours, the active network loss and the unit electricity price of each branch as the third result of each branch; The third results of all branches are summed to obtain the third cost.

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

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